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Archive for December, 2009

Skeptical Science and Climate Denial Crock of the Week in Finnish

Posted by Ari Jokimäki on December 29, 2009

With couple of friends we have started to translate the articles of John Cook to Finnish language, as we felt there’s not enough discussion available on the subject of denialist claims in our language. John’s article collection on the subject is the best available (in my honest opinion), so it is natural place to start. Here’s John’s announcement on the issue. So far there’s just a few articles translated, but we’ll keep adding them. My fellow-workers on this are Kaj Luukko and AJ.

We (Me, Kaj, and Tuomas Helin) have also started translating the Climate Denial Crock of the Week videos by Peter Sinclair. We do that by subtitles. Here’s the first one, Ice area vs Volume, with English and Finnish subtitles (use the up-pointing arrow in the lower left corner to select the language and turn the subtitles on/off). This first one was translated by Kaj Luukko. There are couple of others not yet incorporated to Peter Sinclair’s own videos, but they can be viewed here: The Big Mist Take translated by Tuomas Helin, and The “Temp leads Carbon” Crock translated by me. [UPDATE: as Kaj points out in the comment section below, the other two are also now incorporated to the Peter Sinclair’s videos, and are available at links given by Kaj below. Thanks, Kaj!]

Well, Finnish is a language for only small group of people (few millions), but perhaps some other, larger, languages get a boost from this. 🙂


Posted in General | 10 Comments »

Papers on urban heat island

Posted by Ari Jokimäki on December 27, 2009

This is a list of papers on urban heat island with an emphasis on studies that deal with larger areas (generally no studies dealing with individual cities are included in this list). The list is not complete, and will most likely be updated in the future in order to make it more thorough and more representative. Note that the paperlist on global surface temperature contains several papers that discuss urban heat island issues also.

UPDATE (July 24, 2013): Hausfather et al. (2013), Wickham et al. (2013), Parker (2010) added.
UPDATE (April 5, 2010): Small et al. (2005) added. Full text link was added to Schmidt (2009), Gallo & Owen (1999), and Mitchell (1953).

Quantifying the effect of urbanization on U.S. Historical Climatology Network temperature records – Hausfather et al. (2013) “An assessment quantifying the impact of urbanization on temperature trends from the U.S. Historical Climatology Network (USHCN) is described. Stations were first classified as urban and nonurban (rural) using four different proxy measures of urbanity. Trends from the two station types were then compared using a pairing method that controls for differences in instrument type and via spatial gridding to account for the uneven distribution of stations. The comparisons reveal systematic differences between the raw (unadjusted) urban and rural temperature trends throughout the USHCN period of record according to all four urban classifications. According to these classifications, urbanization accounts for 14–21% of the rise in unadjusted minimum temperatures since 1895 and 6–9% since 1960. The USHCN version 2 homogenization process effectively removes this urban signal such that it becomes insignificant during the last 50–80 years. In contrast, prior to 1930, only about half of the urban signal is removed. Accordingly, the National Aeronautics and Space Administration Goddard Institute for Space Studies urban-correction procedure has essentially no impact on USHCN version 2 trends since 1930, but effectively removes the residual urban-rural temperature trend differences for years before 1930 according to all four urban proxy classifications. Finally, an evaluation of the homogenization of USHCN temperature series using subsets of rural-only and urban-only reference series from the larger U.S. Cooperative Observer (Coop) Network suggests that the composition of Coop stations surrounding USHCN stations is sufficiently “rural” to limit the aliasing of urban heat island signals onto USHCN version 2 temperature trends during homogenization.” Zeke Hausfather, Matthew J. Menne, Claude N. Williams, Troy Masters, Ronald Broberg, David Jones, Journal of Geophysical Research: Atmospheres, Volume 118, Issue 2, pages 481–494, 27 January 2013, DOI: 10.1029/2012JD018509. [Full text]

Influence of Urban Heating on the Global Temperature Land Average using Rural Sites Identified from MODIS Classifications – Wickham et al. (2013) “The effect of urban heating on estimates of global average land surface temperature is studied by applying an urban-rural classification based on MODIS satellite data to the Berkeley Earth temperature dataset compilation of 36,869 sites from 15 different publicly available sources. We compare the distribution of linear temperature trends for these sites to the distribution for a rural subset of 15,594 sites chosen to be distant from all MODISidentified urban areas. While the trend distributions are broad, with one-third of the stations in the US and worldwide having a negative trend, both distributions show significant warming. Time series of the Earth’s average land temperature are estimated using the Berkeley Earth methodology applied to the full dataset and the rural subset; the difference of these is consistent with no urban heating effect over the period 1950 to 2010, with a slope of -0.10 ± 0.24/100yr (95% confidence).” Wickham C, Rohde R, Muller RA, Wurtele J, Curry J, et al. (2013) Influence of Urban Heating on the Global Temperature Land Average using Rural Sites Identified from MODIS Classifications. Geoinfor Geostat: An Overview 1:2. doi:10.4172/2327-4581.1000104. [Full text]

Urban heat island effects on estimates of observed climate change – Parker (2010) “Urban heat islands are a result of the physical properties of buildings and other structures, and the emission of heat by human activities. They are most pronounced on clear, calm nights; their strength depends also on the background geography and climate, and there are often cool islands in parks and less-developed areas. Some old city centers no longer show warming trends relative to rural neighbourhoods, because urban development has stabilised. This article reviews the effects that urban heat islands may have on estimates of global near-surface temperature trends. These effects have been reduced by avoiding or adjusting urban temperature measurements. Comparisons of windy weather with calm-weather air temperature trends for a worldwide set of observing sites suggest that global near-surface temperature trends have not been greatly affected by urban warming trends; this is supported by comparisons with marine surface temperatures. The use of dynamical-model-based reanalyses to estimate urban influences has been hindered by the heterogeneity of the data input to the reanalyses and by biases in the models. However, improvements in reanalyses are increasing their utility for assessing the surface air temperature record. High-resolution climate models and data on changing land use offer potential for future assessment of worldwide urban warming influences. The latest assessments of the likely magnitude of the residual urban trend in available global near-surface temperature records are summarized, along with the uncertainties of these residual trends.” David E. Parker, Wiley Interdisciplinary Reviews: Climate Change, Volume 1, Issue 1, pages 123–133, January/February 2010, DOI: 10.1002/wcc.21.

Spurious correlations between recent warming and indices of local economic activity – Schmidt (2009) “A series of climate model simulations of the 20th Century are analysed to investigate a number of published correlations between indices of local economic activity and recent global warming. These correlations have been used to support a hypothesis that the observed surface warming record has been contaminated in some way and thus overestimates true global warming. However, the basis of the results are correlations over a very restricted set of locations (predominantly western Europe, Japan and the USA) which project strongly onto naturally occurring patterns of climate variability, or are with fields with significant amounts of spatial auto-correlation. Across model simulations, the correlations vary widely due to the chaotic weather component in any short-term record. The reported correlations do not fall outside the simulated distribution, and are probably spurious (i.e. are likely to have arisen from chance alone). Thus, though this study cannot prove that the global temperature record is unbiased, there is no compelling evidence from these correlations of any large-scale contamination.” [Full text]

Global urban land-use trends and climate impacts – Seto & Shepherd (2009) A review article. “Recent research points to mounting evidence that urbanization also affects cycling of water, carbon, aerosols, and nitrogen in the climate system. This review highlights advances in the understanding of urban land-use trends and associated climate impacts, concentrating on peer-reviewed papers that have been published over the last two years.” [Full text]

Urbanization effects in large-scale temperature records, with an emphasis on China – Jones et al. (2008) “We show examples of the UHIs at London and Vienna, where city center sites are warmer than surrounding rural locations. Both of these UHIs however do not contribute to warming trends over the 20th century because the influences of the cities on surface temperatures have not changed over this time. In the main part of the paper, for China, we compare a new homogenized station data set with gridded temperature products and attempt to assess possible urban influences using sea surface temperature (SST) data sets for the area east of the Chinese mainland. We show that all the land-based data sets for China agree exceptionally well and that their residual warming compared to the SST series since 1951 is relatively small compared to the large-scale warming. Urban-related warming over China is shown to be about 0.1°C decade−1 over the period 1951–2004, with true climatic warming accounting for 0.81°C over this period.”

Urban Heat Islands: Observations, Impacts, and Adaptation – Yow (2007) A review article. “Urban heat islands are a clear, well-documented example of an anthropogenic modification to climate that has an atmospheric, biological, and economic impact. This review shows how field-based and modeling studies continue to help unravel the factors that are responsible for heat island development and are providing a basis for the development and application of sustainable adaptation strategies. As urban areas continue to expand, there is a heightened awareness that scientific knowledge of the urban heat island must be more effectively communicated to architects, engineers, and planners and translated into intelligent urban design.”

Urban and rural temperature trends in proximity to large US cities: 1951-2000 – Stone (2007) “In this study, temperature data from urban and proximate rural stations for 50 large US metropolitan areas are analysed to establish the mean decadal rate of change in urban temperatures, rural temperatures, and heat island intensity over five decades. The results of this analysis find the mean decadal rate of change in the heat island intensity of large US cities between 1951 and 2000 to be 0.05 °C and further show a clear division in temperature trends between cities situated in the northeastern and southern regions of the country.” [Full text]

Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data – McKitrick & Michaels (2007) “Using a new database for all available land-based grid cells around the world we test the null hypothesis that the spatial pattern of temperature trends in a widely used gridded climate data set is independent of socioeconomic determinants of surface processes and data inhomogeneities. The hypothesis is strongly rejected (P = 7.1 × 10−14), indicating that extraneous (nonclimatic) signals contaminate gridded climate data. … We conclude that the data contamination likely leads to an overstatement of actual trends over land. Using the regression model to filter the extraneous, nonclimatic effects reduces the estimated 1980–2002 global average temperature trend over land by about half.” [Full text]

A Demonstration That Large-Scale Warming Is Not Urban – Parker (2006) “On the premise that urban heat islands are strongest in calm conditions but are largely absent in windy weather, daily minimum and maximum air temperatures for the period 1950–2000 at a worldwide selection of land stations are analyzed separately for windy and calm conditions, and the global and regional trends are compared. The trends in temperature are almost unaffected by this subsampling, indicating that urban development and other local or instrumental influences have contributed little overall to the observed warming trends.” [Full text]

Spatial analysis of global urban extent from DMSP-OLS night lights – Small et al. (2005) “Previous studies of DMSP-OLS stable night lights have shown encouraging agreement between temporally stable lighted areas and various definitions of urban extent. However, these studies have also highlighted an inconsistent relationship between the actual lighted area and the boundaries of the urban areas considered. Applying detection frequency thresholds can reduce the spatial overextent of lighted area (“blooming”) but thresholding also attenuates large numbers of smaller lights and significantly reduces the information content of the night lights datasets. … Comparison of lighted area with built area estimates from Landsat imagery of 17 cities shows that lighted areas are consistently larger than even maximum estimates of built areas for almost all cities in every light dataset. … Even 100% thresholds significantly overestimate built area for the 1992/1993 and 2000 datasets.” [Full text]

Urban heat island effect on annual mean temperature during the last 50 years in China – Li et al. (2004) “To detect the UHI effect, the annual mean surface air temperature (SAT) time series were firstly classified into 5 subregions by using Rotated Principal Components Analysis (RPCA) according to its high and low frequency climatic change features. Then the average UHI effect on each subregions regional annual mean STA was studied. Results indicate that the UHI effect on the annual mean temperatures includes three aspects: increase of the average values, decrease of variances and change of the climatic trends. The effect on the climatic trends is different from region to region.”

Assessment of Urban Versus Rural In Situ Surface Temperatures in the Contiguous United States: No Difference Found – Peterson (2003) “Using satellite night-lights–derived urban/rural metadata, urban and rural temperatures from 289 stations in 40 clusters were compared using data from 1989 to 1991. Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures. It is postulated that this is due to micro- and local-scale impacts dominating over the mesoscale urban heat island. Industrial sections of towns may well be significantly warmer than rural sites, but urban meteorological observations are more likely to be made within park cool islands than industrial regions.” [Full text]

Satellite-Based Adjustments for the Urban Heat Island Temperature Bias – Gallo & Owen (1999) “Monthly and seasonal relationships between urban–rural differences in minimum, maximum, and average temperatures measured at surface-based observation stations were compared to satellite-derived Advanced Very High Resolution Radiometer estimates of a normalized difference vegetation index (NDVI) and surface radiant temperature (Tsfc). … The use of satellite-derived data may contribute to a globally consistent method for analysis of the urban heat island bias.” [Full text]

A Technique for Using Composite DMSP/OLS ”City Lights” Satellite Data to Map Urban Area – Imhoff et al. (1997) “A Tresholding technique was used to convert a prototype “city lights” data set from the National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NOAAINGDC) into a map of “urban areas” for the continental United States. … We found that a threshold of %89% yielded the best results, removing ephemeral light sources and “blooming” of light onto water when adjacent to cities while still leaving the dense urban core intact. This approach gave very good results when compared with the urban areas as defined by the 1990 U. S. Census; the “urban” area from our analysis being only 5% less than that of the Census.”

Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology – Roth et al. (1989) “NOAA AVHRR satellite infra-red data are used to display the surface radiant temperature heat islands of Vancouver, British Columbia, Seattle, Washington, and Los Angeles, California. Heat island intensities are largest in the day-time and in the warm season. Day-time intra-urban thermal patterns are strongly correlated with land-use; industrial areas are warmest and vegetated, riverine or coastal areas are coolest. Nocturnal heat island intensities and the correlation of the surface radiant temperature distribution with land use are less. This is the reverse of the known characteristics of near-surface air temperature heat islands. Several questions relating to the interpretation and limitations of satellite data in heat island analysis and urban climate modelling are addressed.”

Urbanization: Its Detection and Effect in the United States Climate Record – Karl et al. (1988) “The results indicate that urban effects on temperature are detectable even for small towns with populations under 10 000. Stations with populations near 10 000 are shown to average 0.1°C warmer for the mean annual temperature than nearby stations located in rural areas with populations less than 2000. Urbanization decreases the daily maxima in all seasons except winter and the temperature range in all seasons. It increases the diurnal minima and the daily means in all seasons. The equations indicate that, for the annual mean temperature, urbanization during the twentieth century accounts for a warm bias of about 0.06°C in the U.S. Historical Climatology Network (HCN).” [Full text]

City size and the urban heat island – Oke (1973) “The paper demonstrates the relationship existing between the size of a village, town or city (as measured by its population), and the magnitude of the urban heat island it produces. This is accomplished by analyzing data gathered by automobile traverses in 10 settlements on the St. Lawrence Lowland, whose populations range from 1000 to 2 million inhabitants. The locations of these settlements effectively eliminate all non-urban climatic influences. The results are compared with previously published data.”

On the causes of instrumentally observed secular temperature trends – Mitchell (1953) “Three independent studies of city influence are presented. In the first, recent overlapping observations between the New Haven city and airport stations are used to estimate the local city influence which in turn is used to revise the secular station trend. In the second, evidence of negligible city influence but of real climatic change at Blue Hill Observatory since 1890 is discussed. In the third, a statistical study involving 77 stations in the United States, whose temperature records were observationally homogeneous between 1900 and 1940, bears out the prevalence of important city influence in this country. Except in the period of rapid climatic temperature change occurring since about 1890, observed temperature records, with few individual exceptions, are concluded to be very misleading as direct measures of macroclimatic change over periods longer than a few decades. With their use in climatic studies, particularly those extending back of 1900, isolation of the effects of widespread urban development and frequent thermometer relocation is imperative. At average stations in the United States, urban development has contributed local temperature rises at the rate of more than 1F in a century. The influence of very large cities has not been in proportion.” [Full text]

Closely related

How Researchers Measure Urban Heat Islands – James Voogt

Does Urban Heat Island effect exaggerate global warming trends? – John Cook

Posted in AGW evidence | 3 Comments »

Papers on tropospheric temperatures

Posted by Ari Jokimäki on December 24, 2009

This is a list of papers on the temperature of the troposphere. The list is not complete, and will most likely be updated in the future in order to make it more thorough and more representative.

UPDATE (February 4, 2017): Sherwood & Nishant (2015) added. Thanks to Barry for pointing it out.
UPDATE (August 26, 2013): Gaffen et al. (2000) and Seidel et al. (2004) added, thanks to Barry for pointing them out.
UPDATE (February 8, 2011): Thorne et al. (2010) added, thanks to Jesús Rosino for pointing it out in the discussion of tropospheric hot spot list.
UPDATE (November 11, 2010): Hurrell & Trenberth (1998), Wentz & Schabel (1998), Christy et al. (1997), Hurrell & Trenberth (1997), Hansen et al. (1995), and Jones (1994) added.

Atmospheric changes through 2012 as shown by iteratively homogenized radiosonde temperature and wind data (IUKv2) – Sherwood & Nishant (2015) “We present an updated version of the radiosonde dataset homogenized by Iterative Universal Kriging (IUKv2), now extended through February 2013, following the method used in the original version (Sherwood et al 2008 Robust tropospheric warming revealed by iteratively homogenized radiosonde data J. Clim. 21 5336–52). This method, in effect, performs a multiple linear regression of the data onto a structural model that includes both natural variability, trends, and time-changing instrument biases, thereby avoiding estimation biases inherent in traditional homogenization methods. One modification now enables homogenized winds to be provided for the first time. This, and several other small modifications made to the original method sometimes affect results at individual stations, but do not strongly affect broad-scale temperature trends. Temperature trends in the updated data show three noteworthy features. First, tropical warming is equally strong over both the 1959–2012 and 1979–2012 periods, increasing smoothly and almost moist-adiabatically from the surface (where it is roughly 0.14 K/decade) to 300 hPa (where it is about 0.25 K/decade over both periods), a pattern very close to that in climate model predictions. This contradicts suggestions that atmospheric warming has slowed in recent decades or that it has not kept up with that at the surface. Second, as shown in previous studies, tropospheric warming does not reach quite as high in the tropics and subtropics as predicted in typical models. Third, cooling has slackened in the stratosphere such that linear trends since 1979 are about half as strong as reported earlier for shorter periods. Wind trends over the period 1979–2012 confirm a strengthening, lifting and poleward shift of both subtropical westerly jets; the Northern one shows more displacement and the southern more intensification, but these details appear sensitive to the time period analysed. There is also a trend toward more easterly winds in the middle and upper troposphere of the deep tropics.” Steven C Sherwood and Nidhi Nishant 2015 Environ. Res. Lett. 10 054007, DOI: [Full text]

Tropospheric temperature trends: history of an ongoing controversy – Thorne et al. (2010) “Changes in atmospheric temperature have a particular importance in climate research because climate models consistently predict a distinctive vertical profile of trends. With increasing greenhouse gas concentrations, the surface and troposphere are consistently projected to warm, with an enhancement of that warming in the tropical upper troposphere. Hence, attempts to detect this distinct ‘fingerprint’ have been a focus for observational studies. The topic acquired heightened importance following the 1990 publication of an analysis of satellite data which challenged the reality of the projected tropospheric warming. This review documents the evolution over the last four decades of understanding of tropospheric temperature trends and their likely causes. Particular focus is given to the difficulty of producing homogenized datasets, with which to derive trends, from both radiosonde and satellite observing systems, because of the many systematic changes over time. The value of multiple independent analyses is demonstrated. Paralleling developments in observational datasets, increased computer power and improved understanding of climate forcing mechanisms have led to refined estimates of temperature trends from a wide range of climate models and a better understanding of internal variability. It is concluded that there is no reasonable evidence of a fundamental disagreement between tropospheric temperature trends from models and observations when uncertainties in both are treated comprehensively.” Peter W. Thorne, John R. Lanzante, Thomas C. Peterson, Dian J. Seidel, Keith P. Shine, Wiley Interdisciplinary Reviews: Climate Change, Volume 2, Issue 1, pages 66–88, January/February 2011, DOI: 10.1002/wcc.80. [Full text]

Critically Reassessing Tropospheric Temperature Trends from Radiosondes Using Realistic Validation Experiments – Titchner et al. (2009) “Biases and uncertainties in large-scale radiosonde temperature trends in the troposphere are critically reassessed. … The homogenization system consistently reduces the bias in the daytime tropical, global, and Northern Hemisphere (NH) extratropical trends but underestimates the full magnitude of the bias. … The implications are that tropical tropospheric trends in the unadjusted daytime radiosonde observations, and in many current upper-air datasets, are biased cold, but the degree of this bias cannot be robustly quantified. Therefore, remaining biases in the radiosonde temperature record may account for the apparent tropical lapse rate discrepancy between radiosonde data and climate models. Furthermore, the authors find that the unadjusted global and NH extratropical tropospheric trends are biased cold in the daytime radiosonde observations.” [Full text]

Atmospheric temperature change detection with GPS radio occultation 1995 to 2008 – Steiner et al. (2009) “Existing upper air records of radiosonde and operational satellite data recently showed a reconciliation of temperature trends but structural uncertainties remain. GPS radio occultation (RO) provides a new high-quality record, profiling the upper troposphere and lower stratosphere with stability and homogeneity. Here we show that climate trends are since recently detected by RO data, consistent with earliest detection times estimated by simulations. … The observed trends and warming/cooling contrast across the tropopause agree well with radiosonde data and basically with climate model simulations, the latter tentatively showing less contrast.” [Full text]

Robust Tropospheric Warming Revealed by Iteratively Homogenized Radiosonde Data – Sherwood et al. (2008) “Results are presented from a new homogenization of data since 1959 from 527 radiosonde stations. … Relatively few artifacts were detected in wind shear, and associated adjustments were indistinguishable from random adjustments. Temperature artifacts were detected most often in the late 1980s–early 1990s. … The troposphere warms at least as strongly as the surface, with local warming maxima at 300 hPa in the tropics and in the boundary layer of the extratropical Northern Hemisphere (ENH). Tropospheric warming since 1959 is almost hemispherically symmetric, but since 1979 it is significantly stronger in ENH and weaker in the extratropical Southern Hemisphere (ESH). ESH trends are relatively uncertain because of poor sampling. Stratospheric cooling also remains stronger than indicated by MSU and likely excessive.” [Full text]

Toward Elimination of the Warm Bias in Historic Radiosonde Temperature Records—Some New Results from a Comprehensive Intercomparison of Upper-Air Data – Haimberger et al. (2008) “Both of the new adjusted radiosonde time series are in better agreement with satellite data than comparable published radiosonde datasets, not only for zonal means but also at most single stations. A robust warming maximum of 0.2–0.3K (10 yr)−1 for the 1979–2006 period in the tropical upper troposphere could be found in both homogenized radiosonde datasets.” [Full text]

Assessing Bias and Uncertainty in the HadAT-Adjusted Radiosonde Climate Record – McCarthy et al. (2008) “This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. … Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. … Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homogenization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from radiosondes.” [Full text]

Utility of Radiosonde Wind Data in Representing Climatological Variations of Tropospheric Temperature and Baroclinicity in the Western Tropical Pacific – Allen & Sherwood (2007) “Wind data show a slowing of the midlatitude jets in the Maritime Continent region since 1979, indicating that tropical thicknesses and temperature have increased less than those poleward of 25°N/S. This pattern is consistent with Microwave Sounding Unit temperature trends in the region but is exaggerated south of the equator in trends obtained directly from the temperature data. … These results support the use of the wind field as a way of overcoming heterogeneities in the temperature records in the monitoring of climate change patterns.” [Full text]

Tropospheric temperature change since 1979 from tropical radiosonde and satellite measurements – Christy et al. (2007) “Temperature change of the lower troposphere (LT) in the tropics (20°S–20°N) during the period 1979–2004 is examined using 58 radiosonde (sonde) stations and the microwave-based satellite data sets of the University of Alabama in Huntsville (UAH v5.2) and Remote Sensing Systems (RSS v2.1). … When the largest discontinuities in the sondes are detected and removed through comparison with UAH data, the trend of day and night releases combined becomes +0.09, and using RSS data, +0.12. Relative to several data sets, the RSS data show a warming shift, broadly occurring in 1992, of between +0.07 K and +0.13 K. Because the shift occurs at the time NOAA-12 readings began to be merged into the satellite data stream and large NOAA-11 adjustments were applied, the discrepancy appears to be due to bias adjustment procedures. Several comparisons are consistent with a 26-year trend and error estimate for the UAH LT product for the full tropics of +0.05 ± 0.07, which is very likely less than the tropical surface trend of +0.13 K decade−1.” [Full text]

Temperature Trends in the Lower Atmosphere: Steps for Understanding and Reconciling Differences – Karl et al. (2006) Report about temperature trends in stratosphere and in troposphere. [Full text of chapter 1]

Biases in Stratospheric and Tropospheric Temperature Trends Derived from Historical Radiosonde Data – Randel & Wu (2006) “Detailed comparisons of one radiosonde dataset with collocated satellite measurements from the Microwave Sounding Unit reveal time series differences that occur as step functions or jumps at many stations. These jumps occur at different times for different stations, suggesting that the differences are primarily related to problems in the radiosonde data, rather than in the satellite record. As a result of these jumps, the radiosondes exhibit systematic cooling biases relative to the satellites. A large number of the radiosonde stations in the Tropics are influenced by these biases, suggesting that cooling in the tropical lower stratosphere is substantially overestimated in these radiosonde data. Comparison of trends from stations with larger and smaller biases suggests the cooling bias extends into the tropical upper troposphere. Significant biases are observed in both daytime and nighttime radiosonde measurements.” [Full text]

Radiosonde Daytime Biases and Late-20th Century Warming – Sherwood et al. (2005) “The temperature difference between adjacent 0000 and 1200 UTC weather balloon (radiosonde) reports shows a pervasive tendency toward cooler daytime compared to nighttime observations since the 1970s, especially at tropical stations. Several characteristics of this trend indicate that it is an artifact of systematic reductions over time in the uncorrected error due to daytime solar heating of the instrument and should be absent from accurate climate records. Although other problems may exist, this effect alone is of sufficient magnitude to reconcile radiosonde tropospheric temperature trends and surface trends during the late 20th century.” [Full text]

The Effect of Diurnal Correction on Satellite-Derived Lower Tropospheric Temperature – Mears & Wentz (2005) “Satellite-based measurements of decadal-scale temperature change in the lower troposphere have indicated cooling relative to Earth’s surface in the tropics. Such measurements need a diurnal correction to prevent drifts in the satellites’ measurement time from causing spurious trends. We have derived a diurnal correction that, in the tropics, is of the opposite sign from that previously applied. When we use this correction in the calculation of lower tropospheric temperature from satellite microwave measurements, we find tropical warming consistent with that found at the surface and in our satellite-derived version of middle/upper tropospheric temperature.” [Full text]

Uncertainty in Signals of Large-Scale Climate Variations in Radiosonde and Satellite Upper-Air Temperature Datasets – Seidel et al. (2004) “There is no single reference dataset of long-term global upper-air temperature observations, although several groups have developed datasets from radiosonde and satellite observations for climate-monitoring purposes. The existence of multiple data products allows for exploration of the uncertainty in signals of climate variations and change. This paper examines eight upper-air temperature datasets and quantifies the magnitude and uncertainty of various climate signals, including stratospheric quasi-biennial oscillation (QBO) and tropospheric ENSO signals, stratospheric warming following three major volcanic eruptions, the abrupt tropospheric warming of 1976–77, and multidecadal temperature trends. Uncertainty estimates are based both on the spread of signal estimates from the different observational datasets and on the inherent statistical uncertainties of the signal in any individual dataset. The large spread among trend estimates suggests that using multiple datasets to characterize large-scale upper- air temperature trends gives a more complete characterization of their uncertainty than reliance on a single dataset. For other climate signals, there is value in using more than one dataset, because signal strengths vary. However, the purely statistical uncertainty of the signal in individual datasets is large enough to effectively encompass the spread among datasets. This result supports the notion of an 11th climate-monitoring principle, augmenting the 10 principles that have now been generally accepted (although not generally implemented) by the climate community. This 11th principle calls for monitoring key climate variables with multiple, independent observing systems for measuring the variable, and multiple, independent groups analyzing the data.” Seidel, D. J., and Coauthors, 2004: Uncertainty in Signals of Large-Scale Climate Variations in Radiosonde and Satellite Upper-Air Temperature Datasets. J. Climate, 17, 2225–2240. doi:;2. [Full text]

Stratospheric cooling and the troposphere – Gillett et al. (2004) “Here we apply the method of Fu et al. to output from a state-of-the-art coupled climate model and show that simulated tropospheric temperature trends are consistent with those observed and that their method is robust.”

Stratospheric Influences on MSU-Derived Tropospheric Temperature Trends: A Direct Error Analysis – Fu & Johanson (2004) “Retrievals of tropospheric temperature trends from data of the Microwave Sounding Unit (MSU) are subject to biases related to the strong cooling of the stratosphere during the past few decades. The magnitude of this stratospheric contamination in various retrievals is estimated using stratospheric temperature trend profiles based on observations. It is found that from 1979 to 2001 the stratospheric contribution to the trend of MSU channel-2 brightness temperature is about −0.08 K decade−1, which is consistent with the findings of Fu et al. In the retrieval method developed by Fu et al. based on a linear combination of MSU channels 2 and 4, the stratospheric influence is largely removed, leaving a residual influence of less than ±0.01 K decade−1. This method is also found to be more accurate than the angular scanning retrieval technique of Spencer and Christy to remove the stratospheric contamination.” [Full text]

Contribution of stratospheric cooling to satellite-inferred tropospheric temperature trends – Fu et al. (2004) “Here we show that trends in MSU channel 2 temperatures are weak because the instrument partly records stratospheric temperatures whose large cooling trend offsets the contributions of tropospheric warming. We quantify the stratospheric contribution to MSU channel 2 temperatures using MSU channel 4, which records only stratospheric temperatures. The resulting trend of reconstructed tropospheric temperatures from satellite data is physically consistent with the observed surface temperature trend. For the tropics, the tropospheric warming is ~ 1.6 times the surface warming, as expected for a moist adiabatic lapse rate.” [Full text]

Global Warming Trend of Mean Tropospheric Temperature Observed by Satellites – Vinnikov & Grody (2003) “We have analyzed the global tropospheric temperature for 1978 to 2002 with the use of passive microwave sounding data from the NOAA series of polar orbiters and the Earth Observing System Aqua satellite. To accurately retrieve the climatic trend, we combined the satellite data with an analytic model of temperature that contains three different time scales: a linear trend and functions that define the seasonal and diurnal cycles. Our analysis shows a trend of +0.22° to 0.26°C per 10 years, consistent with the global warming trend derived from surface meteorological stations.”

A Reanalysis of the MSU Channel 2 Tropospheric Temperature Record – Mears et al. (2003) “Results presented herein show a global trend of 0.097 ± 0.020 K decade−1, generally agreeing with the work of Prabhakara et al. but in disagreement with the MSU analysis of Christy and Spencer, which shows significantly less (0.09 K decade−1) warming. Differences in the various methodologies are discussed and it is demonstrated that the principal source of these discrepancies is in the treatment of errors due to variations in the temperature of the MSU hot calibration target.” [Full text]

Error Estimates of Version 5.0 of MSU–AMSU Bulk Atmospheric Temperatures – Christy et al. (2003) “Deep-layer temperatures derived from satellite-borne microwave sensors since 1979 are revised (version 5.0) to account for 1) a change from microwave sounding units (MSUs) to the advanced MSUs (AMSUs) and 2) an improved diurnal drift adjustment for tropospheric products. … Error estimates for TLS temperatures are less well characterized due to significant heterogeneities in the radiosonde data at high altitudes, though evidence is presented to suggest that since 1979 the trend is −0.51° ± 0.10°C decade–1.” [Full text]

Multidecadal Changes in the Vertical Temperature Structure of the Tropical Troposphere – Gaffen et al. (2000) “Trends in global lower tropospheric temperature derived from satellite observations since 1979 show less warming than trends based on surface meteorological observations. Independent radiosonde observations of surface and tropospheric temperatures confirm that, since 1979, there has been greater warming at the surface than aloft in the tropics. Associated lapse-rate changes show a decrease in the static stability of the atmosphere, which exceeds unforced static stability variations in climate simulations with state-of-the-art coupled ocean-atmosphere models. The differential temperature trends and lapse-rate changes seen during the satellite era are not sustained back to 1960.” Dian J. Gaffen, Benjamin D. Santer, James S. Boyle, John R. Christy, Nicholas E. Graham, Rebecca J. Ross, Science 18 February 2000: Vol. 287 no. 5456 pp. 1242-1245, DOI: 10.1126/science.287.5456.1242. [Full text]

Effects of orbital decay on satellite-derived lower-tropospheric temperature trends – Wentz & Schabel (1998) “The 17-year lower-tropospheric temperature record derived from the satellite Microwave Sounding Unit (MSU) shows a global cooling trend, from 1979 to 1995, of -0.05 K per decade at an altitude of about 3.5 km (refs 4, 5). Air temperatures measured at the Earth’s surface, in contrast, have risen by approximately +0.13 K per decade over the same period. The two temperature records are derived from measurements of different physical parameters, and thus are not directly comparable. In fact, the lower stratosphere is cooling substantially (by about -0.5 K per decade), so the warming trend seen at the surface is expected to diminish with altitude and change into a cooling trend at some point in the troposphere. Even so, it has been suggested that the cooling trend seen in the satellite data is excessive. The difficulty in reconciling the information from these different sources has sparked a debate in the climate community about possible instrumental problems and the existence of global warming. Here we identify an artificial cooling trend in the satellite-derived temperature series caused by previously neglected orbital-decay effects. We find a new, corrected estimate of +0.07 K per decade for the MSU-based temperature trend, which is in closer agreement with surface temperatures. We also find that the reported7 cooling of the lower troposphere, relative to the middle troposphere, is another artefact caused by uncorrected orbital-decay effects.” [Full text]

Difficulties in Obtaining Reliable Temperature Trends: Reconciling the Surface and Satellite Microwave Sounding Unit Records – Hurrell & Trenberth (1998) “A chronic difficulty in obtaining reliable climate records from satellites has been changes in instruments, platforms, equator-crossing times, and algorithms. The microwave sounding unit (MSU) tropospheric temperature record has overcome some of these problems, but evidence is presented that it too contains unreliable trends over a 17-yr period (1979–95) because of transitions involving different satellites and complications arising from nonatmospheric signals associated with the surface. The two primary MSU measures of tropospheric temperature contain different error characteristics and trends. The MSU channel 2 record exhibits a slight warming trend since 1979. Its broad vertical weighting function means that the temperature signal originates from throughout the troposphere and part of the lower stratosphere; intersatellite comparisons reveal low noise levels. Off-nadir channel 2 data are combined to provide an adjusted weighting function (called MSU 2R) without the stratospheric signal, but at a cost of an increased influence of surface emissions. Land surface microwave emissions, which account for about 20% of the total signal, depend on ground temperature and soil moisture and are subject to large variations associated with the diurnal cycle. The result is that MSU 2R noise levels are a factor of 3 larger than for MSU 2 and are sufficient to corrupt trends when several satellite records are merged. After allowing for physical differences between the satellite and surface records, large differences remain in temperature trends over the Tropics where there is a strong and deterministic coupling with the surface. The authors use linear regression with observed sea surface temperatures (SSTs) and an atmospheric general circulation model to relate the tropical MSU and surface datasets. These and alternative analyses of the MSU data, radiosonde data, and comparisons between the MSU 2R and channel 2 records, with estimates of their noise, are used to show that the downward trend in tropical MSU 2R temperatures is very likely spurious. Tropical radiosonde records are of limited use in resolving the discrepancies because of artificial trends arising from changes in instruments or sensors;however, comparisons with Australian radiosondes show a spurious downward jump in MSU 2R in mid-1991, which is not evident in MSU 2. Evaluation of reanalyzed tropical temperatures from the National Centers for Environmental Prediction and the European Centre for Medium-Range Weather Forecasts shows that they contain very different and false trends, as the analyses are only as good as the input database. Statistical analysis of the MSU 2R record objectively identifies two stepwise downward discontinuities that coincide with satellite transitions. The first is in mid-1981, prior to which only one satellite was in operation for much of the time so the diurnal cycle was not well sampled. Tropical SST anomalies over these years were small, in agreement with the Southern Oscillation index, yet the MSU 2R values were anomalously warm by 0.25°C. The second transition from NOAA-10 to NOAA-12 in mid-1991 did not involve an overlap except with NOAA-11, which suffered from a large drift in its equator-crossing times. MSU 2R anomalies have remained anomalously cold since mid-1991 by 0.1°C. Adding the two stepwise discontinuities to the tropical MSU 2R record allows it to be completely reconciled with the SST record within expected noise levels. The statistical results also make physical sense as the tropical satellite anomalies are magnified relative to SST anomalies by a factor of 1.3, which is the amplification expected following the saturated adiabatic lapse rate to the level of the peak weighting function of MSU 2R.” [Full text]

How accurate are satellite ‘thermometers’? – Christy et al. (1997) “We believe that lower-tropospheric temperatures measured directly by satellites have excellent long-term accuracy, as seen by comparisons with independent atmospheric measurements from weather balloons. Our results contradict indirect measurements by Hurrell and Trenberth1 who claimed that the satellite data have significant discontinuities.” Nature 389, 342 (25 September 1997) | doi:10.1038/38640.

Spurious trends in satellite MSU temperatures from merging different satellite records – Hurrell & Trenberth (1997) “Analysis of global surface air temperature records has indicated that recent years have been among the warmest since the late nineteenth century1, with 1995 being the warmest year on record2. But the rate of global annual mean surface warming of 0.13 °C per decade during the period 1979–95 differs substantially from the global lower-tropospheric cooling trend of – 0.05 °C per decade3 Inferred from the record (MSU-2R) of radiance measurements by the satellite Microwave Sounder Unit (MSU)4,5. Accordingly, the satellite record has been widely cited by sceptics as evidence against global warming6–10. However, a substantial fraction of the measured radiance originates not from the atmosphere but from the Earth’s surface11, and gives rise to high noise levels. This noise can lead to errors when merging temperature time series obtained from different satellites. Here we present comparisons among different MSU retrievals, sea surface temperatures (SSTs), and equivalent MSU temperatures derived from an atmospheric general circulation model forced with observed SSTs. The comparisons, focused on the tropics where atmospheric temperatures are closely tied to SSTs, strongly suggest that two spurious downward jumps occur in the MSU-2R record coinciding with changes in satellites, and that the real trend in MSU temperatures is likely to be positive, albeit small.” Nature 386, 164 – 167 (13 March 1997); doi:10.1038/386164a0.

Satellite and surface temperature data at odds? Reply to John R. Christy and Roy W. Spencer – Hansen et al. (1995) “Reply to comments by Christy and Spencer regarding Hansen and Wilson (1993), “Commentary on the Significance of Global Temperature Records”, Climatic Change, 25, 896-910.” [Full text]

Recent warming in global temperature series – Jones (1994) “Global mean temperature can be estimated from surface and from tropospheric measurements. Much has been written recently concerning trends in the various time series over short (10–15 year) periods. This paper compares the surface, 850–300 mb and the microwave sounding unit (MSU) channel 2R measurements on the ‘global’ scale. The various series show marked differences in their trends over the 1979–93 period with the surface data showing significant warming and the troposphere no change. The difference can be explained by the shortness of the record and by the transitory nature of volcanic and El Niño/Southern Oscillation effects on global temperatures. Correcting or factoring out these influences and extending the period of analysis leads to a greater conformity in the results, which show temperatures have risen by about 0.1°C per decade since 1958.” Jones, P. D. (1994), Geophys. Res. Lett., 21(12), 1149–1152, doi:10.1029/94GL01042.

Precise Monitoring of Global Temperature Trends from Satellites – Spencer & Christy (1990) “Passive microwave radiometry from satellites provides more precise atmospheric temperature information than that obtained from the relatively sparse distribution of thermometers over the earth’s surface. Accurate global atmospheric temperature estimates are needed for detection of possible greenhouse warming, evaluation of computer models of climate change, and for understanding important factors in the climate system. Analysis of the first 10 years (1979 to 1988) of satellite measurements of lower atmospheric temperature changes reveals a monthly precision of 0.01°C, large temperature variability on time scales from weeks to several years, but no obvious trend for the 10-year period. The warmest years, in descending order, were 1987, 1988, 1983, and 1980. The years 1984, 1985, and 1986 were the coolest.” [Full text]

Closely related

Papers on tropical troposphere hotspot (has some papers that are directly relevant also to the global troposphere measurements)

Apparently flawed papers

There are some papers on the issue that have been found to contain bad flaws. The papers are Douglass et al. (2007) and Klotzbach et al. (2009) and they are included to the page “Anti-AGW papers debunked”.

Posted in AGW evidence | 5 Comments »

Papers on the divergence problem

Posted by Ari Jokimäki on December 19, 2009

This is a list of papers on the divergence problem relating to tree-ring studies. The list is not complete, and will most likely be updated in the future in order to make it more thorough and more representative.

UPDATE (April 3, 2013): Zhang & Wilmking (2010), Loader et al. (2010), Martín-Benito et al. (2010), Porter & Pisaric (2011), Daux et al. (2011), Weijers et al. (2012), and Fang et al. (2012) added. Thanks to Barry for pointing them out.

Tree growth and its association with climate between individual tree-ring series at three mountain ranges in north central China – Fang et al. (2012) “Individual tree-ring series may show changed growth trends and divergent climate–growth associations even within a site, highlighting the need to examine tree growth and its climate association before building a chronology. We provided a case study for the stratification and temporal variability of tree growth and its climate associations of individual cores for three mountain ranges in north central China. Tree growth is mainly limited by moisture conditions in previous July–September and current June–August. Repeated sampling and field investigations of Picea wilsonii at Xinglong Mountain over a growth year of 2004 suggested that the growing season is from about the end of April to the end of September. It appears that the moisture conditions in previous and current growing seasons are crucial for tree growth in this region. However, a decrease in drought limitation was observed for a few tree-ring series. We thereby built the pooled chronology and sub-site chronologies with only drought-sensitive tree rings similar climate–growth relationships from the three mountain slopes. Growth disturbances of tree-ring series are detected by checking the occurrence of successively low values of the biweight series, which are treated by fitting a flexible curve.” Keyan Fang, Xiaohua Gou, Fahu Chen, Yingjun Li, Fen Zhang, Miklos Kazmer, Dendrochronologia, Volume 30, Issue 2, 2012, Pages 113–119,

No divergence in Cassiope tetragona: persistence of growth response along a latitudinal temperature gradient and under multi-year experimental warming – Weijers et al. (2012) “Background and Aims The dwarf shrub Cassiope tetragona (Arctic bell-heather) is increasingly used for arctic climate reconstructions, the reliability of which depends on the existence of a linear climate–growth relationship. This relationship was examined over a high-arctic to sub-arctic temperature gradient and under multi-year artificial warming at a high-arctic site. Methods Growth chronologies of annual shoot length, as well as total leaf length, number of leaves and average leaf length per year, were constructed for three sites. Cassiope tetragona was sampled near its cold tolerance limit at Ny-Ålesund, Svalbard, at its assumed climatic optimum in Endalen, Svalbard, and near its European southern limit at Abisko, Sweden. Together these sites represent the entire temperature gradient of this species. Leaf life span was also determined. Each growing season from 2004 to 2010, 17 open top chambers (OTCs) were placed near Ny-Ålesund, thus increasing the daily mean temperatures by 1·23°C. At the end of the 2010 growing season, shoots were harvested from OTCs and control plots, and growth parameters were measured. Key Results All growth parameters, except average leaf length, exhibited a linear positive response (R2 between 0·63 and 0·91) to mean July temperature over the temperature gradient. Average leaf life span was 1·4 years shorter in sub-arctic Sweden compared with arctic Svalbard. All growth parameters increased in response to the experimental warming; the leaf life span was, however, not significantly affected by OTC warming. Conclusions The linear July temperature–growth relationships, as well as the 7 year effect of experimental warming, confirm that the growth parameters annual shoot length, total leaf length and number of leaves per year can reliably be used for monitoring and reconstructing temperature changes. Furthermore, reconstructing July temperature from these parameters is not hampered by divergence.” Stef Weijers, Inger Greve Alsos, Pernille Bronken Eidesen, Rob Broekman, Maarten J.J.E. Loonen, and Jelte Rozema, Ann Bot (2012) 110 (3): 653-665. doi: 10.1093/aob/mcs123. [Full text]

Can climate variations be inferred from tree-ring parameters and stable isotopes from Larix decidua? Juvenile effects, budmoth outbreaks, and divergence issue – Daux et al. (2011) “Larch wood has been used for centuries as a building material. Hence, the study of the tree-ring width, the latewood maximal density, and the oxygen and carbon isotope composition of the cellulose of this tree provides potential and valuable insights when reconstructing past climate variability. We explore the qualities and limitations of these proxies, focusing on a forest standing the Névache valley (French Alps). The analysis of 15 trees demonstrates a small intra-tree variability in comparison with the inter-tree variability of δ13C and δ18O, and shows that 6 trees, at least, must be pooled to make a population-representative sample. Our results show no juvenile effect for δ13C. Unlike tree-ring width and density, δ13C and δ18O are not altered by budmouth outbreaks. These two parameters therefore appear well suited for climate reconstructions, and depict a strong correlation with July–August temperature and relative humidity. The δ18O of larch cellulose is also strongly linked with the previous winter (December–March) oxygen isotopic composition of the precipitation. This is consistent with a winter water recharge of soil and ground. The past variations of July–August maximum temperature and relative humidity were established using two different combinations of the isotopic ratios. Uncertainties on the reconstructions are estimated respectively at ± 1.4 °C and 3.6%. Inter-annual variations of temperature and relative humidity are well reproduced. However, the reconstructed July–August temperature series diverges from the instrumental one, being lower, after ca. 1990. The effects of the variation through time of the depth of source water and of the ecophysiological response of trees to rising CO2 on the isotope composition are discussed as possible causes of divergence. This ‘divergence problem’ strongly questions the possibility of producing appropriate isotope-based temperature calibration.The relationships between isotopes and the July–August relative humidity are more stationary than those with temperature. This may reflect the first order control of the relative humidity on δ13C via the stomatal conductance and its influence on the evaporative enrichment of the oxygen of the leaf-water. Our study suggests that past variations of relative humidity in the French Alps can be accurately estimated using the stable isotope composition of larch cellulose.” V. Daux, J.L. Edouard, V. Masson-Delmotte, M. Stievenard, G. Hoffmann, M. Pierre, O. Mestre, P.A. Danis, F. Guibal, Earth and Planetary Science Letters, Volume 309, Issues 3–4, 15 September 2011, Pages 221–233, [Full text]

Temperature-growth divergence in white spruce forests of Old Crow Flats, Yukon Territory, and adjacent regions of northwestern North America – Porter & Pisaric (2011) “We present a new 23-site network of white spruce ring-width chronologies near boreal treeline in Old Crow Flats, Yukon Territory, Canada. Most chronologies span the last 300 years and some reach the mid-16th century. The chronologies exhibit coherent growth patterns before the 1930s. However, since the 1930s, they diverge in trend and exhibit one of two contrasting, but well-replicated patterns we call Group 1 and Group 2. Over the instrumental period (1930–2007) Group 1 sites were inversely correlated with previous-year July temperatures while Group 2 sites were positively correlated with growth-year June temperatures. At the broader northwestern North America (NWNA) scale, we find that the Group 1 and Group 2 patterns are common to a number of white spruce chronologies, which we call NWNA 1 and NWNA 2 chronologies. The NWNA 1 and NWNA 2 chronologies also share a single coherent growth pattern prior to their divergence (ca. 1950s). Comparison of the NWNA 1/NWNA 2 chronologies against gridded 20th-century temperatures for NWNA and reconstructed northern hemisphere summer temperatures (ad 1300–2000) indicates that all sites responded positively to temperature prior to the mid-20th century (at least back to ad 1300), but that some changed to a negative response (NWNA 1) while others maintained a positive response (NWNA 2). The spatial extent of divergence implies a large-scale forcing. As the divergence appears to be restricted to the 20th century, we suggest that the temperature response shift represents a moisture stress caused by an anomalously warm, dry 20th-century climate in NWNA, as indicated by paleoclimatic records. However, because some sites do not diverge and are located within a few kilometres of divergent sites, we speculate that site-level factors have been important in determining the susceptibility of sites to the large-scale drivers of divergence.” Trevor J. Porter, Michael F. J. Pisaric, Global Change Biology, Volume 17, Issue 11, pages 3418–3430, November 2011, DOI: 10.1111/j.1365-2486.2011.02507.x.

Twentieth-century summer temperature variability in the southern Altai Mountains: A carbon and oxygen isotope study of tree-rings – Loader et al. (2010) “The Altai mountains, southern Siberia, represent an area of significant scientific interest and exceptional palaeoecological potential. To assess the influence of climate on the stable isotopic composition of tree-ring cellulose and the potential of this record as a palaeoclimate proxy, replicated stable oxygen and carbon isotope time-series were developed for the twentieth century from four Siberian Pine (Pinus sibirica Du Tour) trees growing near the settlement of Aktash, Russian Altai mountains, southern Siberia. Bootstrapped calibrations for the local instrumental period (AD 1954—2000) reveal strong correlations between summer (July—August) growing season temperatures and tree-ring oxygen (r2=0.55) and carbon (r2=0.30) isotopes. Covariance observed between both carbon and oxygen isotope data suggest a common (stomatal) control. The resulting empirical model was used to reconstruct regional summer temperatures for the twentieth century. No divergence is observed between the non-detrended tree-ring isotope series and instrumental data nor is there any twentieth-century summer warming trend. The strong isotopic signal preserved in the tree-ring series supports the wider application of this approach to explore climatic variability and environmental trends during past millennia through analysis of new and existing long tree-ring chronologies from this region.” N.J. Loader, G. Helle, S.O. Los, F. Lehmkuhl, G.H. Schleser, The Holocene November 2010 vol. 20 no. 7 1149-1156, doi: 10.1177/0959683610369507.

Divergent growth responses and increasing temperature limitation of Qinghai spruce growth along an elevation gradient at the northeast Tibet Plateau – Zhang & Wilmking (2010) “Divergent responses between tree growth and climate factors have been widely reported at high latitudes in the northern hemisphere. Here we show variable climate-growth relationships and divergent growth responses of Qinghai spruce (Picea crassfolia) along an elevation gradient at a mid-latitude site at the northeastern Tibetan Plateau, China. Trees from higher elevations, limited mainly by temperature, show divergent growth trends over time and two responses to climate. Some trees show increasing positive and some increasing negative responses to growing season temperature during the last decades. Trees from lower treeline show a strengthening drought stress signal over time and no divergent growth trends within sites. Our results indicate that single tree analysis might be a worthwhile tool to (1) uncover spatial–temporal changes in climate-growth relationships of trees, (2) better understand future growth performance and (3) help overcome current limitations of tree ring based climatic reconstructions.” Yongxiang Zhang, Martin Wilmking, Forest Ecology and Management, Volume 260, Issue 6, 15 August 2010, Pages 1076–1082, [Full text]

Black pine (Pinus nigra Arn.) growth divergence along a latitudinal gradient in Western Mediterranean mountains – Martín-Benito et al. (2010) “Most studies of tree-growth and climate report positive responses to global warming in high latitudes and negative responses at lower ones. We analyzed tree-ring width of Pinus nigra Arn. along a 500 km latitudinal transect in the Iberian Peninsula to study the temporal trend and climate forcing in tree radial growth during the last century. Tree growth was enhanced by cool summers and moist cold seasons. Increased moisture stress has decreased tree growth rates. However, we present evidence of growth increases in some trees in all sampled populations after 1980’s. Climate change negatively (positively) affected between 72% (5%) of trees in the southern populations and 40% (25%) in the north Trees with positive growth trends were favored by winter temperatures and their abundance was inversely correlated with forest productivity. Our findings add evidences of tree growth divergence in the Mediterranean basin and show the gradual transition between forests where positive (temperate and boreal) and negative (Mediterranean) growth trends dominate.” Darío Martín-Benito, Miren del Río and Isabel Cañellas, Ann. For. Sci. Volume 67, Number 4, June 2010, DOI: [Full text]

Divergence pitfalls in tree-ring research – Esper & Frank (2009) No abstract, but here’s a quote from the text: “Rather than re-iterating these arguments, or adding on non-linear statistics or other methodologies to ‘handle’ DP, we wish to take a step back to the basics and describe a number of pitfalls that may be encountered when processing and analyzing tree-ring and temperature data, and that can lead to an accidental detection of DP.” Jan Esper and David Frank, Climatic Change, Volume 94, Numbers 3-4, 261-266, DOI: 10.1007/s10584-009-9594-2. [Full text]

Changing relationships between tree growth and climate in Northwest China – Zhang et al. (2009) “Recently, several studies have shown changing relationships between tree growth and climate factors, mostly in the circumpolar north. There, changing relationships with climate seem to be linked to emergent subpopulation behavior. Here, we test for these phenomena in Northwest China using three tree species (Pinus tabulaeformis, Picea crassifolia, and Sabina przewalskii) that had been collected from six sites at Qilian Mts. and Helan Mts. in Northwest China. We first checked for growth divergence of individual sites and then investigated the relationship between tree growth and climate factors using moving correlation functions (CF). Two species, Pinus and Sabina, from two sites clearly showed growth divergence, not only in the late twentieth century as reported in other studies, but also over nearly the whole record. In divergent sites, one chronology shows more stable relationships with climate factors (usually precipitation). In non-divergent sites, nearly all relationships either vary in strength or become non-significant at one point. While this might possibly be related to increased stress on some trees due to increasing temperature, the exact causes for this shift in sensitivity remain unclear. We would like to highlight the necessity for additional studies investigating possible non-stationary growth responses of trees with climate, especially at sites that are used for climate reconstruction as our sites in Northwest China.” Yongxiang Zhang, Martin Wilmking and Xiaohua Gou, Plant Ecology, Volume 201, Number 1, 39-50, DOI: 10.1007/s11258-008-9478-y. [Full text]

Testing for tree-ring divergence in the European Alps – Büntgen et al. (2008) “Evidence for reduced sensitivity of tree growth to temperature has been reported from multiple forests along the high northern latitudes. This alleged circumpolar phenomenon described the apparent inability of temperature-sensitive tree-ring width and density chronologies to parallel increasing instrumental temperature measurements since the mid-20th century. In addition to such low-frequency trend offset, the inability of formerly temperature-sensitive tree growth to reflect high-frequency temperature signals in a warming world is indicated at some boreal sites, mainly in Alaska, the Yukon and Siberia. Here, we refer to both of these findings as the ‘divergence problem’ (DP), with their causes and scale being debated. If DP is widespread and the result of climatic forcing, the overall reliability of tree-ring-based temperature reconstructions should be questioned. Testing for DP benefits from well-replicated tree-ring and instrumental data spanning from the 19th to the 21st century. Here, we present a network of 124 larch and spruce sites across the European Alpine arc. Tree-ring width chronologies from 40 larch and 24 spruce sites were selected based on their correlation with early (1864–1933) instrumental temperatures to assess their ability of tracking recent (1934–2003) temperature variations. After the tree-ring series of both species were detrended in a manner that allows low-frequency variations to be preserved and scaled against summer temperatures, no unusual late 20th century DP is found. Independent tree-ring width and density evidence for unprecedented late 20th century temperatures with respect to the past millennium further reinforces our results.” Ulf Büntgen, David Frank, Rob Wilson, Marco Carrer, Carlo Urbinati, Jan Esper, Global Change Biology, Volume 14, Issue 10, pages 2443–2453, October 2008, DOI: 10.1111/j.1365-2486.2008.01640.x. [Full text]

On the ‘Divergence Problem’ in Northern Forests: A review of the tree-ring evidence and possible causes – D’Arrigo et al. (2007) “An anomalous reduction in forest growth indices and temperature sensitivity has been detected in tree-ring width and density records from many circumpolar northern latitude sites since around the middle 20th century. This phenomenon, also known as the “divergence problem”, is expressed as an offset between warmer instrumental temperatures and their underestimation in reconstruction models based on tree rings. The divergence problem has potentially significant implications for large-scale patterns of forest growth, the development of paleoclimatic reconstructions based on tree-ring records from northern forests, and the global carbon cycle. Herein we review the current literature published on the divergence problem to date, and assess its possible causes and implications. The causes, however, are not well understood and are difficult to test due to the existence of a number of covarying environmental factors that may potentially impact recent tree growth. … Although limited evidence suggests that the divergence may be anthropogenic in nature and restricted to the recent decades of the 20th century, more research is needed to confirm these observations.” [Full text]

A matter of divergence: Tracking recent warming at hemispheric scales using tree ring data – Wilson et al. (2007) “In this study, we compiled TR data and published local/regional reconstructions that show no divergence against local temperatures. These data have not been included in other large-scale temperature reconstructions. Utilizing this data set, we developed a new, completely independent reconstruction of ENH annual temperatures (1750–2000). This record is not meant to replace existing reconstructions but allows some degree of independent validation of these earlier studies as well as demonstrating that TR data can better model recent warming at large scales when careful selection of constituent chronologies is made at the local scale. Although the new series tracks the increase in ENH annual temperatures over the last few decades better than any existing reconstruction, it still slightly under predicts values in the post-1988 period.” [Full text]

Growth/climate response shift in a long subalpine spruce chronology – Büntgen et al. (2006) “Decreasing (increasing) moving correlations with monthly mean temperatures (precipitation) indicate instable growth/climate response during the 1760–2002 period. Crucial June–August temperatures before ∼1900 shift towards May-July temperature plus August precipitation sensitivity after ∼1900. Numerous of comparable subalpine spruce chronologies confirm increased late-summer drought stress, coincidently with the recent warming trend.”

Divergent tree growth response to recent climatic warming, Lake Clark National Park and Preserve, Alaska – Driscoll et al. (2005) “One subpopulation diverges from historical temperature data after 1950 and one shows increased growth consistent with warming or exceeds expected growth increases. The growth decline may be due to temperature‐induced drought stress that acts on some trees. Unprecedented climatic changes are triggering diverse growth responses between and within study sites that may greatly complicate dendroclimatic reconstructions of past climate conditions.”

Recent climate warming forces contrasting growth responses of white spruce at treeline in Alaska through temperature thresholds – Wilmking et al. (2004) “Our findings of both positive and negative growth responses to climate warming at treeline challenge the widespread assumption that arctic treeline trees grow better with warming climate. High mean temperatures in July decreased the growth of 40% of white spruce at treeline areas in Alaska, whereas warm springs enhance growth of additional 36% of trees and 24% show no significant correlation with climate. Even though these opposing growth responses are present in all sampled sites, their relative proportion varies between sites and there is no overall clear relationship between growth response and landscape position within a site. Growth increases and decreases appear in our sample above specific temperature index values (temperature thresholds), which occurred more frequently in the late 20th century.” [Full text]

Thresholds for warming-induced growth decline at elevational tree line in the Yukon Territory, Canada – D’Arrigo et al. (2004) “Here we identify a temperature threshold for decline in a tree ring record from a well-established temperature-sensitive site at elevational tree line in northwestern Canada. The positive ring width/temperature relationship has weakened such that a pre-1965 linear model systematically overpredicts tree ring widths from 1965 to 1999. A nonlinear model shows an inverted U-shaped relationship between this chronology and summer temperatures, with an optimal July–August average temperature of 11.3°C based on a nearby station. This optimal value has been consistently exceeded since the 1960s, and the concurrent decline demonstrates that even at tree line, trees can be negatively affected when temperatures warm beyond a physiological threshold.” [Full text]

Large-scale temperature inferences from tree rings: a review – Briffa et al. (2004) “However, in many tree-ring chronologies, we do not observe the expected rate of ring density increases that would be compatible with observed late 20th century warming. This changing climate sensitivity may be the result of other environmental factors that have, since the 1950s, increasingly acted to reduce tree-ring density below the level expected on the basis of summer temperature changes. This prevents us from claiming unprecedented hemispheric warming during recent decades on the basis of these tree-ring density data alone. Here we show very preliminary results of an investigation of the links between recent changes in MXD and ozone (the latter assumed to be associated with the incidence of UV radiation at the ground).”

Dendroclimatic reconstruction of maximum summer temperatures from upper treeline sites in Interior British Columbia, Canada – Wilson & Luckman (2003) “Significant changes are also noted in the relationships between summer mean, maximum and minimum temperatures in this region in the last few decades with a greater absolute rate of increase in mean and minimum temperatures. These changing relationships suggest it is prudent to model tree-ring response to a variety of temperature parameters rather than using mean-temperature values.” [Full text]

Boreal temperature variability inferred from maximum latewood density and tree-ring width data, Wrangell Mountain region, Alaska – Davi et al. (2003) “After around 1970 the RW series show a decrease in growth, while station data show continued warming, which may be related to increasing moisture stress or other factors.”

Spatial and Temporal Variability in the Growth and Climate Response of Treeline Trees in Alaska – Lloyd & Fastie (2002) “We found that there was substantial regional variability in response to climate variation. Contrary to our expectations, we found that after 1950 warmer temperatures were associated with decreased tree growth in all but the wettest region, the Alaska Range. Although tree growth increased from 1900–1950 at almost all sites, significant declines in tree growth were common after 1950 in all but the Alaska Range sites. We also found that there was substantial variability in response to climate variation according to distance to treeline. Inverse growth responses to temperature were more common at sites below the forest margin than at sites at the forest margin. Together, these results suggest that inverse responses to temperature are widespread, affecting even the coldest parts of the boreal forest. Even in such close proximity to treeline, warm temperatures after 1950 have been associated with reduced tree growth. Growth declines were most common in the warmer and drier sites, and thus support the hypothesis that drought-stress may accompany increased warming in the boreal forest.” [Full text]

Long-Term Temperature Trends and Tree Growth in the Taymir Region of Northern Siberia – Jacoby et al. (2000) “These warm-season temperatures correlate with annual temperatures and indicate unusual warming in the 20th century. However, there is a loss of thermal response in ring widths since about 1970. Previously the warmer temperatures induced wider rings.” [Full text]

Reduced growth of Alaskan white spruce in the twentieth century from temperature-induced drought stress – Barber et al. (1999) “Here we present multi-proxy tree-ring data (ring width, maximum late-wood density and carbon-isotope composition) from 20 productive stands of white spruce in the interior of Alaska. The tree-ring records show a strong and consistent relationship over the past 90 years and indicate that, in contrast with earlier predictions, radial growth has decreased with increasing temperature. Our data show that temperature-induced drought stress has disproportionately affected the most rapidly growing white spruce, suggesting that, under recent climate warming, drought may have been an important factor limiting carbon uptake in a large portion of the North American boreal forest. If this limitation in growth due to drought stress is sustained, the future capacity of northern latitudes to sequester carbon may be less than currently expected.”

Influence of snowfall and melt timing on tree growth in subarctic Eurasia – Vaganov et al. (1999) “Here we report an analysis of tree-ring and climate data from the forest–tundra zone, in combination with a mechanistic model of tree-ring growth, to argue that an increasing trend of winter precipitation over the past century in many subarctic regions led to delayed snow melt in these permafrost environments. As a result, the initiation of cambial activity (necessary for the formation of wood cells) has been delayed relative to the pre-1960 period in the Siberian subarctic. Since the early 1960s, less of the growth season has been during what had previously been the period of maximal growth sensitivity to temperature. This shift results not only in slower growth, but also in a reduced correlation between growth and temperature.” [Full text]

Trees tell of past climates: but are they speaking less clearly today? – Briffa et al. (1998) “However, a dramatic change in the sensitivity of hemispheric tree–growth to temperature forcing has become apparent during recent decades, and there is additional evidence of major tree–growth (and hence, probably, ecosystem biomass) increases in the northern boreal forests, most clearly over the last century. These possibly anthropogenically related changes in the ecology of tree growth have important implications for modelling future atmospheric CO2 concentrations. Also, where dendroclimatology is concerned to reconstruct longer (increasingly above centennial) temperature histories, such alterations of ‘normal’ (pre–industrial) tree–growth rates and climate–growth relationships must be accounted for in our attempts to translate the evidence of past tree growth changes.” [Full text]

Reduced sensitivity of recent tree-growth to temperature at high northern latitudes – Briffa et al. (1998) “During the second half of the twentieth century, the decadal-scale trends in wood density and summer temperatures have increasingly diverged as wood density has progressively fallen. The cause of this increasing insensitivity of wood density to temperature changes is not known, but if it is not taken into account in dendroclimatic reconstructions, past temperatures could be overestimated. Moreover, the recent reduction in the response of trees to air-temperature changes would mean that estimates of future atmospheric CO2 concentrations, based on carbon-cycle models that are uniformly sensitive to high-latitude warming, could be too low.”

Tree Ring Width and Density Evidence of Climatic and Potential Forest Change in Alaska – Jacoby & D’Arrigo (1995) “Ring width and density measurements from the same trees can produce distinctly different climatic information. … The recent increase in temperatures combined with drier years may be changing the tree response to climate and raising the potential for some forest changes in Alaskan and other boreal forests.” [Full text]

Closely related

Delayed.oscillator – Yamal Emulation II: Divergence
John Cook – The hockey stick divergence problem

Posted in Climate science | 9 Comments »

Papers on climate feedback

Posted by Ari Jokimäki on December 17, 2009

This is a list of papers on climate feedback. Basically, this is second part of papers on climate sensitivity, but here papers that concentrate on determining climate feedback parameter are listed. Note that list contains both model and observational analyses. The list is not complete, and will most likely be updated in the future in order to make it more thorough and more representative.

UPDATE (November 21, 2019): Dessler (2013) added.
UPDATE (March 23, 2011): Coakley (1977), Hansen et al. (1984), and Cacuci & Hall (1984) added.
UPDATE (October 23, 2010): Kellogg (1983) added.
UPDATE (September 9, 2010): Cess (1976) added.

Shortwave and longwave radiative contributions to global warming under increasing CO2 – Donohoe et al. (2014) [Full text]
Abstract: In response to increasing concentrations of atmospheric CO2, high-end general circulation models (GCMs) simulate an accumulation of energy at the top of the atmosphere not through a reduction in outgoing longwave radiation (OLR)—as one might expect from greenhouse gas forcing—but through an enhancement of net absorbed solar radiation (ASR). A simple linear radiative feedback framework is used to explain this counterintuitive behavior. It is found that the timescale over which OLR returns to its initial value after a CO2 perturbation depends sensitively on the magnitude of shortwave (SW) feedbacks. If SW feedbacks are sufficiently positive, OLR recovers within merely several decades, and any subsequent global energy accumulation is because of enhanced ASR only. In the GCM mean, this OLR recovery timescale is only 20 y because of robust SW water vapor and surface albedo feedbacks. However, a large spread in the net SW feedback across models (because of clouds) produces a range of OLR responses; in those few models with a weak SW feedback, OLR takes centuries to recover, and energy accumulation is dominated by reduced OLR. Observational constraints of radiative feedbacks—from satellite radiation and surface temperature data—suggest an OLR recovery timescale of decades or less, consistent with the majority of GCMs. Altogether, these results suggest that, although greenhouse gas forcing predominantly acts to reduce OLR, the resulting global warming is likely caused by enhanced ASR.
Citation: Aaron DonohoeKyle C. ArmourAngeline G. PendergrassDavid S. Battisti (2014).

Observations of Climate Feedbacks over 2000–10 and Comparisons to Climate Models – Dessler (2013) [Full text]
Abstract: Feedbacks in response to climate variations during the period 2000–10 have been calculated using reanalysis meteorological fields and top-of-atmosphere flux measurements. Over this period, the climate was stabilized by a strongly negative temperature feedback (~−3 W m−2 K−1); climate variations were also amplified by a strong positive water vapor feedback (~+1.2 W m−2 K−1) and smaller positive albedo and cloud feedbacks (~+0.3 and +0.5 W m−2 K−1, respectively). These observations are compared to two climate model ensembles, one dominated by internal variability (the control ensemble) and the other dominated by long-term global warming (the A1B ensemble). The control ensemble produces global average feedbacks that agree within uncertainties with the observations, as well as producing similar spatial patterns. The most significant discrepancy was in the spatial pattern for the total (shortwave + longwave) cloud feedback. Feedbacks calculated from the A1B ensemble show a stronger negative temperature feedback (due to a stronger lapse-rate feedback), but that is cancelled by a stronger positive water vapor feedback. The feedbacks in the A1B ensemble tend to be more smoothly distributed in space, which is consistent with the differences between El Niño–Southern Oscillation (ENSO) climate variations and long-term global warming. The sum of all of the feedbacks, sometimes referred to as the thermal damping rate, is −1.15 ± 0.88 W m−2 K−1 in the observations and −0.60 ± 0.37 W m−2 K−1 in the control ensemble. Within the control ensemble, models that more accurately simulate ENSO tend to produce thermal damping rates closer to the observations. The A1B ensemble average thermal damping rate is −1.26 ± 0.45 W m−2 K−1.
Citation: Dessler, A.E., 2013: Observations of Climate Feedbacks over 2000–10 and Comparisons to Climate Models. J. Climate, 26, 333–342,

Climate feedbacks determined using radiative kernels in a multi-thousand member ensemble of AOGCMs – Sanderson et al. (2009) “We apply the radiative kernel technique to transient simulations from a multi-thousand member perturbed physics ensemble of coupled atmosphere-ocean general circulation models, comparing distributions of model feedbacks with those taken from the CMIP-3 multi GCM ensemble. Although the range of clear sky longwave feedbacks in the perturbed physics ensemble is similar to that seen in the multi-GCM ensemble, the kernel technique underestimates the net clear-sky feedbacks (or the radiative forcing) in some perturbed models with significantly altered humidity distributions.” [Full text]

Atmospheric radiative feedbacks associated with transient climate change and climate variability – Colman & Power (2009) “This study examines in detail the ‘atmospheric’ radiative feedbacks operating in a coupled General Circulation Model (GCM). These feedbacks (defined as the change in top of atmosphere radiation per degree of global surface temperature change) are due to responses in water vapour, lapse rate, clouds and surface albedo. Two types of radiative feedback in particular are considered: those arising from century scale ‘transient’ warming (from a 1% per annum compounded CO2 increase), and those operating under the model’s own unforced ‘natural’ variability.”

Tropospheric Adjustment Induces a Cloud Component in CO2 Forcing – Gregory & Webb (2008) “The radiative forcing of CO2 and the climate feedback parameter are evaluated in several climate models with slab oceans by regressing the annual-mean global-mean top-of-atmosphere radiative flux against the annual-mean global-mean surface air temperature change ΔT following a doubling of atmospheric CO2 concentration. The method indicates that in many models there is a significant rapid tropospheric adjustment to CO2 leading to changes in cloud, and reducing the effective radiative forcing, in a way analogous to the indirect and semidirect effects of aerosol. … Tropospheric adjustment to CO2 may be responsible for some of the model spread in equilibrium climate sensitivity and could affect time-dependent climate projections.” [Full text]

CO2 forcing induces semi-direct effects with consequences for climate feedback interpretations – Andrews & Forster (2008) “All models show a cloud semi-direct effect, indicating a rapid cloud response to CO2; cloud typically decreases, enhancing the warming. Similarly there is evidence of semi-direct effects from water-vapour, lapse-rate, ice and snow. Previous estimates of climate feedbacks are unlikely to have taken these semi-direct effects into account and so misinterpret processes as feedbacks that depend only on the forcing, but not the global surface temperature.” [Full text]

An assessment of the primary sources of spread of global warming estimates from coupled atmosphere-ocean models – Dufresne & Bony (2008) “Here these contributions from the classical feedback analysis framework are defined and quantified for an ensemble of 12 third phase of the Coupled Model Intercomparison Project (CMIP3)/Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) coupled atmosphere–ocean GCMs. In transient simulations, the multimodel mean contributions to global warming associated with the combined water vapor–lapse-rate feedback, cloud feedback, and ocean heat uptake are comparable.” [Full text]

Potential Biases in Feedback Diagnosis from Observational Data: A Simple Model Demonstration – Spencer & Braswell (2008) “Here a simple model is used to demonstrate that any nonfeedback source of top-of-atmosphere radiative flux variations can cause temperature variability, which then results in a positive bias in diagnosed feedbacks. This effect is demonstrated with daily random flux variations, as might be caused by stochastic fluctuations in low cloud cover.” [Full text]

The Climate Sensitivity and Its Components Diagnosed from Earth Radiation Budget Data – Forster & Gregory (2006) “Here, data are combined from the 1985–96 Earth Radiation Budget Experiment (ERBE) with surface temperature change information and estimates of radiative forcing to diagnose the climate sensitivity. Importantly, the estimate is completely independent of climate model results. A climate feedback parameter of 2.3 ± 1.4 W m−2 K−1 is found. This corresponds to a 1.0–4.1-K range for the equilibrium warming due to a doubling of carbon dioxide (assuming Gaussian errors in observable parameters, which is approximately equivalent to a uniform “prior” in feedback parameter).” [Full text]

How Well Do We Understand and Evaluate Climate Change Feedback Processes? – Bony et al. (2006) A review article. “By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations.” [Full text]

An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models – Soden & Held (2006) “Water vapor is found to provide the largest positive feedback in all models and its strength is consistent with that expected from constant relative humidity changes in the water vapor mixing ratio. The feedbacks from clouds and surface albedo are also found to be positive in all models, while the only stabilizing (negative) feedback comes from the temperature response. Large intermodel differences in the lapse rate feedback are observed and shown to be associated with differing regional patterns of surface warming.” [Full text]

A new method for diagnosing radiative forcing and climate sensitivity – Gregory et al. (2004) “We describe a new method for evaluating the radiative forcing, the climate feedback parameter (W m−2 K−1) and hence the effective climate sensitivity from any GCM experiment in which the climate is responding to a constant forcing. The method is simply to regress the top of atmosphere radiative flux against the global average surface air temperature change. … We show that for CO2 and solar forcing in a slab model and an AOGCM the method gives results consistent with those obtained by conventional methods.” [Full text]

Interactions among Cloud, Water Vapor, Radiation, and Large-Scale Circulation in the Tropical Climate. Part I: Sensitivity to Uniform Sea Surface Temperature Changes – Larson & Hartmann (2003) “The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model is used with doubly periodic boundary conditions and a uniform constant sea surface temperature (SST). The SST is varied and the equilibrium statistics of cloud properties, water vapor, and circulation at different temperatures are compared. … The clear-sky mean temperature and water vapor feedbacks have similar magnitudes to each other and opposite signs. The net clear-sky feedback is thus about equal to the lapse rate feedback, which is about −2 W m−2 K−1. … The clear-sky outgoing longwave radiation (OLR) thus increases with SST, but the high cloud-top temperature is almost constant with SST, and the high cloud amount increases with SST. The result of these three effects is an increase of cloud longwave forcing with SST and a mean OLR that is almost independent of SST. The high cloud albedo remains almost constant with increasing SST, but the increase in high cloud area causes a negative shortwave cloud radiative forcing feedback, which partly cancels the longwave cloud feedback.” [Full text]

Intercomparison and Interpretation of Climate Feedback Processes in 19 Atmospheric General Circulation Models – Cess et al. (1990) “The need to understand differences among general circulation model projections of CO2-induced climatic change has motivated the present study, which provides an intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. … A roughly threefold variation in one measure of global climate sensitivity is found among the 19 models. The important conclusion is that most of this variation is attributable to differences in the models’ depiction of cloud feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as reliable climate predictors.” [Full text]

Climate sensitivity: Analysis of feedback mechanisms – Hansen et al. (1984) “We study climate sensitivity and feedback processes in three independent ways: (1) by using three dimensional (3-D) global climate model for experiments in which solar irradiance So is increased 2 percent or CO2 is doubled, (2) by using CLIMAP climate boundary conditions to analyze the contributions of different physical processes to the cooling of the last ice age (18K years ago), and (3) by using estimated changes in global temperature and the abundance of atmospheric greenhouse gases to deduce an empirical climate sensitivity for the period 1850-1980. Our 3-D global climate model yields a warming of ~4˚C for either a 2 percent increase of So or doubled CO2. This indicates a net feedback factor of f = 3-4, because either of these forcings would cause the earth’s surface temperature to warm 1.2-1.3˚C to restore radiative balance with space, if other factors remained unchanged. Principal positive feedback processes in the model are changes in atmospheric water vapor, clouds and snow/ice cover. Feedback factors calculated for these processes, with atmospheric dynamical feedbacks implicitly incorporated, are respectively fwater vapor ~ 1.6, fclouds ~ 1.3 and fsnow/ice ~ 1.1, with the latter mainly caused by sea ice changes. A number of potential feedbacks, such as land ice cover, vegetation cover and ocean heat transport were held fixed in these experiments. We calculate land ice, sea ice and vegetation feedbacks for the 18K climate to be fland ice ~ 1.2-1.3, fsea ice ~ 1.2, and fvegetation ~ 1.05-1.1 from their effect on the radiation budget at the top of the atmosphere. This sea ice feedback at 18K is consistent with smaller fsnow/ice ~ 1.1 in the So and CO2 experiments, which applied to a warmer earth with less sea ice. We also obtain an empirical estimate of f = 2-4 for the fast feedback processes (water vapor, clouds, sea ice) operating on 10-100 year time scales by comparing the cooling due to slow or specified changes (land ice, CO2, vegetation) to the total cooling at 18K. The temperature increase believed to have occurred in the past 130 years (approximately 0.5˚C) is also found to imply a climate sensitivity of 2.5-5˚C for doubled CO2 (f = 2-4), if (1) the temperature increase is due to added greenhouse gases, (2) the 1850 CO2 abundance was 270±10 ppm, and (3) the heat perturbation is mixed like a passive tracer in the ocean with vertical mixing coefficient k ~ 1 cm2 s-1. These analyses indicate that f is substantially greater than unity on all time scales. Our best estimate for the current climate due to processes operating on the 10-100 year time scale is f = 2-4, corresponding to a climate sensitivity of 2.5-5˚C for doubled CO2. The physical process contributing the greatest uncertainty to f on this time scale appears to be cloud feedback. We show that the ocean’s thermal relaxation time depends strongly on f. The e-folding time constant for response of the isolated ocean mixed layer is about 15 years, for the estimated value of f. This time is sufficiently long to allow substantial heat exchange between the mixed layer and deeper layers. For f = 3-4 the response time of the surface temperature to a heating perturbation is of order 100 years, if the perturbation is sufficiently small that it does not alter the rate of heat exchange with the deeper ocean. The climate sensitivity we have inferred is larger than that stated in the Carbon Dioxide Assessment Committee report (CDAC, 1983). Their result is based on the empirical temperature increase in the past 130 years, but their analysis did not account for the dependence of the ocean response time on climate sensitivity. Their choice of a fixed 15 year response time biased their result to low sensitivities. We infer that, because of recent increases in atmospheric CO2 and trace gases, there is a large, rapidly growing gap between current climate and the equilibrium climate for current atmospheric composition. Based on the climate sensitivity we have estimated, the amount of greenhhouse gases presently in the atmosphere will cause an eventual global mean warming of about 1˚C, making the global temperature at least comparable to that of the Altithermal, the warmest period in the past 100,000 years. Projection of future climate trends on the 10-100 year time scale depends crucially upon improved understanding of ocean dynamics, particularly upon how ocean mixing will respond to climate change at the ocean surface.” [Full text]

Efficient Estimation of Feedback Effects with Application to Climate Models – Cacuci & Hall (1984) “This work presents an efficient method for calculating the sensitivity of a mathematical model’s result to feedback. Feedback is defined in terms of an operator acting on the model’s dependent variables. The sensitivity to feedback is defined as a functional derivative, and a method is presented to evaluate this derivative using adjoint functions. Typically, this method allows the individual effect of many different feedbacks to be estimated with a total additional computing time comparable to only one recalculation. The effects on a C02-doubling experiment of actually incorporating surface albedo and water vapor feedbacks in a radiative-convective model are compared with sensitivities calculated using adjoint functions. These sensitivities predict the actual effects of feedback with at least the correct sign and order of magnitude. It is anticipated that this method of estimating the effect of feedback will be useful for more complex models where extensive recalculations for each of a variety of different feedbacks is impractical.” Cacuci, Dan G., Matthew C. G. Hall, 1984, J. Atmos. Sci., 41, 2063–2068. [Full text]

Feedback Mechanisms in the Climate System Affecting Future Levels of Carbon Dioxide – Kellogg (1983) “The rate of increase of concentration of atmospheric carbon dioxide depends on the consumption of fossil fuels (the major source of ‘new’ carbon dioxide) and the natural sinks for this trace constituent, primarily the oceans and the biosphere. (It is now fairly well established that the biosphere cannot be a major source, as has been claimed.) The rate of operation of these sinks depends on several factors determined by the state of the climate system, and they will therefore presumably change as the greenhouse effect of increasing carbon dioxide warms the earth. Five specific feedback loops are discussed, two of which are positive (amplifying the rate of increase), two are weakly negative (damping the rate of increase), and one is indeterminate but probably positive. It is concluded that it would be well to be prepared for the possibility that carbon dioxide may increase faster than predicted by models based on the current or past state of the climate system.” Kellogg, W. W. (1983), J. Geophys. Res., 88(C2), 1263–1269, doi:10.1029/JC088iC02p01263.

Feedbacks in Vertical-Column Energy Balance Models – Coakley (1977) “Order-of-magnitude estimates are made for the feedbacks between the global mean surface temperature and the mean tropospheric lapse rate, the mean surface relative humidity and the mean atmospheric relative humidity profile. It is found that with the possible exception of the surface temperature-surface relative humidity feedback the magnitudes of these feedback are not sufficient to significantly alter the sensitivity of the global mean surface temperature to changes in the solar constant as calculated using a vertical-column energy balance model of the earth’s atmosphere.” Coakley, James A., 1977, J. Atmos. Sci., 34, 465–470. [Full text]

Climate Change: An Appraisal of Atmospheric Feedback Mechanisms Employing Zonal Climatology – Cess (1976) “The sensitivity of the earth’s surface temperature to factors which can induce long-term climate change, such as a variation in solar constant, is estimated by employing two readily observable climate changes. One is the latitudinal change in annual mean climate, for which an interpretation of climatological data suggests that cloud amount is not a significant climate feedback mechanism, irrespective of how cloud amount might depend upon surface temperature, since there are compensating changes in both the solar and infrared optical properties of the atmosphere. It is further indicated that all other atmospheric feedback mechanisms, resulting, for example, from temperature-induced changes in water vapor amount, cloud altitude and lapse rate, collectively double the sensitivity of global surface temperature to a change in solar constant. The same conclusion is reached by considering a second type of climate change, that associated with seasonal variations for a given latitude zone. The seasonal interpretation further suggests that cloud amount feedback is unimportant zonally as well as globally. Application of the seasonal data required a correction for what appears to be an important seasonal feedback mechanism. This is attributed to a variability in cloud albedo due to seasonal changes in solar zenith angle. No attempt was made to individually interpret the collective feedback mechanisms which contribute to the doubling in surface temperature sensitivity. It is suggested, however, that the conventional assumption of fixed relative humidity for describing feedback due to water vapor amount might not be as applicable as is generally believed. Climate models which additionally include ice-albedo feedback are discussed within the framework of the present results.” Cess, Robert D., 1976, J. Atmos. Sci., 33, 1831–1843. [Full text]

Posted in AGW evidence | 7 Comments »

Papers on CO2 emissions from volcanoes

Posted by Ari Jokimäki on December 14, 2009

This is a list of papers on carbon dioxide emissions from the volcanoes. Some global analysis papers are given and some examples of analysis of individual volcanoes. The list is not complete, and will most likely be updated in the future in order to make it more thorough and more representative.

UPDATE (December 26, 2014): Burton et al. (2013) added, thanks to mlnfr for pointing it out.
UPDATE (November 24, 2014): Gerlach (2011) added, thanks to Barry for pointing it out.

Deep carbon emissions from volcanoes – Burton et al. (2013) No abstract. Michael R. Burton, Georgina M. Sawyer, Domenico Granieri, 2013: Deep carbon emissions from volcanoes, Reviews in Mineralogy and Geochemistry, 75, 1, 323-354, doi: 10.2138/rmg.2013.75.11 .” [Full text]

Volcanic versus anthropogenic carbon dioxide – Gerlach (2011) “Which emits more carbon dioxide (CO2): Earth’s volcanoes or human activities? Research findings indicate unequivocally that the answer to this frequently asked question is human activities. However, most people, including some Earth scientists working in fields outside volcanology, are surprised by this answer. The climate change debate has revived and reinforced the belief, widespread among climate skeptics, that volcanoes emit more CO2 than human activities [Gerlach, 2010; Plimer, 2009]. In fact, present-day volcanoes emit relatively modest amounts of CO2, about as much annually as states like Florida, Michigan, and Ohio.” Gerlach, T., (2011), Volcanic versus anthropogenic carbon dioxide, Eos Trans. AGU, 92(24), 201, DOI: 10.1029/2011EO240001. [Full text]

Feedback between deglaciation and volcanic emissions of CO2 – Huybers & Langmuir (2009) “A global reconstruction of subaerial volcanic activity over the last 40 Kyr shows a pervasive high-latitude increase in volcanism between 12 Ka and 7 Ka that more than doubles global volcanic activity. This increase can be understood as a consequence of melt generated in response to deglacial decompression. We estimate that increased volcanism during this 5 Ka period emitted an additional 1000 to 5000 Gt of CO2 into the atmosphere. Such a flux is consistent in timing and magnitude with ice core observations of a 40 ppm increase in atmospheric CO2 concentration during the second half of the last deglaciation. Anomalous volcanic emissions also persist later into the Holocene, and it appears that elevated volcanic activity helps maintain high levels of CO2 during interglacials.” [Full text]

Volcanic Contributions to the Global Carbon Cycle – Hards (2005) “The contribution to the present day atmospheric CO2 loading from volcanic emissions is, however, relatively insignificant, and it has been estimated that subaerial volcanism releases around 300 Mt/yr CO2, equivalent to just 1 % of anthropogenic emissions (Morner & Etiope, 2002).” [Full text]

Carbon degassing from the lithosphere – Mörner & Etiope (2002) “…it seems realistic to assume 300 Mt/year as lower limit of the global CO2 emission from subaerial volcanoes.” [Full text]

Global carbon dioxide emission to the atmosphere by volcanoes – Williams et al. (1992) “Global emission of carbon dioxide by subaerial volcanoes is calculated, using CO2/SO2 from volcanic gas analyses and SO2 flux, to be 34 ± 24 x 1012 g CO2/yr from passive degassing and 31 ± 22 x 1012 g CO2/yr from eruptions. Volcanic CO2 presently represents only 0.22% of anthropogenic emissions but may have contributed to significant “greenhouse” effects at times in Earth history.”

Carbon sources in arc volcanism, with implications for the carbon cycle – Varekamp et al. (1992) “The modem combined processes of MOR volcanism, slab alteration, and subduction volcanism do not produce a substantial carbon flux into the exosphere, and rate-changes in ocean floor spreading are unlikely to cause major changes in atmospheric CO2 as a result of changes in the volcanic CO2 fluxes.”

Volatile fluxes from volcanoes – Le Cloarec & Marty (1991) “Volatile fluxes from Mid Ocean Ridge (MOR) and subaerial volcanism have been estimated or re-evaluated using several natural tracers-3He, 210Po, SO2-and chemical ratios of volatile species in lavas and volcanic gases. These estimates confirm the net predominance of anthropogenic fluxes over volcanic fluxes for CO2, SO2 and trace metals.”

Annual volcanic carbon dioxide emission: An estimate from eruption chronologies – Leavitt (1982) “This study examines eruption chronologies to determine a new estimate of the volcanic CO2 input and to test if temporal fluctuations may be resolved. Employing representative average values of 2.7 g cm−3 as density of erupted material, 0.2 wt percent CO2 in the original melt, 60 percent degassing during eruption, and an average volume of 0.1 km3 for each of the eruptions in the recently published eruption chronology of Hirschboeck (1980), a volcanic input of about 1.5 · 1011 moles CO2 yr−1 was determined for the period 1800–1969. … This input is well below man’s current CO2 production of 4–5 · 1014 moles CO2 yr−1.”

Volcanic Contributions to the Atmosphere and Ocean – Cotton (1944) “IF it be assumed, as is now again the fashion, that the nascent earth passed through a liquid stage, it is obvious that “the molten spheroid … retained, occluded within itself, some large part of the water in the present hydrosphere, as well as much ot the carbon dioxide represented by the present carbonates and carbonaceous deposits”1. Most of the carbon dioxide that has become available as a source of carbon is undoubtedly of volcanic origin, being derived from magma.”

Individual volcanoes

CO2 emissions from the Yellowstone volcanic system – Werner & Brantley (2003) “Two methods are used to estimate CO2 degassing from the Yellowstone magmatic-hydrothermal system. … Comparison of modeled estimates with surface measurements suggests that 3.7 ± 1.3 × 1011 mol y−1 (45 ± 16 kt d−1) of CO2 are released from Yellowstone due to diffuse degassing. … The contribution of CO2 from Yellowstone to global volcanic CO2 emissions (∼6–7 × 1012 mol y−1) is comparable to the CO2 contribution from other large volcanic systems like Popocatepetl, Mexico and the combined contribution from the Hawaii hot spot.” [Full text]

Carbon dioxide emission rate of Kīlauea Volcano: Implications for primary magma and the summit reservoir – Gerlach et al. (2002) “We report a CO2 emission rate of 8500 metric tons per day (t d−1) for the summit of Kīlauea Volcano, several times larger than previous estimates.”

Degassing of SO2 and CO2 at Mount Etna (Sicily) as an indicator of pre-eruptive ascent and shallow emplacement of magma – Bruno et al. (2001) “We studied soil CO2 emissions together with crater SO2 fluxes from Mt Etna during the period July 1997 to March 1999. … Based on the temporal variations of measured soil CO2 and crater SO2 data, five intervals of anomalous degassing were recognized.”

Remote sensing of CO2 and H2O emission rates from Masaya volcano, Nicaragua – Burton et al. (2000) “We report the first precise field measurements of volcanic CO2, and H2O, in addition to HCl, HF, and SO2, in the plume of Masaya volcano, Nicaragua, a basaltic volcano with a record of Plinian activity. The molar ratios for CO2: SO2 (2.3–2.5) and H2O: SO2 (66–69) observed in February–March 1998 and March 1999 show no significant variation over the 12 month period. … Emission rates of SO2 from the summit crater, determined by correlation spectroscopy, averaged 21 kg s–1 during the study periods, indicating CO2, H2O, HCl, and HF emission rates of 32–36, 380–420, 7.0–7.8, and 0.86–0.95 kg s–1, respectively.”

Hydrothermal CO2 emission from the Taupo Volcanic Zone, New Zealand – Seward & Kerrick (1996) “The CO2 flux to the atmosphere from active geothermal systems in the back-arc marginal basin environment of the Taupo Volcanic Zone (TVZ), North Island, New Zealand, has been estimated. … The integrated CO2 flux for 20 geothermal systems in the TVZ is estimated to be approximately 1010 mol yr−1. It is concluded that the global hydrothermal CO2 flux from subareal environments alone may be comparable to that estimated for direct volcanic emanations.”

Posted in AGW evidence | 6 Comments »

Unchallenging Copenhagen Climate Challenge

Posted by Ari Jokimäki on December 10, 2009

Yet another statement signed by N number of mind-numbingly relevant people is being spammed onto climate related blogs and discussions. This one is called Copenhagen Climate Challenge. Here, I will just comment on some of the things in their statement:

Therefore, there is no sound reason to impose expensive and restrictive public policy decisions on the peoples of the Earth without first providing convincing evidence that human activities are causing dangerous climate change beyond that resulting from natural causes.

So you don’t think that the potential risk of worldwide catastrophe to both mankind and the global ecosystem as a whole is not “sound reason”? Do you take fire insurance only after your house has burned down?

Before any precipitate action is taken, we must have solid observational data demonstrating that recent changes in climate differ substantially from changes observed in the past…

Past has nothing to do with it. All we need to know is that it has created a very risky situation now.

We the undersigned, being qualified in climate-related scientific disciplines, challenge the UNFCCC and supporters of the United Nations Climate Change Conference to produce convincing OBSERVATIONAL EVIDENCE for their claims of dangerous human-caused global warming and other changes in climate. Projections of possible future scenarios from unproven computer models of climate are not acceptable substitutes for real world data obtained through unbiased and rigorous scientific investigation.

Ok, here goes:

– Earth’s global temperature has increased, as evidenced by surface measurements (latest 10 year means are shown here) and ocean measurements for example, and indicated by changes in biosphere, melting glaciers and polar ice sheets, rising sea level, and vanishing sea ice, to name a few things.

– Carbon dioxide has been shown to be able to absorb heat by laboratory measurements and direct measurements from atmosphere.

– Carbon dioxide content of the atmosphere has been observed increasing by direct measurements from atmosphere and it also has been observed that the increase is from anthropogenic sources.

– We have observed, directly from the atmosphere, how outgoing longwave radiation has decreased in carbon dioxide absorption bands and how downward longwave radiation has increased accordingly.

– Everyone who has studied the subject knows that currently our biggest source of uncertainty is the climate feedbacks, but even there we have direct observations of positive water vapor feedback and the latest research on low-level clouds (where the biggest uncertainty has been) also shows positive feedback.

– Just about all major things are working as expected in greenhouse gas warmed world. For example, stratosphere has been observed cooling, as expected. The scientific research also shows that greenhouse gases have been major factor in past climate changes.

– Other forcings are not present strongly enough. It is not the Sun, the clouds, or the cosmic rays.

– None of the above is based on climate models, it’s all observational. There also seems to be others who agree with me on this with well written words.

The biosphere is already reacting badly to the warming, so it’s not something that might happen in the future, it’s already happening.

I ask you this, dear “climate realist scientists”, as most of the information presented above has been available for a long time, how could you have missed this body of evidence?


I forgot to say this:

It is not the responsibility of ‘climate realist’ scientists to prove that dangerous human-caused climate change is not happening.

It is your responsibility. Mainstream climate science has already presented the proof in the scientific literature. Now would be your turn to do the same. I won’t hold my breath for that, though.

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Papers on climate in Denmark

Posted by Ari Jokimäki on December 6, 2009

In the spirit of COP15, which is about to start, I give you a list of papers dealing with the climate of Denmark (mostly excluding Greenland) in various ways.

Daily ocean monitoring since the 1860s shows record warming of northern European seas – Mackenzie & Schiedek (2007) “We use four of the world’s longest calibrated daily time series to show that trends in surface temperatures in the North and Baltic Seas now exceed those at any time since instrumented measurements began in 1861 and 1880. Temperatures in summer since 1985 have increased at nearly triple the global warming rate, which is expected to occur during the 21st century and summer temperatures have risen two to five times faster than those in other seasons. These warm temperatures and rates of change are due partly to an increase in the frequency of extremely warm years. The recent warming event is exceeding the ability of local species to adapt and is consequently leading to major changes in the structure, function and services of these ecosystems.” [Full text]

Environmental response to the cold climate event 8200 years ago as recorded at Højby Sø, Denmark – Rasmussen et al. (2007) “The need for accurate predictions of future environmental change under conditions of global warming has led to a great interest in the most pronounced climate change known from the Holocene: an abrupt cooling event around 8200 years before present (present = A.D. 1950), also known as the ‘8.2 ka cooling event’ (ka = kilo-annum = 1000 years). … In an ongoing project, the influence of the 8.2 ka cooling event on a Danish terrestrial and lake ecosystem is being investigated using a variety of biological and geochemical proxy data from a sediment core extracted from Højby Sø, north-west Sjælland (Fig. 2). Here we present data on changes in lake hydrology and terrestrial vegetation in response to climate change, inferred from macrofossil data and pollen analysis, respectively.” [Full text]

The influence of climate change on stream flow in Danish rivers – Thodsen (2006) A paper that has to wait a bit for confirmation. “The influence of climate change on river discharges in five major Danish rivers divided into 29 sub-catchments is investigated for the future period of 2071–2100. … Mean annual precipitation is found to increase 7%, potential evapotranspiration to increase 3% and river discharges to increase 12% on average, between a control period (1961–1990) and the future period.”

The Climate of Denmark 2002 – Cappelen & Jørgensen (2003) Technical report. “Despite a cold ending 2002 was considerable warm with a annual mean temperature of 9,2°C. At the same time the year was wet and very sunny. The combination of extraordinary heat and at the same time a lot of sunshine and precipitation are remarkable. Globally the year was the second warmest on record.” [Full text]

The effects of climate change on the birch pollen season in Denmark – Rasmussen (2002) “During the last two decades the climate in Denmark has become warmer and in climate scenarios (IPCC, 2001) it is foreseen that the temperature will increase in the coming decades. … In this study the already observed effects on the birch pollen season are studied. … In Copenhagen there is a marked shift to an earlier season – it starts about 14 days earlier in year 2000 than in 1977, the peak-date is 17 days earlier and the season-end is 9 days earlier.” [Full text]

Lake-level changes in the Late Weichselian Lake Toevelde, Moen, Denmark: induced by changes in climate and base level – Noe-Nygaard & Heiberg (2001) “The lacustrine Toevelde basin is a key locality for the study of Late Weichselian sedimentological, geochemical and climatic development in Denmark. … The sediment record covers a time span from about 14,700 to 9000 cal yr BP and starts with resedimented till. … Sedimentological, geochemical and palaeoecological data show that Lake Toevelde underwent significant fluctuations. Some of which can be directly linked to climate changes. Some rises correlate, however, with damming of the Baltic Basin caused by isostatic uplift of barriers. High-resolution stratigraphy may thus allow distinction between lake-level changes caused by climate fluctuations and base-level changes which are ultimately related to isostasy.”

Large-scale aeolian sand movement on the west coast of Jutland, Denmark in late Subboreal to early Subatlantic time – a record of climate change or cultural impact? – Clemmensen et al. (2001) “Holocene dunefield construction on the west coast of Jutland was episodic. One of the most intense phases of inland sand movement and dunefield construction took place in late Subboreal to early Subatlantic time. … The onset of this phase of dunefield construction may be related to an abrupt climatic change in the North Atlantic region at about 800 BC and a likely increase in storminess.”

Eemian Lake development, hydrology and climate: a multi-stratigraphic study of the Hollerup site in Denmark – Björck et al. (2000) “A classic northwest European open section with lacustrine Eemian sediments, Hollerup, has been studied with respect to sedimentology, geochemistry, stable isotopes, diatoms and mineral magnetic analyses, and correlated by geochemistry and diatoms to a previously pollen analysed section by Andersen (1965). … Our studies show that the onset of the Eemian was characterized by a major lake level rise followed by an almost 3000 yr long period of high, but oscillating lake levels. It is argued that the latter part of this period of highly maritime climate can be defined as the Eemian climatic optimum. This period was interrupted by a few hundred years long phase of low lake level, coinciding with the immigration of spruce, followed by medium-high lake levels. The next c. 3500 yr, coinciding with the Carpinus pollen zone, seem to have been characterized by fairly humid and mild conditions, although slightly more arid than during the preceding optimum. The Carpinus period ended with a more than 1000 yr long gradual lake level fall, and this period of lake level change, concurring with the transition into the Pinus pollen dominated period, terminated with an extreme low lake level event. This 300–500 yr long arid phase coincides with a distinct peak in pine pollen, and was followed by higher but slightly oscillating lake levels in a cooler climate. The end of the Eemian seems to have been characterised by a gradual cooling, until almost pure clastic sedimentation and a marked expansion of herb pollen grains mark the onset of the Weichselian.”

Observed Air Temperature, Humidity, Pressure, Cloud Cover and Weather in Denmark – with Climatological Standard Normals, 1961-90 – Laursen et al. (1999) Technical report. “This report presents the observed air temperature, relative and absolute humidity, mean sea level atmospheric pressure, cloud cover, snow cover, snowfall, fog and thunder in Denmark on a monthly basis from up to 44 Danish stations. The observations mainly cover the climatological standard normal period 1961-1990, but some series covering periods of less than 30 years between 1961 and 1998 are also included.” [Full text]

Interglacial and glacial climate oscillations in a marine shelf sequence from Northern Denmark — a multidisciplinary study – Kristensen et al. (1998) “A 22.5 m long marine shelf sequence in northern Denmark covers the climatic shifts from glacial environments, through interglacial and into early glacial conditions. The interglacial was interrupted by two cool intervals. Also the early glacial succession experienced oscillations of the climate, and a period with ameliorated temperature conditions has been separated as an interstadial. … The climatic changes in this eastern part of the North Sea region are closely linked with changes in the North Atlantic circulation pattern, and the environmental fluctuations at Nørre Lyngby are therefore believed to reflect fluctuations in the past regional climatic and oceanic system.”

Morphology and vegetation of a dune system in SE Denmark in relation to climate change and sea level rise – Vestergaard et al. (1991) “Recordings by the Danish Meteorological Institute show, that the mean temperature of Denmark has remained fairly constant and the mean precipitation in winter has increased very slightly during the last c. 100 years, and that the relative sea level rise in Danish waters amounted to between + 9 cm and -3 cm during the same period of time. For the W Baltic area a doubling of CO2-level in the atmosphere is predicted to cause an increase in mean temperature by 3–4°C, an increase in length of growing season by c. 55 days, an increase in aridity, and a sea level rise of between 25 and 165 cm. Based on recent observations of morphology, soil and vegetation of a W Baltic dune system, possible effects of these changes upon vegetational composition, phytogeography, nutrient economy, stability, and ground water level of coastal dunes are discussed.”

Closely related

Climate Research in Denmark

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Comments on Lindzen & Choi (2009)

Posted by Ari Jokimäki on December 5, 2009

Lindzen & Choi (2009) (“L&C” from hereafter) are studying how outgoing longwave radiation (OLR) and outgoing shortwave radiation (SWR) are responding to the changes in sea surface temperature (SST). L&C already have been criticized by others, but I’ll add some comments. Some of the comments I present here I have already made in some online-discussions. First, I’ll take a look at what others have said on this so far.

James Annan noted that L&C selection of models was curious. Instead of more generally used AOGCM’s, L&C selected to use AMIP model runs. Gavin Schmidt also noticed that. He said:

Additionally, AMIP runs are known to have very counter-intuitive behaviour when it comes to surface energy fluxes (for instance, an oceanic warm anomaly caused by atmospheric anomalies in the real world is associated with an anomalous downward flux of heat – however in an AMIP run, the flux anomaly is upwards (completely opposite)). AMIP runs are useful, but it may be that Lindzen’s analysis is one of those things that is particularly sensitive to that.

So, L&C used model runs that are known to sometimes give exactly the opposite behaviour than in the real world. Also Roy Spencer (of all people) noted the AMIP model selection, and he said that he had got different results (indicating positive feedback) using CMIP model runs.

Rob Dekker had noted that L&C had offset of 4 W/m2 in the shortwave data in their Figure 3. He said:

Of course, after correcting this error, the conclusions of his paper would need to be adjusted as well. Not only is the ERBE data essentially is in line with the model predictions, but also the ERBE data shows that there is NO feedback at all (feedback factor 0) for short-term sea surface temperature changes.

“AJ” also noted the same problem (link is to Finnish discussion, I also make there some of the comments presented below), and so did Luboš Motl. Below, I’ll discuss this problem more thoroughly.

Roy Spencer also noted the smoothing problems I’ll discuss next.

Knowingly working with faulty data

L&C say:

The anomalies include a semiannual signal due to the temporal aliasing effect that needs to be eliminated [Trenberth, 2002]. The relevant sampling error of the tropical monthly ERBE data is about 1.7 W m–2 for SWR and 0.4 W m–2 for OLR [Wielicki et al., 2002a, b]. This spurious signal, particularly in the SWR, can be removed in a 36-day average, reducing the SWR error to the order of 0.3 W m–2.

So, they know that there is a false signal in the data, and they know how to eliminate it. Nevertheless, their action is not to correct the data:

However, in this study, the 36-day average was not applied because we wish to relate monthly SSTs to monthly ERBE TOA fluxes.

It seems to me that there’s not much point to this study after this. Whatever you find, you cannot be sure if it’s real or an artifact due to the false signal in the data.

Unsmooth smoothing

Above we saw L&C say that they are not correcting the data because they want to compare monthly values, but then they say anyway:

Instead, the moving average with a 7-month smoother was used for the SWR anomalies alone;

They left data uncorrected because they don’t want to mess monthly values, but then they go ahead and mess them for one dataset? And they do it by using strange value of 7 months, which they don’t explain. They do say that they are later going to show that this smoothing doesn’t affect their results. But later they show it by comparing it to equally strange smoothing values of 3 and 5 months. Let us also emphasize this: they only smoothed one dataset out of the three they are using.

Temporal and spatial coverage

The study of L&C is limited to tropics. They try to generalize this by suggesting that higher latitudes are neutral and therefore the negative feedback factor would reduce by a factor of two. I would rather have it measured.

The study is limited to short time variations (from few months to less than 2 years), of which they use only 9. They don’t study long time response at all. They say:

Simple calculations as well as GCM results suggest response times on the order of decades for positive feedbacks and years or less for negative feedbacks [Lindzen and Giannitsis, 1998, and references therein].

Assuming that this claim is correct, by limiting their study to timescales of years or less, they are focusing their study only to the feedbacks they are claiming to be negative. In other words, they are not even trying to study positive feedbacks by their own words.

Keep also in mind that they are not trying to measure the feedbacks relating to the theory of AGW where the changes in the forcing are slow, measured on interdecadal scales. Here in this blog we already have seen that water vapor feedback has been measured to be positive. Cloud feedback has been more uncertain, but recent paper by Clement et al. (2009) measured a positive feedback for low level clouds which has been the primary cause for the general feedback uncertainty. For clouds, situation has been uncertain because we haven’t had observations that are stable enough in decadal timescales, but that situation has improved in recent years (Loeb et al. (2007)).

Some minor issues

In the first paragraph of their introduction L&C say:

This is important since most current estimates of climate sensitivity are based on global climate model (GCM) results, and these obviously need observational testing.

One would need to quantify the “most” and “current” here, but it seems to me that this statement is belittling the situation, as there are many empirical estimates made on climate sensitivity in recent years.

Let’s also remember that the Iris hypothesis of Lindzen has been tested by others with bad results, no closing iris is seen.

Here’s some earlier discussion where Lindzen apparently didn’t use most recent data that was available, and when most recent data was used, the results changed remarkably, and not in Lindzen’s favor.

Forcing the feedback

As mentioned above, there is a problem in L&C handling of direct response of SST change. When temperature of SST increases, the sea surface starts to radiate more thermal energy, which causes OLR to increase according to Stefan-Boltzmann law. L&C correctly noted that when trying to determine the feedback component from OLR, one has to subtract the direct response from the OLR, leaving only the possible component caused by feedbacks. L&C say:

In the observed ΔOLR/ΔT, the nonfeedback change of 4 W m–2 K–1 is included. Also ΔSWR/ΔT needs to be balanced with ΔOLR/ΔT.

First part is correct, the nonfeedback change is the direct response I described above. But it is the second part here that is wrong. ΔSWR/ΔT is basically the change in the albedo of the Earth, it is the amount of change in the reflected sunlight. ΔOLR/ΔT is the change in OLR. Now, when the SST changes, it directly affects the amount of OLR, but L&C are suggesting here that it has a direct opposite effect of equal size to the reflected sunlight. Why would Earth’s reflectance change directly in accordance to changes in SST? There is a known feedback effect that affects the reflectance; the amount of high level clouds changes and causes a negative feedback, but remember that here we are not dealing with feedbacks yet, we are dealing with direct response. What direct response a warming event in SST could cause Earth to reflect more sunlight? That is what L&C are claiming here. Perhaps the reflecting properties of sea surface changes when it warms? Perhaps the reflecting properties of clouds change when the warmer thermal radiation from sea surface hits them? There might be some minor effects like that but L&C claim that they are of equal size to the change in OLR.

There is no reason why the Earth’s reflectance should be balanced with direct response shown in OLR. With this action, they are adding an extra 4 W/m2 to the SWR which works in the negative direction, so basically L&C are forcing (perhaps not intentionally) the feedback to be negative.


For argument’s sake, despite what we said above, let us assume that it is reasonable to balance the OLR and outgoing SWR like L&C suggested. So, how do we do balancing? We have a situation where there is an extra component of 4 W/m2 in OLR side. In that situation we can balance the situation by either A) taking the 4 W/m2 out of OLR or B) adding the 4 W/m2 to SWR. How L&C decided to do it? Here’s what they say about it:

From the consideration, FLW = –ΔOLR/ΔT + 4 and FSW = –ΔSWR/ΔΤ – 4.

As you can see, they did both. They took the 4 W/m2 out of OLR and added 4 W/m2 to the SWR. That means that they didn’t balance the situation, they just moved the unbalance to the other side.

I thank Kaj Luukko and AJ for enlightening discussions on this paper.

UPDATE (January 9, 2010): There has been some studies published dealing with L&C. They are still “in press” so there’s not much to link to yet, but see these RealClimate articles: First published response to Lindzen and Choi and Lindzen and Choi Unraveled. The comments presented are largely different than the ones presented here, so the articles are “must read” for anyone interested in this particular issue. The comment sections there also contain interesting stuff.


Clement, Amy C., Robert Burgman, and Joel R. Norris (2009), Observational and Model Evidence for Positive Low-Level Cloud Feedback, Science, Vol. 325. no. 5939, pp. 460 – 464, DOI: 10.1126/science.1171255 [ABSTRACT] [FULL TEXT]

Lindzen, R. S., and Y.-S. Choi (2009), On the determination of climate feedbacks from ERBE data, Geophys. Res. Lett., 36, L16705, doi:10.1029/2009GL039628 [ABSTRACT] [FULL TEXT]

Loeb, N. G., B. A. Wielicki, F. G. Rose, and D. R. Doelling (2007), Variability in global top-of-atmosphere shortwave radiation between 2000 and 2005, Geophys. Res. Lett., 34, L03704, doi:10.1029/2006GL028196 [ABSTRACT]

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Papers on global sea surface temperature observations

Posted by Ari Jokimäki on December 2, 2009

This is a list of papers on the sea surface temperature (SST) observations with emphasis on global analysis. The list is not complete, and will most likely be updated in the future in order to make it more thorough and more representative.

Trend patterns in global sea surface temperature – Barbosa & Andersen (2009) “Isolating long-term trend in sea surface temperature (SST) from El Niño southern oscillation (ENSO) variability is fundamental for climate studies. In the present study, trend-empirical orthogonal function (EOF) analysis, a robust space-time method for extracting trend patterns, is applied to isolate low-frequency variability from time series of SST anomalies for the 1982-2006 period. The first derived trend pattern reflects a systematic decrease in SST during the 25-year period in the equatorial Pacific and an increase in most of the global ocean.”

Inter-comparison and evaluation of global sea surface temperature products – Iwasaki et al. (2008) “To clarify the characteristics of global sea surface temperature (SST) products, we have compared the Reynolds product with four other products: the Center for Atmospheric and Oceanic Studies (CAOS) SST, the microwave optimum interpolation (MWOI) SST, the merged satellite and in-situ data global daily (MGD) SST and the real time global (RTG) SST. Furthermore, we have validated these five products with SST data observed by moored buoys.”

Daily High-Resolution-Blended Analyses for Sea Surface Temperature – Reynolds et al. (2007) “Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data.” [Full text]

OSTIA : An operational, high resolution, real time, global sea surface temperature analysis system – Stark et al. (2007 “A new global, operational, high-resolution, combined sea surface temperature (SST) and sea ice analysis system (OSTIA) has been developed at the Met Office. The output is a daily, global coverage 1/20deg (~6 km) combined SST and sea ice concentration product, which is generated in near-real time.”

The Global Trend in Sea Surface Temperature from 20 Years of Advanced Very High Resolution Radiometer Data – Good et al. (2007) “The trend in sea surface temperature has been determined from 20 yr of Advanced Very High Resolution Radiometer Pathfinder data (version 5). The data span the period from January 1985 to December 2004, inclusive. The linear trends were calculated to be 0.18° ± 0.04° and 0.17° ± 0.05°C decade−1 from daytime and nighttime data, respectively.” [Full text]

The Measurement of the Sea Surface Temperature by Satellites from 1991 to 2005 – O’Carroll et al. (2006) “A near-continuous series of global retrievals of sea surface temperature (SST) has been made from the Along-Track Scanning Radiometer (ATSR) series of instruments from 1991 to 2005. To analyze possible long-term trends in the global or regional SST throughout the period daily anomalies are computed using a 1961–90 daily climatology, averaged into global monthly means, and plotted as a global time series. … The results of the study show the high accuracy of the Advanced Along Track Scanning Radiometer (AATSR) SSTs, but there are concerns with the NOAA-14 AVHRR data (1996–2000) being biased cold, especially in the Northern Hemisphere, and the AMSR-E SSTs (version 4), which show unexplained biases. Since 1999 TMI SSTs appear to have a consistently warm (0.2 K) bias relative to the infrared sensors and HadISST1.” [Full text]

Improved Analyses of Changes and Uncertainties in Sea Surface Temperature Measured In Situ since the Mid-Nineteenth Century: The HadSST2 Dataset – Rayner et al. (2006) “A new flexible gridded dataset of sea surface temperature (SST) since 1850 is presented and its uncertainties are quantified. This analysis [the Second Hadley Centre Sea Surface Temperature dataset (HadSST2)] is based on data contained within the recently created International Comprehensive Ocean–Atmosphere Data Set (ICOADS) database and so is superior in geographical coverage to previous datasets and has smaller uncertainties. … The linear warming between 1850 and 2004 was 0.52° ± 0.19°C (95% confidence interval) for the globe, 0.59° ± 0.20°C for the Northern Hemisphere, and 0.46° ± 0.29°C for the Southern Hemisphere. Decadally filtered differences for these regions over this period were 0.67° ± 0.04°C, 0.71° ± 0.06°C, and 0.64° ± 0.07°C.” [Full text]

Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection – Ishii et al. (2005) “Data for the 20th century from the International Comprehensive Ocean and Atmosphere Data Set and the Kobe Collection have been used as input data for global objective analyses of sea-surface temperatures (SSTs) and other marine meteorological variables. This study seeks a better understanding of the historical marine meteorological data and an evaluation of the quality of the data in the Kobe Collection. … An SST analysis used widely in climatological studies was verified against HadISST from the Hadley Centre and an SST analysis derived from satellite and in situ observations.” [Full text]

Improved Extended Reconstruction of SST (1854–1997) – Smith & Reynolds (2004) “An improved SST reconstruction for the 1854–1997 period is developed. Compared to the version 1 analysis, in the western tropical Pacific, the tropical Atlantic, and Indian Oceans, more variance is resolved in the new analysis.” [Full text]

An Improved In Situ and Satellite SST Analysis for Climate – Reynolds et al. (2002) “A weekly 1° spatial resolution optimum interpolation (OI) sea surface temperature (SST) analysis has been produced at the National Oceanic and Atmospheric Administration (NOAA) using both in situ and satellite data from November 1981 to the present.” [Full text]

Global Sea Surface Temperature Analyses: Multiple Problems and Their Implications for Climate Analysis, Modeling, and Reanalysis – Hurrell & Trenberth (1999) “A comprehensive comparison is made among four sea surface temperature (SST) datasets: the optimum interpolation (OI) and the empirical orthogonal function reconstructed SST analyses from the National Centers for Environmental Prediction (NCEP), the Global Sea-Ice and SST dataset (GISST, version 2.3b) from the United Kingdom Meteorological Office, and the optimal smoothing SST analysis from the Lamont-Doherty Earth Observatory (LDEO). Significant differences exist between the GISST and NCEP 1961–90 SST climatologies, especially in the marginal sea-ice zones and in regions of important small-scale features, such as the Gulf Stream, which are better resolved by the NCEP product. Significant differences also exist in the SST anomalies that relate strongly to the number of in situ observations available.” [Full text]

Analyses of global sea surface temperature 1856–1991 – Kaplan et al. (1998) “Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kaiman filter (KF) and optimal interpolation (OI). … The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the El Niño – Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.” [Full text]

Twentieth-Century Sea Surface Temperature Trends – Cane et al. (1997) “An analysis of historical sea surface temperatures provides evidence for global warming since 1900, in line with land-based analyses of global temperature trends, and also shows that over the same period, the eastern equatorial Pacific cooled and the zonal sea surface temperature gradient strengthened. Recent theoretical studies have predicted such a pattern as a response of the coupled ocean-atmosphere system to an exogenous heating of the tropical atmosphere. This pattern, however, is not reproduced by the complex ocean-atmosphere circulation models currently used to simulate the climatic response to increased greenhouse gases. Its presence is likely to lessen the mean 20th-century global temperature change in model simulations.” [Full text]

A High-Resolution Global Sea Surface Temperature Climatology – Reynolds & Smith (1995) “In response to the development of a new higher-resolution sea surface temperature (SST) analysis at the National Meteorological Center (NMC), a new monthly 1° global sea surface temperature climatology was constructed from two intermediate climatologies: the 2° SST climatology presently used at NMC and a 1° SST climatology derived from the new analysis. … The use of 12 years of satellite SST retrievals makes this new climatology useful for many additional purposes because its effective resolution actually approaches 1° everywhere over the global ocean and because the mean SST values are more accurate south of 40°S than climatologies without these data.” [Full text]

Improved Global Sea Surface Temperature Analyses Using Optimum Interpolation – Reynolds & Smith (1994) “The new NOAA operational global sea surface temperature (SST) analysis is described. The analyses use 7 days of in situ (ship and buoy) and satellite SST. These analyses are produced weekly and daily using optimum interpolation (OI) on a 1° grid.” [Full text]

A Global Monthly Sea Surface Temperature Climatology – Shea et al. (1992) “A new global 2°×2° monthly sea surface temperature (SST) climatology, primarily derived from a 1950–1979-based SST climatology from the Climate Analysis Center (CAC), is presented and described. … This new SST climatology, which we call the Shea-Trenberth-Reynolds (STR) climatology, is compared with the Alexander and Mobley (AM) SST climatology often used as a lower boundary condition by general circulation models. Significant differences are noted. Generally, the STR climatology is warmer in the Northern Hemisphere and in the subtropics of the Southern Hemisphere during the northern winter.” [Full text]

A Real-Time Global Sea Surface Temperature Analysis – Reynolds (1988) “A global monthly sea surface temperature analysis is described which uses real-lime in situ (ship and buoy) and satellite data. The method combines the advantages of both types of data: the ground truth of in situ data and the improved coverage of satellite data. The technique also effectively eliminates most of the bias differences between the in situ and satellite data. Examples of the method are shown to illustrate these points.” [Full text]

Monthly Average Sea–Surface Temperatures and Ice–Pack Limits on a 1° Global Grid – Alexander & Mobley (1976) “Climatological monthly ocean-surface temperatures obtained from the National Center for Atmospheric Research and from Fleet Numerical Weather Central are merged and interpolated onto a 1° global grid.” [Full text]

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