AGW Observer

Observations of anthropogenic global warming

Papers on global cloud cover trends

Posted by Ari Jokimäki on September 10, 2009

This list of papers contains observations of trends in global cloud cover. 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 15, 2012): Stowe et al. (1988), Stowe et al. (1989), Stowe et al. (1991), and Eastman et al. (2011) added.
UPDATE (January 27, 2011): Clapp (1964) added.

Variations in Cloud Cover and Cloud Types over the Ocean from Surface Observations, 1954–2008 – Eastman et al. (2011) “Synoptic weather observations from ships throughout the World Ocean have been analyzed to produce a climatology of total cloud cover and the amounts of nine cloud types. About 54 million observations contributed to the climatology, which now covers 55 years from 1954 to 2008. In this work, interannual variations of seasonal cloud amounts are analyzed in 10° grid boxes. Long-term variations O(5–10 yr), coherent across multiple latitude bands, remain present in the updated cloud data. A comparison to coincident data on islands indicates that the coherent variations are probably spurious. An exact cause for this behavior remains elusive. The globally coherent variations are removed from the gridbox time series using a Butterworth filter before further analysis. Before removing the spurious variation, the global average time series of total cloud cover over the ocean shows low-amplitude, long-term variations O(2%) over the 55-yr span. High-frequency, year-to-year variation is seen O(1%–2%). Among the cloud types, the most widespread and consistent relationship is found for the extensive marine stratus and stratocumulus clouds (MSC) over the eastern parts of the subtropical oceans. Substantiating and expanding upon previous work, strong negative correlation is found between MSC and sea surface temperature (SST) in the eastern North Pacific, eastern South Pacific, eastern South Atlantic, eastern North Atlantic, and the Indian Ocean west of Australia. By contrast, a positive correlation between cloud cover and SST is seen in the central Pacific. High clouds show a consistent low-magnitude positive correlation with SST over the equatorial ocean. In regions of persistent MSC, time series show decreasing MSC amount. This decrease could be due to further spurious variation within the data. However, the decrease combined with observed increases in SST and the negative correlation between marine stratus and sea surface temperature suggests a positive cloud feedback to the warming sea surface. The observed decrease of MSC has been partly but not completely offset by increasing cumuliform clouds in these regions; a similar decrease in stratiform and increase in cumuliform clouds had previously been seen over land. Interannual variations of cloud cover in the tropics show strong correlation with an ENSO index.” Stowe, Larry L., H. Y. Michael Yeh, Thomas F. Eck, Charlie G. Wellemeyer, H. Lee Kyle, The Nimbus-7 Cloud Data Processing Team, 1989: Nimbus-7 Global Cloud Climatology. Part II: First Year Results. J. Climate, 2, 671–709. [Full text]

Trends in Observed Cloudiness and Earth’s Radiation Budget What Do We Not Know and What Do We Need to Know? – Norris & Slingo (2009) “Previous investigators have documented multidecadal variations in various cloud and radiation parameters, but no conclusive results are yet available. Problems include the lack of global and quantitative surface measurements, the shortness of the available satellite record, the inability to determine correctly cloud and aerosol properties from satellite data, many different kinds of inhomogeneities in the data, and insuffi cient precision to measure the small changes in cloudiness and radiation that nevertheless can have large impacts on the Earth’s climate.” [Full text]

A Survey of Changes in Cloud Cover and Cloud Types over Land from Surface Observations, 1971–96 – Warren et al. (2007) “The global average trend of total cloud cover over land is small, -0.7% decade-1, offsetting the small positive trend that had been found for the ocean, and resulting in no significant trend for the land–ocean average.” [Full text]

Observed Interdecadal Changes in Cloudiness: Real or Spurious? – Norris (2007) “Substantial agreement exists between global mean time series of surface- and satellite-observed upper-level cloud cover, indicating that the reported variations in this cloud type are likely to be real. … Global mean time series of surface- and satellite-observed low-level and total cloud cover exhibit very large discrepancies, however, implying that artifacts exist in one or both data sets. The global mean satellite total cloud cover time series appears spurious because the spatial pattern of correlations between grid box time series and the global mean time series closely resembles the fields of view of geostationary satellites rather than geophysical phenomena. The surface-observed low-level cloud cover time series averaged over the global ocean appears suspicious because it reports a very large 5%-sky-cover increase between 1952 and 1997. Unless low-level cloud albedo substantially decreased during this time period, the reduced solar absorption caused by the reported enhancement of cloud cover would have resulted in cooling of the climate system that is inconsistent with the observed temperature record.” [Full text]

Arguments against a physical long-term trend in global ISCCP cloud amounts – Evan et al. (2007) “Here we show that trends observed in the ISCCP data are satellite viewing geometry artifacts and are not related to physical changes in the atmosphere. Our results suggest that in its current form, the ISCCP data may not be appropriate for certain long-term global studies, especially those focused on trends.” [Full text]

Diurnal and angular variability of cloud detection: consistency between polar and geosynchronous ISCCP products – Campbell (2006) “There is a view angular dependence for cloud detection from the ISCCP cloud analysis algorithm. Consistency can be demonstrated between polar and geosynchronous satellite observations for this effect. This leads to an angular correction model which can be applied to the either the geosynchronous or polar satellite data time series to correct for systematic angular sampling biases. Similarly there are diurnal sampling biases in the polar ISCCP observations, especially evident over the land areas. Using the ISCCP geosynchronous diurnal sampling, adjustments consistent with the polar ISCCP analysis are derived. Both these corrections lead to a more time consistent regional cloud time series from 1983 to 2001. It is important to correct both time series so that they can be used to corroborate each other in measuring regional cloud changes over the last 20 decades.” [Full text]

Trends in Global Cloud Cover in Two Decades of HIRS Observations – Wylie et al. (2005) “The frequency of cloud detection and the frequency with which these clouds are found in the upper troposphere have been extracted from NOAA High Resolution Infrared Radiometer Sounder (HIRS) polar-orbiting satellite data from 1979 to 2001. The HIRS/2 sensor was flown on nine satellites from the Television Infrared Observation Satellite-Next Generation (TIROS-N) through NOAA-14, forming a 22-yr record. Carbon dioxide slicing was used to infer cloud amount and height. Trends in cloud cover and high-cloud frequency were found to be small in these data. High clouds show a small but statistically significant increase in the Tropics and the Northern Hemisphere. The HIRS analysis contrasts with the International Satellite Cloud Climatology Project (ISCCP), which shows a decrease in both total cloud cover and high clouds during most of this period.” [Full text]

Multidecadal changes in near-global cloud cover and estimated cloud cover radiative forcing – Norris (2005) “This study examines variability in zonal mean surface-observed upper-level (combined midlevel and high-level) and low-level cloud cover over land during 1971–1996 and over ocean during 1952–1997. These data were averaged from individual synoptic reports in the Extended Edited Cloud Report Archive (EECRA). … Zonal mean estimated longwave CCRF [= cloud cover radiative forcing] has decreased over most of the globe. Estimated shortwave CCRF has become slightly stronger over northern midlatitude oceans and slightly weaker over northern midlatitude land areas.” [Full text]

View angle dependence of cloudiness and the trend in ISCCP cloudiness – Campbell (2004) “Over the twenty year ISCCP record, more geosynchronous satellites have been added to the analysis and the mean view angle over the globe has become more vertical. This systematic change in view point convolved with the view angle dependence in cloudiness produces much of the decreasing trend in ISCCP cloud amount, both regionally and globally.” [Full text]

On Trends and Possible Artifacts in Global Ocean Cloud Cover between 1952 and 1995 – Norris (1999) “Synoptic surface cloud observations are used to examine interdecadal variability in global ocean cloud cover between 1952 and 1995. Global mean total cloud cover over the ocean is observed to increase by 1.9% (sky cover) between 1952 and 1995. Global mean low cloud cover over the ocean is observed to increase by 3.6% between 1952 and 1995. … On the other hand, the fact that ships with a common observing practice travel over most of the global ocean suggests a possible observational artifact may be largely responsible for the upward trends observed at all latitudes. Potential causes of artifacts are examined but do not provide likely explanations for the observed interdecadal variability.”

Seasonal Variation of Surface and Atmospheric Cloud Radiative Forcing Over the Globe Derived From Satellite Data – Gupta et al. (1993) “Global distributions of surface and atmospheric cloud radiative forcing parameters have been derived using parameterized radiation models with satellite meteorological data from the International Satellite Cloud Climatology Project, and directly measured top-of-atmosphere radiative fluxes from the Earth Radiation Budget Experiment. … The globally averaged total cloud forcing amounts to a cooling throughout the year…”

Global distribution of cloudcover derived from NOAA/AVHRR operational satellite data – Stowe et al. (1991) “NOAA/NESDIS is developing an algorithm for the remote sensing of globalcloudcover using multi-spectral radiance measurements from the Advanced Very High Resolution Radiometer (AVHRR) on-board NOAA polar orbiting satellites. The current (Phase 1) algorithm uses a sequence of “universal” threshold tests to classify all 2×2 pixel arrays of GAC (4 km) observations into clear, mixed and cloudy categories. A subsequent version of the algorithm (Phase II) will analyze the previous 9-day series of mapped (1/2 degree) “clear” array data to replace the “universal” thresholds with space and time specific values. This will provide more accurate estimates of cloud amount for each pixel. The current algorithm is being implemented into the operational data processing stream for testing and evaluation of experimental products. Eventually, it is intended for use operationally to support weather and climate diagnosis and forecasting programs, as well as to provide clear sky radiance data sets for other remote sensing parameters, e.g., vegetation index, aerosol optical thickness, and sea surface temperature.” L.L. Stowe, E.P. McClain, R. Carey, P. Pellegrino, G.G. Gutman, P. Davis, C. Long, S. Hart,
Advances in Space Research, Volume 11, Issue 3, 1991, Pages 51–54, http://dx.doi.org/10.1016/0273-1177(91)90402-6.

Cloud-Radiative Forcing and Climate: Results from the Earth Radiation Budget Experiment – Ramanathan et al. (1989) “Quantitative estimates of the global distributions of cloud-radiative forcing have been obtained from the spaceborne Earth Radiation Budget Experiment (ERBE) launched in 1984. For the April 1985 period, the global shortwave cloud forcing [-44.5 watts per square meter (W/m2)] due to the enhancement of planetary albedo, exceeded in magnitude the longwave cloud forcing (31.3 W/m2) resulting from the greenhouse effect of clouds. Thus, clouds had a net cooling effect on the earth.” [Full text]

Nimbus-7 Global Cloud Climatology. Part II: First Year Results – Stowe et al. (1989) “Regional and seasonal variations in global cloud cover observed by the Nimbus-7 satellite over 1 year are analyzed by examining the 4 midseason months—April, July and October 1979 and January 1980. The Nimbus-7 data set is generated from the Temperature Humidity Infrared Radiometer (THIR) 11.5 micron radiances together with Total Ozone Mapping Spectometer (TOMS)-derived UV reflectivities, climatological atmospheric temperature lapse rates, and concurrent surface temperature and snow/ice information from the Air Force three-dimensional-nephanalysis (3DN) archive. The analysis presented here includes total cloud amount, cloud amounts at high, middle and low altitudes, cirrus and deep convective clouds and cloud and cloud-sky 11.5 micron-derived radiances. Also, noon versus midnight cloud amounts are examined and the Nimbus-7 data are compared to three previously published cloud climatologies. The Nimbus-7 bispectral algorithm gives a monthly mean global noontime cloud cover of 51%, averaged over the 4 months. When only the IR is used, this cloud cover is 49% at noontime and 56% at midnight, indicating that the Earth’s cloud cover has a substantial diurnal cycle. Each hemisphere shows a cloud cover maximum in its summer and a minimum in its winter. The Southern Hemisphere shows more clouds than the Northern Hemisphere except for the month of July. The difference between the cloud-top and clear-scene radiance has maxima in the equatorial cloud belt and minima in the polar regions. Because of thew polar minima and the frequent presence of snow, Nimbus-7 cloud traction estimates are less reliable in the polar regions. In the tropics the data show more clouds at midnight than at noon. Over the tropical ocean, overcast regions show lower cloud top radiation temperatures at noon than at midnight, but over land the reverse occurs. In July, cloud amounts in the intertropical convergence zone (ITCZ) peak at about 10°N latitude with local maxima greater than 70% around the west coasts of Africa and Central America, and from India east to the dateline. Cloud-top radiances indicate that mid- and high-level clouds predominate in the ITCZ, with 5% to 15% each of cirrus and deep convective clouds, respectively. In January, the peak of the ITCZ shifts to 10°S with local cloud maxima greater than 90% over Brazil and to the north and northwest of Australia. Comparison is made with several other cloud data sets, including a look at the new preliminary International Satellite Cloud Climatology Project (ISCCP) results. There are considerable differences among the several data sets examined.” Stowe, Larry L., H. Y. Michael Yeh, Thomas F. Eck, Charlie G. Wellemeyer, H. Lee Kyle, The Nimbus-7 Cloud Data Processing Team, 1989: Nimbus-7 Global Cloud Climatology. Part II: First Year Results. J. Climate, 2, 671–709. [Full text]

Nimbus-7 Global Cloud Climatology. part I: Algorithms and Validation – Stowe et al. (1988) “Data from the Temperature Humidity Infrared Radiometer (THIR) and the Total Ozone Mapping Spectrometer (TOMS), both aboard the Nimbus-7 satellite, are used to determine cloudiness parameters for the globe. The 11.5 μm THIR radiances and the 0.36 μm and 0.38 μm TOMS reflectivities, along with concurrent surface temperature data from the Air Force 3-D nephanalysis, are the primary data sources. They are processed by an algorithm that determines total cloud amount, cloud amount in three altitude categories, cirrus cloud, deep convective cloud, warm cloud, and the radiance of radiation emitted by the clouds. and the underlying surface. The algorithm is of the bispectral threshold type, which yields two independent estimates of total cloud, one from the infrared algorithm and one from the UV reflectivity algorithm. For the daytime observations (local noon at the equator), these two independent estimates are combined to determine a composite estimate, while at night (local midnight at the equator), only the infrared threshold algorithm is used in the estimate. Quantitative validation of total cloud amount was performed by comparing the algorithm results with estimates derived by an analyst interpreting geosynchronous satellite (GOES) images, along with auxiliary meteorological data. It has been concluded that the systematic errors of the Nimbus-7 total cloud amount algorithm relative to the analyst are less than 10%, and that the random errors of daily estimates range between 7% and 16%, day or night. These empirical results are consistent with results from a theoretical sensitivity study. Qualitative validation has also been performed by making comparisons with GOES visible and infrared images for specific days. Results indicate that the TOMS cloud estimates improve the IR algorithm estimates of low cloud amount and provide for the identification of cirrus and deep convective cloud, but cloud amounts over humid tropical regions tend to be overestimated even with the use of TOMS. These results suggest that the spatial and temporal characteristics of daily and monthly averaged global cloud cover, including cirrus acid deep convective cloud types, which are presented in Part II, are generally well represented by the Nimbus-7 dataset, which covers a six-year period from April 1979 to March 1985.” Stowe, L. L., C. G. Wellemeyer, H. Y. M. Yeh, T. F. Eck, The Nimbus-7 CLOUD DATA PROCecessing TEAM, 1988: Nimbus-7 Global Cloud Climatology. part I: Algorithms and Validation. J. Climate, 1, 445–470. [Full text]

Global distribution of total cloud cover and cloud type amounts over the ocean – Warren et al. (1988) “The third atlas (NCAR/TN-273+STR) described, for the land areas of the earth, the average total cloud cover and the amounts of each cloud type, and their geographical, diurnal, seasonal, and interannual variations, as well as the average base heights of the low clouds. The present atlas does the same for the ocean areas of the earth.” [Full text, size of the file is over 20 MB]

Global cloud cover for seasons using TIROS nephanalyses – Clapp (1964) “TIROS nephanalyses are used to obtain global maps and latitudinal profiles of average cloud amount for the four seasons for the year March 1962 through February 1963. It is found that the gross patterns and season-to-season variations of these cloud distributions bear a striking resemblance to corresponding features of normal cloudiness, although there are some differences which call for further study. In many cases anomalies in cloudiness can be related to corresponding anomalies of the general circulation. In considering the magnitude as distinct from the pattern of cloudiness, there is some suggestion that during the chosen period the TIROS nephanalyses gave too much cloudiness for large cloud amount, and too little for small cloud amount.” Clapp, Philip F., 1964, Mon. Wea. Rev., 92, 495–507. [Full text]

Closely related

Comparison of cloud statistics from spaceborne lidar systems – Berthier et al. (2008) This paper relates to the issue of the problems with ISCCP data. “Comparisons of CTH developed from LITE, for 2 weeks of data in 1994, with ISCCP (International Satellite Cloud Climatology Project) cloud products show that the cloud fraction observed from spaceborne lidar is much higher than that from ISCCP. Another key result is that ISCCP products tend to underestimate the CTH of optically thin cirrus clouds.” [Full text]

Quite often claims about global cloud cover trends being responsible for the warming of recent decades are accompanied by claims that cosmic rays are causing them, so this is relevant in that case:
Papers on the non-significant role of cosmic rays in climate

3 Responses to “Papers on global cloud cover trends”

  1. Ari Jokimäki said

    I added Clapp (1964).

  2. barry said

    Maybe appropriate here in the ‘closely related’ section, some of the papers posted at SkS on clouds.

    Advances in Understanding Top-of-Atmosphere Radiation Variability from Satellite Observations – (Loeb et al 2012)

    http://meteora.ucsd.edu/~jnorris/reprints/Loeb_et_al_ISSI_Surv_Geophys_2012.pdf

    This paper highlights how the emerging record of satellite observations from the Earth Observation System (EOS) and A-Train constellation are advancing our ability to more completely document and understand the underlying processes associated with variations in the Earth’s top-of-atmosphere (TOA) radiation budget [...] At the global scale, outgoing LW flux anomalies are partially compensated for by decreases in midlatitude cloud fraction and cloud height, as observed by Moderate Resolution Imaging Spectrometer and Multi-angle Imaging SpectroRadiometer, respectively. CERES data show that clouds have a net radiative warming influence during La Nin˜a conditions and a net cooling influence during El Nin˜o, but the magnitude of the anomalies varies greatly from one ENSO event to another.

    and

    Global Cloud Cover and the Earth’s Mean Surface Temperature – (Erlykin and Wolfendale 2010)

    http://www.springerlink.com/content/101j152645206257/ (full version)

    The well-known 11-year cycle in low cloud cover amount for Solar Cycle Number 22 and the trend with time for Solar Cycle Number 23 are interpreted as being due to similar changes, but of opposite phase, in the mean global surface temperature of the Earth. An analysis of cloud amounts in two higher altitude bands shows that they, and the surface temperature, are roughly in phase with each other. The suggested mechanism to explain this result is that a warming of the Earth’s surface causes low clouds to rise and to be reclassified in the next upper category. The energetics of the process are shown to be satisfactory for this to be the correct explanation.

  3. Ari Jokimäki said

    I added Stowe et al. (1988), Stowe et al. (1989), Stowe et al. (1991), and Eastman et al. (2011).

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