New research from last week 11/2011

Here is the new research published last week. I’m not including everything that was published but just some papers that got my attention. Those who follow my Facebook page (and/or Twitter) have already seen most of these, as I post these there as soon as they are published. Here, I’ll just put them out in one batch. Sometimes I might also point out to some other news as well, but the new research will be the focus here. Here’s the archive for the news of previous weeks. By the way, if this sort of thing interests you, be sure to check out A Few Things Illconsidered, they have a weekly posting containing lots of links to new research and other climate related news. Planet 3.0 also reports new research.

Published last week:

Early predictability was low for 2010 Russian heat wave

Was there a basis for anticipating the 2010 Russian heat wave? – Dole et al. (2011) “The 2010 summer heat wave in western Russia was extraordinary, with the region experiencing the warmest July since at least 1880 and numerous locations setting all-time maximum temperature records. This study explores whether early warning could have been provided through knowledge of natural and human-caused climate forcings. Model simulations and observational data are used to determine the impact of observed sea surface temperatures (SSTs), sea ice conditions and greenhouse gas concentrations. Analysis of forced model simulations indicates that neither human influences nor other slowly evolving ocean boundary conditions contributed substantially to the magnitude of this heat wave. They also provide evidence that such an intense event could be produced through natural variability alone. Analysis of observations indicate that this heat wave was mainly due to internal atmospheric dynamical processes that produced and maintained a strong and long-lived blocking event, and that similar atmospheric patterns have occurred with prior heat waves in this region. We conclude that the intense 2010 Russian heat wave was mainly due to natural internal atmospheric variability. Slowly varying boundary conditions that could have provided predictability and the potential for early warning did not appear to play an appreciable role in this event.” Dole, R., M. Hoerling, J. Perlwitz, J. Eischeid, P. Pegion, T. Zhang, X.-W. Quan, T. Xu, and D. Murray (2011), Geophys. Res. Lett., 38, L06702, doi:10.1029/2010GL046582. [full text]

Argo pressure sensor drift analysis

Pressure sensor drifts in Argo and their impacts – Barker et al. (2011) “In recent years, autonomous profiling floats have become the prime component of the in-situ ocean observing system through the implementation of the Argo Programme. These data are now the dominant input to estimates of the evolution of the global ocean heat content and associated thermosteric sea level rise. APEX is the dominant type of Argo float (~62%), and a large portion of these floats report pressure measurements that are uncorrected for sensor drift, the size and source of which we describe. The remaining Argo float types are designed to automatically self-correct for any pressure drift. Only about 57% of APEX float profiles (or ~38% Argo profiles) can be corrected but this typically has not been done by the data centres which distribute the data (as of January 2009). A pressure correction method for APEX floats is described and applied to the Argo data set. A comparison between estimates using our corrected Argo data set and the publically available uncorrected data set (as of January 2009) reveals that our pressure corrections remove significant regional errors from ocean temperature, salinity and thermosteric sea level fields. In the global mean, the 43% of uncorrectable APEX float profiles (or ~28% Argo profiles) appear to largely offset the effect of the correctable APEX float profiles with positive pressure drifts. While about half of the uncorrectable APEX profiles can, in principle, be recovered in the near future (after inclusion of technical information that allows for corrections), the other half have negative pressure drifts truncated to zero (due to firmware limitations) which do not allow for corrections. Therefore, any Argo pressure profile that cannot be corrected for biases should be excluded from global change research. This study underscores the ongoing need for careful analyses to detect and remove subtle but systematic errors in ocean observations.” Paul M. Barker, Jeff R. Dunn, Catia M. Domingues, Susan E. Wijffels, Journal of Atmospheric and Oceanic Technology 2011.

Freshening episode in Alaska Coastal Current due to glacier melting and rain

Freshening of the Alaska Coastal Current recorded by coralline algal Ba/Ca ratios – Chan et al. (2011) “Arctic Ocean freshening can exert a controlling influence on global climate, triggering strong feedbacks on ocean-atmospheric processes and affecting the global cycling of the world’s oceans. Glacier-fed ocean currents such as the Alaska Coastal Current are important sources of freshwater for the Bering Sea shelf, and may also influence the Arctic Ocean freshwater budget. Instrumental data indicate a multiyear freshening episode of the Alaska Coastal Current in the early 21st century. It is uncertain whether this freshening is part of natural multidecadal climate variability or a unique feature of anthropogenically induced warming. In order to answer this, a better understanding of past variations in the Alaska Coastal Current is needed. However, continuous long-term high-resolution observations of the Alaska Coastal Current have only been available for the last 2 decades. In this study, specimens of the long-lived crustose coralline alga Clathromorphum nereostratum were collected within the pathway of the Alaska Coastal Current and utilized as archives of past temperature and salinity. Results indicate that coralline algal Mg/Ca ratios provide a 60 year record of sea surface temperatures and track changes of the Pacific Decadal Oscillation, a pattern of decadal-to-multidecadal ocean-atmosphere climate variability centered over the North Pacific. Algal Ba/Ca ratios (used as indicators of coastal freshwater runoff) are inversely correlated to instrumentally measured Alaska Coastal Current salinity and record the period of freshening from 2001 to 2006. Similar multiyear freshening events are not evident in the earlier portion of the 60 year Ba/Ca record. This suggests that the 21st century freshening of the Alaska Coastal Current is a unique feature related to increasing glacial melt and precipitation on mainland Alaska.” Chan, P., J. Halfar, B. Williams, S. Hetzinger, R. Steneck, T. Zack, and D. E. Jacob (2011), J. Geophys. Res., 116, G01032, doi:10.1029/2010JG001548.

Global warming driven enhancement of Arctic biosphere might decline in the future

Ecological controls on net ecosystem productivity of a mesic arctic tundra under current and future climates – Grant et al. (2011) “Changes in arctic C stocks with climate are thought to be caused by rising net primary productivity (NPP) during longer and warmer growing seasons, offset by rising heterotrophic respiration (Rh) in warmer and deeper soil active layers. In this study, we used the process model ecosys to test hypotheses for these changes with CO2 and energy fluxes measured by eddy covariance over a mesic shrub tundra at Daring Lake, Canada, under varying growing seasons. These tests corroborated substantial rises in NPP, smaller rises in Rh, and, hence, rises in net ecosystem productivity (NEP) from 17 to 45 g C m⁻² yr⁻¹ (net C sink), modeled with higher Ta and longer growing seasons. However, NEP was found to decline briefly during midsummer warming events (Ta > 20°C). A model run under climate change predicted for Daring Lake indicated that rises in NPP would exceed those in Rh during the first 100 years, causing NEP to rise. Rises in NPP were driven by more rapid net N mineralization from more rapid Rh in warming soils. However, greater declines in NEP were modeled during more frequent and intense midsummer warming events as climate change progressed. Consequently, average annual NEP (± interannual variability) rose from 30 (±13) g C m⁻² yr⁻¹ under current climate to 57 (±40) g C m⁻² yr⁻¹ after 90 years but declined to 44 (±51) g C m⁻² yr⁻¹ after 150 years, indicating that gains in tundra NEP under climate change may not be indefinite.” Grant, R. F., E. R. Humphreys, P. M. Lafleur, and D. D. Dimitrov (2011), J. Geophys. Res., 116, G01031, doi:10.1029/2010JG001555.

NH snow cover decrease has accelerated since 1970

Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty – Brown & Robinson (2011) ” An update is provided of Northern Hemisphere (NH) spring (March, April) snow cover extent (SCE) over the 1922–2010 period incorporating the new climate data record (CDR) version of the NOAA weekly SCE dataset, with annual 95% confidence intervals estimated from regression analysis and intercomparison of multiple datasets. The uncertainty analysis indicates a 95% confidence interval in NH spring SCE of ±5–10% over the pre-satellite period and ±3–5% over the satellite era. The multi-dataset analysis shows larger uncertainties monitoring spring SCE over Eurasia (EUR) than North America (NA) due to the more complex regional character of the snow cover variability and larger between-dataset variability over northern Europe and north-central Russia. Trend analysis of the updated SCE series provides evidence that NH spring snow cover extent has undergone significant reductions over the past ~90 yr and that the rate of decrease has accelerated over the past 40 yr. The rate of decrease in March and April NH SCE over the 1970–2010 period is ~0.8 million km2 per decade corresponding to a 7% and 11% decrease in NH March and April SCE respectively from pre-1970 values. In March, most of the change is being driven by Eurasia (NA trends are not significant) but both continents exhibit significant SCE reductions in April. The observed trends in SCE are being mainly driven by warmer air temperatures, with NH mid-latitude air temperatures explaining ~50% of the variance in NH spring snow cover over the 89-yr period analyzed. However, there is also evidence that changes in atmospheric circulation around 1980 involving the North Atlantic Oscillation and Scandinavian pattern have contributed to reductions in March SCE over Eurasia.” Brown, R. D. and Robinson, D. A.: Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty, The Cryosphere, 5, 219-229, doi:10.5194/tc-5-219-2011, 2011. [full text]

Was anomalous winter 1783-1784 analogous to winter 2009-2010?

The anomalous winter of 1783–1784: Was the Laki eruption or an analog of the 2009–2010 winter to blame? – D’Arrigo et al. (2011) “The multi-stage eruption of the Icelandic volcano Laki beginning in June, 1783 is speculated to have caused unusual dry fog and heat in western Europe and cold in North America during the 1783 summer, and record cold and snow the subsequent winter across the circum-North Atlantic. Despite the many indisputable impacts of the Laki eruption, however, its effect on climate, particularly during the 1783–1784 winter, may be the most poorly constrained. Here we test an alternative explanation for the unusual conditions during this time: that they were caused primarily by a combined negative phase of the North Atlantic Oscillation (NAO) and an El Niño-Southern Oscillation (ENSO) warm event. A similar combination of NAO-ENSO phases was identified as the cause of record cold and snowy conditions during the 2009–2010 winter in Europe and eastern North America. 600-year tree-ring reconstructions of NAO and ENSO indices reveal values in the 1783–1784 winter second only to their combined severity in 2009–2010. Data sources and model simulations support our hypothesis that a combined, negative NAO-ENSO warm phase was the dominant cause of the anomalous winter of 1783–1784, and that these events likely resulted from natural variability unconnected to Laki.” D’Arrigo, R., R. Seager, J. E. Smerdon, A. N. LeGrande, and E. R. Cook (2011), Geophys. Res. Lett., 38, L05706, doi:10.1029/2011GL046696.