Interpretation of snow-climate feedback as produced by 17 general circulation models

Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer Earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.     

Inner core differential motion confirmed by earthquake waveform doublets

We analyzed 18 high-quality waveform doublets with time separations of up to 35 years in the South Sandwich Islands region, for which the seismic signals have traversed the inner core as PKP(DF). The doublets show a consistent temporal change of travel times at up to 58 stations in and near Alaska, and they show a dissimilarity of PKP(DF) coda. Using waveform doublets avoids artifacts of earthquake mislocations and contamination from small-scale heterogeneities. Our results confirm that Earth's inner core is rotating faster than the mantle and crust at about 0.3 degrees to 0.5 degrees per year.     

Interpretation of cloud-climate feedback as produced by 14 atmospheric general circulation models

Understanding the cause of differences among general circulation model projections of carbon dioxide-induced climatic change is a necessary step toward improving the models. An intercomparison of 14 atmospheric general circulation models, for which sea surface temperature perturbations were used as a surrogate climate change, showed that there was a roughly threefold variation in global climate sensitivity. Most of this variation is attributable to differences in the models' depictions of cloud-climate 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 climatic predictors.