NATURE GEOSCIENCE | ADVANCE ONLINE PUBLICATION | www.nature.com/naturegeoscience 1 T he Arctic cryosphere is an integral part of Earth's climate system and has undergone unprecedented changes within the past few decades. Rapid warming and sea-ice loss has had significant impacts locally, particularly in late summer and early autumn. September sea ice has declined at a rate of 12.4% per decade since 1979 (ref. 1), so that by summer 2012, nearly half of the areal coverage had disappeared. This decrease in ice extent has been accompanied by an approximately 1.8 m (40%) decrease in mean winter ice thickness since 1980 (ref.2) and a 75-80% loss in volume 3 . Though sea-ice loss has received most of the research and media attention, snow cover in spring and summer has decreased at an even greater rate than sea ice. June snow cover alone has decreased at nearly double the rate of September sea ice 4 . The decrease in spring snow cover has contributed to both the rise in warm season surface temperatures over the Northern Hemisphere extratropical landmasses and the decrease in summer Arctic sea ice 5 . The combined rapid loss of sea ice and snow cover in the spring and summer has played a role in amplifying Arctic warming. However, snow cover and sea-ice trends diverge in the autumn and winter with sea ice decreasing in all months while snow cover has exhibited a neutral to positive trend in autumn and winter 6 . Climate change and Arctic amplificationWhile the global-mean surface temperature has unequivocally risen over the instrumental record 7 , spatial heterogeneity of this warming plays an important role in the resulting climate impacts. In particular, the near-surface of the Northern Hemisphere high latitudes are warming at rates double that of lower latitudes [8][9][10] . This observed The Arctic region has warmed more than twice as fast as the global average -a phenomenon known as Arctic amplification. The rapid Arctic warming has contributed to dramatic melting of Arctic sea ice and spring snow cover, at a pace greater than that simulated by climate models. These profound changes to the Arctic system have coincided with a period of ostensibly more frequent extreme weather events across the Northern Hemisphere mid-latitudes, including severe winters. The possibility of a link between Arctic change and mid-latitude weather has spurred research activities that reveal three potential dynamical pathways linking Arctic amplification to mid-latitude weather: changes in storm tracks, the jet stream, and planetary waves and their associated energy propagation. Through changes in these key atmospheric features, it is possible, in principle, for sea ice and snow cover to jointly influence mid-latitude weather. However, because of incomplete knowledge of how high-latitude climate change influences these phenomena, combined with sparse and short data records, and imperfect models, large uncertainties regarding the magnitude of such an influence remain. We conclude that improved process understanding, sustained and additional...

This paper describes an instrument designed to distinguish frozen from thawed land surfaces from an Earth satellite by bouncing signals back to Earth from deployable mesh antennas.

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Soil moisture controls the partitioning of moisture and energy fluxes at the land surface and is a key variable in weather and climate prediction. The performance of the ensemble Kalman filter (EnKF) for soil moisture estimation is assessed by assimilating L-band (1.4 GHz) microwave radiobrightness observations into a land surface model. An optimal smoother (a dynamic variational method) is used as a benchmark for evaluating the filter's performance. In a series of synthetic experiments the effect of ensemble size and non-Gaussian forecast errors on the estimation accuracy of the EnKF is investigated. With a state vector dimension of 4608 and a relatively small ensemble size of 30 (or 100; or 500), the actual errors in surface soil moisture at the final update time are reduced by 55% (or 70%; or 80%) from the value obtained without assimilation (as compared to 84% for the optimal smoother). For robust error variance estimates, an ensemble of at least 500 members is needed. The dynamic evolution of the estimation error variances is dominated by wetting and drying events with high variances during drydown and low variances when the soil is either very wet or very dry. Furthermore, the ensemble distribution of soil moisture is typically symmetric except under very dry or wet conditions when the effects of the nonlinearities in the model become significant. As a result, the actual errors are consistently larger than ensemble-derived forecast and analysis error variances. This suggests that the update is suboptimal. However, the degree of suboptimality is relatively small and results presented here indicate that the EnKF is a flexible and robust data assimilation option that gives satisfactory estimates even for moderate ensemble sizes.

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