Satellite-based cloud, radiation flux, and sea ice records covering 34 years are used 1) to investigate autumn cloud cover trends over the Arctic, 2) …Satellite-based cloud, radiation flux, and sea ice records covering 34 years are used 1) to investigate autumn cloud cover trends over the Arctic, 2) to assess its relation with declining sea ice using Granger causality (GC) analysis, and 3) to discuss the contribution of the cloud–sea ice (CSI) feedback to Arctic amplification. This paper provides strong evidence for a positive CSI feedback with the capability to contribute to autumnal Arctic amplification. Positive low-level cloud fractional cover (CFClow) trends over the Arctic ice pack are found in October and November (ON) with magnitudes of up to about 19.6% per decade locally. Statistically significant anticorrelations between sea ice concentration (SIC) and CFClow are observed in ON over melting zones, suggesting an association. The GC analysis indicated a causal two-way interaction between SIC and CFClow. Interpreting the resulting F statistic and its spatial distribution as a relation strength proxy, the influence of SIC on CFClow is likely stronger than the reverse. ERA-Interim reanalysis data suggest that ON CFClow is impacted by sea ice melt through surface–atmosphere coupling via turbulent heat and moisture fluxes. Due to weak solar insolation in ON, net cloud radiative forcing (CRF) exerts a warming effect on the Arctic surface. Increasing CFClow induces a large-scale surface warming trend reaching magnitudes of up to about 18.3 W m22 per decade locally. Sensitivities of total CRF to CFClow ranges between 10.22 and 10.66 W m22 per percent CFClow. Increasing surface warming can cause a melt season lengthening and hinders formation of perennial ice. Ó 2020 American Meteorological Society.more
With the accelerating impact of global warming, the changes of Arctic sea ice has become a focal point of research. Due to the spatial heterogeneity a…With the accelerating impact of global warming, the changes of Arctic sea ice has become a focal point of research. Due to the spatial heterogeneity and the complexity of its evolution, long-term prediction of Arctic sea ice remains a challenge. In this article, a spatial attention U-Net (SAU-Net) method integrated with a gated spatial attention mechanism is proposed. Extracting and enhancing the spatial features from the historical atmospheric and SIC data, this method improves the accuracy of Arctic sea ice prediction. During the test periods (2018-2020), our method can skillfully predict the Arctic sea ice up to 12 months, outperforming the naive U-Net, linear trend models, and dynamical models, especially in extreme sea ice scenarios. The importance of different atmospheric factors affecting sea ice prediction are also analyzed for further exploration.more
Within Chapter 2, changes are assessed from in situ and remotely sensed data and products and from indirect evidence of longer-term changes based upon…Within Chapter 2, changes are assessed from in situ and remotely sensed data and products and from indirect evidence of longer-term changes based upon a diverse range of climate proxies. The time-evolving availability of observations and proxy information dictate the periods that can be assessed. Wherever possible, recent changes are assessed for their significance in a longer-term context, including target proxy periods, both in terms of mean state and rates of changemore
This chapter assesses past and projected changes in the ocean, cryosphere and sea level using paleo reconstructions, instrumental observations and mod…This chapter assesses past and projected changes in the ocean, cryosphere and sea level using paleo reconstructions, instrumental observations and model simulations. In the following summary, we update and expand the related assessments from the IPCC Fifth Assessment Report (AR5), the Special Report on Global Warming of 1.5ºC (SR1.5) and the Special Report on Ocean and Cryosphere in a Changing Climate (SROCC). Major advances in this chapter since the SROCC include the synthesis of extended and new observations, which allows for improved assessment of past change, processes and budgets for the last century, and the use of a hierarchy of models and emulators, which provide improved projections and uncertainty estimates of future change. In addition, the systematic use of model emulators makes our projections of ocean heat content, land-ice loss and sea level rise fully consistent both with each other and with the assessed equilibrium climate sensitivity and projections of global surface air temperature across the entire report. In this executive summary, uncertainty ranges are reported as very likely ranges and expressed by square brackets, unless otherwise noted.more
One of the clearest manifestations of ongoing global climate change is the dramatic retreat and thinning of the Arctic sea-ice cover1. While all state…One of the clearest manifestations of ongoing global climate change is the dramatic retreat and thinning of the Arctic sea-ice cover1. While all state-of-the-art climate models consistently reproduce the sign of these changes, they largely disagree on their magnitude1,2,3,4, the reasons for which remain contentious3,5,6,7. As such, consensual methods to reduce uncertainty in projections are lacking7. Here, using the CMIP5 ensemble, we propose a process-oriented approach to revisit this issue. We show that intermodel differences in sea-ice loss and, more generally, in simulated sea-ice variability, can be traced to differences in the simulation of seasonal growth and melt. The way these processes are simulated is relatively independent of the complexity of the sea-ice model used, but rather a strong function of the background thickness. The larger role played by thermodynamic processes as sea ice thins8,9 further suggests that the recent10 and projected11 reductions in sea-ice thickness induce a transition of the Arctic towards a state with enhanced volume seasonality but reduced interannual volume variability and persistence, before summer ice-free conditions eventually occur. These results prompt modelling groups to focus their priorities on the reduction of sea-ice thickness biases.more