Debjani Ghatak

Arctic sea-ice variability revisited

Abstract This paper explores spatial and temporal relationships between variations in Arctic sea-ice concentration (summer and winter) and near-surface atmospheric temperature and atmospheric pressure using multivariate statistical techniques. Trend, empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses are used to identify spatial patterns associated with covariances and correlations between these fields. Results show that (1) in winter, the Arctic Oscillation still explains most of the variability in sea-ice concentration from 1979 to 2006; and (2) in summer, a decreasing sea-ice trend centered in the Pacific sector of the Arctic basin is clearly correlated to an Arctic-wide air temperature warming trend. These results demonstrate the applicability of multivariate methods, and in particular SVD analysis, which has not been used in earlier studies for assessment of changes in the Arctic sea-ice cover. Results are consistent with the interpretation that a warming signal has now emerged from the noise in the Arctic sea-ice record during summer. Our analysis indicates that such a signal may also be forthcoming during winter.

Role of snow-albedo feedback in higher elevation warming over the Himalayas, Tibetan Plateau and Central Asia

Recent literature has shown that surface air temperature (SAT) in many high elevation regions, including the Tibetan Plateau (TP) has been increasing at a faster rate than at their lower elevation counterparts. We investigate projected future changes in SAT in the TP and the surrounding high elevation regions (between 25°–45°N and 50°–120°E) and the potential role snow-albedo feedback may have on amplified warming there. We use the Community Climate System Model version 4 (CCSM4) and Geophysical Fluid Dynamics Laboratory (GFDL) model which have different spatial resolutions as well as different climate sensitivities. We find that surface albedo (SA) decreases more at higher elevations than at lower elevations owing to the retreat of the 0 °C isotherm and the associated retreat of the snow line. Both models clearly show amplified warming over Central Asian mountains, the Himalayas, the Karakoram and Pamir during spring. Our results suggest that the decrease of SA and the associated increase in absorbed solar radiation (ASR) owing to the loss of snowpack play a significant role in triggering the warming over the same regions. Decreasing cloud cover in spring also contributes to an increase in ASR over some of these regions in CCSM4. Although the increase in SAT and the decrease in SA are greater in GFDL than CCSM4, the sensitivity of SAT to changes in SA is the same at the highest elevations for both models during spring; this suggests that the climate sensitivity between models may differ, in part, owing to their corresponding treatments of snow cover, snow melt and the associated snow/albedo feedback.

North American Temperature, Snowfall, and Snow-Depth Response to Winter Climate Modes

The snowpack is an important seasonal surface water storage reservoir that affects the availability of water resources during the spring and summer seasons in mid‐high latitudes. Not surprisingly, interannual variations in snow cover extent and snow water equivalent have been extensively studied in arid regions such as western North America. This study broadens the focus by examining snow depth as an alternative snowpack metric, and considers its variability over different parts of North America. The authors use singular value decomposition (SVD) in conjunction with linear and partial correlation to show that regional snow-depth variations can be largely explained by the winter North Atlantic Oscillation (NAO) and the Pacific‐North American (PNA) modes of atmospheric variability through distinct mechanistic pathways involving regional winter circulation patterns and hydrologic fluxes. The high index phase of the NAO generates positive winter air temperature anomalies over eastern parts of North America, causing thinning of the winter snowpack via snowmelt. Meanwhile, the high index phase of the PNA generates negative winter snowfall anomalies across midlatitudinal areas of North America, which also serve to thin the snowpack. Positive PNA anomalies have also been shown to increase temperatures and decrease snow depths over western North America. The PNA influence extends across the continent, whereas the NAO influence is limited to eastern North America. The winter snow-depth variations associated with all of these pathways exhibit seasonal persistence, which ultimately yield regional-scale spring snow-depth anomalies throughout much of North America.

Potential Climate and Hydrological Changes in the Aral Sea Region

This chapter describes the climatic setting of the Aral Sea region, investigates how the climate might change during the 21st century, and discusses potential impacts on water resources. Temperature and precipitation fields are analyzed to describe the mean climate for the Aral Sea region. Composite analysis has been employed on the precipitation field from the Global Precipitation Climatology Project (GPCP v2.2) to assess the spatial pattern of changes in precipitation during the last several decades. Furthermore, temperature and precipitation projections available from the 2007 Intergovernmental Panel on Climate Change report are synthesized to examine the nature of climate change during this century. Cold season precipitation has increased during recent decades, particularly over the mountainous terrain east of the Aral Sea. Climate models also project increases (5−20%) in winter precipitation during the 21st century; however, several models suggest decreases (0 to −15%) in precipitation during summer. Despite the increases in cold season precipitation, the large increases in temperature (4°C) during the 21st century are likely to cause increased evaporation which could exacerbate the regional water budget deficit. This may constrain the water supply in the region, particularly during summer and autumn when water demand is highest. To fully understand the impacts of future climate change on regional water resources, hydrologic models that include anthropogenic management of water will be required.