Arctic air temperature, precipitation, ground temperature, river runoff, clouds, and radiation are all changing quickly in a warming climate. Interactions and feedbacks between these features are not well understood. In particular, the relative role of local climate processes and large-scale ocean-atmosphere dynamics in driving observed Arctic changes is difficult to ascertain because of the sparsity of observations, inaccuracy of those that do exist, biases in global circulation models and analyses, and fundamental physics of the Arctic region.

Four studies of Arctic hydroclimatology herein attempt to overcome these challenges. The first study, analysis of the Lena river basin hydroclimatology, shows canonical acceleration of the hydrologic cycle and amplification of global warming. Winter and spring are warming and increased frozen precipitation is contributing to permafrost melting by increasing soil insulation. Increasing runoff and soil moisture is leading to increasing evapotranspiration and changes in clouds. Changes in clouds are cooling summer days but warming summer nights, melting additional permafrost. Model simulations suggests that a deepening active layer will lead to an increasingly wet Arctic.

The second two studies describe the development of the Pan-Arctic Snowfall Reconstruction (PASR). This product addresses the problem of cold season precipitation gauge biases for 1940-1999. The NASA Interannual-to-Seasonal Prediction Project Catchment-based Land Surface Model is used to reconstruct solid precipitation from observed snow depth and surface air temperatures. Error estimation is done via controlled simulations at Reynolds Creek Experimental Watershed, in Idaho. The method is then applied to stations in the pan-Arctic hydrological catchment. Comparison with existing products suggests that the PASR is a better estimate of actual snowfall for hydroclimatological studies.

The final chapter is a case study on hydroclimatological variability driven by a large-scale mode of climate, the North Atlantic Oscillation (NAO). Variation in the NAO index explains 55% of the variance of streamflow in Norway and up to 30% of the variance in Norway's hydropower output. It is also possible to identify the influence of NAO anomalies on electricity consumption and prices. The model offers a possible tool for predicting the effects of future NAO variability on hydropower production and energy prices in Scandinavia.