Svetlana I. Kuzmina

Sea-ice change and its connection with climate change in the Arctic in CMIP2 simulations

[1] In this work, we analyze the two-dimensional distribution of mean and intermodel spread of Arctic sea ice and climate change at the time of CO2 doubling and their connection using the simulations from the second phase of the Coupled Model Intercomparison Project (CMIP2). Arctic surface warming at the time of CO2 doubling is found to be not evenly distributed and ranges from 1 to 5C. The intermodel spread is pronounced in the Arctic Ocean, particularly in the Barents Sea. Reduction of sea-ice thickness (SIT) is in the range 0.3–1.8 m and mainly appears in the Greenland-Barents Seas. Meanwhile, sea-ice concentrations (SIC) decrease more than 10% in most regions of the Arctic Ocean. The sensitivity of Arctic surface air temperature change with respect to sea-ice area change is model-dependent. For some models, the sensitivity is different even in different periods of the transient integration. Values of the sensitivity vary from 2.0 to 0.5C/10 6 km 2 for most CMIP2 models. A colder (warmer) Arctic climate may favor a higher (lower) sensitivity. The simulated mean and intermodel spread patterns of surface air temperature (SAT) change are similar to those of SIT and sea level pressure (SLP) changes. This implies that the mean and intermodel spread of projected Arctic climate change are influenced by the interaction between sea ice and the atmosphere. Both SIT and SIC are sensitive to the increase in greenhouse gas concentrations, and are connected with SAT and SLP changes in the Arctic. The average of all model simulations indicates that the north-south SLP gradient and the mean westerly winds are enhanced by CO2 doubling. Finally, both the mean and intermodel spread patterns show considerable differences between models with and without flux adjustment in some regions. INDEX TERMS: 1620 Global Change: Climate dynamics (3309); 1854 Hydrology: Precipitation (3354); 3349 Meteorology and Atmospheric Dynamics: Polar meteorology; 3309 Meteorology and Atmospheric Dynamics: Climatology (1620); KEYWORDS: Arctic sea ice, global warming, CMIP2 simulations

Surface air temperature variability and trends in the Arctic: new amplification assessment and regionalisation

Arctic amplification of temperature change is theorised to be an important feature of the Earths climate system. For observational assessment and understanding of mechanisms of this amplification, which remain uncertain, thorough and detailed analyses of surface air temperature (SAT) variability and trends in the Arctic are needed. Here we present an analysis of Arctic SAT variability in comparison with mid-latitudes and the Northern Hemisphere (NH), based on an advanced SAT dataset – NansenSAT. We define an index for the Arctic amplification as the ratio between absolute values of the Arctic (65–90°N) and NH 30-yr running linear SAT trends. It is demonstrated that the temperature amplification in the Arctic is characteristic not only for the recent warming but also the early 20th century warming (ETCW) and subsequent cooling. The amplification appears to be weaker during the recent warming than in the ETCW, simply because the index values reflect the more pervasive nature of the recent warming that reflects the background of anthropogenic global warming. We also produced a new Arctic regionalisation created from hierarchical cluster analysis, which identifies six major natural regions in the Arctic that reflect SAT variability. Statistical comparison with several climate indices shows that the Atlantic Multidecadal Oscillation (AMO) is the mode of variability that is most significantly associated with the amplified warming–cooling in the Arctic, with a stronger correlation during the ETCW and recent warming than during the intermediate period. Regionally, differences are identified in terms of annual and seasonal rates of change and in their correlations with modes of variability.

Projecting the global macroeconomic dynamics under high-end temperature scenarios and strongly nonlinear climate damage functions

Projections of the gross world product (GWP) for the 21 century are computed on a simple climate–macroeconomic model using different global mean surface air temperature projections provided by General Circulation Models (GCMs) as input data. Two alternative specifications of climate damage functions proposed by Nordhaus and Weitzman are considered. High uncertainty of long-term global macroeconomic dynamics with respect to the choice of climate scenarios and climate damage functions is revealed. Strong nonlinearity of the Weitzman function combined with the “worst-case” temperature scenario yields a very dramatic scenario of long-term global economic development. A high degree of uncertainty accompanying existing assessments of climate–socioeconomic projections urgently calls for more detailed and better justified estimations of anticipated climate damages at high temperature increases above pre-industrial level.