Evaluating Model Parameterizations of Arctic Processes

An understanding of the arctic climate system has become a high priority research area because of its importance to global climate change (IPCC 1990). Unfortunately, our studies of this region are in their infancy and we lack a broad knowledge of the Arctic. This deficiency is due to the scarcity of observations and the difficulties in remotely sensing arctic clouds from satellites (Curry et al. 2000). Of fundamental importance is both a better understanding of and more accurate simulations of cloud and radiation processes over the Arctic. A complex combination of drastic seasonal changes, complicated cloud microphysics, turbulent transport, and frequent boundary layer inversions have presented many challenges in developing model parameterizations for these regions (Curry et al. 1996). To combat this lack of knowledge of the arctic climate system, the United States Department of Energy’s Atmospheric Radiation Measurement (ARM) Program has arranged a relatively dense concentration of instruments at the North Slope of Alaska (NSA). The primary purpose of this long-term monitoring site is to improve parameterizations of cloud and radiation processes in models. A desire to improve understanding of (e.g., Curry 1986; Curry et al. 1996; Harrington et al. 1999; Harrington and Olsson 2001b) and to create better model parameterizations for arctic cloud processes are of primary scientific research interest in the meteorological community (e.g., Harrington and Olsson 2001a; Doran et al. 2002; Girard and Blanchet 2001). Arctic clouds play a potentially important role in both the arctic and global climate system. Large seasonal and spatial cloud coverage in the Arctic creates a large impact on the radiation budget of the arctic climate system. For example, cloud/radiation feedback is associated with snow/ice albedo feedback, thereby providing a significant positive feedback on global climate change (Curry et al. 1996). Arctic clouds also are linked to changes in the arctic hydrological cycle and the thermohaline circulation (Nakamura 1996). This need to understand arctic clouds is the prime motivation for the comparison of ARM NSA cloud observations with numerical model results. We first provide a description of the data used for the comparison and then describe the methods used in the analysis. Next, we provide a description of our current results and offer a conclusion as well as future plans for research.