38CVC The use of Arctic-CORDEX Regional Climate Change Predictions to Anticipate Impacts on Coastal Alaska Villages

The rapidly warming Arctic climate is driving significant weather shifts, creating unique challenges for Alaska's coastal communities. Adjusted climate model predictions are needed to accurately reflect region-specific conditions in rural coastal Alaska, where current models often lack precision. The present research addresses the gap by refining predictions, particularly focusing on trends in weather conditions that impact human housing and comfort, and making necessary bias corrections. The aim is to enhance the accuracy of these models for housing system design, including addressing critical issues such as mold and energy efficiency in these communities.

Our methodology integrates ARCTIC-CORDEX climate model outputs with up to 50 years of surface observations for each station, using a 9-member ensemble, to project future climate impacts on six Alaskan coastal cities under RCP8.5. We evaluate 32 climate indices related to temperature, humidity, precipitation, and wind speed, with bias corrections based on historical data ensuring the most accurate predictions of future weather pattern shifts. The study projects that daily winter temperatures will be 8°F to 10°F warmer annually, alongside a 20-30% extension in the melting season length, with a more than 20% increase in average specific humidity, and a slight decrease in mold-prone days. The insights gained from comparative analysis suggest that although the trends predicted by climate models do not always align perfectly with observed trends, they exhibit close correspondence in most cases.

In comparing climate models for different cities, the water-to-land ratio in the grid box emerges as a crucial factor, with higher ratios leading to colder model predictions than observations, with lower ratios leading to warm biases, necessitating variable bias correction in coastal regions and adjustments to climate indices in response to these varying conditions.