Global models are the most widely used tools for understanding and assessing climatic variability and changes. However, coarse-resolution limits their capability to capture detailed finer-scale meteorological features, including heterogeneous spatial distributions and high-frequency temporal variability. In this study, the mesoscale Weather Research and Forecasting (WRF) model is used to dynamically downscale a CMIP5 global model simulation (CCSM MOAR output) for a portion of the Arctic marginal zone, encompassing Alaska and surrounding areas, with the aim to improve understanding, representation, and future projection of high-resolution climate changes in the area. Dynamic downscaling of the twentieth century simulation was conducted for the period 1991-2005 and validated against in situ observations archived by the NCDC. Downscaled results generally capture observed conditions well. However, cold biases exist across most of the study area, except for a weak warm bias along the western and northern Alaskan coasts. In addition, downscaled winds are stronger than observations and precipitation is overestimated along the Alaskan panhandle. The biases in the downscaled temperature, wind speed, and precipitation are correctable. The downscaled temperature bias exhibits strong seasonality, with a warm bias in the cold months and a cold bias in the warm months, particularly along the western and northern Alaskan coasts. Seasonality in the wind speed and precipitation biases, however, is relatively small. Under the RCP6 scenario, downscaled regional climate over Alaska and the surrounding areas demonstrate a significant warming trend over the entire study area during the twenty-first century, with the strongest warming occurring over the Arctic Ocean. Precipitation is also projected to increase along Alaska’s coastal areas and over the Arctic Ocean. Interior Alaska, on the other hand, becomes drier in the future climate scenario.
Zhang, J., J. R. Krieger, U. Bhatt, C. Lu, X. Zhang. 2015. Alaskan Regional Climate Changes in Dynamically Downscaled CMIP5 Simulations. Proceedings of the 2013 National Conference on Advances in Environmental Science and Technology. 47-60. http://link.springer.com/chapter/10.1007/978-3-319-19923-8_5. DOI: doi: 10.1007/978-3-319-19923-8_5.