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Preprints
https://doi.org/10.5194/tc-2016-87
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-2016-87
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 May 2016

31 May 2016

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This preprint has been withdrawn by the authors.

Dynamical Downscaling Data for Studying Climatic Impacts on Hydrology, Permafrost, and Ecosystems in Arctic Alaska

Lei Cai1, Vladimir A. Alexeev1, Christopher D. Arp2, Benjamin M. Jones3, Anna Liljedahl2, and Anne Gädeke2 Lei Cai et al.
  • 1International Arctic Research Center, University of Alaska Fairbanks, 930 Koyukuk Dr. Fairbanks, AK 99775, USA
  • 2Water and Environment Research Center, University of Alaska Fairbanks, 306 Tanana Loop Rd., Fairbanks, AK 99775, USA
  • 3United States Geological Survey, Alaska Science Center, 4210 University Dr. Anchorage, AK 99508-4626, USA

Abstract. Climatic changes are most pronounced in northern high latitude regions. Yet, there is a paucity of observational data, both spatially and temporally, such that regional-scale dynamics are not fully captured, limiting our ability to make reliable projections. In this study, a group of dynamical downscaling products were created for the period 1950 to 2100 to better understand climate change and its impacts on hydrology, permafrost, and ecosystems at a resolution suitable for northern Alaska. An ERA-interim reanalysis dataset and the Community Earth System Model (CESM) served as the forcing mechanisms in this dynamical downscaling framework, and the Weather Research & Forecast (WRF) model, embedded with an optimization for the Arctic (Polar WRF), served as the Regional Climate Model (RCM). This downscaled output consists of multiple climatic variables (precipitation, temperature, wind speed, dew point temperature, and surface air pressure) for a 10 km grid spacing at three-hour intervals. The modeling products were evaluated and calibrated using a bias-correction approach. The ERA-interim forced WRF (ERA-WRF) produced reasonable climatic variables as a result, yielding a more closely correlated temperature field than precipitation field when long-term monthly climatology was compared with its forcing and observational data. A linear scaling method then further corrected the bias, based on ERA-interim monthly climatology, and bias-corrected ERA-WRF fields were applied as a reference for calibration of both the historical and the projected CESM forced WRF (CESM-WRF) products. Biases, such as, a cold temperature bias during summer and a warm temperature bias during winter as well as a wet bias for annual precipitation that CESM holds over northern Alaska persisted in CESM-WRF runs. The linear scaling of CESM-WRF eventually produced high-resolution downscaling products for the Alaskan North Slope for hydrological and ecological research, together with the calibrated ERA-WRF run, and its capability extends far beyond that. Other climatic research has been proposed, including exploration of historical and projected climatic extreme events and their possible connections to low-frequency sea-atmospheric oscillations, as well as near-surface permafrost degradation and ice regime shifts of lakes. These dynamically downscaled, bias corrected climatic datasets provide improved spatial and temporal resolution data necessary for ongoing modeling efforts in northern Alaska focused on reconstructing and projecting hydrologic changes, ecosystem processes and responses, and permafrost thermal regimes. The dynamical downscaling methods presented in this study can also be used to create more suitable model input datasets for other sub-regions of the Arctic.

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Lei Cai et al.

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Short summary
This paper introduces the development process of a data set that specifically made for climatic impacts research over the Alaskan North Slope. This data set can offset to some extent the sparseness of observation on spatial and temporal scales, retrieving high-resolution climatic backgrounds that enable various studies in the fields of climatology, hydrology, ecology, etc.
This paper introduces the development process of a data set that specifically made for climatic...
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