The Western Arctic Linkage Experiment (WALE): Overview and Synthesis

Author:

McGuire A. D.1,Walsh J. E.2,Kimball J. S.3,Clein J. S.4,Euskirchen S. E.4,Drobot S.5,Herzfeld U. C.5,Maslanik J.5,Lammers R. B.6,Rawlins M. A.6,Vorosmarty C. J.6,Rupp T. S.7,Wu W.8,Calef M.9

Affiliation:

1. U.S. Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit, University of Alaska Fairbanks, Fairbanks, Alaska

2. International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, Alaska

3. Flathead Lake Biological Station, Division of Biological Sciences, University of Montana, Polson, Montana

4. Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska

5. University of Colorado, Boulder, Colorado

6. Water Systems Analysis Group, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire

7. Department of Forest Sciences, University of Alaska Fairbanks, Fairbanks, Alaska

8. National Center for Atmospheric Research, Boulder, Colorado

9. Department of Geography and Planning, University at Albany, State University of New York, Albany, New York

Abstract

Abstract The primary goal of the Western Arctic Linkage Experiment (WALE) was to better understand uncertainties of simulated hydrologic and ecosystem dynamics of the western Arctic in the context of 1) uncertainties in the data available to drive the models and 2) different approaches to simulating regional hydrology and ecosystem dynamics. Analyses of datasets on climate available for driving hydrologic and ecosystem models within the western Arctic during the late twentieth century indicate that there are substantial differences among the mean states of datasets for temperature, precipitation, vapor pressure, and radiation variables. Among the studies that examined temporal trends among the alternative climate datasets, there is not much consensus on trends among the datasets. In contrast, monthly and interannual variations of some variables showed some correlation across the datasets. The application of hydrology models driven by alternative climate drivers revealed that the simulation of regional hydrology was sensitive to precipitation and water vapor differences among the driving datasets and that accurate simulation of regional water balance is limited by biases in the forcing data. Satellite-based analyses for the region indicate that vegetation productivity of the region increased during the last two decades of the twentieth century because of earlier spring thaw, and the temporal variability of vegetation productivity simulated by different models from 1980 to 2000 was generally consistent with estimates based on the satellite record for applications driven with alternative climate datasets. However, the magnitude of the fluxes differed by as much as a factor of 2.5 among applications driven with different climate data, and spatial patterns of temporal trends in carbon dynamics were quite different among simulations. Finally, the study identified that the simulation of fire by ecosystem models is particularly sensitive to alternative climate datasets, with little or no fire simulated for some datasets. The results of WALE identify the importance of conducting retrospective analyses prior to coupling hydrology and ecosystem models with climate system models. For applications of hydrology and ecosystem models driven by projections of future climate, the authors recommend a coupling strategy in which future changes from climate model simulations are superimposed on the present mean climate of the most reliable datasets of historical climate.

Publisher

American Meteorological Society

Subject

General Earth and Planetary Sciences

Reference42 articles.

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