Biogeosciences Discuss., 10, 17145-17192, 2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
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This discussion paper is under review for the journal Biogeosciences (BG).
What is the importance of climate model bias when projecting the impacts of climate change on land surface processes?
M. Liu1, K. Rajagopalan1, S. H. Chung1, X. Jiang2, J. Harrison3, T. Nergui1, A. Guenther2, C. Miller3, J. Reyes1, C. Tague4, J. Choate4, E. P. Salathé5, C. O. Stöckle6, and J. C. Adam1
1Civil and Environmental Engineering, Washington State University, Pullman, WA, USA
2Atmospheric Chemistry Division, NCAR Earth System Laboratory, Boulder, CO, USA
3School of the Environment, Washington State University, Vancouver, WA, USA
4Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, USA
5School of Science Technology Engineering and Mathematics, University of Washington, Bothell, WA, USA
6Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA

Abstract. Regional climate change impact (CCI) studies have widely involved downscaling and bias-correcting (BC) Global Climate Model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables (evapotranspiration, ET; runoff; snow water equivalent, SWE; and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) Region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ Andrews). Simulation results from the coupled ECHAM5/MPI-OM model with A1B emission scenario were firstly dynamically downscaled to 12 km resolutions with WRF model. Then a quantile mapping based statistical downscaling model was used to downscale them into 1/16th degree resolution daily climate data over historical and future periods. Two series climate data were generated according to the option of bias-correction (i.e. with bias-correction (BC) and without bias-correction, NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological datasets. These impact models include a macro-scale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrologic model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS).

Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ Andrews's ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at region scales.

We conclude that there are trade-offs between using BC climate data for offline CCI studies vs. direct modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; e.g., BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality (where VOCs are a primary indicator).

Citation: Liu, M., Rajagopalan, K., Chung, S. H., Jiang, X., Harrison, J., Nergui, T., Guenther, A., Miller, C., Reyes, J., Tague, C., Choate, J., Salathé, E. P., Stöckle, C. O., and Adam, J. C.: What is the importance of climate model bias when projecting the impacts of climate change on land surface processes?, Biogeosciences Discuss., 10, 17145-17192, doi:10.5194/bgd-10-17145-2013, 2013.
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