Biogeosciences, 11, 2601-2622, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
16 May 2014
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. Guenther1,4, C. Miller3, J. Reyes1, C. Tague5, J. Choate5, E. P. Salathé6, C. O. Stöckle7, and J. C. Adam1 1Civil and Environ 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
4Atmospheric Sciences and Global Change, Pacific Northwest National Laboratory, Richland WA, USA
5Bren School of Environmental Science & Management, University of California, Santa Barbara, CA, USA
6School of Science Technology Engineering and Mathematics, University of Washington, Bothell, WA, USA
7Department 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 first dynamically downscaled to 12 km resolution with the WRF model. Then a quantile-mapping-based statistical downscaling model was used to downscale them into 1/16° resolution daily climate data over historical and future periods. Two climate data series were generated, 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 data sets. These impact models include a macroscale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrological 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 regional scales.

We conclude that there are trade-offs between using BC climate data for offline CCI studies versus directly modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; for example, BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality, for which 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, 11, 2601-2622,, 2014.
Publications Copernicus