A combined dynamical and statistical downscaling technique to reduce biases in climate projections: an example for winter precipitation and snowpack in the western United States
- 335 Downloads
Large biases associated with climate projections are problematic when it comes to their regional application in the assessment of water resources and ecosystems. Here, we demonstrate a method that can reduce systematic biases in regional climate projections. The global and regional climate models employed to demonstrate the technique are the Community Climate System Model (CCSM) and the Weather Research and Forecasting (WRF) model. The method first utilized a statistical regression technique and a global reanalysis dataset to correct biases in the CCSM-simulated variables (e.g., temperature, geopotential height, specific humidity, and winds) that are subsequently used to drive the WRF model. The WRF simulations were conducted for the western United States and were driven with (a) global reanalysis, (b) original CCSM, and (c) bias-corrected CCSM data. The bias-corrected CCSM data led to a more realistic regional climate simulation of precipitation and associated atmospheric dynamics, as well as snow water equivalent (SWE), in comparison to the original CCSM-driven WRF simulation. Since most climate applications rely on existing global model output as the forcing data (i.e., they cannot re-run or change the global model), which often contain large biases, this method provides an effective and economical tool to reduce biases in regional climate downscaling simulations of water resource variables.
KeywordsRoot Mean Square Deviation Snow Water Equivalent Dynamical Downscaling Community Climate System Model Ageostrophic Wind
We would like to acknowledge our funding from Bureau of Reclamation (project No: R11AC81456 and R13AC80039) in support of this research. We thank the reviewers and editor for their helpful comments.
- Brekke L, Raff D, Werner K, White K, Wood A (2012) Anticipating, preparing for, and managing through extreme weather and climate events: water resource managers’ needs for improved weather and climate prediction information. 10th Annual Climate Prediction Applications Science Workshop,March 13–15, 2012, MiamiGoogle Scholar
- Brekke L, Thrasher B, Pruitt T, Maurer EP, Tebaldi C, Arnold JR, Long J (2013) New CMIP5 downscaled climate and hydrology projections. 11th Annual Climate Prediction Applications Science Workshop, April 23–25, 2013, LoganGoogle Scholar
- Holton JR (1992) An introduction to dynamic meteorology, 3rd edn. Academic, San Diego, p 511Google Scholar
- Jin J, Wen L (2012) Evaluation of snowmelt simulation in the weather research and forecasting model. J Geophys Res-Atmos 117:D10110Google Scholar
- Jin J, Wang SY, Gillies RR (2011) Improved Dynamical Downscaling of Climate Projections for the Western United States. Climate Change: Research and Technology for Adaptation and Mitigation, J. Blanco and H. Kheradmand eds., InTech, 23–38Google Scholar
- Nakicenvoic et al (2000) Special report on emissions scenarios. A special report of working group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 599Google Scholar
- Qian Y, Ghan SJ, Leung LR (2010) Downscaling hydroclimatic changes over the Western US based on CAM subgrid scheme and WRF regional climate simulations. Int J Climatol 30:675–693Google Scholar
- USGS (2014) Snowmelt - The Water Cycle (available at http://water.usgs.gov/edu/watercyclesnowmelt.html). accessed on June 10, 2014