Statistical Downscaling of General Circulation Models (GCMs); History, Principles, and Methods

Document Type : Technical paper

Authors

• Dina Yazdany
• Azar Zarrin
• Abbasali Dadashi-Roudbari

Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran.

10.22067/jwsd.v11i2.2401-1305

Abstract

The climate system is very complex and has made the modeling and predicting/projecting face many challenges. Although climate variability may be detected and identified through a time series of observations, it cannot express the interaction of various components of the Earth's climate system. General circulation models (GCMs) are essential for simulating the physical processes governing the atmosphere and the interaction of the components involved in the Earth's climate system. Statistical downscaling extracts empirical relationships between small-scale observational variables (often at the station level) and the direct GCM output by applying three approaches: Perfect Prognosis (PP), Model Output Statistics (MOS), and Weather Generators (WGs). Bias correction, widely used in climate change studies, is the MOS statistical downscaling approach. To clarify the role of using the inappropriate method and software in increasing uncertainty, two scaling methods from the model output statistics (MOS) approach are compared to correct the bias of the minimum and maximum temperatures. In this research, the outputs of R and CMhyd software are compared to check the uncertainty caused by using inappropriate software. The output of the EC-Earth3-CC model for two variables of the minimum and maximum temperatures was examined using CMhyd and R software. Examining the results showed that the CMhyd software has a significant error in both extracting the direct model output and the bias correction method. For example, the PBIAS of direct output of maximum temperature in Abadan was 2.10%, while CMhyd software gives 5.10%. The result of this research shows the need to use the correct methods and software for processing the output of GCMs. 

Keywords

• Statistical Downscaling
• Model Output Statistics (MOS)
• Bias Correction
• CMhyd Software

Main Subjects

• Water, Ecosystem and Environment

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References

زرین، آذر، و داداشی رودباری، عباسعلی. (1399). پیش‌نگری چشم‌انداز بلندمدت دمای آینده ایران مبتنی بر برونداد پروژه مقایسه مدل‌های جفت‌شده فاز ششم (CMIP6). فیزیک زمین و فضا، 46(3)، 583-602. doi: 10.22059/jesphys.2020.304870.1007226
شعبان‌پور، فاطمه، بذرافشان، جواد، و عراقی‌نژاد، شهاب. (1399). ارزیابی تاثیر روش‌های تصحیح اریبی بر مهارت پیش‌بینی فصلی بارش مدل اقلیمی CFSv2. تحقیقات آب و خاک ایران، 51(12)، 3017-3032. doi: 10.22059/ijswr.2020.306717.668680
یزدانی، دینا، زرین، آذر، و داداشی رودباری، عباسعلی. (1403). بررسی کارایی روش‌های تصحیح اریبی در بهبود برونداد مستقیم دمای مدل‌های CMIP بر روی ایران. فیزیک زمین و فضا، 50(2)، 429-450. doi: 10.22059/jesphys.2024.363498.1007547
Chen, J., Brissette, F. P., Poulin, A., & Leconte, R. (2011). Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed. Water Resources Research, 47(12). https://doi.org/10.1029/2011WR010602.
Cho, R. (2023). What uncertainties remain in climate science? Columbia Climate School. Retrieved March 26, 2024, from https://news.climate.columbia.edu/2023/01/12/what-uncertainties-remain-in-climate-science/. Columbia, USA.
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arneth, A., Arsouze, T., & Zhang, Q. (2021). The EC-earth3 Earth system model for the climate model intercomparison project 6. Geoscientific Model Development Discussions, 2021, 1-90. https://doi.org/10.5194/gmd-15-2973-2022
Gates, W. L. (1985). The use of general circulation models in the analysis of the ecosystem impacts of climatic change. Climatic Change, 7(3), 267-284. https://doi.org/10.1007/BF00144171
Glahn, H. R., & Lowry, D. A. (1972). The use of model output statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology and Climatology, 11(8), 1203-1211. https://doi.org/10.1175/1520-0450(1972)011%3C1203:TUOMOS%3E2.0.CO;2
Klein, W. H. (1948). Winter precipitation as related to the 700–mb circulation. Bulletin of the American Meteorological Society, 29(9), 439-453.
Klein, W. H., Lewis, B. M., & Enger, I. (1959). Objective prediction of five-day mean temperatures during winter. Journal of the Atmospheric Sciences, 16(6), 672-682. https://doi.org/10.1175/1520-0469(1959)016%3C0672:OPOFDM%3E2.0.CO;2
Lambert, S., & Boer, G. (2001). CMIP1 evaluation and intercomparison of coupled climate models. Climate Dynamics, 17, 83–106. https://doi.org/10.1007/PL00013736
Lenderink, G., Buishand, A., & Van Deursen, W. (2007). Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrology and Earth System Sciences, 11(3), 1145–1159. https://doi.org/10.5194/hess-11-1145-2007
Manabe, S., & Wetherald, R. T. (1975). The effects of doubling the CO 2 concentration on the climate of a general circulation model. Journal of Atmospheric Sciences, 32(1), 3-15. https://doi.org/10.1175/1520-0469(1975)032%3C0003:TEODTC%3E2.0.CO;2
Maraun, D. (2019). Statistical downscaling for Climate Science. In Oxford Research Encyclopedia of Climate Science. https://doi.org/10.1093/acrefore/9780190228620.013.712
Maraun, D., & Widmann, M. (2018). Statistical downscaling and bias correction for climate research. Vol.1. Cambridge University Press. Cambridge, England. https://doi.org/10.1017/9781107588783
Meehl, G. A., Boer, G. J., Covey, C., Latif, M., & Stouffer, R. J. (1997). Intercomparison makes for a better climate model. Eos, Transactions American Geophysical Union, 78(41), 445–451. https://doi.org/10.1029/97EO00276
Navarra, A. (2021). The role of systematic errors. National Science Review, 8(10), nwab082. https://doi.org/10.1093/nsr/nwab082
Neelin, J. D. (2010). Climate change and climate modeling. Vol.1. Cambridge University Press. Cambridge, England.
Rathjens, H., Bieger, K., Srinivasan, R., Chaubey, I., & Arnold, J. G. (2016). CMhyd user manual. Doc. Prep. Simulated Clim. Change Data Hydrol. Impact Study, 1413. https://swat.tamu.edu/software/cmhyd/
Rummukainen, M. (2016). Added value in regional climate modeling. Wiley Interdisciplinary Reviews: Climate Change, 7(1), 145-159. https://doi.org/10.1002/wcc.378
Schoof, J. T. (2013). Statistical downscaling in climatology. Geography Compass, 7(4), 249–265. https://doi.org/10.1111/gec3.12036
Society, T. R. (2021). Next generation climate models: a step change for net zero and climate adaptation. Royal Society. https://policycommons.net/artifacts/1724705/next-generation-climate-models/
Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456, 12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052
Trenberth, K. E. (Ed.). (1992). Climate system modeling. Vol.1. Cambridge University Press. Cambridge, England.