Assessment of bias correction techniques for historical precipitation simulations in Sudan

• Autorzy: Hilal Khalid , Salieva Kamar

Identyfikatory

DOI

10.26491/mhwm/214425

• Języki publikacji: EN

Abstrakty

EN

This study evaluates the performance of several bias correction techniques applied to CMIP6 precipitation simulations over Sudan for the period 1991-2014, using the high-resolution CHIRPS observational dataset as a reference. Four widely used bias correction methods: Empirical Quantile Mapping (EQM), Gamma Quantile Mapping (Gamma-QM), Local Intensity Scaling (LOCI), and the Delta Method were applied to ten CMIP6 models to assess their ability to reduce systematic biases and improve consistency with observed climatology. The raw simulations reveal pronounced seasonal biases, characterized by overestimation during the pre-monsoon season (MAM) and underestimation during the monsoon season (JJAS), whereas annual biases are moderate but exhibit notable spatial heterogeneity. Among the tested techniques, EQM and Gamma-QM consistently yield the most effective corrections, achieving median bias reductions of 94-100% across both annual and seasonal timescales, and markedly enhancing Kling–Gupta Efficiency (KGE) values. Among the evaluated models, EC-Earth3, GFDL-ESM4, and INMCM4-8 demonstrate the best performance annually and during June-September, whereas NESM3 performs better during MarchMay, highlighting model-specific strengths in simulating seasonal precipitation variability. Spatial analyses further confirm that bias corrections effectively align precipitation variability with observations, with statistically significant improvements across most regions of Sudan. These findings highlight the critical role of quantile-based correction methods in producing reliable CMIP6 precipitation outputs over Sudan and establish a robust framework for assessing both model skill and bias correction performance in regions characterized by complex, seasonally varying rainfall regimes.

Słowa kluczowe

EN

climate modeling; downscaling; quantile mapping; seasonal rainfall; ensemble simulations

• Wydawca: Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
• Czasopismo: Meteorology Hydrology and Water Management. Research and Operational Applications
• Rocznik: 2025
• Tom: Vol. 13, Iss. 2
• Strony: 53--70
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Hilal Khalid

• The author conducted this research independently

autor

Salieva Kamar

• Kazhydromet, Kazakhstan

Bibliografia

• Ayugi B.O., Shilenje Z.W., Babaousmail H., Lim Kam Sian K.T.C., Mumo R., Dike V.N., Iyakaremye V., Chehbouni A., Ongoma V., 2022, Projected changes in meteorological drought over East Africa inferred from bias-adjusted CMIP6 models, Natural Hazards, 113 (2), 1151-1176, DOI: 10.1007/s11069-022-05341-8.
• Byun Y.-H., Lim Y.-J., Sung H.-M., Kim J., Sun M., Kim B.-H., Lee J.-H., Moon H., 2019, NIMS-KMA KACE1.0-G model output prepared for CMIP6 CMIP historical, Earth System Grid Federation, DOI: 10.22033/ESGF/CMIP6.8378.
• Cao J., Wang B., Yang Y.-M., Ma L., Li J., Sun B., Bao Y., He J., Zhou X., Wu L., 2018, The NUIST Earth System Model (NESM) version 3: Description and preliminary evaluation, Geoscientific Model Development, 11 (7), 2975-2993, DOI: 10.5194/gmd-11-2975-2018.
• Döscher R., Acosta M., Alessandri A., Anthoni P., Arsouze T., Bergman T., Bernardello R., Boussetta S., Caron L,-P., Carver G., Castrillo M., Catalano F., Cvijanovic I., Davini P., Dekker E., Doblas-Reyes F.J., Docquier D., Echevarria P., Fladrich U., Fuentes-Franco R., Gröger M., v. Hardenberg J., Hieronymus J., Pasha Karami M., Keskinen J.-P., Koenigk T., Makkonen R., Massonnet F., Ménégoz M., Miller P.A., Moreno-Chamarro E., Nieradzik L., van Noije T., Nolan P., O'Donnell D., Ollinaho P., van den Oord G., Ortega P., Tintó Prims O., Ramos A., Reerink T., Rousset C., Ruprich-Robert Y., Le Sager P., Schmith T., Schrödner R., Serva F., Sicardi V., Sloth Madsen M., Smith B., Tian T., Tourigny E., Uotila P., Vancoppenolle M., Wang S., Wårlind D., Willén U., Wyser K., Yang S., Yepes-Arbós X., Zhang Q., 2022, The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geoscientific Model Development, 15 (1), 2973-3020, DOI: 10.5194/gmd-15-2973-2022.
• Efron B., Tibshirani R.J., 1993, An Introduction to the Bootstrap, Chapman & Hall/CRC, New York, 456 pp., DOI: 10.1201/9780429246593.
• Eyring V., Bony S., Meehl G.A., Senior C.A., Stevens B., Stouffer R.J., Taylor K.E., 2016, Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geoscientific Model Development, 9 (5), 1937-1958, DOI: 10.5194/gmd-9-1937-2016.
• Funk C., Peterson P., Landsfeld M., Pedreros D., Verdin J., Shukla S., Husak G., Rowland J., Harrison L., Hoell A., Michaelsen J., 2015, The climate hazards infrared precipitation with stations - a new environmental record for monitoring extremes. Scientific Data, 2, DOI: 10.1038/sdata.2015.66.
• Gebrechorkos S.H., Hülsmann S., Bernhofer C., 2019, Long-term trends in rainfall and temperature using high-resolution climate datasets in East Africa, Scientific Reports, 9, DOI: 10.1038/s41598-019-47933-8.
• Gudmundsson L., Bremnes J.B., Haugen J.E., Engen-Skaugen T., 2012, Technical note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods, Hydrology and Earth System Sciences, 16 (9), 3383-3390, DOI: 10.5194/hess-16-3383-2012.
• Hay L.E., Wilby R.L., Leavesley G.H., 2000, A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States, Journal of the American Water Resources Association, 36 (2), 387-397, DOI: https://doi.org/10.1111/j.1752-1688.2000.tb04276.x.
• Held I.M., Gou H., Adcroft A., Dunne J.P., Horowitz L.W., Krasting J., Shevliakova E., Winton M., Zhao M., Bushuk M., Wittenberg A.T., Wyman B., Xiang B., Zhang R., Anderson W., Balaji V., Donner L., Dunne K., Durachta J., Gauthier P.P.G., Ginoux P., Golaz J.-C., Griffies S.M., Hallberg R., Harris L., Harrison M., Hurlin W., John J., Lin P., Lin S.-J., Malyshev S., Menzel R., Milly P.C.D., Ming Y., Naik V., Paynter D., Paulot F., Ramaswamy V., Reichl B., Robinson T., Rosati A., Seman C., Silvers L.G., Underwood S., Zadeh N., 2019, Structure and performance of GFDL's CM4.0 climate model, Journal of Advances in Modeling Earth Systems, 11 (11), 3691-3727, DOI: 10.1029/2019MS001829.
• ICPAC, 2020, State of the Climate of the Greater Horn of Africa 2020, IGAD Climate Prediction and Applications Centre, Nairobi, Kenya, available online at: https://www.icpac.net/ (data access 19.11.2025).
• Jackson L.S., Declan L.F., Kendon E.J., Marsham H., Parker D.J., Stratton R.A., Tomassini L., Tucker S., 2020, The effect of explicit convection on couplings between rainfall, humidity, and ascent over Africa under climate change, Journal of Climate, 33 (19), 8315-8337, DOI: 10.1175/JCLI-D-19-0322.1.
• Kling H., Fuchs M., Paulin M., 2012, Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, Journal of Hydrology, 424-425, 264-277, DOI: 10.1016/j.jhydrol.2012.01.011.
• Lee W.-L., Wang Y.-C., Shiu C.-J., Tsai I., Tu C.-Y., Lan Y.-Y., Chen J.-P., Pan H.-L., Hsu H.-H., 2020, Taiwan Earth System Model Version 1: description and evaluation of mean state, Geoscientific Model Development, 13 (8), 3887-3904, DOI: 10.5194/gmd-13-3887-2020.
• Li H., Sheffield J., Wood E.F., 2010, Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching, Journal of Geophysical Research: Atmospheres, 115 (D10), DOI: 1029/2009JD012882.
• Maraun D., 2013, Bias correction, quantile mapping, and downscaling: revisiting the inflation issue, Journal of Climate, 26, 2137-2143, DOI: 10.1175/JCLI-D-12-00821.1.
• Maraun D., 2016, Bias correcting climate change simulations - a critical review, Current Climate Change Reports, 2 (4), 211-221, DOI: 10.1007/s40641-016-0050-x.
• Nicholson S.E., 2018, The ITCZ and the seasonal cycle over equatorial Africa, Bulletin of the American Meteorological Society, 99 (2), 337-348, DOI: 10.1175/BAMS-D-16-0287.1.
• Omay P.O., Muthama N.J., Oludhe C., Kinama J.M., Artan G., Atheru Z., 2023, Evaluation of CMIP6 historical simulations over IGAD region of Eastern Africa, Discover Environment, 11 (1), DOI: 10.1007/s44274-023-00012-2.
• RCCC, 2022, Sudan Climate Profile, Country-Level Summary, Red Cross Climate Centre, available online at https://www.climatecentre.org/wp-content/uploads/RCCC-Country-profiles-Sudan-2022-Final-1.pdf (data access 19.11.2025).
• Saltelli A., Ratto M., Andres T., Campolongo F., Cariboni J., Gatelli D., Saisana M., Tarantola S., 2008, Global Sensitivity Analysis: The Primer, John Wiley & Sons, Chichester, UK, 292 pp., DOI: 10.1002/9780470725184.
• Schmidli J., Frei C., Vidale P.L., 2006, Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods, International Journal of Climatology, 26 (5), 679-689, DOI: 10.1002/joc.1287.
• Semmler T., Danilov S., Rackow T., Sidorenko D., Barbi D., Hegewald J., Pradhan H.K., Sein D., Wang Q., Jung T., 2019, AWI AWI-CM1.1MR model output prepared for CMIP6 ScenarioMIP ssp370. World Data Center for Climate (WDCC) at DKRZ, DOI: WDCC/AR6.C6SPAWAWMs370.
• Siddig M.S.A., Ibrahim S., Yu Q., Abdalla A., Osman Y., Atiem I.A., 2022, Bias adjustment of four satellite-based rainfall products using ground-based measurements over Sudan, Water, 14 (9), DOI: 10.3390/w14091475.
• Switanek M. B., Troch P. A., Castro C. L., Leuprecht A., Chang H.-I., Mukherjee R., Demaria E.M., 2017, Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes, Hydrology and Earth System Sciences, 21 (6), 2649-2666, DOI: 10.5194/hess-2016-435.
• Tatebe H., Ogura T., Nitta T., Komuro Y., Ogochi K., Takemura T., Sudo K., Skiguchi M., Abe M., Saito F., Chikira M., Watanabe S., Mori M., Hirota N., Kawatani Y., Mochizuki T., Yoshimura K., Takata K., O’ishi R., Yamazaki D., Suzuki T., Kurogi M., Kataoka T., Watanabe M., Kimoto M., 2019, Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12 (7), 2727-2765, DOI: 10.5194/gmd-12-2727-2019.
• Taylor K.E., 2001, Summarizing multiple aspects of model performance in a single diagram, Journal of Geophysical Research: Atmospheres, 106 (D7), 7183-7192, DOI: 10.1029/2000JD900719.
• 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, 456-457, 12-29, DOI: 10.1016/j.jhydrol.2012.05.052.
• Tiku T.W., Tarekegn G.B., Sahlu D., Tashebo G.B., Enyew F.B., Umer Y., Debele S.E., 2025, Evaluating CMIP6 precipitation simulations across different rainfall regimes in the Amhara region, Ethiopia, Theoretical and Applied Climatology, 5 (3), 689-704, DOI: 10.1016/j.nhres.2025.03.002.
• Toolan C.A., Amooli J.A., Wilvox L.J., Samset B.H., Turenr A.G., Westervelt D.M., 2025, Strong intermodel differences and biases in CMIP6 simulations of PM2.5, aerosol optical depth, and precipitation over Africa, Atmospheric Chemistry and Physics, 25 (18), 10523-10557, DOI: 10.5194/acp-25-10523-2025.
• Vogel P., Knippertz P., Fink A.H., Schlueter A., Gneiting T., 2018, Skill of global raw and postprocessed ensemble predictions of rainfall over northern tropical Africa, Weather and Forecasting, 33, 369-388, DOI: 10.1175/WAF-D-17-0127.1.
• Volodin E.M., Mortikov E.V., Kostrykin S.V., Galin V.Y., Lykossov V.N., Gritsun A.S., Diansky N.A., Gusev A.V., Iakovlev N.G., Shestakova A.A., Emelina S.V., 2018, Simulation of the modern climate using the INM-CM48 climate model, Russian Journal of Numerical Analysis and Mathematical Modelling, 33 (6), 367-374, DOI: 10.1515/rnam-2018-0032.
• Wilks D.S., 2011, Statistical Methods in the Atmospheric Sciences (3rd ed.). Academic Press, San Diego.
• Williams K.D., Copsey D., Blockley E.W., Bodas-Salcedo A., Calvert D., Comer R., Davis P., Graham T., Hewitt H.T., Hill R., Hyder P., Ineson S., Johns T.C., Keen A.B., Lee R.W., Megann A., Milton S.F., Rae J.G.L., Roberts M.J., Scaife A.A., Schiemann R., Storkey D., Thorpe L., Watterson I.G., Walters D.N, West A., Wood R.A., Woolings R., Xavier P.K., 2018, The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 & GC3.1) configurations, Journal of Advances in Modeling Earth Systems, 10 (2), 357-380, DOI: 10.1002/2017MS001115.
• Yukimoto S., Koshiro T., Kawai H., Koshiro T., Oshima N., Yoshida K., Urakawa S., Tsujino H., Deushi M., Tanaka T., Hosaka M., Yabu S., Yoshimura H., Shindo E., Mizuta R., Obata A., Adachi Y., Ishii M., 2019, The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component, Journal of the Meteorological Society of Japan, 97 (5), 931-965, DOI: 10.2151/jmsj.2019-051.