Abstract
General circulation models (GCMs) are limited in their representation of regional climates. Thus, the selection and downscaling of the most suitable models for regional/local studies are crucial prior to climate change impact studies. This study addressed the selection and downscaling of GCM models from 100 ensembles each from the Shared Socioeconomic Pathways (SSP4.5 and SSP8.5) emission scenarios from the CMIP6 archive using an advanced envelop-based selection approach for Northern Nigeria. We used 2021–2050 as the short-term and 2051–2080 as the long-term study periods. The selection approach revealed that CanESM5 models are more skilful in simulating the warm and wet season, while HadGEM3-GC31-LL in the warm and dry season, whereas MPI-ESM1-2-HR and MPI-ESM1-2-LR are skilful in the cold and dry season. Furthermore, we downscaled the three most skilled models from each season and calculated their spatial averages over Northern Nigeria to provide a more precise illustration of the temperature and precipitation patterns. Under the SSP4.5 emission scenario, the ensemble mean of the downscaled and the (raw) GCMs projected about 13% (8–17%) and 20% (11–35%) increase in average annual precipitation during the short-term and long-term periods, respectively. Similarly, for SSP8.5, the models projected about 23% (5–38%) and 41% (29–60%) increase in the average annual precipitation during short-term and long-term periods respectively. For the temperature, under SSP4.5, the GCMs projected a 1.1 °C (0.26–1.6 °C) and 2.5 °C (0.87–4.04 °C) increase in average annual temperature for short-term and long-term periods respectively. Similarly, an increase of 1.2 °C (0.01–1.78 °C) and 2.7 °C (0.01–4.3 °C) is projected for SSP8.5 during the short-term and long-term periods respectively. These findings can be used for climate impact studies in the region.
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The full set of raw data and the analysed output that supports the findings of this study are available for future analysis.
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The codes that support the findings of this study are available from the corresponding author (wada@fuwukari.edu.ng).
References
Abiodun BJ, Adedoyin A (2016) Chapter 23-A modelling perspective of future climate change. In: Letcher TM (ed) Climate Change, 2nd edn. Elsevier, Boston, pp 355–371
Adakayi, P. E. (2012). An assesment of rainfall and temperature variations in selected stations in parts of Northern Nigeria.
Adenuga KI, Mahmoud AS, Dodo YA, Albert M, Kori SA, Danlami NJ (2021) Climate change adaptation and mitigation in sub-Saharan African countries. In: Asif M (ed) Energy And Environmental Security In Developing Countries. Springer International Publishing, Cham, pp 393–409
Adeosun OT, Popogbe OO (2021) Population growth and human resource utilization nexus in Nigeria. J Hum Appl Soc Sci 3(4):281–298. https://doi.org/10.1108/Jhass-06-2020-0088
Aladejana OO, Salami AT, Adetoro O-IO (2018) Hydrological responses to land degradation in the northwest Benin Owena River Basin, Nigeria. J Environ Manage 225:300–312. https://doi.org/10.1016/J.Jenvman.2018.07.095
Almazroui M, Saeed F, Saeed S, Nazrul Islam M, Ismail M, Klutse NAB, Siddiqui MH (2020) Projected change in temperature and precipitation over Africa from CMIP6. Earth Systems And Environment 4(3):455–475. https://doi.org/10.1007/S41748-020-00161-X
Amodu M, Ejieji C (2017) Performance Of some general circulation models on predicting temperature and rainfall in the Sudan-Sahel Region Of Nigeria. Arid Zone J Eng Technol Environ
Ayugi B, Dike V, Ngoma H, Babaousmail H, Mumo R, Ongoma V (2021) Future changes in precipitation extremes over East Africa based on CMIP6 models. Water 13(17):2358
Bhat KS, Haran M, Terando A, Keller K (2011) Climate projections using Bayesian model averaging and space—time dependence. J Agric Biol Environ Stat 16(4):606–628
Biemans, H., Speelman, L. H., Ludwig, F., Moors, E. J., Wiltshire, A. J., Kumar, P., . . . Kabat, P. (2013). Future water resources for food production in five South Asian river basins and potential for adaptation — a modeling study. Sci Total Environ, 468-469, S117-S131. https://doi.org/10.1016/J.Scitotenv.2013.05.092
Brunner L, Lorenz R, Zumwald M, Knutti R (2019) Quantifying uncertainty in European climate projections using combined performance-independence weighting. Environ Res Lett 14(12):124010. https://doi.org/10.1088/1748-9326/Ab492f
Chen H, Xu C-Y, Guo SJJOH (2012) Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. J Hydrol 434-435:36–45
Ekeu-Wei IT, Blackburn GA, Giovannettone J (2020) Accounting for the effects of climate variability in regional flood frequency estimates in Western Nigeria. J Water Resource Prot 12(08):690–731. https://doi.org/10.4236/Jwarp.2020.128042
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9(5):1937–1958. https://doi.org/10.5194/Gmd-9-1937-2016
Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27(12):1547–1578. https://doi.org/10.1002/Joc.1556
Fragoso TM, Bertoli W, Louzada F (2018) Bayesian model averaging: a systematic review and conceptual classification. Int Stat Rev 86(1):1–28. https://doi.org/10.1111/Insr.12243
Gebresellase SH, Wu Z, Xu H, Muhammad WI (2022) Evaluation and selection of CMIP6 climate models in Upper Awash Basin (UBA). In: Theoretical And Applied Climatology, Ethiopia. https://doi.org/10.1007/S00704-022-04056-X
Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Technical note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrol Earth Syst Sci 16(9):3383–3390. https://doi.org/10.5194/Hess-16-3383-2012
Hernanz A, Correa C, Domínguez M, Rodríguez-Guisado E, Rodríguez-Camino E (2023) Statistical downscaling in the tropics and mid-latitudes: A COMPARATIVE ASSESSMENT OVER TWO REPRESENTATIVE REGIONS. J Appl Meteorol Climatol. https://doi.org/10.1175/Jamc-D-22-0164.1
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., . . . Thépaut, J.-N. (2020). The ERA5 global reanalysis 146(730), 1999-2049. https://doi.org/10.1002/Qj.3803
Hersbach H, Dee D (2016) ERA5 reanalysis is in production. Ecmwf Newsletter 147 Https://Www.Ecmwf.Int/En/Newsletter/147/News/Era5-Reanalysis-Production
Hertig E, Jacobeit J (2008) Downscaling future climate change: temperature scenarios for the Mediterranean area. Global Planet Change 63(2-3):127–131
Ibebuchi CC (2021a) Circulation type analysis of regional hydrology: the added value in using CMIP6 over CMIP5 simulations as exemplified from the MPI-ESM-LR model. J Water Clim Change 13(2):1046–1055. https://doi.org/10.2166/Wcc.2021.262
Ibebuchi CC (2021b) On the relationship between circulation patterns, the southern annular mode, and rainfall variability in Western Cape. Atmosphere 12(6):753
Ibebuchi CC (2021c) Revisiting the 1992 severe drought episode in South Africa: the role of El Niño in the anomalies of atmospheric circulation types in Africa south of the equator. Theor Appl Climatol 146(1):723–740. https://doi.org/10.1007/S00704-021-03741-7
Ibebuchi CC (2022) Future trends in atmospheric circulation patterns over Africa south of the equator. J Water Clim Change 13(12):4194–4212. https://doi.org/10.2166/Wcc.2022.172
Ibebuchi CC (2023) On The representation of atmospheric circulation modes in regional climate models over Western Europe. Int J Climatol 43(1):668–682. https://doi.org/10.1002/Joc.7807
Jin L, Whitehead PG, Appeaning Addo K, Amisigo B, Macadam I, Janes T, Rodda HJE (2018) Modeling future flows of the Volta River system: impacts of climate change and socio-economic changes. Sci Total Environ 637-638:1069–1080. https://doi.org/10.1016/J.Scitotenv.2018.04.350
Jose DM, Vincent AM, Dwarakish GS (2022) Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning techniques. Sci Rep 12(1):4678. https://doi.org/10.1038/S41598-022-08786-W
Joshua W (2021) Climate change and extreme climate events: a threat to water security in Northern Nigeria. J Environ Sci, Toxicol Food Technol 15(1):47–54
Kaini S, Nepal S, Pradhananga S, Gardner T, Sharma AK (2020) Representative general circulation models selection and downscaling of climate data for the transboundary Koshi River Basin In China and Nepal. Int J Climatol 40(9):4131–4149. https://doi.org/10.1002/Joc.6447
Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–472. https://doi.org/10.1175/1520-0477(1996)077<0437:Tnyrp>2.0.Co;2
Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) Ncep–Doe Amip-Ii Reanalysis (R-2). Bull Am Meteorol Soc 83(11):1631–1644. https://doi.org/10.1175/Bams-83-11-1631
Khan, F., & Pilz, J. (2017). A Bayesian approach for GCMs selection and ensemble projections under the latest emission scenarios. Https://Ui.Adsabs.Harvard.Edu/Abs/2017eguga..19.5259k
Kim B-S, Kim B-K, Kwon H-H (2011) Assessment of the impact of climate change on the flow regime of the Han River Basin using indicators of hydrologic alteration. Hydrol Process 25(5):691–704. https://doi.org/10.1002/Hyp.7856\
Koutsouris AJ, Chen D, Lyon SW (2016) Comparing global precipitation data sets in Eastern Africa: a case study of Kilombero Valley, Tanzania. Int J Climatol 36(4):2000–2014. https://doi.org/10.1002/Joc.4476
Lee J, Sperber KR, Gleckler PJ, Bonfils CJW, Taylor KE (2019) Quantifying the agreement between observed and simulated extratropical modes of interannual variability. Climate Dynam 52(7):4057–4089. https://doi.org/10.1007/S00382-018-4355-4
Li C, Zhao T, Shi C, Liu Z (2021) Assessment of precipitation from the CRA40 dataset and new generation reanalysis datasets in the global domain. Int J Climatol 41(11):5243–5263. https://doi.org/10.1002/Joc.7127
Li M-H, Tien W, Tung C-P (2009) Assessing the impact of climate change on the land hydrology in Taiwan. Paddy And Water Environment 7(4):283. https://doi.org/10.1007/S10333-009-0175-9
Li YL, Tao H, Yao J, Zhang Q (2016) Application of a distributed catchment model to investigate hydrological impacts of climate change within Poyang Lake catchment (China). Hydrology Res 47(S1):120–135. https://doi.org/10.2166/Nh.2016.234
Liang-Liang L, Jian L, Ru-Cong Y (2022) Evaluation of CMIP6 HighResMIP models in simulating precipitation over Central Asia. Adv Clim Change Res 13(1):1–13. https://doi.org/10.1016/J.Accre.2021.09.009
Lutz AF, Ter Maat HW, Biemans H, Shrestha AB, Wester P, Immerzeel WW (2016) Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach. Int J Climatol 36(12):3988–4005. https://doi.org/10.1002/Joc.4608
Maghsood FF, Moradi H, Massah Bavani AR, Panahi M, Berndtsson R, Hashemi H (2019) Climate change impact on flood frequency and source area in Northern Iran under CMIP5 scenarios. Water 11(2):273. https://doi.org/10.3390/W11020273
Maraun D (2013) Bias correction, quantile mapping, and downscaling: revisiting the inflation issue. J Climate 26(6):2137–2143. https://doi.org/10.1175/Jcli-D-12-00821.1
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., . . . Thiele-Eich, I. (2010). Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3). https://doi.org/10.1029/2009rg000314
Meehl, G. A., Covey, C., Delworth, T., Latif, M., Mcavaney, B., Mitchell, J. F. B., . . . Taylor, K. E. (2007). The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc, 88(9), 1383-1394. https://doi.org/10.1175/Bams-88-9-1383
Monerie P-A, Wainwright CM, Sidibe M, Akinsanola AA (2020) Model uncertainties in climate change impacts on Sahel precipitation in ensembles of CMIP5 And CMIP6 simulations. Climate Dynam 55(5):1385–1401. https://doi.org/10.1007/S00382-020-05332-0
Ndulue EL, Mbajiorgu CC (2019) Modeling climate and land-use change impacts on streamflow and sediment yield of an agricultural watershed using SWAT. Agric Eng Int: Cigr J 20(4):15–25
O'Neill BC, Tebaldi C, Van Vuuren DP, Eyring V, Friedlingstein P, Hurtt G, Knutti R, Kriegler E, Lamarque JF, Lowe J, Meehl GA (2016) The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci Model Dev 9(9):3461–3482. https://doi.org/10.5194/Gmd-9-3461-2016
Ofori SA, Cobbina SJ, Obiri S (2021) Climate change, land, water, and food security: perspectives from sub-Saharan Africa. Front Sustain Food Systems 5:680924. https://doi.org/10.3389/Fsufs.2021.680924
Ogunjo ST, Olusegun CF, Fuwape IA (2022) Evaluation of monthly precipitation data from three gridded climate data products over Nigeria. Remote Sensing In Earth Systems Sci 5(3):119–128. https://doi.org/10.1007/S41976-022-00069-2
Ometto JP, Bun R, Jonas M, Nahorski Z, Gusti MI (2014) Uncertainties in greenhouse gases inventories – expanding our perspective. Clim Change 124(3):451–458. https://doi.org/10.1007/S10584-014-1149-5
Perkins SE, Pitman AJ, Holbrook NJ, Mcaneney J (2007) Evaluation of the AR4 climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J Climate 20(17):4356–435s. https://doi.org/10.1175/Jcli4253.1
Peterson TC (2005) Climate change indices. Wmo Bulletin 54:83–86
Piani C, Haerter JO, Coppola E (2009) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99(1-2):187–192. https://doi.org/10.1007/S00704-009-0134-9
Pierce DW, Barnett TP, Santer BD, Gleckler PJ (2009) Selecting global climate models for regional climate change studies. Proc Natl Acad Sci 106(21):8441. https://doi.org/10.1073/Pnas.0900094106
Rajagopalan B, Lall U, Zebiak SE (2002) Categorical climate forecasts through regularization and optimal combination of multiple GCM ensembles. Mon Weather Rev 130(7):1792–1811. https://doi.org/10.1175/1520-0493(2002)130<1792:Ccftra>2.0.Co;2
Raju KS, Kumar DN (2020) Review of approaches for selection and ensembling of GCMs. J Water Clim Change 11(3):577–599. https://doi.org/10.2166/Wcc.2020.128
Rapaić M, Brown R, Markovic M, Chaumont D (2015) An evaluation of temperature and precipitation surface-based and reanalysis datasets for the Canadian Arctic, 1950–2010. Atmosphere-Ocean 53(3):283–303. https://doi.org/10.1080/07055900.2015.1045825
Räty O, Räisänen J, Ylhäisi JS (2014) Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ensembles simulations. Climate Dynam 42(9):2287–2303. https://doi.org/10.1007/S00382-014-2130-8
Robertson AW, Lall U, Zebiak SE, Goddard L (2004) Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Mon Weather Rev 132(12):2732–2744. https://doi.org/10.1175/Mwr2818.1
Sanchez E, Romera R, Gaertner C, Gallardo C, Castro M (2009) A weighting proposal for an ensemble of regional climate models over Europe driven by 1961–2000 ERA40 based on monthly precipitation probability density functions. Atmos Sci Lett 10(4):241–248. https://doi.org/10.1002/Asl.230
Sawa BA, Ati OF, Jaiyeoba IA, Oladipo EO (2015) Trends in aridity of the arid and semi-arid regions of Northern Nigeria. J Environ Earth Sci 5:61–68
Schulzweida U (2020) Cdo User Guide. In Zenodo (Ed.). https://doi.org/10.5281/Zenodo.4246983
Seager R, Ting M, Held I, Kushnir Y, Lu J, Vecchi G, Huang HP, Harnik N, Leetmaa A, Lau NC, Li C (2007) Model projections of an imminent transition to a more arid climate in southwestern North America. Science 316(5828):1181. https://doi.org/10.1126/Science.1139601
Semenov MA, Stratonovitch P (2010) Use of multi-model ensembles from global climate models for assessment of climate change impacts. Climate Res 41(1):1–14
Shiru MS, Chung E-S, Shahid S, Alias N (2020a) GCM selection and temperature projection of Nigeria under different RCPs of the CMIP5 GCMs. Theor Appl Climatol 141(3):1611–1627. https://doi.org/10.1007/S00704-020-03274-5
Shiru MS, Chung E-SJT, Climatology A (2021) Performance evaluation of CMIP6 global climate models for selecting models for climate projection over Nigeria. Theor Appl Climatol 146(1):599–615
Shiru MS, Shahid S, Dewan A, Chung E-S, Alias N, Ahmed K, Hassan QK (2020b) Projection of meteorological droughts in Nigeria during growing seasons under climate change scenarios. Sci Rep 10(1):10107. https://doi.org/10.1038/S41598-020-67146-8
Sorg A, Huss M, Rohrer M, Stoffel M (2014) The days of plenty might soon be over in glacierized Central Asian catchments. Environ Res Lett 9(10):104018. https://doi.org/10.1088/1748-9326/9/10/104018
Tang G, Clark MP, Papalexiou SM, Ma Z, Hong Y (2020) Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sens Environ 240:111697. https://doi.org/10.1016/J.Rse.2020.111697
Tanko AI, Momale SB (2013) Geography of the Kano Region. Southbank House Black Prince Road London Se1 7sj. Adonis & Abbey Publishers Ltd., United Kingdom
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192. https://doi.org/10.1029/2000jd900719
Taylor KE (2005) Taylor Diagram Primer, pp 1–4
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93(4):485–498. https://doi.org/10.1175/Bams-D-11-00094.1
Tebaldi C, Debeire K, Eyring V, Fischer E, Fyfe J, Friedlingstein P, Knutti R, Lowe J, O'Neill B, Sanderson B, Van Vuuren D (2021) Climate model projections from the scenario model intercomparison project (ScenarioMIP) of CMIP6. Earth Syst Dynam 12(1):253–293. https://doi.org/10.5194/Esd-12-253-2021
Umar DUA, Ramli MF, Aris AZ, Jamil NR, Abdulkareem JH (2018) Runoff irregularities, trends, and variations in tropical semi-arid river catchment. J Hydrol Reg Stud 19:335–348. https://doi.org/10.1016/J.Ejrh.2018.10.008
Warszawski L, Frieler K, Huber V, Piontek F, Serdeczny O, Schewe J (2014) The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): project framework. Proc Natl Acad Sci 111(9):3228. https://doi.org/10.1073/Pnas.1312330110
Werner AT, Cannon AJ (2016) Hydrologic extremes -- an intercomparison of multiple gridded statistical downscaling methods. Hydrol Earth Syst Sci 20(4):1483–1508. https://doi.org/10.5194/Hess-20-1483-2016
Wilby RL, Dawson CW, Barrow EM (2002) SDSM — a decision support tool for the assessment of regional climate change impacts. Environ Model Software 17(2):145–157. https://doi.org/10.1016/S1364-8152(01)00060-3
Wu Y, Miao C, Fan X, Gou J, Zhang Q, Zheng H (2022) Quantifying the uncertainty sources of future climate projections and narrowing uncertainties with bias correction techniques. 10(11):E2022ef002963. https://doi.org/10.1029/2022ef002963
Yang Y, Tang J, Xiong Z, Wang S, Yuan J (2019) An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: present climate evaluations. Climate Dynam 53(7):4629–4649. https://doi.org/10.1007/S00382-019-04809-X
Zhan W, Guan K, Sheffield J, Wood EF (2016) Depiction of drought over sub-Saharan Africa using reanalyses precipitation data sets. J Geophys Res Atmos 121(18):555–510. https://doi.org/10.1002/2016jd024858
Zhang S, Li Z, Lin X, Zhang C (2019) Assessment of climate change and associated vegetation cover change on watershed-scale runoff and sediment yield. Water 11(7). https://doi.org/10.3390/W11071373
Zollo, A. L., Turco, M., & Mercogliano, P. (2015). Assessment of hybrid downscaling techniques for precipitation over the Po River Basin.
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The authors would like to acknowledge the Earth System Grid Federation (ESGF), NCEP DOE, ERA5, and ERA-1 reanalysis for archiving and accessing their dataset.
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Wada Idris Muhammad contributed to conceptualization, data curation, conducted formal analysis, methodology, coding, investigation, simulation of datasets, results, visualisation, writing the original manuscript, review, and editing. Haruna Shehu Usman contributed to the simulation of data sets, revision, and edited the manuscript. Amechi S. Nwankwegu, Makhai Nwunuji Usman, and Selamawit Haftu Gebresellase contributed to review, editing, and curation.
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Wada, I.M., Usman, H.S., Nwankwegu, A.S. et al. Selection and downscaling of CMIP6 climate models in Northern Nigeria. Theor Appl Climatol 153, 1157–1175 (2023). https://doi.org/10.1007/s00704-023-04534-w
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DOI: https://doi.org/10.1007/s00704-023-04534-w