Abstract
This study establishes the improvements in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) simulations as compared to its previous version, CMIP5. First, the historical simulations are compared with the reanalysis products from the 5th generation European Centre for Medium-Range Weather Forecasts (ERA5). Quality improvement in CMIP6 is assured through its correspondence with ERA5 in terms of mean, standard deviation and mean bias. Global fields of three hydrometeorological variables, i.e. temperature, precipitation and soil moisture, are considered from multiple General Circulation Models. Among the three variables, maximum improvement is noticed in case of soil moisture followed by precipitation, especially in the tropical belt. In case of temperature, the mean bias has reduced by ± 3 °C across the parts of North America, Africa, and South Asia. Better reliance on the CMIP6 motivates for a trend analysis to peek into the future. The results indicate a significant increasing trend for precipitation in the temperate, polar and sub-polar regions, whereas a significant increase in temperature is noticed almost all across the world with highest slope in the polar and sub-polar regions. Furthermore, soil moisture shows a significant trend that can be grouped continent-wise, e.g. Africa, Central and South Asia exhibit an increasing trend, whereas North and Central America and Northern parts of South America exhibit an overall decreasing trend. Apart from underlining the better reliance on CMIP6, the findings of this study will also be useful across different parts of the world for many climate related studies using CMIP6.
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Availability of data and materials
The data that support the findings of this study are available from; https://esgf-node.llnl.gov/search/cmip6/, https://esgf-node.llnl.gov/search/cmip5/, https://esgf-data.dkrz.de/search/cordex-dkrz/ and https://www.ecmwf.int/en/forecasts/datasets/ reanalysis-datasets/era5. These datasets are freely available. It was accessed by the authors in April 2021.
Code availability
The codes required for the analysis are written in MATLAB R2018a (version 9.4). These codes can be made available on request.
References
Almazroui M, Saeed F, Saeed S et al (2020a) Projected change in temperature and precipitation over Africa from CMIP6. Earth Syst Environ 4:455–475. https://doi.org/10.1007/s41748-020-00161-x
Almazroui M, Saeed S, Saeed F et al (2020b) Projections of precipitation and temperature over the South Asian countries in CMIP6. Earth Syst Environ 4:297–320. https://doi.org/10.1007/s41748-020-00157-7
Chen S, Yuan X (2021) CMIP6 projects less frequent seasonal soil moisture droughts over China in response to different warming levels. Environ Res Lett. https://doi.org/10.1088/1748-9326/abe782
Cook BI, Mankin JS, Marvel K et al (2020) Twenty-first century drought projections in the CMIP6 forcing scenarios. Earth’s Futur 8:1–20. https://doi.org/10.1029/2019EF001461
Deng K, Azorin-Molina C, Minola L et al (2020) Global near-surface wind speed changes over the last decades revealed by reanalyses and CMIP6 model simulations. J Clim 34:2219–2234. https://doi.org/10.1175/jcli-d-20-0310.1
Dosio A, Panitz HJ, Schubert-Frisius M, Lüthi D (2015) Dynamical downscaling of CMIP5 global circulation models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value. Clim Dyn 44:2637–2661. https://doi.org/10.1007/s00382-014-2262-x
Eyring V, Bony S, Meehl GA et al (2016) Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organisation. Geosci Model Dev Discuss 8:10539–10583. https://doi.org/10.5194/gmdd-8-10539-2015
Eyring V, Cox PM, Flato GM et al (2019) Taking climate model evaluation to the next level. Nat Clim Chang 9:102–110. https://doi.org/10.1038/s41558-018-0355-y
Fan X, Duan Q, Shen C et al (2020a) Global surface air temperatures in CMIP6: historical performance and future changes. Environ Res Lett. https://doi.org/10.1088/1748-9326/abb051
Fan X, Miao C, Duan Q et al (2020b) The performance of CMIP6 versus CMIP5 in simulating temperature extremes over the global land surface. J Geophys Res Atmos 125:1–16. https://doi.org/10.1029/2020JD033031
Giorgi F, Jones C, Asrar G (2009) Addressing climate information needs at the regional level: the CORDEX framework. WMO Bull 58:175–183
Gusain A, Ghosh S, Karmakar S (2020) Added value of CMIP6 over CMIP5 models in simulating Indian summer monsoon rainfall. Atmos Res 232:104680. https://doi.org/10.1016/j.atmosres.2019.104680
Hermans THJ, Gregory JM, Palmer MD et al (2021) Projecting global mean sea-level change using CMIP6 models. Geophys Res Lett 48:1–11. https://doi.org/10.1029/2020GL092064
Hirabayashi Y, Tanoue M, Sasaki O et al (2021) Global exposure to flooding from the new CMIP6 climate model projections. Sci Rep 11:1–7. https://doi.org/10.1038/s41598-021-83279-w
Huntington TG (2010) Chapter one-climate warming-induced intensification of the hydrologic cycle: an assessment of the published record and potential impacts on agriculture. In: Donald L (ed) Advances in agronomy, vol 09. Academic Press, Sparks, pp 1–53. https://doi.org/10.1016/B978-0-12-385040-9.00001-3
IPCC (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, New York, p 582
Jiang D, Hu D, Tian Z, Lang X (2020) Differences between CMIP6 and CMIP5 models in simulating climate over China and the East Asian monsoon. Adv Atmos Sci 37:1102–1118. https://doi.org/10.1007/s00376-020-2034-y
Kendall MG (1975) Rank Correlation Methods, 4th edn. Charles Griffin, London
Kim YH, Min SK, Zhang X et al (2020) Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Weather Clim Extrem 29:100269. https://doi.org/10.1016/j.wace.2020.100269
Liu X, Li C, Zhao T, Han L (2020a) Future changes of global potential evapotranspiration simulated from CMIP5 to CMIP6 models. Atmos Ocean Sci Lett 13:568–575. https://doi.org/10.1080/16742834.2020.1824983
Liu Y, Zhu Y, Zhang L et al (2020b) Flash droughts characterization over China: from a perspective of the rapid intensification rate. Sci Total Environ 704:135373. https://doi.org/10.1016/j.scitotenv.2019.135373
Liu X, Yuan X, Zhu E (2021) Global warming induces significant changes in the fraction of stored precipitation in the surface soil. Glob Planet Change 205:103616. https://doi.org/10.1016/j.gloplacha.2021.103616
Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259
Narsey SY, Brown JR, Colman RA et al (2020) Climate change projections for the Australian monsoon from CMIP6 models. Geophys Res Lett 47:1–9. https://doi.org/10.1029/2019GL086816
Nikiema PM, Sylla MB, Ogunjobi K et al (2017) Multi-model CMIP5 and CORDEX simulations of historical summer temperature and precipitation variabilities over West Africa. Int J Climatol 37:2438–2450. https://doi.org/10.1002/joc.4856
O’Neill BC, Tebaldi C, Van Vuuren DP et al (2016) The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci Model Dev 9:3461–3482. https://doi.org/10.5194/gmd-9-3461-2016
Pendergrass AG (2020) The global-mean precipitation response to CO2-induced warming in CMIP6 models. Geophys Res Lett 47:1–10. https://doi.org/10.1029/2020GL089964
Qiao L, Zuo Z, Xiao D (2022) Evaluation of soil moisture in CMIP6 simulations. J Clim 35:779–800. https://doi.org/10.1175/JCLI-D-20-0827.1
Sante DF, Coppola E, Giorgi F (2021) Projections of river floods in Europe using EURO-CORDEX, CMIP5 and CMIP6 simulations. Int J Climatol. https://doi.org/10.1002/joc.7014
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63:1379–1389. https://doi.org/10.1080/01621459.1968.10480934
Stouffer RJ, Eyring V, Meehl GA et al (2017) CMIP5 scientific gaps and recommendations for CMIP6. Bull Am Meteorol Soc 98:95–105. https://doi.org/10.1175/BAMS-D-15-00013.1
Sung HM, Kim J, Lee J-H et al (2021) Future changes in the global and regional sea level rise and sea surface temperature based on CMIP6 models. Atmosphere (basel) 12:90. https://doi.org/10.3390/atmos12010090
Sylla MB, Gaye AT, Jenkins GS (2012) On the fine-scale topography regulating changes in atmospheric hydrological cycle and extreme rainfall over West Africa in a regional climate model projections. Int J Geophys. https://doi.org/10.1155/2012/981649
Theil H (1950) A rank-invariant method of linear and polynomial regression analysis. Indag Math 12:173
Ukkola AM, De Kauwe MG, Roderick ML et al (2020) Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation. Geophys Res Lett 47:1–9. https://doi.org/10.1029/2020GL087820
Wang B, Jin C, Liu J (2020) Understanding future change of global monsoons projected by CMIP6 models. J Clim 33:6471–6489. https://doi.org/10.1175/JCLI-D-19-0993.1
Wang X, Li Y, Wang M et al (2021) Changes in daily extreme temperature and precipitation events in mainland China from 1960 to 2016 under global warming. Int J Climatol 41:1465–1483. https://doi.org/10.1002/joc.6865
Zhu Y, Yang S (2021) Interdecadal and interannual evolution characteristics of the global surface precipitation anomaly shown by CMIP5 and CMIP6 models. Int J Climatol 41:E1100–E1118. https://doi.org/10.1002/joc.6756
Acknowledgements
This work is supported by the Ministry of Earth Science, Government of India through a sponsored project. The data that support the findings of this study are available from; https://esgf-node.llnl.gov/search/cmip6/, https://esgf-node.llnl.gov/search/cmip5/, https://esgf-data.dkrz.de/search/cordex-dkrz/ and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. These datasets are freely available. It was accessed by the authors in March 2022.
Funding
This work is supported by the Ministry of Earth Science, Government of India through a sponsored project.
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RD: Methodology, Investigation, Writing—original draft. RM: Conceptualization, Methodology, Investigation, Writing—review & editing, Supervision, Funding acquisition.
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Edited by Dr. Achilleas G. Samaras (ASSOCIATE EDITOR) / Dr. Michael Nones (CO-EDITOR-IN-CHIEF).
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Dutta, R., Maity, R. Value addition in coupled model intercomparison project phase 6 over phase 5: global perspectives of precipitation, temperature and soil moisture fields. Acta Geophys. 70, 1401–1415 (2022). https://doi.org/10.1007/s11600-022-00793-9
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DOI: https://doi.org/10.1007/s11600-022-00793-9