Abstract
Projected changes in global to regional precipitation remain highly model-dependent and are driven by both fast atmospheric adjustments and slower ocean-mediated responses to increasing concentrations of greenhouse gases. Understanding the relative influence of these multiple drivers is one of the main objectives of the Cloud Feedback Model Intercomparison Project. Here the focus is on the daily precipitation response to an abrupt quadrupling of atmospheric CO2, as simulated by the CNRM-CM6-1 global climate model. Extended atmosphere-only experiments with prescribed sea surface temperature (SST) are used to decompose the precipitation changes into separate responses to uniform SST warming, the pattern of SST anomalies, as well as fast radiative and physiological CO2 effects. The uniform SST warming dominates the global and regional changes in annual mean and annual maximum daily precipitation intensity. In contrast, the annual mean number of wet days and the annual maximum of consecutive dry days show a strong fast adjustment. They are also sensitive to the SST warming pattern that strongly influences changes in large-scale circulation. The increase in daily precipitation intensity drives the global-mean magnitude of the annual precipitation change. In contrast, the response of wet day frequency shapes the geographical distribution and interannual variability of the annual mean precipitation, especially in the subtropics, and is more sensitive to changes in near-surface relative humidity than in the total water column over land. Although the annual precipitation response does not seem highly sensitive to the base state, these results deserve further investigation and model intercomparison within CFMIP.
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References
Adler RF, Huffman GJ, Chang A et al (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J Hydrometeorol 4:1147–1167. https://doi.org/10.1175/1525-7541(2003)004%3c1147:TVGPCP%3e2.0.CO;2
Alexander LV, Arblaster JM (2017) Historical and projected trends in temperature and precipitation extremes in Australia in observations and CMIP5. Weather Clim Extrem 15:34–56. https://doi.org/10.1016/j.wace.2017.02.001
Allan RP, Liepert BG (2010) Anticipated changes in the global atmospheric water cycle. Environ Res Lett 5:86–88. https://doi.org/10.1088/1748-9326/5/2/025201
Allan RP, Barlow M, Byrne MP et al (2020) Advances in understanding large-scale responses of the water cycle to climate change. Ann N Y Acad Sci. https://doi.org/10.1111/nyas.14337
Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419:224–232. https://doi.org/10.1038/nature01092
Andrews T, Forster PM, Boucher O et al (2010) Precipitation, radiative forcing and global temperature change. Geophys Res Lett. https://doi.org/10.1029/2010GL043991
Behrangi A, Richardson M (2018) Observed high-latitude precipitation amount and pattern and CMIP5 model projections. Remote Sens 10:1–17. https://doi.org/10.3390/rs10101583
Benestad RE (2018) Implications of a decrease in the precipitation area for the past and the future. Environ Res Lett 13:44022. https://doi.org/10.1088/1748-9326/aab375
Berg P, Moseley C, Haerter JO (2013) Strong increase in convective precipitation in response to higher temperatures. Nat Geosci 6:181–185. https://doi.org/10.1038/ngeo1731
Berg A, Findell K, Lintner B et al (2016) Land-atmosphere feedbacks amplify aridity increase over land under global warming. Nat Clim Chang 6:869–874. https://doi.org/10.1038/nclimate3029
Berg A, Lintner BR, Findell K, Giannini A (2017) Uncertain soil moisture feedbacks in model projections of Sahel precipitation. Geophys Res Lett 44:6124–6133. https://doi.org/10.1002/2017GL073851
Birch CE, Roberts MJ, Garcia-Carreras L et al (2015) Sea-breeze dynamics and convection initiation: the influence of convective parameterization in weather and climate model biases. J Clim 28:8093–8108. https://doi.org/10.1175/JCLI-D-14-00850.1
Bony S, Bellon G, Klocke D et al (2013) Robust direct effect of carbon dioxide on tropical circulation and regional precipitation. Nat Geosci 6:447–451. https://doi.org/10.1038/ngeo1799
Chadwick R (2016) Which aspects of CO2forcing and SST warming cause most uncertainty in projections of tropical rainfall change over land and ocean? J Clim 29:2493–2509. https://doi.org/10.1175/JCLI-D-15-0777.1
Chadwick R, Boutle I, Martin G (2013) Spatial patterns of precipitation change in CMIP5: why the rich do not get richer in the tropics. J Clim 26:3803–3822. https://doi.org/10.1175/JCLI-D-12-00543.1
Chadwick R, Douville H, Skinner CB (2017) Timeslice experiments for understanding regional climate projections: applications to the tropical hydrological cycle and European winter circulation. Clim Dyn 49:3011–3029. https://doi.org/10.1007/s00382-016-3488-6
Chadwick R, Ackerley D, Ogura T, Dommenget D (2019) Separating the influences of land warming, the direct CO2 effect, the plant physiological effect, and SST warming on regional precipitation changes. J Geophys Res Atmos 124:624–640. https://doi.org/10.1029/2018JD029423
Chen J, Dai A, Zhang Y, Rasmussen KL (2020) Changes in convective available potential energy and convective inhibition under global warming. J Clim 33:2025–2050. https://doi.org/10.1175/jcli-d-19-0461.1
Covey C, Doutriaux C, Gleckler PJ et al (2018) High frequency intermittency in observed and model-simulated precipitation. Geophys Res Lett 45:12512–514522. https://doi.org/10.1029/2018GL078926
Dagan G, Stier P, Watson-Parris D (2019) Analysis of the atmospheric water budget for elucidating the spatial scale of precipitation changes under climate change. Geophys Res Lett. https://doi.org/10.1029/2019GL084173
Decharme B, Delire C, Minvielle M et al (2019) Recent changes in the ISBA-CTRIP land surface system for use in the CNRM-CM6 climate model and in global off-line hydrological applications. J Adv Model Earth Syst. https://doi.org/10.1029/2018MS001545
Donat MG, Lowry AL, Alexander LV et al (2016) More extreme precipitation in the world’s dry and wet regions. Nat Clim Chang 6:508–513. https://doi.org/10.1038/nclimate2941
Douville H (2005) Limitations of time-slice experiments for predicting regional climate change over South Asia. Clim Dyn 24:373–391. https://doi.org/10.1007/s00382-004-0509-7
Douville H, Decharme B, Delire C et al (2020) Drivers of the enhanced decline of land near-surface relative humidity to abrupt 4xCO2 in CNRM-CM6-1. Clim Dyn 55:1613–1629. https://doi.org/10.1007/s00382-020-05351-x
Feng X, Haines K, Liu C et al (2018) Improved SST-precipitation intraseasonal relationships in the ECMWF coupled climate reanalysis. Geophys Res Lett 45:3664–3672. https://doi.org/10.1029/2018GL077138
Fereday D, Chadwick R, Knight J, Scaife AA (2018) Atmospheric dynamics is the largest source of uncertainty in future winter European rainfall. J Clim 31:963–977. https://doi.org/10.1175/JCLI-D-17-0048.1
Fischer EM, Beyerle U, Schleussner CF et al (2018) Biased estimates of changes in climate extremes from prescribed SST simulations. Geophys Res Lett 45:8500–8509. https://doi.org/10.1029/2018GL079176
Fläschner D, Mauritsen T, Stevens B (2016) Understanding the intermodel spread in global-mean hydrological sensitivity. J Clim 29:801–817. https://doi.org/10.1175/JCLI-D-15-0351.1
Gershunov A, Shulgina TM, Clemesha RES, et al (2019) Precipitation regime change in Western North America: The role of Atmospheric Rivers. In: Nature Communications. submitted
He J, Soden BJ (2017) A re-examination of the projected subtropical precipitation decline. Nat Clim Chang 7:53–57. https://doi.org/10.1038/nclimate3157
Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming (vol 19, pg 5686, 2006). J Clim 19:5686–5699. https://doi.org/10.1175/2010JCLI4045.1
Lambert FH, Ferraro AJ, Chadwick R (2017) Land-ocean shifts in tropical precipitation linked to surface temperature and humidity change. J Clim 30:4527–4545. https://doi.org/10.1175/JCLI-D-16-0649.1
Lavers DA, Ralph FM, Waliser DE et al (2015) Climate change intensification of horizontal water vapor transport in CMIP5. Geophys Res Lett 42:5617–5625. https://doi.org/10.1002/2015GL064672
Lehner F, Deser C, Maher N et al (2020) Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6. Earth Syst Dyn Discuss. https://doi.org/10.5194/esd-2019-93
Muller CJ, O’Gorman PA (2011) An energetic perspective on the regional response of precipitation to climate change. Nat Clim Chang 1:266–271. https://doi.org/10.1038/nclimate1169
Myhre G, Kramer RJ, Smith CJ et al (2018) Quantifying the importance of rapid adjustments for global precipitation changes. Geophys Res Lett 45:11399–11405. https://doi.org/10.1029/2018GL079474
O’Gorman PA, Schneider T (2009) The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc Natl Acad Sci 106:14773–14777. https://doi.org/10.1073/pnas.0907610106
Oudar T, Cattiaux J, Douville H et al (2020) Robustness and drivers of the Northern Hemisphere extratropical atmospheric circulation response to a CO$$_2$$2-induced warming in CNRM-CM6-1. Clim Dyn 54:2267–2285. https://doi.org/10.1007/s00382-019-05113-4
Pendergrass AG (2020) The global-mean precipitation response to CO2-induced warming in CMIP6 models. Geophys Res Lett 47: 2020GL089964. https://doi.org/10.1029/2020GL089964
Pendergrass AG, Lehner F, Sanderson BM, Xu Y (2015) Does extreme precipitation intensity depend on the emissions scenario? Geophys Res Lett 42:8767–8774. https://doi.org/10.1002/2015GL065854
Pfahl S, O’Gorman PA, Fischer EM (2017) Understanding the regional pattern of projected future changes in extreme precipitation. Nat Clim Chang 7:423–427. https://doi.org/10.1038/nclimate3287
Polade SD, Pierce DW, Cayan DR et al (2014) The key role of dry days in changing regional climate and precipitation regimes. Sci Rep 4:1–8. https://doi.org/10.1038/srep04364
Polade SD, Gershunov A, Cayan DR et al (2017) Precipitation in a warming world: assessing projected hydro-climate changes in California and other Mediterranean climate regions. Sci Rep 7:10783. https://doi.org/10.1038/s41598-017-11285-y
Power S, Delage F, Chung C et al (2013) Robust twenty-first-century projections of El Niño and related precipitation variability. Nature 502:541–545. https://doi.org/10.1038/nature12580
Richardson TB, Forster PM, Andrews T et al (2018) Carbon dioxide physiological forcing dominates projected eastern amazonian drying. Geophys Res Lett. https://doi.org/10.1002/2017GL076520
Samset BH, Myhre G, Forster PM et al (2016) Fast and slow precipitation responses to individual climate forcers: a PDRMIP multimodel study. Geophys Res Lett 43:2782–2791. https://doi.org/10.1002/2016GL068064
Stocker TF, Qin D, Plattner G-K, et al (2013) Technical Summary. In: Stocker TF, Qin D, Plattner G-K, et al. (eds) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, pp 33–115
Sun Q, Miao C, Duan Q et al (2018) A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev Geophys 56:79–107. https://doi.org/10.1002/2017RG000574
Tapiador FJ, Roca R, Del Genio A et al (2019) Is precipitation a good metric for model performance? Bull Am Meteorol Soc 100:223–233. https://doi.org/10.1175/BAMS-D-17-0218.1
Thackeray CW, DeAngelis AM, Hall A et al (2018) On the connection between global hydrologic sensitivity and regional wet extremes. Geophys Res Lett. https://doi.org/10.1029/2018GL079698
Tomassini L, Office M, Kingdom U (2020) The interaction between moist convection and the atmospheric circulation in the tropics ∗ Corresponding Early Online Release : This preliminary version has been accepted for publication in Bulletin of the American Meteorological Society , may be fully cit. https://doi.org/10.1175/BAMS-D-19-0180.1
Trenberth KE (2011) Changes in precipitation with climate change. Clim Res 47:123–138. https://doi.org/10.3354/cr00953
Voldoire A, Saint-Martin D, Sénési S et al (2019) Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. J Adv Model Earth Syst 11:2177–2213. https://doi.org/10.1029/2019MS001683
Walsh RPD, Lawler DM (1981) Rainfall seasonality: description, spatial patterns and change through time (British Isles, Africa). Weather 36:201–208. https://doi.org/10.1002/j.1477-8696.1981.tb05400.x
Wan H, Zhang X, Zwiers F, Min S-K (2015) Attributing northern high-latitude precipitation change over the period 1966–2005 to human influence. Clim Dyn 45:1713–1726. https://doi.org/10.1007/s00382-014-2423-y
Webb MJ, Andrews T, Bodas-Salcedo A et al (2017) The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. Geosci Model Dev 10:359–384. https://doi.org/10.5194/gmd-10-359-2017
Westra S, Alexander LV, Zwiers FW (2013) Global increasing trends in annual maximum daily precipitation. J Clim. https://doi.org/10.1175/JCLI-D-12-00502.1
Wilks DS (2016) “The stippling shows statistically significant grid points”: how research results are routinely overstated and overinterpreted, and what to do about it. Bull Am Meteorol Soc 97:2263–2273. https://doi.org/10.1175/BAMS-D-15-00267.1
Ye H, Fetzer EJ, Wong S, Lambrigtsen BH (2017) Rapid decadal convective precipitation increase over Eurasia during the last three decades of the 20th century. Sci Adv 3:1–8. https://doi.org/10.1126/sciadv.1600944
Acknowledgements
We would like to thank all people at CNRM and CERFACS who have been involved in the development of the CNRM-CM6-1 model. We are particularly grateful to Romain Roehrig for useful discussions about this manuscript, Richard Stchepounoff for the preparation of the SST and SIC boundary conditions of the AGCM experiments and to Laurent Franchisteguy for the publication of the CRNM-CM6-1 model outputs on the ESGF. The CNRM-CM6-1 model outputs from the CMIP6 DECK and the standard CFMIP experiments can be downloaded from the ESGF data nodes, but the extended atmospheric simulations will be only available on demand. This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Marie Sklodowska-Curie grant agreement No 813844.
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Douville, H., John, A. Fast adjustment versus slow SST-mediated response of daily precipitation statistics to abrupt 4xCO2. Clim Dyn 56, 1083–1104 (2021). https://doi.org/10.1007/s00382-020-05522-w
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DOI: https://doi.org/10.1007/s00382-020-05522-w