Abstract
The Intergovernmental Panel on Climate Change (IPCC) suggests limiting global warming to 1.5 °C compared to 2 °C would avoid dangerous impacts of anthropogenic climate change and ensure a more sustainable society. As the vulnerability to global warming is regionally dependent, this study assesses the effects of 0.5 °C less global warming on climate extremes in the United States. Eight climate extreme indices are calculated based on Coupled Model Intercomparison Project—phase 5 (CMIP5), and North American—Coordinated Regional Climate Downscaling Experiments (NA-CORDEX) with and without bias correction. We evaluate the projected changes in temperature and precipitation extremes, and examine their differences between the 1.5 and 2 °C warming targets. Under a warming climate, both CMIP5 and NA-CORDEX show intensified heat extremes and reduced cold extremes across the country, intensified and more frequent heavy precipitation in large areas of the North, prolonged dry spells in some regions of the West, South, and Midwest, and more frequent drought events in the West. Results suggest that the 0.5 °C less global warming would avoid the intensification of climate extremes by 32–46% (35–42%) for heat extremes intensity (frequency) across the country and, by 23–41% for heavy precipitation intensity in the North, South, and Southeast. The changes in annual heavy precipitation intensity are mainly contributed by winter and spring. However, impacts of the limited warming on the frequency of heavy precipitation, dry spell, and drought frequency are only evident in a few regions. Although uncertainties are found among the climate models and emission scenarios, our results highlight the benefits of limiting warming at 1.5 °C in order to reduce the risks of climate extremes associated with global warming.
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Data analysis is performed using NCAR Command Language (NCL). NCL codes are available from the corresponding author upon reasonable request.
References
Abatzoglou JT (2013) Development of gridded surface meteorological data for ecological applications and modelling. Int J Climatol 33:121–131. https://doi.org/10.1002/joc.3413
Akinsanola AA, Kooperman GJ, Reed KA, Pendergrass AG, Hannah WM (2020) Projected changes in seasonal precipitation extremes over the United States in CMIP6 simulations. Environ Res Lett. https://doi.org/10.1088/1748-9326/abb397
Alexander LV et al (2006) Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res. https://doi.org/10.1029/2005JD006290
Allan RP, Soden BJ (2008) Atmospheric warming and the amplification of precipitation extremes. Science 321:1481–1484. https://doi.org/10.1126/science.1160787
Anderson BT (2012) Intensification of seasonal extremes given a 2°C global warming target. Clim Change 112:325–337. https://doi.org/10.1007/s10584-011-0213-7
Cannon AJ (2017a) Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Clim Dyn. https://doi.org/10.1007/s00382-017-3580-6
Cannon AJ (2017b) Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Clim Dyn 50:31–49. https://doi.org/10.1007/s00382-017-3580-6
Chen L (2020) Impacts of climate change on wind resources over North America based on NA-CORDEX. Renew Energy. https://doi.org/10.1016/j.renene.2020.02.090
Chou C, Chiang JCH, Lan C-W, Chung C-H, Liao Y-C, Lee C-J (2013) Increase in the range between wet and dry season precipitation. Nat Geosci 6:263–267. https://doi.org/10.1038/ngeo1744
Collins M et al (2013) Long-term climate change: projections, commitments and irreversibility. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (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, New York
Culp K, Tonelli S (2019) Heat-related illness in Midwestern Hispanic farmworkers: a descriptive analysis of hydration status and reported symptoms. Workplace Health Saf 67:168–178. https://doi.org/10.1177/2165079918813380
Di Luca A, de Elía R, Laprise R (2012) Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations. Clim Dyn 38:1229–1247. https://doi.org/10.1007/s00382-011-1068-3
Dong L, Leung LR, Lu J, Song F (2019) Mechanisms for an amplified precipitation seasonal cycle in the U.S. West Coast under global warming. J Clim 32:4681–4698. https://doi.org/10.1175/JCLI-D-19-0093.1
Fischer EM, Knutti R (2015) Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat Clim Change 5:560–564. https://doi.org/10.1038/nclimate2617
Fischer EM, Beyerle U, Knutti R (2013) Robust spatially aggregated projections of climate extremes. Nat Clim Chang. 3:1033–1038. https://doi.org/10.1038/nclimate2051
Frei C, Schöll R, Fukutome S, Schmidli J, Vidale PL (2006) Future change of precipitation extremes in Europe: intercomparison of scenarios from regional climate models. J Geophys Res. https://doi.org/10.1029/2005JD005965
Gibson PB, Waliser DE, Lee H, Tian B, Massoud E (2019) Climate model evaluation in the presence of observational uncertainty: precipitation indices over the contiguous United States. J Hydrometeor 20:1339–1357. https://doi.org/10.1175/JHM-D-18-0230.1
Giorgi F, Gutowski WJ (2015) Regional dynamical downscaling and the CORDEX initiative. Annu Rev Environ Resour 40:467–490. https://doi.org/10.1146/annurev-environ-102014-021217
Guirguis K et al (2018) Heat, disparities, and health outcomes in San Diego County’s diverse climate zones. GeoHealth 2:212–223. https://doi.org/10.1029/2017GH000127
Guttman NB (1999) Accepting the standardized precipitation index: a calculation algorithm1. JAWRA J Am Water Resour Assoc 35:311–322. https://doi.org/10.1111/j.1752-1688.1999.tb03592.x
Hoegh-Guldberg O, Jacob D, Taylor M, Bindi M, Brown S, Camilloni I, Diedhiou A, Djalante R, Ebi KL, Engelbrecht F, Guiot J, Hijioka Y, Mehrotra S, Payne A, Seneviratne SI, Thomas A, Warren R, Zhou G (2018) Impacts of 1.5ºC Global Warming on Natural and Human Systems. In: Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W,. Péan C, Pidcock R, Connors S, Matthews JBR,. Chen Y, ZhouX, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds) Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (in press)
Hoerster KD, Mayer JA, Gabbard S, Kronick RG, Roesch SC, Malcame VL, Zuniga ML (2011) Impact of individual-, environmental-, and policy-level factors on health care utilization among US farmworkers. Am J Public Health 101:685–692. https://doi.org/10.2105/AJPH.2009.190892
IPCC (2014) Climate Change 2014: synthesis report. contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. In: Core Writing Team, Pachauri RK, Meyer LA (eds) IPCC, Geneva, Switzerland, p 151. https://www.ipcc.ch/report/ar5/syr/
IPCC (2018) Summary for policymakers. In: Masson-Delmotte V, Zhai P, Pörtner HO, Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T, Tignor M, Waterfield T (eds) Global warming of 1.5°C. An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. World Meteorological Organization, Geneva, Switzerland, p 32. https://www.ipcc.ch/sr15/chapter/spm/
James R, Washington R, Schleussner C-F, Rogelj J, Conway D (2017) Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. WIREs Clim Change. https://doi.org/10.1002/wcc.457
Janssen E, Sriver RL, Wuebbles DJ, Kunkel KE (2016) Seasonal and regional variations in extreme precipitation event frequency using CMIP5. Geophys Res Lett 43:5385–5393. https://doi.org/10.1002/2016GL069151
Karl TR, Koss WJ (1984) Regional and national monthly, seasonal, and annual temperature weighted by area, 1895–1983. Historical Climatology Series 4–3, National Climatic Data Center, Asheville, NC, p 38
Karl TR, Nicholls N, Ghazi A (1999) CLIVAR/GCOS/WMO workshop on indices and indicators for climate extremes workshop summary. In: Karl TR, Nicholls N, Ghazi A (eds) Weather and climate extremes. Springer, Dordrecht, pp 3–7
Karmalkar AV, Bradley RS (2017) Consequences of global warming of 15 °C and 2 °C for regional temperature and precipitation changes in the contiguous United States. PLoS ONE 12:e0168697. https://doi.org/10.1371/journal.pone.0168697
Kharin VV, Zwiers FW, Zhang X, Wehner M (2013) Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim Change 119:345–357. https://doi.org/10.1007/s10584-013-0705-8
Kumar S, Kinter J, Dirmeyer PA, Pan Z, Adams J (2013) Multidecadal Climate Variability and the “Warming Hole” in North America: results from CMIP5 Twentieth- and Twenty-First-Century Climate Simulations. J Clim 26:3511–3527. https://doi.org/10.1175/JCLI-D-12-00535.1
Lehtonen I, Ruosteenoja K, Jylhä K (2014) Projected changes in European extreme precipitation indices on the basis of global and regional climate model ensembles. Int J Climatol 34:1208–1222. https://doi.org/10.1002/joc.3758
Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J Geophys Res 115:D10101. https://doi.org/10.1029/2009JD012882
Li D, Zhou T, Zhang W (2019) Extreme precipitation over East Asia under 1.5 °C and 2 °C global warming targets: a comparison of stabilized and overshoot projections. Environ Res Commun 1:085002. https://doi.org/10.1088/2515-7620/ab3971
Lopez H, West R, Dong S, Goni G, Kirtman B, Lee S-K, Atlas R (2018) Early emergence of anthropogenically forced heat waves in the western United States and Great Lakes. Nat Clim Change 8:414–420. https://doi.org/10.1038/s41558-018-0116-y
Madrigano J, Lane K, Petrovic N, Ahmed M, Blum M, Matte T (2018) Awareness, risk perception, and protective behaviors for extreme heat and climate change in New York City. Int J Environ Res Public Health 15:1433. https://doi.org/10.3390/ijerph15071433
Magaña CG, Hovey JD (2003) Psychosocial stressors associated with Mexican migrant farmworkers in the Midwest United States. J Immigr Health 5:75–86. https://doi.org/10.1023/A:1022955825650
Mahoney K, Scott JD, Alexander M, McCrary R, Hughes M, Swales D, Bukovsky M (2021) Cool season precipitation projections for California and the Western United States in NA-CORDEX models. Clim Dyn. https://doi.org/10.1007/s00382-021-05632-z
Maloney ED, Coauthors, (2014) North American Climate in CMIP5 experiments: part III: assessment of Twenty-First-Century Projections*. J Clim 27:2230–2270. https://doi.org/10.1175/JCLI-D-13-00273.1
McGinnis SA, Mearns L (2016) Bias-Correction of Extreme Temperatures and Precipitation in NA-CORDEX Regional Climate Model Output. AGU Fall Meeting, American Geophysical Union, San Francisco
McGinnis SA, Bukovsky MS, McCrary RR, Mearns L (2019) Effects of observational dataset choice on multivariate bias correction. AGU Fall Meeting, American Geophysical Union, San Francisco
Mearns LO et al (2017) The NA-CORDEX dataset, version 1.0. NCAR Climate Data Gateway, Boulder. https://doi.org/10.5065/D6SJ1JCH. Accessed on 03 Feb 2019
Meyer JDD, Jin J (2017) The response of future projections of the North American monsoon when combining dynamical downscaling and bias correction of CCSM4 output. Clim Dyn 49:433–447. https://doi.org/10.1007/s00382-016-3352-8
Morton LW, Hobbs J, Arbuckle JG, Loy A (2015) Upper midwest climate variations: farmer responses to excess water risks. J Environ Qual 44:810–822. https://doi.org/10.2134/jeq2014.08.0352
Mueller ND, Butler EE, McKinnon KA, Rhines A, Tingley M, Holbrook NM, Huybers P (2015) Cooling of US Midwest summer temperature extremes from cropland intensification. Nat Clim Change 6:317–322. https://doi.org/10.1038/nclimate2825
Naumann G, Coauthors, (2018) Global changes in drought conditions under different levels of warming. Geophys Res Lett 45:3285–3296. https://doi.org/10.1002/2017GL076521
Navarro-Racines C, Tarapues J, Thornton P, Jarvis A, Ramirez-Villegas J (2020) High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci Data 7:7. https://doi.org/10.1038/s41597-019-0343-8
Nikulin G et al (2018) The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble. Environ Res Lett 13:065003. https://doi.org/10.1088/1748-9326/aab1b1
Ning L, Riddle EE, Bradley RS (2015) Projected changes in climate extremes over the Northeastern United States. J Clim 28:3289–3310. https://doi.org/10.1175/JCLI-D-14-00150.1
O’Gorman PA (2015) Precipitation extremes under climate change. Curr Clim Change Rep 1:49–59. https://doi.org/10.1007/s40641-015-0009-3
Pan Z, Liu X, Kumar S, Gao Z, Kinter J (2013) Intermodel variability and mechanism attribution of central and southeastern U.S. anomalous cooling in the twentieth century as simulated by CMIP5 models. J Clim 26:6215–6237. https://doi.org/10.1175/JCLI-D-12-00559.1
Park C-E, Coauthors, (2018) Keeping global warming within 1.5 °C constrains emergence of aridification. Nat Clim Change 8:70–74. https://doi.org/10.1038/s41558-017-0034-4
Peng D, Zhou T, Zhang L, Zhang W, Chen X (2019) Observationally constrained projection of the reduced intensification of extreme climate events in Central Asia from 0.5 °C less global warming. Clim Dyn. https://doi.org/10.1007/s00382-019-05014-6
Pfahl S, O’Gorman PA, Fischer EM (2017) Understanding the regional pattern of projected future changes in extreme precipitation. Nat Clim Change 7:423–427. https://doi.org/10.1038/nclimate3287
Prein AF, Rasmussen RM, Ikeda K, Liu C, Clark MP, Holland GJ (2016) The future intensification of hourly precipitation extremes. Nat Clim Change 7:48–52. https://doi.org/10.1038/nclimate3168
Riahi K, Coauthors, (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57. https://doi.org/10.1007/s10584-011-0149-y
Schleussner C-F, Coauthors, (2016) Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C. Earth Syst Dyn 7:327–351. https://doi.org/10.5194/esd-7-327-2016
Shiu C-J, Liu SC, Fu C, Dai A, Sun Y (2012) How much do precipitation extremes change in a warming climate? Geophys Res Lett 39:L17707. https://doi.org/10.1029/2012GL052762
Sillmann J, Kharin VV, Zwiers FW, Zhang X, Bronaugh D (2013) Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J Geophys Res Atmos 118:2473–2493. https://doi.org/10.1002/jgrd.50188
Sillmann J, Stjern CW, Myhre G, Forster PM (2017) Slow and fast responses of mean and extreme precipitation to different forcing in CMIP5 simulations. Geophys Res Lett 44:6383–6390. https://doi.org/10.1002/2017GL073229
Singh D, Tsiang M, Rajaratnam B, Diffenbaugh NS (2013) Precipitation extremes over the continental United States in a transient, high-resolution, ensemble climate model experiment. J Geophys Res Atmos 118:7063–7086. https://doi.org/10.1002/jgrd.50543
Tamoffo AT et al (2019) Process-oriented assessment of RCA4 regional climate model projections over the Congo Basin under 1.5 °C and 2 °C global warming levels: influence of regional moisture fluxes. Clim Dyn. https://doi.org/10.1007/s00382-019-04751-y
Thibeault JM, Seth A (2015) Toward the credibility of Northeast United States summer precipitation projections in CMIP5 and NARCCAP simulations. J Geophys Res Atmos 120:10050–10073. https://doi.org/10.1002/2015JD023177
Thomson AM, Calvin KV, Smith SJ et al (2011) RCP4.5: a pathway for stabilization of radiative forcing by 2100. Clim Change 109:77–94. https://doi.org/10.1007/s10584-011-0151-4
Ting M, Camargo SJ, Li C, Kushnir Y (2015) Natural and forced north Atlantic hurricane potential intensity change in CMIP5 models. J Clim 28:3926–3942. https://doi.org/10.1175/JCLI-D-14-00520.1
Trenberth KE, Dai A, Rasmussen RM, Parsons DB (2003) The changing character of precipitation. Bull Am Meteor Soc 84:1205–1217. https://doi.org/10.1175/BAMS-84-9-1205
Urban DW, Roberts MJ, Schlenker W, Lobell DB (2015) The effects of extremely wet planting conditions on maize and soybean yields. Clim Change 130:247–260. https://doi.org/10.1007/s10584-015-1362-x
Voelkel J, Hellman D, Sakuma R, Shadas V (2018) Assessing vulnerability to urban heat: a study of disproportionate heat exposure and access to refuge by socio-demographic status in Portland, Oregon. Int J Environ Res Public Health 15:640. https://doi.org/10.3390/ijerph15040640
Wang X, Zhang J, Shahid S, Guan E, Wu Y, Gao J, He R (2016) Adaptation to climate change impacts on water demand. Mitig Adapt Strateg Glob Change 21:81–99. https://doi.org/10.1007/s11027-014-9571-6
Wehner MF (2013) Very extreme seasonal precipitation in the NARCCAP ensemble: model performance and projections. Clim Dyn 40:59–80. https://doi.org/10.1007/s00382-012-1393-1
Wuebbles DJ, Hayhoe K, Parzen J (2010) Introduction: assessing the effects of climate change on Chicago and the Great Lakes. J Great Lakes Res 36:1–6. https://doi.org/10.1016/j.jglr.2009.09.009
Wuebbles D et al (2014) CMIP5 climate model analyses: climate extremes in the United States. Bull Am Meteor Soc 95:571–583. https://doi.org/10.1175/BAMS-D-12-00172.1
Yettella V, Kay JE (2017) How will precipitation change in extratropical cyclones as the planet warms? Insights from a large initial condition climate model ensemble. Clim Dyn 49:1765–1781. https://doi.org/10.1007/s00382-016-3410-2
Zhang X, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Clim Change 2:851–870. https://doi.org/10.1002/wcc.147
Zhang W, Zhou T, Zou L, Zhang L, Chen X (2018) Reduced exposure to extreme precipitation from 0.5 °C less warming in global land monsoon regions. Nat Commun 9:3153. https://doi.org/10.1038/s41467-018-05633-3
Zhao T, Dai A (2017) Uncertainties in historical changes and future projections of drought. Part II: model-simulated historical and future drought changes. Clim Change 144:535–548. https://doi.org/10.1007/s10584-016-1742-x
Zobel Z, Wang J, Wuebbles DJ, Kotamarthi VR (2017) High-resolution dynamical downscaling ensemble projections of future extreme temperature distributions for the United States. Earth’s Future 5:1234–1251. https://doi.org/10.1002/2017EF000642
Acknowledgements
This work was supported by the Illinois State Water Survey, Prairie Research Institute, University of Illinois in Urbana-Champaign. All views and opinions expressed do not necessarily reflect those of these institutions. The authors would like to thank David Kristovich for his review of this manuscript. We acknowledge the NA-CORDEX climate modeling groups for producing and making available the model output. All the NA-CORDEX data are obtained from the NCAR Climate Data Gateway (https://www.earthsystemgrid.org/search/cordexsearch.html). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. The authors acknowledge the Texas Advanced Computing Center (TACC, http://www.tacc.utexas.edu) at The University of Texas at Austin for providing computing resources that have contributed to data analysis in this study. We also are grateful to the reviewers whose insightful comments helped improve our manuscript.
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This work was supported by the Illinois State Water Survey, Prairie Research Institute, University of Illinois in Urbana-Champaign.
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LC designed the study and performed data analysis. All authors contributed to the writing and revising of the paper.
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All the NA-CORDEX data are obtained from the NCAR Climate Data Gateway (https://www.earthsystemgrid.org/search/cordexsearch.html). CMIP5 data are downloaded through the ESGF@DOE/LLNL node (https://esgf-node.llnl.gov/search/cmip5/).
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Chen, L., Ford, T.W. Effects of 0.5 °C less global warming on climate extremes in the contiguous United States. Clim Dyn 57, 303–319 (2021). https://doi.org/10.1007/s00382-021-05717-9
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DOI: https://doi.org/10.1007/s00382-021-05717-9