Bias-corrected climate projections for South Asia from Coupled Model Intercomparison Project-6.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
12 10 2020
12 10 2020
Historique:
received:
11
06
2020
accepted:
01
09
2020
entrez:
13
10
2020
pubmed:
14
10
2020
medline:
14
10
2020
Statut:
epublish
Résumé
Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951-2014) and projected (2015-2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 General Circulation Models (GCMs) from Coupled Model Intercomparison Project-6 (CMIP6). The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3-5°C) and wetter (13-30%) climate in South Asia in the 21
Identifiants
pubmed: 33046709
doi: 10.1038/s41597-020-00681-1
pii: 10.1038/s41597-020-00681-1
pmc: PMC7550601
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
338Références
Suarez-Gutierrez, L., Müller, W. A., Li, C. & Marotzke, J. Dynamical and thermodynamical drivers of variability in European summer heat extremes. Clim. Dyn. 54, 4351–4366 (2020).
doi: 10.1007/s00382-020-05233-2
Diffenbaugh, N. S. & Giorgi, F. Climate change hotspots in the CMIP5 global climate model ensemble. Clim. Change 114, 813–822 (2012).
pubmed: 24014154
pmcid: 3765072
doi: 10.1007/s10584-012-0570-x
Knox, J., Hess, T., Daccache, A. & Wheeler, T. Climate change impacts on crop productivity in Africa and South Asia. Environ. Res. Lett. 7(3), 034032 (2012).
Turner, A. G. & Annamalai, H. Climate change and the South Asian summer monsoon. Nature Climate Change 2, 587–595 (2012).
doi: 10.1038/nclimate1495
Lobell, D. B. & Burke, M. B. Why are agricultural impacts of climate change so uncertain? the importance of temperature relative to precipitation. Environ. Res. Lett. 3(3), 034007 (2008).
Immerzeel, W. W., van Beek, L. P. H. & Bierkens, M. F. P. Climate change will affect the Asian water towers. Science 328, 1382–5 (2010).
pubmed: 20538947
doi: 10.1126/science.1183188
Aadhar, S. & Mishra, V. A Substantial rise in the area and population affected by dryness in South Asia under 1.5, 2.0 and 2.5C warmer worlds. Environ. Res. Lett. 14, 114021 (2019).
doi: 10.1088/1748-9326/ab4862
Aadhar, S. & Mishra, V. Increased drought risk in South Asia under warming climate: Implications of uncertainty in potential evapotranspiration estimates. J. Hydrometeorol. 3 (2020).
Ali, H., Modi, P. & Mishra, V. Increased flood risk in Indian sub-continent under the warming climate. Weather Clim. Extrem. 25, 100212 (2019).
doi: 10.1016/j.wace.2019.100212
Hirabayashi, Y. et al. Global flood risk under climate change. Nat. Clim. Chang. 3, 816–821 (2013).
doi: 10.1038/nclimate1911
Arnell, N. W. & Gosling, S. N. The impacts of climate change on river flood risk at the global scale. Clim. Change 134, 387–401 (2016).
doi: 10.1007/s10584-014-1084-5
Winsemius, H. C. et al. Global drivers of future river flood risk. Nat. Clim. Chang. 6, 381–385 (2016).
doi: 10.1038/nclimate2893
Mishra, V., Aadhar, S., Pai, S. & Kumar, R. Supp: On the frequency of the 2015 monsoon season drought in the Indo-Gangetic Plain.
Webster, P. J., Toma, V. E. & Kim, H. M. Were the 2010 Pakistan floods predictable? Geophys. Res. Lett. 38, 1–5 (2011).
doi: 10.1029/2010GL046346
Hunt, K. M. R. & Menon, A. The 2018 Kerala floods: a climate change perspective. Clim. Dyn. 54, 2433–2446 (2020).
doi: 10.1007/s00382-020-05123-7
Mishra, V. & Shah, H. L. Hydroclimatological Perspective of the Kerala Flood of 2018. J. Geol. Soc. INDIA 92, 645–650 (2018).
doi: 10.1007/s12594-018-1079-3
Mishra, V., Mukherjee, S., Kumar, R. & Stone, D. A. Heat wave exposure in India in current, 1.5 °C, and 2.0 °C worlds. Environ. Res. Lett. 12, 124012 (2017).
doi: 10.1088/1748-9326/aa9388
JA, N. et al. A seven-fold rise in the probability of exceeding the observed hottest summer in India in a 2 °C warmer world. Environ. Res. Lett. 15(4), 044028 (2020).
Mukherjee, S. & Mishra, V. A sixfold rise in concurrent day and night-time heatwaves in India under 2 °C warming. Sci. Rep. 8, 16922 (2018).
pubmed: 30446705
pmcid: 6240077
doi: 10.1038/s41598-018-35348-w
Im, E. S., Pal, J. S. & Eltahir, E. A. B. Deadly heat waves projected in the densely populated agricultural regions of South Asia. Sci. Adv. 3, e1603322 (2017).
pubmed: 28782036
pmcid: 5540239
doi: 10.1126/sciadv.1603322
Mazdiyasni, O. et al. Increasing probability of mortality during Indian heat waves. Sci. Adv. 3, 1–6 (2017).
doi: 10.1126/sciadv.1700066
Mukherjee, S., Aadhar, S., Stone, D. & Mishra, V. Increase in extreme precipitation events under anthropogenic warming in India. Weather Clim. Extrem. 20, 45–53 (2018).
doi: 10.1016/j.wace.2018.03.005
Christensen, J. H., Boberg, F., Christensen, O. B. & Lucas-Picher, P. On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys. Res. Lett. 35(20) (2008).
Cayan, D. R. et al. Future dryness in the Southwest US and the hydrology of the early 21st century drought. Proc. Natl. Acad. Sci. USA 107, 21271–21276 (2010).
pubmed: 21149687
doi: 10.1073/pnas.0912391107
Maurer, E. P., Hidalgo, H. G., Das, T., Dettinger, M. D. & Cayan, D. R. The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California. Hydrol. Earth Syst. Sci. 14, 1125–1138 (2010).
doi: 10.5194/hess-14-1125-2010
Barbero, R., Fowler, H. J., Lenderink, G. & Blenkinsop, S. Is the intensification of precipitation extremes with global warming better detected at hourly than daily resolutions? Geophys. Res. Lett. 44, 974–983 (2017).
doi: 10.1002/2016GL071917
Mishra, V. et al. Reliability of regional and global climate models to simulate precipitation extremes over India. J. Geophys. Res. Atmos. 119, 9301–9323 (2014).
doi: 10.1002/2014JD021636
Ashfaq, M., Rastogi, D., Mei, R., Touma, D. & Ruby Leung, L. Sources of errors in the simulation of south Asian summer monsoon in the CMIP5 GCMs. Clim. Dyn. 49, 193–223 (2017).
doi: 10.1007/s00382-016-3337-7
Maraun, D. et al. Towards process-informed bias correction of climate change simulations. Nat. Clim. Chang. 7, 764–773 (2017).
doi: 10.1038/nclimate3418
Piani, C., Haerter, J. O. & Coppola, E. Statistical bias correction for daily precipitation in regional climate models over Europe. Theor. Appl. Climatol. 99, 187–192 (2010).
doi: 10.1007/s00704-009-0134-9
Eisner, S., Voss, F. & Kynast, E. Statistical bias correction of global climate projections - Consequences for large scale modeling of flood flows. Adv. Geosci. 31, 75–82 (2012).
doi: 10.5194/adgeo-31-75-2012
Wood, A. W., Leung, L. R., Sridhar, V. & Lettenmaier, D. P. Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs. Clim. Change 62, 189–216 (2004).
doi: 10.1023/B:CLIM.0000013685.99609.9e
Pierce, D. W., Cayan, D. R., Maurer, E. P., Abatzoglou, J. T. & Hegewisch, K. C. Improved bias correction techniques for hydrological simulations of climate change. J. Hydrometeorol. 16, 2421–2442 (2015).
doi: 10.1175/JHM-D-14-0236.1
Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci. 16, 3309–3314 (2012).
doi: 10.5194/hess-16-3309-2012
Giorgi, F. & Gutowski, W. J. Regional Dynamical Downscaling and the CORDEX Initiative. Annu. Rev. Environ. Resour. 40, 467–490 (2015).
doi: 10.1146/annurev-environ-102014-021217
Mearns, L. O. et al. Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP). Clim. Change 120, 965–975 (2013).
doi: 10.1007/s10584-013-0831-3
Abatzoglou, J. T. & Brown, T. J. A comparison of statistical downscaling methods suited for wildfire applications. Int. J. Climatol. 32, 772–780 (2012).
doi: 10.1002/joc.2312
Maurer, E. P. & Hidalgo, H. G. Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods. Hydrol. Earth Syst. Sci. 14, 1125–1138 (2008).
doi: 10.5194/hess-14-1125-2010
White, R. H. & Toumi, R. The limitations of bias correcting regional climate model inputs. Geophys. Res. Lett. 40, 2907–2912 (2013).
doi: 10.1002/grl.50612
Gutmann, E. et al. An intercomparison of statistical downscaling methods used for water resource assessments in the United States. Water Resour. Res. Res. 50, 7167–7186 (2014).
doi: 10.1002/2014WR015559
Xu, L. & Wang, A. Application of the Bias Correction and Spatial Downscaling Algorithm on the Temperature Extremes From CMIP5 Multimodel Ensembles in China. Earth Sp. Sci. 6, 2508–2524 (2019).
doi: 10.1029/2019EA000995
Shah, H. L. & Mishra, V. Hydrologic Changes in Indian Sub-Continental River Basins (1901-2012). J. Hydrometeorol. 17, 2667–2687 (2016).
doi: 10.1175/JHM-D-15-0231.1
Pai, D. S. et al. Development of a new high spatial resolution (0. 25 ° × 0. 25 °) Long Period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 65, 1–18 (2014).
High-resolution near real-time drought monitoring in South Asia.
Mishra, V. Long-term (1870–2018) drought reconstruction in context of surface water security in India. J. Hydrol. 580, 124228 (2020).
doi: 10.1016/j.jhydrol.2019.124228
Srivastava, A. K., Rajeevan, M. & Kshirsagar, S. R. Development of a high resolution daily gridded temperature data set (1969 – 2005) for the Indian region. Atmos. Sci. Lett. 10, 249–254 (2009).
Shah, R. & Mishra, V. Evaluation of the Reanalysis Products for the Monsoon Season Droughts in India. J. Hydrometeorol. 15, 1575–1591 (2014).
doi: 10.1175/JHM-D-13-0103.1
Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).
doi: 10.1175/JCLI3790.1
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
doi: 10.5194/gmd-9-1937-2016
Gidden, M. J. et al. Global emissions pathways under different socioeconomic scenarios for use in CMIP6: A dataset of harmonized emissions trajectories through the end of the century. Geosci. Model Dev. 12, 1443–1475 (2019).
doi: 10.5194/gmd-12-1443-2019
Wood, A. W., Maurer, E. P., Kumar, A. & Lettenmaier, D. P. Long-range experimental hydrologic forecasting for the eastern United States. J. Geophys. Res. D Atmos. 107, 1–15 (2002).
Julien, B. L., T., F., H. & E., M. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. Int. J. Climatol. 27, 1643–1655 (2007).
doi: 10.1002/joc.1602
Jakob Themeßl, M., Gobiet, A. & Leuprecht, A. Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int. J. Climatol. 31, 1530–1544 (2011).
doi: 10.1002/joc.2168
Cannon, A. J. Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Comput. Geosci. 37, 1277–1284 (2011).
doi: 10.1016/j.cageo.2010.07.005
Gudmundsson, L., Bremnes, J. B., Haugen, J. E. & Engen-Skaugen, T. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – A comparison of methods. Hydrol. Earth Syst. Sci. 16, 3383–3390 (2012).
doi: 10.5194/hess-16-3383-2012
Bürger, G., Murdock, T. Q., Werner, A. T., Sobie, S. R. & Cannon, A. J. Downscaling extremes-an intercomparison of multiple statistical methods for present climate. J. Clim. 25, 4366–4388 (2012).
doi: 10.1175/JCLI-D-11-00408.1
Mishra, V., Bhatia, U. & Tiwari, A. D. Bias corrected climate projections from CMIP6 models for Indian sub-continental river basins. Zenodo https://doi.org/10.5281/zenodo.3874046 (2020).
Mishra, V., Bhatia, U. & Tiwari, A. D. Bias Corrected Climate Projections from CMIP6 Models for South Asia. Zenodo https://doi.org/10.5281/zenodo.3987736 (2020).
Meng, Q. & Mourshed, M. Degree-day based non-domestic building energy analytics and modelling should use building and type specific base temperatures. Energy Build. 155, 260–268 (2017).
doi: 10.1016/j.enbuild.2017.09.034
Ben-Ari, T. et al. Identifying indicators for extreme wheat and maize yield losses. Agric. For. Meteorol. 220, 130–140 (2016).
doi: 10.1016/j.agrformet.2016.01.009