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References

Published online by Cambridge University Press:  05 February 2021

Rao Kotamarthi ,
Katharine Hayhoe ,
Linda O. Mearns ,
Donald Wuebbles ,
Jennifer Jacobs  and
Jennifer Jurado
Rao Kotamarthi
Affiliation:
Argonne National Laboratory, Illinois
Katharine Hayhoe
Affiliation:
Texas Tech University
Linda O. Mearns
Affiliation:
National Center for Atmospheric Research, Boulder, Colorado
Donald Wuebbles
Affiliation:
University of Illinois, Urbana-Champaign
Jennifer Jacobs
Affiliation:
University of New Hampshire
Jennifer Jurado
Affiliation:
Environmental Planning and Community Resilience Division, Broward County, Florida
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Downscaling Techniques for High-Resolution Climate Projections
From Global Change to Local Impacts
 , pp. 166 - 187
Publisher: Cambridge University Press
Print publication year: 2021

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References

Abatzoglou, J. T. and Brown, T. J. (2012). A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology, 32: 772–80.Google Scholar
Adarsh, S. and Reddy, M. Janga (2018a). Developing hourly intensity duration frequency curves for urban areas in India using multivariate empirical mode decomposition and scaling theory. Stochastic Environmental Research and Risk Assessment: Research Journal, 32(6): 1889–902.Google Scholar
Adarsh, S. and Reddy, M. Janga (2018b). Multiscale characterization and prediction of monsoon rainfall in India using Hilbert–Huang transform and time-dependent intrinsic correlation analysis. Meteorology and Atmospheric Physics, 130(6): 667–88.Google Scholar
Adger, W. N., Agrawala, S., and Mirza, M. (2007). Assessment of adaptation practices, options, constraints and capacity. In IPCC (ed.), Climate Change 2007: Climate Change Impacts, Adaptation, and Vulnerability. Cambridge: Cambridge University Press: 718–43.Google Scholar
Adger, W. N., Nigel, W. A., and Tompkins, E. L. (2005). Successful adaptation to climate change across scales. Global Environmental Change: Human and Policy Dimensions, 15(2): 77–86.CrossRefGoogle Scholar
Alam, Shahabul and Elshorbagy, Amin (2015). Quantification of the climate change-induced variations in intensity–duration–frequency curves in the Canadian prairies. Journal of Hydrology, 527(August): 990–1005.CrossRefGoogle Scholar
Allen, E. B. and Forman, R. T. T. (1976). Plant species removals and old-field community structure and stability. Ecology, 57(6): 1233–43.Google Scholar
Aloysius, N. R., Sheffield, J., Saiers, J. E., Li, H., and Wood, E. F. (2016). Evaluation of historical and future simulations of precipitation and temperature in Central Africa from CMIP5 climate models. Journal of Geophysical Research, D: Atmospheres, 121(1): 130–52.Google Scholar
Anis, M. R. and Rode, M. (2015). A new magnitude category disaggregation approach for temporal high-resolution rainfall intensities. Hydrological Processes, 29(6): 1119–28.CrossRefGoogle Scholar
Annamalai, H., Hamilton, K., and Sperber, K. R. (2007). The South Asian summer monsoon and its relationship with ENSO in the IPCC AR4 simulations. Journal of Climate, 20(6): 1071–92.CrossRefGoogle Scholar
Arrhenius, S. (1896). On the influence of carbonic acid in the air upon the temperature of the ground. Philosophical Magazine and Journal of Science, 41: 237–76.Google Scholar
Arrhenius, S. (1906). Världarnas Utveckling. Stockholm: H Geber.Google Scholar
Avissar, R. and Werth, D. (2005). Global hydroclimatological teleconnections resulting from tropical deforestation. Journal of Hydrometeorology, 6(2): 134–45.CrossRefGoogle Scholar
Barros, V. R., Field, C. B., Dokken, D. J., et al. (eds.) (2014). IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge and New York: Cambridge University Press.Google Scholar
Barsugli, Joseph J., Guentchev, Galina, Horton, Radley M., et al. (2013). The practitioner's dilemma: How to assess the credibility of downscaled climate projections. Eos, Transactions American Geophysical Union, 94(46): 424–5.Google Scholar
Basha, G., Kishore, P., Ratnam, M. Venkat, et al. (2017). Historical and projected surface temperature over India during the 20th and 21st century. Scientific Reports, 7(1): 2987.CrossRefGoogle Scholar
Bedsworth, L., Cayan, D., Franco, G., Fisher, L., and Ziaja, S. (2018). California Governor’s Office of Planning and Research, Scripps Institution of Oceanography, California Energy Commission, California Public Utilities Commission. Statewide Summary Report. California’s Fourth Climate Change Assessment. Publication number: SUM- CCCA4–2018-013.Google Scholar
Benestad, R. E. (2017). A mental picture of the greenhouse effect. Theoretical and Applied Climatology, 128: 679–88. Available at: http://dx.doi.org/10.1007/s00704–016-1732-yGoogle Scholar
Benestad, R. E., Hanssen-Bauer, I., and Chen, D. (2008).  Empirical-Statistical Downscaling. Singapore: World Scientific: 228CrossRefGoogle Scholar
Benestad, R. E., Nychka, D., and Mearns, L. O. (2012). Spatially and temporally consistent prediction of heavy precipitation from mean values. Nature Climate Change, 2 (April): 544.Google Scholar
Benoit, L. and Mariethoz, G. (2017). Generating synthetic rainfall with geostatistical simulations. Wiley Interdisciplinary Reviews: Water, 4(2): e1199.Google Scholar
Bergen, Karianne J., Johnson, Paul A., de Hoop, Maarten V., and Beroza, Gregory C. (2019). Machine learning for data-driven discovery in solid earth geoscience. Science, 363(6433). Available at: https://doi.org/10.1126/science.aau0323Google Scholar
BOM and CSIRO (2012). Annual Australian Climate Statement. Available at: www.bom.gov.au/climate/current/annual/aus/2012/Google Scholar
BOM and CSIRO (2014). Annual Australian Climate Statement. Available at: www.bom.gov.au/climate/current/annual/aus/2014/Google Scholar
BOM and CSIRO (2016). Annual Australian Climate Statement. Available at: www.bom.gov.au/climate/current/annual/aus/2016/Google Scholar
BOM and CSIRO (2018). Annual Australian Climate Statement. Available at: www.bom.gov.au/climate/current/annual/aus/2018/Google Scholar
Braconnot, P., Harrison, S. P., Kageyama, M., et al. (2012). Evaluation of climate models using palaeoclimatic data. Nature Climate Change, 2: 417–24. Available at: http://dx.doi.org/10.1038/nclimate1456Google Scholar
Brasseur, G. P. and Gallardo, Laura (2016). Climate services: Lessons learned and future prospects. Earth’s Future, 4(3): 79–89.Google Scholar
Brasseur, G. P., Jacob, D., and Schuck-Zoller, S. (eds.) (2017). Klimawandel in Deutschland: Openn Access Publication, e-online, Springler Spektrum.Google Scholar
Breinl, Korbinian, Turkington, Thea, and Stowasser, Markus (2015). Simulating daily precipitation and temperature: A weather generation framework for assessing hydrometeorological hazards. Meteorological Applications, 22(3): 334–47.CrossRefGoogle Scholar
Brown, Casey (2011). Decision-scaling for robust planning and policy under climate uncertainty. World Resources Report Uncertainty Series, 14.Google Scholar
Brown, M. E., Antle, J. M., Backlund, P., et al. (2015). Climate Change, Global Food Security, and the U.S. Food System. Available at: www.usda.gov/oce/climate_change/FoodSecurity2015Assessment/FullAssessment.pdfGoogle Scholar
Bruyère, C. L., Done, J. M., Holland, G. J., and Fredrick, S. (2014). Bias corrections of global models for regional climate simulations of high-impact weather. Climate Dynamics, 43(7): 1847–56.Google Scholar
Budyko, M. I. (1969). The effect of solar radiation variations on the climate of the Earth. Tellus, 21: 611–19,Google Scholar
Bukovsky, Melissa S., Carrillo, Carlos M., Gochis, David J., et al. (2015). Toward assessing NARCCAP regional climate model credibility for the North American monsoon: future climate simulations. Journal of Climate, 28(17): 6707–28.Google Scholar
Bukovsky, M. S., Gochis, D. J., and Mearns, L. O. (2013). Towards assessing NARCCAP Regional Climate Model credibility for the North American monsoon: Current climate simulations. Journal of Climate, 26(22): 8802–26.Google Scholar
Bukovsky, Melissa S., Thompson, Joshua A., and Mearns, Linda O. (2019). Weighting a regional climate model ensemble: Does it make a difference? Can it make a difference? Climate Research, 77 (1): 23–43.Google Scholar
Bukovsky, M. S., McCrary, R. R., Seth, A., and Mearns, L. O. (2017). A mechanistically credible, poleward shift in warm-season precipitation projected for the U.S. Southern Great Plains? Journal of Climate 30: 8275–98.CrossRefGoogle Scholar
Bush, E. and Lemmen, D. S. (eds.) 2019. Canada’s Changing Climate Report; Government of Canada, Ottawa, ON. 444pGoogle Scholar
Busuioc, A., Tomozeiu, R., and Cacciamani, C. (2008). Statistical downscaling model based on canonical correlation analysis for winter extreme precipitation events in the Emilia‐Romagna region. International Journal of Climatology, 28: 449–64.CrossRefGoogle Scholar
Cammalleri, C., Barbosa, P., Micale, F., Vogt, J. V.. (2017) Change impacts and adaptation in Europe, focusing on extremes and adaptation until the 2030s. PESETA-3 Project, Final Sector Report on Task 9: Droughts, European Commission, JRC Ispra.Google Scholar
Cash, D., Clark, W. C., Alcock, F., et al. (2002). Salience, credibility, legitimacy and boundaries: Linking research, assessment and decision making. Available at: https://doi.org/10.2139/ssrn.372280Google Scholar
Castro, C. L., Chang, H.-I., Dominguez, F., et al. (2012). Can a regional climate model improve the ability to forecast the North American Monsoon? Journal of Climate, 25(23): 8212–37.Google Scholar
Charney, Jule, Arakawa, A., Baker, D. J., et al. (1979). Carbon dioxide and climate: A scientific assessment. A report of the ad hoc Study Group on Carbon Dioxide and Climate to the Climate Research Board of the National Research Board. Available at: www.bnl.gov/envsci/schwartz/charney_report1979.pdfGoogle Scholar
Cheung, W. W., Lam, V. W., Sarmiento, J. L., et al. (2009). Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries, 10: 235–51. DOI:10.1111/j.1467-2979.2008.00315.xGoogle Scholar
Chong-Hai, X. and Ying, X.. (2012). The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 Multi-Model Ensemble. Atmospheric and Oceanic Science Letters, 5(6): 527–33. DOI:10.1080/16742834.2012.11447042Google Scholar
Christensen, Jens Hesselbjerg, Hewitson, Bruce, Busuioc, Aristita, et al. (2007). Regional climate projections. In Climate Change, 2007: The Physical Science Basis. Contribution of Working group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, University Press, Cambridge, Chapter 11: 847–940.Google Scholar
Ciscar, J.-C., Iglesias, A., Szabó, L. F. László, et al. (2011). Physical and economic consequences of climate change in Europe. Proceedings of the National Academy of Sciences of the United States of America, 108(7): 2678–83.Google ScholarPubMed
Crowley, T. J. (1990). Are there any satisfactory geologic analogs for a future greenhouse warming? Journal of Climate, 3(11): 1282–92.2.0.CO;2>CrossRefGoogle Scholar
CSIRO and Bureau of Meteorology (2015). Climate Change in Australia Information for Australia’s Natural Resource Management Regions: Technical Report, CSIRO and Bureau of Meteorology, Australia.Google Scholar
Cui, M., Storch, H. V. O. N., and Zorita, E. (1995). Coastal sea level and the large-scale climate state a downscaling exercise for the Japanese islands. Tellus, A 47(1): 132–44.Google Scholar
Dai, A. (2012). Increasing drought under global warming in observations and models. Nature Climate Change, 3(August): 52.CrossRefGoogle Scholar
Department of Environmental Affairs (2017). South Africa’s 2nd Annual Climate Change Report: Department of Environmental Affairs, South Africa. Available at: www.environment.gov.za/sites/default/files/reports/southafrica_secondnational_climatechnage_report2017.pdfGoogle Scholar
Deser, C., Phillips, A. S., Alexander, M. A., and Smoliak, B. V. (2014). Projecting North American climate over the next 50 years: Uncertainty due to internal variability. Journal of Climate, 27: 2271–96. Available at: http://dx.doi.org/10.1175/JCLI-D-13-00451.1Google Scholar
Deser, C., Solomon, S., Knutti, R., and Phillips, A. S. (2012). Communication of the role of natural variability in future North American climate. Nature Climate Change, 2: 775–9. Available at: http://dx.doi.org/10.1038/nclimate1562Google Scholar
Dessai, Suraje and Hulme, Mike (2004). Does climate adaptation policy need probabilities? Climate Policy, 4(2): 107–28.Google Scholar
Dettinger, M. D., Cayan, D. R., Meyer, M. K., et al. (2004). Simulated hydrologic responses to climate variations and change in the Merced, Carson, and American River Basins, Sierra Nevada, California, 1900–2099. Climatic Change, 62: 283–317.Google Scholar
Di Luca, A., de Elía, R., and Laprise, R. (2012). Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations. Climate Dynamics, 38(5): 1229–47.Google Scholar
Di Luca, A., de Elía, R., and Laprise, R. (2015). Challenges in the quest of added value of regional climate dynamical downscaling. Current Climate Change Reports, 1:10–21.Google Scholar
Dickinson, R., Errico, R., Giorgi, F., and Bates, G. (1989). A regional climate model for the Western United States. Climatic Change. Available at: https://doi.org/10.1007/bf00240465Google Scholar
Dilling, L. and Lemos, M. (2011). Creating useable science: Opportunities and constraints for climate knowledge use and their implications for science policy. Global and Environmental Change, 21: 680–9Google Scholar
Dixon, K. W., Lanzante, J. R., Nath, M. J., et al. (2016). Evaluating the stationarity assumption in statistically downscaled climate projections: Is past performance an indicator of future results? Climatic Change, 135: 395–408. Available at: http://dx.doi.org/10.1007/s10584–016-1598-0CrossRefGoogle Scholar
Dosio, A. and Panitz, H. (2016). Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models. Climate Dynamics, 46(5): 1599–625.Google Scholar
Dutton, J. A. (1995). Dynamics of Atmospheric Motion. Mineola, New York: Dover Publications.Google Scholar
Easterling, D. R., Kunkel, K. E., Arnold, J. R., et al. (2017). Precipitation change in the United States. In Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., et al. (eds.), Climate Science Special Report: Fourth National Climate Assessment, Volume I, Washington, DC: U.S. Global Change Research Program: 207–230. DOI: 10.7930/J0H993CCGoogle Scholar
Edenhofer, O., Pichs-Madruga, R., Sokona, Y. et al. (2014). Technical Summary. In Edenhofer, O., Pichs-Madruga, R., Sokona, Y., et al. (eds.), Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge and New York: Cambridge University Press: 1–1419.Google Scholar
Edwards, P. N. (2011). History of climate modeling. WIREs Climate Change, 2: 128–39.Google Scholar
Ekström, Marie, Grose, Michael R., and Whetton, Penny H., et al. (2015). An appraisal of downscaling methods used in climate change research. Wiley Interdisciplinary Reviews: Climate Change. Available at: https://doi.org/10.1002/wcc.339CrossRefGoogle Scholar
Erfanian, A., Wang, G., and Fomenko, L. (2017). Unprecedented drought over tropical South America in 2016: Significantly under-predicted by tropical SST. Scientific Reports, 7(1): 5811.Google Scholar
Evans, J. P. and McCabe, M. F., (2013). Effect of model resolution on a regional model simulation over southeast Australia. Climate Research, 56: 131–45.CrossRefGoogle Scholar
Eyring, V., Flato, G., Lamarque, J.-F., et al. (2019). CMIP6 Analysis Workshop. Barcelona, Spain. Available at: https://cmip6workshop19.sciencesconf.org/data/CMIP6_CMIP6AnalysisWorkshop_Barcelona_190325_FINAL.pdfGoogle Scholar
Fahad, M. G., S.,, Islam, A. K., Nazari, R. A. et al. (2018). Regional changes of precipitation and temperature over Bangladesh using bias‐corrected multi‐model ensemble projections considering high‐emission pathways. International Journal of Climatology, 38: 1634–48. doi:10.1002/joc.5284Google Scholar
Famien, Adjoua Moise, Janicot, Serge, Ochou, Abe Delfin, et al. (2018). A bias-corrected CMIP5 dataset for Africa using the CDF-T Method – a contribution to agricultural impact studies. Earth System Dynamics. Available at: https://doi.org/10.5194/esd-9-313-2018Google Scholar
Felber, Raphael, Stoeckli, Sibylle, and Calanca, Pierluigi (2018). Generic calibration of a simple model of diurnal temperature variations for spatial analysis of accumulated degree-days. International Journal of Biometeorology, 62(4): 621–30.Google Scholar
Feser, Frauke, Rockel, Burkhardt, von Storch, Hans, Winterfeldt, Jörg, and Zahn, Matthias (2011). Regional climate models add value to global model data: A review and selected examples. Bulletin of the American Meteorological Society 92, (9): 1181–92.CrossRefGoogle Scholar
Field, C. B., Barros, V. R., Mach, K. J., et al. (2014). Technical summary. In Field, C. B., Barros, V. R., Dokken, D. J., et al. (eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York: Cambridge University Press: 35–94.Google Scholar
Förster, Kristian, Hanzer, Florian, Winter, Benjamin, Marke, Thomas, and Strasser, Ulrich (2016). An Open-Source MEteoroLOgical Observation Time Series DISaggregation Tool (MELODIST v0.1.1). Geoscientific Model Development, 9(7): 2315–33.Google Scholar
Fowler, H. J. and Blenkinsop, S. (2007). Linking climate change modelling to impacts studies: Recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology. Available at: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.1556.Google Scholar
Fox-Rabinovitz, M. S., Berbery, E. H., Takacs, L. L., and Govindaraju, R. C. (2005). A Multiyear Ensemble Simulation of the US Climate with a Stretched-Grid GCM. Monthly Weather Review, 133(9): 2505–25.Google Scholar
Füssel, H., Jol, A., Marx, A., et al. (2017). Climate Change, Impacts and Vulnerability in Europe 2016-An Indicator-Based Report. Luxembourg: EEA Report, No 1/2017, Publications Office of the European Union.Google Scholar
Fyfe, J. C., Meehl, G. A., England, M. H., et al. (2016). Making sense of the early-2000s warming slowdown. Nature Climate Change, 6: 224–8. Available at: http://dx.doi.org/10.1038/nclimate2938Google Scholar
Ganguli, Poulomi and Coulibaly, Paulin (2019). Assessment of future changes in intensity-duration-frequency curves for Southern Ontario using North American (NA)-CORDEX models with nonstationary methods. Journal of Hydrology: Regional Studies, 22: 100587.Google Scholar
Gates, W. L. (1985). The use of general circulation models in the analysis of the ecosystem impacts of climatic change. Climatic Change, 7(3): 267–84CrossRefGoogle Scholar
Giorgi, F. and Bates, G. T. (1989). The climatological skill of a regional model over complex terrain. Monthly Weather Review, 117(11): 2325–47.Google Scholar
Giorgi, F. and Coppola, E. (2010). Does the model regional bias affect the projected regional climate change? An analysis of global model projections. Climatic Change, 100, 787–95. Available at: http://dx.doi.org/10.1007/s10584–010-9864-zGoogle Scholar
Giorgi, F. and Gao, X.-J. (2018). Regional earth system modeling: Review and future directions. Atmospheric and Oceanic Science Letters, 11: 189–97. DOI: 10.1080/16742834.2018.1452520CrossRefGoogle Scholar
Giorgi, F. and Gutowski, W. J. (2015). Regional dynamical downscaling and the CORDEX initiative. Annual Review of Environment and Resources, 40: 467–90.Google Scholar
Giorgi, F. and Mearns, Linda O. (1991). Approaches to the simulation of regional climate change: A review. Reviews of Geophysics, SCOPE, 29(2): 191.Google Scholar
Giorgi, F., Torma, C., Coppola, E., et al. (2016). Enhanced summer convective rainfall at Alpine high elevations in response to climate warming. Nature Geoscience, 9(8): 584.Google Scholar
Glahn, H. R. and Lowry, D. A. (1972). The use of Model Output Statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology, 11: 1203–11Google Scholar
Gudmundsson, L., Bremnes, J. B., Haugen, J. (2012). Scaling RCM precipitation to the station scale using statistical transformations – a comparison of methods. Hydrology and Earth System Sciences, 16: 3383–90. doi:10.5194/hess-16-3383-2012Google Scholar
Gutiérrez, J. M., Maraun, D., Widmann, M., et al. (2019). An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment. International Journal of Climatology, 39: 3750– 85. https://doi.org/10.1002/joc.5462Google Scholar
Gutmann, E., Pruitt, T., Clark, M. P., et al. (2014). An intercomparison of statistical downscaling methods used for water resource assessments in the United States, Water Resources Journal,  50: 7167– 86. doi:10.1002/2014WR015559Google Scholar
Gutmann, E. D., Rasmussen, R. M., Liu, C., et al. (2012). A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain. Journal of Climate, 25: 262–81.Google Scholar
Havard, M., Jean, J., Saddier, M., et al. (2015). French National Climate Change Impact Adaptation Plan 2011–2015 (INIS-FR--19-0198). France.Google Scholar
Hawkins, E. and Sutton, R., (2009). The potential to narrow uncertainty in regional climate predictions. Bulletin of the American Meteorological Society, 90: 1095–107. Available at: http://dx.doi.org/10.1175/2009BAMS2607.1Google Scholar
Hawkins, E. and Sutton, R. (2011). The potential to narrow uncertainty in projections of regional precipitation change. Climate Dynamics, 37: 407–8. Available at: http://dx.doi.org/10.1007/s00382–010-0810-6Google Scholar
Hay, L. and Clark, M. P. (2003). Use of statistically and dynamically downscaled atmospheric model output for hydrological simulations in three mountainous basins in the western US. Journal of Hydrology, 282: 56–75.Google Scholar
Hayhoe, K., Edmonds, J., Kopp, R. E., et al. (2017). Climate models, scenarios, and projections. In Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., et al. (eds.), Climate Science Special Report: Fourth National Climate Assessment, Volume I. U.S. Global Change Research Program: 133–60. doi:10.7930/J0WH2N54Google Scholar
Hayhoe, K., Scott-Fleming, I., and Stoner, A. M. K., (2020). STAR-ESDM: High-Resolution Station and Grid-Based Climate Projections, 3A.3, American Meteorological Society Annual Meeting, Boston, January 13.Google Scholar
Hayhoe, K., van Dorn, J., Croley, T. II, and Schlegal, N. (2010). Regional climate change projections for Chicago and the US Great Lakes. Journal of Great Lakes Research. Available at: www.sciencedirect.com/science/article/pii/S0380133010000559CrossRefGoogle Scholar
Hayhoe, K., Wuebbles, D. J., Easterling, D. R., et al. (2018). Our changing climate. In Reidmiller, D. R., Avery, C. W., Easterling, D. R., et al. (eds.), Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II. Washington, DC: US Global Change Research Program, Washington, DC: 72–144. doi: 10.7930/NCA4.2018.CH2Google Scholar
Hewitson, B. C. and Crane, R. G.. (1996). Climate downscaling: Techniques and application. Climate Research, 7: 85–95.Google Scholar
Hewitson, Bruce, Waagsaether, Katinka, Wohland, Jan, Kloppers, Kate, and Kara, Teizeen (2017). Climate information websites: an evolving landscape. Wiley Interdisciplinary Reviews: Climate Change, 8(5): e470.Google Scholar
Hidalgo, H. G., Dettinger, M. D., and Cayan, D. R. (2008). Downscaling with Constructed Analogues: Daily Precipitation and Temperature Fields Over the United States. California Energy Commission, PIER Energy‐Related Environmental Research. CEC‐500‐2007‐123.Google Scholar
Hijioka, Y., Lin, E., Pereira, J. J., et al. (2014). Asia. In Barros, V. R., Field, C. B., Dokken, D. J., et al. (eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York: Cambridge University Press: 1327–70Google Scholar
Hijmans, R. J., Cameron, Susan, Parra, Juan, et al. (2005). WorldClim, Version 1.3. Berkeley CA.: University of California.Google Scholar
Hong, C., Zhang, Q., Zhang, Y., et al. (2017). Multi-year downscaling application of two-way coupled WRF v3. 4 and CMAQ v5. 0.2 over East Asia for regional climate and air quality modeling: Model evaluation and aerosol direct effects. Geoscientific Model Development, 10 (6): 2447–70.Google Scholar
Horton, R., Bader, D., Kushnir, Y., et al. (2015). New York City Panel on Climate Change 2015 Report Chapter 1: Climate observations and projections. Annals of the New York Academy of Sciences’ 1336: 18–35. doi:10.1111/nyas.12586Google Scholar
Horton, Radley M., Coffel, Ethan D., Winter, Jonathan M., and Bader, Daniel A. (2015). Projected changes in extreme temperature events based on the NARCCAP Model Suite. Geophysical Research Letters, 42(18): 7722–31.Google Scholar
Huang, J., Ji, M., Xie, Y., Wang, S., He, Y., and Ran, J. (2016). Global semi-arid climate change over last 60 years. Climate Dynamics, 46(3–4): 1131–50.Google Scholar
Huth, R. (2002). Statistical downscaling of daily temperature in central Europe. Journal of Climate, 15(13): 1731–42.Google Scholar
Im, E.-S., Pal, J. S., and Eltahir, E. A. B. (2017). Deadly heat waves projected in the densely populated agricultural regions of South Asia. Science Advances, 3(8): e1603322.Google Scholar
Imbach, P., Chou, S. C., Lyra, A., et al. (2018). Future Climate Change Scenarios in Central America at High Spatial Resolution. PloS One, 13(4): e0193570.Google Scholar
IPCC (1996). Climate Change 1995: Impacts, Adaptation and Mitigation of Climate Change: Scientific-technical Analysis. Cambridge, UK and New York: Cambridge University Press.Google Scholar
IPCC (2007). Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team, Pachauri, R. K. and Reisinger, A. (eds.). Geneva, Switzerland: IPCC:104 pp.Google Scholar
INCCA (2010). Climate Change and India: A 4x4 Assessment – A Sectoral and Regional Analysis for 2030s. New Delhi, India: Indian Network for Climate Change Assessment, Ministry of Environment and Forests, Government of India.Google Scholar
IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T. F., Qin, D., and Plattner, G.-K., (eds.), Cambridge, UK and New York, NY: Cambridge University Press: 1535 pp.Google Scholar
Irving, Damien B., Whetton, Penny, and Moise, Aurel F. (2012). Climate projections for Australia: A first glance at CMIP5. Australian Meteorological and Oceanographic Journal, 62(4): 211–25.Google Scholar
Jacob, D., Petersen, J., Eggert, B., et al. (2014). EURO-CORDEX: New high-resolution climate change projections for European impact research. Regional Environmental Change, 14(2): 563–78.Google Scholar
Jarosińska, Elżbieta and Pierzga, Katarzyna (2015). Estimating flood quantiles on the basis of multi-event rainfall simulation – case study. Acta Geophysica. Available at: https://doi.org/10.1515/acgeo-2015-0046.Google Scholar
Jenkins, Geoff, Perry, Matthew, Prior, John, and (as contributor) Woodworth, Phil (2009). UKCIP08: The Climate of the United Kingdom and Recent Trends. Exeter: Met Office Hadley Centre.Google Scholar
Jevrejeva, Svetlana, Jackson, L. P., Grinsted, Aslak, Lincke, Daniel, and Marzeion, Ben (2018). Flood damage costs under the sea level rise with warming of 1.5 C and 2 C. Environmental Research Letters, 13(7): 074014.Google Scholar
Jin, Zhenong, Zhuang, Qianlai, Wang, Jiali, et al. (2017). The combined and separate impacts of climate extremes on the current and future US rainfed maize and soybean production under elevated CO2. Global Change Biology, 23(7): 2687–704.Google Scholar
Johnson, F. and Sharma, A. (2012). A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Water Resources Research, 48(1): 10.1029/2011WR010464Google Scholar
Jun, M., Knutti, R., and Nychka, D. W. (2008). Local eigenvalue analysis of CMIP3 climate model errors. Tellus A, 60: 992–1000. Available at: http://dx.doi.org/10.1111/j.1600-0870.2008.00356.xGoogle Scholar
Jungclaus, J. H., Keenlyside, Noel, Botzet, M., et al. (2006). Ocean circulation and tropical variability in the coupled model ECHAM5/MPI-OM. Journal of Climate, 19(16): 3952–72.Google Scholar
Kanamitsu, Masao and DeHaan, Laurel (2011). The Added Value Index: A new metric to quantify the added value of regional models. Journal of Geophysical Research: Atmospheres, 116(D11). doi: 116, D11106, doi:10.1029/2011JD015597.Google Scholar
Kannan, S. and Ghosh, S. (2013). A nonparametric kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin, Water Resources Research, 49: 1360–85. doi:10.1002/wrcr.20118Google Scholar
Kaptué, Armel T., Hanan, Niall P., Prihodko, Lara, and Ramirez, Jorge A. (2015). Spatial and temporal characteristics of rainfall in Africa: Summary statistics for temporal downscaling. Water Resources Research, 51(4): 2668–79.Google Scholar
Karl, Thomas R., Wang, Wei-Chyung, Schlesinger, Michael E., Knight, Richard W., and Portman, David (1990). A method of relating general circulation model simulated climate to the observed local climate. Part I: Seasonal statistics. Journal of Climate, 3(10): 1053–79.Google Scholar
Kay, Jennifer E., Deser, Clara, Phillips, A., et al. (2015). The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bulletin of the American Meteorological Society, 96(8): 1333–49.Google Scholar
Kendon, Elizabeth J., Roberts, Nigel M., Fowler, Hayley J., et al. (2014). Heavier summer downpours with climate change revealed by weather forecast resolution model. Nature Climate Change, 4(June): 570.Google Scholar
Kim, D., Cho, H., Ono, C., and Choi, M. (2017a). Let-It-Rain: A web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling. Stochastic Environmental Research and Risk Assessment, 31(4): 1023–43.Google Scholar
Kim, Jang-Gyeong, Kwon, Hyun-Han, and Kim, Dongkyun (2017b). A hierarchical Bayesian approach to the modified Bartlett-Lewis Rectangular Pulse Model for a joint estimation of model parameters across stations. Journal of Hydrology, 544(January): 210–23.Google Scholar
Kling, G. W., Hayhoe, K., Johnson, L. B., J. J. et al. (2003). Confronting Climate Change in the Great Lakes Region: Impacts on Our Communities and Ecosystems. Washington, DC: Ecological Society of America.Google Scholar
Knox, Jerry, Hess, Tim, Daccache, Andre, and Wheeler, Tim (2012). Climate change impacts on crop productivity in Africa and South Asia. Environmental Research Letters: ERL, 7(3): 034032.Google Scholar
Knutson, T. R., Zhang, R., and Horowitz, L. W. (2016). Prospects for a prolonged slowdown in global warming in the early 21st century. Nature Communcations, 7: 13676. Available at: http://dx.doi.org/10.1038/ncomms13676Google Scholar
Knutti, R. and Hegerl, G. C. (2008). The equilibrium sensitivity of the Earth’s temperature to radiation changes. Nature Geoscience, 1(11): 735.Google Scholar
Knutti, R., Masson, D., and Gettelman, A. (2013). Climate model genealogy: Generation CMIP5 and how we got there. Geophysical Research Letters, 40: 1194–9. Available at: http://dx.doi.org/10.1002/grl.50256Google Scholar
Knutti, R., Rogelj, J., Sedláček, J., and Fischer, E. M. (2016). A scientific critique of the two-degree climate change target. Nature Geoscience, 9: 13–18. Available at: http://dx.doi.org/10.1038/ngeo2595Google Scholar
Knutti, R. and Sedláček, J. (2013). Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change, 3: 369–73. Available at: http://dx.doi.org/10.1038/nclimate1716.Google Scholar
Knutti, R., Sedláček, J., Sanderson, B. M., et al. (2017). A climate model projection weighting scheme accounting for performance and interdependence. Geophysical Research Letters, 44: 1909–18. Available at: http://dx.doi.org/10.1002/2016GL072012Google Scholar
Kompas, Tom, Pham, Van Ha, and Che, Tuong Nhu (2018). The effects of climate change on GDP by country and the global economic gains from complying with the Paris Climate Accord. Earth’s Future, 6(8): 1153–73.Google Scholar
Kopp, R. E., Hayhoe, K., Easterling, D. R., et al. (2017). Potential surprises – compound extremes and tipping elements. In Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., et al., (eds.), Climate Science Special Report: Fourth National Climate Assessment, Volume I. Washington, DC.: U.S. Global Change Research Program: 411–29. doi: 10.7930/J0GB227JGoogle Scholar
Kossieris, Panagiotis, Makropoulos, Christos, Onof, Christian, and Koutsoyiannis, Demetris (2018). A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures. Journal of Hydrology, 56(January): 980–92.Google Scholar
Kotamarthi, Rao, Mearns, Linda, Hayhoe, Katharine, Castro, Christoper L., and Wuebbles, Donald (2016). Use of Climate Information for Decision-Making and Impacts Research: State of Our Understanding. Argonne, WI: Argonne National Laboratory. Available at: https://apps.dtic.mil/docs/citations/AD1029525Google Scholar
Kotlarski, S., Keuler, K., and Christensen, O. B. (2014). Regional climate modeling on European scales: A joint standard evaluation of the EURO-CORDEX RCM Ensemble. Geoscientific Model. Available at: https://pure.mpg.de/rest/items/item_2060613/component/file_2060614/contentGoogle Scholar
Krasnopolsky, Vladimir M., Fox-Rabinovitz, Michael S., and Belochitski, Alexei A. (2013). Using ensemble of neural networks to learn stochastic convection parameterizations for climate and numerical weather prediction models from data simulated by a cloud resolving model. Advances in Artificial Neural Systems. Available at: https://doi.org/10.1155/2013/485913CrossRefGoogle Scholar
Kreienkamp, Frank, Huebener, Heike, Linke, Carsten, and Spekat, Arne (2012). Good practice for the usage of climate model simulation results – a discussion paper. Environmental Systems Research 1(1): 9.Google Scholar
Larson, V. E. and Golaz, J. (2005). Using probability density functions to derive consistent closure relationships among higher-order moments. Monthly Weather Review, 133:1023–42. Available at: https://doi.org/10.1175/MWR2902.1Google Scholar
Lazante, J. R., Dixon, K. W., Nath, M. J., Whitlock, C. E., and Adams-Smith, D. (2018). Some pitfalls in statistical downscaling of future climate. Bulletin of the American Meteorological Society. DOI:10.1175/BAMS-D-17-0046.1Google Scholar
Lee, M.‐H., Lu, M., Im, E.-S., Bae, D.-H. (2019). Added value of dynamical downscaling for hydrological projections in the Chungju Basin, Korea. International Journal of Climatology, 39: 516–31. Available at: https://doi.org/10.1002/joc.5825Google Scholar
Lemos, Maria Carmen, Kirchhoff, Christine J., and Ramprasad, Vijay (2012). Narrowing the climate information usability gap. Nature Climate Change. Available at: https://doi.org/10.1038/nclimate1614Google Scholar
Lemos, Maria Carmen and Rood, Richard B. (2010). Climate projections and their impact on policy and practice. Wiley Interdisciplinary Reviews: Climate Change, 1(5): 670–82.Google Scholar
Lempert, Robert J., Popper, Steven W., and Bankes, Steven C. (2010). Robust decision making: Coping with uncertainty. The Futurist, 44(1): 47.Google Scholar
Leung, L. R., Ringler, T., Collins, W. D., Taylor, M., and Ashfaq, M. (2013).  A hierarchical evaluation of regional climate simulations. Eos Trans. AGU, 94(34): 297Google Scholar
Li, H., Kanamitsu, M., Hong, S.-Y., et al. (2014). Projected climate change scenario over California by a regional ocean–atmosphere coupled model system. Climatic Change, 122(4): 609–19.Google Scholar
Li, R., Lv, S., Han, B., Gao, Y., and Meng, X. (2017). Projections of South Asian summer monsoon precipitation based on 12 CMIP5 Models. International Journal of Climatology, 37(1): 94–108.Google Scholar
Li, S., Mote, P. W., Rupp, D. E., et al. (2015). Evaluation of a regional climate modeling effort for the Western United States using a Superensemble from Weather@ Home. Journal of Climate, 28(19): 7470–88.Google Scholar
Li Liu, D. and Zuo, H. (2012). Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia. Climatic Change, 115(3–4): 629–66.Google Scholar
Liang, X.-Z., Xu, M., Yuan, X., et al. (2012). Regional climate–weather research and forecasting model. Bulletin of the American Meteorological Society, 93(9): 1363–87.Google Scholar
Lichter, M., Vafeidis, A., Nicholls, R., and Kaiser, G. (2011). Exploring data-related uncertainties in analyses of land area and population in the “Low-Elevation Coastal Zone” (LECZ). Journal of Coastal Research, 27(4): 757–68.Google Scholar
Lipscomb, W. H., Fyke, J. G., Vizcaíno, M., et al. (2013). Implementation and initial evaluation of the Glimmer Community ice sheet model in the Community Earth System Model. Journal of Climate, 26: 7352–71. Available at: https://doi.org/10.1175/JCLI-D-12-00557.1Google Scholar
Liston, Glen E. and Elder, Kelly (2006). A meteorological distribution system for high-resolution terrestrial modeling (MicroMet). Journal of Hydrometeorology. Available at: https://doi.org/10.1175/jhm486.1Google Scholar
Liu, Y., Stanturf, J., and Goodrick, S. (2010). Trends in global wildfire potential in a changing climate. Forest Ecology and Management, 259(4): 685–97. Available at: https://doi.org/10.1016/j.foreco.2009.09.00Google Scholar
Lo, J. C.-F., Yang, Z.-L., and Pielke, R. A. Sr. (2008). Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) Model. Journal of Geophysical Research, 113(D9): 1306.Google Scholar
Lombardo, F., Volpi, E., Koutsoyiannis, D., and Serinaldi, F. (2017). A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall. Water Resources Research, 53(6): 4586–605.Google Scholar
Lorenz, Susanne, Dessai, Suraje, Forster, Piers M., and Paavola, Jouni (2017). Adaptation planning and the use of climate change projections in local government in England and Germany. Regional Environmental Change, 17(2): 425–35.Google Scholar
Lu, J., Carbone, G. J., and Grego, J. M. (2019). Uncertainty and hotspots in 21st century projections of agricultural drought from CMIP5 models. Nature, Scientific Reports, 9: 4922. Available at: https://doi.org/10.1038/s41598–019-41196-zGoogle Scholar
Lukas, J., Barsugli, J., Doesken, N., Rangwala, I., and Wolter, K. (2014). Climate Change in Colorado: A Synthesis to Support Water Resources Management and Adaptation. A report for the Colorado Water Conservation Board by the Western Water Assessment. 114 pp.Google Scholar
Lv, Z., Zhu, Y., Liu, X., et al. (2018). Climate change impacts on regional rice production in China. Climatic Change, 147: 523–37Google Scholar
Mabuchi, K., Sato, Y., and Kida, H. (2002). Verification of the climatic features of a regional climate model with BAIM. Journal of the Meteorological Society of Japan. Available at: https://doi.org/10.2151/jmsj.80.621Google Scholar
Magrin, G. O., Marengo, J. A., Boulanger, J.-P., et al. (2014). Central and South America. In: Barros, V. R., Field, C. B., Dokken, D. J., et al. (eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York: Cambridge University Press: 1499–556.Google Scholar
Manabe, S. and Bryan, K. (1969). Climate calculations with a combined ocean-atmosphere model. Journal of the Atmospheric Science, 26: 786–9.Google Scholar
Manabe, S. and Wetherald, R. (1975). The effects of doubling with CO2 concentration on the climate of a general circulation model. Journal of the Atmospheric Science, 32: 3–15,Google Scholar
Manzanas, R., Gutiérrez, J. M., Fernández, J., et al. (2018). Dynamical and statistical downscaling of seasonal temperature forecasts in Europe: Added value for user applications. Climate Services, 9: 44–56.Google Scholar
Maraun, D. and Widmann, M. (2018). Statistical Downscaling and Bias Correction for Climate Research. Cambridge: Cambridge University Press: 347 pp.Google Scholar
Maurer, E. P. and Hidalgo, H. G. (2008). Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods, Hydrology and Earth System Science, 12: 551–63. Available at: https://doi.org/10.5194/hess-12-551-2008Google Scholar
Maurer, J. M., Schaefer, J. M., Rupper, S., and Corley, A. (2019). Acceleration of ice loss across the Himalayas over the past 40 years. Science Advances, 5(6): eaav7266.Google Scholar
Maurer, E. P., Wood, A. W., Adam, J. C., Lettenmaier, D. P., and Nijssen, B. (2002). A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. Journal of Climate, 15(22): 3237–51.Google Scholar
McFarlane, N. (2011). Parameterizations: Representing key processes in climate models without resolving them. Wiley Interdisciplinary Reviews: Climate Change, 2(4): 482–97.Google Scholar
McGinnis, S., Nychka, D., and Mearns, L. O. (2015). A new distribution mapping technique for climate model bias correction. In Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M., (eds.), Machine Learning and Data Mining Approaches to Climate Science: Proceedings of the Fourth International Workshop on Climate Informatics. Cham, Switzerland: Springer: 91–99. doi:10.1007/978-3-319-17220-0Google Scholar
McGranahan, Gordon, Balk, Deborah, and Anderson, Bridget (2007). The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones. Environment and Urbanization, 19(1): 17–37.Google Scholar
Md Hafijur Rahaman Khan, A. Rahman, Luo, C., Kumar, S., G. M. A. and Islam, M. A. Hossain, (2019). Detection of changes and trends in climatic variables in Bangladesh during 1988–2017, Heliyon, 5(3). doi:10.1016/j.heliyon.2019.e01268Google Scholar
Mearns, L. O., Arritt, R., Biner, S., et al. (2012). The North American Regional Climate Change Assessment Program: Overview of Phase I results. Bulletin of the American Meteorological Society, 93: 1337–62. Available at: https://doi.org/10.1175/BAMS-D-11-00223.1Google Scholar
Mearns, L. O., Bogardi, I., Giorgi, F., Matyasovszky, I., and Palecki, M. (1999). Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling. Journal of Geophysical Research, 104(D6): 6603–21.Google Scholar
Mearns, Linda O., Bukovsky, Melissa S., Pryor, Sarah C., and Magaña, Victor (2014). Downscaling of climate information. In George Ohring (ed.), Climate Change in North America. Cham: Springer International Publishing: 201–50.Google Scholar
Mearns, Linda O., Bukovsky, Melissa S., and Schweizer, Vanessa J. (2017). Potential value of expert elicitation for determining differential credibility of regional climate change simulations: An exercise with the NARCCAP co-PIs for the Southwest Monsoon region of North America. Bulletin of the American Meteorological Society, 98 (1): 29–35.Google Scholar
Mearns, L. O., Easterling, W., Hays, C., and Marx, D. (2001). Comparison of agricultural impacts of climate change calculated from high and low resolution climate change scenarios: Part I. The uncertainty due to spatial scale. Climatic Change, 51(2): 131–72.Google Scholar
Mearns, L. O., Giorgi, F., Whetton, P., et al. (2003). Guidelines for Use of Climate Scenarios Developed from Regional Climate Model Experiments. Available at: http://climate-action.engin.umich.edu/downscaling/Mearns_IPCC_Downscaling_Guidelines_IPCC_2004.pdfGoogle Scholar
Mearns, L. O., Lettenmaier, D. P., and McGinnis, S. (2015). Uses of results of regional climate model experiments for impacts and adaptation studies: The example of NARCCAP. Current Climate Change Reports, 1(1): 1–9.Google Scholar
Mearns, L. O., Sain, S., Leung, L. R., et al. (2013). Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP). Climatic Change, 120(4): 965–75.Google Scholar
Meehl, G. A., Covey, C., Delworth, T., et al. (2007). The WCRP CMIP3 multimodel dataset: A new era in climate model research. Bulletin of the American Meteorological Society, 88: 1383–94.Google Scholar
Meehl, G. A., Hu, A., Santer, B. D., and Xie, S.-P. (2016). Contribution of the Interdecadal Pacific Oscillation to twentieth-century global surface temperature trends. Nature Climate Change, 6: 1005–8. Available at: http://dx.doi.org/10.1038/nclimate3107Google Scholar
Michelangeli, P.-A., Vrac, M., and Loukos, H. (2009). Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophysical Research Letters, 36(11): 136.Google Scholar
Miguez-Macho, G., Stenchikov, G. L., and Roboc, A.. (2005). Regional climate simulations over North America: Interaction of local processes with improved large-scale flow. Journal of Climate, 18(8): 1227–46.Google Scholar
Ministry of Environment, Ministry of Agriculture, Forestry and Fisheries (2018). Climate change in Japan and its impacts. Available at: www.env.go.jp/earth/tekiou/pamph2018_full_Eng.pdfGoogle Scholar
Monerie, P., Fontaine, B., and Roucou, P. (2012). Expected future changes in the African Monsoon between 2030 and 2070 using some CMIP3 and CMIP5 models under a medium-low RCP RCPScenario. Journal of Geophysical Research, D: Atmospheres, 117 (D16). Available at: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2012JD017510Google Scholar
Morrison, H. and Gettelman, A. (2008). A new two-moment bulk stratiform cloud microphysics scheme in the community atmosphere model, Version 3 (cam3). Part I: Description and numerical tests. Journal of Climate, 21: 3642–59. Available at: https://doi.org/10.1175/2008JCLI2105.1Google Scholar
Moser, S. C., Davidson, M. A., Kirshen, P., et al. (2014). Ch. 25: Coastal zone development and ecosystems. In Melillo, J. M., Richmond, Terese (T. C.), and Yohe, G. W., (eds.), Climate Change Impacts in the United States: The Third National Climate Assessment, U.S. Global Change Research Program: 579–618. doi:10.7930/J0MS3QNWGoogle Scholar
Moss, R. H., Edmonds, J. A., Hibbard, K. A., et al. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463: 747–56. http://dx.doi.org/10.1038/nature08823Google Scholar
Mukherjee, S., Aadhar, S., Stone, D., and Mishra, V. (2018). Increase in extreme precipitation events under anthropogenic warming in India. Weather and Climate Extremes, 20(June): 45–53.Google Scholar
Müller, H. and Haberlandt, U. (2018). Temporal rainfall disaggregation using a multiplicative cascade model for spatial application in urban hydrology. Journal of Hydrology, 556(January): 847–64.Google Scholar
Muluye, Getnet Y. (2011). Implications of medium-range numerical weather model output in hydrologic applications: Assessment of skill and economic value. Journal of Hydrology, 400(3–4): 448–64.Google Scholar
Murakami, H., Wang, B., and Kitoh, A. (2011). Future change of Western North Pacific typhoons: Projections by a 20-km-mesh global atmospheric model. Journal of Climate, 24: 1154–69. Available at: https://doi.org/10.1175/2010JCLI3723.1Google Scholar
Murphy, J. M., Booth, B. B. B., Collins, M., et al. (2007). A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 365(1857): 1993–2028.Google Scholar
Murphy, J. M., Harris, G. R., Sexton, D. M. H., et al. (2018). UKCP18 Land Projections: Science Report. Bracknell: Met Office. Available at: www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP18-Land-report.pdfGoogle Scholar
Myhre, G., Highwood, E., Shine, K., and Stordal, F. (1998). New estimates of radiative forcing due to well mixed greenhouse gases. Geophysical Research Letters, 25(14): 2715–18. doi:10.1029/98GL01908Google Scholar
Nakicenovic, N., Alcamo, J., Davis, G., et al. (2000). IPCC Special Report on Emissions Scenarios. Nakicenovic, N. and Swart, R. (eds.). Cambridge: Cambridge University Press, 2000. Available at: www.ipcc.ch/ipccreports/sres/emission/index.php?idp=0Google Scholar
NAS, 2012: A National Strategy for Advancing Climate Modeling. Washington, DC.: National Academy Press.Google Scholar
National Oceanic and Atmospheric Administration (NOAA) (2013). National Coastal Population Report. A product of the NOAA State of the Coast Report Series, a publication of the National Oceanic and Atmospheric Administration, Department of Commerce, developed in partnership with the U.S. Census Bureau. Available at: http://stateofthecoast.noaa.govGoogle Scholar
Niang, I., Ruppel, O. C., Abdrabo, M. A., et al. (2014). Africa. In Barros, V. R., Field, C. B., Dokken, D. J., et al. (eds.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York: Cambridge University Press: 1199–265.Google Scholar
Nicholls, R. J., Hanson, S. E., Lowe, J. A., et al. (2014). Sea-level scenarios for evaluating coastal impacts. Wiley Interdisciplinary Reviews. Climate Change, 5(1): 129–50.Google Scholar
Nowicki, S. M. J., Payne, T., Larour, E., et al. (2016). Ice sheet model intercomparison project (ISMIP6) contribution to CMIP6. Geoscientific Model Development, 9(12): 4521.Google Scholar
NYC Mayors’ Office of Recovery and Resiliency (2019). Climate Resiliency Design Guidelines. Available at: www1.nyc.gov/assets/orr/pdf/NYC_Climate_Resiliency_Design_Guidelines_v3-0.pdfGoogle Scholar
NYCPCC, New York City Panel on Climate Change (2019). Report Executive Summary 2019. Annals of the New York Academy of Sciences, 1439: 11–21. doi:10.1111/nyas.14008Google Scholar
O’Neill, Brian C., Kriegler, Elmar, Riahi, Keywan, et al. (2014). A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Climatic Change, 122(3): 387–400.Google Scholar
Oh, S.-G., Park, J.-H., Lee, S.-H., and Suh, M.-S. (2014). Assessment of the RegCM4 over East Asia and future precipitation change adapted to the RCP Scenarios. Journal of Geophysical Research, D: Atmospheres, 119(6): 2913–27.Google Scholar
Pachauri, R. K., Allen, M. R., Barros, V. R., et al. (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, (eds.) Pachauri, R. K. and Meyer, L.. Geneva, Switzerland: IPCC.Google Scholar
Paeth, H. and Manning, B. (2013). On the added value of regional climate modeling in climate change Assessment. Climate Dynamics, 41: 1057–66.Google Scholar
Pal, J. S., Giorgi, F., Bi, X., et al. (2007). Regional climate modeling for the developing world: The ICTP RegCM3 and RegCNET. Bulletin of the American Meteorological Society, 88(9): 1395–410.Google Scholar
Palomino-Lemus, Re., Córdoba-Machado, S., Gámiz-Fortis, S. R., Castro-Díez, Y., and Esteban-Parra, M. J. (2018). High-resolution boreal winter precipitation projections over tropical America from CMIP5 models. Climate Dynamics, 51(5–6): 1773–92.Google Scholar
Parmesan, C. and Yohe, G., (2003). A globally coherent fingerprint of climate change impacts across natural systems, Nature, 421(6918): 37–42.Google Scholar
Parry, M. and Carter, T. (1996). Climate Impact and Adaptation Assessment. New York: Earthscan.Google Scholar
PBMC (2013). Executive summary: Scientific basis of climate change – Contribution from Grupo de Trabalho 1 (GT1, acronym for the Working Group 1) to the Primeiro Relatório de Avaliação Nacional sobre Mudanças Climáticas (RAN1) of the Painel Brasileiro de Mudanças Climáticas (PBMC), (eds.) Ambrizzi, T., Araujo, M.. Rio de Janeiro: Universidade Federal do Rio de Janeiro: 24 pp.Google Scholar
Perry, W. J., and Abizaid, J. P. (2014). Ensuring a strong US Defense for the future: The National Defense Panel Review of the 2014 Quadrennial Defense Review. UNITED STATES INST OF PEACE WASHINGTON DC. Available at: https://apps.dtic.mil/docs/citations/ADA604218.Google Scholar
Peters, G. P., Andrew, R. M., Boden, T., et al. (2013). The challenge to keep global warming below 2 °C. Nature Climate Change, 3(4–6). Available at: https://doi.org/10.1038/nclimate1783.Google Scholar
Piao, S. L. et al. (2010). The impacts of climate change on water resources and agriculture in China. Nature, 467: 43–51Google Scholar
Pierce, D. W., Cayan, D. R., and Thrasher, B. L. (2014). Statistical downscaling using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology, 15(6): 2558–85.Google Scholar
Pinto, J. G., Neuhaus, C. P., and Leckebusch, G. C. (2010). Estimation of Wind Storm Impacts over Western Germany under Future Climate Conditions Using a Statistical–Dynamical Downscaling Approach. Tellus A: Dynamic. Available at: www.tandfonline.com/doi/abs/10.1111/j.1600-0870.2009.00424.x.Google Scholar
Prein, Andreas F., Langhans, Wolfgang, Fosser, Giorgia et al. (2015). A review on regional convection permitting climate modeling: Demonstrations, prospects, and challenges. Reviews of Geophysics, 53: 323–61.Google Scholar
Prudhomme, Christel and Davies, Helen (2009). Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: Future climate. Climatic Change, 93(1–2): 177–95. Available at: https://doi.org/10.1007/s10584–008-9461-6.Google Scholar
Ramanathan, V. and Coakley, J. R., Jr. (1978). Climate modeling through radiative-convective models. Reviews of Geophysics and Space Physics, 16: 465–89,Google Scholar
Rhoades, Alan M., Huang, Xingying, Ullrich, Paul A., and Zarzycki, Colin M. (2016). Characterizing Sierra Nevada snowpack using variable-resolution CESM. Journal of Applied Meteorology and Climatology, 55(1): 173–96. Available at: https://doi.org/10.1175/jamc-d-15-0156.1.Google Scholar
Richardson, M., Cowtan, K., Hawkins, E., and Stolpe, M. B. (2016). Reconciled climate response estimates from climate models and the energy budget of Earth. Nature Climate Change, 6: 931–5. Available at: http://dx.doi.org/10.1038/nclimate3066.Google Scholar
Rockel, B., Will, A., and Hense, A. (2008). The Regional Climate Model COSMO-CLM (CCLM). Meteorologische Zeitschrift. Available at: https://doi.org/10.1127/0941-2948/2008/0309.Google Scholar
Rössler, O., Fischer, A. M., Maraun, D. et al. (2017). Challenges to link climate change data provision and user needs – perspective from the COST-action VALUE. International Journal of Climatology, 50: 7541Google Scholar
Rummukainen, M. (2016). Added value in regional climate modeling. Wires Climate Change, 7: 145–59.Google Scholar
Ruth, Matthias and Coelho, Dana (2007). Understanding and Managing the Complexity of Urban Systems under Climate Change. Climate Policy. Available at: https://doi.org/10.3763/cpol.2007.0716.Google Scholar
Ryu, J.-H. and Hayhoe, K. (2013). Understanding the sources of Caribbean precipitation biases in CMIP3 and CMIP5 simulations. Climate Dynamics, 42: 3233–52. Available at: http://dx.doi.org/10.1007/s00382–013-1801-1Google Scholar
Sachindra, D. A., Huang, F., Barton, A., and Perera, B. J. C. (2014). Statistical downscaling of General Circulation Model outputs to precipitation – Part 2: Bias-correction and future projections. International Journal of Climatology, 34(11): 3282–303.Google Scholar
Salvi, K., Ghosh, S., and Ganguly, A. (2016). Credibility of statistical downscaling under nonstationary climate. Climate Dynamics, 46(5): 1991–2023. doi: 10.1007/s00382-015-2688-9Google Scholar
Salvi, K., Kannan, S., and Ghosh, S. (2013). High-resolution multisite daily rainfall projections in India with statistical downscaling for climate change impacts assessment, Journal of Geophysical Research: Atmospheres, 118: 3557–78. doi:10.1002/jgrd.50280.Google Scholar
Sanderson, B. M. (2011). A multimodel study of parametric uncertainty in predictions of climate response to rising greenhouse gas concentrations. Journal of Climate, 24(5): 1362–77.Google Scholar
Sanderson, B. M., Knutti, R., and Caldwell, P. (2015). A representative democracy to reduce interdependency in a multimodel ensemble. Journal of Climate, 28: 5171–94. Available at: http://dx.doi.org/10.1175/JCLI-D-14-00362.1Google Scholar
Sanderson, B. M., Wehner, M., and Knutti, R. (2017). Skill and independence weighting for multi-model assessments. Geoscientific Model Development, 10: 2379–95.Google Scholar
Santer, B. D., Solomon, S., Pallotta, G., et al. (2017). Comparing tropospheric warming in climate models and satellite data. Journal of Climate, 30: 373–92. Available at: http://dx.doi.org/10.1175/JCLI-D-16-0333.1Google Scholar
Schellnhuber, H. J., Rahmstorf, S., and Winkelmann, R. (2016). Why the right climate target was agreed in Paris. Nature Climate Change, 6, 649–53. Available at: http://dx.doi.org/ 10.1038/nclimate301Google Scholar
Scher, S. (2018). Toward data‐driven weather and climate forecasting: Approximating a simple general circulation model with deep learning. Geophysical Research Letters. Available at: https://doi.org/10.1029/2018gl080704Google Scholar
Schlesinger, M. and Mitchell, J. F. B. (1985). Model projections of the equilibrium climatic response to increases carbon dioxide: The potential climatic effects of increasing carbon dioxide, Rep. DOE/ER-0237: 83–147.Google Scholar
Schlesinger, M. and Mitchell, J. F. B. (1986).  Model Projections of the Equilibrium Climatic Response to Increased Carbon Dioxide. No. UCRL-15807. Corvallis, OR: Oregon State Univ; Bracknell: Meteorological Office (UK).Google Scholar
Schneider, Tapio, Lan, Shiwei, Stuart, Andrew, and Teixeira, João (2017). Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations. Geophysical Research Letters, 44(24): 12–396.Google Scholar
Sellers, W. D. (1969). A global climatic model based on the energy balance of the Earth atmosphere system. Journal of Applied Meteorology, 21: 391–400.Google Scholar
Seo, K.-H., Ok, J., Son, J.-H., and Cha, D.-H. (2013). Assessing future changes in the East Asian summer monsoon using CMIP5 coupled models. Journal of Climate, 26 (19): 7662–75.Google Scholar
Shao, Q., Zhang, L., and Wang, Q. J. (2016). A hybrid stochastic-weather-generation method for temporal disaggregation of precipitation with consideration of seasonality and within-month variations. Stochastic Environmental Research and Risk Assessment, 30(6): 1705–24.Google Scholar
Sheffield, Justin, Goteti, Gopi, and Wood, Eric F. (2006). Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. Journal of Climate, 19(13): 3088–111.Google Scholar
Sheffield, J. and Wood, E. F. (2007). Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. Journal of Geophysical Research, D: Atmospheres, 112(D17). Available at: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2006JD008288Google Scholar
Shtiliyanova, Anastasiya, Bellocchi, Gianni, Borras, David, et al. (2017). Kriging-based approach to predict missing air temperature data. Computers and Electronics in Agriculture, 142(November): 440–9.Google Scholar
Sikorska, A. E. and Seibert, J. (2018). Appropriate temporal resolution of precipitation data for discharge modelling in pre-alpine catchments. Hydrological Sciences Journal. Available at: www.tandfonline.com/doi/abs/10.1080/02626667.2017.1410279Google Scholar
Skamarock, W., Klemp, J. B., Dudhia, J., et al. (2008). A description of the Advanced Research WRF Version 3. NCAR Technical Note, NCAR/ TN-475+STR, 123 ppGoogle Scholar
Skelton, M., Porter, J. J., Dessai, S., et al. (2017). The social and scientific values that shape national climate scenarios: A comparison of the Netherlands, Switzerland and the UK. Regional Environmental Change, 17(8): 2325–38.Google Scholar
Skourkeas, Anastasios, Kolyva-Machera, Fotini, and Maheras, Panagiotis (2013). Improved statistical downscaling models based on canonical correlation analysis, for generating temperature scenarios over Greece. Environmental and Ecological Statistics. Available at: https://doi.org/10.1007/s10651–012-0228-xGoogle Scholar
Smith, M. J., Palmer, P. I., Purves, , et al. (2014). Changing how earth system modeling is done to provide more useful information for decision making, science, and society. Bulletin of the American Meteorological Society, 95: 1453–64. doi:10.1175/BAMS-D-13-00080.1Google Scholar
Smith, T. and Bookhagen, B. (2018). changes in seasonal snow water equivalent distribution in high mountain Asia (1987 to 2009). Science Advances, 4(1): e1701550.Google Scholar
SNAP (Scenarios Network for Alaska and Arctic Planning), (2016). About SNAP Data, University of Alaska. Available at www.snap.uaf.edu/tools/data-downloadsGoogle Scholar
So, Byung-Jin, Kim, Jin-Young, Kwon, Hyun-Han, and Lima, Carlos H. R. (2017). Stochastic extreme downscaling model for an assessment of changes in rainfall intensity-duration-frequency curves over South Korea using multiple regional climate models. Journal of Hydrology, 553(October): 321–37.Google Scholar
Soares, P. M. M., Maraun, D, Brands, S. et al. (2019). Process‐based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods. International Journal of Climatology. 39: 3868–93. Available at https://doi.org/10.1002/joc.5911Google Scholar
Solomon, S., Qin, D., Manning, M., et al. (eds.) (2007). Climate Change 2007 – The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC (Vol. 4). Cambridge: Cambridge University Press.Google Scholar
Spak, Scott, Holloway, Tracey, Lynn, Barry, and Goldberg, Richard (2007). A comparison of statistical and dynamical downscaling for surface temperature in North America. Journal of Geophysical Research, 112(D8): 1645.Google Scholar
Stocker, Thomas F., Qin, Dahe, Plattner, Gian-Kasper, et al. (2013). Climate phenomena and their relevance for future regional climate change. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment of the Intergovernmental Panel on Climate Change, (eds.) Stocker, Thomas F., Qin, Dahe, Plattner, Gian-Kasper et al. 1217–308. Cambridge: Cambridge University Press.Google Scholar
Stoner, Anne M. K., Hayhoe, Katharine, Yang, Xiaohui, and Wuebbles, Donald J. (2013). An asynchronous regional regression model for statistical downscaling of daily climate variables. International Journal of Climatology, 33(11): 2473–94.Google Scholar
Stoner, A., Hayhoe, K. Dixon, Lanzante, J., and Scott-Fleming, I. (2017). Comparing the Performance of Multiple Statistical Downscaling Approaches Using a Perfect Model Framework. Presented at the Annual Meeting of the American Meteorological Society. Seattle WA.Google Scholar
Subyani, Ali M. and Al-Amri, Nassir S. (2015). IDF curves and daily rainfall generation for Al-Madinah City, Western Saudi Arabia. Arabian Journal of Geosciences, 8(12): 11107–19.Google Scholar
Sun, F., Berg, N., Hall, A., Schwartz, M., and Walton, D. (2019).  Understanding end‐of‐century snowpack changes over California's Sierra Nevada. Geophysical Research Letters,  46:  933–43. Available at: https://doi.org/10.1029/2018GL080362Google Scholar
Sun, L., Kunkel, K. E., Stevens, L. E., et al. (2015b). Regional Surface Climate Conditions in CMIP3 and CMIP5 for the United States: Differences, Similarities, and Implications for the U.S. National Climate Assessment. National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 111 pp. Available at: http://dx.doi.org/10.7289/V5RB72KG.Google Scholar
Sun, F., Walton, D. B., and Hall, A., (2015a). A hybrid dynamical-statistical technique: Part II: End of century warming projections predict a new climate state in the Los Angeles region. Journal of Climate, 28: 4618–36Google Scholar
Suppiah, R., Collier, M., Jeffrey, S., L. et al. (2013). Simulated and projected summer rainfall in tropical Australia: Links to atmospheric circulation using the CSIRO-Mk3.6 climate model. Australian Meteorological and Oceanographic Journal, 63(1): 15–26.Google Scholar
Swaminathan, Ranjini, Sridharan, Mohan, and Hayhoe, Katharine (2018). A computational framework for modelling and analyzing ice storms. arXiv preprint arXiv:1805.04907.Google Scholar
Swart, R. J., de Bruin, K., Dhenain, S. et al. (2017). Developing climate information portals with users: Promises and pitfalls. Climate Services, 6: 12–22.Google Scholar
Sweet, W. V., Kopp, R. E., Weaver, C. P., et al. (2017). Global and Regional Sea Level Rise Scenarios for the United States. NOAA Technical Report NOS CO-OPS 083. NOAA/NOS Center for Operational Oceanographic Products and Services.Google Scholar
Syafrina, A. H., Norzaida, A., and Shazwani, O. Noor (2018). Stochastic modeling of rainfall series in Kelantan using an advanced weather generator. ETASR, 8: 2537–41.Google Scholar
Tang, Jianping, Niu, Xiaorui, Wang, Shuyu et al. (2016). Statistical downscaling and dynamical downscaling of regional climate in China: Present climate evaluations and future climate projections. Journal of Geophysical Research, D: Atmospheres, 121(5): 2110–29.Google Scholar
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research, WMO TD-732, 106(D7): 7183–92.Google Scholar
Taylor, K. E., Stouffer, R. J., and Meehl, G. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 92: 485–98. doi: 10.1175/BAMS-D-11-00094.1Google Scholar
Tebaldi, Claudia and Knutti, Reto (2007). The use of the multi-model ensemble in probabilistic climate projections. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 365(1857): 2053–75.Google Scholar
Thi, Phuong Cu and Ball, James E. (2015). Estimating design flood magnitude for a Vietnamese catchment. In: 36th Hydrology and Water Resources Symposium: The Art and Science of Water, 1370. Barton, ACT, Australia: Engineers Australia. Available at: https://search.informit.com.au/documentSummary;dn=824364044355354;res=IELENGGoogle Scholar
Tomassetti, Barbara, Verdecchia, Marco, and Giorgi, Filippo (2009). NN5: A neural network based approach for the downscaling of precipitation fields–model description and preliminary results. Journal of Hydrology, 367(1–2): 14–26.Google Scholar
Torma, C., Giorgi, F., and Coppola, E. (2015). added value of regional climate modeling over areas characterized by complex terrain–precipitation over the Alps. Journal of Geophysical Research, D: Atmospheres, 120(9): 3957–72.Google Scholar
Trenberth, K. E. (2015). Has there been a hiatus? Science, 349: 691–2. Available at: http://dx.doi.org/10.1126/science.aac9225Google Scholar
UKCP18, United Kingdom Climate Projections (2018). Available at: www.metoffice.gov.uk/research/collaboration/ukcp/Google Scholar
Urwin, K. and Jordan, A. (2008). Does public policy support or undermine climate change adaptation? Exploring policy interplay across different scales of governance. Global Environmental Change: Human and Policy Dimensions, 18(1): 180–91.Google Scholar
USGCRP (2000). National Assessment Synthesis Team, Climate Change Impacts on the United States: The Potential Consequences of Climate Variability and Change. Washington DC: US Global Change Research Program.Google Scholar
USGCRP (2016). US Global Change Research Program, Washington, DC.Google Scholar
USGCRP (2017). Climate Science Special Report: Fourth National Climate Assessment, Volume 1 [Wuebbles, D. J., Fahey, D. W., K. A. et al. (eds.)]. US Global Change Research Program: Washington, DC.Google Scholar
USGCRP (2018a). Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report [Cavallaro, N., Shrestha, G., Birdsey, R., et al. (eds.)]. US Global Change Research Program, Washington, DC, 878 pp., doi.org/10.7930/SOCCR2.2018.Google Scholar
USGCRP (2018b). Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment, Volume II [Reidmiller, D. R., Avery, C. W., Easterling, D. R., et al. (eds.)]. US Global Change Research Program, Washington, DC. doi: 10.7930/NCA4.2018.Google Scholar
Van Oldenborgh, G. J., Collins, Matthew, Arblaster, Julie, et al. (2013). Annex I: Atlas of global and regional climate projections. Climate Change 2013: The Physical Sci- ence Basis Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.Google Scholar
Vano, Julie A., Arnold, Jeffrey R., Nijssen, Bart et al. (2018). DOs and DON'Ts for using climate change information for water resource planning and management: Guidelines for study design. Climate Services, 12: 1–13.Google Scholar
VanRheenen, Nathan T., Wood, Andrew W., Palmer, Richard N., and Lettenmaier, Dennis P. (2004). Potential implications of PCM climate change scenarios for Sacramento–San Joaquin River Basin hydrology and water resources. Climatic Change, 62(1): 257–81.Google Scholar
Vavrus, Stephen J. and Behnke, Ruben J. (2014). A comparison of projected future precipitation in Wisconsin using global and downscaled climate model simulations: Implications for public health. International Journal of Climatology, 34(10): 3106–24.Google Scholar
Vigaud, N., Vrac, M., and Caballero, Y. (2013). Probabilistic downscaling of GCM scenarios over Southern India. International Journal of Climatology. https://doi.org/10.1002/joc.3509.Google Scholar
von Trentini, Fabian, Leduc, Martin, and Ludwig, Ralf (2019). Assessing natural variability in RCM signals: Comparison of a multi model EURO-CORDEX ensemble with a 50-member single model large ensemble. Climate Dynamics, 53 (3–4): 1963–79. Available at: https://doi.org/10.1007/s00382–019-04755-8.Google Scholar
Vose, James M., Clark, James S., Luce, Charles H., and Patel-Weynand, Toral, (eds.) (2016). Effects of Drought on Forests and Rangelands in the United States: A Comprehensive Science Synthesis. Gen. Tech. Rep. WO-93b. Washington, DC: US Department of Agriculture, Forest Service, Washington Office. 289 p.Google Scholar
Vose, R. S., Easterling, D. R., Kunkel, K. E., LeGrande, A. N., and Wehner, M. F. (2017). Temperature changes in the United States. In Wuebbles, D. J., Fahey, D. W., Hibbard, K. A. et al. (eds.), Climate Science Special Report: Fourth National Climate Assessment, Volume I. US Global Change Research Program, Washington, DC.: 185–206. doi: 10.7930/J0N29V45.Google Scholar
Vrac, M., Stein, M., and Hayhoe, K. (2007). Statistical Downscaling of Precipitation through Nonhomogeneous Stochastic Weather Typing. Climate Research. Available at: https://doi.org/10.3354/cr00696.Google Scholar
Vu, Tue M., Mishra, Ashok K., Konapala, Goutam, and Liu, Di (2018). Evaluation of multiple stochastic rainfall generators in diverse climatic regions. Stochastic Environmental Research and Risk Assessment: Research Journal, 32(5): 1337–53.Google Scholar
Walsh, C. L., Roberts, D., Dawson, R. J., et al. (2013). Experiences of integrated assessment of climate impacts, adaptation and mitigation modelling in London and Durban. Environment and Urbanization, 25(2): 361–80. Available at:  https://doi.org/10.1177/0956247813501121Google Scholar
Walton, D. B., Sun, F., Hall, A., and Capps, S. (2015). A hybrid dynamical-statistical technique: Part I: Development and validation of the technique. Journal of Climate, 28: 4597–617Google Scholar
Wang, Jiali, Balaprakash, Prasanna, and Kotamarthi, Rao (2019a). Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model. Geoscientific Model Development, 12(10). Available at: https://doi.org/10.5194/gmd-2019-79Google Scholar
Wang, L., Chen, W., Zhou, W. (2014b). Assessment of future drought in Southwest China based on CMIP5 Multimodel projections. Advances in Atmospheric Sciences, 33: 1035–50.Google Scholar
Wang, B., Hong, G., Cui, C. Q., et al. (2019b). Front. Eng. Manag. 6: 52. https://doi.org/10.1007/s42524–019-0002-yGoogle Scholar
Wang, J. and Kotamarthi, V. R. (2014). Downscaling with a nested regional climate model in near-surface fields over the contiguous United States. Journal of Geophysical Research, D: Atmospheres, 119(14): 8778–97.Google Scholar
Wang, J. and Kotamarthi, V. R. (2015). High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America. Earth’s Future, 3(7): 268–88.Google Scholar
Wang, C., Lin, B., Chen, C., and Lo, S. (2015b). Quantifying the effects of long-term climate change on tropical cyclone rainfall using a cloud-resolving model: Examples of two landfall typhoons in Taiwan. Journal of Climate, 28: 66–85. Available at: https://doi.org/10.1175/JCLI-D-14-00044.1Google Scholar
Wang, M., Overland, J. E., Kattsov, V., Walsh, J. E., Zhang, X., and Pavlova, T. (2007). Intrinsic versus forced variation in coupled climate model simulations over the Arctic during the twentieth century. Journal of Climate, 20: 1093–107. Available at: http://dx.doi.org/10.1175/JCLI4043.1Google Scholar
Wang, J. F. N., Swati, U., Stein, M. L., and Kotamarthi, V. R. (2015a). Model performance in spatiotemporal patterns of precipitation: New methods for identifying value added by a regional climate model. Journal of Geophysical Research: Atmospheres. Available at: https://doi.org/10.1002/2014jd022434Google Scholar
Wang, C., Zhang, L., Lee, S.-K., Wu, L., and Mechoso, C. R. (2014a). A global perspective on CMIP5 climate model biases. Nature Climate Change, 4: 201–5. Available at: http://dx.doi.org/10.1038/nclimate2118Google Scholar
Warren, F. J. and Lemmen, D. S.., (eds.) (2014). Canada in a Changing Climate: Sector Perspectives on Impacts and Adaptation. Government of Canada, Ottawa, ON, 286p.Google Scholar
Warrick, R., Ye, W., Li, Y., Dooley, M., and Urich, P. (2009). SimCLIM: A Software System for Modelling the Impacts of Climate Variability and Change. Hamilton, New Zealand: CLIMsystems Ltd.Google Scholar
Wasko, Conrad, Pui, Alexander, Sharma, Ashish, Mehrotra, Rajeshwar, and Jeremiah, Erwin (2015). Representing low-frequency variability in continuous rainfall simulations: A hierarchical random B Artlett L Ewis Continuous Rainfall Generation Model. Water Resources Research, 51(12): 9995–10007.Google Scholar
Wasko, Conrad, Sharma, Ashish, and Johnson, Fiona ( 2015). Does storm duration modulate the extreme precipitation-temperature scaling relationship? Geophysical Research Letters, 42(20): 8783–90.Google Scholar
Watson, A., Reece, J., Tirpak, B. E., et al. (2015). The Gulf Coast Vulnerability Assessment: Mangrove, Tidal Emergent Marsh, Barrier Islands, and Oyster Reef. 132 pp. Available from: http://gulfcoastprairielcc.org/science/science-projects/gulf-coast-vulnerability-assessment/Google Scholar
Watson, Robert T., Zinyowera, Marufu C., and Moss, Richard H. (1996). Climate change 1995 1996. Impacts, adaptations and mitigation of climate change: Scientific-technical analyses. In: Watson, Robert T., Zinyowera, Marufu C., Moss, Richard H. and Dokken, David J. (eds.), Climate change 1995–Impacts, adaptations and mitigation of climate change: Scientific‐technical analyses. Cambridge: Cambridge University Press: 879 .Google Scholar
Weart, S. (2015). CLIMATE AND CLIMATE CHANGE | History of Scientific Work on Climate Change. Encyclopedia of Atmospheric Sciences. Available at: https://doi.org/10.1016/b978–0-12-382225-Google Scholar
Wehner, M. F., Arnold, J. R., Knutson, T., Kunkel, K. E., and LeGrande, A. N. (2017). Droughts, floods, and wildfires. In: Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., et al. (eds.), Climate Science Special Report: Fourth National Climate Assessment, Volume I. Washington, DC.: U.S. Global Change Research Program: 231–56.Google Scholar
Weigel, A. P., Knutti, R., Liniger, M. A., and Appenzeller, C. (2010). Risks of model weighting in multimodel climate projections. Journal of Climate, 23: 4175–91. Available at: http://dx.doi.org/10.1175/2010jcli3594.1Google Scholar
Wigley, T. M. L., Jones, P. D., Briffa, K. R., and Smith, G. (1990). Obtaining sub-grid-scale information from coarse-resolution general circulation model output. Journal of Geophysical Research, D: Atmospheres, 95 (D2): 1943–53.Google Scholar
Wigley, T. M. L. and Raper, S. C. B. (1990). Natural variability of the climate system and detection of the greenhouse effect. Nature, 344(6264): 324.Google Scholar
Wilby, Robert L., Charles, S. P., Zorita, Eduardo, et al. (2004). Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods. Supporting Material of the Intergovernmental Panel on Climate Change, Available from the DDC of IPCC TGCIA 27. http://www.narccap.ucar.edu/doc/tgica-guidance-2004.pdfGoogle Scholar
Wilby, Robert L., Dawson, Christian W., and Barrow, Elaine M. (2002). SDSM–a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 17(2): 145–57.Google Scholar
Wilby, Robert L., Dawson, Christian W., Murphy, Conor, Connor, P. O., and Hawkins, Ed (2014). The Statistical DownScaling Model-Decision Centric (SDSM-DC): Conceptual basis and applications. Climate Research, 61(3): 259–76.Google Scholar
Wilby, R. L. and Wigley, T. M. L. (1997). Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography: Earth and Environment, 21(4): 530–48.Google Scholar
Wilby, R. and Wigley, T. M. L. (2000). Hydrologic responses to dynamically and statistically downscaled climate model output. Geophysical Research Letters, 27: 1199–202.Google Scholar
Wilby, R. L., Wigley, T. M. L, Conway, D. et al. (1998). Statistical downscaling of general circulation model output: A comparison of methods, Water Resource Research, 34(11): 2995–3008. Doi:10.1029/98WR02577Google Scholar
Wilks, D. S. and Wilby, R. L. (1999). The weather generation game: A review of stochastic weather models. Progress in Physical Geography: Earth and Environment, 23 (3): 329–57.Google Scholar
Wilson, L. and Barnett, W. (1983). Degree-days: An aid in crop and pest management. California Agriculture, 37(1): 4–7.Google Scholar
Woo, S., Singh, G. P., Oh, J. H. et al. (2019). Projection of seasonal summer precipitation over Indian sub-continent with a high-resolution AGCM based on the RCP scenarios. Meteorology and Atmospheric Physics, 131: 897. Available at: https://doi.org/10.1007/s00703–018-0612-7Google Scholar
Wood, A. W., Leung, Lai R., Venkataramana, Sridhar, and Lettenmaier, D. P. (2004). Hydrological implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62: 1890216.Google Scholar
Wood, A. W., Maurer, E. P., Kumar, A., and Lettenmaier, D. P. (2002). Long range experimental hydrologic forecasting for the Eastern U.S., Journal of Geophysical Research, 107(D20): 4429. Doi:10.1029/2001JD000659Google Scholar
Wuebbles, D., Fahey, D. W., and Hibbard, K. A. (2017). How will climate change affect the United States in decades to come? EOS, 98. Available at: https://doi.org/10.1029/2017EO086015Google Scholar
Xu, Chong-Yu (1999). From GCMs to river flow: A review of downscaling methods and hydrologic modelling approaches. Progress in Physical Geography: Earth and Environmen, 23(2): 229–49.Google Scholar
Xu, Z. and Yang, Z.-L. (2012). An improved dynamical downscaling method with gcm bias corrections and its validation with 30 years of climate simulations. Journal of Climate, 25(18): 6271–86.Google Scholar
Zeebe, Richard E., Ridgwell, Andy, and Zachos, James C. (2016). Anthropogenic carbon release rate unprecedented during the past 66 million years. Nature Geoscience, 9(4): 325.Google Scholar
Zhang, Yongfang, Guan, Dexin, Jin, Changjie, et al. (2011). Analysis of impacts of climate variability and human activity on streamflow for a river basin in Northeast China. Journal of Hydrology, 410(3): 239–47.Google Scholar
Zhang, G. J. and McFarlane, N. A. (1995). Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian climate centre general circulation model, Atmosphere-Ocean, 33(3): 407–46. DOI: 10.1080/07055900.1995.9649539Google Scholar
Zobel, Z., Wang, J., Wuebbles, D. J., and Kotamarthi, V. R. (2017). High-resolution dynamical downscaling ensemble projections of future extreme temperature distributions for the United States. Earth’s Future, 5(12): 1234–51.Google Scholar
Zobel, Z., Wang, J., Wuebbles, D. J., and Kotamarthi, V. R. (2018). Evaluations of high-resolution dynamically downscaled ensembles over the contiguous United States. Climate Dynamics, 50(3–4): 863–84. Available at: https://doi.org/10.1007/s00382–017-3645-6Google Scholar

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Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

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