Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2265-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2265-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
20 Mar 2024
Model experiment description paper |
| 20 Mar 2024
| 20 Mar 2024
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Lei Huang
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Jesse Norris
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Alex Hall
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Naomi Goldenson
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Will Krantz
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Benjamin Bass
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Chad Thackeray
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Henry Lin
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Di Chen
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Eli Dennis
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Ethan Collins
Department of Atmospheric Science, University of Wyoming, Laramie, WY 82071, USA
Zachary J. Lebo
School of Meteorology, University of Oklahoma, Norman, OK 73019, USA
Emily Slinskey
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Sara Graves
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Surabhi Biyani
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Bowen Wang
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
Stephen Cropper
Center for Climate Science, University of California, Los Angeles, Los Angeles, CA 90095, USA
A full list of authors appears at the end of the paper.
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Cited articles
Bass, B., Rahimi, S., Goldenson, N., Hall, A., Norris, J., and Lebow, Z. J.: Achieving Realistic Runoff in the Western United States with a Land Surface Model Forced by Dynamically Downscaled Meteorology, J. Hydrometeorol., 24, 269–283, https://doi.org/10.1175/JHM-D-22-0047.1, 2023.
Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., Law, R., Harman, I., Srbinovsky, J., Rashid, H. A., Dobrohotoff, P., Mackallah, C., Yan, H., Hirst, A., Savita, A., Dias, F. B., Woodhouse, M., Fiedler, R., Heerdegen, A., Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., Law, R., Harman, I., Srbinovsky, J., Rashid, H. A., Dobrohotoff, P., Mackallah, C., Yan, H., Hirst, A., Savita, A., Dias, F. B., Woodhouse, M., Fiedler, R., and Heerdegen, A.: Configuration and spin-up of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model, JSHESS, 70, 225–251, https://doi.org/10.1071/ES19040, 2020.
Bruyère, C. L., Done, J. M., Holland, G. J., and Fredrick, S.: Bias corrections of global models for regional climate simulations of high-impact weather, Clim. Dynam., 43, 1847–1856, https://doi.org/10.1007/s00382-013-2011-6, 2014.
Bukovsky, M. S. and Karoly, D. J.: A Regional Modeling Study of Climate Change Impacts on Warm-Season Precipitation in the Central United States, J. Climate, 24, 1985–2002, https://doi.org/10.1175/2010JCLI3447.1, 2011.
Bukovsky, M. S., Gao, J., Mearns, L. O., and O'Neill, B. C.: SSP-Based Land-Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections, Earth's Future, 9, e2020EF001782, https://doi.org/10.1029/2020EF001782, 2021.
Chen, D., Norris, J., Thackeray, C., and Hall, A.: Increasing precipitation whiplash in climate change hotspots, Environ. Res. Lett., 17, 124011, https://doi.org/10.1088/1748-9326/aca3b9, 2022.
Colman, R. and Soden, B. J.: Water vapor and lapse rate feedbacks in the climate system, Rev. Mod. Phys., 93, 045002, https://doi.org/10.1103/RevModPhys.93.045002, 2021.
Coppola, E., Sobolowski, S., Pichelli, E., Raffaele, F., Ahrens, B., Anders, I., Ban, N., Bastin, S., Belda, M., Belusic, D., Caldas-Alvarez, A., Cardoso, R. M., Davolio, S., Dobler, A., Fernandez, J., Fita, L., Fumiere, Q., Giorgi, F., Goergen, K., Güttler, I., Halenka, T., Heinzeller, D., Hodnebrog, Ø., Jacob, D., Kartsios, S., Katragkou, E., Kendon, E., Khodayar, S., Kunstmann, H., Knist, S., Lavín-Gullón, A., Lind, P., Lorenz, T., Maraun, D., Marelle, L., van Meijgaard, E., Milovac, J., Myhre, G., Panitz, H.-J., Piazza, M., Raffa, M., Raub, T., Rockel, B., Schär, C., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Stocchi, P., Tölle, M. H., Truhetz, H., Vautard, R., de Vries, H., and Warrach-Sagi, K.: A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean, Clim. Dynam., 55, 3–34, https://doi.org/10.1007/s00382-018-4521-8, 2020.
Coppola, E., Nogherotto, R., Ciarlo', J. M., Giorgi, F., van Meijgaard, E., Kadygrov, N., Iles, C., Corre, L., Sandstad, M., Somot, S., Nabat, P., Vautard, R., Levavasseur, G., Schwingshackl, C., Sillmann, J., Kjellström, E., Nikulin, G., Aalbers, E., Lenderink, G., Christensen, O. B., Boberg, F., Sørland, S. L., Demory, M.-E., Bülow, K., Teichmann, C., Warrach-Sagi, K., and Wulfmeyer, V.: Assessment of the European Climate Projections as Simulated by the Large EURO-CORDEX Regional and Global Climate Model Ensemble, J. Geophys. Res.-Atmos., 126, e2019JD032356, https://doi.org/10.1029/2019JD032356, 2021.
Daly, C., Neilson, R. P., and Phillips, D. L.: A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain, J. Appl. Meteorol. Clim., 33, 140–158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2, 1994.
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020.
Dennis, E. J. and Berbery, E. H.: The Role of Soil Texture in Local Land Surface–Atmosphere Coupling and Regional Climate, J. Hydrometeorol., 22, 313–330, https://doi.org/10.1175/JHM-D-20-0047.1, 2021.
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022.
Durack, P. J., Taylor, K. E., Eyring, V., Ames, S. K., Doutriaux, C., Hoang, T., Nadeau, D., Stockhause, M., and Gleckler, P. J.: input4MIPs: Making [CMIP] model forcing more transparent, United States, United States, Report No. LLNL-JRNL-729886, 880514, https://doi.org/10.2172/1463030, 2017.
Goldenson, N., Leung, L. R., Mearns, L. O., Pierce, D. W., Reed, K. A., Simpson, I. R., Ullrich, P., Krantz, W., Hall, A., Jones, A., and Rahimi, S.: Use-Inspired, Process-Oriented GCM Selection: Prioritizing Models for Regional Dynamical Downscaling, B. Am. Meteorol. Soc., 104, E1619–29, https://doi.org/10.1175/BAMS-D-23-0100.1, 2023.
Gutjahr, O., Putrasahan, D., Lohmann, K., Jungclaus, J. H., von Storch, J.-S., Brüggemann, N., Haak, H., and Stössel, A.: Max Planck Institute Earth System Model (MPI-ESM1.2) for the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 12, 3241–3281, https://doi.org/10.5194/gmd-12-3241-2019, 2019.
Hall, A.: The Role of Surface Albedo Feedback in Climate, J. Climate, 17, 1550–1568, https://doi.org/10.1175/1520-0442(2004)017<1550:TROSAF>2.0.CO;2, 2004.
Hansen, J., Lacis, A., Rind, D., Russell, G., Stone, P., Fung, I., Ruedy, R., and Lerner, J.: Climate Sensitivity: Analysis of Feedback Mechanisms, in: Climate Processes and Climate Sensitivity, American Geophysical Union (AGU), 130–163, https://doi.org/10.1029/GM029p0130, 1984.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P. de, Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Huang, X., Swain, D. L., Walton, D. B., Stevenson, S., and Hall, A. D.: Simulating and Evaluating Atmospheric River-Induced Precipitation Extremes Along the U.S. Pacific Coast: Case Studies From 1980–2017, J. Geophys. Res.-Atmos., 125, e2019JD031554, https://doi.org/10.1029/2019JD031554, 2020.
Huang, X., Swain, D. L., and Hall, A. D.: Future precipitation increase from very high resolution ensemble downscaling of extreme atmospheric river storms in California, Sci. Adv., 6, eaba1323, https://doi.org/10.1126/sciadv.aba1323, 2021.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Miller, R. L., Murray, L. T., Oinas, V., Orbe, C., García-Pando, C. P., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: GISS-E2.1: Configurations and Climatology, J. Adv. Model. Earth Sy., 12, e2019MS002025, https://doi.org/10.1029/2019MS002025, 2020.
Komurcu, M., Emanuel, K. A., Huber, M., and Acosta, R. P.: High-Resolution Climate Projections for the Northeastern United States Using Dynamical Downscaling at Convection-Permitting Scales, Earth Space Sci., 5, 801–826, https://doi.org/10.1029/2018EA000426, 2018.
Krantz, W., Pierce, D., Goldenson, N., and Cayan, D.: Memorandum on Evaluating Global Climate Models for Studying Regional Climate Change in California, The California Energy Commision, https://www.energy.ca.gov/sites/default/files/2022-09/20220907_CDAWG_MemoEvaluating_GCMs_EPC-20-006_Nov2021-ADA.pdf (last access: January 2024), 2021.
Lanzante, J. R., Dixon, K. W., Nath, M. J., Whitlock, C. E., and Adams-Smith, D.: Some Pitfalls in Statistical Downscaling of Future Climate, B. Am. Meteorol. Soc., 99, 791–803, https://doi.org/10.1175/BAMS-D-17-0046.1, 2018.
Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., Dong, L., Zhou, T., Liu, L., Wang, L., Pu, Y., Chen, X., Chen, L., Xie, Z., Liu, H., Zhang, L., Huang, X., Feng, T., Zheng, W., Xia, K., Liu, H., Liu, J., Wang, Y., Wang, L., Jia, B., Xie, F., Wang, B., Zhao, S., Yu, Z., Zhao, B., and Wei, J.: The Flexible Global Ocean-Atmosphere-Land System Model Grid-Point Version 3 (FGOALS-g3): Description and Evaluation, J. Adv. Model. Earth Sy., 12, e2019MS002012, https://doi.org/10.1029/2019MS002012, 2020.
Liu, C., Ikeda, K., Thompson, G., Rasmussen, R., and Dudhia, J.: High-Resolution Simulations of Wintertime Precipitation in the Colorado Headwaters Region: Sensitivity to Physics Parameterizations, Mon. Weather Rev., 139, 3533–3553, https://doi.org/10.1175/MWR-D-11-00009.1, 2011.
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A. F., Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J., Eidhammer, T., Gochis, D., Gutmann, E., Kurkute, S., Li, Y., Thompson, G., and Yates, D.: Continental-scale convection-permitting modeling of the current and future climate of North America, Clim. Dynam., 49, 71–95, https://doi.org/10.1007/s00382-016-3327-9, 2017.
Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S.: Our Skill in Modeling Mountain Rain and Snow is Bypassing the Skill of Our Observational Networks, B. Am. Meteorol. Soc., 100, 2473–2490, https://doi.org/10.1175/BAMS-D-19-0001.1, 2019.
Mahoney, K., Scott, J. D., Alexander, M., McCrary, R., Hughes, M., Swales, D., and Bukovsky, M.: Cool season precipitation projections for California and the Western United States in NA-CORDEX models, Clim. Dynam., 56, 3081–3102, https://doi.org/10.1007/s00382-021-05632-z, 2021.
Mankin, J. S., Simpson, I., Hoell, A., Fu, R., Lisonbee, J., Sheffield, A., and Barrie, D.: NOAA Drought Task Force Report on the 2020–2021 Southwestern U.S. Drought, NOAA Drought Task Force, MAPP, and NIDIS, 2021.
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la-Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038, https://doi.org/10.1029/2018MS001400, 2019.
Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., Raper, S. C. B., Watterson, I. G., Weaver, A. J., and Zhao, Z. C.: Global 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, Cambridge University Press, Cambridge, ISBN 978-0521-70596-7, 2007.
Mearns, L. O., Arritt, R., Biner, S., Bukovsky, M. S., McGinnis, S., Sain, S., Caya, D., Correia, J., Flory, D., Gutowski, W., Takle, E. S., Jones, R., Leung, R., Moufouma-Okia, W., McDaniel, L., Nunes, A. M. B., Qian, Y., Roads, J., Sloan, L., and Snyder, M.: The North American Regional Climate Change Assessment Program: Overview of Phase I Results, B. Am. Meteorol. Soc., 93, 1337–1362, https://doi.org/10.1175/BAMS-D-11-00223.1, 2012.
Mitchell, D. L., Ivanova, D., Rabin, R., Brown, T. J., and Redmond, K.: Gulf of California Sea Surface Temperatures and the North American Monsoon: Mechanistic Implications from Observations, J. Climate, 15, 2261–2281, https://doi.org/10.1175/1520-0442(2002)015<2261:GOCSST>2.0.CO;2, 2002.
Morrison, H. and Milbrandt, J.: Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests, J. Atmos. Sci., 72, 287–311, https://doi.org/10.1175/JAS-D-14-0065.1, 2015.
Neelin, J. D., Langenbrunner, B., Meyerson, J. E., Hall, A., and Berg, N.: California Winter Precipitation Change under Global Warming in the Coupled Model Intercomparison Project Phase 5 Ensemble, J. Climate, 26, 6238–6256, https://doi.org/10.1175/JCLI-D-12-00514.1, 2013.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Norris, J., Carvalho, L. M. V., Jones, C., and Cannon, F.: Deciphering the contrasting climatic trends between the central Himalaya and Karakoram with 36 years of WRF simulations, Clim. Dynam., 52, 159–180, https://doi.org/10.1007/s00382-018-4133-3, 2019.
Norris, J., Hall, A., Chen, D., Thackeray, C. W., and Madakumbura, G. D.: Assessing the Representation of Synoptic Variability Associated With California Extreme Precipitation in CMIP6 Models, J. Geophys. Res.-Atmos., 126, e2020JD033938, https://doi.org/10.1029/2020JD033938, 2021.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Qu, X. and Hall, A.: Assessing Snow Albedo Feedback in Simulated Climate Change, J. Climate, 19, 2617–2630, https://doi.org/10.1175/JCLI3750.1, 2006.
Rahimi, S. and Huang, L.: CMIP6 GCMs downscaled using WRF, Amazon S3 [data], https://registry.opendata.aws/wrf-cmip6/ (last access: January 2024), 2023a.
Rahimi, S. and Huang, L.: Rahimi, S., and Huang, L.: Data tier descriptions, directory structure, and data access of the Western U.S. Dynamically Downscaled Dataset (WUS-D3) version 1, https://dept.atmos.ucla.edu/sites/default/files/alexhall/files/aws_tiers_dirstructure_nov22.pdf (last access: October 2023), 2023b.
Rahimi, S. and Huang, L.: Data tier descriptions, directory structure, and data access of the Western U.S. Dynamically Downscaled Dataset (WUS-D3) version 1, https://www.researchgate.net/publication/374504614_Data_ tier_ descriptions_directory_structure_and_data_ access_ of_the_ Western_US_Dynamically_Downscaled_Dataset_WUS-D3_ version_1 (last access: October 2023), 2023c.
Rahimi, S. and Huang, L.: Versions of WRF, Figure Jupyter notebooks, and geo files for WUS-D3. In Geoscientific Model Development, Zenodo [code and data set], https://doi.org/10.5281/zenodo.10635867, 2024.
Rahimi, S., Krantz, W., Lin, Y.-H., Bass, B., Goldenson, N., Hall, A., Lebo, Z. J., and Norris, J.: Evaluation of a Reanalysis-Driven Configuration of WRF4 Over the Western United States From 1980 to 2020, J. Geophys. Res.-Atmos., 127, e2021JD035699, https://doi.org/10.1029/2021JD035699, 2022.
Rahimi, S., Huang, L., Norris, J., Hall, A., Thackeray, C., Goldenson, N., Risser, M., Feldman, D., and Lebo, Z. J.: Understanding the Cascade: Removing GCM biases improves dynamically downscaled climate projections, Geophys Res. Lett., in review, 2024.
Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Yu, W., Miller, K., Arsenault, K., Grubišić, V., Thompson, G., and Gutmann, E.: High-Resolution Coupled Climate Runoff Simulations of Seasonal Snowfall over Colorado: A Process Study of Current and Warmer Climate, J. Climate, 24, 3015–3048, https://doi.org/10.1175/2010JCLI3985.1, 2011.
Rasmussen, R., Ikeda, K., Liu, C., Gochis, D., Clark, M., Dai, A., Gutmann, E., Dudhia, J., Chen, F., Barlage, M., Yates, D., and Zhang, G.: Climate Change Impacts on the Water Balance of the Colorado Headwaters: High-Resolution Regional Climate Model Simulations, J. Hydrometeorol., 15, 1091–1116, https://doi.org/10.1175/JHM-D-13-0118.1, 2014.
Rastogi, D., Kao, S.-C., and Ashfaq, M.: How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections?, Earth's Future, 10, e2022EF002734, https://doi.org/10.1029/2022EF002734, 2022.
Risser, M., Rahimi, S., Goldenson, G., Lebo, Z. J., Hall, A., and Feldman, D.: Is bias correction in dynamical downscaling defensible?, Geophys Res. Lett., in press, 2024.
Rupp, D. E., Hawkins, L. R., Li, S., Koszuta, M., and Siler, N.: Spatial patterns of extreme precipitation and their changes under ∼ 2 °C global warming: a large-ensemble study of the western USA, Clim. Dynam., 59, 2363–2379, https://doi.org/10.1007/s00382-022-06214-3, 2022.
Scalzitti, J., Strong, C., and Kochanski, A.: Climate change impact on the roles of temperature and precipitation in western US snowpack variability, Geophys. Res. Lett., 43, 5361–5369, https://doi.org/10.1002/2016GL068798, 2016.
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E., Geoffroy, O., Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A., Rocher, M., Roehrig, R., Salas-y-Mélia, D., Sanchez, E., Terray, L., Valcke, S., Waldman, R., Aumont, O., Bopp, L., Deshayes, J., Éthé, C., and Madec, G.: Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System Processes in Present-Day and Future Climate, J. Adv. Model. Earth Sy., 11, 4182–4227, https://doi.org/10.1029/2019MS001791, 2019.
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020.
Sellar, A. A., Walton, J., Jones, C. G., Wood, R., Abraham, N. L., Andrejczuk, M., Andrews, M. B., Andrews, T., Archibald, A. T., de Mora, L., Dyson, H., Elkington, M., Ellis, R., Florek, P., Good, P., Gohar, L., Haddad, S., Hardiman, S. C., Hogan, E., Iwi, A., Jones, C. D., Johnson, B., Kelley, D. I., Kettleborough, J., Knight, J. R., Köhler, M. O., Kuhlbrodt, T., Liddicoat, S., Linova-Pavlova, I., Mizielinski, M. S., Morgenstern, O., Mulcahy, J., Neininger, E., O’Connor, F. M., Petrie, R., Ridley, J., Rioual, J.-C., Roberts, M., Robertson, E., Rumbold, S., Seddon, J., Shepherd, H., Shim, S., Stephens, A., Teixiera, J. C., Tang, Y., Williams, J., Wiltshire, A., and Griffiths, P. T.: Implementation of U.K. Earth System Models for CMIP6, J. Adv. Model. Earth Sy., 12, e2019MS001946, https://doi.org/10.1029/2019MS001946, 2020
Siler, N. and Roe, G.: How will orographic precipitation respond to surface warming? An idealized thermodynamic perspective, Geophys. Res. Lett., 41, 2606–2613, https://doi.org/10.1002/2013GL059095, 2014.
Simpson, I. R., Bacmeister, J., Neale, R. B., Hannay, C., Gettelman, A., Garcia, R. R., Lauritzen, P. H., Marsh, D. R., Mills, M. J., Medeiros, B., and Richter, J. H.: An Evaluation of the Large-Scale Atmospheric Circulation and Its Variability in CESM2 and Other CMIP Models, J. Geophys. Res.-Atmos., 125, e2020JD032835, https://doi.org/10.1029/2020JD032835, 2020.
Skamarock, C., Klemp, B., Dudhia, J., Gill, O., Liu, Z., Berner, J., Wang, W., Powers, G., Duda, G., Barker, D., and Huang, X.: A Description of the Advanced Research WRF Model Version 4, No. NCAR/TN-556+STR, https://doi.org/10.5065/1dfh-6p97, 2019.
Spero, T. L., Otte, M. J., Bowden, J. H., and Nolte, C. G.: Improving the representation of clouds, radiation, and precipitation using spectral nudging in the Weather Research and Forecasting model, J. Geophys. Res.-Atmos., 119, 11682–11694, https://doi.org/10.1002/2014JD022173, 2014.
Swain, D. L., Langenbrunner, B., Neelin, J. D., and Hall, A.: Increasing precipitation volatility in twenty-first-century California, Nat. Clim. Change, 8, 427–433, https://doi.org/10.1038/s41558-018-0140-y, 2018.
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019.
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019.
Thackeray, C. W. and Fletcher, C. G.: Snow albedo feedback: Current knowledge, importance, outstanding issues and future directions, Prog. Phys. Geogr., 40, 392–408, https://doi.org/10.1177/0309133315620999, 2016.
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models, Mon. Weather Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989.
Walton, D. B., Hall, A., Berg, N., Schwartz, M., and Sun, F.: Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California's Sierra Nevada, J. Climate, 30, 1417–1438, https://doi.org/10.1175/JCLI-D-16-0168.1, 2017.
Wang, J. and Kotamarthi, V. R.: Assessment of Dynamical Downscaling in Near-Surface Fields with Different Spectral Nudging Approaches Using the Nested Regional Climate Model (NRCM), J. Appl. Meteorol. Clim., 52, 1576–1591, https://doi.org/10.1175/JAMC-D-12-0302.1, 2013.
Wang, J. and Kotamarthi, V. R.: High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America, Earth's Future, 3, 268–288, https://doi.org/10.1002/2015EF000304, 2015.
Wang, Y.-C., Hsu, H.-H., Chen, C.-A., Tseng, W.-L., Hsu, P.-C., Lin, C.-W., Chen, Y.-L., Jiang, L.-C., Lee, Y.-C., Liang, H.-C., Chang, W.-M., Lee, W.-L., and Shiu, C.-J.: Performance of the Taiwan Earth System Model in Simulating Climate Variability Compared With Observations and CMIP6 Model Simulations, J. Adv. Model. Earth Sy., 13, e2020MS002353, https://doi.org/10.1029/2020MS002353, 2021.
White, A. B., Moore, B. J., Gottas, D. J., and Neiman, P. J.: Winter Storm Conditions Leading to Excessive Runoff above California's Oroville Dam during January and February 2017, B. Am. Meteorol. Soc., 100, 55–70, https://doi.org/10.1175/BAMS-D-18-0091.1, 2019.
White, R. H., Anderson, S., Booth, J. F., Braich, G., Draeger, C., Fei, C., Harley, C. D. G., Henderson, S. B., Jakob, M., Lau, C.-A., Mareshet Admasu, L., Narinesingh, V., Rodell, C., Roocroft, E., Weinberger, K. R., and West, G.: The unprecedented Pacific Northwest heatwave of June 2021, Nat. Commun., 14, 727, https://doi.org/10.1038/s41467-023-36289-3, 2023.
Zhang, C., Wang, Y., and Hamilton, K.: Improved Representation of Boundary Layer Clouds over the Southeast Pacific in ARW-WRF Using a Modified Tiedtke Cumulus Parameterization Scheme, Mon. Weather Rev., 139, 3489–3513, https://doi.org/10.1175/MWR-D-10-05091.1, 2011.
Zhou, S., Williams, A. P., Berg, A. M., Cook, B. I., Zhang, Y., Hagemann, S., Lorenz, R., Seneviratne, S. I., and Gentine, P.: Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity, P. Natl. Acad. Sci USA, 116, 18848–18853, https://doi.org/10.1073/pnas.1904955116, 2019.
Zobel, Z., Wang, J., Wuebbles, D. J., and Kotamarthi, V. R.: High-Resolution Dynamical Downscaling Ensemble Projections of Future Extreme Temperature Distributions for the United States, Earth's Future, 5, 1234–1251, https://doi.org/10.1002/2017EF000642, 2017.
Zobel, Z., Wang, J., Wuebbles, D. J., and Kotamarthi, V. R.: Evaluations of high-resolution dynamically downscaled ensembles over the contiguous United States, Clim. Dynam., 50, 863–884, https://doi.org/10.1007/s00382-017-3645-6, 2018.
Short summary
Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Here, we project future climate across the western United States through the end of the 21st...
