Articles | Volume 14, issue 1
https://doi.org/10.5194/esd-14-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-121-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States
South Central Climate Adaptation Science Center, University of
Oklahoma, Norman, OK 73019, USA
Elias C. Massoud
Computational Sciences and Engineering Division, Oak Ridge National
Laboratory, Oak Ridge, TN 37830, USA
Duane E. Waliser
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Huikyo Lee
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
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How can we systematically understand what causes high levels of atmospheric humidity and thus heat stress? Here we argue that atmospheric rivers can be a useful tool, based on our finding that in several US regions, atmospheric rivers and humid heat occur close together in space and time. Most typically, an atmospheric river transports moisture which heightens heat stress, with precipitation following a day later. These effects tend to be larger for stronger and more extensive systems.
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In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
Sudip Chakraborty, Bin Guan, Duane E. Waliser, and Arlindo M. da Silva
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The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Yan Yu, Olga V. Kalashnikova, Michael J. Garay, Huikyo Lee, Myungje Choi, Gregory S. Okin, John E. Yorks, James R. Campbell, and Jared Marquis
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Given the current uncertainties in the simulated diurnal variability of global dust mobilization and concentration, observational characterization of the variations in dust mobilization and concentration will provide a valuable benchmark for evaluating and constraining such model simulations. The current study investigates the diurnal cycle of dust loading across the global tropics, subtropics, and mid-latitudes by analyzing aerosol observations from the International Space Station.
Cited articles
Abatzoglou, J.: Development of gridded surface meteorological data for
ecological applications and modeling, Int. J. Clim.,
33, 121–131, https://doi.org/10.1002/joc.3413, 2013.
Allstadt, A. J., Vavrus, S. J., Heglund, P. J., Pidgeon, A. M., Thogmartin,
W. E., and Radelhoff, V. C.: Spring plant phenology and false springs in the
conterminous US during the 21st century, Environ. Res.
Lett., 10, 104008, https://doi.org/10.1088/1748-9326/10/10/104008, 2015.
Amante, C. and Eakins, B. W.: ETOPO1 1 arcmin Global Relief Model:
Procedures, Data Sources and Analysis; NOAA Technical Memorandum NESDIS
NGDC-24, National Geophysical Data Center, NOAA, Boulder, CO, USA, NGDC-24, 2009.
Amos, M., Young, P. J., Hosking, J. S., Lamarque, J.-F., Abraham, N. L., Akiyoshi, H., Archibald, A. T., Bekki, S., Deushi, M., Jöckel, P., Kinnison, D., Kirner, O., Kunze, M., Marchand, M., Plummer, D. A., Saint-Martin, D., Sudo, K., Tilmes, S., and Yamashita, Y.: Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence, Atmos. Chem. Phys., 20, 9961–9977, https://doi.org/10.5194/acp-20-9961-2020, 2020.
Balhane, S., Driouech, F., Chafki, O., Manzanas, R., Chehbouni, A., and
Moufouma-Okia, W.: Changes in mean and extreme temperature and precipitation
events from different weighted multi-model ensembles over the northern half
of Morocco, Clim. Dynam., 58, 389–404, https://doi.org/10.1007/s00382-021-05910-w,
2022.
Basso, B., Hyndman, D. W., Kendall, A. D., Grace, P. R., and Robertson, G. P.:
Can impacts of climate change agricultural adaptation strategies be
accurately quantified if crop models are annually re-initialized?, PLoS One,
10, e0127333, https://doi.org/10.1371/journal.pone.0127333, 2015.
Befort, D. J., Brunner, L., Borchert, L. F., O'Reilly, C. H., Mignot, J.,
Ballinger, A. P., Hergerl, G. C., Murphy, J. M., and Weisheimer, A.: Combination
of Decadal Predictions and Climate Projections in Time: Challenges and
Potential Solutions, Geophys. Res. Lett., 49, e2022GL098568, https://doi.org/10.1029/2022GL098568, 2022.
Behnke, R., Vavrus, S., Allstadt, A., Thogmartin, W., and Radelhoff, V. C.:
Evaluation of downscaled gridded climate data for the conterminous United
States, Ecol. Appl., 26, 1338–1351,
https://doi.org/10.1002/15-1061, 2016.
Bishop, C. H. and Shanley, K. T.: Bayesian model averaging's problematic
treatment of extreme weather and a paradigm shift that fixes it, Mon. Weather Rev., 136, 4641–4652, 2008.
Brunner, L., Lorenz, R., Zumwald, M., and Knutti, R.: Quantifying
uncertainty in European climate projections using combined
performance-independence weighting, Environ. Res. Lett., 14, 124010,
https://doi.org/10.1088/1748-9326/ab492f, 2019.
Brunner, L., McSweeney, C., Ballinger, A. P., Befort, D. J., Benassi, M.,
Booth, B., and Coppola, E.: Comparing methods to constrain future European
climate projections using a consistent framework, J. Climate, 33,
20, 8671–8692, 2020a.
Brunner, L., Pendergrass, A. G., Lehner, F., Merrifield, A. L., Lorenz, R., and Knutti, R.: Reduced global warming from CMIP6 projections when weighting models by performance and independence, Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, 2020b.
Caldwell, P. M., Zelinka, M. D., and Klein, S. A.: Evaluating Emergent
Constraints on Equilibrium Climate Sensitivity, J. Climate,
31, 3921–3942, https://doi.org/10.1175/JCLI-D-17-0631.1, 2018.
Cesana, G., Suselj, K., and Brient, F.: On the Dependence of Cloud Feedbacks
on Physical Parameterizations in WRF Aquaplanet Simulations, Geophys. Res. Lett., 44, 10762–10771, https://doi.org/10.1002/2017GL074820, 2017.
CMIP5 Data Search – ESGF CoG: https://esgf-node.llnl.gov/search/cmip5/ [data set], last access: 11 January 2023.
Dilling, L. and Berrgren, J.: What do stakeholders need to manage for
climate change and variability? A document-based analysis from three
mountain states in the Western USA, Reg. Environ. Change, 15,
657–667, https://doi.org/10.1007/s10113-014-0668-y, 2014.
Dixon, K. W., Lanzante, J. R., Nath, M. J., Hayhoe, K., Stoner, A.,
Radhakrishnan, A., Balaji, V., and Gaitán, C.: Evlauting the assumption
in statistically downscaled climate projections: is past performance an
indicator of future results?, Clim. Change, 135, 395–408, https://doi.org/10.1007/s10584-016-1598-0, 2016.
Duan, Q., Newsha, K., Ajami, X. G., and Sorooshian, S.: Multi-model ensemble
hydrologic prediction using Bayesian model averaging, Adv. Water
Res., 30, 1371–1386, 2007.
Elshall, A., Ye, M., Kranz, S. A., Harrington, J., Yang, X., Wan, Y., and
Maltrud, M.: Application-specific optimal model weighting of global climate
models: A red tide example, Clim. Serv., 28, 100334, https://doi.org/10.1016/j.cliser.2022.100334, 2022
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G.,
Caldwell, P., and Collins, W. D.: Taking climate model evaluation to the next
level, Nat. Clim. Change, 9, 102–110, 2019.
Fan, Y., Olson, R., and Evans, J. P.: A Bayesian posterior predictive framework for weighting ensemble regional climate models, Geosci. Model Dev., 10, 2321–2332, https://doi.org/10.5194/gmd-10-2321-2017, 2017.
Gergel, D. R., Nijssen, B., Abatzoglour, J. T., Lettenmaier, D. P., and
Stumbaugh, M. R.: Effects of climate change on snowpack and fire potential in
the western USA, Clim. Change, 141, 287–299, https://doi.org/10.1007/s10584-017-1899-y, 2017.
Gibson, P. B., Waliser, D. E., Lee, H., Tian, B., and Massoud, E.: Climate
model evaluation in the presence of observational uncertainty: precipitation
indices over the Contiguous United States, J. Hydrometeorol.,
2019, 1339–1357, https://doi.org/10.1175/JHM-D-18-0230.1 2019.
Gneiting, T. and Raftery, A. E.: Weather forecasting with ensemble methods,
Science, 310, 248–249, 2005.
GRDC: Major River Basins of the World/Global Runoff Data Centre, GRDC,
2nd ed., Federal Institute of Hydrology (BfG), Koblenz, Germany, https://www.bafg.de/GRDC/EN/02_srvcs/22_gslrs/221_MRB/Techdoc.pdf?__blob=publicationFile (last acccess: 16 January 2023), 2020.
Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W., and
Zelinka, M.: Climate simulations: recognize the “hot model” problem, Nature,
605, 26–29, https://doi.org/10.1038/d41586-022-01192-2, 2022.
Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T.: Bayesian
model averaging: a tutorial, Stat. Sci., 382–401, 1999.
Karl, T. R., Williams, C. N., Young, P. J., and Wendland, W. M.: A Model to
Estimate the Time of Observation Bias Associated with Monthly Mean Maximum,
Minimum, and Mean Temperatures for the United States, J. Clim. Appl. Meteorol., 25, 145–160, 1986.
Keupp, L., Hertig, E., Kaspar-Ott, I., Pollinger, F., Ring, C., Paeth, H.,
and Jacobeit, J.: Weighted multi-model ensemble projection of extreme
precipitation in the Mediterranean region using statistical downscaling,
Theor. Appl. Climatol., 138, 1269–1295, https://doi.org/10.1007/s00704-019-02851-7, 2019.
Knutti, R.: The end of model democracy?, Clim. Change, 102, 395–404,
https://doi.org/10.1007/s10584-010-9800-2, 2010.
Knutti, R., Sedlacek, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and
Eyring, V.: A climate model weighting scheme accounting for performance and
independence, Geophys. Res. Lett., 44, 1909–1918, https://doi.org/10.1002/2016GL072012,
2017.
Kolosu, S. R., Siderius, C., Todd, M. C., Bhave, A., Conway, D., James, R.,
Washington, R., Geressu, R., Harou, J. J., and Kashaigili, J. J.: Sensitivity
of projected climate impacts to climate model weighting: multi-sector
analysis in eastern Africa, Clim. Change, 164, 1–20, https://doi.org/10.1007/s10584-021-02991-8, 2021.
Lee, H., Goodman, A., McGibbney, L., Waliser, D. E., Kim, J., Loikith, P. C., Gibson, P. B., and Massoud, E. C.: Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies, Geosci. Model Dev., 11, 4435–4449, https://doi.org/10.5194/gmd-11-4435-2018, 2018.
Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis,
K. M., Maurer, E. P., and Lettenmaier, D. P.: A long-term hydrologically based
dataset of land surface fluxes and states for the conterminous United
States: Update and extensions, J. Climate, 26, 9384–9392, 2013.
Livneh, B., Bohn, T. J., Pierce, D. W., Muñoz-Arriola, F., Nijssen, B., Vose, R., Cayan, D. R., and Brekke, L.: A spatially comprehensive, meteorological data set for Mexico, the U.S., and southern Canada (NCEI Accession 0129374), NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/v5x34vf6, 2015.
Maher, N., Lehner, F., and Marotzke, J.: Quantifying the role of internal
variability in the temperature we expect to observe in the coming decades,
Environ. Res. Lett., 15, 054014, https://doi.org/10.1088/1748-9326/ab7d02, 2020.
Massoud, E. C., Espinoza, V., Guan, B., and Waliser, D. E.: Global Climate Model
Ensemble Approaches for Future Projections of Atmospheric Rivers, Earth's
Future, 7, 1136–1151, https://doi.org/10.1029/2019EF001249, 2019.
Massoud, E. C., Lee, H., Gibson, P. B., Loikith, P., and Waliser, D. E.:
Bayesian model averaging of climate model projections constrained by
precipitation observations over the contiguous United States, J. Hydrometeorol., 21, , 2401–2418, 2020a.
Massoud, E. C., Massoud, T., Guan, B., Sengupta, A., Espinoza, V., De Luna,
M., Raymond, C., and Waliser, D. E.: Atmospheric rivers and precipitation in
the middle east and north Africa (Mena), Water, 12, https://doi.org/10.3390/w12102863, 2863, 2020b.
Min, S.-K. and Hense, A.: A Bayesian approach to climate model evaluation
and multi-model averaging with and application to global mean surface
temperatures, Geophys. Res. Lett., 33, L08708, https://doi.org/10.1029/2006GL025779, 2006.
Nijsse, F. J. M. M., Cox, P. M., and Williamson, M. S.: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models, Earth Syst. Dynam., 11, 737–750, https://doi.org/10.5194/esd-11-737-2020, 2020.
Olson, R., Fan, Y., and Evans, J. P.: A simple method for Bayesian model
averaging of regional climate model projections: Application to southeast
Australian temperatures, Geophys. Res. Lett., 43, 14, 7661–7669,
2016.
Olson, R., An, S.-I., Fan, Y., and Evans, J. P.: Accounting for skill in
trend, variability, and autocorrelation facilitates better multi-model
projections: Application to the AMOC and temperature time series, PloS One,
14, e0214535, https://doi.org/10.1371/journal.pone.0214535,
2019.
Parding, K. M., Dobler, A., McSweeney, C., Landgren, O. A., Benestad, R.,
Erlandsen, H. B., Mezghani, A., Gregow, H., Räty, O., and Viktor, E.:
GCMeval – An interactive tool for evaluation and selection of climate model
ensembles, Clim. Serv., 18, 100167, https://doi.org/10.1016/j.cliser.2020.100167, 2020.
Peña, M., and van den Dool, H.: Consolidation of Multimodel Forecasts by
Ridge Regressison: Application to Pacific Sea Surface Temperature, J. Climate, 21, 6521–6538, https://doi.org/10.1175/2008JCLI2226.1, 2008.
Pickler, C. and Mölg, T.: General Circulation Model Selection Technique
for Downscaling: Exemplary Application to East Africa, J. Geophys. Res.-Atmos., 126, e2020JD033033, https://doi.org/10.1029/2020JD033033, 2021.
Pierce, D. W., Cayan, D. R., and Thrasher, B. L.,: Statistical downscaling
using Localized Constructed Analogs (LOCA), J. Hydrometeorol., 15, 2558–2585,
https://doi.org/10.1175/JHM-D-14-0082.1, 2014.
Pourmoktharian, A., Driscoll, C. T., Campbell, J. L., Hayhoe, K., and Stoner,
A. M. K.: The effects of climate downscaling technique and observations
dataset on modeled ecological responses, Ecol. Appl., 26,
1321–1337, https://doi.org/10.1890/15-0745, 2016.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using
Bayesian model averaging to calibrate forecast ensembles, Mon. Weather
Rev., 133, 1155–1174, 2005.
Rummukainen, M.: State-of-the-art with regional climate models, Wires. Clim.
Change, 1, 82–96, https://doi.org/10.1002/wcc.8, 2010.
Sanderson, B. M., Knutti, R., and Caldwell, P.: A representative democracy to
reduce interdependency in a multimodel ensemble, J. Climate, 28,
5171–5194, 2015a.
Sanderson, B. M., Knutti, R., and Caldwell, P.: Addressing interdependency in
a multimodel ensemble by interpolation of model properties, J. Climate, 28, 13, 5150–5170, 2015b.
Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017.
Sanderson, B. M. and Wehner, M. F.: Model weighting strategy, in: Climate
Science Special Report: Fourth National Climate Assessment, Volume I, edited by: Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., Dokken, D. J., Stewart, B. C., and
Maycock, T. K., U.S. Global Change Research Program, Washington, DC,
USA, 436–442, https://doi.org/10.7930/J06T0JS3, 2017.
Schäfer Rodrigues Silva, A., Weber, T. K. D., Gayler, S., Guthke, A.,
Höge, M., Nowak, W., and Streck, T.: Diagnosing Similarities in
probabilistic multi-model ensembles: an application to
soil-plant-growth-modeling, Model. Earth Sys. Environ., 8,
5143–5175, https://doi.org/10.1007/s40808-022-01427-1, 2022.
Schoof, J. T.: Statistical downscaling in climatology, Geogr. Comp., 7,
249–265, 2013.
Shin, Y., Lee, Y., and Park, J.-S.: A Weighting Scheme in A Multi-Model
Ensemble for Bias-Corrected Climate Simulation, Atmosphere, 11, p. 775,
https://doi.org/10.3390/atmos11080775, 2020.
Skahill, B., Berenguer, B., and Stoll, M.: Ensembles for Viticulture Climate
Classifications of the Willamette Valley Wine Region, Climate, 9, 140,
https://doi.org/10.3390/cli9090140, 2021.
Smith, L. and Stern, N.: Uncertainty in science and its role in climate
policy, Philos. T. Roy. Soc. A, 369, 1–24,
https://doi.org/10.1098/rsta.2011.0149, 2011.
Sperna Weiland, F. C., Visser, R. D., Greve, P., Bisselink, B., Brunner, L.,
and Weerts, A. H.: Estimating Regionalized Hydrological Impacts of Climate Change
Over Europe by Performance-Based Weighting of CORDEX projections, Front. Water, 3, 713537, https://doi.org/10.3389/frwa.2021.713537, 2021.
Tapiador, F. J., Roca, R., Genio, A. D., Dewitte, B., Petersen, W., and Zhang,
F.: Is Precipitation a Good Metric for Model Performance?, B. Am. Meteorol. Soc., 100, 223–233, https://doi.org/10.1175/bams-d-17-0218.1, 2019.
Taylor, A., Gregory, J. M., Webb, M. J., and Taylor, K. E.: Forcing, feedbacks
and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models,
Geophys. Res. Lett., 39, L09712, https://doi.org/10.1029/2012GL051607, 2012.
USGCRP: Climate Science Special Report: Fourth National Climate Assessment,
Volume I, edited by: Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., Dokken, D. J., Stewart, B. C.,
and Maycock, T. K., U.S. Global Change Research Program,
Washington, DC, USA, 470 pp., https://doi.org/10.7930/J0J964J6, 2017.
Vrugt, J. A. and Robinson, B. A.: Treatment of uncertainty using ensemble
methods: Comparison of sequential data assimilation and Bayesian model
averaging, Water Resour. Res., 43, W01411, https://doi.org/10.1029/2005WR004838, 2007.
Vrugt, J. A. and Massoud, E. C.: Uncertainty quantification of complex system
models: Bayesian Analysis, Handbook of Hydrometeorological Ensemble
Forecasting, edited by: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H. L., and Schaake, J. C., https://doi.org/10.1007/978-3-642-39925-1, 2018.
Vrugt, J. A., Cajo, J. F., Ter Braak, M. P. C., Hyman, J. M., and Robinson, B. A.:
Treatment of input uncertainty in hydrologic modeling: Doing hydrology
backward with Markov chain Monte Carlo simulation, Water Resour. Res.,
44, W00B09, https://doi.org/10.1029/2007WR006720, 2008.
Weart, S.: The development of general circulation models of climate, Studies
in History and Philosophy of Science Part B – Studies in History and
Philosophy of Modern Physics, 41, 208–217, https://doi.org/10.1016/j.shpsb.2010.06.002,
2010.
Weigel, A. P., Liniger, M. A., and Appenzeller, C.: Can multi-model
combination really enhance the prediction skill of probabilistic ensemble
forecasts?, Q. J. Roy. Meteor. Soc., 134, 630,
https://doi.org/10.1002/qj.210, 2008.
Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Emergent
Constraints on climate-carbon cycle feedbacks in the CMIP5 Earth System
Models, J. Geophys. Res.-Biogeo., 119, 794–807, https://doi.org/10.1002/2013JG002591, 2014.
Wootten, A. M., Massoud, E. C., Sengupta, A., Waliser, D. E., and Lee, H.: The
Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles
of Precipitation, Climate, 8, 138, https://doi.org/10.3390/cli8120138, 2020a.
Wootten, A. M., Dixon, K. W., Adams-Smith, D. J., and McPherson, R. A.:
Statistically downscaled precipitation sensitivity to gridded observation
data and downscaling technique, Int. J. Climatol., 41, 980–1001, https://doi.org/10.1002/joc.6716, 2020b.
Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., DeAngelo, B., Doherty, S.,
Hayhoe, K., Horton, R., Kossin, J. P., Taylor, P. C., Waple, A. M., and Weaver,
C. P.: Executive summary, in: Climate Science Special Report: Fourth National
Climate Assessment, Volume I, edited by: Wuebbles, D. J., Fahey, D. W., Hibbard, K. A., Dokken, D. J.,
Stewart, B. C., and Maycock, T. K., U.S. Global Change Research
Program, Washington, DC, USA, 12–34, https://doi.org/10.7930/J0DJ5CTG,
2017.
Short summary
Climate projections and multi-model ensemble weighting are increasingly used for climate assessments. This study examines the sensitivity of projections to multi-model ensemble weighting strategies in the south-central United States. Model weighting and ensemble means are sensitive to the domain and variable used. There are numerous findings regarding the improvement in skill with model weighting and the sensitivity associated with various strategies.
Climate projections and multi-model ensemble weighting are increasingly used for climate...
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