Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States
暂无分享,去创建一个
[1] M. Maltrud,et al. Application-specific optimal model weighting of global climate models: A red tide example , 2022, Climate services.
[2] Tobias K. D. Weber,et al. Diagnosing similarities in probabilistic multi-model ensembles: an application to soil–plant-growth-modeling , 2022, Modeling Earth Systems and Environment.
[3] G. Schmidt,et al. Climate simulations: recognize the ‘hot model’ problem , 2022, Nature.
[4] A. Weerts,et al. Estimating Regionalized Hydrological Impacts of Climate Change Over Europe by Performance-Based Weighting of CORDEX Projections , 2021, Frontiers in Water.
[5] M. Stoll,et al. Ensembles for Viticulture Climate Classifications of the Willamette Valley Wine Region , 2021, Climate.
[6] A. Chehbouni,et al. Changes in mean and extreme temperature and precipitation events from different weighted multi-model ensembles over the northern half of Morocco , 2021, Climate Dynamics.
[7] D. Conway,et al. Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa , 2021, Climatic Change.
[8] D. Waliser,et al. The Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles of Precipitation , 2020, Climate.
[9] D. Waliser,et al. Atmospheric Rivers and Precipitation in the Middle East and North Africa (MENA) , 2020 .
[10] R. McPherson,et al. Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique , 2020, International Journal of Climatology.
[11] P. Cox,et al. Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models , 2020 .
[12] Jeong‐Soo Park,et al. A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation , 2020, Atmosphere.
[13] J. Marotzke,et al. Quantifying the role of internal variability in the temperature we expect to observe in the coming decades , 2020, Environmental Research Letters.
[14] R. Knutti,et al. Reduced global warming from CMIP6 projections when weighting models by performance and independence , 2020, Earth System Dynamics.
[15] Hilppa Gregow,et al. GCMeval – An interactive tool for evaluation and selection of climate model ensembles , 2020 .
[16] M. Kunze,et al. Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence , 2020, Atmospheric Chemistry and Physics.
[17] R. Knutti,et al. Quantifying uncertainty in European climate projections using combined performance-independence weighting , 2019, Environmental Research Letters.
[18] H. Paeth,et al. Weighted multi-model ensemble projection of extreme precipitation in the Mediterranean region using statistical downscaling , 2019, Theoretical and Applied Climatology.
[19] Bettina K. Gier,et al. Taking climate model evaluation to the next level , 2019, Nature Climate Change.
[20] Yanan Fan,et al. Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series , 2018, PloS one.
[21] Jinwon Kim,et al. Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies , 2018 .
[22] Yanan Fan,et al. A Bayesian posterior predictive framework for weighting ensemble regional climate models , 2017 .
[23] J. Abatzoglou,et al. Effects of climate change on snowpack and fire potential in the western USA , 2017, Climatic Change.
[24] R. Knutti,et al. Skill and independence weighting for multi-model assessments , 2016 .
[25] C. Driscoll,et al. The effects of climate downscaling technique and observational data set on modeled ecological responses. , 2016, Ecological applications : a publication of the Ecological Society of America.
[26] S. Vavrus,et al. Evaluation of downscaled, gridded climate data for the conterminous United States. , 2016, Ecological applications : a publication of the Ecological Society of America.
[27] J. Lanzante,et al. Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results? , 2016, Climatic Change.
[28] Veronika Eyring,et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .
[29] S. Vavrus,et al. Spring plant phenology and false springs in the conterminous US during the 21st century , 2015 .
[30] D. Cayan,et al. A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013 , 2015, Scientific Data.
[31] G. Robertson,et al. Can Impacts of Climate Change and Agricultural Adaptation Strategies Be Accurately Quantified if Crop Models Are Annually Re-Initialized? , 2015, PloS one.
[32] Lisa Dilling,et al. What do stakeholders need to manage for climate change and variability? A document-based analysis from three mountain states in the Western USA , 2015, Regional Environmental Change.
[33] J. Schoof. Statistical Downscaling in Climatology , 2013 .
[34] J. Abatzoglou. Development of gridded surface meteorological data for ecological applications and modelling , 2013 .
[35] Nicholas Stern,et al. Uncertainty in science and its role in climate policy , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[36] Spencer R. Weart,et al. The development of general circulation models of climate , 2010 .
[37] Reto Knutti,et al. The end of model democracy? , 2010 .
[38] M. Rummukainen. State‐of‐the‐art with regional climate models , 2010 .
[39] Mark A. Liniger,et al. Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts? , 2007 .
[40] S. Sorooshian,et al. Multi-model ensemble hydrologic prediction using Bayesian model averaging , 2007 .
[41] Adrian E. Raftery,et al. Weather Forecasting with Ensemble Methods , 2005, Science.