Bayesian Model Averaging of Climate Model Projections Constrained by Precipitation Observations over the Contiguous United States

This study utilizes Bayesian model averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project phase 5 (CMIP5). We evaluate the representation of five precipitation summary metrics in the historical simulations using observations from the NASA Tropical Rainfall Measuring Mission (TRMM) satellites. The summary metrics include mean, annual and interannual variability, and maximum and minimum extremes of precipitation. The estimated model average produced with BMA is shown to have higher accuracy in simulating mean rainfall than the ensemble mean (RMSE of 0.49 for BMA versus 0.65 for ensemble mean), and a more constrained spread of uncertainty with roughly a third of the total uncertainty than is produced with the multimodel ensemble. The results show that, by the end of the century, the mean daily rainfall is projected to increase for most of the East Coast and the Northwest, may decrease in the southern United States, and with little change expected for the Southwest. For extremes, the wettest year on record is projected to become wetter for the majority of CONUS and the driest year to become drier. We show that BMA offers a framework to more accurately estimate and to constrain the spread of uncertainties of future climate, such as precipitation changes over CONUS.

[1]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[2]  C. Kummerow,et al.  The Tropical Rainfall Measuring Mission (TRMM) Sensor Package , 1998 .

[3]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[4]  T. N. Krishnamurti,et al.  The status of the tropical rainfall measuring mission (TRMM) after two years in orbit , 2000 .

[5]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[6]  Adrian E. Raftery,et al.  Weather Forecasting with Ensemble Methods , 2005, Science.

[7]  S. Sorooshian,et al.  Multi-model ensemble hydrologic prediction using Bayesian model averaging , 2007 .

[8]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[9]  Bruce A. Robinson,et al.  Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging , 2007 .

[10]  Reto Knutti,et al.  The use of the multi-model ensemble in probabilistic climate projections , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  G. Meehl,et al.  A strategy for climate change stabilization experiments , 2007 .

[12]  C. Bishop,et al.  Bayesian Model Averaging’s Problematic Treatment of Extreme Weather and a Paradigm Shift That Fixes It , 2008 .

[13]  Cajo J. F. ter Braak,et al.  Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation , 2008 .

[14]  G. Meehl,et al.  Decadal prediction: Can it be skillful? , 2009 .

[15]  Reto Knutti,et al.  Challenges in Combining Projections from Multiple Climate Models , 2010 .

[16]  M. Dettinger.  Climate Change, Atmospheric Rivers, and Floods in California – A Multimodel Analysis of Storm Frequency and Magnitude Changes 1 , 2011 .

[17]  Thomas Reichler,et al.  On the Effective Number of Climate Models , 2011 .

[18]  M. Dettinger,et al.  Atmospheric Rivers, Floods and the Water Resources of California , 2011 .

[19]  R. Knutti,et al.  Climate model genealogy , 2011 .

[20]  M. Almazroui.  Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009 , 2011 .

[21]  J. Annan,et al.  On the generation and interpretation of probabilistic estimates of climate sensitivity , 2011 .

[22]  A. V. Vecchia,et al.  Monitoring and Understanding Changes in Heat Waves, Cold Waves, Floods, and Droughts in the United States: State of Knowledge , 2013 .

[23]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[24]  Reto Knutti,et al.  On the interpretation of constrained climate model ensembles , 2012 .

[25]  C. Bishop,et al.  Climate model dependence and the replicate Earth paradigm , 2013, Climate Dynamics.

[26]  R. Knutti,et al.  Robustness and uncertainties in the new CMIP5 climate model projections , 2013 .

[27]  M. Kanamitsu,et al.  The Key Role of Heavy Precipitation Events in Climate Model Disagreements of Future Annual Precipitation Changes in California , 2013 .

[28]  M. Dettinger,et al.  Drought and the California Delta—A Matter of Extremes , 2014 .

[29]  T. Shepherd.  Atmospheric circulation as a source of uncertainty in climate change projections , 2014 .

[30]  P. Cox,et al.  Emergent constraints on climate‐carbon cycle feedbacks in the CMIP5 Earth system models , 2014 .

[31]  D. Wuebbles,et al.  Observational‐ and model‐based trends and projections of extreme precipitation over the contiguous United States , 2014 .

[32]  K.,et al.  The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability , 2015 .

[33]  G. Magnusdottir,et al.  An evaluation of atmospheric rivers over the North Pacific in CMIP5 and their response to warming under RCP 8.5 , 2015 .

[34]  Quantifying the effects of long-term climate change on tropical cyclone rainfall using a cloud-resolving model: Examples of two landfall typhoons in Taiwan , 2015 .

[35]  C. Bishop,et al.  Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections , 2015 .

[36]  Alex J. Cannon,et al.  Future changes in autumn atmospheric river events in British Columbia, Canada, as projected by CMIP5 global climate models , 2015 .

[37]  Y. Qian,et al.  Dynamical and thermodynamical modulations on future changes of landfalling atmospheric rivers over western North America , 2015 .

[38]  Steve Easterbrook,et al.  The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations , 2015 .

[39]  C. Mass,et al.  Changes in Winter Atmospheric Rivers along the North American West Coast in CMIP5 Climate Models , 2015 .

[40]  E. Alfaro,et al.  Skill of CMIP5 climate models in reproducing 20th century basic climate features in Central America , 2015 .

[41]  Reto Knutti,et al.  Addressing interdependency in a multimodel ensemble by interpolation of model properties , 2015 .

[42]  D. Wuebbles,et al.  Seasonal and regional variations in extreme precipitation event frequency using CMIP5 , 2016 .

[43]  L. Leung,et al.  A projection of changes in landfalling atmospheric river frequency and extreme precipitation over western North America from the Large Ensemble CESM simulations , 2016 .

[44]  Yanan Fan,et al.  A simple method for Bayesian model averaging of regional climate model projections: Application to southeast Australian temperatures , 2016 .

[45]  L. Leung,et al.  More frequent intense and long-lived storms dominate the springtime trend in central US rainfall , 2016, Nature Communications.

[46]  Jasper A. Vrugt,et al.  Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation , 2016, Environ. Model. Softw..

[47]  R. Knutti,et al.  Skill and independence weighting for multi-model assessments , 2016 .

[48]  J. Kiehl,et al.  Simulating the Pineapple Express in the half degree Community Climate System Model, CCSM4 , 2016 .

[49]  Adrian E. Raftery,et al.  Bayesian Model Averaging: A Tutorial , 2016 .

[50]  J. Kiehl,et al.  Atmospheric river landfall‐latitude changes in future climate simulations , 2016 .

[51]  Ruth Lorenz,et al.  A climate model projection weighting scheme accounting for performance and interdependence , 2017 .

[52]  J. Neelin,et al.  Pareto‐Optimal Estimates of California Precipitation Change , 2017 .

[53]  D. Lavers,et al.  Global Analysis of Climate Change Projection Effects on Atmospheric Rivers , 2017 .

[54]  Yanan Fan,et al.  A Bayesian posterior predictive framework for weighting ensemble regional climate models , 2017 .

[55]  Karsten Lehmann,et al.  Selecting a climate model subset to optimise key ensemble properties , 2017 .

[56]  J. Famiglietti,et al.  Projecting groundwater storage changes in California’s Central Valley , 2018, Scientific Reports.

[57]  Jasper A. Vrugt,et al.  Uncertainty Quantification of Complex System Models: Bayesian Analysis , 2018 .

[58]  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 .

[59]  Bettina K. Gier,et al.  Taking climate model evaluation to the next level , 2019, Nature Climate Change.

[60]  D. Waliser,et al.  Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers , 2019, Earth's Future.

[61]  B. Tian,et al.  Climate Model Evaluation in the Presence of Observational Uncertainty: Precipitation Indices over the Contiguous United States , 2019, Journal of Hydrometeorology.

[62]  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.

[63]  N. McDowell,et al.  Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES) , 2019, Geoscientific Model Development.

[64]  M. Turmon,et al.  Cascading Dynamics of the Hydrologic Cycle in California Explored through Observations and Model Simulations , 2020, Geosciences.