Multi-model ensemble analysis of runoff extremes for climate change impact assessments

Summary In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades.

[1]  S. Klein,et al.  The new GFDL global atmosphere and land model AM2-LM2: Evaluation with prescribed SST simulations , 2004 .

[2]  J. Christensen,et al.  A summary of the PRUDENCE model projections of changes in European climate by the end of this century , 2007 .

[3]  P. Pech,et al.  Evaluation of Simple Statistical Downscaling Methods for Monthly Regional Climate Model Simulations with Respect to the Estimated Changes in Runoff in the Czech Republic , 2013, Water Resources Management.

[4]  H. Moradkhani,et al.  Ensemble Streamflow Prediction: Climate signal weighting methods vs. Climate Forecast System Reanalysis , 2012 .

[5]  P. Linden,et al.  ENSEMBLES: Climate Change and its Impacts - Summary of research and results from the ENSEMBLES project , 2009 .

[6]  S. Coles,et al.  An Introduction to Statistical Modeling of Extreme Values , 2001 .

[7]  Noel A Cressie,et al.  Combining outputs from the North American Regional Climate Change Assessment Program by using a Bayesian hierarchical model , 2012 .

[8]  D. Lettenmaier,et al.  A simple hydrologically based model of land surface water and energy fluxes for general circulation models , 1994 .

[9]  In-Sik Kang,et al.  Optimal Multi-model Ensemble Method in Seasonal Climate Prediction , 2008 .

[10]  F. Giorgi,et al.  Weight assignment in regional climate models , 2010 .

[11]  D. Lettenmaier,et al.  Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .

[12]  Soroosh Sorooshian,et al.  Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method , 2012 .

[13]  D. Lettenmaier,et al.  A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States* , 2002 .

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

[15]  J. M. Sloughter,et al.  Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging , 2007 .

[16]  Francis W. Zwiers,et al.  Attribution of Arctic temperature change to greenhouse-gas and aerosol influences , 2015 .

[17]  Richard L. Smith,et al.  Bayesian Modeling of Uncertainty in Ensembles of Climate Models , 2009 .

[18]  John F. B. Mitchell,et al.  The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments , 2000 .

[19]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

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

[21]  F. Zwiers,et al.  Climate Predictions with Multimodel Ensembles , 2002 .

[22]  Hamid Moradkhani,et al.  Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging , 2014 .

[23]  D. Caya,et al.  Future changes in intense precipitation over Canada assessed from multi‐model NARCCAP ensemble simulations , 2012 .

[24]  D. Nychka,et al.  Bayesian Spatial Modeling of Extreme Precipitation Return Levels , 2007 .

[25]  Alan E. Gelfand,et al.  Hierarchical modeling for extreme values observed over space and time , 2009, Environmental and Ecological Statistics.

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

[27]  R. Schumer,et al.  Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models , 2013, Climatic Change.

[28]  Bodo Ahrens,et al.  On the Weighting of Multimodel Ensembles in Seasonal and Short-Range Weather Forecasting , 2009 .

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

[30]  Reto Knutti,et al.  Risks of Model Weighting in Multimodel Climate Projections , 2010 .

[31]  Mohammad Najafi,et al.  Air demand in gated tunnels - a Bayesian approach to merge various predictions , 2012 .

[32]  Bin Wang,et al.  Reducing biases in regional climate downscaling by applying Bayesian model averaging on large-scale forcing , 2012, Climate Dynamics.

[33]  Richard G. Jones,et al.  A Regional Climate Change Assessment Program for North America , 2009 .

[34]  H. Moradkhani,et al.  Analysis of precipitation extremes with the assessment of regional climate models over the Willamette River Basin, USA , 2013 .

[35]  Alan F. Hamlet,et al.  PACIFIC NORTHWEST REGIONAL ASSESSMENT: THE IMPACTS OF CLIMATE VARIABILITY AND CLIMATE CHANGE ON THE WATER RESOURCES OF THE COLUMBIA RIVER BASIN 1 , 2000 .

[36]  R. Toumi,et al.  Climate projections: Past performance no guarantee of future skill? , 2009 .

[37]  H. Fowler,et al.  Estimating change in extreme European precipitation using a multimodel ensemble , 2007 .

[38]  Mohammad Najafi,et al.  Statistical Downscaling of Precipitation Using Machine Learning with Optimal Predictor Selection , 2011 .

[39]  Richard L. Smith,et al.  Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles , 2005 .

[40]  John F. B. Mitchell,et al.  THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research , 2007 .

[41]  H. Moradkhani,et al.  Assessing the uncertainties of hydrologic model selection in climate change impact studies , 2011 .

[42]  J. M. Sloughter,et al.  Probabilistic Wind Speed Forecasting Using Ensembles and Bayesian Model Averaging , 2010 .

[43]  W. Collins,et al.  The Community Climate System Model Version 3 (CCSM3) , 2006 .

[44]  H. Moradkhani,et al.  A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes , 2014 .

[45]  Stephan R. Sain,et al.  Spatial Hierarchical Modeling of Precipitation Extremes From a Regional Climate Model , 2008 .

[46]  R. Sakia.  The Box-Cox transformation technique: a review , 1992 .

[47]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[48]  Mohammad Najafi,et al.  Analysis of runoff extremes using spatial hierarchical Bayesian modeling , 2013 .

[49]  H. Fowler,et al.  Multi‐model ensemble estimates of climate change impacts on UK seasonal precipitation extremes , 2009 .

[50]  Richard A. L. Jones,et al.  The North American Regional Climate Change Assessment Program: Overview of Phase I Results , 2012 .

[51]  Stephan R. Sain,et al.  A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling , 2010 .

[52]  Hamid Moradkhani,et al.  Toward a reliable prediction of seasonal forecast uncertainty: Addressing model and initial condition uncertainty with ensemble data assimilation and Sequential Bayesian Combination , 2014 .

[53]  Hamid Moradkhani,et al.  Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation , 2012 .

[54]  Sw. Banerjee,et al.  Hierarchical Modeling and Analysis for Spatial Data , 2003 .