A Multiresolution Approach to Estimating the Value Added by Regional Climate Models

AbstractClimate models have emerged as an essential tool for studying the earth’s climate. Global models are computationally expensive, and so a relatively coarse spatial resolution must be used within the model. This hinders direct application for many impacts studies that require regional and local climate information. A regional model with boundary conditions taken from the global model achieves a finer spatial scale for local analysis. In this paper the authors propose a new method for assessing the value added by these higher-resolution models, and they demonstrate the method within the context of regional climate models (RCMs) from the North American Regional Climate Change Assessment Program (NARCCAP) project. This spectral approach using the discrete cosine transformation (DCT) is based on characterizing the joint relationship between observations, coarser-scale models, and higher-resolution models to identify how the finer scales add value over the coarser output. The joint relationship is comput...

[1]  L. Mearns,et al.  Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results , 2015 .

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

[3]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[4]  H. Storch,et al.  Regional climate models add value to global model data, A Review and Selected Examples , 2011 .

[5]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[6]  B. Silverman,et al.  Nonparametric regression and generalized linear models , 1994 .

[7]  R. Laprise,et al.  Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations , 2012, Climate Dynamics.

[8]  S. Malyshev,et al.  Climate change in the 21st Century - Interim Characterizations based on the new IPCC Emissions Scenarios , 2000 .

[9]  Kenji Matsuura,et al.  Smart Interpolation of Annually Averaged Air Temperature in the United States , 1995 .

[10]  Martin Widmann,et al.  Evaluation of the skill and added value of a reanalysis‐driven regional simulation for Alpine temperature , 2009 .

[11]  Jeremy S. Pal,et al.  Simulation of regional‐scale water and energy budgets: Representation of subgrid cloud and precipitation processes within RegCM , 2000 .

[12]  D. Mondal,et al.  An h‐likelihood method for spatial mixed linear models based on intrinsic auto‐regressions , 2015 .

[13]  Song-You Hong,et al.  The NCEP Regional Spectral Model: An Update , 1997 .

[14]  Jean Côté,et al.  Spectral Decomposition of Two-Dimensional Atmospheric Fields on Limited-Area Domains Using the Discrete Cosine Transform (DCT) , 2002 .

[15]  T. D. Mitchell,et al.  An improved method of constructing a database of monthly climate observations and associated high‐resolution grids , 2005 .

[16]  René Laprise,et al.  A Semi-Implicit Semi-Lagrangian Regional Climate Model: The Canadian RCM , 1999 .

[17]  F. Feser,et al.  Enhanced Detectability of Added Value in Limited-Area Model Results Separated into Different Spatial Scales , 2006 .

[18]  F. Giorgi,et al.  Development of a Second-Generation Regional Climate Model (RegCM2). Part II: Convective Processes and Assimilation of Lateral Boundary Conditions , 1993 .

[19]  M. Plummer,et al.  CODA: convergence diagnosis and output analysis for MCMC , 2006 .

[20]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[21]  B. Silverman,et al.  Nonparametric Regression and Generalized Linear Models: A roughness penalty approach , 1993 .

[22]  J. Houghton,et al.  Climate Change 2013 - The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , 2014 .

[23]  Linda O. Mearns,et al.  Downscaling Climate Information , 2021, Lectures in Climate Change.

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

[25]  R. Laprise,et al.  Potential for added value in temperature simulated by high-resolution nested RCMs in present climate and in the climate change signal , 2012, Climate Dynamics.

[26]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[27]  A. Gelfand,et al.  Proper multivariate conditional autoregressive models for spatial data analysis. , 2003, Biostatistics.

[28]  K. Mardia Multi-dimensional multivariate Gaussian Markov random fields with application to image processing , 1988 .

[29]  L. Leung,et al.  Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP) , 2013, Climatic Change.

[30]  M. Kanamitsu,et al.  NCEP–DOE AMIP-II Reanalysis (R-2) , 2002 .

[31]  F. Giorgi,et al.  Development of a Second-Generation Regional Climate Model (RegCM2). Part I: Boundary-Layer and Radiative Transfer Processes , 1993 .

[32]  Raquel V. Francisco,et al.  Regional Climate Modeling for the Developing World: The ICTP RegCM3 and RegCNET , 2007 .

[33]  M. Kanamitsu,et al.  The NMC Nested Regional Spectral Model , 1994 .

[34]  H. Paeth,et al.  On the added value of regional climate modeling in climate change assessment , 2013, Climate Dynamics.

[35]  W. Collins,et al.  Evaluation of climate models , 2013 .

[36]  Alexei G. Sankovski,et al.  Special report on emissions scenarios , 2000 .

[37]  M. Kanamitsu,et al.  The Added Value Index: A new metric to quantify the added value of regional models , 2011 .

[38]  J. Besag,et al.  Bayesian image restoration, with two applications in spatial statistics , 1991 .

[39]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .