On Discriminating between GCM Forcing Configurations Using Bayesian Reconstructions of Late-Holocene Temperatures*

AbstractSeveral climate modeling groups have recently generated ensembles of last-millennium climate simulations under different forcing scenarios. These experiments represent an ideal opportunity to establish the baseline feasibility of using proxy-based reconstructions of late-Holocene climate as out-of-calibration tests of the fidelity of the general circulation models used to project future climate. This paper develops a formal statistical model for assessing the agreement between members of an ensemble of climate simulations and the ensemble of possible climate histories produced from a hierarchical Bayesian climate reconstruction. As the internal variabilities of the simulated and reconstructed climate are decoupled from one another, the comparison is between the two latent, or unobserved, forced responses. Comparisons of the spatial average of a 600-yr high northern latitude temperature reconstruction to suites of last-millennium climate simulations from the GISS-E2 and CSIRO models, respectively, ...

[1]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

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

[3]  Andrei P. Sokolov,et al.  Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations , 2002, Science.

[4]  N. Graham,et al.  Continental-scale temperature variability during the past two millennia , 2013 .

[5]  J. Pongratz,et al.  Climate forcing reconstructions for use in PMIP simulations of the last millennium (v1.0) , 2011 .

[6]  C. Fröhlich,et al.  Total solar irradiance during the Holocene , 2009 .

[7]  Axel Timmermann,et al.  Using paleoclimate proxy-data to select optimal realisations in an ensemble of simulations of the climate of the past millennium , 2006 .

[8]  Rolf Sundberg,et al.  Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium - Part 1: Theory , 2012 .

[9]  A. Timmermann,et al.  Using palaeo-climate comparisons to constrain future projections in CMIP5 , 2013 .

[10]  Peter Huybers,et al.  Recent temperature extremes at high northern latitudes unprecedented in the past 600 years , 2013, Nature.

[11]  B. Christiansen Straight Line Fitting and Predictions: On a Marginal Likelihood Approach to Linear Regression and Errors-In-Variables Models , 2014 .

[12]  M. Claussen,et al.  Radiative forcing from anthropogenic land cover change since A.D. 800 , 2009 .

[13]  Peter John Huybers,et al.  A Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time. Part I: Development and Applications to Paleoclimate Reconstruction Problems , 2010 .

[14]  Stephan R. Sain,et al.  Emulating and calibrating the multiple‐fidelity Lyon–Fedder–Mobarry magnetosphere–ionosphere coupled computer model , 2015 .

[15]  A. Moberg Comparisons of simulated and observed Northern Hemisphere temperature variations during the past millennium – selected lessons learned and problems encountered , 2013 .

[16]  T. Crowley,et al.  Volcanism and the Little Ice Age , 2008 .

[17]  Luis Barboza,et al.  Reconstructing past temperatures from natural proxies and estimated climate forcings using short- and long-memory models , 2014, 1403.3260.

[18]  M. Haran,et al.  Fast dimension-reduced climate model calibration and the effect of data aggregation , 2013, 1303.1382.

[19]  R. Carroll Measurement, Regression, and Calibration , 1994 .

[20]  G. Faluvegi,et al.  Global Signatures and Dynamical Origins of the Little Ice Age and Medieval Climate Anomaly , 2009, Science.

[21]  Douglas W. Nychka,et al.  The ‘hockey stick’ and the 1990s: a statistical perspective on reconstructing hemispheric temperatures , 2007 .

[22]  B. Sansó,et al.  Inferring climate system properties using a computer model , 2008 .

[23]  Murali Haran,et al.  Piecing together the past: statistical insights into paleoclimatic reconstructions , 2010 .

[24]  P. Guttorp,et al.  Uncertainty analysis in climate change assessments , 2013 .

[25]  C. Tebaldi,et al.  Long-term Climate Change: Projections, Commitments and Irreversibility , 2013 .

[26]  P. Stott,et al.  Estimating signal amplitudes in optimal fingerprinting, part I: theory , 2003 .

[27]  Myles R. Allen,et al.  Optimal detection and attribution of climate change: sensitivity of results to climate model differences , 2000 .

[28]  Rolf Sundberg,et al.  Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 2: A pseudo-proxy study addressing the amplitude of solar forcing , 2012 .

[29]  Murali Haran,et al.  Inferring likelihoods and climate system characteristics from climate models and multiple tracers , 2012 .

[30]  Peter John Huybers,et al.  A Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time. Part II: Comparison with the Regularized Expectation–Maximization Algorithm , 2010 .

[31]  James O. Berger,et al.  A Framework for Validation of Computer Models , 2007, Technometrics.

[32]  J. Overpeck,et al.  Recent Warming Reverses Long-Term Arctic Cooling , 2009, Science.

[33]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[34]  G. Hegerl,et al.  Influence of human and natural forcing on European seasonal temperatures , 2011 .

[35]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[36]  A. Moberg,et al.  Past millennial solar forcing magnitude , 2013, Climate Dynamics.

[37]  L. Gleser Measurement, Regression, and Calibration , 1996 .

[38]  G. Hegerl,et al.  Detection of Human Influence on a New, Validated 1500-Year Temperature Reconstruction , 2007 .

[39]  P. Huybers,et al.  Temperature reconstructions from tree‐ring densities overestimate volcanic cooling , 2014 .

[40]  Tom Simkin,et al.  Volcanoes of the World: A Regional Directory, Gazetteer, and Chronology of Volcanism During the Last 10,000 Years , 1994 .

[41]  Gregory J. Hakim,et al.  Assimilation of Time-Averaged Pseudoproxies for Climate Reconstruction , 2014 .

[42]  P. Huybers,et al.  Arctic tree rings as recorders of variations in light availability , 2014, Nature Communications.

[43]  D. Nychka,et al.  The Value of Multiproxy Reconstruction of Past Climate , 2010 .

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

[45]  Axel Timmermann,et al.  Reconstructing surface temperature changes over the past 600 years using climate model simulations with data assimilation , 2010 .

[46]  J. Guiot,et al.  Data-model comparison using fuzzy logic in paleoclimatology , 1999 .

[47]  Rolf Sundberg,et al.  Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 3: Practical considerations, relaxed assumptions, and using tree-ring data to address the amplitude of solar forcing , 2014 .

[48]  Michael R. Chernick,et al.  Wavelet Methods for Time Series Analysis , 2001, Technometrics.

[49]  A. Robock,et al.  Volcanic forcing of climate over the past 1500 years: An improved ice core-based index for climate models , 2006 .

[50]  Michael Schulz,et al.  Information from paleoclimate archives , 2013 .

[51]  Steven J. Phipps,et al.  Paleoclimate Data–Model Comparison and the Role of Climate Forcings over the Past 1500 Years* , 2013 .

[52]  J. Smerdon,et al.  A Pseudoproxy Evaluation of Bayesian Hierarchical Modeling and Canonical Correlation Analysis for Climate Field Reconstructions over Europe , 2013 .

[53]  Jed O. Kaplan,et al.  Holocene carbon emissions as a result of anthropogenic land cover change , 2011 .

[54]  S. Solanki,et al.  Evolution of the solar irradiance during the Holocene , 2011, 1103.4958.