Incorporating model quality information in climate change detection and attribution studies Export

@article{citeulike:5778152, abstract = {10.1073/pnas.0901736106 In a recent multimodel detection and attribution (D\&A) study using the pooled results from 22 different climate models, the simulated \^{a}€œfingerprint\^{a}€ pattern of anthropogenically caused changes in water vapor was identifiable with high statistical confidence in satellite data. Each model received equal weight in the D\&A analysis, despite large differences in the skill with which they simulate key aspects of observed climate. Here, we examine whether water vapor D\&A results are sensitive to model quality. The \^{a}€œtop 10\^{a}€ and \^{a}€œbottom 10\^{a}€ models are selected with three different sets of skill measures and two different ranking approaches. The entire D\&A analysis is then repeated with each of these different sets of more or less skillful models. Our performance metrics include the ability to simulate the mean state, the annual cycle, and the variability associated with El Ni\~{A}±o. We find that estimates of an anthropogenic water vapor fingerprint are insensitive to current model uncertainties, and are governed by basic physical processes that are well-represented in climate models. Because the fingerprint is both robust to current model uncertainties and dissimilar to the dominant noise patterns, our ability to identify an anthropogenic influence on observed multidecadal changes in water vapor is not affected by \^{a}€œscreening\^{a}€ based on model quality.}, author = {Santer, B. D. and Taylor, K. E. and Gleckler, P. J. and Bonfils, C. and Barnett, T. P. and Pierce, D. W. and Wigley, T. M. L. and Mears, C. and Wentz, F. J. and Br\~{A}¼ggemann, W. and Gillett, N. P. and Klein, S. A. and Solomon, S. and Stott, P. A. and Wehner, M. F.}, citeulike-article-id = {5778152}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0901736106}, citeulike-linkout-1 = {http://www.pnas.org/content/106/35/14778.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/106/35/14778.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19706477}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19706477}, day = {1}, doi = {10.1073/pnas.0901736106}, journal = {Proceedings of the National Academy of Sciences}, keywords = {attribution, climate, detection, multimodel, skill, water-vapor}, month = {September}, number = {35}, pages = {14778--14783}, posted-at = {2009-09-12 13:43:55}, priority = {2}, title = {Incorporating model quality information in climate change detection and attribution studies}, url = {http://dx.doi.org/10.1073/pnas.0901736106}, volume = {106}, year = {2009} }

TY - JOUR ID - citeulike:5778152 L3 - citeulike-article-id:5778152 N2 - 10.1073/pnas.0901736106 In a recent multimodel detection and attribution (D&A) study using the pooled results from 22 different climate models, the simulated “fingerprint” pattern of anthropogenically caused changes in water vapor was identifiable with high statistical confidence in satellite data. Each model received equal weight in the D&A analysis, despite large differences in the skill with which they simulate key aspects of observed climate. Here, we examine whether water vapor D&A results are sensitive to model quality. The “top 10” and “bottom 10” models are selected with three different sets of skill measures and two different ranking approaches. The entire D&A analysis is then repeated with each of these different sets of more or less skillful models. Our performance metrics include the ability to simulate the mean state, the annual cycle, and the variability associated with El Niño. We find that estimates of an anthropogenic water vapor fingerprint are insensitive to current model uncertainties, and are governed by basic physical processes that are well-represented in climate models. Because the fingerprint is both robust to current model uncertainties and dissimilar to the dominant noise patterns, our ability to identify an anthropogenic influence on observed multidecadal changes in water vapor is not affected by “screening” based on model quality. IS - 35 JF - Proceedings of the National Academy of Sciences EP - 14783 TI - Incorporating model quality information in climate change detection and attribution studies VL - 106 SP - 14778 KW - attribution KW - climate KW - detection KW - multimodel KW - skill KW - water-vapor AU - Santer, BD AU - Taylor, KE AU - Gleckler, PJ AU - Bonfils, C AU - Barnett, TP AU - Pierce, DW AU - Wigley, TML AU - Mears, C AU - Wentz, FJ AU - Brüggemann, W AU - Gillett, NP AU - Klein, SA AU - Solomon, S AU - Stott, PA AU - Wehner, MF PY - 2009/09/01/ UR - http://dx.doi.org/10.1073/pnas.0901736106 ER -