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GHG-attributable warming ( Hegerl et al. 2007 ; Stott et al. 2003 ) by fitting observed temperature changes to simulated GHG, other anthropogenic (OTH), and NAT (consisting of volcanic and solar forcing) responses by regression and then multiplying the simulated GHG-induced warming trend by the GHG regression coefficient. Here, we require an estimate of the CO 2 -attributable warming. We do not attempt to constrain CO 2 -attributable warming directly in a detection and attribution analysis
GHG-attributable warming ( Hegerl et al. 2007 ; Stott et al. 2003 ) by fitting observed temperature changes to simulated GHG, other anthropogenic (OTH), and NAT (consisting of volcanic and solar forcing) responses by regression and then multiplying the simulated GHG-induced warming trend by the GHG regression coefficient. Here, we require an estimate of the CO 2 -attributable warming. We do not attempt to constrain CO 2 -attributable warming directly in a detection and attribution analysis
. The calculation of the yearly average of vegetation carbon and NPP of model outputs and sums of vegetation carbon and NPP for each biome and globe, as well as the regridding of all data were performed with the NCAR Command Language, version 6.1.2 ( UCAR/NCAR/CISL/VETS 2013 ). g. Statistical analysis The goodness of fit of vegetation carbon of ESMs simulated at the grid and biome scale was quantified by the coefficient of determination of linear regressions ( R 2 ) and root-mean-square error (RMSE
. The calculation of the yearly average of vegetation carbon and NPP of model outputs and sums of vegetation carbon and NPP for each biome and globe, as well as the regridding of all data were performed with the NCAR Command Language, version 6.1.2 ( UCAR/NCAR/CISL/VETS 2013 ). g. Statistical analysis The goodness of fit of vegetation carbon of ESMs simulated at the grid and biome scale was quantified by the coefficient of determination of linear regressions ( R 2 ) and root-mean-square error (RMSE
). There also exists the possibility of “GCM–scenario interaction” if the differences in simulated climate between GCMs vary between scenarios. The aim of this study is to quantify the relative importance with time of GCM and scenario variability and to estimate the future time beyond which scenario variability dominates GCM variability. The importance of the GCM–scenario interaction term will be quantified as a tool to understanding the response of GCMs to different scenarios. Analysis of variance
). There also exists the possibility of “GCM–scenario interaction” if the differences in simulated climate between GCMs vary between scenarios. The aim of this study is to quantify the relative importance with time of GCM and scenario variability and to estimate the future time beyond which scenario variability dominates GCM variability. The importance of the GCM–scenario interaction term will be quantified as a tool to understanding the response of GCMs to different scenarios. Analysis of variance
( ) with an estimated rate of change of ALT max with temperature sampled from the CMIP5 ensemble. For each CMIP5 model and each RCP scenario the rate of change of ALT max per degree (ALT sensitivity ) was calculated using a linear regression fit between ALT max and the local near surface annual mean air temperature on a grid point by grid point basis for all grid points where there is permafrost in the top 3 m. If ALT max becomes greater than 3 m all subsequent times are excluded from the analysis
( ) with an estimated rate of change of ALT max with temperature sampled from the CMIP5 ensemble. For each CMIP5 model and each RCP scenario the rate of change of ALT max per degree (ALT sensitivity ) was calculated using a linear regression fit between ALT max and the local near surface annual mean air temperature on a grid point by grid point basis for all grid points where there is permafrost in the top 3 m. If ALT max becomes greater than 3 m all subsequent times are excluded from the analysis
( Schneider von Deimling et al. 2012 ; Harden et al. 2012 ; Burke et al. 2012 ). Table 1. List of models used in this analysis, the modeling groups that developed them, model attributes, and references. The model attributes listed here are relevant to soil physics at high latitudes, including whether the model includes a multilayer snow model, whether the snow acts to insulate between the soil and atmosphere, the inclusion of soil water latent heat and differing frozen- and unfrozen-soil thermal
( Schneider von Deimling et al. 2012 ; Harden et al. 2012 ; Burke et al. 2012 ). Table 1. List of models used in this analysis, the modeling groups that developed them, model attributes, and references. The model attributes listed here are relevant to soil physics at high latitudes, including whether the model includes a multilayer snow model, whether the snow acts to insulate between the soil and atmosphere, the inclusion of soil water latent heat and differing frozen- and unfrozen-soil thermal
protocol represents real-world historical emissions. A brief evaluation of the global atmosphere–land and atmosphere–ocean CO 2 fluxes is presented here, and we refer to Anav et al. (2013) for a more in-depth analysis of the carbon cycle in ESMs. The best estimates of the ocean sinks come from both cumulative carbon inventories over the historical period ( Sabine et al. 2004 , and updates) and from combined oceanic ( p CO 2 measurements and oceanic inversions) and atmospheric estimates (atmospheric
protocol represents real-world historical emissions. A brief evaluation of the global atmosphere–land and atmosphere–ocean CO 2 fluxes is presented here, and we refer to Anav et al. (2013) for a more in-depth analysis of the carbon cycle in ESMs. The best estimates of the ocean sinks come from both cumulative carbon inventories over the historical period ( Sabine et al. 2004 , and updates) and from combined oceanic ( p CO 2 measurements and oceanic inversions) and atmospheric estimates (atmospheric
fractions are directly linked to the World Meteorological Organization. Since the strong westerly winds result in the atmospheric p CO 2 being well mixed across the Drake Passage, in the following analysis we will use the discrete flask measurement as representative of atmospheric p CO 2 across the whole passage. Comparisons between the discrete and underway measurements yielded differences of less than 0.15 μ atm. Given temperature corrections and other artifacts associated with converting the dry
fractions are directly linked to the World Meteorological Organization. Since the strong westerly winds result in the atmospheric p CO 2 being well mixed across the Drake Passage, in the following analysis we will use the discrete flask measurement as representative of atmospheric p CO 2 across the whole passage. Comparisons between the discrete and underway measurements yielded differences of less than 0.15 μ atm. Given temperature corrections and other artifacts associated with converting the dry
