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. Res. Lett. , 40 , 1391 – 1395 , https://doi.org/10.1002/grl.50301 . Friederichs , P. , and A. Hense , 2007 : Statistical downscaling of extreme precipitation events using censored quantile regression . Mon. Wea. Rev. , 135 , 2365 – 2378 , https://doi.org/10.1175/MWR3403.1 . Gao , M. , and C. L. E. Franzke , 2017 : Quantile regression–based spatiotemporal analysis of extreme temperature change in China . J. Climate , 30 , 9897 – 9914 , https://doi.org/10.1175/JCLI-D-17
. Res. Lett. , 40 , 1391 – 1395 , https://doi.org/10.1002/grl.50301 . Friederichs , P. , and A. Hense , 2007 : Statistical downscaling of extreme precipitation events using censored quantile regression . Mon. Wea. Rev. , 135 , 2365 – 2378 , https://doi.org/10.1175/MWR3403.1 . Gao , M. , and C. L. E. Franzke , 2017 : Quantile regression–based spatiotemporal analysis of extreme temperature change in China . J. Climate , 30 , 9897 – 9914 , https://doi.org/10.1175/JCLI-D-17
. For psl, δ is actually negative for this set of models, although relatively small compared to Δ. The three variables combine to produce the consistency of the average scores with the 66-model regression line ( Fig. 3a ). The scores for tas appear to provide a clear indication of improvements that are in addition to those from resolution. Further analysis suggests that these are partly due to improved agreement of the global mean temperature to that in ERA-Interim. Results are included in Table
. For psl, δ is actually negative for this set of models, although relatively small compared to Δ. The three variables combine to produce the consistency of the average scores with the 66-model regression line ( Fig. 3a ). The scores for tas appear to provide a clear indication of improvements that are in addition to those from resolution. Further analysis suggests that these are partly due to improved agreement of the global mean temperature to that in ERA-Interim. Results are included in Table
spatiotemporal temperature pattern in observations to that produced by models and employ sophisticated statistical analysis to separate the forced from unforced variability. Typically these rely on optimized linear regression, a technique first proposed by Hasselmann (1993) , that was further developed by Hegerl et al. (1997) , Allen and Tett (1999) , Allen and Stott (2003) , and Ribes et al. (2013) to determine the combination of the responses to different forcings, often referred to as their
spatiotemporal temperature pattern in observations to that produced by models and employ sophisticated statistical analysis to separate the forced from unforced variability. Typically these rely on optimized linear regression, a technique first proposed by Hasselmann (1993) , that was further developed by Hegerl et al. (1997) , Allen and Tett (1999) , Allen and Stott (2003) , and Ribes et al. (2013) to determine the combination of the responses to different forcings, often referred to as their
) period (1958–present). The NEM extends from October to December, consistent with the India Meteorological Department’s definition ( http://www.imdchennai.gov.in/northeast_monsoon.htm ); NEM plots are constructed by averaging the OND fields. Analysis of the IPCC-AR5 simulations and projections is based on each model’s ensemble mean; the number of ensemble members is noted in Table 1 . The ENSO–NEM relationship is characterized from the temporally leading (OND) regressions of precipitation on the
) period (1958–present). The NEM extends from October to December, consistent with the India Meteorological Department’s definition ( http://www.imdchennai.gov.in/northeast_monsoon.htm ); NEM plots are constructed by averaging the OND fields. Analysis of the IPCC-AR5 simulations and projections is based on each model’s ensemble mean; the number of ensemble members is noted in Table 1 . The ENSO–NEM relationship is characterized from the temporally leading (OND) regressions of precipitation on the
–2000 showed a tendency to be smaller than expected from our analysis of the historical runs. Assuming that aerosol ERF calculated from the fixed-SST runs is applicable in the historicalAA runs (which is uncertain), changes in GMST suggested values of E ranging from 0.58 to 1.40, with an average of 1.11 for the six models. We also used the regression method of Forster et al. (2013) to estimate time series of aerosol ERF directly from these runs, and obtained aerosol ERF on average 20% smaller in
–2000 showed a tendency to be smaller than expected from our analysis of the historical runs. Assuming that aerosol ERF calculated from the fixed-SST runs is applicable in the historicalAA runs (which is uncertain), changes in GMST suggested values of E ranging from 0.58 to 1.40, with an average of 1.11 for the six models. We also used the regression method of Forster et al. (2013) to estimate time series of aerosol ERF directly from these runs, and obtained aerosol ERF on average 20% smaller in
relation (see section 3 below). For reasons explained in section 3 we think that the second assumption in Ho et al. (2012) , called “change factor,” has little practical relevance. Boberg and Christensen (2012) and Christensen and Boberg (2012) analyze the relation between climate model biases and the anticipated climate change. They find that models having large biases are likely to show large climate change signals. Accordingly, they then propose a regression type of correction to the climate
relation (see section 3 below). For reasons explained in section 3 we think that the second assumption in Ho et al. (2012) , called “change factor,” has little practical relevance. Boberg and Christensen (2012) and Christensen and Boberg (2012) analyze the relation between climate model biases and the anticipated climate change. They find that models having large biases are likely to show large climate change signals. Accordingly, they then propose a regression type of correction to the climate
Met Office Unified Model (HadCM3); see Gordon et al. 2000 ] to around 3.75° × 3.75° [e.g., the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 (CGCM3.1); see Flato et al. 2000 ]. Although quite reasonable for large-scale processes, this spatial resolution is not tractable for hydrological impacts and adaptation applications or modeling (i.e., skillful scale; see e.g., Grotch and MacCracken 1991 ; Huth and Kyselý 2000 ). There is thus a
Met Office Unified Model (HadCM3); see Gordon et al. 2000 ] to around 3.75° × 3.75° [e.g., the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 (CGCM3.1); see Flato et al. 2000 ]. Although quite reasonable for large-scale processes, this spatial resolution is not tractable for hydrological impacts and adaptation applications or modeling (i.e., skillful scale; see e.g., Grotch and MacCracken 1991 ; Huth and Kyselý 2000 ). There is thus a
multiregression technique in order to quantify the statistical significance of the anthropogenic signal in temperature trends as simulated by a range of climate models. In these studies, long GCM control simulations are used to estimate internal variability on the temporal and spatial scales that are retained in the analysis. Although these authors are careful to attempt the inclusion of model uncertainty in the regression model and test the robustness of their results under changes in the amplitude of the
multiregression technique in order to quantify the statistical significance of the anthropogenic signal in temperature trends as simulated by a range of climate models. In these studies, long GCM control simulations are used to estimate internal variability on the temporal and spatial scales that are retained in the analysis. Although these authors are careful to attempt the inclusion of model uncertainty in the regression model and test the robustness of their results under changes in the amplitude of the
shows the results of the optimal fingerprint analysis applied to the 1979–2005 Antarctic SIE trend, using the model “historical” simulations. The reader will notice that some models do not have a result for certain seasons; in these cases, the regression model failed the consistency check described in section 2b at all levels of EOF truncation. For the seasons of maximum sea ice cover, June–August (JJA) and SON ( Figs. 3c and 3d ), the results clearly show that the observed trend is within the
shows the results of the optimal fingerprint analysis applied to the 1979–2005 Antarctic SIE trend, using the model “historical” simulations. The reader will notice that some models do not have a result for certain seasons; in these cases, the regression model failed the consistency check described in section 2b at all levels of EOF truncation. For the seasons of maximum sea ice cover, June–August (JJA) and SON ( Figs. 3c and 3d ), the results clearly show that the observed trend is within the
. 2014 ). We thus hypothesize that while, as in pattern scaling ( Mitchell 2003 ; Tebaldi and Arblaster 2014 ), global warming sets the overall amplitude of the climate change response, different patterns in the atmospheric circulation response can result from the competing effects of these three remote drivers of midlatitude circulation change: tropical and polar amplification of global warming and changes in the stratospheric vortex strength. A novel regression framework incorporating these
. 2014 ). We thus hypothesize that while, as in pattern scaling ( Mitchell 2003 ; Tebaldi and Arblaster 2014 ), global warming sets the overall amplitude of the climate change response, different patterns in the atmospheric circulation response can result from the competing effects of these three remote drivers of midlatitude circulation change: tropical and polar amplification of global warming and changes in the stratospheric vortex strength. A novel regression framework incorporating these
