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Regression Analysis. 2nd ed. John Wiley and Sons, 557 pp . Shongwe , M. E. , C. A. T. Ferro , C. A. S. Coelho , and G. J. V. Oldenborgh , 2007 : Predictability of cold spring seasons in Europe . Mon. Wea. Rev. , 135 , 4185 – 4201 . Shukla , J. , 1981 : Dynamical predictability of monthly means . J. Atmos. Sci. , 38 , 2547 – 2572 . Shukla , J. , 1983 : Comments on “Natural variability and predictability.” Mon. Wea. Rev. , 111 , 581 – 585 . Shukla , J. , and D. S. Gutzler
Regression Analysis. 2nd ed. John Wiley and Sons, 557 pp . Shongwe , M. E. , C. A. T. Ferro , C. A. S. Coelho , and G. J. V. Oldenborgh , 2007 : Predictability of cold spring seasons in Europe . Mon. Wea. Rev. , 135 , 4185 – 4201 . Shukla , J. , 1981 : Dynamical predictability of monthly means . J. Atmos. Sci. , 38 , 2547 – 2572 . Shukla , J. , 1983 : Comments on “Natural variability and predictability.” Mon. Wea. Rev. , 111 , 581 – 585 . Shukla , J. , and D. S. Gutzler
, including North Atlantic cyclones ( Mailier et al. 2006 ; Vitolo et al. 2009 ; Pinto et al. 2013 ; Dacre and Pinto 2020 ) and atmospheric rivers in western North America ( Payne and Magnusdottir 2014 ). Statistical analyses by Barton et al. (2016) and Tuel and Martius (2021) both used Ripley’s K function to detect statistically significant TCEP in southern Switzerland and globally respectively. Villarini et al. (2011) implemented Poisson regressions on annual extreme precipitation event
, including North Atlantic cyclones ( Mailier et al. 2006 ; Vitolo et al. 2009 ; Pinto et al. 2013 ; Dacre and Pinto 2020 ) and atmospheric rivers in western North America ( Payne and Magnusdottir 2014 ). Statistical analyses by Barton et al. (2016) and Tuel and Martius (2021) both used Ripley’s K function to detect statistically significant TCEP in southern Switzerland and globally respectively. Villarini et al. (2011) implemented Poisson regressions on annual extreme precipitation event
stepwise multilinear regression model of the monthly air-stagnation variability in various regions within Europe. Robust, statistically significant relationships were found between air stagnation and the model predictors (synoptic-scale weather systems and large-scale flow features), which were able to explain between 40% and 70% of the variability in air stagnation. Therefore, this statistical model provides an alternative approach to ASIs for describing air stagnation in GCMs. As this model only
stepwise multilinear regression model of the monthly air-stagnation variability in various regions within Europe. Robust, statistically significant relationships were found between air stagnation and the model predictors (synoptic-scale weather systems and large-scale flow features), which were able to explain between 40% and 70% of the variability in air stagnation. Therefore, this statistical model provides an alternative approach to ASIs for describing air stagnation in GCMs. As this model only
SSTs. The datasets for this analysis are described in section 3 . The result of applying the above methods to observations and dynamical model hindcasts is described in section 4 . We show that none of the regression models estimated from observations have significant prediction skill. Next, regression models are estimated from dynamical model output without direct use of any observational data. In this case, the regression models derived from most dynamical models have skill with respect to
SSTs. The datasets for this analysis are described in section 3 . The result of applying the above methods to observations and dynamical model hindcasts is described in section 4 . We show that none of the regression models estimated from observations have significant prediction skill. Next, regression models are estimated from dynamical model output without direct use of any observational data. In this case, the regression models derived from most dynamical models have skill with respect to
limited the quality of internal variability estimates in the regression due to small sample sizes. We therefore opt for a compromise; assuming that all CMIP6 models adequately simulate preindustrial internal variability and that the sample across many models suitably represents the internal variability in the simulations of our four ESMs, we improve the quality of internal variability estimates in our analysis. We select the most recent 50 years of the CMIP6 historical period (1965–2014) from all
limited the quality of internal variability estimates in the regression due to small sample sizes. We therefore opt for a compromise; assuming that all CMIP6 models adequately simulate preindustrial internal variability and that the sample across many models suitably represents the internal variability in the simulations of our four ESMs, we improve the quality of internal variability estimates in our analysis. We select the most recent 50 years of the CMIP6 historical period (1965–2014) from all
://doi.org/10.1029/2003JD004414 . 10.1029/2003JD004414 Seidou , O. , and T. B. M. J. Ouarda , 2007 : Recursion-based multiple changepoint detection in multiple linear regression and application to river streamflows . Water Resour. Res. , 43 , W07404 , https://doi.org/10.1029/2006WR005021 . 10.1029/2006WR005021 Serinaldi , F. , and C. G. Kilsby , 2016 : The importance of prewhitening in change point analysis under persistence . Stochastic Environ. Res. Risk Assess. , 30 , 763 – 777
://doi.org/10.1029/2003JD004414 . 10.1029/2003JD004414 Seidou , O. , and T. B. M. J. Ouarda , 2007 : Recursion-based multiple changepoint detection in multiple linear regression and application to river streamflows . Water Resour. Res. , 43 , W07404 , https://doi.org/10.1029/2006WR005021 . 10.1029/2006WR005021 Serinaldi , F. , and C. G. Kilsby , 2016 : The importance of prewhitening in change point analysis under persistence . Stochastic Environ. Res. Risk Assess. , 30 , 763 – 777
MAM shifted eastward to nearly 140°E during JJASON, leading to the opposite anomalies of large-scale conditions west of 140°E ( Figs. 3 and 4 ). The coherence in TC genesis and large-scale environmental conditions indicates that the changes of large-scale environmental conditions are responsible for this trans-season out-of-phase variation of TC genesis frequency over the WNP and the SCS. To confirm the results of the composite analysis, we conduct a regression analysis of the large
MAM shifted eastward to nearly 140°E during JJASON, leading to the opposite anomalies of large-scale conditions west of 140°E ( Figs. 3 and 4 ). The coherence in TC genesis and large-scale environmental conditions indicates that the changes of large-scale environmental conditions are responsible for this trans-season out-of-phase variation of TC genesis frequency over the WNP and the SCS. To confirm the results of the composite analysis, we conduct a regression analysis of the large
we present the Gaussian (linear) regression forecast, demonstrate its reliability with population parameters, and use those parameters to characterize the reliability of the underlying uncorrected forecast ensemble in a procedure that parallels detection and attribution analysis ( Allen and Tett 1999 ). In section 3 we demonstrate that applying regression to seasonal precipitation forecasts improves reliability on dependent data but that sampling errors in the regression parameter estimates
we present the Gaussian (linear) regression forecast, demonstrate its reliability with population parameters, and use those parameters to characterize the reliability of the underlying uncorrected forecast ensemble in a procedure that parallels detection and attribution analysis ( Allen and Tett 1999 ). In section 3 we demonstrate that applying regression to seasonal precipitation forecasts improves reliability on dependent data but that sampling errors in the regression parameter estimates
VOLUME8 JOURNAL OF CLIMATE MAY 1995Trend Estimation and Regression Analysis in Climatological Time Series:An Application of Structural Time Series Models and the Kalman Filter H. VISSERKEMA Environmental Services, the Netherlands J. MOLENAARFaculty of Mathematics and Computing Science, Eindhoven University of Technology, the Netherlands(Manuscript received 10 April 1992, in
VOLUME8 JOURNAL OF CLIMATE MAY 1995Trend Estimation and Regression Analysis in Climatological Time Series:An Application of Structural Time Series Models and the Kalman Filter H. VISSERKEMA Environmental Services, the Netherlands J. MOLENAARFaculty of Mathematics and Computing Science, Eindhoven University of Technology, the Netherlands(Manuscript received 10 April 1992, in
quantiles, which are often computed using a sorting approach. Its main advantage comes to light when considering quantile regression. Analogously to linear least squares regression, we obtain a linear quantile regression by computing the following ( Koenker 2005 ): For the median ( ) this is equivalent to the minimization of the sum of the absolute differences. Linear quantile regression has often been used; for example, for the determination of time-dependent thresholds in peaks-over-threshold analysis
quantiles, which are often computed using a sorting approach. Its main advantage comes to light when considering quantile regression. Analogously to linear least squares regression, we obtain a linear quantile regression by computing the following ( Koenker 2005 ): For the median ( ) this is equivalent to the minimization of the sum of the absolute differences. Linear quantile regression has often been used; for example, for the determination of time-dependent thresholds in peaks-over-threshold analysis
