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quantile level changes linearly in time. Unlike Reich’s model-dependent method, Chapman et al. (2013) and Stainforth et al. (2013) derived a local trend parameter of temperature change; thus, the relative changes of temperature between different quantiles and between different geographical locations for the same quantiles could be evaluated. An alternative approach to detect spatial variation patterns of climate extremes is by combining quantile regression and cluster analysis ( Barbosa et al. 2011
quantile level changes linearly in time. Unlike Reich’s model-dependent method, Chapman et al. (2013) and Stainforth et al. (2013) derived a local trend parameter of temperature change; thus, the relative changes of temperature between different quantiles and between different geographical locations for the same quantiles could be evaluated. An alternative approach to detect spatial variation patterns of climate extremes is by combining quantile regression and cluster analysis ( Barbosa et al. 2011
predictor analysis (PPA) are pattern methods specifically tailored for use in linear regression models and, unlike CCA and MCA, are asymmetric in their treatment of the two datasets, identifying one dataset as the predictor and the other as the predictand. RDA selects predictor components that maximize explained variance ( von Storch and Zwiers 1999 ; Wang and Zwiers 2001 ). PPA selects predictor components that maximize the sum of squared correlations ( Thacker 1999 ). Another commonly used pattern
predictor analysis (PPA) are pattern methods specifically tailored for use in linear regression models and, unlike CCA and MCA, are asymmetric in their treatment of the two datasets, identifying one dataset as the predictor and the other as the predictand. RDA selects predictor components that maximize explained variance ( von Storch and Zwiers 1999 ; Wang and Zwiers 2001 ). PPA selects predictor components that maximize the sum of squared correlations ( Thacker 1999 ). Another commonly used pattern
regarding directionality, correlation-based methods, such as lagged linear regression, remain popular and useful tools for identifying lagged relationships between climate variables. A lagged regression model can provide a straightforward assessment of spatial and temporal variability. Lagged regression analysis has been a popular technique in climate science for nearly 100 years (e.g., Walker 1923 , 1924 ). Since 1988, the phrases “lagged regression,” “lag regression,” “lagged correlation,” and “lag
regarding directionality, correlation-based methods, such as lagged linear regression, remain popular and useful tools for identifying lagged relationships between climate variables. A lagged regression model can provide a straightforward assessment of spatial and temporal variability. Lagged regression analysis has been a popular technique in climate science for nearly 100 years (e.g., Walker 1923 , 1924 ). Since 1988, the phrases “lagged regression,” “lag regression,” “lagged correlation,” and “lag
methodology used to train and create the linear regression models as well as the metrics that will be used to evaluate the forecasts. Additionally, a summary of the adjusted framework conceptualization is included. Section 3 covers the different predictors that will be used in the analysis, which are also detailed in Table 1 . An analysis of the much longer preindustrial control run from the Community Earth System Model Large Ensemble (CESM LENS) version 1 configuration is presented in section 4
methodology used to train and create the linear regression models as well as the metrics that will be used to evaluate the forecasts. Additionally, a summary of the adjusted framework conceptualization is included. Section 3 covers the different predictors that will be used in the analysis, which are also detailed in Table 1 . An analysis of the much longer preindustrial control run from the Community Earth System Model Large Ensemble (CESM LENS) version 1 configuration is presented in section 4
canonical regression analysis to reconstruct the July–September streamflow for the Potomac River using tree-ring chronologies from nearby sites. Cook and Jacoby (1977) also examined the drought in the Hudson River Valley by reconstructing the Palmer Drought Severity Index (PDSI) using a stepwise regression analysis. Recently, Maxwell et al. (2011) reconstructed the Potomac River streamflow dating back to 950 using a network of tree-ring chronologies from multiple species. Kauffman and Vonck (2011
canonical regression analysis to reconstruct the July–September streamflow for the Potomac River using tree-ring chronologies from nearby sites. Cook and Jacoby (1977) also examined the drought in the Hudson River Valley by reconstructing the Palmer Drought Severity Index (PDSI) using a stepwise regression analysis. Recently, Maxwell et al. (2011) reconstructed the Potomac River streamflow dating back to 950 using a network of tree-ring chronologies from multiple species. Kauffman and Vonck (2011
detailed measures of vertical stability and synoptic circulation (e.g., vertical stability at every model grid point for every hour) for regression analysis is self-defeating and amounts to overfitting. The goal is to build simple and elegant relationships that are predictive but not complicated ones that appear to fit well but have low predictive skill and difficult interpretation. Hence we seek general proxies and diagnostics that summarize the overall vertical stability and synoptic weather
detailed measures of vertical stability and synoptic circulation (e.g., vertical stability at every model grid point for every hour) for regression analysis is self-defeating and amounts to overfitting. The goal is to build simple and elegant relationships that are predictive but not complicated ones that appear to fit well but have low predictive skill and difficult interpretation. Hence we seek general proxies and diagnostics that summarize the overall vertical stability and synoptic weather
distribution under the null hypothesis of independence are invariant to reversing x and y . It follows that testing the hypothesis in model (1) is equivalent to testing the hypothesis in the model Thus, testing multivariate independence of x and y by multivariate regression or by canonical correlation analysis yields precisely the same decision rule, regardless of which variables are identified as predictors and predictands. These equivalences are somewhat surprising, given that the regression
distribution under the null hypothesis of independence are invariant to reversing x and y . It follows that testing the hypothesis in model (1) is equivalent to testing the hypothesis in the model Thus, testing multivariate independence of x and y by multivariate regression or by canonical correlation analysis yields precisely the same decision rule, regardless of which variables are identified as predictors and predictands. These equivalences are somewhat surprising, given that the regression
described by statistical measures such as the mean, standard deviation, skewness, and kurtosis. Quantiles, which are widely used in hydrologic frequency analysis, represent the relative magnitudes of particular values in the historical records. For example, in this study an extremely low ice cover is represented by a small quantile, while an extremely high ice cover is represented by a large quantile. Past studies about changes in sea ice are more based on a linear regression that describes the average
described by statistical measures such as the mean, standard deviation, skewness, and kurtosis. Quantiles, which are widely used in hydrologic frequency analysis, represent the relative magnitudes of particular values in the historical records. For example, in this study an extremely low ice cover is represented by a small quantile, while an extremely high ice cover is represented by a large quantile. Past studies about changes in sea ice are more based on a linear regression that describes the average
. Therefore, the choice of level or levels to use as predictors in an index is somewhat arbitrary. Since microwave satellite retrievals of column-integrated water vapor are available, it seems reasonable to consider this quantity as a predictor in place of reanalysis products. Given the possible biases in either satellite retrievals or reanalysis products, it should be noted that the regression can implicitly correct systematic errors in its inputs. In this study, we address the first three limitations
. Therefore, the choice of level or levels to use as predictors in an index is somewhat arbitrary. Since microwave satellite retrievals of column-integrated water vapor are available, it seems reasonable to consider this quantity as a predictor in place of reanalysis products. Given the possible biases in either satellite retrievals or reanalysis products, it should be noted that the regression can implicitly correct systematic errors in its inputs. In this study, we address the first three limitations
(MDR) and a reduced set is retained. Variability is defined in terms of daily anomalies against reference averaging time periods of 10 and 15 days; for the purposes of brevity, we report on the 15-day results here. 1 This base time period is chosen to capture the evolution of active and inactive TCG periods of around 2–3 weeks observed throughout the hurricane season (e.g., Gray 1988 ). By applying principal component analysis (PCA), the variables are transformed into an uncorrelated set of
(MDR) and a reduced set is retained. Variability is defined in terms of daily anomalies against reference averaging time periods of 10 and 15 days; for the purposes of brevity, we report on the 15-day results here. 1 This base time period is chosen to capture the evolution of active and inactive TCG periods of around 2–3 weeks observed throughout the hurricane season (e.g., Gray 1988 ). By applying principal component analysis (PCA), the variables are transformed into an uncorrelated set of
