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[vectors; see a scale at the top right of (a)] based on ERA-Interim from 1998 to 2012. While moisture mode theory has been mainly employed for the winter MJO, a recent diagnostic study on MJO moisture budget by Adames et al. (2016) suggests that distinct MJO propagation between winter and summer is associated with seasonal variations in the mean moisture pattern ( Fig. 1 ). In this study, by conducting a detailed moisture entropy budget analysis for the northward propagation of the summer MJO over
[vectors; see a scale at the top right of (a)] based on ERA-Interim from 1998 to 2012. While moisture mode theory has been mainly employed for the winter MJO, a recent diagnostic study on MJO moisture budget by Adames et al. (2016) suggests that distinct MJO propagation between winter and summer is associated with seasonal variations in the mean moisture pattern ( Fig. 1 ). In this study, by conducting a detailed moisture entropy budget analysis for the northward propagation of the summer MJO over
slope of that relationship differs between models. We chose to focus here on the share of explained variance rather than the sensitivity, arguing that it better represents the strength of the relationship between SM and ET. In any case, Fig. 10c also shows that the value of the regression slope and the correlation actually remain strongly correlated over much of the land surface, including over regions of larger spread in SM–ET correlation where our analysis has been focused. Fig . 10. (a
slope of that relationship differs between models. We chose to focus here on the share of explained variance rather than the sensitivity, arguing that it better represents the strength of the relationship between SM and ET. In any case, Fig. 10c also shows that the value of the regression slope and the correlation actually remain strongly correlated over much of the land surface, including over regions of larger spread in SM–ET correlation where our analysis has been focused. Fig . 10. (a
2) for the North Atlantic, the across-model covariability of the spatial regressions of T DIFF and/or σ BI and the surface storm tracks is also strong. Thus, in the North Atlantic we find indicators suggesting that the biases in the SST create dominant biases in the surface storm track. An analysis of the multimodel mean using only the CMIP model without the large bias in the surface storm track also shows forcing from the SST biases impact the surface storm track in the North Atlantic
2) for the North Atlantic, the across-model covariability of the spatial regressions of T DIFF and/or σ BI and the surface storm tracks is also strong. Thus, in the North Atlantic we find indicators suggesting that the biases in the SST create dominant biases in the surface storm track. An analysis of the multimodel mean using only the CMIP model without the large bias in the surface storm track also shows forcing from the SST biases impact the surface storm track in the North Atlantic
Force (MDTF) ( Maloney et al. 2019 ). Similarly to what was done in the analysis of the TCs in the HWG project ( Shaevitz et al. 2014 ; Daloz et al. 2015 ; Nakamura et al. 2017 ; Ramsay et al. 2018 ), we are considering the tracking provided by each modeling group as part of the model package. This is an ensemble of opportunity; that is, we use the model simulations and TC tracks that are available to us, as they are. These model simulations were not produced for this purpose. Therefore, there
Force (MDTF) ( Maloney et al. 2019 ). Similarly to what was done in the analysis of the TCs in the HWG project ( Shaevitz et al. 2014 ; Daloz et al. 2015 ; Nakamura et al. 2017 ; Ramsay et al. 2018 ), we are considering the tracking provided by each modeling group as part of the model package. This is an ensemble of opportunity; that is, we use the model simulations and TC tracks that are available to us, as they are. These model simulations were not produced for this purpose. Therefore, there
similar manner ( Fig. 4d ). Defining the inverse of the slope of the regression line in Fig. 4c as the precipitable water limit pw lim and the intercept as E 0 /pw lim , (5) NMFC = P − E 0 p w lim . Before moving the analysis further, a brief discussion of NMFC in the context of previous work is warranted. The normalization introduced in Eq. (3) is also relevant to the concept of gross moist stability (GMS) put forward by Neelin and Held (1987) and further developed by Raymond et al. (2009
similar manner ( Fig. 4d ). Defining the inverse of the slope of the regression line in Fig. 4c as the precipitable water limit pw lim and the intercept as E 0 /pw lim , (5) NMFC = P − E 0 p w lim . Before moving the analysis further, a brief discussion of NMFC in the context of previous work is warranted. The normalization introduced in Eq. (3) is also relevant to the concept of gross moist stability (GMS) put forward by Neelin and Held (1987) and further developed by Raymond et al. (2009
section 2 and describe our diagnostics and analysis methodology in section 3 . The application of these diagnostics to the six models will be described in section 4 , with a discussion of their implications in section 5 . We provide a summary of the results and conclusions in section 6 . 2. Model simulations a. Models We explore TC intensification processes in six high-resolution climate model long-term (>20 year) historical simulations ( Table 1 ). Several of these simulations were also examined
section 2 and describe our diagnostics and analysis methodology in section 3 . The application of these diagnostics to the six models will be described in section 4 , with a discussion of their implications in section 5 . We provide a summary of the results and conclusions in section 6 . 2. Model simulations a. Models We explore TC intensification processes in six high-resolution climate model long-term (>20 year) historical simulations ( Table 1 ). Several of these simulations were also examined
represent ENSO. An NAO index is calculated as the difference in the standardized SLP between Reykjavik, Iceland, and Lisbon, Portugal ( Jones et al. 1997 ). A cutoff of ±0.5 standard deviation is used to select the ENSO and NAO years in composite analysis. b. Performance metrics The anomaly correlation coefficient (ACC) is calculated to evaluate the model prediction skill. The ACC is calculated by The metric evaluates the anomalies of the forecast and observations, which are defined with respect to the
represent ENSO. An NAO index is calculated as the difference in the standardized SLP between Reykjavik, Iceland, and Lisbon, Portugal ( Jones et al. 1997 ). A cutoff of ±0.5 standard deviation is used to select the ENSO and NAO years in composite analysis. b. Performance metrics The anomaly correlation coefficient (ACC) is calculated to evaluate the model prediction skill. The ACC is calculated by The metric evaluates the anomalies of the forecast and observations, which are defined with respect to the
statistics in the select period are typical of each model. GCM outputs are saved with a 6-h time interval at the models’ native grids and later interpolated to pressure levels for our analysis. b. TC detection algorithm TC-like vortices are detected and tracked from the model fields using the tracking algorithm described in detail in Murakami et al. (2015) . The tracking scheme mainly uses local sea level pressure minimum and warm-core conditions to detect TCs and impose a 3-day duration threshold on
statistics in the select period are typical of each model. GCM outputs are saved with a 6-h time interval at the models’ native grids and later interpolated to pressure levels for our analysis. b. TC detection algorithm TC-like vortices are detected and tracked from the model fields using the tracking algorithm described in detail in Murakami et al. (2015) . The tracking scheme mainly uses local sea level pressure minimum and warm-core conditions to detect TCs and impose a 3-day duration threshold on
per pixel basis. Note that the ensembles of models available do not necessarily overlap similarly for each pair of variables; in the interest of maximizing the number of models used in these correlations (given the overall low number of available models), for each variable that we cross with ET partitioning, we use the maximum number of common models available. Thus, rather than having a common set of models for the whole analysis, the number of models considered for different combinations of
per pixel basis. Note that the ensembles of models available do not necessarily overlap similarly for each pair of variables; in the interest of maximizing the number of models used in these correlations (given the overall low number of available models), for each variable that we cross with ET partitioning, we use the maximum number of common models available. Thus, rather than having a common set of models for the whole analysis, the number of models considered for different combinations of
