Search Results
blue), or poleward (PW) type (light green) based on the ITCZ latitude. Black dashed line in (b) indicates regression between the simulated PLP index and ITCZ latitudes. Also shown are correlation coefficients (shading) between the PLP index and (c) SST or (d) precipitation over the tropical Pacific Ocean based on the CMIP6 model ensemble. The observed climatological SST and precipitation are shown as contours in (c) and (d), respectively. Stippling represents the regions in which the correlation is
blue), or poleward (PW) type (light green) based on the ITCZ latitude. Black dashed line in (b) indicates regression between the simulated PLP index and ITCZ latitudes. Also shown are correlation coefficients (shading) between the PLP index and (c) SST or (d) precipitation over the tropical Pacific Ocean based on the CMIP6 model ensemble. The observed climatological SST and precipitation are shown as contours in (c) and (d), respectively. Stippling represents the regions in which the correlation is
elaborate on these issues, here we take a two-step approach. First, we use maximum covariance analysis (MCA) to objectively search for the leading modes of observed covariability between monthly NH atmospheric circulation and tropical SSTs. These modes represent the most prevalent forms of atmospheric circulation response in the extratropics to tropical SST forcing throughout the year. Second, we apply the MCA method on the same variables derived from Coupled Model Intercomparison Project phase 6 (CMIP6
elaborate on these issues, here we take a two-step approach. First, we use maximum covariance analysis (MCA) to objectively search for the leading modes of observed covariability between monthly NH atmospheric circulation and tropical SSTs. These modes represent the most prevalent forms of atmospheric circulation response in the extratropics to tropical SST forcing throughout the year. Second, we apply the MCA method on the same variables derived from Coupled Model Intercomparison Project phase 6 (CMIP6
30–60 days ( Lee et al. 2013 ). The active ISO phases adopted in this study are defined as (PC1 2 + PC2 2 ) 1/2 > 1. Linear correlation, regression, and composite analysis are the major methods adopted here, and the statistical significance is tested by the two-tailed Student’s t test. 3. Delayed ENSO impacts on the SEHD frequency in the AMR based on the observations a. Delayed ENSO impacts on the SEHD frequency The delayed impacts of ENSO on the SEHD frequency in the AMR are almost
30–60 days ( Lee et al. 2013 ). The active ISO phases adopted in this study are defined as (PC1 2 + PC2 2 ) 1/2 > 1. Linear correlation, regression, and composite analysis are the major methods adopted here, and the statistical significance is tested by the two-tailed Student’s t test. 3. Delayed ENSO impacts on the SEHD frequency in the AMR based on the observations a. Delayed ENSO impacts on the SEHD frequency The delayed impacts of ENSO on the SEHD frequency in the AMR are almost
al. 2020 ), it has not yet been shown whether this is true for reanalyses as well. Other factors, such as how synoptic-scale environments impact storm-level processes ( McBride and Zehr 1981 ; Elsberry et al. 1988 ; Jones 1995 ; DeMaria 1996 ; Hill and Lackmann 2009 ), may also influence reanalysis representation of TCs ( Slocum et al. 2022 ) but are not examined here. The remainder of this paper is structured as follows: section 2 describes the datasets used and analysis methods. To provide
al. 2020 ), it has not yet been shown whether this is true for reanalyses as well. Other factors, such as how synoptic-scale environments impact storm-level processes ( McBride and Zehr 1981 ; Elsberry et al. 1988 ; Jones 1995 ; DeMaria 1996 ; Hill and Lackmann 2009 ), may also influence reanalysis representation of TCs ( Slocum et al. 2022 ) but are not examined here. The remainder of this paper is structured as follows: section 2 describes the datasets used and analysis methods. To provide
of intermodel spread in tropical rainfall change, the atmospheric circulation change is mainly governed by the SST warming pattern over tropical oceans. We conduct the intermodel SVD analysis between the SST and atmospheric circulation changes in the CMIP6 1%CO2 run. In the zonal mean, the first two SVD modes show an interhemispheric dipole asymmetry and an equatorial peak, respectively ( Fig. 3 ), consistent with the CMIP5 results. The regression of the zonal-mean rainfall change against PC1
of intermodel spread in tropical rainfall change, the atmospheric circulation change is mainly governed by the SST warming pattern over tropical oceans. We conduct the intermodel SVD analysis between the SST and atmospheric circulation changes in the CMIP6 1%CO2 run. In the zonal mean, the first two SVD modes show an interhemispheric dipole asymmetry and an equatorial peak, respectively ( Fig. 3 ), consistent with the CMIP5 results. The regression of the zonal-mean rainfall change against PC1
surface, respectively, showing that both interannual and decadal time-scale variability was reduced in the temperature-coordinate analysis and the long-term warming trend was more robustly identifiable. Watermass-based analyses also have other advantages. For instance, budget analyses (such as heat or salt budgets) performed in watermass coordinates can be simpler than Eulerian budgets because only smoother diabatic terms are present. Attribution of model biases to the representation of specific
surface, respectively, showing that both interannual and decadal time-scale variability was reduced in the temperature-coordinate analysis and the long-term warming trend was more robustly identifiable. Watermass-based analyses also have other advantages. For instance, budget analyses (such as heat or salt budgets) performed in watermass coordinates can be simpler than Eulerian budgets because only smoother diabatic terms are present. Attribution of model biases to the representation of specific
analysis building on advances in understanding the thermodynamic environments of precipitation (e.g., Bretherton et al. 2004 ; Neelin et al. 2009 ; Kuo et al. 2018 ; Chen et al. 2020 ) and their role in modes of variability (e.g., Wolding et al. 2020 ), and in tracking weather features such as atmospheric rivers (e.g., Shields et al. 2018 ). While examples of the above metrics have been reported in recent literature (e.g., Klingaman et al. 2017 ; Ahmed et al. 2020 ; Feng et al. 2021a ), they
analysis building on advances in understanding the thermodynamic environments of precipitation (e.g., Bretherton et al. 2004 ; Neelin et al. 2009 ; Kuo et al. 2018 ; Chen et al. 2020 ) and their role in modes of variability (e.g., Wolding et al. 2020 ), and in tracking weather features such as atmospheric rivers (e.g., Shields et al. 2018 ). While examples of the above metrics have been reported in recent literature (e.g., Klingaman et al. 2017 ; Ahmed et al. 2020 ; Feng et al. 2021a ), they
partitioning analysis in R by using the “hier.part” package. Linear regression and the R 2 goodness-of-fit measure were used. Formulations were logarithmically transformed if the formulation was not expressed as the sum of variables. 3. Results a. Converging but still underestimated near-present land C storage We first compared intermodel spread in the near-present terrestrial C storage between CMIP5 and CMIP6. The across-model variation in the global terrestrial C storage decreased from CMIP5 to
partitioning analysis in R by using the “hier.part” package. Linear regression and the R 2 goodness-of-fit measure were used. Formulations were logarithmically transformed if the formulation was not expressed as the sum of variables. 3. Results a. Converging but still underestimated near-present land C storage We first compared intermodel spread in the near-present terrestrial C storage between CMIP5 and CMIP6. The across-model variation in the global terrestrial C storage decreased from CMIP5 to
