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Abstract
The Indian Ocean dipole (IOD) is a prominent interannual phenomenon in the tropical Indian Ocean (TIO), influencing weather and climate globally, particularly during extreme IOD events. The IOD shows notable amplitude asymmetry in both observations and historical simulations from the phase 6 of Coupled Model Intercomparison Project (CMIP6), with positive events having a greater magnitude than negative events, mainly due to the negative nonlinear dynamical heating. However, simulations under the shared socioeconomic pathway 5-8.5 (SSP5-8.5) scenario indicate a notable reduction in IOD asymmetry. It shows that this reduction points to an increased frequency of extreme negative IOD events under global warming. The primary cause of this reduced IOD asymmetry is less negative nonlinear dynamical heating in future simulations, especially the nonlinear zonal advection. Under global warming, the increased atmospheric static stability weakens the large-scale atmospheric response to sea surface temperature (SST) anomalies forcing. This leads to reduced strength of nonlinear zonal advection, resulting in a decreased IOD asymmetry. Nevertheless, nonlinear vertical advection, another key factor in IOD asymmetry, remains comparable due to the increased upper-ocean stratification in the eastern TIO. The reduced inhibition of negative nonlinear zonal advection and the increased SST response to deepening thermocline contribute to the increased frequency of extreme negative IOD events. These changes underscore the potential risks associated with negative IOD events in a warming world, emphasizing the importance of understanding IOD dynamics for improved climate impact prediction and future preparedness.
Abstract
The Indian Ocean dipole (IOD) is a prominent interannual phenomenon in the tropical Indian Ocean (TIO), influencing weather and climate globally, particularly during extreme IOD events. The IOD shows notable amplitude asymmetry in both observations and historical simulations from the phase 6 of Coupled Model Intercomparison Project (CMIP6), with positive events having a greater magnitude than negative events, mainly due to the negative nonlinear dynamical heating. However, simulations under the shared socioeconomic pathway 5-8.5 (SSP5-8.5) scenario indicate a notable reduction in IOD asymmetry. It shows that this reduction points to an increased frequency of extreme negative IOD events under global warming. The primary cause of this reduced IOD asymmetry is less negative nonlinear dynamical heating in future simulations, especially the nonlinear zonal advection. Under global warming, the increased atmospheric static stability weakens the large-scale atmospheric response to sea surface temperature (SST) anomalies forcing. This leads to reduced strength of nonlinear zonal advection, resulting in a decreased IOD asymmetry. Nevertheless, nonlinear vertical advection, another key factor in IOD asymmetry, remains comparable due to the increased upper-ocean stratification in the eastern TIO. The reduced inhibition of negative nonlinear zonal advection and the increased SST response to deepening thermocline contribute to the increased frequency of extreme negative IOD events. These changes underscore the potential risks associated with negative IOD events in a warming world, emphasizing the importance of understanding IOD dynamics for improved climate impact prediction and future preparedness.
Abstract
Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air-sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several CMIP6 models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m/s) compared to observations (about 2.5 m/s). This slow propagation is also identified in wavenumber-frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers implying the biased incorporation of higher baroclinic modes in model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models.
Abstract
Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air-sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several CMIP6 models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m/s) compared to observations (about 2.5 m/s). This slow propagation is also identified in wavenumber-frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers implying the biased incorporation of higher baroclinic modes in model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models.
Abstract
Westerly wind events (WWEs) are anomalously strong, long-lasting westerlies over the Indian or Pacific Oceans that are capable of forcing oceanic wave modes, which in turn can impact the evolution of coupled ocean-atmosphere phenomena such as the El Niño Southern Oscillation (ENSO). This work examines the fidelity of equatorial WWEs over the Pacific Ocean in 30 CMIP6 historical simulations against observations. WWEs are identified using equatorially-averaged zonal wind stress anomaly duration, zonal extent, and intensity criteria. Most simulations correctly place the majority of WWEs over the west Pacific, although they are skewed westward and generally occur less frequently compared to observations. Simulated WWEs tend to be weaker than observations for a given duration and zonal extent with several models having shorter durations and zonal extents than observations. Biases in simulated WWEs are associated with biases in Madden-Julian Oscillation (MJO) and convectively-coupled Rossby wave (CRW) variability. Models that underpredict WWE forcing in the west Pacific also severely underpredict MJO and CRW variance. Further, the multi-model mean shows a smaller fraction of WWEs associated with both the MJO and CRW than observations.
Abstract
Westerly wind events (WWEs) are anomalously strong, long-lasting westerlies over the Indian or Pacific Oceans that are capable of forcing oceanic wave modes, which in turn can impact the evolution of coupled ocean-atmosphere phenomena such as the El Niño Southern Oscillation (ENSO). This work examines the fidelity of equatorial WWEs over the Pacific Ocean in 30 CMIP6 historical simulations against observations. WWEs are identified using equatorially-averaged zonal wind stress anomaly duration, zonal extent, and intensity criteria. Most simulations correctly place the majority of WWEs over the west Pacific, although they are skewed westward and generally occur less frequently compared to observations. Simulated WWEs tend to be weaker than observations for a given duration and zonal extent with several models having shorter durations and zonal extents than observations. Biases in simulated WWEs are associated with biases in Madden-Julian Oscillation (MJO) and convectively-coupled Rossby wave (CRW) variability. Models that underpredict WWE forcing in the west Pacific also severely underpredict MJO and CRW variance. Further, the multi-model mean shows a smaller fraction of WWEs associated with both the MJO and CRW than observations.
Abstract
The biases generated by state-of-the-art climate models in simulating dust optical depth (DOD) remain to be detailed. Here a site-scale DOD dataset in March–August over northern China (NC) during 1980–2001 was reconstructed using the empirical relationship between MODIS-retrieved DOD and dust-event frequencies during 2001–2021. Then, through the combined use of MODIS-based and reconstructed DOD, we evaluated the reproducibility of DOD from 10 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the historical period (1980–2001 and 2002–2014) and under different Shared Socioeconomic Pathways (SSPs) during 2015–2021. The results demonstrate that CMIP6 models and multi-model ensemble mean (MEM) are capable of capturing the spatial pattern of DOD, but with considerable uncertainty and inter-model variability in magnitude. Regionally-averaged DOD is underestimated by 56.09% during 1980–2001 and overestimated by 30.97% during 2002–2014 in MEM over NC. Simultaneously, the inter-model standard deviations are greater than MEM during 2002–2014, suggesting large discrepancies among individual models. Very few models accurately capture the trends in DOD, which can mainly be attributed to the different trends in simulated wind speed (WS), soil moisture, and vegetation cover, and their contributions to dust evolution. Under 4 SSPs, despite the best correlation between SSP1-2.6-modeled and MODIS DOD over Gobi Desert (GD), overestimation of DOD is still observed. More models under SSP1-2.6 capture the positive DOD trend, mainly attributable to positive changes in simulated WS over GD.
Abstract
The biases generated by state-of-the-art climate models in simulating dust optical depth (DOD) remain to be detailed. Here a site-scale DOD dataset in March–August over northern China (NC) during 1980–2001 was reconstructed using the empirical relationship between MODIS-retrieved DOD and dust-event frequencies during 2001–2021. Then, through the combined use of MODIS-based and reconstructed DOD, we evaluated the reproducibility of DOD from 10 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the historical period (1980–2001 and 2002–2014) and under different Shared Socioeconomic Pathways (SSPs) during 2015–2021. The results demonstrate that CMIP6 models and multi-model ensemble mean (MEM) are capable of capturing the spatial pattern of DOD, but with considerable uncertainty and inter-model variability in magnitude. Regionally-averaged DOD is underestimated by 56.09% during 1980–2001 and overestimated by 30.97% during 2002–2014 in MEM over NC. Simultaneously, the inter-model standard deviations are greater than MEM during 2002–2014, suggesting large discrepancies among individual models. Very few models accurately capture the trends in DOD, which can mainly be attributed to the different trends in simulated wind speed (WS), soil moisture, and vegetation cover, and their contributions to dust evolution. Under 4 SSPs, despite the best correlation between SSP1-2.6-modeled and MODIS DOD over Gobi Desert (GD), overestimation of DOD is still observed. More models under SSP1-2.6 capture the positive DOD trend, mainly attributable to positive changes in simulated WS over GD.
Abstract
Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature, θe , and saturation equivalent potential temperature, θes , in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θes vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θes measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θes lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θe /θes structure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.
Abstract
Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature, θe , and saturation equivalent potential temperature, θes , in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of the θes vertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and a θes measure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between larger θes lapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poor θe /θes structure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.
Abstract
The Indian Ocean dipole (IOD) is the dominant mode of interannual variability in the tropical Indian Ocean (TIO), characterized by warming (cooling) in western TIO and cooling (warming) in eastern TIO during its positive (negative) phase. Observed IOD events exhibit distinct amplitude asymmetry in relation to negative nonlinear dynamic heating. Nearly all models in phase 5 of the Coupled Model Intercomparison Project (CMIP) simulate a less-skewed IOD than observed, but 6 out of 20 CMIP6 models can reproduce realistic high skewness. Analysis of less-skewed models indicates that the positive IOD-like biases in the mean state, which can be traced back to their weaker simulations of the preceding Indian summer monsoon, reduce the convective response to positive sea surface temperature anomalies in the western TIO, resulting in a weaker zonal wind response and weaker nonlinear zonal advection during positive IOD events. Besides, ocean stratification in the eastern TIO influences the IOD skewness: stronger stratification leads to larger mixed-layer temperature response to thermocline changes, contributing to larger anomalous vertical temperature gradient, larger nonlinear vertical advection, and thus stronger positive IOD skewness. Our findings underscore the importance of reducing Indian summer monsoon biases and eastern TIO stratification biases, for properly representing the IOD in Earth system models.
Abstract
The Indian Ocean dipole (IOD) is the dominant mode of interannual variability in the tropical Indian Ocean (TIO), characterized by warming (cooling) in western TIO and cooling (warming) in eastern TIO during its positive (negative) phase. Observed IOD events exhibit distinct amplitude asymmetry in relation to negative nonlinear dynamic heating. Nearly all models in phase 5 of the Coupled Model Intercomparison Project (CMIP) simulate a less-skewed IOD than observed, but 6 out of 20 CMIP6 models can reproduce realistic high skewness. Analysis of less-skewed models indicates that the positive IOD-like biases in the mean state, which can be traced back to their weaker simulations of the preceding Indian summer monsoon, reduce the convective response to positive sea surface temperature anomalies in the western TIO, resulting in a weaker zonal wind response and weaker nonlinear zonal advection during positive IOD events. Besides, ocean stratification in the eastern TIO influences the IOD skewness: stronger stratification leads to larger mixed-layer temperature response to thermocline changes, contributing to larger anomalous vertical temperature gradient, larger nonlinear vertical advection, and thus stronger positive IOD skewness. Our findings underscore the importance of reducing Indian summer monsoon biases and eastern TIO stratification biases, for properly representing the IOD in Earth system models.
Abstract
Previous studies have hypothesized that climatologically thick salinity-stratified barrier layers (BLs) in the north Indian Ocean (NIO) influence the upper ocean heat budget, sea surface temperature (SST), and monsoons. Here, we investigate how state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) climate models simulate the NIO barrier layer thickness (BLT). CMIP6 models generally reproduce the BLT seasonal cycle and spatial distribution, but with shallow November–February (NDJF) biases in regions with thick observed BLT: the eastern equatorial Indian Ocean (EEIO), Bay of Bengal (BoB), and southeastern Arabian Sea (SEAS). We show that the intensity of the CMIP6 equatorial easterly wind bias controls the EEIO shallow isothermal layer depth (ILD) and BLT biases. It also controls the BoB shallow BLT bias, both through the propagation of the EEIO shallow ILD bias into the NIO coastal waveguide and because it is linked to the BoB dry and cold bias through the Bjerknes feedback, hence also controlling the mixed layer depth (MLD) deep bias there. Finally, the SEAS shallow BLT bias is due to a too-deep MLD, in response to subdued monsoonal currents around India, which do not bring enough BoB low-salinity water. The BL insulating effect mentioned in literature does not seem to dominate in CMIP6. Rather, the CMIP6 salinity-related deep MLD biases diminish the BoB cooling rate by winter upward surface heat fluxes, reducing cold SST biases. This suggests that salinity effects alleviate the easterly equatorial wind, cold, and dry BoB biases that develop through the positive Bjerknes feedback loop in CMIP6.
Abstract
Previous studies have hypothesized that climatologically thick salinity-stratified barrier layers (BLs) in the north Indian Ocean (NIO) influence the upper ocean heat budget, sea surface temperature (SST), and monsoons. Here, we investigate how state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) climate models simulate the NIO barrier layer thickness (BLT). CMIP6 models generally reproduce the BLT seasonal cycle and spatial distribution, but with shallow November–February (NDJF) biases in regions with thick observed BLT: the eastern equatorial Indian Ocean (EEIO), Bay of Bengal (BoB), and southeastern Arabian Sea (SEAS). We show that the intensity of the CMIP6 equatorial easterly wind bias controls the EEIO shallow isothermal layer depth (ILD) and BLT biases. It also controls the BoB shallow BLT bias, both through the propagation of the EEIO shallow ILD bias into the NIO coastal waveguide and because it is linked to the BoB dry and cold bias through the Bjerknes feedback, hence also controlling the mixed layer depth (MLD) deep bias there. Finally, the SEAS shallow BLT bias is due to a too-deep MLD, in response to subdued monsoonal currents around India, which do not bring enough BoB low-salinity water. The BL insulating effect mentioned in literature does not seem to dominate in CMIP6. Rather, the CMIP6 salinity-related deep MLD biases diminish the BoB cooling rate by winter upward surface heat fluxes, reducing cold SST biases. This suggests that salinity effects alleviate the easterly equatorial wind, cold, and dry BoB biases that develop through the positive Bjerknes feedback loop in CMIP6.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations.
Significance Statement
High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations.
Significance Statement
High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.
Abstract
The central role of tropical sea surface temperature (SST) variability in modulating Northern Hemisphere (NH) extratropical climate has long been known. However, the prevailing pathways of teleconnections in observations and the ability of climate models to replicate these observed linkages remain elusive. Here, we apply maximum covariance analysis between atmospheric circulation and tropical SST to reveal two coexisting tropical–extratropical teleconnections albeit with distinctive spatiotemporal characteristics. The first mode, resembling the Pacific–North American (PNA) pattern, favors a tropical–Arctic in-phase (warm Pacific–warm Arctic) teleconnection in boreal spring and winter. However, the second mode, with a slight seasonal preference of summer, is manifested as an elongated Rossby wave train emanating from the tropical eastern Pacific that features an out-of-phase relationship (cold Pacific–warm Arctic) between tropical central Pacific SSTs and temperature variability over the Arctic (referred to as the PARC mode). While climate models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) appear to successfully simulate the PNA mode and its temporal characteristics, the majority of models’ skill in reproducing the PARC mode is obstructed to some extent by biases in simulating low-frequency SST and rainfall variability over the tropical eastern Pacific and the climatological mean flow over the North Pacific during boreal summer. Considering the contribution of the PARC mode in shaping low-frequency climate variations over the past 42 years from the tropics to the Arctic, improving models’ capability to capture the PARC mode is essential to reduce uncertainties associated with decadal prediction and climate change projection over the NH.
Significance Statement
This study focuses on the skill of models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating two leading observed Northern Hemisphere (NH) teleconnections that show distinctive spatial and temporal characteristics. The first one, the Pacific–North American (PNA) mode, exhibits a warm Pacific–warm Arctic pattern in boreal spring and winter, and the second one, the Pacific–Arctic (PARC) mode, features a cold Pacific–warm Arctic out-of-phase relationship. We find that models are skillful in simulating the PNA mode but not the PARC mode. This limitation may be rooted in unrealistic simulations of the mean state of winds and the low-frequency sea surface temperature variability in the tropical eastern Pacific. These biases call for caution when interpreting current models’ projections of extratropical circulations on multidecadal time scales.
Abstract
The central role of tropical sea surface temperature (SST) variability in modulating Northern Hemisphere (NH) extratropical climate has long been known. However, the prevailing pathways of teleconnections in observations and the ability of climate models to replicate these observed linkages remain elusive. Here, we apply maximum covariance analysis between atmospheric circulation and tropical SST to reveal two coexisting tropical–extratropical teleconnections albeit with distinctive spatiotemporal characteristics. The first mode, resembling the Pacific–North American (PNA) pattern, favors a tropical–Arctic in-phase (warm Pacific–warm Arctic) teleconnection in boreal spring and winter. However, the second mode, with a slight seasonal preference of summer, is manifested as an elongated Rossby wave train emanating from the tropical eastern Pacific that features an out-of-phase relationship (cold Pacific–warm Arctic) between tropical central Pacific SSTs and temperature variability over the Arctic (referred to as the PARC mode). While climate models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) appear to successfully simulate the PNA mode and its temporal characteristics, the majority of models’ skill in reproducing the PARC mode is obstructed to some extent by biases in simulating low-frequency SST and rainfall variability over the tropical eastern Pacific and the climatological mean flow over the North Pacific during boreal summer. Considering the contribution of the PARC mode in shaping low-frequency climate variations over the past 42 years from the tropics to the Arctic, improving models’ capability to capture the PARC mode is essential to reduce uncertainties associated with decadal prediction and climate change projection over the NH.
Significance Statement
This study focuses on the skill of models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating two leading observed Northern Hemisphere (NH) teleconnections that show distinctive spatial and temporal characteristics. The first one, the Pacific–North American (PNA) mode, exhibits a warm Pacific–warm Arctic pattern in boreal spring and winter, and the second one, the Pacific–Arctic (PARC) mode, features a cold Pacific–warm Arctic out-of-phase relationship. We find that models are skillful in simulating the PNA mode but not the PARC mode. This limitation may be rooted in unrealistic simulations of the mean state of winds and the low-frequency sea surface temperature variability in the tropical eastern Pacific. These biases call for caution when interpreting current models’ projections of extratropical circulations on multidecadal time scales.
Abstract
Global models are frequently used for tropical cyclone (TC) prediction and climate projections but have biases in their representation of TCs that are not fully understood. The objective of this work is to assess how well and how robustly physical processes that are important for TC development are represented in modern reanalysis products and to consider whether reanalyses can serve as an observationally constrained reference against which model representation of these physical processes can be evaluated. Differences in the representation of large-scale environmental variables relevant to TC development do not readily explain the spread in TC climatologies across climate models, as found in prior work, or across reanalysis datasets, as shown here. This motivates the use of process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled and can be used to identify areas to target for model improvement. Using the column-integrated moist static energy (MSE) variance budget, we analyze radiative and surface flux feedbacks across five different reanalyses. We construct an intensity-bin composite of the MSE variance budget to compare storms of similar intensity. Our results point to some fundamental differences across reanalyses in how they represent MSE variance and surface flux and radiative feedbacks in TCs, which could contribute to differences across reanalyses in how they represent TCs, but other factors also likely contribute. Any future work that evaluates these diagnostics in GCMs against reanalyses should do so cautiously, and efforts should be undertaken to provide a true observational estimate of these processes.
Abstract
Global models are frequently used for tropical cyclone (TC) prediction and climate projections but have biases in their representation of TCs that are not fully understood. The objective of this work is to assess how well and how robustly physical processes that are important for TC development are represented in modern reanalysis products and to consider whether reanalyses can serve as an observationally constrained reference against which model representation of these physical processes can be evaluated. Differences in the representation of large-scale environmental variables relevant to TC development do not readily explain the spread in TC climatologies across climate models, as found in prior work, or across reanalysis datasets, as shown here. This motivates the use of process-oriented diagnostics that focus on how convection, moisture, clouds, and related processes are coupled and can be used to identify areas to target for model improvement. Using the column-integrated moist static energy (MSE) variance budget, we analyze radiative and surface flux feedbacks across five different reanalyses. We construct an intensity-bin composite of the MSE variance budget to compare storms of similar intensity. Our results point to some fundamental differences across reanalyses in how they represent MSE variance and surface flux and radiative feedbacks in TCs, which could contribute to differences across reanalyses in how they represent TCs, but other factors also likely contribute. Any future work that evaluates these diagnostics in GCMs against reanalyses should do so cautiously, and efforts should be undertaken to provide a true observational estimate of these processes.
