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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 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
Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.
Abstract
Process-oriented diagnostics (PODs) aim to provide feedback for model developers through model analysis based on physical hypotheses. However, the step from a diagnostic based on relationships among variables, even when hypothesis driven, to specific guidance for revising model formulation or parameterizations can be substantial. The POD may provide more information than a purely performance-based metric, but a gap between POD principles and providing actionable information for specific model revisions can remain. Furthermore, in coordinating diagnostics development, there is a trade-off between freedom for the developer, aiming to capture innovation, and near-term utility to the modeling center. Best practices that allow for the former, while conforming to specifications that aid the latter, are important for community diagnostics development that leads to tangible model improvements. Promising directions to close the gap between principles and practice include the interaction of PODs with perturbed physics experiments and with more quantitative process models as well as the inclusion of personnel from modeling centers in diagnostics development groups for immediate feedback during climate model revisions. Examples are provided, along with best-practice recommendations, based on practical experience from the NOAA Model Diagnostics Task Force (MDTF). Common standards for metrics and diagnostics that have arisen from a collaboration between the MDTF and the Department of Energy’s Coordinated Model Evaluation Capability are advocated as a means of uniting community diagnostics efforts.
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.
Abstract
El Niño–Southern Oscillation (ENSO) is the most influential interannual climate variability on Earth. The tendency of the mature phase of ENSO, characterized by the strongest sea surface temperature (SST) anomalies, to appear during the boreal winter is known as seasonal phase locking. Climate models are challenged by biases in simulating ENSO seasonal phase locking. Here, we evaluated the ENSO phase-locking simulation performance in 50 models of phase 6 of the Coupled Model Intercomparison Project (CMIP6) and found that the models with the intertropical convergence zone (ITCZ) poleward bias tended to simulate more ENSO events that peaked out of the boreal winter season. The contributions of the ITCZ poleward bias to the ENSO phase-locking bias were also evaluated, yielding a correlation coefficient of 0.55, which can explain approximately 30% of the ENSO seasonal phase-locking bias. The mechanism that influences the simulation of ENSO seasonal phase locking was also assessed. The ITCZ poleward bias induces a dry bias over the equatorial Pacific, especially during the boreal summer. During ENSO events, the meridional movement of the ITCZ is prevented, and the equatorial precipitation and convection anomalies that respond to ENSO events are also restrained. The restrained convection anomaly weakens the ENSO-related zonal wind anomaly, triggering a weaker eastern tropical Pacific thermocline anomaly during the following autumn. The weakened thermocline anomaly cannot sustain further development of ENSO-related SST anomalies. Therefore, ENSO events in models containing the ITCZ poleward bias are restrained during the boreal summer and autumn and, thus, tend to peak out of the winter season.
Significance Statement
We aimed to better understand the mechanism that induces bias when simulating ENSO seasonal phase locking, that is, what disturbs the simulated ENSO events peaking during the boreal winter. As previous studies have primarily focused on the South Pacific convergence zone (SPCZ) bias and other biases, this study is the first to propose the effects of the poleward ITCZ latitude bias and clarify the corresponding mechanism. We show that latitudinal bias can explain approximately 30% of the ENSO seasonal phase-locking bias. This is important because the biases in simulating ENSO seasonal phase locking have long hampered the prediction of ENSO. Our study highlights the importance of the latitude of the ITCZ and provides a basis for the future development of climate models.
Abstract
El Niño–Southern Oscillation (ENSO) is the most influential interannual climate variability on Earth. The tendency of the mature phase of ENSO, characterized by the strongest sea surface temperature (SST) anomalies, to appear during the boreal winter is known as seasonal phase locking. Climate models are challenged by biases in simulating ENSO seasonal phase locking. Here, we evaluated the ENSO phase-locking simulation performance in 50 models of phase 6 of the Coupled Model Intercomparison Project (CMIP6) and found that the models with the intertropical convergence zone (ITCZ) poleward bias tended to simulate more ENSO events that peaked out of the boreal winter season. The contributions of the ITCZ poleward bias to the ENSO phase-locking bias were also evaluated, yielding a correlation coefficient of 0.55, which can explain approximately 30% of the ENSO seasonal phase-locking bias. The mechanism that influences the simulation of ENSO seasonal phase locking was also assessed. The ITCZ poleward bias induces a dry bias over the equatorial Pacific, especially during the boreal summer. During ENSO events, the meridional movement of the ITCZ is prevented, and the equatorial precipitation and convection anomalies that respond to ENSO events are also restrained. The restrained convection anomaly weakens the ENSO-related zonal wind anomaly, triggering a weaker eastern tropical Pacific thermocline anomaly during the following autumn. The weakened thermocline anomaly cannot sustain further development of ENSO-related SST anomalies. Therefore, ENSO events in models containing the ITCZ poleward bias are restrained during the boreal summer and autumn and, thus, tend to peak out of the winter season.
Significance Statement
We aimed to better understand the mechanism that induces bias when simulating ENSO seasonal phase locking, that is, what disturbs the simulated ENSO events peaking during the boreal winter. As previous studies have primarily focused on the South Pacific convergence zone (SPCZ) bias and other biases, this study is the first to propose the effects of the poleward ITCZ latitude bias and clarify the corresponding mechanism. We show that latitudinal bias can explain approximately 30% of the ENSO seasonal phase-locking bias. This is important because the biases in simulating ENSO seasonal phase locking have long hampered the prediction of ENSO. Our study highlights the importance of the latitude of the ITCZ and provides a basis for the future development of climate models.
Abstract
Many coupled climate models suffer from a late retreat bias in North American monsoon (NAM) simulations, which is manifested by overestimated precipitation in October. The overestimated precipitation has long been attributed to the negative sea surface temperature (SST) biases in the tropical Atlantic and insufficient model resolution to resolve mesoscale features. However, we found little correlation between CMIP6 model resolutions and the simulated NAM retreat-season precipitation in October. Instead, we showed that tropical eastern North Pacific SST biases and the associated large-scale circulation biases play a dominant role in inducing the retreat-season biases, with SST biases in other ocean basins playing a secondary role. As revealed by simulations using a hierarchy of models, the positive SST biases in the tropical eastern North Pacific enhance local convection and lead to positive diabatic heating biases throughout the troposphere; the diabatic heating biases generate a Matsuno–Gill type of response that strengthens the subtropical high over the North Atlantic and weakens the subtropical high over the North Pacific, enhancing the low-level northward moisture transport from the tropics to the NAM region. The conclusion is robust across phase 6 of CMIP (CMIP6) models. The precipitation seasonality in the NAM region is used to constrain future projection. The “good” CMIP6 models project that the timing of the NAM peak season remains the same, but the peak-season precipitation is reduced and monsoon retreat is delayed, while the “poor” CMIP6 models project a delayed monsoon peak season with slightly enhanced peak-season precipitation. Both model groups project a drier dry season in the NAM region.
Abstract
Many coupled climate models suffer from a late retreat bias in North American monsoon (NAM) simulations, which is manifested by overestimated precipitation in October. The overestimated precipitation has long been attributed to the negative sea surface temperature (SST) biases in the tropical Atlantic and insufficient model resolution to resolve mesoscale features. However, we found little correlation between CMIP6 model resolutions and the simulated NAM retreat-season precipitation in October. Instead, we showed that tropical eastern North Pacific SST biases and the associated large-scale circulation biases play a dominant role in inducing the retreat-season biases, with SST biases in other ocean basins playing a secondary role. As revealed by simulations using a hierarchy of models, the positive SST biases in the tropical eastern North Pacific enhance local convection and lead to positive diabatic heating biases throughout the troposphere; the diabatic heating biases generate a Matsuno–Gill type of response that strengthens the subtropical high over the North Atlantic and weakens the subtropical high over the North Pacific, enhancing the low-level northward moisture transport from the tropics to the NAM region. The conclusion is robust across phase 6 of CMIP (CMIP6) models. The precipitation seasonality in the NAM region is used to constrain future projection. The “good” CMIP6 models project that the timing of the NAM peak season remains the same, but the peak-season precipitation is reduced and monsoon retreat is delayed, while the “poor” CMIP6 models project a delayed monsoon peak season with slightly enhanced peak-season precipitation. Both model groups project a drier dry season in the NAM region.
Abstract
Significant anomalies in frequency of summer extreme hot days (SEHDs) are broadly observed in the Asian monsoon region (AMR) in the post-ENSO summers. The delayed ENSO impacts are mainly conveyed by provoking the Indo-western Pacific Ocean capacitor (IPOC) effect that maintains the anomalous anticyclone in the western North Pacific. The related diabatic heating anomaly can trigger the westward-propagating Rossby wave to the Indian subcontinent, which increases the geopotential heights, reduces the cloud cover, and thus increases the seasonal surface temperature and SEHD frequency in the southern AMR. Besides, the reduced atmospheric moisture in the western North Pacific hinders the northward propagation of intraseasonal oscillation (ISO) and modulates the occurrence frequency of individual ISO phases, contributing to the significantly increased/decreased SEHDs in eastern China/Hokkaido, Japan, in the post–El Niño summers. The 25-model-ensemble mean of CMIP6 historical runs can reproduce well the observed SEHD anomalies in the southern AMR in the post-ENSO summers mainly due to the realistic simulation of ENSO impacts on the seasonal surface temperature, although a large intermodel spread exists due to different strengths of IPOC effect in each model owing to model biases in the mean state of the eastern tropical Pacific, the ENSO variance, and teleconnection to the Indian Ocean. Furthermore, future projections under the SSP5-8.5 scenario show that the delayed ENSO impacts on the southern AMR remain stable under global warming via a similar mechanism as in the observations and historical runs.
Abstract
Significant anomalies in frequency of summer extreme hot days (SEHDs) are broadly observed in the Asian monsoon region (AMR) in the post-ENSO summers. The delayed ENSO impacts are mainly conveyed by provoking the Indo-western Pacific Ocean capacitor (IPOC) effect that maintains the anomalous anticyclone in the western North Pacific. The related diabatic heating anomaly can trigger the westward-propagating Rossby wave to the Indian subcontinent, which increases the geopotential heights, reduces the cloud cover, and thus increases the seasonal surface temperature and SEHD frequency in the southern AMR. Besides, the reduced atmospheric moisture in the western North Pacific hinders the northward propagation of intraseasonal oscillation (ISO) and modulates the occurrence frequency of individual ISO phases, contributing to the significantly increased/decreased SEHDs in eastern China/Hokkaido, Japan, in the post–El Niño summers. The 25-model-ensemble mean of CMIP6 historical runs can reproduce well the observed SEHD anomalies in the southern AMR in the post-ENSO summers mainly due to the realistic simulation of ENSO impacts on the seasonal surface temperature, although a large intermodel spread exists due to different strengths of IPOC effect in each model owing to model biases in the mean state of the eastern tropical Pacific, the ENSO variance, and teleconnection to the Indian Ocean. Furthermore, future projections under the SSP5-8.5 scenario show that the delayed ENSO impacts on the southern AMR remain stable under global warming via a similar mechanism as in the observations and historical runs.
Abstract
Climate model fidelity in representing ENSO-induced teleconnection is assessed with process-oriented diagnostics that examine a chain of processes, from equatorial Pacific precipitation to the midlatitude circulation pattern over the Pacific–North American regions. Such processes are rarely addressed during model development. Using an upper-tropospheric divergent level, local vorticity gradient of the ambient zonal flow (
Significance Statement
The seasonal changes in tropical Pacific sea surface temperatures associated with El Niño events can have a significant impact in the atmospheric circulation through the North Pacific and on the annual climate variations over North America. Our skill in predicting these impacts depends critically on the ability of climate models to represent these global-scale connections accurately. We show a number of metrics that describe critical processes along this North Pacific pathway that can be used to examine the progress in climate model skill. In the future, these models could benefit significantly from using these metrics with the end goal of much improved predictions of El Niño–related variability.
Abstract
Climate model fidelity in representing ENSO-induced teleconnection is assessed with process-oriented diagnostics that examine a chain of processes, from equatorial Pacific precipitation to the midlatitude circulation pattern over the Pacific–North American regions. Such processes are rarely addressed during model development. Using an upper-tropospheric divergent level, local vorticity gradient of the ambient zonal flow (
Significance Statement
The seasonal changes in tropical Pacific sea surface temperatures associated with El Niño events can have a significant impact in the atmospheric circulation through the North Pacific and on the annual climate variations over North America. Our skill in predicting these impacts depends critically on the ability of climate models to represent these global-scale connections accurately. We show a number of metrics that describe critical processes along this North Pacific pathway that can be used to examine the progress in climate model skill. In the future, these models could benefit significantly from using these metrics with the end goal of much improved predictions of El Niño–related variability.
Abstract
In the last decade, three persistent warm blob events (2013/14, 2015, and 2019/20) in the northeast Pacific (NEP) have been hotly debated given their substantial effects on climate, ecosystems, and the socioeconomy. This study investigates the changes of such long-lived NEP warm blobs in terms of their intensity, duration, structure, and occurrence frequency under Shared Socioeconomic Pathway (SSP) 119 and 126 low-warming scenarios of phase 6 of the Coupled Model Intercomparison Project. Results show that the peak timing of the warm blobs shifts from the cold season to boreal summer. For the summer-peak warm blobs, their maximum intensity increases by 6.7% (10.0%) under the SSP119 (SSP126) scenario, but their duration reduces by 31.0% (20.4%) under the SSP119 (SSP126) scenario. In terms of their vertical structure, the most pronounced temperature signal is located at the surface, and their vertical penetration is mostly confined to the mixed layer, which becomes shallower in warming climates. Based on a mixed layer heat budget analysis, we reveal that a shoaling mixed layer depth plays a dominant role in driving the stronger intensity of the warm blobs under low-warming scenarios, while the stronger magnitude of ocean heat loss after their peaks explains the faster decay and thus shorter duration. Regarding occurrence frequency, the total number of the warm blobs does not change robustly in the low-warming climates. Following the summer peak of the warm blobs, extreme El Niño events may occur more frequently under the low-warming scenarios, possibly through stronger air–sea coupling induced by tropical Pacific southwesterly anomalies.
Abstract
In the last decade, three persistent warm blob events (2013/14, 2015, and 2019/20) in the northeast Pacific (NEP) have been hotly debated given their substantial effects on climate, ecosystems, and the socioeconomy. This study investigates the changes of such long-lived NEP warm blobs in terms of their intensity, duration, structure, and occurrence frequency under Shared Socioeconomic Pathway (SSP) 119 and 126 low-warming scenarios of phase 6 of the Coupled Model Intercomparison Project. Results show that the peak timing of the warm blobs shifts from the cold season to boreal summer. For the summer-peak warm blobs, their maximum intensity increases by 6.7% (10.0%) under the SSP119 (SSP126) scenario, but their duration reduces by 31.0% (20.4%) under the SSP119 (SSP126) scenario. In terms of their vertical structure, the most pronounced temperature signal is located at the surface, and their vertical penetration is mostly confined to the mixed layer, which becomes shallower in warming climates. Based on a mixed layer heat budget analysis, we reveal that a shoaling mixed layer depth plays a dominant role in driving the stronger intensity of the warm blobs under low-warming scenarios, while the stronger magnitude of ocean heat loss after their peaks explains the faster decay and thus shorter duration. Regarding occurrence frequency, the total number of the warm blobs does not change robustly in the low-warming climates. Following the summer peak of the warm blobs, extreme El Niño events may occur more frequently under the low-warming scenarios, possibly through stronger air–sea coupling induced by tropical Pacific southwesterly anomalies.
