Abstract
The export of the North Atlantic Deep Water (NADW) from the subpolar North Atlantic is known to affect the variability in the lower limb of the Atlantic meridional overturning circulation (AMOC). However, the respective impact from the transport in the upper NADW (UNADW) and lower NADW (LNADW) layers, and from the various transport branches through the boundary and interior flows, on the subpolar overturning variability remains elusive. To address this, the spatiotemporal characteristics of the circulation of NADW throughout the eastern subpolar basins are examined, mainly based on the 2014–20 observations from the transatlantic Overturning in the Subpolar North Atlantic Program (OSNAP) array. It reveals that the time-mean transport within the overturning’s lower limb across the eastern subpolar gyre [−13.0 ± 0.5 Sv (1 Sv ≡ 106 m3 s−1)] mostly occurs in the LNADW layer (−9.4 Sv or 72% of the mean), while the lower limb variability is mainly concentrated in the UNADW layer (57% of the total variance). This analysis further demonstrates a dominant role in the lower limb variability by coherent intraseasonal changes across the region that result from a basinwide barotropic response to changing wind fields. By comparison, there is just a weak seasonal cycle in the flows along the western boundary of the basins, in response to the surface buoyancy-induced water mass transformation.
Abstract
The export of the North Atlantic Deep Water (NADW) from the subpolar North Atlantic is known to affect the variability in the lower limb of the Atlantic meridional overturning circulation (AMOC). However, the respective impact from the transport in the upper NADW (UNADW) and lower NADW (LNADW) layers, and from the various transport branches through the boundary and interior flows, on the subpolar overturning variability remains elusive. To address this, the spatiotemporal characteristics of the circulation of NADW throughout the eastern subpolar basins are examined, mainly based on the 2014–20 observations from the transatlantic Overturning in the Subpolar North Atlantic Program (OSNAP) array. It reveals that the time-mean transport within the overturning’s lower limb across the eastern subpolar gyre [−13.0 ± 0.5 Sv (1 Sv ≡ 106 m3 s−1)] mostly occurs in the LNADW layer (−9.4 Sv or 72% of the mean), while the lower limb variability is mainly concentrated in the UNADW layer (57% of the total variance). This analysis further demonstrates a dominant role in the lower limb variability by coherent intraseasonal changes across the region that result from a basinwide barotropic response to changing wind fields. By comparison, there is just a weak seasonal cycle in the flows along the western boundary of the basins, in response to the surface buoyancy-induced water mass transformation.
Abstract
Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.
Abstract
Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.
Abstract
We explore the skill in predicting Southwest United States (SWUS) October to March precipitation and associated large-scale teleconnections in an ensemble of hindcasts from seasonal prediction systems. We identify key model biases that degrade the models’ capability to predict SWUS precipitation. The subtropical jet in the Pacific sector is generally too zonal and elongated. This is reflected in the models’ North Pacific ENSO teleconnections that are generally too weak with exaggerated northwest-southeast tilt, compared to observations. Also, the models are too dependent on tropical, El Niño-like, wave train anomalies for producing high seasonal SWUS precipitation, when in observations there is a larger influence of zonal Rossby wave trains such as the one observed in 2016/17. Overall, this is consistent with biases in the basic flow inducing errors in the propagation of zonal wave trains in the North Pacific, which affects SWUS precipitation downstream. Although higher skill may be gained from reducing mean flow biases in the models, a case study of the 2016/17 winter illustrates the great challenge behind skillful seasonal prediction of SWUS precipitation. Unsurprisingly, the almost record-breaking precipitation observed that year in the absence of ENSO is not predicted in the hindcasts, and model perturbation experiments suggest that even a perfect prediction of tropical sea surface temperature and tropical atmospheric variability would not have sufficed to produce a reasonable seasonal precipitation prediction. On a more positive note, our perturbation experiments suggest a potential role for Arctic variability that supports findings from prior studies and suggests re-examining high-latitude drivers of SWUS precipitation.
Abstract
We explore the skill in predicting Southwest United States (SWUS) October to March precipitation and associated large-scale teleconnections in an ensemble of hindcasts from seasonal prediction systems. We identify key model biases that degrade the models’ capability to predict SWUS precipitation. The subtropical jet in the Pacific sector is generally too zonal and elongated. This is reflected in the models’ North Pacific ENSO teleconnections that are generally too weak with exaggerated northwest-southeast tilt, compared to observations. Also, the models are too dependent on tropical, El Niño-like, wave train anomalies for producing high seasonal SWUS precipitation, when in observations there is a larger influence of zonal Rossby wave trains such as the one observed in 2016/17. Overall, this is consistent with biases in the basic flow inducing errors in the propagation of zonal wave trains in the North Pacific, which affects SWUS precipitation downstream. Although higher skill may be gained from reducing mean flow biases in the models, a case study of the 2016/17 winter illustrates the great challenge behind skillful seasonal prediction of SWUS precipitation. Unsurprisingly, the almost record-breaking precipitation observed that year in the absence of ENSO is not predicted in the hindcasts, and model perturbation experiments suggest that even a perfect prediction of tropical sea surface temperature and tropical atmospheric variability would not have sufficed to produce a reasonable seasonal precipitation prediction. On a more positive note, our perturbation experiments suggest a potential role for Arctic variability that supports findings from prior studies and suggests re-examining high-latitude drivers of SWUS precipitation.
Abstract
A critical issue is determining the factors that control the year-to-year variability in precipitation over southern Asia. In this study, we employ a cyclostationary linear inverse model (CS-LIM) to quantify the relative contribution of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) to the interannual variability of the Asian monsoon, especially Indian summer monsoon rainfall (ISMR). Through a series of CS-LIM experiments, we isolate the impacts of the direct forcing from Pacific SSTAs, Indian Ocean SSTAs, and their interaction on Asian monsoon rainfall variability. Our results reveal distinct patterns of influence with the direct forcing from the Pacific (Indian) Ocean tending to enhance (reduce) the magnitude of precipitation variability, while the Indo-Pacific interaction acts to strongly damp the variability of Asian monsoon precipitation, especially over India. We further investigate these specific impacts on ISMR by analyzing the relationship between tropical Indo-Pacific SSTAs and the leading three empirical orthogonal functions (EOFs) of ISMR. The results from our CS-LIM experiments indicate that the direct forcing from El Niño–Southern Oscillation (ENSO) enhances the variability of the first and third EOFs, while the Indian Ocean SSTA opposes ENSO’s effects, which is consistent with previous studies. Our new results show that the tropical Indo-Pacific interaction strongly damps ISMR variability, which is due to the ENSO-induced Indian Ocean dipole (IOD) opposing the direct impacts from ENSO on ISMR. Additionally, reduced ENSO amplitude and duration associated with the Indo-Pacific interaction may also contribute to the damping effect on ISMR, but this requires further study to understand the relevant mechanisms.
Abstract
A critical issue is determining the factors that control the year-to-year variability in precipitation over southern Asia. In this study, we employ a cyclostationary linear inverse model (CS-LIM) to quantify the relative contribution of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) to the interannual variability of the Asian monsoon, especially Indian summer monsoon rainfall (ISMR). Through a series of CS-LIM experiments, we isolate the impacts of the direct forcing from Pacific SSTAs, Indian Ocean SSTAs, and their interaction on Asian monsoon rainfall variability. Our results reveal distinct patterns of influence with the direct forcing from the Pacific (Indian) Ocean tending to enhance (reduce) the magnitude of precipitation variability, while the Indo-Pacific interaction acts to strongly damp the variability of Asian monsoon precipitation, especially over India. We further investigate these specific impacts on ISMR by analyzing the relationship between tropical Indo-Pacific SSTAs and the leading three empirical orthogonal functions (EOFs) of ISMR. The results from our CS-LIM experiments indicate that the direct forcing from El Niño–Southern Oscillation (ENSO) enhances the variability of the first and third EOFs, while the Indian Ocean SSTA opposes ENSO’s effects, which is consistent with previous studies. Our new results show that the tropical Indo-Pacific interaction strongly damps ISMR variability, which is due to the ENSO-induced Indian Ocean dipole (IOD) opposing the direct impacts from ENSO on ISMR. Additionally, reduced ENSO amplitude and duration associated with the Indo-Pacific interaction may also contribute to the damping effect on ISMR, but this requires further study to understand the relevant mechanisms.
Abstract
In February 2022, an extreme wet and cold event hits South China, with the regional precipitation ranking the second highest on record, while the temperature ranked the third lowest since 1979. In this study, the physical mechanisms of this extreme event are investigated from the perspective of multiple time-scale interactions. Results show that the strong confrontation between the warm and moist air advection by the India–Burma trough (IBT) and the invasion of cold air activity related to the strengthening of the East Asian winter monsoon (EAWM) is the key to trigger this extreme event. Further analyses show that the multitime-scale coupling of the South Asian jet wave train and Eurasian (EU) teleconnection is the main reason for the strong cold and warm–moist airflow. The EU teleconnection on both intraseasonal and synoptic time scales plays a key role in triggering this extreme event by strengthening the EAWM. On the synoptic time scale, not only the EU teleconnection but also the South Asian jet wave train plays a key role. They show a stronger intensity on this time scale, and their coupling is obvious. The South Asian jet wave train enhances the moisture supply by deepening the IBT, which further conflicts with the strong EAWM modulated by the EU teleconnection over South China, leading to this extreme wet–cold event. The forecast skills in the Subseasonal to Seasonal (S2S) Prediction project models of this event are evaluated in this paper, and results show that the ECMWF model can successfully predict the extreme precipitation by capturing the coupling of the two wave trains with a 5-day lead time.
Abstract
In February 2022, an extreme wet and cold event hits South China, with the regional precipitation ranking the second highest on record, while the temperature ranked the third lowest since 1979. In this study, the physical mechanisms of this extreme event are investigated from the perspective of multiple time-scale interactions. Results show that the strong confrontation between the warm and moist air advection by the India–Burma trough (IBT) and the invasion of cold air activity related to the strengthening of the East Asian winter monsoon (EAWM) is the key to trigger this extreme event. Further analyses show that the multitime-scale coupling of the South Asian jet wave train and Eurasian (EU) teleconnection is the main reason for the strong cold and warm–moist airflow. The EU teleconnection on both intraseasonal and synoptic time scales plays a key role in triggering this extreme event by strengthening the EAWM. On the synoptic time scale, not only the EU teleconnection but also the South Asian jet wave train plays a key role. They show a stronger intensity on this time scale, and their coupling is obvious. The South Asian jet wave train enhances the moisture supply by deepening the IBT, which further conflicts with the strong EAWM modulated by the EU teleconnection over South China, leading to this extreme wet–cold event. The forecast skills in the Subseasonal to Seasonal (S2S) Prediction project models of this event are evaluated in this paper, and results show that the ECMWF model can successfully predict the extreme precipitation by capturing the coupling of the two wave trains with a 5-day lead time.
Abstract
A comprehensive understanding of snowfall microphysics is crucial for enhancing the accuracy of remote sensing snowfall retrievals. However, variations in regional and seasonal snow particle size distributions (PSDs) contribute substantial uncertainty. Here, we examine snowfall PSDs from across the Northern Hemisphere, applying Principal Component Analysis (PCA) to disdrometer observations with the aim of identifying dominant modes of variability across varying regional climates. The PCA revealed three Empirical Orthogonal Functions (EOFs) that account for a combined 95% of the variability across the dataset, which are attributed to latent linear embeddings of snowfall intensity (EOF1), snowfall character (EOF2) and snowfall regime (EOF3). Examining point clusters with the highest combined EOF values reveals six distinct modes of variability (i.e., Principal Component [PC] groups) with unique PSD traits. These groups are then correlated with environmental factors using data from collocated vertically pointing radar, surface meteorology, and reanalysis to assist in assigning physical attributes. The first and second PC groups, linked to EOF1’s intensity embedding, are described by their PSD intercepts, snowfall rates, and reflectivity and Doppler velocity values, representing low and high intensity snowfall modes, respectively. The third and fourth PC groups, associated with EOF2’s character embedding, are defined by temperature, fall speed, and density, indicative of cold, fluffy snowfall and warm, dense snowfall, respectively. The fifth and sixth PC groups, related to EOF3’s regime embedding, are distinguished by their PSD slope, snowfall rate, and reflectivity profiles, signifying shallow, weak convective systems with small particles, and deep, stratiform snowfall events with large aggregates, respectively.
Abstract
A comprehensive understanding of snowfall microphysics is crucial for enhancing the accuracy of remote sensing snowfall retrievals. However, variations in regional and seasonal snow particle size distributions (PSDs) contribute substantial uncertainty. Here, we examine snowfall PSDs from across the Northern Hemisphere, applying Principal Component Analysis (PCA) to disdrometer observations with the aim of identifying dominant modes of variability across varying regional climates. The PCA revealed three Empirical Orthogonal Functions (EOFs) that account for a combined 95% of the variability across the dataset, which are attributed to latent linear embeddings of snowfall intensity (EOF1), snowfall character (EOF2) and snowfall regime (EOF3). Examining point clusters with the highest combined EOF values reveals six distinct modes of variability (i.e., Principal Component [PC] groups) with unique PSD traits. These groups are then correlated with environmental factors using data from collocated vertically pointing radar, surface meteorology, and reanalysis to assist in assigning physical attributes. The first and second PC groups, linked to EOF1’s intensity embedding, are described by their PSD intercepts, snowfall rates, and reflectivity and Doppler velocity values, representing low and high intensity snowfall modes, respectively. The third and fourth PC groups, associated with EOF2’s character embedding, are defined by temperature, fall speed, and density, indicative of cold, fluffy snowfall and warm, dense snowfall, respectively. The fifth and sixth PC groups, related to EOF3’s regime embedding, are distinguished by their PSD slope, snowfall rate, and reflectivity profiles, signifying shallow, weak convective systems with small particles, and deep, stratiform snowfall events with large aggregates, respectively.
Abstract
Recent field campaigns, observational studies, and modeling work have demonstrated that extratropical tropopause-overshooting convection has a substantial and previously underestimated impact on stratospheric water vapor concentrations. This necessitates improved understanding of how tropopause-overshooting convection will respond to a warming climate. A growing body of research indicates that environments conducive to severe thunderstorms will occur more often and be increasingly unstable in the future, but no study has examined how this may be related to increased overshooting. To rectify this, this study leverages an existing pseudo–global warming (PGW) experiment to evaluate potential future changes in tropopause-overshooting convection over North America. We examine two 10-yr simulations consisting of 1) a retrospective period (2003–12) forced by ERA-Interim initial and boundary conditions (the control simulation) and 2) the same retrospective period with CMIP5 ensemble-mean high-end emission scenario climate changes added to the initial and boundary conditions (the PGW simulation). Tropopause-overshooting convection in the control simulation is validated against observed overshoots from both ground-based radar observations in the United States and GOES observations over North America. The model is shown to effectively simulate the observed regional distribution, annual cycle, and diurnal cycle of tropopause-overshooting convection. In the PGW simulation, tropopause-overshooting convection is found to increase more than 250% across the model domain, and the projected seasonal period of frequent tropopause-overshooting convection is shown to extend into late summer. Additionally, tropopause-overshooting convection with extreme tropopause-relative heights (>4 km) is more frequent in a warmed climate scenario.
Abstract
Recent field campaigns, observational studies, and modeling work have demonstrated that extratropical tropopause-overshooting convection has a substantial and previously underestimated impact on stratospheric water vapor concentrations. This necessitates improved understanding of how tropopause-overshooting convection will respond to a warming climate. A growing body of research indicates that environments conducive to severe thunderstorms will occur more often and be increasingly unstable in the future, but no study has examined how this may be related to increased overshooting. To rectify this, this study leverages an existing pseudo–global warming (PGW) experiment to evaluate potential future changes in tropopause-overshooting convection over North America. We examine two 10-yr simulations consisting of 1) a retrospective period (2003–12) forced by ERA-Interim initial and boundary conditions (the control simulation) and 2) the same retrospective period with CMIP5 ensemble-mean high-end emission scenario climate changes added to the initial and boundary conditions (the PGW simulation). Tropopause-overshooting convection in the control simulation is validated against observed overshoots from both ground-based radar observations in the United States and GOES observations over North America. The model is shown to effectively simulate the observed regional distribution, annual cycle, and diurnal cycle of tropopause-overshooting convection. In the PGW simulation, tropopause-overshooting convection is found to increase more than 250% across the model domain, and the projected seasonal period of frequent tropopause-overshooting convection is shown to extend into late summer. Additionally, tropopause-overshooting convection with extreme tropopause-relative heights (>4 km) is more frequent in a warmed climate scenario.
Abstract
The Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) product combines CERES and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites to create a record of earth radiation budget (ERB) and the associated cloud properties. As the Terra and Aqua orbits are no longer maintained at a fixed mean local time, EBAF recently transitioned to the CERES and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on NOAA-20 to avoid introducing a time-dependent bias in the record. To ensure smooth transitions between the Terra, combined Terra and Aqua (Terra+Aqua), and NOAA-20 portions of the record, regional climatological adjustments derived from the overlap period between missions are applied to anchor the entire record to Terra+Aqua. We estimate the random error in global monthly anomalies following the transitions to be <0.15 W m−2 for top-of-atmosphere (TOA) flux and <0.1% for cloud fraction, much smaller than the standard deviation in the corresponding anomalies. As the number of ERB instruments will decrease from four to one in just 10 years, there is a high probability that a data gap in the EBAF record will occur, making it challenging to maintain continuity. We estimate that there is a 33% probability of a data gap in 2028 and a 60% probability in 2035. Bridging a data gap using computed TOA fluxes from one satellite product and one atmospheric reanalysis results in errors that are a factor of 4 larger than those obtained when there is overlap between successive missions.
Abstract
The Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) product combines CERES and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites to create a record of earth radiation budget (ERB) and the associated cloud properties. As the Terra and Aqua orbits are no longer maintained at a fixed mean local time, EBAF recently transitioned to the CERES and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on NOAA-20 to avoid introducing a time-dependent bias in the record. To ensure smooth transitions between the Terra, combined Terra and Aqua (Terra+Aqua), and NOAA-20 portions of the record, regional climatological adjustments derived from the overlap period between missions are applied to anchor the entire record to Terra+Aqua. We estimate the random error in global monthly anomalies following the transitions to be <0.15 W m−2 for top-of-atmosphere (TOA) flux and <0.1% for cloud fraction, much smaller than the standard deviation in the corresponding anomalies. As the number of ERB instruments will decrease from four to one in just 10 years, there is a high probability that a data gap in the EBAF record will occur, making it challenging to maintain continuity. We estimate that there is a 33% probability of a data gap in 2028 and a 60% probability in 2035. Bridging a data gap using computed TOA fluxes from one satellite product and one atmospheric reanalysis results in errors that are a factor of 4 larger than those obtained when there is overlap between successive missions.
Abstract
Although links between the atmospheric convergence zone and the local ocean dipole in the South Atlantic are well established, relationships between the South Pacific convergence zone (SPCZ) and the South Pacific quadrupole (SPQ) remain largely unexplored. Based on maximum covariance analysis applied to a 110-yr monthly coupled atmosphere–ocean reanalysis, we describe a coupled quadrupole mode (CQM) that connects the SPCZ and SPQ during austral summer [December–February (DJF)]. The CQM is linked to the “enhanced SPCZ” mode in the atmosphere and the SPQ in the ocean, with the atmospheric signal leading the ocean signal by about 1 month. This coupled mode essentially represents the atmospheric and oceanic responses to a stationary Rossby wave train that propagates from low- to high latitudes before reflecting back toward lower latitudes around 150°E. Coupled atmosphere–ocean feedbacks help to maintain anomalous convective activity in the SPCZ and related circulation anomalies. The stationary waves that organize the CQM are often rooted in anomalous convection over the Maritime Continent and have close connections with the atmospheric wavenumber-4 mode in the midlatitude Southern Hemisphere.
Significance Statement
In this study, we investigate the relationships between coherent large-scale patterns in the South Pacific Ocean and the overlying atmosphere. These patterns, which we refer to as a coupled quadrupole for their four centers of action, impact both local communities and the global climate by shaping rainfall and temperature anomalies across the “four corners” of the South Pacific: east–west and north–south. We show that this coupled quadrupole arises as the joint atmospheric and oceanic response to a large-scale wave that arcs across the entire South Pacific basin more than 10 km above the surface and that feedbacks from the ocean to the atmosphere help it to last longer.
Abstract
Although links between the atmospheric convergence zone and the local ocean dipole in the South Atlantic are well established, relationships between the South Pacific convergence zone (SPCZ) and the South Pacific quadrupole (SPQ) remain largely unexplored. Based on maximum covariance analysis applied to a 110-yr monthly coupled atmosphere–ocean reanalysis, we describe a coupled quadrupole mode (CQM) that connects the SPCZ and SPQ during austral summer [December–February (DJF)]. The CQM is linked to the “enhanced SPCZ” mode in the atmosphere and the SPQ in the ocean, with the atmospheric signal leading the ocean signal by about 1 month. This coupled mode essentially represents the atmospheric and oceanic responses to a stationary Rossby wave train that propagates from low- to high latitudes before reflecting back toward lower latitudes around 150°E. Coupled atmosphere–ocean feedbacks help to maintain anomalous convective activity in the SPCZ and related circulation anomalies. The stationary waves that organize the CQM are often rooted in anomalous convection over the Maritime Continent and have close connections with the atmospheric wavenumber-4 mode in the midlatitude Southern Hemisphere.
Significance Statement
In this study, we investigate the relationships between coherent large-scale patterns in the South Pacific Ocean and the overlying atmosphere. These patterns, which we refer to as a coupled quadrupole for their four centers of action, impact both local communities and the global climate by shaping rainfall and temperature anomalies across the “four corners” of the South Pacific: east–west and north–south. We show that this coupled quadrupole arises as the joint atmospheric and oceanic response to a large-scale wave that arcs across the entire South Pacific basin more than 10 km above the surface and that feedbacks from the ocean to the atmosphere help it to last longer.
Abstract
The tropical Pacific convergence zone plays a crucial role in the global climate system. Previous research studies emphasized the cross-seasonal influence of the South Pacific quadrupole (SPQ) mode on the tropical Pacific climate. This study assesses the relationship between austral summer SPQ and austral winter tropical precipitation in phase 6 of the Coupled Model Intercomparison Project (CMIP6) models. The analysis emphasizes the historical experiments conducted within this time frame, spanning from 1979 to 2014. Our findings reveal that the SPQ is accurately represented in all CMIP6 models, but the connection between SPQ and precipitation is inadequately simulated in most models. To investigate the reasons behind these intermodel differences in reproducing SPQ-related processes, we categorize models into two groups. The comparisons demonstrate that the fidelity of model simulations in replicating the SPQ–tropical precipitation relationship hinges significantly on their capacity to reproduce the positive wind–evaporation–sea surface temperature (WES; SST) feedback over both the southwestern Pacific (25°–10°S; 150°E–160°W) and the southeastern Pacific (30°–10°S; 140°–80°W). This positive WES feedback propagates the SPQ signal into the tropics, intensifying the meridional gradient of SST anomaly in the tropical western-central Pacific, which consequently amplifies convection and rainfall in that area. In the group of models that failed to simulate this relationship accurately, the weakened WES feedback can be traced back to biases in wind speed and its variation. Furthermore, this WES feedback establishes a connection between SPQ and El Niño–Southern Oscillation (ENSO). A better rendition of the SPQ–tropical rainfall connection tends to result in a better simulation of the onset of SPQ-related ENSO events. As a result, this study advances our comprehension of extratropical impacts on the tropics, with the potential to enhance the accuracy of tropical climate simulation and prediction.
Significance Statement
Tropical rainfall plays an important role in the global climate system. Beyond the well-known influence of El Niño–Southern Oscillation (ENSO) on the tropical rainfall, the sea surface temperature (SST) anomaly in the South Pacific has a cross-seasonal impact on the precipitation over the tropical Pacific via air–sea coupled processes. Such SST anomaly pattern shows a quadrupole structure in the extratropical South Pacific, known as the South Pacific quadrupole (SPQ) mode. However, the relationship between SPQ and tropical precipitation remains poorly simulated in most state-of-the-art climate models. One primary reason for this gap between observed and simulated relationships is the underestimation of wind speed and its variation over the south tropical Pacific in these models. This limitation undermines their ability to accurately represent the air–sea interactions that drive tropical precipitation patterns, leading to inaccuracies in simulations. Our study aims to bridge this knowledge gap by enhancing our understanding of the extratropical effects on the tropical Pacific. By exploring the mechanisms underlying the SPQ–precipitation connection, we expect to improve the simulation and prediction capabilities of tropical climate models, thereby enhancing our ability to forecast and adapt to future climatic changes.
Abstract
The tropical Pacific convergence zone plays a crucial role in the global climate system. Previous research studies emphasized the cross-seasonal influence of the South Pacific quadrupole (SPQ) mode on the tropical Pacific climate. This study assesses the relationship between austral summer SPQ and austral winter tropical precipitation in phase 6 of the Coupled Model Intercomparison Project (CMIP6) models. The analysis emphasizes the historical experiments conducted within this time frame, spanning from 1979 to 2014. Our findings reveal that the SPQ is accurately represented in all CMIP6 models, but the connection between SPQ and precipitation is inadequately simulated in most models. To investigate the reasons behind these intermodel differences in reproducing SPQ-related processes, we categorize models into two groups. The comparisons demonstrate that the fidelity of model simulations in replicating the SPQ–tropical precipitation relationship hinges significantly on their capacity to reproduce the positive wind–evaporation–sea surface temperature (WES; SST) feedback over both the southwestern Pacific (25°–10°S; 150°E–160°W) and the southeastern Pacific (30°–10°S; 140°–80°W). This positive WES feedback propagates the SPQ signal into the tropics, intensifying the meridional gradient of SST anomaly in the tropical western-central Pacific, which consequently amplifies convection and rainfall in that area. In the group of models that failed to simulate this relationship accurately, the weakened WES feedback can be traced back to biases in wind speed and its variation. Furthermore, this WES feedback establishes a connection between SPQ and El Niño–Southern Oscillation (ENSO). A better rendition of the SPQ–tropical rainfall connection tends to result in a better simulation of the onset of SPQ-related ENSO events. As a result, this study advances our comprehension of extratropical impacts on the tropics, with the potential to enhance the accuracy of tropical climate simulation and prediction.
Significance Statement
Tropical rainfall plays an important role in the global climate system. Beyond the well-known influence of El Niño–Southern Oscillation (ENSO) on the tropical rainfall, the sea surface temperature (SST) anomaly in the South Pacific has a cross-seasonal impact on the precipitation over the tropical Pacific via air–sea coupled processes. Such SST anomaly pattern shows a quadrupole structure in the extratropical South Pacific, known as the South Pacific quadrupole (SPQ) mode. However, the relationship between SPQ and tropical precipitation remains poorly simulated in most state-of-the-art climate models. One primary reason for this gap between observed and simulated relationships is the underestimation of wind speed and its variation over the south tropical Pacific in these models. This limitation undermines their ability to accurately represent the air–sea interactions that drive tropical precipitation patterns, leading to inaccuracies in simulations. Our study aims to bridge this knowledge gap by enhancing our understanding of the extratropical effects on the tropical Pacific. By exploring the mechanisms underlying the SPQ–precipitation connection, we expect to improve the simulation and prediction capabilities of tropical climate models, thereby enhancing our ability to forecast and adapt to future climatic changes.