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Abstract
Extratropical storms, particularly explosive storms or “weather bombs” with exceptionally high deepening rates, present substantial risks and are susceptible to climate change. Individual storms may exhibit a complex and hardly detectable response to human-driven climate change because of the atmosphere’s chaotic nature and variability at the regional level. It is thus essential to understand changes in specific storms for building local resilience and advancing our overall comprehension of storm trends. To address this challenge, this study compares analogs—storms with a similar backward track until making landfall—in two climates of three explosive storms impacting different European locations: Alex (October 2020), Eunice (January 2022), and Xynthia (February 2010). We use a large ensemble dataset of 105 members from the Community Earth System Model, version 1 (CESM1). These analogs are identified in two periods: the present-day climate (1991–2001) and a future climate scenario characterized by high anthropogenic greenhouse gas emissions [representative concentration pathway 8.5 (RCP8.5), 2091–2101]. We evaluate future changes in the frequency of occurrence of the storms and intensity, as well as in meteorological hazards and the underlying dynamics. For all storms, our analysis reveals an increase in precipitation and wind severity over land associated with the explosive analogs in the future climate. These findings underscore the potential consequences of explosive storms modified by climate change and their subsequent hazards in various regions of Europe, offering evidence that can be used to prepare and enhance adaptation processes.
Significance Statement
This study investigates the impact of climate change on explosive storms, or weather bombs, and their potential consequences for European regions. We project future scenarios of three specific storms, Alex, Eunice, and Xynthia, using a state-of-the-art climate model. Our findings reveal an increase in precipitation and wind over land associated with these storms, emphasizing the heightened risks associated with climate change. The significance lies in understanding the local implications of explosive storms, aiding in the development of resilient strategies and adaptation measures.
Abstract
Extratropical storms, particularly explosive storms or “weather bombs” with exceptionally high deepening rates, present substantial risks and are susceptible to climate change. Individual storms may exhibit a complex and hardly detectable response to human-driven climate change because of the atmosphere’s chaotic nature and variability at the regional level. It is thus essential to understand changes in specific storms for building local resilience and advancing our overall comprehension of storm trends. To address this challenge, this study compares analogs—storms with a similar backward track until making landfall—in two climates of three explosive storms impacting different European locations: Alex (October 2020), Eunice (January 2022), and Xynthia (February 2010). We use a large ensemble dataset of 105 members from the Community Earth System Model, version 1 (CESM1). These analogs are identified in two periods: the present-day climate (1991–2001) and a future climate scenario characterized by high anthropogenic greenhouse gas emissions [representative concentration pathway 8.5 (RCP8.5), 2091–2101]. We evaluate future changes in the frequency of occurrence of the storms and intensity, as well as in meteorological hazards and the underlying dynamics. For all storms, our analysis reveals an increase in precipitation and wind severity over land associated with the explosive analogs in the future climate. These findings underscore the potential consequences of explosive storms modified by climate change and their subsequent hazards in various regions of Europe, offering evidence that can be used to prepare and enhance adaptation processes.
Significance Statement
This study investigates the impact of climate change on explosive storms, or weather bombs, and their potential consequences for European regions. We project future scenarios of three specific storms, Alex, Eunice, and Xynthia, using a state-of-the-art climate model. Our findings reveal an increase in precipitation and wind over land associated with these storms, emphasizing the heightened risks associated with climate change. The significance lies in understanding the local implications of explosive storms, aiding in the development of resilient strategies and adaptation measures.
Abstract
The likely changes to precipitation seasonality with warming are both impactful and not well understood. This work aims to describe areas that experience similar changes to seasonal precipitation irrespective of the original underlying precipitation seasonality. We train a self-organizing map on the difference between the seasonal cycle of precipitation in the past and in a high-warming future climate as represented by the Community Earth System Model, version 2, to create regions with similar changes in precipitation seasonality. This method is applied separately over land and ocean surfaces because of the differing processes leading to precipitation over each. This method indicates that future changes in seasonal precipitation are most varied in the tropics because of a southward shift in the intertropical convergence zone. The seasonal shifts found over midlatitude oceans indicate a poleward shift in atmospheric river activity. We find a correspondence between certain land-based precipitation changes and Köppen climate classification. The seasonality of large-scale and convective precipitation is examined for each region. The relationship between the seasonal changes to precipitation and associated atmospheric processes is discussed. These processes include atmospheric rivers, the intertropical convergence zone, tropical cyclones, and monsoons.
Abstract
The likely changes to precipitation seasonality with warming are both impactful and not well understood. This work aims to describe areas that experience similar changes to seasonal precipitation irrespective of the original underlying precipitation seasonality. We train a self-organizing map on the difference between the seasonal cycle of precipitation in the past and in a high-warming future climate as represented by the Community Earth System Model, version 2, to create regions with similar changes in precipitation seasonality. This method is applied separately over land and ocean surfaces because of the differing processes leading to precipitation over each. This method indicates that future changes in seasonal precipitation are most varied in the tropics because of a southward shift in the intertropical convergence zone. The seasonal shifts found over midlatitude oceans indicate a poleward shift in atmospheric river activity. We find a correspondence between certain land-based precipitation changes and Köppen climate classification. The seasonality of large-scale and convective precipitation is examined for each region. The relationship between the seasonal changes to precipitation and associated atmospheric processes is discussed. These processes include atmospheric rivers, the intertropical convergence zone, tropical cyclones, and monsoons.
Abstract
This study presents a framework to assess climate variability and change through atmospheric circulation patterns (CPs) and their link with regional processes across time scales. We evaluate the CP impacts on daily rainfall and maximum and minimum temperatures in the Iberian Peninsula using sea level pressure (SLP) during 1950–2022. Different sensitivity analyses are performed, employing multiple spatial domains and number of patterns. An optimal classification is found in midlatitudes, centered over the Mediterranean basin and covering part of the North Atlantic Ocean, which can identify atmospheric configurations significantly related to discriminated rainfall and temperature anomalies, with clear seasonal behavior. The temporal variability of CPs is studied across time scales showing, e.g., that transitions between patterns are faster in autumn and spring, and that CPs exhibit distinct temporal variability at intraseasonal, seasonal, interannual, and decadal scales, including significant long-term trends on their frequency. CPs influence temperature and precipitation variations throughout the year. The winter season exhibits the largest atmospheric circulation variability, while the summer is dominated by persistent high-pressure structures—the subtropical Azores high—leading to warm and dry conditions. Based on an interannual correlation analysis, some CPs are significantly associated with the North Atlantic Oscillation (NAO), stronger during winter, indicating the NAO modulation on the regional-to-local climatic features. Overall, this approach arises as a dynamic cross-time-scale framework that can be adapted to specific user needs and levels of regional detail, being useful to study climate drivers for climate change and to perform a process-based evaluation of climate models.
Abstract
This study presents a framework to assess climate variability and change through atmospheric circulation patterns (CPs) and their link with regional processes across time scales. We evaluate the CP impacts on daily rainfall and maximum and minimum temperatures in the Iberian Peninsula using sea level pressure (SLP) during 1950–2022. Different sensitivity analyses are performed, employing multiple spatial domains and number of patterns. An optimal classification is found in midlatitudes, centered over the Mediterranean basin and covering part of the North Atlantic Ocean, which can identify atmospheric configurations significantly related to discriminated rainfall and temperature anomalies, with clear seasonal behavior. The temporal variability of CPs is studied across time scales showing, e.g., that transitions between patterns are faster in autumn and spring, and that CPs exhibit distinct temporal variability at intraseasonal, seasonal, interannual, and decadal scales, including significant long-term trends on their frequency. CPs influence temperature and precipitation variations throughout the year. The winter season exhibits the largest atmospheric circulation variability, while the summer is dominated by persistent high-pressure structures—the subtropical Azores high—leading to warm and dry conditions. Based on an interannual correlation analysis, some CPs are significantly associated with the North Atlantic Oscillation (NAO), stronger during winter, indicating the NAO modulation on the regional-to-local climatic features. Overall, this approach arises as a dynamic cross-time-scale framework that can be adapted to specific user needs and levels of regional detail, being useful to study climate drivers for climate change and to perform a process-based evaluation of climate models.
Abstract
We analyze biases in subseasonal forecast models and their effect on Southwest United States (SWUS) precipitation prediction (2–6-week time scale). Cluster analyses identify three primary wave trains associated with SWUS precipitation: a meridional El Niño–Southern Oscillation (ENSO)–type wave train, an arching Pacific–North American (PNA)–type wave train, and a circumglobal zonal wave train. Compared to reanalysis, the models overrepresent the arching pattern, underrepresent the zonal pattern, and produce mixed results for the meridional pattern. The arching pattern overrepresentation is linked to model mean flow biases in the midlatitude–subpolar North Pacific, which cause a westward retraction of the region of forbidden linear Rossby wave propagation. The zonal pattern underrepresentation is linked to westerly biases in the subtropical jet, which cause a westward retraction of the waveguide in the midlatitude eastern North Pacific and divert wave trains southward. These results are confirmed using linear, barotropic ray-tracing analysis. In addition to mean state biases, the models also contain errors in their representation of the Madden–Julian oscillation (MJO). Tropical convection anomalies associated with the MJO are too weak and incoherent at lead times greater than 2 weeks when compared to reanalysis. Additionally, there is a strong SWUS precipitation signal as far out as 5 weeks after a strong MJO in reanalysis, associated with its persistent eastward propagation, but this signal is absent in the models. Our results indicate that there is still significant room for improvement in subseasonal predictions if we can reduce model biases in the background flow and improve the representation of the MJO.
Abstract
We analyze biases in subseasonal forecast models and their effect on Southwest United States (SWUS) precipitation prediction (2–6-week time scale). Cluster analyses identify three primary wave trains associated with SWUS precipitation: a meridional El Niño–Southern Oscillation (ENSO)–type wave train, an arching Pacific–North American (PNA)–type wave train, and a circumglobal zonal wave train. Compared to reanalysis, the models overrepresent the arching pattern, underrepresent the zonal pattern, and produce mixed results for the meridional pattern. The arching pattern overrepresentation is linked to model mean flow biases in the midlatitude–subpolar North Pacific, which cause a westward retraction of the region of forbidden linear Rossby wave propagation. The zonal pattern underrepresentation is linked to westerly biases in the subtropical jet, which cause a westward retraction of the waveguide in the midlatitude eastern North Pacific and divert wave trains southward. These results are confirmed using linear, barotropic ray-tracing analysis. In addition to mean state biases, the models also contain errors in their representation of the Madden–Julian oscillation (MJO). Tropical convection anomalies associated with the MJO are too weak and incoherent at lead times greater than 2 weeks when compared to reanalysis. Additionally, there is a strong SWUS precipitation signal as far out as 5 weeks after a strong MJO in reanalysis, associated with its persistent eastward propagation, but this signal is absent in the models. Our results indicate that there is still significant room for improvement in subseasonal predictions if we can reduce model biases in the background flow and improve the representation of the MJO.
Abstract
This study reveals the remarkable interdecadal changes in the influence of boreal winter Arctic sea ice concentration (SIC) anomalies (ASICAs) in the Greenland–Barents Seas on the subsequent El Niño–Southern Oscillation (ENSO) development. Winter ASICA is strongly associated with the subsequent winter ENSO before the late 1980s and after the late 2000s, but their connection is weak during the 1990s and the 2000s. The interdecadal variations in the influence of ASICA on ENSO are associated with changes in the spatial structure of the ASICA-induced North Pacific atmospheric anomalies. During high-correlation periods, winter SIC increases in the Greenland–Barents Seas lead to tropospheric cooling via the suppression of upward surface heat fluxes, which further trigger an atmospheric teleconnection from the Arctic to the North Pacific. The accompanying North Pacific Oscillation–like atmospheric anomalies result in sea surface temperature (SST) warming in the subtropical North Pacific, which extends southward into the tropical Pacific via wind–evaporation–SST feedback and leads to surface westerly anomalies over the tropical western Pacific in the following summer. The tropical western Pacific westerly wind anomalies impact the subsequent ENSO development via triggering positive Bjerknes air–sea interaction. During low-correlation periods, atmospheric anomalies over the North Pacific generated by the winter ASICA are located more northward and cannot induce marked subtropical North Pacific SST anomalies and thus have a weak impact on the following ENSO development. Numerical experiments suggest that the interdecadal variation in the spatial structure of the North Pacific atmospheric anomalies induced by the winter ASICA is partly attributed to change in the atmospheric mean flow.
Significance Statement
Arctic sea ice is an important component of the global climate system. Studies have shown that Arctic sea ice has decreased significantly in recent decades, which is considered to be one of the most important responses of Earth’s climate system to global climate change. A number of studies have shown that Arctic sea ice anomalies have significant impacts on weather and climate over mid–high latitudes through tropospheric and stratospheric processes. Recent studies have suggested that the effects of Arctic sea ice anomalies could extend to the tropics via air–sea interactions. El Niño–Southern Oscillation (ENSO) is the strongest air–sea coupled system in the tropics and can have a significant impact on climate over the globe. ENSO is also considered to be one of the most important sources of subseasonal–seasonal climate predictability over many parts of the globe. It is therefore important to study the factors for the ENSO occurrence. A recent study showed that Arctic sea ice anomalies during boreal winter in the Greenland–Barents Seas have a significant impact on the following winter ENSO. In this study, we further reveal that the influence of winter Arctic sea ice anomalies in the Greenland–Barents Seas on the subsequent ENSO is unstable and has undergone significant interdecadal variation. We then investigate the mechanisms underlying the interdecadal variation via observational analysis and numerical simulations. The results of this study not only have implications for improving the prediction of ENSO but also could improve our understanding of the physical link between high-latitude climate systems and tropical air–sea coupling systems.
Abstract
This study reveals the remarkable interdecadal changes in the influence of boreal winter Arctic sea ice concentration (SIC) anomalies (ASICAs) in the Greenland–Barents Seas on the subsequent El Niño–Southern Oscillation (ENSO) development. Winter ASICA is strongly associated with the subsequent winter ENSO before the late 1980s and after the late 2000s, but their connection is weak during the 1990s and the 2000s. The interdecadal variations in the influence of ASICA on ENSO are associated with changes in the spatial structure of the ASICA-induced North Pacific atmospheric anomalies. During high-correlation periods, winter SIC increases in the Greenland–Barents Seas lead to tropospheric cooling via the suppression of upward surface heat fluxes, which further trigger an atmospheric teleconnection from the Arctic to the North Pacific. The accompanying North Pacific Oscillation–like atmospheric anomalies result in sea surface temperature (SST) warming in the subtropical North Pacific, which extends southward into the tropical Pacific via wind–evaporation–SST feedback and leads to surface westerly anomalies over the tropical western Pacific in the following summer. The tropical western Pacific westerly wind anomalies impact the subsequent ENSO development via triggering positive Bjerknes air–sea interaction. During low-correlation periods, atmospheric anomalies over the North Pacific generated by the winter ASICA are located more northward and cannot induce marked subtropical North Pacific SST anomalies and thus have a weak impact on the following ENSO development. Numerical experiments suggest that the interdecadal variation in the spatial structure of the North Pacific atmospheric anomalies induced by the winter ASICA is partly attributed to change in the atmospheric mean flow.
Significance Statement
Arctic sea ice is an important component of the global climate system. Studies have shown that Arctic sea ice has decreased significantly in recent decades, which is considered to be one of the most important responses of Earth’s climate system to global climate change. A number of studies have shown that Arctic sea ice anomalies have significant impacts on weather and climate over mid–high latitudes through tropospheric and stratospheric processes. Recent studies have suggested that the effects of Arctic sea ice anomalies could extend to the tropics via air–sea interactions. El Niño–Southern Oscillation (ENSO) is the strongest air–sea coupled system in the tropics and can have a significant impact on climate over the globe. ENSO is also considered to be one of the most important sources of subseasonal–seasonal climate predictability over many parts of the globe. It is therefore important to study the factors for the ENSO occurrence. A recent study showed that Arctic sea ice anomalies during boreal winter in the Greenland–Barents Seas have a significant impact on the following winter ENSO. In this study, we further reveal that the influence of winter Arctic sea ice anomalies in the Greenland–Barents Seas on the subsequent ENSO is unstable and has undergone significant interdecadal variation. We then investigate the mechanisms underlying the interdecadal variation via observational analysis and numerical simulations. The results of this study not only have implications for improving the prediction of ENSO but also could improve our understanding of the physical link between high-latitude climate systems and tropical air–sea coupling systems.
Abstract
Past studies have shown a significant observed poleward trend in the latitude at which tropical cyclones reach their lifetime maximum intensity (LMI), especially in the northwest Pacific basin. Given the brevity of the historical record, it remains difficult to separate the forced trend from internal variability of the climate system. A recently developed tropical cyclone downscaling model is used to downscale the Community Earth System Model, version 2 (CESM2), preindustrial control simulation. It is found that the observed trend in the latitude at which tropical cyclones reach their LMI in the northwest Pacific is very unlikely to be caused by internal variability. The same downscaling model is then used to downscale CESM2 simulations under historical forcing. The resulting trend distribution shows a significant poleward migration of tropical cyclone LMI even after regressing out both natural variability and the part of the forced warming pattern that projects onto natural variability. The results indicate that the observed poleward migration of the latitude at which tropical cyclones reach their LMI in the northwest Pacific basin is likely to be, at least in part, forced. However, the magnitude of the projected poleward trend in climate models can be significantly modulated by the simulated spatial pattern of ocean warming. This highlights how discrepancies between models and observations, with regard to projected changes to the equatorial zonal sea surface temperature gradient under anthropogenic forcing, can lead to large uncertainties in projected changes to the LMI latitude of tropical cyclones.
Significance Statement
Observations in the northwest Pacific basin show that the latitude at which tropical cyclones are at their most intense has been trending northward in the recent half century. These changes are important since tropical cyclones could bring hazardous weather to coastal areas that are poorly equipped to handle them. Here, we show that natural variations in Earth’s climate are very unlikely to explain the observed poleward trend in the latitude that tropical cyclone reach their maximum intensity. We find that it is much more likely that the observed trend is forced by human-related emissions, though the spatial pattern of warming in response to greenhouse emissions can have significant impacts on the magnitude of the trend.
Abstract
Past studies have shown a significant observed poleward trend in the latitude at which tropical cyclones reach their lifetime maximum intensity (LMI), especially in the northwest Pacific basin. Given the brevity of the historical record, it remains difficult to separate the forced trend from internal variability of the climate system. A recently developed tropical cyclone downscaling model is used to downscale the Community Earth System Model, version 2 (CESM2), preindustrial control simulation. It is found that the observed trend in the latitude at which tropical cyclones reach their LMI in the northwest Pacific is very unlikely to be caused by internal variability. The same downscaling model is then used to downscale CESM2 simulations under historical forcing. The resulting trend distribution shows a significant poleward migration of tropical cyclone LMI even after regressing out both natural variability and the part of the forced warming pattern that projects onto natural variability. The results indicate that the observed poleward migration of the latitude at which tropical cyclones reach their LMI in the northwest Pacific basin is likely to be, at least in part, forced. However, the magnitude of the projected poleward trend in climate models can be significantly modulated by the simulated spatial pattern of ocean warming. This highlights how discrepancies between models and observations, with regard to projected changes to the equatorial zonal sea surface temperature gradient under anthropogenic forcing, can lead to large uncertainties in projected changes to the LMI latitude of tropical cyclones.
Significance Statement
Observations in the northwest Pacific basin show that the latitude at which tropical cyclones are at their most intense has been trending northward in the recent half century. These changes are important since tropical cyclones could bring hazardous weather to coastal areas that are poorly equipped to handle them. Here, we show that natural variations in Earth’s climate are very unlikely to explain the observed poleward trend in the latitude that tropical cyclone reach their maximum intensity. We find that it is much more likely that the observed trend is forced by human-related emissions, though the spatial pattern of warming in response to greenhouse emissions can have significant impacts on the magnitude of the trend.
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
Two common methods used to develop a process-level understanding of global cloud cover are 1) analyzing large-scale meteorological variables (cloud controlling factors) associated with cloud variability and 2) classifying cloud types using clustering algorithms applied to satellite data, such as the International Satellite Cloud Climatology Project (ISCCP) weather states. The cloud controlling factor method is advantageous to apply to climate models, as it does not rely on cloud parameterizations or the availability of satellite simulator output. The purpose of this study is to document the relationship between cloud controlling factors and the ISCCP weather states in the observational record, providing a benchmark for the application of cloud controlling factors to study individual cloud types in future studies. Most ISCCP weather states are linked to distinct dynamical regimes characterized by unique combinations of six cloud controlling factors. These relationships are present in both the long-term mean climatology and daily-to-monthly climate variability. For example, deep convective and midlatitude storm clouds dominate ascending regions. In descending regions, shallow cumulus is more frequent in regimes characterized by weak boundary layer temperature inversions [estimated inversion strength (EIS)] and strong subsidence, and stratocumulus is more frequent in regimes with larger values of EIS, weaker subsidence, and relatively weak near-surface cold advection. Midlevel clouds are prominent in descending regions with strong cold advection. Overall, the results of this study suggest promise in using cloud controlling factors to identify dynamical regimes where individual cloud types are more or less likely and to understand the physical processes responsible for the transitions among them.
Abstract
Two common methods used to develop a process-level understanding of global cloud cover are 1) analyzing large-scale meteorological variables (cloud controlling factors) associated with cloud variability and 2) classifying cloud types using clustering algorithms applied to satellite data, such as the International Satellite Cloud Climatology Project (ISCCP) weather states. The cloud controlling factor method is advantageous to apply to climate models, as it does not rely on cloud parameterizations or the availability of satellite simulator output. The purpose of this study is to document the relationship between cloud controlling factors and the ISCCP weather states in the observational record, providing a benchmark for the application of cloud controlling factors to study individual cloud types in future studies. Most ISCCP weather states are linked to distinct dynamical regimes characterized by unique combinations of six cloud controlling factors. These relationships are present in both the long-term mean climatology and daily-to-monthly climate variability. For example, deep convective and midlatitude storm clouds dominate ascending regions. In descending regions, shallow cumulus is more frequent in regimes characterized by weak boundary layer temperature inversions [estimated inversion strength (EIS)] and strong subsidence, and stratocumulus is more frequent in regimes with larger values of EIS, weaker subsidence, and relatively weak near-surface cold advection. Midlevel clouds are prominent in descending regions with strong cold advection. Overall, the results of this study suggest promise in using cloud controlling factors to identify dynamical regimes where individual cloud types are more or less likely and to understand the physical processes responsible for the transitions among them.
Abstract
Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability.
Significance Statement
Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.
Abstract
Forecasts of sea surface temperature anomalies (SSTAs) provide essential information to stakeholders of marine resources in coastal ecosystems, such as the California Current Large Marine Ecosystem (CCLME), at management-relevant monthly-to-annual time scales. Diagnosing dynamical sources of predictability and the mechanisms differentiating skill among forecasts is required for verification and improvement in operational forecasting systems. Using retrospective forecasts (1982–2020) from a four-member subset of the North American Multi-Model Ensemble (NMME), we evaluate the conditional skill of SSTA forecasts in the CCLME at monthly resolution for lead times up to 10.5 months. Forecasts from ensemble members with relatively small SSTA errors at shorter lead times retain higher skill at longer lead times, with the most substantial and long-lasting increases for forecasts initialized in the fall and early spring. The “best” low-error SSTA forecasts are characterized by increased skill in the prediction of North Pacific atmospheric circulation [sea level pressure (SLP) and 200-hPa geopotential height] the month prior to the evaluation of SSTA errors in the CCLME and exhibit more realistic progressions of anomalous SLP. The Pacific meridional mode (PMM) emerges as a diagnostic of skillful North Pacific atmosphere–ocean coupling, as forecasts that correctly simulate the PMM and its associated SLP variability increase the SSTA prediction skill in the CCLME in the fall through spring. Predictable coupled ocean–atmosphere modes provide a target for enhancing predictability with early detection of the onset of a deterministic progression emerging from stochastic atmospheric variability.
Significance Statement
Global forecast systems provide near-term climate predictions that inform the management of marine resources, such as those of the California Current Large Marine Ecosystem. In this study, we probe the processes which lead forecasts to succeed or fail at predicting sea surface temperatures in the California Current at seasonal time scales among retrospective forecasts from the North American Multimodel Ensemble. We demonstrate that forecasts which best simulate sea surface temperatures at the earliest lead times sustain advantages in forecast skill and find that correctly simulating extratropical atmospheric circulation increases the predictive skill of sea surface temperatures in the northeast Pacific in the following lead times. Our results offer North Pacific atmospheric circulation as a target for forecast model improvement that would additionally enhance ocean forecasts.
Abstract
Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales.
Significance Statement
The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported.
Abstract
Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales.
Significance Statement
The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported.
