Abstract
Absolute calibration of spaceborne microwave radiometer observations consists of accurate determination of antenna cold space spillover, cross-polarization contamination, and non-linearity coefficients of the receivers. We deem the GMI sensor to be the most accurate calibrated spaceborne microwave radiometer due to its unique calibration design features and its carefully planned orbit maneuvers. We demonstrate how to transfer the GMI calibration to other spaceborne radiometers, whose operations have sufficient time overlap with GMI. Specifically, we show results for WindSat and AMSR2. The sensor intercalibration is based on brightness temperature match-ups between GMI and the other instruments over both open ocean and rainforest scenes. In order to assess the calibration accuracy, we compare the intercalibrated brightness temperatures with radiative transfer model calculations. In addition, we provide in-situ validation results for wind speed and water vapor retrievals from the intercalibrated sensors. The intercalibration methodology allows for the creation of a multi-decadal climate data record from passive microwave satellite observations.
Abstract
Absolute calibration of spaceborne microwave radiometer observations consists of accurate determination of antenna cold space spillover, cross-polarization contamination, and non-linearity coefficients of the receivers. We deem the GMI sensor to be the most accurate calibrated spaceborne microwave radiometer due to its unique calibration design features and its carefully planned orbit maneuvers. We demonstrate how to transfer the GMI calibration to other spaceborne radiometers, whose operations have sufficient time overlap with GMI. Specifically, we show results for WindSat and AMSR2. The sensor intercalibration is based on brightness temperature match-ups between GMI and the other instruments over both open ocean and rainforest scenes. In order to assess the calibration accuracy, we compare the intercalibrated brightness temperatures with radiative transfer model calculations. In addition, we provide in-situ validation results for wind speed and water vapor retrievals from the intercalibrated sensors. The intercalibration methodology allows for the creation of a multi-decadal climate data record from passive microwave satellite observations.
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
The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model.
Significance Statement
As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed.
Abstract
The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model.
Significance Statement
As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed.
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
This study examined future changes in the microphysical properties of surface solid precipitation over Hokkaido, Japan. A process-tracking model that predicts the mass of the hydrometeors generated by each cloud microphysical process was implemented in a meteorological model. This implementation aimed to analyze the mass fraction of hydrometeors resulting from depositional growth and the riming process to the total mass of surface solid precipitation. Results from pseudo–global warming experiments suggest two potential future changes in the characteristics of surface solid precipitation over Hokkaido. First, the rimed particles are expected to increase and be dominant over the west and northwest coast of Hokkaido, where heavy snowfall occurs primarily due to the lake effect. Second, the mass fraction from depositional growth under relatively higher temperatures is expected to increase. This increase is anticipated to be dominant over the eastern part and mountainous area of Hokkaido. Additionally, the fraction of liquid precipitation to total precipitation is expected to increase in the future. These results suggest that the microphysical properties of solid precipitation in Hokkaido are expected to be similar to those observed in the current climate over Hokuriku, the central part of Japan even in warmer climate conditions.
Significance Statement
This study examines potential future changes in the growth processes contributing to surface precipitation particles in Hokkaido, Japan. The surface solid precipitation particles in the western and eastern regions of Hokkaido are mainly generated through depositional growth that occurs within the temperature ranges −36° to −20°C and −20° to −10°C, respectively. A future shift is anticipated, with riming becoming the primary process. This shift suggests that snowfall particles will be heavier than those in the current climate, which would increase the snow-removal workload. The change in precipitation characteristics could influence adaptation and mitigation strategies for climate change in cold regions.
Abstract
This study examined future changes in the microphysical properties of surface solid precipitation over Hokkaido, Japan. A process-tracking model that predicts the mass of the hydrometeors generated by each cloud microphysical process was implemented in a meteorological model. This implementation aimed to analyze the mass fraction of hydrometeors resulting from depositional growth and the riming process to the total mass of surface solid precipitation. Results from pseudo–global warming experiments suggest two potential future changes in the characteristics of surface solid precipitation over Hokkaido. First, the rimed particles are expected to increase and be dominant over the west and northwest coast of Hokkaido, where heavy snowfall occurs primarily due to the lake effect. Second, the mass fraction from depositional growth under relatively higher temperatures is expected to increase. This increase is anticipated to be dominant over the eastern part and mountainous area of Hokkaido. Additionally, the fraction of liquid precipitation to total precipitation is expected to increase in the future. These results suggest that the microphysical properties of solid precipitation in Hokkaido are expected to be similar to those observed in the current climate over Hokuriku, the central part of Japan even in warmer climate conditions.
Significance Statement
This study examines potential future changes in the growth processes contributing to surface precipitation particles in Hokkaido, Japan. The surface solid precipitation particles in the western and eastern regions of Hokkaido are mainly generated through depositional growth that occurs within the temperature ranges −36° to −20°C and −20° to −10°C, respectively. A future shift is anticipated, with riming becoming the primary process. This shift suggests that snowfall particles will be heavier than those in the current climate, which would increase the snow-removal workload. The change in precipitation characteristics could influence adaptation and mitigation strategies for climate change in cold regions.
Abstract
Humid heat and associated heat stress have increased in frequency, intensity, and duration across the globe, particularly at lower latitudes. One of the more robust metrics for heat stress impacts on the human body is wet-bulb globe temperature (WBGT), because it incorporates temperature, humidity, wind speed, and solar radiation. WBGT can typically only be measured using nonstandard instrumentation (e.g., black globe thermometers). However, estimation formulas have been developed to calculate WBGT using standard surface meteorological variables. This study evaluates several WBGT estimation formulas for the southeastern United States using North Carolina Environment and Climate Observing Network (ECONet) and U.S. Military measurement campaign data as verification. The estimation algorithm with the smallest mean absolute error was subsequently chosen to evaluate summer WBGT trends and extremes at 39 ASOS stations with long continuous (1950–2023) data records. Trend results showed that summer WBGT has increased throughout much of the southeastern United States, with larger increases at night than during the day. Although there were some surprisingly large WBGT trends at higher elevation locations far from coastlines, the greatest increases were predominantly located in the Florida Peninsula and Louisiana. Increases in the intensity and frequency of extreme (90th percentile) WBGTs were particularly stark in large coastal urban centers (e.g., New Orleans, Tampa, and Miami). Some locations like New Orleans and Tampa have experienced more than two additional extreme heat stress days and nights per decade since 1950, with an exponential escalation in the number of extreme summer nights during the most recent decade.
Significance Statement
Humid heat and associated heat stress pose threats to health in the moist subtropical climate of the southeastern United States. Wet-bulb globe temperature (WBGT) is a robust metric for heat stress but must be estimated using complex algorithms. We first evaluated the accuracy of three WBGT algorithms in the southeastern United States, using measured verification data. Subsequently, we used the most accurate algorithm to investigate WBGT trends and extremes since 1950 in 39 cities. Results showed that summer heat stress has increased throughout the region, especially at night. Increases in the intensity and frequency of extreme heat stress were most prevalent at urban coastal locations in Florida and Louisiana, emphasizing the impacts of increased urbanization and evaporation on heat stress.
Abstract
Humid heat and associated heat stress have increased in frequency, intensity, and duration across the globe, particularly at lower latitudes. One of the more robust metrics for heat stress impacts on the human body is wet-bulb globe temperature (WBGT), because it incorporates temperature, humidity, wind speed, and solar radiation. WBGT can typically only be measured using nonstandard instrumentation (e.g., black globe thermometers). However, estimation formulas have been developed to calculate WBGT using standard surface meteorological variables. This study evaluates several WBGT estimation formulas for the southeastern United States using North Carolina Environment and Climate Observing Network (ECONet) and U.S. Military measurement campaign data as verification. The estimation algorithm with the smallest mean absolute error was subsequently chosen to evaluate summer WBGT trends and extremes at 39 ASOS stations with long continuous (1950–2023) data records. Trend results showed that summer WBGT has increased throughout much of the southeastern United States, with larger increases at night than during the day. Although there were some surprisingly large WBGT trends at higher elevation locations far from coastlines, the greatest increases were predominantly located in the Florida Peninsula and Louisiana. Increases in the intensity and frequency of extreme (90th percentile) WBGTs were particularly stark in large coastal urban centers (e.g., New Orleans, Tampa, and Miami). Some locations like New Orleans and Tampa have experienced more than two additional extreme heat stress days and nights per decade since 1950, with an exponential escalation in the number of extreme summer nights during the most recent decade.
Significance Statement
Humid heat and associated heat stress pose threats to health in the moist subtropical climate of the southeastern United States. Wet-bulb globe temperature (WBGT) is a robust metric for heat stress but must be estimated using complex algorithms. We first evaluated the accuracy of three WBGT algorithms in the southeastern United States, using measured verification data. Subsequently, we used the most accurate algorithm to investigate WBGT trends and extremes since 1950 in 39 cities. Results showed that summer heat stress has increased throughout the region, especially at night. Increases in the intensity and frequency of extreme heat stress were most prevalent at urban coastal locations in Florida and Louisiana, emphasizing the impacts of increased urbanization and evaporation on heat stress.
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.
