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Abstract
The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s−1). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction.
Significance Statement
Storm size has long been speculated to play a crucial role in modulating the TC self-induced sea surface temperature (SST) cooling and thus potentially influence TC intensification through ocean negative feedback. Nevertheless, systematic analysis is lacking. Here we show that larger TCs tend to generate stronger SST cooling and have longer exposure to the cooling effect, both of which enhance the strength of the negative feedback. Consequently, larger TCs undergo weaker intensification and are less likely to experience rapid intensification than smaller TCs. These results demonstrate that storm size can influence TC intensification not only from the atmospheric pathway, but also via the oceanic pathway. Accurate characterization of this oceanic pathway in coupled models is important to accurately forecast TC intensity.
Abstract
The effect of tropical cyclone (TC) size on TC-induced sea surface temperature (SST) cooling and subsequent TC intensification is an intriguing issue without much exploration. Via compositing satellite-observed SST over the western North Pacific during 2004–19, this study systematically examined the effect of storm size on the magnitude, spatial extension, and temporal evolution of TC-induced SST anomalies (SSTA). Consequential influence on TC intensification is also explored. Among the various TC wind radii, SSTA are found to be most sensitive to the 34-kt wind radius (R34) (1 kt ≈ 0.51 m s−1). Generally, large TCs generate stronger and more widespread SSTA than small TCs (for category 1–2 TCs, R34: ∼270 vs 160 km; SSTA: −1.7° vs −0.9°C). Despite the same effect on prolonging residence time of TC winds, the effect of doubling R34 on SSTA is more profound than halving translation speed, due to more wind energy input into the upper ocean. Also differing from translation speed, storm size has a rather modest effect on the rightward shift and timing of maximum cooling. This study further demonstrates that storm size regulates TC intensification through an oceanic pathway: large TCs tend to induce stronger SST cooling and are exposed to the cooling for a longer time, both of which reduce the ocean’s enthalpy supply and thereby diminish TC intensification. For larger TCs experiencing stronger SST cooling, the probability of rapid intensification is half of smaller TCs. The presented results suggest that accurately specifying storm size should lead to improved cooling effect estimation and TC intensity prediction.
Significance Statement
Storm size has long been speculated to play a crucial role in modulating the TC self-induced sea surface temperature (SST) cooling and thus potentially influence TC intensification through ocean negative feedback. Nevertheless, systematic analysis is lacking. Here we show that larger TCs tend to generate stronger SST cooling and have longer exposure to the cooling effect, both of which enhance the strength of the negative feedback. Consequently, larger TCs undergo weaker intensification and are less likely to experience rapid intensification than smaller TCs. These results demonstrate that storm size can influence TC intensification not only from the atmospheric pathway, but also via the oceanic pathway. Accurate characterization of this oceanic pathway in coupled models is important to accurately forecast TC intensity.
Abstract
Climate model projections of atmospheric circulation patterns, their frequency, and associated temperature and precipitation anomalies under a high-end global warming scenario are assessed over the Pacific Northwest of North America for the final three decades of the twenty-first century. Model simulations are from phase 6 of the Coupled Model Intercomparison Project (CMIP6) and circulation patterns are identified using the self-organizing maps (SOMs) approach, applied to 500-hPa geopotential height (Z500) anomalies. Overall, the range of projected circulation patterns is similar to that in the current climate, especially in winter, whereas in summer the models project a general reduction in the magnitude of Z500 anomalies. Significant changes in pattern frequencies are also projected in summer, with an overall decrease in the frequency of patterns with large Z500 anomalies. In winter, patterns historically associated with anomalously cold weather in northern latitudes are projected to warm the most, and in summer the largest temperature increases are projected over inland areas. Precipitation is found to increase across all seasons and most SOM patterns. However, some summer patterns that are associated with above-average precipitation in the current climate are projected to become significantly drier by the end of the century.
Significance Statement
This paper uses a novel method to analyze projections of large-scale atmospheric circulation over the Pacific Northwest of North America, reducing the uncertainty of changes to the circulation patterns over the region under a high-emissions scenario of global warming.
Abstract
Climate model projections of atmospheric circulation patterns, their frequency, and associated temperature and precipitation anomalies under a high-end global warming scenario are assessed over the Pacific Northwest of North America for the final three decades of the twenty-first century. Model simulations are from phase 6 of the Coupled Model Intercomparison Project (CMIP6) and circulation patterns are identified using the self-organizing maps (SOMs) approach, applied to 500-hPa geopotential height (Z500) anomalies. Overall, the range of projected circulation patterns is similar to that in the current climate, especially in winter, whereas in summer the models project a general reduction in the magnitude of Z500 anomalies. Significant changes in pattern frequencies are also projected in summer, with an overall decrease in the frequency of patterns with large Z500 anomalies. In winter, patterns historically associated with anomalously cold weather in northern latitudes are projected to warm the most, and in summer the largest temperature increases are projected over inland areas. Precipitation is found to increase across all seasons and most SOM patterns. However, some summer patterns that are associated with above-average precipitation in the current climate are projected to become significantly drier by the end of the century.
Significance Statement
This paper uses a novel method to analyze projections of large-scale atmospheric circulation over the Pacific Northwest of North America, reducing the uncertainty of changes to the circulation patterns over the region under a high-emissions scenario of global warming.
Abstract
For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden–Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describing MJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a direct MJO influence on precipitation corresponding to propagation of the MJO’s convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October–November) and southwest monsoon (SWM; May–September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method.
Significance Statement
Rainfall variability at the subseasonal (weeks–months) time scale is critical to societal well-being, given its fundamental importance for agriculture, flood risk, hydropower generation, and disease incidence. Our work describes how such rainfall variability in Sri Lanka is impacted by the Madden–Julian oscillation, in which a region of enhanced rainfall and cloudiness, paired with a region of decreased rainfall and cloudiness, circles the globe every 30–60 days. Our results suggest that its influence on Sri Lankan rainfall may be strong enough that incorporating knowledge of the Madden–Julian oscillation into forecasts can improve the accuracy of rainfall prediction for Sri Lanka. Future work should develop a more comprehensive forecast method to assess viability in real-world forecasting scenarios.
Abstract
For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden–Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describing MJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a direct MJO influence on precipitation corresponding to propagation of the MJO’s convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October–November) and southwest monsoon (SWM; May–September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method.
Significance Statement
Rainfall variability at the subseasonal (weeks–months) time scale is critical to societal well-being, given its fundamental importance for agriculture, flood risk, hydropower generation, and disease incidence. Our work describes how such rainfall variability in Sri Lanka is impacted by the Madden–Julian oscillation, in which a region of enhanced rainfall and cloudiness, paired with a region of decreased rainfall and cloudiness, circles the globe every 30–60 days. Our results suggest that its influence on Sri Lankan rainfall may be strong enough that incorporating knowledge of the Madden–Julian oscillation into forecasts can improve the accuracy of rainfall prediction for Sri Lanka. Future work should develop a more comprehensive forecast method to assess viability in real-world forecasting scenarios.
Abstract
Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean–atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wet-season precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The “Forgotten Drought” of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to first-hand descriptions by Louis Agassiz, his Brazilian colleague João Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of “First Street” in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century.
Significance Statement
Proxy tree-ring and historical evidence for precipitation extremes during the preinstrumental nineteenth century indicate that recent floods and droughts on the Amazon River may have not yet exceeded the range of natural hydroclimatic variability.
Abstract
Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean–atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wet-season precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The “Forgotten Drought” of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to first-hand descriptions by Louis Agassiz, his Brazilian colleague João Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of “First Street” in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century.
Significance Statement
Proxy tree-ring and historical evidence for precipitation extremes during the preinstrumental nineteenth century indicate that recent floods and droughts on the Amazon River may have not yet exceeded the range of natural hydroclimatic variability.
Abstract
This study reveals that South China precipitation (SCP) anomalies tend to persist well from winter to the following spring after the late 1990s, favoring long-lasting drought or flood events over South China. Mechanism analysis indicates that the interdecadal changes in El Niño–Southern Oscillation (ENSO) and the preceding November central Asian snow cover could contribute to the increased persistence of winter-to-spring SCP anomalies. ENSO has a stable impact on winter SCP, whereas its impact on spring SCP is significantly enhanced after the late 1990s. With a weakened intensity and faster decay rate in the recent two decades, the ENSO-related spring SST anomalies over the tropical Pacific are relatively weaker, inducing a weakened and more southward-located western North Pacific anticyclone. This further leads to an interdecadal migration of the spring rainfall belt anomaly, consequently favoring the persistence of winter-to-spring SCP anomalies after the late 1990s. Additionally, the impacts of November central Asian snow cover on winter and spring SCP are both strengthened after the late 1990s. In the most recent two decades, the snow-cover-related cooling effect has become stronger, which induces winter cyclonic anomalies over Lake Baikal, favoring increased winter SCP. In addition, increased snow cover excites upward-propagating waves from the troposphere to the stratosphere, consequently weakening the stratospheric polar vortex. In spring, the stratospheric polar vortex signals propagate downward and result in a negative Arctic Oscillation in the troposphere, favoring more spring SCP. Therefore, central Asian snow cover is also conductive to the persistence of winter-to-spring SCP anomalies after the late 1990s.
Abstract
This study reveals that South China precipitation (SCP) anomalies tend to persist well from winter to the following spring after the late 1990s, favoring long-lasting drought or flood events over South China. Mechanism analysis indicates that the interdecadal changes in El Niño–Southern Oscillation (ENSO) and the preceding November central Asian snow cover could contribute to the increased persistence of winter-to-spring SCP anomalies. ENSO has a stable impact on winter SCP, whereas its impact on spring SCP is significantly enhanced after the late 1990s. With a weakened intensity and faster decay rate in the recent two decades, the ENSO-related spring SST anomalies over the tropical Pacific are relatively weaker, inducing a weakened and more southward-located western North Pacific anticyclone. This further leads to an interdecadal migration of the spring rainfall belt anomaly, consequently favoring the persistence of winter-to-spring SCP anomalies after the late 1990s. Additionally, the impacts of November central Asian snow cover on winter and spring SCP are both strengthened after the late 1990s. In the most recent two decades, the snow-cover-related cooling effect has become stronger, which induces winter cyclonic anomalies over Lake Baikal, favoring increased winter SCP. In addition, increased snow cover excites upward-propagating waves from the troposphere to the stratosphere, consequently weakening the stratospheric polar vortex. In spring, the stratospheric polar vortex signals propagate downward and result in a negative Arctic Oscillation in the troposphere, favoring more spring SCP. Therefore, central Asian snow cover is also conductive to the persistence of winter-to-spring SCP anomalies after the late 1990s.
Abstract
The Indian monsoon is of utmost concern to agriculture, the economy, and the livelihoods of billions in South Asia. However, little attention has been paid to the possibility of distinct subseasonal episodes phase-locked in the Indian monsoon annual cycle. This study addresses this gap by utilizing the self-organizing map (SOM) method to objectively classify six distinct subseasonal stages based on the 850-hPa wind fields. Each subseasonal stage ranges from 23 to 90 days. The Indian summer monsoon (ISM) consists of three substages, the ISM-onset, ISM-peak, and ISM-withdrawal, altogether contributing to 82% of the annual precipitation. The three substages signify the rapid northward advance, dominance, and gradual southward retreat of southwesterlies from mid-May to early October. The winter monsoon also comprises three substages (fall, winter, and spring), distinguishable by the latitude of the Arabian Sea high pressure ridge and hydrological conditions. This study proposes two compact indices based on zonal winds in the northern and southern Arabian Sea to measure the winter and summer monsoons, respectively. These indices capture the development and turnabouts of the six SOM-derived stages and can be used for subseasonal monsoon monitoring and forecasts. The spring and the ISM-onset episodes are highly susceptible to compound hazards of droughts and heatwaves, while the greatest flood risk occurs during the ISM-peak stage. The fall stage heralds the peak season for tropical storms over the Arabian Sea and the Bay of Bengal. The annual start and end dates of the ISM-peak are highly correlated (0.6–0.8) with the criteria-based dates proposed previously, supporting the delineation of the Indian monsoon subseasonal features.
Significance Statement
This research explores the existence of subseasonal features in the Indian monsoon annual cycle. Through the use of machine learning, we discover that the Indian summer monsoon and winter monsoon each consist of three substages. These substages’ evolution can be measured by two compact indices proposed herein, which can aid in subseasonal monsoon monitoring and forecasts in South Asia. Pertaining to hazard adaptations, this work pinpoints the subseasonal episodes most susceptible to droughts, heatwaves, floods, and tropical storms. High correlations are obtained when validating the substages’ yearly start and end dates against those documented in the existing literature, offering credibility to the subseasonal features of the Indian monsoon.
Abstract
The Indian monsoon is of utmost concern to agriculture, the economy, and the livelihoods of billions in South Asia. However, little attention has been paid to the possibility of distinct subseasonal episodes phase-locked in the Indian monsoon annual cycle. This study addresses this gap by utilizing the self-organizing map (SOM) method to objectively classify six distinct subseasonal stages based on the 850-hPa wind fields. Each subseasonal stage ranges from 23 to 90 days. The Indian summer monsoon (ISM) consists of three substages, the ISM-onset, ISM-peak, and ISM-withdrawal, altogether contributing to 82% of the annual precipitation. The three substages signify the rapid northward advance, dominance, and gradual southward retreat of southwesterlies from mid-May to early October. The winter monsoon also comprises three substages (fall, winter, and spring), distinguishable by the latitude of the Arabian Sea high pressure ridge and hydrological conditions. This study proposes two compact indices based on zonal winds in the northern and southern Arabian Sea to measure the winter and summer monsoons, respectively. These indices capture the development and turnabouts of the six SOM-derived stages and can be used for subseasonal monsoon monitoring and forecasts. The spring and the ISM-onset episodes are highly susceptible to compound hazards of droughts and heatwaves, while the greatest flood risk occurs during the ISM-peak stage. The fall stage heralds the peak season for tropical storms over the Arabian Sea and the Bay of Bengal. The annual start and end dates of the ISM-peak are highly correlated (0.6–0.8) with the criteria-based dates proposed previously, supporting the delineation of the Indian monsoon subseasonal features.
Significance Statement
This research explores the existence of subseasonal features in the Indian monsoon annual cycle. Through the use of machine learning, we discover that the Indian summer monsoon and winter monsoon each consist of three substages. These substages’ evolution can be measured by two compact indices proposed herein, which can aid in subseasonal monsoon monitoring and forecasts in South Asia. Pertaining to hazard adaptations, this work pinpoints the subseasonal episodes most susceptible to droughts, heatwaves, floods, and tropical storms. High correlations are obtained when validating the substages’ yearly start and end dates against those documented in the existing literature, offering credibility to the subseasonal features of the Indian monsoon.
Abstract
In this paper, we present a late summer (August–September) temperature reconstruction over the period 1792–2020 based on a tree-ring maximum latewood density (MXD) chronology for the southern Tibetan Plateau (TP). The reconstruction explained 66.2% of the variance in the instrumental temperature records during the calibration period 1960–2020 and captured the warming trend since the 1960s, which would support the current warming on the TP. In addition, a warming hiatus existed during 2001–12 and the last 20 years (2000–20) were the warmest period in the past two centuries. The reconstruction matched other MXD- and mean latewood density (LWD)-based late summer temperature reconstructions from neighboring regions, and fluctuated in synchrony with the Climatic Research Unit (CRU) Northern Hemisphere land surface temperature during 1850–2020. Multitaper method analysis and wavelet analysis revealed significant periodicities of 2–3, 20–30, and 40–60 years in the reconstructed series. Our reconstructed series was very consistent and highly correlated with the Atlantic multidecadal oscillation (AMO). During the warm phase of the AMO, higher pressure and divergent horizontal winds over the TP contribute to warmer summers in the region. In addition, we found that the southern TP experienced the lowest temperature and downward solar radiation in the second year following large volcanic eruptions. The decrease in downward solar radiation may be directly responsible for the occurrence of the lowest temperatures. The results indicate that the AMO and large volcanic eruptions were impacting factors on temperature in our study area.
Abstract
In this paper, we present a late summer (August–September) temperature reconstruction over the period 1792–2020 based on a tree-ring maximum latewood density (MXD) chronology for the southern Tibetan Plateau (TP). The reconstruction explained 66.2% of the variance in the instrumental temperature records during the calibration period 1960–2020 and captured the warming trend since the 1960s, which would support the current warming on the TP. In addition, a warming hiatus existed during 2001–12 and the last 20 years (2000–20) were the warmest period in the past two centuries. The reconstruction matched other MXD- and mean latewood density (LWD)-based late summer temperature reconstructions from neighboring regions, and fluctuated in synchrony with the Climatic Research Unit (CRU) Northern Hemisphere land surface temperature during 1850–2020. Multitaper method analysis and wavelet analysis revealed significant periodicities of 2–3, 20–30, and 40–60 years in the reconstructed series. Our reconstructed series was very consistent and highly correlated with the Atlantic multidecadal oscillation (AMO). During the warm phase of the AMO, higher pressure and divergent horizontal winds over the TP contribute to warmer summers in the region. In addition, we found that the southern TP experienced the lowest temperature and downward solar radiation in the second year following large volcanic eruptions. The decrease in downward solar radiation may be directly responsible for the occurrence of the lowest temperatures. The results indicate that the AMO and large volcanic eruptions were impacting factors on temperature in our study area.
Abstract
In the context of global climate change, recent studies indicate a poleward migration trend of tropical cyclones (TCs) in the western North Pacific (WNP), while little attention has been paid to the TC cyclolysis (hereafter Lysis). With respect to two different datasets, this study identifies the poleward migration of the annual-mean latitude of TC Lysis during 1979–2018, being more significant for the intensified TCs, although this trend is suppressed by the reduction in the TC frequency over the sea. It is found that the TC migration is more like a poleward translation of the overall movement rather than the expansion of a specific phase’s distance span. Subsequently, the trends of several environmental parameters related to TC development are also analyzed. The large-scale sea surface temperature warming leads to the increase of potential intensity and enhances the possibility of TC poleward migration. Through controlling the TC formation in the eastern WNP tropics, the variations of vertical wind shear and horizontal winds affect TC Lysis latitude, facilitate the TC development environment around East Asia offshore and island chain areas, and steer TCs poleward migration through the southerly wind anomalies in the area north of 30°N. Regarding the cyclonic vorticity in the lower troposphere and the divergence in the upper troposphere, their influence on TC Lysis latitude is mainly by adjusting the numbers of TCs rather than directly interfering with the TC movement process. The present results indicate that the northern WNP coastal region will also become a TC-prone area in the future, which needs to be treated with caution.
Abstract
In the context of global climate change, recent studies indicate a poleward migration trend of tropical cyclones (TCs) in the western North Pacific (WNP), while little attention has been paid to the TC cyclolysis (hereafter Lysis). With respect to two different datasets, this study identifies the poleward migration of the annual-mean latitude of TC Lysis during 1979–2018, being more significant for the intensified TCs, although this trend is suppressed by the reduction in the TC frequency over the sea. It is found that the TC migration is more like a poleward translation of the overall movement rather than the expansion of a specific phase’s distance span. Subsequently, the trends of several environmental parameters related to TC development are also analyzed. The large-scale sea surface temperature warming leads to the increase of potential intensity and enhances the possibility of TC poleward migration. Through controlling the TC formation in the eastern WNP tropics, the variations of vertical wind shear and horizontal winds affect TC Lysis latitude, facilitate the TC development environment around East Asia offshore and island chain areas, and steer TCs poleward migration through the southerly wind anomalies in the area north of 30°N. Regarding the cyclonic vorticity in the lower troposphere and the divergence in the upper troposphere, their influence on TC Lysis latitude is mainly by adjusting the numbers of TCs rather than directly interfering with the TC movement process. The present results indicate that the northern WNP coastal region will also become a TC-prone area in the future, which needs to be treated with caution.
Abstract
Climate change detection and attribution have played a central role in establishing the influence of human activities on climate. Optimal fingerprinting, a linear regression with errors in variables (EIVs), has been widely used in detection and attribution analyses of climate change. The method regresses observed climate variables on the expected climate responses to the external forcings, which are measured with EIVs. The reliability of the method depends critically on proper point and interval estimations of the regression coefficients. The confidence intervals constructed from the prevailing method, total least squares (TLS), have been reported to be too narrow to match their nominal confidence levels. We propose a novel framework to estimate the regression coefficients based on an efficient, bias-corrected estimating equations approach. The confidence intervals are constructed with a pseudo residual bootstrap variance estimator that takes advantage of the available control runs. Our regression coefficient estimator is unbiased, with a smaller variance than the TLS estimator. Our estimation of the sampling variability of the estimator has a low bias compared to that from TLS, which is substantially negatively biased. The resulting confidence intervals for the regression coefficients have coverage rates close to the nominal level, which ensures valid inferences in detection and attribution analyses. In applications to the annual mean near-surface air temperature at the global, continental, and subcontinental scales during 1951–2020, the proposed method led to shorter confidence intervals than those based on TLS in most of the analyses.
Significance Statement
Optimal fingerprinting is an important statistical tool for estimating human influences on the climate and for quantifying the associated uncertainty. Nonetheless, the estimators from the prevailing practice are not as optimal as believed, and their uncertainties are underestimated, both owing to the unreliable estimation of the optimal weight matrix that is critical to the method. Here we propose an estimation method based on the theory of estimating equations; to assess the uncertainty of the resulting estimator, we propose a pseudo bootstrap procedure. Through extensive numerical studies commonly used in statistical investigations, we demonstrate that the new estimator has a smaller mean-square error, and its uncertainty is estimated much closer to the true uncertainty than the prevailing total least squares method.
Abstract
Climate change detection and attribution have played a central role in establishing the influence of human activities on climate. Optimal fingerprinting, a linear regression with errors in variables (EIVs), has been widely used in detection and attribution analyses of climate change. The method regresses observed climate variables on the expected climate responses to the external forcings, which are measured with EIVs. The reliability of the method depends critically on proper point and interval estimations of the regression coefficients. The confidence intervals constructed from the prevailing method, total least squares (TLS), have been reported to be too narrow to match their nominal confidence levels. We propose a novel framework to estimate the regression coefficients based on an efficient, bias-corrected estimating equations approach. The confidence intervals are constructed with a pseudo residual bootstrap variance estimator that takes advantage of the available control runs. Our regression coefficient estimator is unbiased, with a smaller variance than the TLS estimator. Our estimation of the sampling variability of the estimator has a low bias compared to that from TLS, which is substantially negatively biased. The resulting confidence intervals for the regression coefficients have coverage rates close to the nominal level, which ensures valid inferences in detection and attribution analyses. In applications to the annual mean near-surface air temperature at the global, continental, and subcontinental scales during 1951–2020, the proposed method led to shorter confidence intervals than those based on TLS in most of the analyses.
Significance Statement
Optimal fingerprinting is an important statistical tool for estimating human influences on the climate and for quantifying the associated uncertainty. Nonetheless, the estimators from the prevailing practice are not as optimal as believed, and their uncertainties are underestimated, both owing to the unreliable estimation of the optimal weight matrix that is critical to the method. Here we propose an estimation method based on the theory of estimating equations; to assess the uncertainty of the resulting estimator, we propose a pseudo bootstrap procedure. Through extensive numerical studies commonly used in statistical investigations, we demonstrate that the new estimator has a smaller mean-square error, and its uncertainty is estimated much closer to the true uncertainty than the prevailing total least squares method.
Abstract
Extratropical cyclones (ETCs) are a common source of natural hazards, from heavy rain to high winds, and the direction and speed of ETC propagation influence where impacts occur and for how long. Eighteen models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) are used to examine the response of Northern Hemisphere ETC propagation to global warming. In winter, simulations show that ETCs become slower over North America and the Arctic but faster over the Pacific Ocean and part of Europe. In summer, storm propagation becomes slightly slower throughout much of the midlatitudes (30°–60°N). Trends in both seasons relate closely to the impact of global warming on upper-level (250 hPa) winds and the 850–250-hPa thickness gradient. Wherever local thickness gradients weaken in the future, ETCs travel more slowly; conversely, wherever they strengthen, ETCs travel more quickly. In contrast to past work, we find that winter storm propagation becomes more zonal over the Pacific and Atlantic Oceans, which may link to decreased atmospheric blocking and less-sinuous flow at 500 hPa. The importance of model projections of the 850–250-hPa thickness gradient for meridionality of ETC propagation remains uncertain for these regions. However, for North America, models that project stronger thickness gradients also project less-sinuous flow and more-zonal ETC propagation. Overall, this work highlights strong regional variation in how the speed and direction of ETC propagation, and the upper-level circulation patterns that govern them, respond to continued warming.
Significance Statement
Extratropical storms are common sources of natural hazards like heavy rain and high winds. In our analysis of projections from 18 climate models, we find that winter storms tend to move more slowly over midlatitude North America and the Arctic as the world warms but move faster over the North Pacific Ocean and part of Europe. Slight slowing of summer storms is projected throughout much of the midlatitudes. When storms move slower, their attendant hazards (like heavy precipitation) last longer for the areas they impact. Further, Atlantic winter storms travel more west to east instead of southwest to northeast, so they impact Iceland less often and the British Isles more often. Changes become more dramatic with each additional degree of global warming.
Abstract
Extratropical cyclones (ETCs) are a common source of natural hazards, from heavy rain to high winds, and the direction and speed of ETC propagation influence where impacts occur and for how long. Eighteen models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) are used to examine the response of Northern Hemisphere ETC propagation to global warming. In winter, simulations show that ETCs become slower over North America and the Arctic but faster over the Pacific Ocean and part of Europe. In summer, storm propagation becomes slightly slower throughout much of the midlatitudes (30°–60°N). Trends in both seasons relate closely to the impact of global warming on upper-level (250 hPa) winds and the 850–250-hPa thickness gradient. Wherever local thickness gradients weaken in the future, ETCs travel more slowly; conversely, wherever they strengthen, ETCs travel more quickly. In contrast to past work, we find that winter storm propagation becomes more zonal over the Pacific and Atlantic Oceans, which may link to decreased atmospheric blocking and less-sinuous flow at 500 hPa. The importance of model projections of the 850–250-hPa thickness gradient for meridionality of ETC propagation remains uncertain for these regions. However, for North America, models that project stronger thickness gradients also project less-sinuous flow and more-zonal ETC propagation. Overall, this work highlights strong regional variation in how the speed and direction of ETC propagation, and the upper-level circulation patterns that govern them, respond to continued warming.
Significance Statement
Extratropical storms are common sources of natural hazards like heavy rain and high winds. In our analysis of projections from 18 climate models, we find that winter storms tend to move more slowly over midlatitude North America and the Arctic as the world warms but move faster over the North Pacific Ocean and part of Europe. Slight slowing of summer storms is projected throughout much of the midlatitudes. When storms move slower, their attendant hazards (like heavy precipitation) last longer for the areas they impact. Further, Atlantic winter storms travel more west to east instead of southwest to northeast, so they impact Iceland less often and the British Isles more often. Changes become more dramatic with each additional degree of global warming.
