Abstract
We present the formulation and optimization of a Runge–Kutta-type time-stepping scheme for solving the shallow-water equations, aimed at substantially increasing the effective allowable time step over that of comparable methods. This scheme, called FB-RK(3,2), uses weighted forward–backward averaging of thickness data to advance the momentum equation. The weights for this averaging are chosen with an optimization process that employs a von Neumann–type analysis, ensuring that the weights maximize the admittable Courant number. Through a simplified local truncation error analysis and numerical experiments, we show that the method is at least second-order in time for any choice of weights and exhibits low dispersion and dissipation errors for well-resolved waves. Further, we show that an optimized FB-RK(3,2) can take time steps up to 2.8 times as large as a popular three-stage, third-order strong stability-preserving Runge–Kutta method in a quasi-linear test case. In fully nonlinear shallow-water test cases relevant to oceanic and atmospheric flows, FB-RK(3,2) outperforms SSPRK3 in admittable time step by factors roughly between 1.6 and 2.2, making the scheme approximately twice as computationally efficient with little to no effect on solution quality.
Significance Statement
The purpose of this work is to develop and optimize time-stepping schemes for models relevant to oceanic and atmospheric flows. Specifically, for the shallow-water equations we optimize for schemes that can take time steps as large as possible while retaining solution quality. We find that our optimized schemes can take time steps between 1.6 and 2.2 times larger than schemes that cost the same number of floating point operations, translating directly to a corresponding speedup. Our ultimate goal is to use these schemes in climate-scale simulations.
Abstract
We present the formulation and optimization of a Runge–Kutta-type time-stepping scheme for solving the shallow-water equations, aimed at substantially increasing the effective allowable time step over that of comparable methods. This scheme, called FB-RK(3,2), uses weighted forward–backward averaging of thickness data to advance the momentum equation. The weights for this averaging are chosen with an optimization process that employs a von Neumann–type analysis, ensuring that the weights maximize the admittable Courant number. Through a simplified local truncation error analysis and numerical experiments, we show that the method is at least second-order in time for any choice of weights and exhibits low dispersion and dissipation errors for well-resolved waves. Further, we show that an optimized FB-RK(3,2) can take time steps up to 2.8 times as large as a popular three-stage, third-order strong stability-preserving Runge–Kutta method in a quasi-linear test case. In fully nonlinear shallow-water test cases relevant to oceanic and atmospheric flows, FB-RK(3,2) outperforms SSPRK3 in admittable time step by factors roughly between 1.6 and 2.2, making the scheme approximately twice as computationally efficient with little to no effect on solution quality.
Significance Statement
The purpose of this work is to develop and optimize time-stepping schemes for models relevant to oceanic and atmospheric flows. Specifically, for the shallow-water equations we optimize for schemes that can take time steps as large as possible while retaining solution quality. We find that our optimized schemes can take time steps between 1.6 and 2.2 times larger than schemes that cost the same number of floating point operations, translating directly to a corresponding speedup. Our ultimate goal is to use these schemes in climate-scale simulations.
Abstract
Projections of precipitation extremes over land are crucial for socioeconomic risk assessments, yet model discrepancies limit their application. Here we use a pattern-filtering technique to identify low-frequency changes in individual members of a multimodel ensemble to assess discrepancies across models in the projected pattern and magnitude of change. Specifically, we apply low-frequency component analysis (LFCA) to the intensity and frequency of daily precipitation extremes over land in 21 CMIP-6 models. LFCA brings modest but statistically significant improvements in the agreement between models in the spatial pattern of projected change, particularly in scenarios with weak greenhouse forcing. Moreover, we show that LFCA facilitates a robust identification of the rates at which increasing precipitation extremes scale with global temperature change within individual ensemble members. While these rates approximately match expectations from the Clausius-Clapeyron relation on average across models, individual models exhibit considerable and significant differences. Monte Carlo simulations indicate that these differences contribute to uncertainty in the magnitude of projected change at least as much as differences in the climate sensitivity. Last, we compare these scaling rates with those identified from observational products, demonstrating that virtually all climate models significantly underestimate the rates at which increases in precipitation extremes have scaled with global temperatures historically. Constraining projections with observations therefore amplifies the projected intensification of precipitation extremes as well as reducing the relative error of their distribution.
Abstract
Projections of precipitation extremes over land are crucial for socioeconomic risk assessments, yet model discrepancies limit their application. Here we use a pattern-filtering technique to identify low-frequency changes in individual members of a multimodel ensemble to assess discrepancies across models in the projected pattern and magnitude of change. Specifically, we apply low-frequency component analysis (LFCA) to the intensity and frequency of daily precipitation extremes over land in 21 CMIP-6 models. LFCA brings modest but statistically significant improvements in the agreement between models in the spatial pattern of projected change, particularly in scenarios with weak greenhouse forcing. Moreover, we show that LFCA facilitates a robust identification of the rates at which increasing precipitation extremes scale with global temperature change within individual ensemble members. While these rates approximately match expectations from the Clausius-Clapeyron relation on average across models, individual models exhibit considerable and significant differences. Monte Carlo simulations indicate that these differences contribute to uncertainty in the magnitude of projected change at least as much as differences in the climate sensitivity. Last, we compare these scaling rates with those identified from observational products, demonstrating that virtually all climate models significantly underestimate the rates at which increases in precipitation extremes have scaled with global temperatures historically. Constraining projections with observations therefore amplifies the projected intensification of precipitation extremes as well as reducing the relative error of their distribution.
Abstract
Solar radiation management (SRM) with injections of aerosols into the stratosphere has emerged as a research area of focus with the potential to cool the planet. However, the amount of SRM required to achieve a given level of cooling, and how this relationship evolves in response to increasing greenhouse gas emissions, remains uncertain. Here, we explore the evolution of solar dimming efficacy over time by defining and quantifying a new SRM feedback term, which is analogous to conventional radiative feedbacks. Using Earth system model simulations that dynamically adjust the amount of insolation to offset global mean warming from increasing CO2, we find that positive SRM feedbacks decrease global planetary albedo and diminish the efficacy of solar dimming. Physically, the decrease in albedo is primarily due to reductions in optically thick tropical cloud fraction in the boundary layer and midtroposphere, which is driven by a drying and destabilization of the tropical mid- to lower troposphere. These results offer an energetic explanation for reduced cloud fraction commonly observed in idealized SRM experiments, as well as reaffirm the need to understand the troposphere response, particularly from clouds, in realizable geoengineering experiments and their potential to feed back onto SRM efficacy.
Significance Statement
The goal of this study is to understand how the effectiveness of solar geoengineering may evolve over time. Using a climate model with the ability to directly tune the amount of incoming sunlight, we explore the potential for feedback loops in the climate system to diminish or amplify the desired effect of solar tuning, which is to offset greenhouse gas–induced warming. For this climate model and this solar geoengineering proxy, in particular, we find that feedback loops reduce Earth’s albedo and therefore diminish the desired effect of turning down the sun over time. This study lays the groundwork for understanding potential feedback loops in climate model simulations that represent solar geoengineering in a more realistic way.
Abstract
Solar radiation management (SRM) with injections of aerosols into the stratosphere has emerged as a research area of focus with the potential to cool the planet. However, the amount of SRM required to achieve a given level of cooling, and how this relationship evolves in response to increasing greenhouse gas emissions, remains uncertain. Here, we explore the evolution of solar dimming efficacy over time by defining and quantifying a new SRM feedback term, which is analogous to conventional radiative feedbacks. Using Earth system model simulations that dynamically adjust the amount of insolation to offset global mean warming from increasing CO2, we find that positive SRM feedbacks decrease global planetary albedo and diminish the efficacy of solar dimming. Physically, the decrease in albedo is primarily due to reductions in optically thick tropical cloud fraction in the boundary layer and midtroposphere, which is driven by a drying and destabilization of the tropical mid- to lower troposphere. These results offer an energetic explanation for reduced cloud fraction commonly observed in idealized SRM experiments, as well as reaffirm the need to understand the troposphere response, particularly from clouds, in realizable geoengineering experiments and their potential to feed back onto SRM efficacy.
Significance Statement
The goal of this study is to understand how the effectiveness of solar geoengineering may evolve over time. Using a climate model with the ability to directly tune the amount of incoming sunlight, we explore the potential for feedback loops in the climate system to diminish or amplify the desired effect of solar tuning, which is to offset greenhouse gas–induced warming. For this climate model and this solar geoengineering proxy, in particular, we find that feedback loops reduce Earth’s albedo and therefore diminish the desired effect of turning down the sun over time. This study lays the groundwork for understanding potential feedback loops in climate model simulations that represent solar geoengineering in a more realistic way.
Developing a Data-Driven Transfer Learning Model to Locate Tropical Cyclone Centers on Satellite Infrared ImageryAbstract
In this paper, a data-driven transfer learning (TL) model for locating tropical cyclone (TC) centers from satellite infrared images in the northwest Pacific is developed. A total of 2450 satellite infrared TC images derived from 97 TCs between 2015 and 2018 were used for this paper. The TC center location model (ResNet-TCL) with added residual fully connected modules is built for the TC center location. The MAE of the ResNet-TCL model is 34.8 km. Then TL is used to improve the model performance, including obtaining a pretrained model based on the ImageNet dataset, transferring the pretrained model parameters to the ResNet-TCL model, and using TC satellite infrared imagery to fine-train the ResNet-TCL model. The results show that the TL-based model improves the location accuracy by 14.1% (29.3 km) over the no-TL model. The model performance increases logarithmically with the amount of training data. When the training data are large, the benefit of increasing the training samples is smaller than the benefit of using TL. The comparison of model results with the best track data of TCs shows that the MAEs of TCs center is 29.3 km for all samples and less than 20 km for H2–H5 TCs. In addition, the visualization of the TL-based TC center location model shows that the TL model can accurately extract the most important features related to TC center location, including TC eye, TC texture, and contour. On the other hand, the no-TL model does not accurately extract these features.
Abstract
In this paper, a data-driven transfer learning (TL) model for locating tropical cyclone (TC) centers from satellite infrared images in the northwest Pacific is developed. A total of 2450 satellite infrared TC images derived from 97 TCs between 2015 and 2018 were used for this paper. The TC center location model (ResNet-TCL) with added residual fully connected modules is built for the TC center location. The MAE of the ResNet-TCL model is 34.8 km. Then TL is used to improve the model performance, including obtaining a pretrained model based on the ImageNet dataset, transferring the pretrained model parameters to the ResNet-TCL model, and using TC satellite infrared imagery to fine-train the ResNet-TCL model. The results show that the TL-based model improves the location accuracy by 14.1% (29.3 km) over the no-TL model. The model performance increases logarithmically with the amount of training data. When the training data are large, the benefit of increasing the training samples is smaller than the benefit of using TL. The comparison of model results with the best track data of TCs shows that the MAEs of TCs center is 29.3 km for all samples and less than 20 km for H2–H5 TCs. In addition, the visualization of the TL-based TC center location model shows that the TL model can accurately extract the most important features related to TC center location, including TC eye, TC texture, and contour. On the other hand, the no-TL model does not accurately extract these features.
Abstract
The Victoria mode (VM), similar to the Pacific meridional mode (PMM), is forced by North Pacific Oscillation atmospheric variability. Both the boreal spring VM and PMM can trigger the onset of El Niño–Southern Oscillation (ENSO) events in the following winter. Previous studies have examined the precursor relationship between the PMM and ENSO based on a subset of models drawn from the North American Multimodel Ensemble (NMME) system. They suggested that the PMM can act as a precursor to El Niño events, whereas it fails to predict La Niña events. Utilizing the hindcasts of these models from NMME, this study further investigates the role of the VM as an ENSO predictor to examine the real usefulness of the VM for ENSO prediction. Compared with the PMM, the VM can predict both El Niño and La Niña events with some skill, showing that the VM seems to be a more reliable predictor of ENSO. We found that the unique role of the VM in ENSO prediction originates from the symmetric impact of the VM on ENSO events. The VM, as a basin-scale sea surface temperature (SST) pattern, combines the role of the SST over the subtropical northeastern Pacific that is similar to the PMM in initializing El Niño events with that of the SST over the western North Pacific that is different from PMM in initializing La Niña events, resulting in the symmetric effect of the VM on ENSO prediction. Thus, it is useful to consider VM variability as a reference for ENSO prediction.
Abstract
The Victoria mode (VM), similar to the Pacific meridional mode (PMM), is forced by North Pacific Oscillation atmospheric variability. Both the boreal spring VM and PMM can trigger the onset of El Niño–Southern Oscillation (ENSO) events in the following winter. Previous studies have examined the precursor relationship between the PMM and ENSO based on a subset of models drawn from the North American Multimodel Ensemble (NMME) system. They suggested that the PMM can act as a precursor to El Niño events, whereas it fails to predict La Niña events. Utilizing the hindcasts of these models from NMME, this study further investigates the role of the VM as an ENSO predictor to examine the real usefulness of the VM for ENSO prediction. Compared with the PMM, the VM can predict both El Niño and La Niña events with some skill, showing that the VM seems to be a more reliable predictor of ENSO. We found that the unique role of the VM in ENSO prediction originates from the symmetric impact of the VM on ENSO events. The VM, as a basin-scale sea surface temperature (SST) pattern, combines the role of the SST over the subtropical northeastern Pacific that is similar to the PMM in initializing El Niño events with that of the SST over the western North Pacific that is different from PMM in initializing La Niña events, resulting in the symmetric effect of the VM on ENSO prediction. Thus, it is useful to consider VM variability as a reference for ENSO prediction.
Abstract
The Indian Ocean basin (IOB) mode is the dominant mode of the interannual sea surface temperature (SST) variability in the Indian Ocean, with the Indian Ocean dipole (IOD) as the second mode. An IOB event normally occurs after an El Niño or a concurrent IOD–El Niño event, the dynamics of which are traditionally believed as forced by ENSO through the Walker circulation anomalies over the tropical Indian Ocean. A strong IOB in 2020 took place after the strongest 2019 IOD on record but independent of El Niño, which challenges the traditional atmospheric bridge dynamics of the IOB event. In this study, the dynamics of the 2020 IOB event are investigated using the numerical seasonal climate prediction system of the National Marine Environmental Forecasting Center of China. It is found that the initialization of the Indian Ocean subsurface temperature during the 2019 IOD event has led to the outburst of the 2020 IOB event successfully, the dynamics of which are the propagation and the western boundary reflection of the equatorial and off-equatorial Rossby waves, inducing heat content recharge over the tropical Indian Ocean upper thermocline. In comparison, experiments of SST initialization over the tropical Indian Ocean, with the subsurface temperature in a climatological state, were unable to reproduce the onset of the 2020 IOB event, suggesting that the local air–sea interaction within the Indian Ocean basin is of secondary importance. The numerical experiments suggest that the thermocline ocean wave dynamics play an important role in forcing the IOB event. The revealed thermocline dynamics are potentially useful in climate prediction associated with IOB events.
Abstract
The Indian Ocean basin (IOB) mode is the dominant mode of the interannual sea surface temperature (SST) variability in the Indian Ocean, with the Indian Ocean dipole (IOD) as the second mode. An IOB event normally occurs after an El Niño or a concurrent IOD–El Niño event, the dynamics of which are traditionally believed as forced by ENSO through the Walker circulation anomalies over the tropical Indian Ocean. A strong IOB in 2020 took place after the strongest 2019 IOD on record but independent of El Niño, which challenges the traditional atmospheric bridge dynamics of the IOB event. In this study, the dynamics of the 2020 IOB event are investigated using the numerical seasonal climate prediction system of the National Marine Environmental Forecasting Center of China. It is found that the initialization of the Indian Ocean subsurface temperature during the 2019 IOD event has led to the outburst of the 2020 IOB event successfully, the dynamics of which are the propagation and the western boundary reflection of the equatorial and off-equatorial Rossby waves, inducing heat content recharge over the tropical Indian Ocean upper thermocline. In comparison, experiments of SST initialization over the tropical Indian Ocean, with the subsurface temperature in a climatological state, were unable to reproduce the onset of the 2020 IOB event, suggesting that the local air–sea interaction within the Indian Ocean basin is of secondary importance. The numerical experiments suggest that the thermocline ocean wave dynamics play an important role in forcing the IOB event. The revealed thermocline dynamics are potentially useful in climate prediction associated with IOB events.
Abstract
Organized deep convective activity has been routinely monitored by satellite precipitation radar from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM). Organized deep convective activity is found to not only increase with SST above 27°C, but also with low-level wind shear. Precipitation shows a similar increasing relationship with both SST and low-level wind shear, except for the highest low-level wind shear. These observations suggest that the threshold for organized deep convection and precipitation in the tropics should consider not only SST, but also vertical wind shear. The longwave cloud radiative feedback, measured as the tropospheric longwave cloud radiative heating per amount of precipitation, is found to generally increase with stronger organized deep convective activity as SST and low-level wind shear increase. Organized deep convective activity, the longwave cloud radiative feedback, and cirrus ice cloud cover per amount of precipitation also appear to be controlled more strongly by SST than by the deviation of SST from its tropical mean. This study hints at the importance of non-thermodynamic factors such as vertical wind shear for impacting tropical convective structure, cloud properties, and associated radiative energy budget of the tropics.
Abstract
Organized deep convective activity has been routinely monitored by satellite precipitation radar from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM). Organized deep convective activity is found to not only increase with SST above 27°C, but also with low-level wind shear. Precipitation shows a similar increasing relationship with both SST and low-level wind shear, except for the highest low-level wind shear. These observations suggest that the threshold for organized deep convection and precipitation in the tropics should consider not only SST, but also vertical wind shear. The longwave cloud radiative feedback, measured as the tropospheric longwave cloud radiative heating per amount of precipitation, is found to generally increase with stronger organized deep convective activity as SST and low-level wind shear increase. Organized deep convective activity, the longwave cloud radiative feedback, and cirrus ice cloud cover per amount of precipitation also appear to be controlled more strongly by SST than by the deviation of SST from its tropical mean. This study hints at the importance of non-thermodynamic factors such as vertical wind shear for impacting tropical convective structure, cloud properties, and associated radiative energy budget of the tropics.
Abstract
Time-varying processes contribute to ocean heat transport and are important to understand for accurate climate modeling. While past studies have quantified time-varying contributions to advective transport, less attention has been given to diabatic processes such as surface forcing and mixing. Using a global eddy-permitting ocean model we quantify the contribution of time-variable processes to meridional and diathermal (warm to cold) heat transport at different time scales using a temporal eddy-mean decomposition performed in the temperature–latitude plane. Time-varying contributions to meridional heat transport occur predominantly at mesoscale eddy-dominated midlatitudes and in the tropics, associated with the seasonal cycle and tropical instability waves. The seasonal cycle is a dominant driver of surface flux– and mixing-driven diathermal heat transports. Nonseasonal (and nondiurnal) processes contribute up to about 10% of the total. We show that transient contributions to diathermal heat transport can be interpreted as sources of Eulerian temperature variance. We thus extend recent work on the drivers of temperature variability by evaluating the role of mixing. Mixing dampens seasonal and diurnal temperature variability, except near the equator where it can be a source of seasonal variability. At mesoscale time scales mixing drives variability within and near the base of the boundary layer, the mechanisms of which are explored using a column model. We suggest that climate models that do not resolve the mesoscale may be missing the rectified heat transport associated with high-frequency diabatic processes, in addition to the adiabatic eddy fluxes that are commonly parameterized.
Significance Statement
Ocean heat transport plays a key role in determining how the climate responds to changes in forcing. This transport is influenced by a range of processes that vary with time. Previous research has quantified time-varying sources of lateral heat transport, such as mesoscale eddies and overturning circulation cells. However, time-varying “diabatic processes,” such as surface forcing and unresolved turbulent mixing, have received less attention. Here, we quantify these effects using a global ocean model. We find a dominant role for the seasonal cycle in driving diabatic heat transport, but processes on shorter time scales also contribute. Our results suggest that temporal variations in turbulent mixing are an important contributor to heat transport but may not be resolved in coarse-resolution climate models.
Abstract
Time-varying processes contribute to ocean heat transport and are important to understand for accurate climate modeling. While past studies have quantified time-varying contributions to advective transport, less attention has been given to diabatic processes such as surface forcing and mixing. Using a global eddy-permitting ocean model we quantify the contribution of time-variable processes to meridional and diathermal (warm to cold) heat transport at different time scales using a temporal eddy-mean decomposition performed in the temperature–latitude plane. Time-varying contributions to meridional heat transport occur predominantly at mesoscale eddy-dominated midlatitudes and in the tropics, associated with the seasonal cycle and tropical instability waves. The seasonal cycle is a dominant driver of surface flux– and mixing-driven diathermal heat transports. Nonseasonal (and nondiurnal) processes contribute up to about 10% of the total. We show that transient contributions to diathermal heat transport can be interpreted as sources of Eulerian temperature variance. We thus extend recent work on the drivers of temperature variability by evaluating the role of mixing. Mixing dampens seasonal and diurnal temperature variability, except near the equator where it can be a source of seasonal variability. At mesoscale time scales mixing drives variability within and near the base of the boundary layer, the mechanisms of which are explored using a column model. We suggest that climate models that do not resolve the mesoscale may be missing the rectified heat transport associated with high-frequency diabatic processes, in addition to the adiabatic eddy fluxes that are commonly parameterized.
Significance Statement
Ocean heat transport plays a key role in determining how the climate responds to changes in forcing. This transport is influenced by a range of processes that vary with time. Previous research has quantified time-varying sources of lateral heat transport, such as mesoscale eddies and overturning circulation cells. However, time-varying “diabatic processes,” such as surface forcing and unresolved turbulent mixing, have received less attention. Here, we quantify these effects using a global ocean model. We find a dominant role for the seasonal cycle in driving diabatic heat transport, but processes on shorter time scales also contribute. Our results suggest that temporal variations in turbulent mixing are an important contributor to heat transport but may not be resolved in coarse-resolution climate models.
Abstract
The intertropical convergence zone (ITCZ) is a zonally elongated band of near-surface convergence and precipitation near the equator. During boreal spring, the eastern Pacific ITCZ migrates latitudinally on daily to subseasonal time scales, and climate models exhibit the greatest ITCZ biases during this time of the year. In this work, we investigate the air–sea interactions associated with the variability in the eastern Pacific ITCZ’s latitudinal location for consecutive days when the ITCZ is only located north of the equator (nITCZ events) compared to when the ITCZ is on both sides of the equator or south of the equator (dsITCZ events) during February–April. The distribution of sea surface temperature (SST) anomalies and surface latent heat flux (SLHF) anomalies during the nITCZ and dsITCZ events follow the classic wind–evaporation–SST (WES) positive feedback mechanism. However, an alternative mechanism, embracing the effect of SST anomalies on vertical stratification and momentum mixing, gives rise to a negative WES feedback. Our results show that in the surface layer, there is a general progression of positive WES feedbacks happening in the weeks leading to the events followed by negative WES feedbacks occurring after the ITCZ events, with an alternate mechanism involving air–sea humidity differences limiting evaporation occurring in between. Additionally, the spatial structures of the components of the feedbacks are nearly mirror images for these opposite ITCZ events over the east Pacific during boreal spring. In closing, we find that understanding the air–sea interactions during daily to weekly varying ITCZ events (nITCZ and dsITCZ) helps to pinpoint how fundamental processes differ for ITCZs in different hemispheres.
Abstract
The intertropical convergence zone (ITCZ) is a zonally elongated band of near-surface convergence and precipitation near the equator. During boreal spring, the eastern Pacific ITCZ migrates latitudinally on daily to subseasonal time scales, and climate models exhibit the greatest ITCZ biases during this time of the year. In this work, we investigate the air–sea interactions associated with the variability in the eastern Pacific ITCZ’s latitudinal location for consecutive days when the ITCZ is only located north of the equator (nITCZ events) compared to when the ITCZ is on both sides of the equator or south of the equator (dsITCZ events) during February–April. The distribution of sea surface temperature (SST) anomalies and surface latent heat flux (SLHF) anomalies during the nITCZ and dsITCZ events follow the classic wind–evaporation–SST (WES) positive feedback mechanism. However, an alternative mechanism, embracing the effect of SST anomalies on vertical stratification and momentum mixing, gives rise to a negative WES feedback. Our results show that in the surface layer, there is a general progression of positive WES feedbacks happening in the weeks leading to the events followed by negative WES feedbacks occurring after the ITCZ events, with an alternate mechanism involving air–sea humidity differences limiting evaporation occurring in between. Additionally, the spatial structures of the components of the feedbacks are nearly mirror images for these opposite ITCZ events over the east Pacific during boreal spring. In closing, we find that understanding the air–sea interactions during daily to weekly varying ITCZ events (nITCZ and dsITCZ) helps to pinpoint how fundamental processes differ for ITCZs in different hemispheres.
Abstract
This study explores the population exposure to an increasing number of hydroclimatic extreme events owing to the warming climate. It is well agreed that the extreme events are increasing in terms of frequency as well as intensity due to climate change and that the exposure to compound extreme events (concurrent occurrence of two or more extreme phenomena) affects population, ecosystems, and a variety of socioeconomic aspects more adversely. Specifically, the compound precipitation–temperature extremes (hot-dry and hot-wet) are considered, and the entire Indian mainland is regarded as the study region that spans over a wide variety of climatic regimes and wide variation of population density. The developed copula-based statistical method evaluates the change in population exposure to the compound extremes across the past (1981–2020) and future (near future: 2021–60 and far future: 2061–2100) due to climate change. The results indicate an increase of more than 10 million person-year exposure from the compound extremes across many regions of the country, considering both near and far future periods. Densely populated regions have experienced more significant changes in hot-wet extremes as compared with the hot-dry extremes in the past, and the same is projected to continue in the future. The increase is as much as sixfold in many parts of the country, including the Indo-Gangetic Plain and southernmost coastal regions, identified as the future hotspots with the maximum increase in exposure under all the projected warming and population scenarios. The study helps to identify the regions that may need greater attention based on the risks of population exposure to compound extremes in a warmer future.
Significance Statement
How is the growing population being affected now, and in the future, how will it be affected due to climate change induced compound extreme events? This study explores this societal consequence in terms of population exposure for the most populous country, India. An increase of more than 10 million person-year exposure from the precipitation–temperature compound extremes across many regions is indicated. Densely populated regions are expected to experience enhanced population exposure to hot-wet extremes as compared with the hot-dry extremes. Furthermore, the maximum increase in population exposure to compound extremes is expected across the Indo-Gangetic Plain and southern coastal regions of India. The outcome of the study will be helpful for adopting socioeconomic decisions toward the welfare of society.
Abstract
This study explores the population exposure to an increasing number of hydroclimatic extreme events owing to the warming climate. It is well agreed that the extreme events are increasing in terms of frequency as well as intensity due to climate change and that the exposure to compound extreme events (concurrent occurrence of two or more extreme phenomena) affects population, ecosystems, and a variety of socioeconomic aspects more adversely. Specifically, the compound precipitation–temperature extremes (hot-dry and hot-wet) are considered, and the entire Indian mainland is regarded as the study region that spans over a wide variety of climatic regimes and wide variation of population density. The developed copula-based statistical method evaluates the change in population exposure to the compound extremes across the past (1981–2020) and future (near future: 2021–60 and far future: 2061–2100) due to climate change. The results indicate an increase of more than 10 million person-year exposure from the compound extremes across many regions of the country, considering both near and far future periods. Densely populated regions have experienced more significant changes in hot-wet extremes as compared with the hot-dry extremes in the past, and the same is projected to continue in the future. The increase is as much as sixfold in many parts of the country, including the Indo-Gangetic Plain and southernmost coastal regions, identified as the future hotspots with the maximum increase in exposure under all the projected warming and population scenarios. The study helps to identify the regions that may need greater attention based on the risks of population exposure to compound extremes in a warmer future.
Significance Statement
How is the growing population being affected now, and in the future, how will it be affected due to climate change induced compound extreme events? This study explores this societal consequence in terms of population exposure for the most populous country, India. An increase of more than 10 million person-year exposure from the precipitation–temperature compound extremes across many regions is indicated. Densely populated regions are expected to experience enhanced population exposure to hot-wet extremes as compared with the hot-dry extremes. Furthermore, the maximum increase in population exposure to compound extremes is expected across the Indo-Gangetic Plain and southern coastal regions of India. The outcome of the study will be helpful for adopting socioeconomic decisions toward the welfare of society.
