Abstract
One of the critical components in understanding the climate system is the interaction between the land and the atmosphere. Whereas previous studies on land–atmosphere coupling mostly focus on its spatial hotspots, we explore the temporal evolution of land surface coupling strength (LCS) during a large-scale flood event in a semiarid region in northern Australia. The LCS indicates the relationship between soil moisture and latent heat flux, and the spatiotemporal variability in precipitation and soil water strongly affects the variability of LCS. The LCS is usually positive in the semiarid climate, where evapotranspiration (ET) occurs under the soil moisture–limited regime and thus increases with soil moisture. However, our analyses of combined land surface modeling and observational datasets show high temporal variability of LCS in the course of the extreme flood event followed by a drying period. The wet regions transferred the ET regime from the soil moisture–limited to the transition section, weakening the linear growth of ET with soil moisture, which resulted in the decline of LCS. The LCS remained weak until the flood retreated and the soil water approached the prestorm average state. Such temporal variation of the LCS has important implications for realistic parameterization of the land–atmosphere coupling and consequently improving subseasonal to seasonal climate forecast.
Abstract
One of the critical components in understanding the climate system is the interaction between the land and the atmosphere. Whereas previous studies on land–atmosphere coupling mostly focus on its spatial hotspots, we explore the temporal evolution of land surface coupling strength (LCS) during a large-scale flood event in a semiarid region in northern Australia. The LCS indicates the relationship between soil moisture and latent heat flux, and the spatiotemporal variability in precipitation and soil water strongly affects the variability of LCS. The LCS is usually positive in the semiarid climate, where evapotranspiration (ET) occurs under the soil moisture–limited regime and thus increases with soil moisture. However, our analyses of combined land surface modeling and observational datasets show high temporal variability of LCS in the course of the extreme flood event followed by a drying period. The wet regions transferred the ET regime from the soil moisture–limited to the transition section, weakening the linear growth of ET with soil moisture, which resulted in the decline of LCS. The LCS remained weak until the flood retreated and the soil water approached the prestorm average state. Such temporal variation of the LCS has important implications for realistic parameterization of the land–atmosphere coupling and consequently improving subseasonal to seasonal climate forecast.
Abstract
Severe thunderstorms can have devastating impacts. Concurrently high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are known to be conducive to severe weather, so high values of PROD = (CAPE)1/2 × SRH have been used to indicate high risk of severe thunderstorms. We consider the extreme values of these three variables for a large area of the contiguous United States over the period 1979–2015 and use extreme-value theory and a multiple testing procedure to show that there is a significant time trend in the extremes for PROD maxima in April, May, and August, for CAPE maxima in April, May, and June, and for maxima of SRH in April and May. These observed increases in CAPE are also relevant for rainfall extremes and are expected in a warmer climate but have not previously been reported. Moreover, we show that El Niño–Southern Oscillation explains variation in the extremes of PROD and SRH in February. Our results suggest that the risk from severe thunderstorms in April and May is increasing in parts of the United States where it was already high and that the risk from storms in February is increased over the main part of the region during La Niña years.
Abstract
Severe thunderstorms can have devastating impacts. Concurrently high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are known to be conducive to severe weather, so high values of PROD = (CAPE)1/2 × SRH have been used to indicate high risk of severe thunderstorms. We consider the extreme values of these three variables for a large area of the contiguous United States over the period 1979–2015 and use extreme-value theory and a multiple testing procedure to show that there is a significant time trend in the extremes for PROD maxima in April, May, and August, for CAPE maxima in April, May, and June, and for maxima of SRH in April and May. These observed increases in CAPE are also relevant for rainfall extremes and are expected in a warmer climate but have not previously been reported. Moreover, we show that El Niño–Southern Oscillation explains variation in the extremes of PROD and SRH in February. Our results suggest that the risk from severe thunderstorms in April and May is increasing in parts of the United States where it was already high and that the risk from storms in February is increased over the main part of the region during La Niña years.
Abstract
Observational analyses suggest that a significant fraction of the tropical Pacific decadal variability (TPDV) (~60%–70%) is energized by the combined action of extratropical precursors of El Niño–Southern Oscillation (ENSO) originating from the North and South Pacific. Specifically, the growth and decay of the basin-scale TPDV pattern (time scale = ~1.5–2 years) is linked to the following sequence: ENSO precursors (extratropics, growth phase) → ENSO (tropics, peak phase) → ENSO successors (extratropics, decay phase) resulting from ENSO teleconnections. This sequence of teleconnections is an important physical basis for Pacific climate predictability. Here we examine the TPDV and its connection to extratropical dynamics in 20 models from phase 5 of the Coupled Model Intercomparison Project (CMIP). We find that most models (~80%) can simulate the observed spatial pattern (R > 0.6) and frequency characteristics of the TPDV. In 12 models, more than 65% of the basinwide Pacific decadal variability (PDV) originates from TPDV, which is comparable with observations (~70%). However, despite reproducing the basic spatial and temporal statistics, models underestimate the influence of the North and South Pacific ENSO precursors to the TPDV, and most of the models’ TPDV originates in the tropics. Only 35%–40% of the models reproduce the observed extratropical ENSO precursor patterns (R > 0.5). Models with a better representation of the ENSO precursors show 1) better basin-scale signatures of TPDV and 2) stronger ENSO teleconnections from/to the tropics that are consistent with observations. These results suggest that better representation of ENSO precursor dynamics in CMIP may lead to improved Pacific decadal variability dynamics and predictability.
Abstract
Observational analyses suggest that a significant fraction of the tropical Pacific decadal variability (TPDV) (~60%–70%) is energized by the combined action of extratropical precursors of El Niño–Southern Oscillation (ENSO) originating from the North and South Pacific. Specifically, the growth and decay of the basin-scale TPDV pattern (time scale = ~1.5–2 years) is linked to the following sequence: ENSO precursors (extratropics, growth phase) → ENSO (tropics, peak phase) → ENSO successors (extratropics, decay phase) resulting from ENSO teleconnections. This sequence of teleconnections is an important physical basis for Pacific climate predictability. Here we examine the TPDV and its connection to extratropical dynamics in 20 models from phase 5 of the Coupled Model Intercomparison Project (CMIP). We find that most models (~80%) can simulate the observed spatial pattern (R > 0.6) and frequency characteristics of the TPDV. In 12 models, more than 65% of the basinwide Pacific decadal variability (PDV) originates from TPDV, which is comparable with observations (~70%). However, despite reproducing the basic spatial and temporal statistics, models underestimate the influence of the North and South Pacific ENSO precursors to the TPDV, and most of the models’ TPDV originates in the tropics. Only 35%–40% of the models reproduce the observed extratropical ENSO precursor patterns (R > 0.5). Models with a better representation of the ENSO precursors show 1) better basin-scale signatures of TPDV and 2) stronger ENSO teleconnections from/to the tropics that are consistent with observations. These results suggest that better representation of ENSO precursor dynamics in CMIP may lead to improved Pacific decadal variability dynamics and predictability.
Abstract
We use a statistical tropical cyclone (TC) model, the North Atlantic Stochastic Hurricane Model (NASHM), in combination with sea surface temperature (SST) projections from climate models, to estimate regional changes in U.S. TC activity into the 2030s. NASHM is trained on historical variations in TC characteristics with two SST indices: global–tropical mean SST and the difference between tropical North Atlantic Ocean (NA) SST and the rest of the global tropics, often referred to as “relative SST.” Testing confirms the model’s ability to reproduce historical U.S. TC activity as well as to make skillful predictions. When NASHM is driven by SST projections into the 2030s, overall NA annual TC counts increase, and the fractional increase is the greatest at the highest wind intensities. However, an eastward anomaly in mean TC tracks and an eastward shift in TC formation region result in a geographically varied signal in U.S. coastal activity. Florida’s Gulf Coast is projected to see significant increases in TC activity relative to the long-term historical mean, and these increases are fractionally greatest at the highest intensities. By contrast, the northwestern U.S. Gulf Coast and the U.S. East Coast will see little change.
Abstract
We use a statistical tropical cyclone (TC) model, the North Atlantic Stochastic Hurricane Model (NASHM), in combination with sea surface temperature (SST) projections from climate models, to estimate regional changes in U.S. TC activity into the 2030s. NASHM is trained on historical variations in TC characteristics with two SST indices: global–tropical mean SST and the difference between tropical North Atlantic Ocean (NA) SST and the rest of the global tropics, often referred to as “relative SST.” Testing confirms the model’s ability to reproduce historical U.S. TC activity as well as to make skillful predictions. When NASHM is driven by SST projections into the 2030s, overall NA annual TC counts increase, and the fractional increase is the greatest at the highest wind intensities. However, an eastward anomaly in mean TC tracks and an eastward shift in TC formation region result in a geographically varied signal in U.S. coastal activity. Florida’s Gulf Coast is projected to see significant increases in TC activity relative to the long-term historical mean, and these increases are fractionally greatest at the highest intensities. By contrast, the northwestern U.S. Gulf Coast and the U.S. East Coast will see little change.
Abstract
The Arctic is rapidly changing, with increasingly dramatic sea ice loss and surface warming in recent decades. Shortwave radiation plays a key role in Arctic warming during summer months, and absorbed shortwave radiation has been increasing largely because of greater sea ice loss. Clouds can influence this ice–albedo feedback by modulating the amount of shortwave radiation incident on the Arctic Ocean. In turn, clouds impact the amount of time that must elapse before forced trends in Arctic shortwave absorption emerge from internal variability. This study determines whether the forced climate response of absorbed shortwave radiation in the Arctic has emerged in the modern satellite record and global climate models. From 18 years of satellite observations from CERES-EBAF, we find that recent declines in sea ice are large enough to produce a statistically significant trend (1.7 × 106 PJ or 3.9% per decade) in observed clear-sky absorbed shortwave radiation. However, clouds preclude any forced trends in all-sky absorption from emerging within the existing satellite record. Across 18 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), the predicted time to emergence of absorbed shortwave radiation trends varies from 8 to 39 and from 8 to 35 years for all-sky and clear-sky conditions, respectively, across two future scenarios. Furthermore, most models fail to reproduce the observed cloud delaying effect because of differences in internal variability. Contrary to observations, one-third of models suggest that clouds may reduce the time to emergence of absorbed shortwave trends relative to clear skies, an artifact that may be the result of inaccurate representations of cloud feedbacks.
Abstract
The Arctic is rapidly changing, with increasingly dramatic sea ice loss and surface warming in recent decades. Shortwave radiation plays a key role in Arctic warming during summer months, and absorbed shortwave radiation has been increasing largely because of greater sea ice loss. Clouds can influence this ice–albedo feedback by modulating the amount of shortwave radiation incident on the Arctic Ocean. In turn, clouds impact the amount of time that must elapse before forced trends in Arctic shortwave absorption emerge from internal variability. This study determines whether the forced climate response of absorbed shortwave radiation in the Arctic has emerged in the modern satellite record and global climate models. From 18 years of satellite observations from CERES-EBAF, we find that recent declines in sea ice are large enough to produce a statistically significant trend (1.7 × 106 PJ or 3.9% per decade) in observed clear-sky absorbed shortwave radiation. However, clouds preclude any forced trends in all-sky absorption from emerging within the existing satellite record. Across 18 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6), the predicted time to emergence of absorbed shortwave radiation trends varies from 8 to 39 and from 8 to 35 years for all-sky and clear-sky conditions, respectively, across two future scenarios. Furthermore, most models fail to reproduce the observed cloud delaying effect because of differences in internal variability. Contrary to observations, one-third of models suggest that clouds may reduce the time to emergence of absorbed shortwave trends relative to clear skies, an artifact that may be the result of inaccurate representations of cloud feedbacks.
Abstract
Decadal mean variables are frequently used to characterize decadal climate variabilities. Decadal means are often calculated using yearly data, which can represent variability at time scales from annual to centennial. Residuals from interannual fluctuations may contribute to the variability in decadal time series. Such variability is more difficult to be predicted at the long range. Removing it from the decadal variability means that the remaining variability is more likely to arise from slowly varying multidecadal or longer time scale external forcing and internal climate dynamics, which are more likely to be predicted. Here, a new approach is proposed to understand the uncertainty, potential predictability, and drivers of decadal mean variables. The covariance matrix of multivariate decadal running means is decomposed into unpredictable fast decadal variability and the potentially predictable slow decadal variability. EOF analysis is then applied to the decomposed matrices to find the dominant modes, which may be related to the drivers of the two types of variabilities in the multivariate decadal means. The methodology has been applied to 140-yr datasets of North Pacific sea surface temperature and the Northern Hemisphere 1000-hPa geopotential height. For sea surface temperature, the Pacific decadal oscillation is the major driver of the fast decadal variability, while the radiative forcing and the Atlantic multidecadal oscillation are major drivers of the slow decadal variability. For the 1000-hPa geopotential height, fast decadal variability is associated with the northern annular mode, the east Atlantic mode, and the Pacific decadal oscillation. Slow decadal variability is associated with the northern annular mode and the Atlantic multidecadal oscillation.
Abstract
Decadal mean variables are frequently used to characterize decadal climate variabilities. Decadal means are often calculated using yearly data, which can represent variability at time scales from annual to centennial. Residuals from interannual fluctuations may contribute to the variability in decadal time series. Such variability is more difficult to be predicted at the long range. Removing it from the decadal variability means that the remaining variability is more likely to arise from slowly varying multidecadal or longer time scale external forcing and internal climate dynamics, which are more likely to be predicted. Here, a new approach is proposed to understand the uncertainty, potential predictability, and drivers of decadal mean variables. The covariance matrix of multivariate decadal running means is decomposed into unpredictable fast decadal variability and the potentially predictable slow decadal variability. EOF analysis is then applied to the decomposed matrices to find the dominant modes, which may be related to the drivers of the two types of variabilities in the multivariate decadal means. The methodology has been applied to 140-yr datasets of North Pacific sea surface temperature and the Northern Hemisphere 1000-hPa geopotential height. For sea surface temperature, the Pacific decadal oscillation is the major driver of the fast decadal variability, while the radiative forcing and the Atlantic multidecadal oscillation are major drivers of the slow decadal variability. For the 1000-hPa geopotential height, fast decadal variability is associated with the northern annular mode, the east Atlantic mode, and the Pacific decadal oscillation. Slow decadal variability is associated with the northern annular mode and the Atlantic multidecadal oscillation.
Abstract
This study examines processes fundamental to the development of South Asian monsoon depressions using an array of integrations of an idealized convection-permitting numerical model. In each integration, a wave of initially small amplitude is subjected to a different amount of vertical and meridional wind shear, with temperature and moisture fields constructed according to realistic constraints. Based on the evolution of this disturbance into monsoon depression-like vortices, two features of the background environment emerge as important: the low-level gradient of moist static energy (MSE) and the low-level meridional shear. As the low-level MSE gradient steepens, the disturbance becomes stronger and produces more rain. This strengthening results from the interaction of the vortex with latent heat release by convection that is in turn organized by positive MSE advection in the northerly flow west of the vortex. In this region of advection, moister air from the north ascends along upward sloping isentropes, driving moist convection. The disturbance also becomes stronger with increasing meridional shear, which makes the environment more barotropically unstable. The absence of either of these two features of the background environment prevents substantial growth of the disturbance. Our results suggest that monsoon depression growth in South Asia is fostered by the coexistence of a strong low-level MSE gradient with strong meridional wind shear associated with the monsoon trough.
Abstract
This study examines processes fundamental to the development of South Asian monsoon depressions using an array of integrations of an idealized convection-permitting numerical model. In each integration, a wave of initially small amplitude is subjected to a different amount of vertical and meridional wind shear, with temperature and moisture fields constructed according to realistic constraints. Based on the evolution of this disturbance into monsoon depression-like vortices, two features of the background environment emerge as important: the low-level gradient of moist static energy (MSE) and the low-level meridional shear. As the low-level MSE gradient steepens, the disturbance becomes stronger and produces more rain. This strengthening results from the interaction of the vortex with latent heat release by convection that is in turn organized by positive MSE advection in the northerly flow west of the vortex. In this region of advection, moister air from the north ascends along upward sloping isentropes, driving moist convection. The disturbance also becomes stronger with increasing meridional shear, which makes the environment more barotropically unstable. The absence of either of these two features of the background environment prevents substantial growth of the disturbance. Our results suggest that monsoon depression growth in South Asia is fostered by the coexistence of a strong low-level MSE gradient with strong meridional wind shear associated with the monsoon trough.
Abstract
The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase K
DP, is introduced. A large 2D-video-disdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient,
Abstract
The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase K
DP, is introduced. A large 2D-video-disdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient,
Abstract
Under global warming, surface air temperature has risen rapidly and sea ice decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-meter air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°∼90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient (ACC) of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the maximum decrease trend of SIC by one month, in association with the heat flux forcing from the ocean surface to lower atmosphere. While the predictive skill is generally much lower for the detrended variations, we find a close relationship between the tropical Pacific El Niño–Southern Oscillation and the Arctic detrended 2-m T anomalies. This indicates potential seasonal to interannual predictability of the Arctic natural variations.
Abstract
Under global warming, surface air temperature has risen rapidly and sea ice decreased markedly in the Arctic. These drastic climate changes have brought about various severe impacts on the vulnerable environment and ecosystem there. Thus, accurate prediction of Arctic climate becomes more important than before. Here we examine the seasonal to interannual predictive skills of 2-meter air temperature (2-m T) and sea ice cover (SIC) over the Arctic region (70°∼90°N) during 1980–2014 with a high-resolution global coupled model called the Met Office Decadal Prediction System version 3 (DePreSys3). The model captures well both the climatology and interannual variability of the Arctic 2-m T and SIC. Moreover, the anomaly correlation coefficient (ACC) of Arctic-averaged 2-m T and SIC shows statistically significant skills at lead times up to 16 months. This is mainly due to the contribution of strong decadal trends. In addition, it is found that the peak warming trend of Arctic 2-m T lags the maximum decrease trend of SIC by one month, in association with the heat flux forcing from the ocean surface to lower atmosphere. While the predictive skill is generally much lower for the detrended variations, we find a close relationship between the tropical Pacific El Niño–Southern Oscillation and the Arctic detrended 2-m T anomalies. This indicates potential seasonal to interannual predictability of the Arctic natural variations.
Abstract
Observations have shown that tropical convection is influenced by fluctuations in temperature and moisture in the lower free troposphere (LFT; 600–850 hPa), as well as moist enthalpy (ME) fluctuations beneath the 850 hPa level, referred to as the deep boundary layer (DBL; 850–1000 hPa). A framework is developed that consolidates these three quantities within the context of the buoyancy of an entraining plume. A “plume buoyancy equation” is derived based on a relaxed version of the weak temperature gradient (WTG) approximation. Analysis of this equation using quantities derived from the Dynamics of the Madden–Julian Oscillation (DYNAMO) sounding array data reveals that processes occurring within the DBL and the LFT contribute nearly equally to the evolution of plume buoyancy, indicating that processes that occur in both layers are critical to the evolution of tropical convection. Adiabatic motions play an important role in the evolution of buoyancy both at the daily and longer time scales and are comparable in magnitude to horizontal moisture advection and vertical moist static energy advection by convection. The plume buoyancy equation may explain convective coupling at short time scales in both temperature and moisture fluctuations and can be used to complement the commonly used moist static energy budget, which emphasizes the slower evolution of the convective envelope in tropical motion systems.
Abstract
Observations have shown that tropical convection is influenced by fluctuations in temperature and moisture in the lower free troposphere (LFT; 600–850 hPa), as well as moist enthalpy (ME) fluctuations beneath the 850 hPa level, referred to as the deep boundary layer (DBL; 850–1000 hPa). A framework is developed that consolidates these three quantities within the context of the buoyancy of an entraining plume. A “plume buoyancy equation” is derived based on a relaxed version of the weak temperature gradient (WTG) approximation. Analysis of this equation using quantities derived from the Dynamics of the Madden–Julian Oscillation (DYNAMO) sounding array data reveals that processes occurring within the DBL and the LFT contribute nearly equally to the evolution of plume buoyancy, indicating that processes that occur in both layers are critical to the evolution of tropical convection. Adiabatic motions play an important role in the evolution of buoyancy both at the daily and longer time scales and are comparable in magnitude to horizontal moisture advection and vertical moist static energy advection by convection. The plume buoyancy equation may explain convective coupling at short time scales in both temperature and moisture fluctuations and can be used to complement the commonly used moist static energy budget, which emphasizes the slower evolution of the convective envelope in tropical motion systems.
