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Abstract
This study investigates the in situ generation of planetary waves (PWs) by zonally asymmetric gravity wave drag (GWD) in the mesosphere using a fully nonlinear general circulation model extending to the lower thermosphere. To isolate the effects of GWD, we establish a highly idealized but efficient framework that excludes stationary PWs propagating from the troposphere and in situ PWs generated by barotropic and baroclinic instabilities. The GWD is prescribed in a zonally sinusoidal form with a zonal wavenumber (ZWN) of either 1 or 2 in the lower mesosphere of the Northern Hemisphere midlatitude. Our idealized simulations clearly show that zonally asymmetric GWD generates PWs by serving as a nonconservative source Z′ of linearized disturbance quasigeostrophic potential vorticity q′. While Z′ initially amplifies PWs through enhancing q′ tendency, the subsequent zonal advection of q′ gradually balances with Z′, thereby attaining steady-state PWs. The GWD-induced PWs predominantly have the same ZWN as the applied GWD with minor contributions from higher ZWN components attributed to nonlinear processes. The amplitude of the induced PWs increases in proportion with the magnitude of the peak GWD, while it decreases in proportion to the square of ZWN. Moreover, the amplitude of PWs increases as the meridional range of GWD expands and as GWD shifts toward lower latitudes. These PWs deposit substantial positive Eliassen–Palm flux divergence (EPFD) of ∼30 m s−1 day−1 at their origin and negative EPFD of 5–10 m s−1 day−1 during propagation. In addition, the in situ PWs exhibit interhemispheric propagation following westerlies that extend into the Southern Hemisphere.
Abstract
This study investigates the in situ generation of planetary waves (PWs) by zonally asymmetric gravity wave drag (GWD) in the mesosphere using a fully nonlinear general circulation model extending to the lower thermosphere. To isolate the effects of GWD, we establish a highly idealized but efficient framework that excludes stationary PWs propagating from the troposphere and in situ PWs generated by barotropic and baroclinic instabilities. The GWD is prescribed in a zonally sinusoidal form with a zonal wavenumber (ZWN) of either 1 or 2 in the lower mesosphere of the Northern Hemisphere midlatitude. Our idealized simulations clearly show that zonally asymmetric GWD generates PWs by serving as a nonconservative source Z′ of linearized disturbance quasigeostrophic potential vorticity q′. While Z′ initially amplifies PWs through enhancing q′ tendency, the subsequent zonal advection of q′ gradually balances with Z′, thereby attaining steady-state PWs. The GWD-induced PWs predominantly have the same ZWN as the applied GWD with minor contributions from higher ZWN components attributed to nonlinear processes. The amplitude of the induced PWs increases in proportion with the magnitude of the peak GWD, while it decreases in proportion to the square of ZWN. Moreover, the amplitude of PWs increases as the meridional range of GWD expands and as GWD shifts toward lower latitudes. These PWs deposit substantial positive Eliassen–Palm flux divergence (EPFD) of ∼30 m s−1 day−1 at their origin and negative EPFD of 5–10 m s−1 day−1 during propagation. In addition, the in situ PWs exhibit interhemispheric propagation following westerlies that extend into the Southern Hemisphere.
Abstract
Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.
Significance Statement
Flash droughts are a subset of droughts with rapid onset and intensification leading to devastating losses to crops. Rapid soil moisture decline is one way to detect flash droughts. Because there is a lack of widespread observational data, we often rely on model outputs of soil moisture. Here, we explore how the representation of vegetation within land surface models influences the U.S. flash drought characteristics covering 1981–2017. We show that the misrepresentation of vegetation status propagates soil moisture biases into flash drought monitoring, impacting our understanding of the onset, magnitude, duration, and trends in flash droughts. Our results suggest that the assimilation of near-real-time vegetation into land surface models could improve the detection, monitoring, and prediction of flash droughts.
Abstract
Predicting and managing the impacts of flash droughts is difficult owing to their rapid onset and intensification. Flash drought monitoring often relies on assessing changes in root-zone soil moisture. However, the lack of widespread soil moisture measurements means that flash drought assessments often use process-based model data like that from the North American Land Data Assimilation System (NLDAS). Such reliance opens flash drought assessment to model biases, particularly from vegetation processes. Here, we examine the influence of vegetation on NLDAS-simulated flash drought characteristics by comparing two experiments covering 1981–2017: open loop (OL), which uses NLDAS surface meteorological forcing to drive a land surface model using prognostic vegetation, and data assimilation (DA), which instead assimilates near-real-time satellite-derived leaf area index (LAI) into the land surface model. The OL simulation consistently underestimates LAI across the United States, causing relatively high soil moisture values. Both experiments produce similar geographic patterns of flash droughts, but OL produces shorter duration events and regional trends in flash drought occurrence that are sometimes opposite to those in DA. Across the Midwest and Southern United States, flash droughts are 4 weeks (about 70%) longer on average in DA than OL. Moreover, across much of the Great Plains, flash drought occurrence has trended upward according to the DA experiment, opposite to the trend in OL. This sensitivity of flash drought to the representation of vegetation suggests that representing plants with greater fidelity could aid in monitoring flash droughts and improve the prediction of flash drought transitions to more persistent and damaging long-term droughts.
Significance Statement
Flash droughts are a subset of droughts with rapid onset and intensification leading to devastating losses to crops. Rapid soil moisture decline is one way to detect flash droughts. Because there is a lack of widespread observational data, we often rely on model outputs of soil moisture. Here, we explore how the representation of vegetation within land surface models influences the U.S. flash drought characteristics covering 1981–2017. We show that the misrepresentation of vegetation status propagates soil moisture biases into flash drought monitoring, impacting our understanding of the onset, magnitude, duration, and trends in flash droughts. Our results suggest that the assimilation of near-real-time vegetation into land surface models could improve the detection, monitoring, and prediction of flash droughts.
Abstract
Pacific Islands present unique challenges for water resource management due to their environmental vulnerability, dynamic climates, and heavy reliance on groundwater. Quantifying connections between meteoric, ground, and surface waters is critical for effective water resource management. Analyses of the stable isotopes of oxygen and hydrogen in the hydrosphere can help illuminate such connections. This study investigates the stable isotope composition of rainfall on O‘ahu in the Hawaiian Islands, with a particular focus on how altitude impacts stable isotope composition. Rainfall was sampled at 20 locations from March 2018 to August 2021. The new precipitation stable isotope data were integrated with previously published data to create the most spatially and topographically diverse precipitation collector network on O‘ahu to date. Results show that δ 18O and δ 2H values in precipitation displayed distinct isotopic signatures influenced by geographical location, season, and precipitation source. Altitude and isotopic compositions were strongly correlated along certain elevation transects, but these relationships could not be extrapolated to larger regions due to microclimate influences. Altitude and deuterium excess were strongly correlated across the study region, suggesting that deuterium excess may be a reliable proxy for precipitation elevation in local water tracer studies. Analysis of spring, rainfall, and fog stable isotope composition from Mount Ka‘ala suggests that fog may contribute up to 45% of total groundwater recharge at the summit. These findings highlight the strong influence of microclimates on the stable isotope composition of rainfall, underscore the need for further investigation into fog’s role in the water budget, and demonstrate the importance of stable isotope analysis for comprehending hydrologic dynamics in environmentally sensitive regions.
Abstract
Pacific Islands present unique challenges for water resource management due to their environmental vulnerability, dynamic climates, and heavy reliance on groundwater. Quantifying connections between meteoric, ground, and surface waters is critical for effective water resource management. Analyses of the stable isotopes of oxygen and hydrogen in the hydrosphere can help illuminate such connections. This study investigates the stable isotope composition of rainfall on O‘ahu in the Hawaiian Islands, with a particular focus on how altitude impacts stable isotope composition. Rainfall was sampled at 20 locations from March 2018 to August 2021. The new precipitation stable isotope data were integrated with previously published data to create the most spatially and topographically diverse precipitation collector network on O‘ahu to date. Results show that δ 18O and δ 2H values in precipitation displayed distinct isotopic signatures influenced by geographical location, season, and precipitation source. Altitude and isotopic compositions were strongly correlated along certain elevation transects, but these relationships could not be extrapolated to larger regions due to microclimate influences. Altitude and deuterium excess were strongly correlated across the study region, suggesting that deuterium excess may be a reliable proxy for precipitation elevation in local water tracer studies. Analysis of spring, rainfall, and fog stable isotope composition from Mount Ka‘ala suggests that fog may contribute up to 45% of total groundwater recharge at the summit. These findings highlight the strong influence of microclimates on the stable isotope composition of rainfall, underscore the need for further investigation into fog’s role in the water budget, and demonstrate the importance of stable isotope analysis for comprehending hydrologic dynamics in environmentally sensitive regions.
Abstract
Producing a quantitative snowfall forecast (QSF) typically requires a model quantitative precipitation forecast (QPF) and snow-to-liquid ratio (SLR) estimate. QPF and SLR can vary significantly in space and time over complex terrain, necessitating fine-scale or point-specific forecasts of each component. Little Cottonwood Canyon (LCC) in Utah’s Wasatch Range frequently experiences high-impact winter storms and avalanche closures that result in substantial transportation and economic disruptions, making it an excellent testbed for evaluating snowfall forecasts. In this study, we validate QPFs, SLR forecasts, and QSFs produced by or derived from the Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) using liquid precipitation equivalent (LPE) and snowfall observations collected during the 2019/20–2022/23 cool seasons at the Alta–Collins snow-study site (2945 m MSL) in upper LCC. The 12-h QPFs produced by the GFS and HRRR underpredict the total LPE during the four cool seasons by 33% and 29%, respectively, and underpredict 50th, 75th, and 90th percentile event frequencies. Current operational SLR methods exhibit mean absolute errors of 4.5–7.7. In contrast, a locally trained random forest algorithm reduces SLR mean absolute errors to 3.7. Despite the random forest producing more accurate SLR forecasts, QSFs derived from operational SLR methods produce higher critical success indices since they exhibit positive SLR biases that offset negative QPF biases. These results indicate an overall underprediction of LPE by operational models in upper LCC and illustrate the need to identify sources of QSF bias to enhance QSF performance.
Significance Statement
Winter storms in mountainous terrain can disrupt transportation and threaten life and property due to road snow and avalanche hazards. Snow-to-liquid ratio (SLR) is an important variable for snowfall and avalanche forecasts. Using high-quality historical snowfall observations and atmospheric analyses, we developed a machine learning technique for predicting SLR at a high mountain site in Utah’s Little Cottonwood Canyon that is prone to closure due to winter storms. This technique produces improved SLR forecasts for use by weather forecasters and snow-safety personnel. We also show that current operational models and SLR techniques underforecast liquid precipitation amounts and overforecast SLRs, respectively, which has implications for future model development.
Abstract
Producing a quantitative snowfall forecast (QSF) typically requires a model quantitative precipitation forecast (QPF) and snow-to-liquid ratio (SLR) estimate. QPF and SLR can vary significantly in space and time over complex terrain, necessitating fine-scale or point-specific forecasts of each component. Little Cottonwood Canyon (LCC) in Utah’s Wasatch Range frequently experiences high-impact winter storms and avalanche closures that result in substantial transportation and economic disruptions, making it an excellent testbed for evaluating snowfall forecasts. In this study, we validate QPFs, SLR forecasts, and QSFs produced by or derived from the Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) using liquid precipitation equivalent (LPE) and snowfall observations collected during the 2019/20–2022/23 cool seasons at the Alta–Collins snow-study site (2945 m MSL) in upper LCC. The 12-h QPFs produced by the GFS and HRRR underpredict the total LPE during the four cool seasons by 33% and 29%, respectively, and underpredict 50th, 75th, and 90th percentile event frequencies. Current operational SLR methods exhibit mean absolute errors of 4.5–7.7. In contrast, a locally trained random forest algorithm reduces SLR mean absolute errors to 3.7. Despite the random forest producing more accurate SLR forecasts, QSFs derived from operational SLR methods produce higher critical success indices since they exhibit positive SLR biases that offset negative QPF biases. These results indicate an overall underprediction of LPE by operational models in upper LCC and illustrate the need to identify sources of QSF bias to enhance QSF performance.
Significance Statement
Winter storms in mountainous terrain can disrupt transportation and threaten life and property due to road snow and avalanche hazards. Snow-to-liquid ratio (SLR) is an important variable for snowfall and avalanche forecasts. Using high-quality historical snowfall observations and atmospheric analyses, we developed a machine learning technique for predicting SLR at a high mountain site in Utah’s Little Cottonwood Canyon that is prone to closure due to winter storms. This technique produces improved SLR forecasts for use by weather forecasters and snow-safety personnel. We also show that current operational models and SLR techniques underforecast liquid precipitation amounts and overforecast SLRs, respectively, which has implications for future model development.
Abstract
Accurate tropical cyclogenesis (TCG) prediction is important because it allows national operational forecasting agencies to issue timely warnings and implement effective disaster prevention measures. In 2020, the Korea Meteorological Administration employed a self-developed operational model called the Korean Integrated Model (KIM). In this study, we verified KIM’s TCG forecast skill over the western North Pacific. Based on 9-day forecasts, TCG in the model was objectively detected and classified as well-predicted, early formation, late formation, miss, or false alarm by comparing their formation times and locations with those of 46 tropical cyclones (TCs) from June to November in 2020–21 documented by the Joint Typhoon Warning Center. The prediction of large-scale environmental conditions relevant to TCG was also evaluated. The results showed that the probability of KIM detection was comparable to or better than that of previously reported statistics of other numerical weather prediction models. The intrabasin comparison revealed that the probability of detection in the Philippine Sea was the highest, followed by the South China Sea and central Pacific. The best TCG prediction performance in the Philippine Sea was supported by unbiased forecasts in large-scale environments. The missed and false alarm cases in all three regions had the largest prediction biases in the large-scale lower-tropospheric relative vorticity. Excessive false alarms may be associated with prediction biases in the vertical gradient of equivalent potential temperature within the boundary layer. This study serves as a primary guide for national forecasters and is useful to model developers for further refinement of KIM.
Abstract
Accurate tropical cyclogenesis (TCG) prediction is important because it allows national operational forecasting agencies to issue timely warnings and implement effective disaster prevention measures. In 2020, the Korea Meteorological Administration employed a self-developed operational model called the Korean Integrated Model (KIM). In this study, we verified KIM’s TCG forecast skill over the western North Pacific. Based on 9-day forecasts, TCG in the model was objectively detected and classified as well-predicted, early formation, late formation, miss, or false alarm by comparing their formation times and locations with those of 46 tropical cyclones (TCs) from June to November in 2020–21 documented by the Joint Typhoon Warning Center. The prediction of large-scale environmental conditions relevant to TCG was also evaluated. The results showed that the probability of KIM detection was comparable to or better than that of previously reported statistics of other numerical weather prediction models. The intrabasin comparison revealed that the probability of detection in the Philippine Sea was the highest, followed by the South China Sea and central Pacific. The best TCG prediction performance in the Philippine Sea was supported by unbiased forecasts in large-scale environments. The missed and false alarm cases in all three regions had the largest prediction biases in the large-scale lower-tropospheric relative vorticity. Excessive false alarms may be associated with prediction biases in the vertical gradient of equivalent potential temperature within the boundary layer. This study serves as a primary guide for national forecasters and is useful to model developers for further refinement of KIM.
Abstract
East Asian atmospheric rivers (ARs) exhibit the most pronounced activity in summer with significant impacts on monsoon rainfall. However, their occurrence mechanisms are yet to be revealed in detail. In this study, we unravel the inherently complex nature of East Asian summer ARs by applying a multiscale index that quantifies the relative importance of high-frequency (HF) and low-frequency (LF) moisture transports in AR development. It is found that both HF and LF processes contribute to shaping the summertime ARs in East Asia, contrasting to the wintertime ARs dominated by HF processes. Stratification of ARs with the multiscale index reveals that HF-dominant ARs are driven by baroclinically deepening extratropical cyclones, analogous to the widely accepted definition of canonical ARs. In contrast, LF-dominant ARs result from an enhanced monsoon southwesterly between a quasi-stationary cyclone and an anticyclone with the latter being the anomalous expansion of the western North Pacific subtropical high. Such a pattern is reminiscent of the classical monsoon rainband. While HF-dominant ARs are transient, LF-dominant ARs are quasi-stationary with a higher potential for prolonged local impacts. The intermediate ARs, constituting a majority of East Asian summer ARs, exhibit synoptic conditions that combine HF- and LF-dominant ARs. Therefore, East Asian summer ARs cannot be explained by a single parent system but should be considered as a continuum of extratropical-cyclone-induced and fluctuating monsoon-flow-induced moisture plumes. This finding would serve as a base for the advanced understanding of hydrological impacts, variability, and projected change of East Asian ARs.
Significance Statement
Despite the accumulation of studies on summertime atmospheric rivers (ARs) in East Asia, a comprehensive explanation for their occurrence mechanisms remains elusive. This study disentangles their complicated nature through case-level multiscale analyses. In contrast to wintertime ARs, summertime ARs are shaped by both high- and low-frequency moisture transports. The high-frequency moisture transport is associated with migratory extratropical cyclones which are suppressed but still active in summer, while the low-frequency moisture transport arises from the fluctuation of a quasi-stationary monsoon southwesterly along the periphery of the western North Pacific subtropical high. The varying relative contribution of high- and low-frequency components from one AR to another suggests that East Asian summer ARs represent a continuum of extratropical and monsoonal moisture plumes.
Abstract
East Asian atmospheric rivers (ARs) exhibit the most pronounced activity in summer with significant impacts on monsoon rainfall. However, their occurrence mechanisms are yet to be revealed in detail. In this study, we unravel the inherently complex nature of East Asian summer ARs by applying a multiscale index that quantifies the relative importance of high-frequency (HF) and low-frequency (LF) moisture transports in AR development. It is found that both HF and LF processes contribute to shaping the summertime ARs in East Asia, contrasting to the wintertime ARs dominated by HF processes. Stratification of ARs with the multiscale index reveals that HF-dominant ARs are driven by baroclinically deepening extratropical cyclones, analogous to the widely accepted definition of canonical ARs. In contrast, LF-dominant ARs result from an enhanced monsoon southwesterly between a quasi-stationary cyclone and an anticyclone with the latter being the anomalous expansion of the western North Pacific subtropical high. Such a pattern is reminiscent of the classical monsoon rainband. While HF-dominant ARs are transient, LF-dominant ARs are quasi-stationary with a higher potential for prolonged local impacts. The intermediate ARs, constituting a majority of East Asian summer ARs, exhibit synoptic conditions that combine HF- and LF-dominant ARs. Therefore, East Asian summer ARs cannot be explained by a single parent system but should be considered as a continuum of extratropical-cyclone-induced and fluctuating monsoon-flow-induced moisture plumes. This finding would serve as a base for the advanced understanding of hydrological impacts, variability, and projected change of East Asian ARs.
Significance Statement
Despite the accumulation of studies on summertime atmospheric rivers (ARs) in East Asia, a comprehensive explanation for their occurrence mechanisms remains elusive. This study disentangles their complicated nature through case-level multiscale analyses. In contrast to wintertime ARs, summertime ARs are shaped by both high- and low-frequency moisture transports. The high-frequency moisture transport is associated with migratory extratropical cyclones which are suppressed but still active in summer, while the low-frequency moisture transport arises from the fluctuation of a quasi-stationary monsoon southwesterly along the periphery of the western North Pacific subtropical high. The varying relative contribution of high- and low-frequency components from one AR to another suggests that East Asian summer ARs represent a continuum of extratropical and monsoonal moisture plumes.
Abstract
The importance of extreme event attribution rises as climate change causes severe damage to populations resulting from unprecedented events. In February 2019, a planetary wave shifted along the U.S.–Canadian border, simultaneously leading to troughing with anomalous cold events and ridging over Alaska and northern Canada with abnormal warm events. Also, a dry-stabilized anticyclonic circulation over low latitudes induced warm extreme events over Mexico and Florida. Most attribution studies compare the climate model simulations under natural or actual forcing conditions and assess probabilistically from a climatological point of view. However, in this study, we use multiple ensembles from an operational forecast model, promising statistical as well as dynamically constrained attribution assessment, often referred to as the storyline approach to extreme event attribution. In the globally averaged results, increasing CO2 concentrations lead to distinct warming signals at the surface, resulting mainly from diabatic heating. Our study finds that CO2-induced warming eventually affects the possibility of extreme events in North America, quantifying the impact of anthropogenic forcing over less than a week’s forecast simulation. Our study assesses the validity of the storyline approach conditional on the forecast lead times, which is hindered by rising noise in CO2 signals and the declining performance of the forecast model. The forecast-based storyline approach is valid for at least half of the land area within a 6-day lead time before the target extreme occurrence. Our attribution results highlight the importance of achieving net-zero emissions ahead of schedule to reduce the occurrence of severe heatwaves.
Abstract
The importance of extreme event attribution rises as climate change causes severe damage to populations resulting from unprecedented events. In February 2019, a planetary wave shifted along the U.S.–Canadian border, simultaneously leading to troughing with anomalous cold events and ridging over Alaska and northern Canada with abnormal warm events. Also, a dry-stabilized anticyclonic circulation over low latitudes induced warm extreme events over Mexico and Florida. Most attribution studies compare the climate model simulations under natural or actual forcing conditions and assess probabilistically from a climatological point of view. However, in this study, we use multiple ensembles from an operational forecast model, promising statistical as well as dynamically constrained attribution assessment, often referred to as the storyline approach to extreme event attribution. In the globally averaged results, increasing CO2 concentrations lead to distinct warming signals at the surface, resulting mainly from diabatic heating. Our study finds that CO2-induced warming eventually affects the possibility of extreme events in North America, quantifying the impact of anthropogenic forcing over less than a week’s forecast simulation. Our study assesses the validity of the storyline approach conditional on the forecast lead times, which is hindered by rising noise in CO2 signals and the declining performance of the forecast model. The forecast-based storyline approach is valid for at least half of the land area within a 6-day lead time before the target extreme occurrence. Our attribution results highlight the importance of achieving net-zero emissions ahead of schedule to reduce the occurrence of severe heatwaves.
Abstract
Episodic cold surges in the East Asian winter monsoon can penetrate deep into the South China Sea (SCS), enhance consequent tropical rainfall, and further strengthen the East Asia meridional overturning circulation. These cold surges can promote strong surface fluxes and lead to a deeper marine boundary layer (MBL). However, there is a lack of boundary layer studies over the SCS, unlike many other well-studied regions such as the North Atlantic Ocean and the central-eastern Pacific Ocean. In this study, we use high-resolution radiosonde data of temperature and humidity profiles over Dongsha Island (20.70°N, 116.69°E) to identify the inversion layer, mixed layer, cloud base, cloud top, and factors controlling low cloud cover for the period of December–February from 2010 to 2020. We perform an energy budget analysis with ERA5 meteorological variables and surface fluxes. Here, we show a strong turbulent flux convergence of both heat and moisture within the SCS MBL during cold surges, which leads to a lifting of the mixed layer to ∼1.0 km and the inversion layer to ∼2.0 km and associated cloud development over Dongsha Island. The cold and dry horizontal advection is balanced by this vertical turbulent flux convergence in the energy budget. Overall, cold surges over the SCS enhance a lower branch of winter monsoon meridional overturning circulation with stronger inversion and higher low cloud covers.
Significance Statement
Cold surges in the East Asian winter monsoon bring cold and dry air from Eurasian continent to the South China Sea where strong air–sea fluxes and pronounced shallow clouds are unique climatological features. The convective boundary layer (CBL) over the SCS and upstream northwest Pacific (NWP) is important in maintaining the East Asia (EA) meridional overturning circulation. However, the CBL over the SCS–NWP is poorly understood and the lack of understanding can lead to unrealistic boundary layer turbulence and energy transport such that the tropical convection and the overturning circulation are incorrectly represented. In this study, we use high-quality radiosonde data at Dongsha, reanalysis, and satellite cloud data to show the CBL structure and their evolution during the passage of cold surges in northern SCS. We anticipate that our study will motivate more atmosphere–ocean joint observation and PBL-related studies over the SCS–NWP.
Abstract
Episodic cold surges in the East Asian winter monsoon can penetrate deep into the South China Sea (SCS), enhance consequent tropical rainfall, and further strengthen the East Asia meridional overturning circulation. These cold surges can promote strong surface fluxes and lead to a deeper marine boundary layer (MBL). However, there is a lack of boundary layer studies over the SCS, unlike many other well-studied regions such as the North Atlantic Ocean and the central-eastern Pacific Ocean. In this study, we use high-resolution radiosonde data of temperature and humidity profiles over Dongsha Island (20.70°N, 116.69°E) to identify the inversion layer, mixed layer, cloud base, cloud top, and factors controlling low cloud cover for the period of December–February from 2010 to 2020. We perform an energy budget analysis with ERA5 meteorological variables and surface fluxes. Here, we show a strong turbulent flux convergence of both heat and moisture within the SCS MBL during cold surges, which leads to a lifting of the mixed layer to ∼1.0 km and the inversion layer to ∼2.0 km and associated cloud development over Dongsha Island. The cold and dry horizontal advection is balanced by this vertical turbulent flux convergence in the energy budget. Overall, cold surges over the SCS enhance a lower branch of winter monsoon meridional overturning circulation with stronger inversion and higher low cloud covers.
Significance Statement
Cold surges in the East Asian winter monsoon bring cold and dry air from Eurasian continent to the South China Sea where strong air–sea fluxes and pronounced shallow clouds are unique climatological features. The convective boundary layer (CBL) over the SCS and upstream northwest Pacific (NWP) is important in maintaining the East Asia (EA) meridional overturning circulation. However, the CBL over the SCS–NWP is poorly understood and the lack of understanding can lead to unrealistic boundary layer turbulence and energy transport such that the tropical convection and the overturning circulation are incorrectly represented. In this study, we use high-quality radiosonde data at Dongsha, reanalysis, and satellite cloud data to show the CBL structure and their evolution during the passage of cold surges in northern SCS. We anticipate that our study will motivate more atmosphere–ocean joint observation and PBL-related studies over the SCS–NWP.
Abstract
The safe and efficient execution of offshore operations requires short-term (1–6 h ahead) high-quality probabilistic forecasts of metocean variables. The development areas for offshore wind projects, potentially in high depths, make it difficult to gather measurement data. This paper explores the use of deep learning for wind speed forecasting at an unobserved offshore location. The proposed convolutional architecture jointly exploits coastal measurements and numerical weather predictions to emulate multivariate probabilistic short-term forecasts. We explore both Gaussian and non-Gaussian neural representations using normalizing flows. We benchmark these approaches with respect to state-of-the-art data-driven schemes, including analog methods and quantile forecasting. The performance of the models and resulting forecast quality are analyzed in terms of probabilistic calibration, probabilistic and deterministic metrics, and as a function of weather situations. We report numerical experiments for a real case study off the French Mediterranean coast. Our results highlight the role of regional numerical weather prediction and coastal in situ measurement in the performance of postprocessing. For single-valued forecasts, a 40% decrease in RMSE is observed compared to the direct use of numerical weather predictions. Significant skill improvements are also obtained for the probabilistic forecasts, in terms of various scores, as well as an acceptable probabilistic calibration. The proposed architecture can process a large amount of heterogeneous input data and offers a versatile probabilistic framework for multivariate forecasting.
Abstract
The safe and efficient execution of offshore operations requires short-term (1–6 h ahead) high-quality probabilistic forecasts of metocean variables. The development areas for offshore wind projects, potentially in high depths, make it difficult to gather measurement data. This paper explores the use of deep learning for wind speed forecasting at an unobserved offshore location. The proposed convolutional architecture jointly exploits coastal measurements and numerical weather predictions to emulate multivariate probabilistic short-term forecasts. We explore both Gaussian and non-Gaussian neural representations using normalizing flows. We benchmark these approaches with respect to state-of-the-art data-driven schemes, including analog methods and quantile forecasting. The performance of the models and resulting forecast quality are analyzed in terms of probabilistic calibration, probabilistic and deterministic metrics, and as a function of weather situations. We report numerical experiments for a real case study off the French Mediterranean coast. Our results highlight the role of regional numerical weather prediction and coastal in situ measurement in the performance of postprocessing. For single-valued forecasts, a 40% decrease in RMSE is observed compared to the direct use of numerical weather predictions. Significant skill improvements are also obtained for the probabilistic forecasts, in terms of various scores, as well as an acceptable probabilistic calibration. The proposed architecture can process a large amount of heterogeneous input data and offers a versatile probabilistic framework for multivariate forecasting.
Abstract
Precipitation in eastern China exhibits large interannual variability during July with the northward movement of the monsoon rain belt. Thus, eastern China usually experiences severe droughts and floods in July. However, the influences of underlying surface thermal drivers, particularly the land factors, remain poorly understood. This study investigates the leading modes of July precipitation in eastern China and their potential influencing factors. The first and second empirical orthogonal function (EOF) modes show meridional dipole and tripolar precipitation anomalies in eastern China, respectively. The EOF1 mode is found to be closely associated with sea surface temperature (SST) anomalies in the tropical Pacific and North Atlantic Oceans in June, while the EOF2 mode is mainly linked to anomalous Indian Ocean SST and Indochina Peninsula soil moisture in June. During years with a strong El Niño–South Oscillation (ENSO) signal, the EOF1 mode is mainly related to the enhanced Walker and Hadley circulations associated with the cold tropical Pacific SST anomalies. In contrast, during years with a weak ENSO signal, the Eurasian midlatitude wave train and the westward zonal overturning circulation associated with tripole-like North Atlantic SST anomalies play a leading role. The EOF2 mode is mainly influenced by Indian Ocean SST anomalies that alter the Walker circulation and by soil moisture anomalies in the Indochina Peninsula that induce an anomalous regional cyclonic circulation. Numerical experiments further demonstrated that the combined effects of soil moisture and SST exert a more substantial impact than their individual effects. These results emphasize the importance of surface thermal factors in understanding regional climate dynamics.
Abstract
Precipitation in eastern China exhibits large interannual variability during July with the northward movement of the monsoon rain belt. Thus, eastern China usually experiences severe droughts and floods in July. However, the influences of underlying surface thermal drivers, particularly the land factors, remain poorly understood. This study investigates the leading modes of July precipitation in eastern China and their potential influencing factors. The first and second empirical orthogonal function (EOF) modes show meridional dipole and tripolar precipitation anomalies in eastern China, respectively. The EOF1 mode is found to be closely associated with sea surface temperature (SST) anomalies in the tropical Pacific and North Atlantic Oceans in June, while the EOF2 mode is mainly linked to anomalous Indian Ocean SST and Indochina Peninsula soil moisture in June. During years with a strong El Niño–South Oscillation (ENSO) signal, the EOF1 mode is mainly related to the enhanced Walker and Hadley circulations associated with the cold tropical Pacific SST anomalies. In contrast, during years with a weak ENSO signal, the Eurasian midlatitude wave train and the westward zonal overturning circulation associated with tripole-like North Atlantic SST anomalies play a leading role. The EOF2 mode is mainly influenced by Indian Ocean SST anomalies that alter the Walker circulation and by soil moisture anomalies in the Indochina Peninsula that induce an anomalous regional cyclonic circulation. Numerical experiments further demonstrated that the combined effects of soil moisture and SST exert a more substantial impact than their individual effects. These results emphasize the importance of surface thermal factors in understanding regional climate dynamics.
