Abstract
Tropical instability waves (TIWs) are oceanic features that form around the equatorial Pacific cold tongue and influence the large-scale circulation and coupled climate variability including El Niño–Southern Oscillation. Local air–sea coupling over TIWs is thought to play an important role in the atmosphere and ocean’s energy and tracer budgets but is not well captured in coarse-resolution models. In this study, we isolate the impacts of TIW thermal (sea surface temperature–driven) and current (surface current–driven) feedbacks by removing TIW signatures in air–sea coupling fields in a high-resolution regional coupled model. The thermal feedback is found to damp TIW temperature variance by a factor of 2, associated both with the direct dependence of surface heat fluxes on SST (∼74%) and indirect impacts on surface winds (∼35%) and air temperature and humidity (∼−9%). These changes lead to cooling of the cold tongue SST by up to 0.1°C through reduced TIW-driven meridional heat fluxes and associated small changes in atmospheric circulation. The current feedback is decomposed into TIW (i.e., mesoscale) and mean (i.e., large-scale) components using separate experiments, with both having distinct impacts on TIWs and the mean state. The mesoscale current feedback reduces TIW eddy kinetic energy (EKE) by 22% through the eddy wind work, while the mean current feedback induces a further reduction of 8% by extracting energy from the mean currents and thus reducing barotropic EKE shear production. An improved understanding of small-scale tropical Pacific processes is needed to address biases in coarse-resolution models that impact their predictions and projections of Pacific climate variability and change.
Significance Statement
Tropical instability waves (TIWs) are oceanic features with ∼1000-km wavelengths that propagate westward on either side of the eastern equatorial Pacific cold tongue. TIWs drive lateral and vertical heat fluxes that impact several aspects of El Niño–Southern Oscillation. While climate models with a moderate, 1/4° ocean resolution can capture some TIW variability, they fail to properly represent many associated processes such as the impact of TIWs on the overlying atmosphere. Using sensitivity studies performed using a high-resolution regional coupled model, we study the impact of TIW air–sea coupling on the eastern Pacific climate system. Increased understanding of small-scale processes from studies such as this is essential to understand and address biases in models used for seasonal climate predictions and projections in the Pacific region.
Abstract
Tropical instability waves (TIWs) are oceanic features that form around the equatorial Pacific cold tongue and influence the large-scale circulation and coupled climate variability including El Niño–Southern Oscillation. Local air–sea coupling over TIWs is thought to play an important role in the atmosphere and ocean’s energy and tracer budgets but is not well captured in coarse-resolution models. In this study, we isolate the impacts of TIW thermal (sea surface temperature–driven) and current (surface current–driven) feedbacks by removing TIW signatures in air–sea coupling fields in a high-resolution regional coupled model. The thermal feedback is found to damp TIW temperature variance by a factor of 2, associated both with the direct dependence of surface heat fluxes on SST (∼74%) and indirect impacts on surface winds (∼35%) and air temperature and humidity (∼−9%). These changes lead to cooling of the cold tongue SST by up to 0.1°C through reduced TIW-driven meridional heat fluxes and associated small changes in atmospheric circulation. The current feedback is decomposed into TIW (i.e., mesoscale) and mean (i.e., large-scale) components using separate experiments, with both having distinct impacts on TIWs and the mean state. The mesoscale current feedback reduces TIW eddy kinetic energy (EKE) by 22% through the eddy wind work, while the mean current feedback induces a further reduction of 8% by extracting energy from the mean currents and thus reducing barotropic EKE shear production. An improved understanding of small-scale tropical Pacific processes is needed to address biases in coarse-resolution models that impact their predictions and projections of Pacific climate variability and change.
Significance Statement
Tropical instability waves (TIWs) are oceanic features with ∼1000-km wavelengths that propagate westward on either side of the eastern equatorial Pacific cold tongue. TIWs drive lateral and vertical heat fluxes that impact several aspects of El Niño–Southern Oscillation. While climate models with a moderate, 1/4° ocean resolution can capture some TIW variability, they fail to properly represent many associated processes such as the impact of TIWs on the overlying atmosphere. Using sensitivity studies performed using a high-resolution regional coupled model, we study the impact of TIW air–sea coupling on the eastern Pacific climate system. Increased understanding of small-scale processes from studies such as this is essential to understand and address biases in models used for seasonal climate predictions and projections in the Pacific region.
Abstract
The turbulent transport of mass, energy, moisture, and momentum between the clouds and the surrounding environment plays a central role in determining the vertical structure of the troposphere. This article investigates the connection between net entrainment, defined here as the net transport of air into individual clouds, and net dilution, defined as the tendencies of passive tracers such as static energy or total water mixing ratio. Entrainment and detrainment rates for 2.6 × 106 individual cloud samples are obtained from a large-eddy simulation of shallow convective boundary layer atmosphere that explicitly calculates the turbulent fluxes across the cloud boundaries. The equations describing the tendencies of cloud mass and tracer concentrations are derived as a function of directly calculated entrainment and detrainment rates of the individual clouds, and used to calculate net entrainment and dilution rates. Directly calculated net entrainment and dilution rates agree well with cloud mass and tracer tendencies and give a dilution time scale of 13 min. In contrast, the traditional bulk-plume approximation overestimates the effect of entrainment and detrainment on the dilution of cloud field properties due to the differential tracer transport through the moist shell surrounding the cloud. Using direct measures of entrainment and detrainment for individual clouds separates different processes that influence the turbulent mass transport between the clouds and their environment.
Abstract
The turbulent transport of mass, energy, moisture, and momentum between the clouds and the surrounding environment plays a central role in determining the vertical structure of the troposphere. This article investigates the connection between net entrainment, defined here as the net transport of air into individual clouds, and net dilution, defined as the tendencies of passive tracers such as static energy or total water mixing ratio. Entrainment and detrainment rates for 2.6 × 106 individual cloud samples are obtained from a large-eddy simulation of shallow convective boundary layer atmosphere that explicitly calculates the turbulent fluxes across the cloud boundaries. The equations describing the tendencies of cloud mass and tracer concentrations are derived as a function of directly calculated entrainment and detrainment rates of the individual clouds, and used to calculate net entrainment and dilution rates. Directly calculated net entrainment and dilution rates agree well with cloud mass and tracer tendencies and give a dilution time scale of 13 min. In contrast, the traditional bulk-plume approximation overestimates the effect of entrainment and detrainment on the dilution of cloud field properties due to the differential tracer transport through the moist shell surrounding the cloud. Using direct measures of entrainment and detrainment for individual clouds separates different processes that influence the turbulent mass transport between the clouds and their environment.
Abstract
The High-Resolution Rapid Refresh (HRRR) model provides hourly updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–20 HRRR, version 4 (HRRRv4), forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts [neural network probability forecasts (NNPFs)] for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day but was all superior to the UH forecast. NNPFs valid at hours between 1800 and 0000 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 0600–1200 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–20 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts.
Significance Statement
Convective hazards, such as hail and tornadoes, are often challenging to predict. To improve hazard predictions, we used machine learning (ML) to generate forecasts of convective hazards across the United States leveraging forecasts of prior events. The ML hazard forecasts were consistently better than a non-ML approach and varied in skill based on the time of day, with nighttime forecasts being particularly challenging. ML forecasts of wind gusts and hail were more skillful than tornadoes. Different strategies of constructing the dataset of prior events led to differences in forecast performance; thus, this work provides recommendations for how to assemble these datasets and train ML models to generate improved forecasts of severe weather events.
Abstract
The High-Resolution Rapid Refresh (HRRR) model provides hourly updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–20 HRRR, version 4 (HRRRv4), forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts [neural network probability forecasts (NNPFs)] for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day but was all superior to the UH forecast. NNPFs valid at hours between 1800 and 0000 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 0600–1200 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–20 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts.
Significance Statement
Convective hazards, such as hail and tornadoes, are often challenging to predict. To improve hazard predictions, we used machine learning (ML) to generate forecasts of convective hazards across the United States leveraging forecasts of prior events. The ML hazard forecasts were consistently better than a non-ML approach and varied in skill based on the time of day, with nighttime forecasts being particularly challenging. ML forecasts of wind gusts and hail were more skillful than tornadoes. Different strategies of constructing the dataset of prior events led to differences in forecast performance; thus, this work provides recommendations for how to assemble these datasets and train ML models to generate improved forecasts of severe weather events.
Abstract
This study uses high-resolution, convection-permitting, dynamically downscaled regional climate simulation output to assess how long-lived, convectively induced, extratropical windstorms known as derechos may change across the CONUS during the 21st century. Three 15-year epochs including a historical period (1990 – 2005), and two, separate late-21st century periods (2085 – 2100) employing intermediate (RCP 4.5) and pessimistic (RCP 8.5) greenhouse gas concentration scenarios are evaluated. A mesoscale convective system (MCS) identification and tracking tool catalogs derecho candidates across epochs using simulated radar reflectivity and maximum 10-meter wind speed as a proxy for near-surface severe wind gusts. Results indicate that MCS-based windstorms, including derechos, are more frequent, widespread, and intense in both future climate scenarios examined for most regions of the central and eastern CONUS. Increases are suggested across all parts of the year, with significant changes in populations concentrated during the early spring and summer months, suggesting the potential for a longer, more extreme MCS windstorm season. This research provides insights for forecasters, emergency managers, and wind-vulnerable stakeholders on how these events may change across the 21st century so that they may mitigate, adapt to, and become resilient against severe convective storm perils.
Abstract
This study uses high-resolution, convection-permitting, dynamically downscaled regional climate simulation output to assess how long-lived, convectively induced, extratropical windstorms known as derechos may change across the CONUS during the 21st century. Three 15-year epochs including a historical period (1990 – 2005), and two, separate late-21st century periods (2085 – 2100) employing intermediate (RCP 4.5) and pessimistic (RCP 8.5) greenhouse gas concentration scenarios are evaluated. A mesoscale convective system (MCS) identification and tracking tool catalogs derecho candidates across epochs using simulated radar reflectivity and maximum 10-meter wind speed as a proxy for near-surface severe wind gusts. Results indicate that MCS-based windstorms, including derechos, are more frequent, widespread, and intense in both future climate scenarios examined for most regions of the central and eastern CONUS. Increases are suggested across all parts of the year, with significant changes in populations concentrated during the early spring and summer months, suggesting the potential for a longer, more extreme MCS windstorm season. This research provides insights for forecasters, emergency managers, and wind-vulnerable stakeholders on how these events may change across the 21st century so that they may mitigate, adapt to, and become resilient against severe convective storm perils.
Abstract
Hail is a significant weather hazard in Canada, but its spatial and temporal distribution is poorly understood. We compiled a Canadian hail report database for 2005–22, containing 7000 unique entries with estimates of the timing and location of the hail reports and estimated hail diameter. We developed a methodology to construct an estimate of the hail climatology across Canada using manual hail observations at airports and a lightning proxy. First, we estimated the probability of hail occurrence at airport locations across the country at any given hour using Bayesian inference. Next, we interpolated in space the probabilities to obtain smooth prior probabilities of hail occurrence at any location in Canada. Then, we refined these probabilities using lightning flash density as a proxy for the likelihood of hail, severe hail (diameter greater than 20 mm), or significant severe hail (diameter greater than 50 mm). Finally, we aggregated the posterior probabilities of hail, severe hail, and significant severe hail over time and space and compared them with the number of reports found in the 2005–22 Canadian hail database. Our results indicate that the posterior probabilities of hail are not consistent with the observed hail reports and suggest that there are many gaps in hail reporting in Canada.
Abstract
Hail is a significant weather hazard in Canada, but its spatial and temporal distribution is poorly understood. We compiled a Canadian hail report database for 2005–22, containing 7000 unique entries with estimates of the timing and location of the hail reports and estimated hail diameter. We developed a methodology to construct an estimate of the hail climatology across Canada using manual hail observations at airports and a lightning proxy. First, we estimated the probability of hail occurrence at airport locations across the country at any given hour using Bayesian inference. Next, we interpolated in space the probabilities to obtain smooth prior probabilities of hail occurrence at any location in Canada. Then, we refined these probabilities using lightning flash density as a proxy for the likelihood of hail, severe hail (diameter greater than 20 mm), or significant severe hail (diameter greater than 50 mm). Finally, we aggregated the posterior probabilities of hail, severe hail, and significant severe hail over time and space and compared them with the number of reports found in the 2005–22 Canadian hail database. Our results indicate that the posterior probabilities of hail are not consistent with the observed hail reports and suggest that there are many gaps in hail reporting in Canada.
Abstract
As the surface ozone (O3) concentration is increasing despite emission control measures, it is urgent to clarify the impact of dynamic transport, such as quantifying the impact of stratosphere-to-troposphere transport (STT) on the surface O3 budget. Based on observations and numerical models, this study explores the relationship between the surface O3 concentration in eastern China (30°–38°N, 112°–122°E) and the location and intensity of the Northeast China cold vortex (NCCV), a predictable weather system. The results show that when the NCCV occurs in the southeastern region (35°–47.5°N, 125°–140°E), there is a corresponding increase in the surface O3 concentration in eastern China. The stronger the intensity of the NCCV is, the greater the increase in surface O3. The strongest 10% of the NCCV may cause an increase in the surface O3 concentration of 12–18 ppbv (30%–45% of the summer average surface O3 concentration). NCCV events and enhanced O3 are related to the STT. Anomalies in the 200-hPa wind field induce downward motion in the NCCV region and its west side. The cold O3-rich stratospheric air is transported southeastward and downward during the NCCV period, leading to an increase in the surface O3 concentration. The correlation and predictability between 500-hPa meteorological fields and surface O3 concentrations are also highlighted in this work.
Significance Statement
Despite the emission control measures taken in the past, the surface O3 concentration continues to increase in most regions of the world. It is urgent to quantify all the factors that impact the surface O3 concentration. This study focuses on a predictable weather system, the Northeast China cold vortex (NCCV), and its connection with stratosphere-to-troposphere transport (STT) that induces enhanced surface O3 concentrations. Our findings provide a fresh perspective for studying the sources and forecasting of O3 concentrations by linking upper-level weather systems and meteorological fields with changes in surface O3 concentrations.
Abstract
As the surface ozone (O3) concentration is increasing despite emission control measures, it is urgent to clarify the impact of dynamic transport, such as quantifying the impact of stratosphere-to-troposphere transport (STT) on the surface O3 budget. Based on observations and numerical models, this study explores the relationship between the surface O3 concentration in eastern China (30°–38°N, 112°–122°E) and the location and intensity of the Northeast China cold vortex (NCCV), a predictable weather system. The results show that when the NCCV occurs in the southeastern region (35°–47.5°N, 125°–140°E), there is a corresponding increase in the surface O3 concentration in eastern China. The stronger the intensity of the NCCV is, the greater the increase in surface O3. The strongest 10% of the NCCV may cause an increase in the surface O3 concentration of 12–18 ppbv (30%–45% of the summer average surface O3 concentration). NCCV events and enhanced O3 are related to the STT. Anomalies in the 200-hPa wind field induce downward motion in the NCCV region and its west side. The cold O3-rich stratospheric air is transported southeastward and downward during the NCCV period, leading to an increase in the surface O3 concentration. The correlation and predictability between 500-hPa meteorological fields and surface O3 concentrations are also highlighted in this work.
Significance Statement
Despite the emission control measures taken in the past, the surface O3 concentration continues to increase in most regions of the world. It is urgent to quantify all the factors that impact the surface O3 concentration. This study focuses on a predictable weather system, the Northeast China cold vortex (NCCV), and its connection with stratosphere-to-troposphere transport (STT) that induces enhanced surface O3 concentrations. Our findings provide a fresh perspective for studying the sources and forecasting of O3 concentrations by linking upper-level weather systems and meteorological fields with changes in surface O3 concentrations.
Abstract
This study explores reasons for differences in discriminations of nontornadic and tornadic supercell environments between a recent study of field project (FP) radiosonde observations and RUC/RAP-based studies. Two differences are explored: 1) differences in the relative skill between near-ground and deeper-layer storm-relative helicity (SRH) and 2) differences in skill for storm-relative winds (SRWs) seen in observed soundings that are not seen in RUC/RAP-based analyses. Results show that RUC/RAP-derived near-ground SRH continues to show larger skill than deeper-layer SRH for springtime, afternoon/evening cases over the plains (the “FP” domain), although 0–1-km SRH becomes more skillful than 0–500-m SRH. The skill of kinematic variables decreases over the FP domain, as the skill of mixed-layer convective available potential energy (MLCAPE) and the percent of the low-level horizontal vorticity that is streamwise increase for significant tornadoes. Large skill is found for mean ground-relative winds (GRWs) over all layers tested, but the skill of SRWs using Bunkers motion is relatively small. The field project dataset is shown to be biased toward particularly high-end nontornadic supercells, with more tornado-favorable mixed-layer lifted condensation levels (MLLCLs), lapse rates, and low-level shear/SRH compared to the nontornadic cases in the RUC/RAP dataset over the FP domain. The skill of deeper-layer SRH, GRWs, SRWs, and MLCAPE is unusually large in the field project sample, which highlights variables that may increase the likelihood of tornadoes when other variables that relate to the supercell tornado production (low-level shear/SRH and MLLCLs) are already in a tornado-favorable range. The skill of deeper-layer kinematic variables is particularly evident when observed storm motions are used instead of Bunkers motion.
Significance Statement
Researchers continue to explore reasons why some storms produce tornadoes and others do not. Some recent studies show conflicting results, including the best layers to analyze the environmental wind shear and the ability of wind speeds relative to the storm to distinguish nontornadic from tornadic storms. One study is rooted in observations taken for field projects that targeted tornadic storms and the others in model analyses. Restricting model analyses to a spatiotemporal region that emulates field projects does not explain the disagreement but reveals how the skill of some other variables changes. The tendency for field projects to target particularly strong storms and the use of actual storm motions versus estimates of storm motions both contribute to the conflicting results.
Abstract
This study explores reasons for differences in discriminations of nontornadic and tornadic supercell environments between a recent study of field project (FP) radiosonde observations and RUC/RAP-based studies. Two differences are explored: 1) differences in the relative skill between near-ground and deeper-layer storm-relative helicity (SRH) and 2) differences in skill for storm-relative winds (SRWs) seen in observed soundings that are not seen in RUC/RAP-based analyses. Results show that RUC/RAP-derived near-ground SRH continues to show larger skill than deeper-layer SRH for springtime, afternoon/evening cases over the plains (the “FP” domain), although 0–1-km SRH becomes more skillful than 0–500-m SRH. The skill of kinematic variables decreases over the FP domain, as the skill of mixed-layer convective available potential energy (MLCAPE) and the percent of the low-level horizontal vorticity that is streamwise increase for significant tornadoes. Large skill is found for mean ground-relative winds (GRWs) over all layers tested, but the skill of SRWs using Bunkers motion is relatively small. The field project dataset is shown to be biased toward particularly high-end nontornadic supercells, with more tornado-favorable mixed-layer lifted condensation levels (MLLCLs), lapse rates, and low-level shear/SRH compared to the nontornadic cases in the RUC/RAP dataset over the FP domain. The skill of deeper-layer SRH, GRWs, SRWs, and MLCAPE is unusually large in the field project sample, which highlights variables that may increase the likelihood of tornadoes when other variables that relate to the supercell tornado production (low-level shear/SRH and MLLCLs) are already in a tornado-favorable range. The skill of deeper-layer kinematic variables is particularly evident when observed storm motions are used instead of Bunkers motion.
Significance Statement
Researchers continue to explore reasons why some storms produce tornadoes and others do not. Some recent studies show conflicting results, including the best layers to analyze the environmental wind shear and the ability of wind speeds relative to the storm to distinguish nontornadic from tornadic storms. One study is rooted in observations taken for field projects that targeted tornadic storms and the others in model analyses. Restricting model analyses to a spatiotemporal region that emulates field projects does not explain the disagreement but reveals how the skill of some other variables changes. The tendency for field projects to target particularly strong storms and the use of actual storm motions versus estimates of storm motions both contribute to the conflicting results.
Abstract
The parameterization of orographic gravity wave drag (OGWD) is essential for accurate numerical weather prediction in regions of complex terrain. Current OGWD schemes assume hydrostatic OGWs but the parameterized OGWs in fine-resolution models with narrow subgrid-scale orography can be significantly affected by the nonhydrostatic effects (NHE). In our recent work, the OGWD scheme in the Model for Prediction Across Scales (MPAS) was revised by accounting for the NHE on the surface wave momentum flux of upward-propagating OGWs. Herein, the revised OGWD scheme is implemented in the Weather Research and Forecasting (WRF) model to evaluate its performance in short-range weather forecast. Two sets of 36-hr WRF simulations are conducted for nine Northeast China cold vortices (NECVs) that occurred in the warm season of 2011 using the original and revised OGWD schemes. Results show that the WRF model tends to underestimate the intensity of the NECVs, producing too high geopotential height. When accounting for the NHE in the OGWD scheme, the NECV intensity biases are significantly reduced. Analyses reveal that the NHE act to weaken the lower-tropospheric OGWD by decreasing the surface wave momentum flux, which strengthens the NECV in the lower troposphere. Consequently, the strengthened low-level cyclonic circulation increases the post-trough cold advection to the southwest of the NECV which in turn enhances the NECV in the mid-upper troposphere with reduced geopotential height. The NHE are found to increase as the model horizontal resolution increases, suggesting greater importance of the NHE in the OGWD parameterization of high-resolution numerical models.
Abstract
The parameterization of orographic gravity wave drag (OGWD) is essential for accurate numerical weather prediction in regions of complex terrain. Current OGWD schemes assume hydrostatic OGWs but the parameterized OGWs in fine-resolution models with narrow subgrid-scale orography can be significantly affected by the nonhydrostatic effects (NHE). In our recent work, the OGWD scheme in the Model for Prediction Across Scales (MPAS) was revised by accounting for the NHE on the surface wave momentum flux of upward-propagating OGWs. Herein, the revised OGWD scheme is implemented in the Weather Research and Forecasting (WRF) model to evaluate its performance in short-range weather forecast. Two sets of 36-hr WRF simulations are conducted for nine Northeast China cold vortices (NECVs) that occurred in the warm season of 2011 using the original and revised OGWD schemes. Results show that the WRF model tends to underestimate the intensity of the NECVs, producing too high geopotential height. When accounting for the NHE in the OGWD scheme, the NECV intensity biases are significantly reduced. Analyses reveal that the NHE act to weaken the lower-tropospheric OGWD by decreasing the surface wave momentum flux, which strengthens the NECV in the lower troposphere. Consequently, the strengthened low-level cyclonic circulation increases the post-trough cold advection to the southwest of the NECV which in turn enhances the NECV in the mid-upper troposphere with reduced geopotential height. The NHE are found to increase as the model horizontal resolution increases, suggesting greater importance of the NHE in the OGWD parameterization of high-resolution numerical models.
Abstract
Seasonal rainfall is critical to lives and livelihoods within the Horn of Africa drylands (HAD), but it is highly variable in space and time. The HAD rainfall seasons are typically defined as March-April-May (MAM) and October-November-December (OND). However, these three-month periods are only generalised definitions of seasonality across the HAD, and local experience of rainfall may depart from these substantially. Here, we use daily rain gauge data with a duration of at least 10 years from 69 stations across Kenya, Somalia, and Ethiopia to locally delineate key rainfall seasons. By calculating local seasonal rainfall timings, totals, and extremes we obtain more accurate estimates of the spatial variability in rainfall delivery across the HAD, as well as climatological patterns. Results show high spatial variability in season onset, cessation, and length across the region, indicating that a homogenous classification of rainfall seasons across the HAD (e.g. MAM and OND) is inadequate for representing local rainfall characteristics. Our results show that the ‘long rains’ season is not significantly longer than ‘short rains’ season over the period of study. This could be related to the previously documented decline of the ‘long rains’ rainfall totals over recent decades. Several rainfall metrics also vary spatially between seasons, and the rainfall on the most extreme days can accumulate to double the mean seasonal total. The locally defined rainfall seasons better capture the bulk of the rainfall during the season, giving improved characterisation of rainfall metrics, consistent with the aim of a better understanding of rainfall impacts on local communities.
Abstract
Seasonal rainfall is critical to lives and livelihoods within the Horn of Africa drylands (HAD), but it is highly variable in space and time. The HAD rainfall seasons are typically defined as March-April-May (MAM) and October-November-December (OND). However, these three-month periods are only generalised definitions of seasonality across the HAD, and local experience of rainfall may depart from these substantially. Here, we use daily rain gauge data with a duration of at least 10 years from 69 stations across Kenya, Somalia, and Ethiopia to locally delineate key rainfall seasons. By calculating local seasonal rainfall timings, totals, and extremes we obtain more accurate estimates of the spatial variability in rainfall delivery across the HAD, as well as climatological patterns. Results show high spatial variability in season onset, cessation, and length across the region, indicating that a homogenous classification of rainfall seasons across the HAD (e.g. MAM and OND) is inadequate for representing local rainfall characteristics. Our results show that the ‘long rains’ season is not significantly longer than ‘short rains’ season over the period of study. This could be related to the previously documented decline of the ‘long rains’ rainfall totals over recent decades. Several rainfall metrics also vary spatially between seasons, and the rainfall on the most extreme days can accumulate to double the mean seasonal total. The locally defined rainfall seasons better capture the bulk of the rainfall during the season, giving improved characterisation of rainfall metrics, consistent with the aim of a better understanding of rainfall impacts on local communities.
Abstract
In the inner core of a tropical cyclone, turbulence not only exists in the boundary layer (BL) but can also be generated above the BL by eyewall and rainband clouds. Thus, the treatment of vertical turbulent mixing must go beyond the conventional scope of the BL. The turbulence schemes formulated based on the turbulent kinetic energy (TKE) are attractive as they are applicable to both deep and shallow convection regimes in the tropical cyclone (TC) inner core provided that the TKE production and dissipation can be appropriately determined. However, TKE schemes are not self-closed. They must be closed by an empirically prescribed vertical profile of mixing length. This motivates this study to investigate the sensitivity of the simulated TC intensification to the sloping curvature and asymptotic length scale of mixing length, the two parameters that determine the vertical distribution of a prescribed mixing length. To tackle the problem, both idealized and real-case TC simulations are performed. The results show that the simulated TC intensification is sensitive to the sloping curvature of mixing length but only exhibits marginal sensitivity to the asymptotic length scale. The underlying reasons for such sensitivities are explored analytically based on the Mellor and Yamada level-2 turbulence model and the analyses of azimuthal-mean tangential wind budget. The results highlight the uncertainty and importance of mixing length in the numerical prediction of TCs and suggest that future research should focus on searching for physical constraints on mixing length, particularly in the low- to midtroposphere, using observations and large-eddy simulations.
Significance Statement
The parametric representation of subgrid-scale turbulent mixing is one of the major sources of uncertainty in numerical predictions of tropical cyclones (TCs). This study investigates how the numerical prediction of TC intensification is affected by the turbulent mixing length, a length scale that is required to close a turbulence scheme formulated based on the turbulent kinetic energy (TKE). The research highlights the uncertainty and importance of mixing length in numerical prediction of TCs and suggests that future research should focus on searching for physical constraints on the mixing length, particularly in the low- to midtroposphere, using observations and large-eddy simulations.
Abstract
In the inner core of a tropical cyclone, turbulence not only exists in the boundary layer (BL) but can also be generated above the BL by eyewall and rainband clouds. Thus, the treatment of vertical turbulent mixing must go beyond the conventional scope of the BL. The turbulence schemes formulated based on the turbulent kinetic energy (TKE) are attractive as they are applicable to both deep and shallow convection regimes in the tropical cyclone (TC) inner core provided that the TKE production and dissipation can be appropriately determined. However, TKE schemes are not self-closed. They must be closed by an empirically prescribed vertical profile of mixing length. This motivates this study to investigate the sensitivity of the simulated TC intensification to the sloping curvature and asymptotic length scale of mixing length, the two parameters that determine the vertical distribution of a prescribed mixing length. To tackle the problem, both idealized and real-case TC simulations are performed. The results show that the simulated TC intensification is sensitive to the sloping curvature of mixing length but only exhibits marginal sensitivity to the asymptotic length scale. The underlying reasons for such sensitivities are explored analytically based on the Mellor and Yamada level-2 turbulence model and the analyses of azimuthal-mean tangential wind budget. The results highlight the uncertainty and importance of mixing length in the numerical prediction of TCs and suggest that future research should focus on searching for physical constraints on mixing length, particularly in the low- to midtroposphere, using observations and large-eddy simulations.
Significance Statement
The parametric representation of subgrid-scale turbulent mixing is one of the major sources of uncertainty in numerical predictions of tropical cyclones (TCs). This study investigates how the numerical prediction of TC intensification is affected by the turbulent mixing length, a length scale that is required to close a turbulence scheme formulated based on the turbulent kinetic energy (TKE). The research highlights the uncertainty and importance of mixing length in numerical prediction of TCs and suggests that future research should focus on searching for physical constraints on the mixing length, particularly in the low- to midtroposphere, using observations and large-eddy simulations.
