Abstract
Surface boundaries in supercells have been suspected of being important in the arrangement and concentration of vorticity for the development and intensification of tornadoes, but there has been little attention given to the effects of the underlying surface roughness on their behavior. This study investigates the impact of surface drag on the structure and evolution of these boundaries, their associated distribution of near-surface vorticity, and tornadogenesis and maintenance. Comparisons between idealized simulations without and with drag introduced in the mature stage of the storm prior to tornadogenesis reveal that the inclusion of surface drag substantially alters the low-level structure, particularly with respect to the number, location, and intensity of surface convergence boundaries. Substantial drag-generated horizontal vorticity induces rotor structures near the surface associated with the convergence boundaries in both the forward and rear flanks of the storm. Stretching of horizontal vorticity and subsequent tilting into the vertical along the convergence boundaries lead to elongated positive vertical vorticity sheets on the ascending branch of the rotors and the opposite on the descending branch. The larger near-surface pressure deficit associated with the faster development of the near-surface cyclone when drag is active creates a downward dynamic vertical pressure gradient force that suppresses vertical growth, leading to a weaker and wider tornado detached from the surrounding convergence boundaries. A conceptual model of the low-level structure of the tornadic supercell is presented that focuses on the contribution of surface drag, with the aim of adding more insight and complexity to previous conceptual models.
Significance Statement
Tornado development is sensitive to near-surface processes, including those associated with front-like boundaries between regions of airflow within the parent storm. However, observations and theory are insufficient to understand these phenomena, and numerical simulation remains vital. In our simulations, we find that a change in a parameter that controls how much the near-surface winds are reduced by friction (or drag) can substantially alter the storm behavior and tornado potential. We investigate how surface drag affects the low-level storm structure, the distribution of regions of near-surface rotation, and the development of tornadoes within the simulation. Our results provide insight into the role of surface drag and lead to an improved conceptual model of the near-surface structure of a tornadic storm.
Abstract
Surface boundaries in supercells have been suspected of being important in the arrangement and concentration of vorticity for the development and intensification of tornadoes, but there has been little attention given to the effects of the underlying surface roughness on their behavior. This study investigates the impact of surface drag on the structure and evolution of these boundaries, their associated distribution of near-surface vorticity, and tornadogenesis and maintenance. Comparisons between idealized simulations without and with drag introduced in the mature stage of the storm prior to tornadogenesis reveal that the inclusion of surface drag substantially alters the low-level structure, particularly with respect to the number, location, and intensity of surface convergence boundaries. Substantial drag-generated horizontal vorticity induces rotor structures near the surface associated with the convergence boundaries in both the forward and rear flanks of the storm. Stretching of horizontal vorticity and subsequent tilting into the vertical along the convergence boundaries lead to elongated positive vertical vorticity sheets on the ascending branch of the rotors and the opposite on the descending branch. The larger near-surface pressure deficit associated with the faster development of the near-surface cyclone when drag is active creates a downward dynamic vertical pressure gradient force that suppresses vertical growth, leading to a weaker and wider tornado detached from the surrounding convergence boundaries. A conceptual model of the low-level structure of the tornadic supercell is presented that focuses on the contribution of surface drag, with the aim of adding more insight and complexity to previous conceptual models.
Significance Statement
Tornado development is sensitive to near-surface processes, including those associated with front-like boundaries between regions of airflow within the parent storm. However, observations and theory are insufficient to understand these phenomena, and numerical simulation remains vital. In our simulations, we find that a change in a parameter that controls how much the near-surface winds are reduced by friction (or drag) can substantially alter the storm behavior and tornado potential. We investigate how surface drag affects the low-level storm structure, the distribution of regions of near-surface rotation, and the development of tornadoes within the simulation. Our results provide insight into the role of surface drag and lead to an improved conceptual model of the near-surface structure of a tornadic storm.
Abstract
This study investigates the relative roles of sea surface temperature-forced climate changes and weather variability in driving the observed eastward shift of Atlantic hurricane tracks over the period from 1970 to 2021. A ten-member initial condition ensemble with a ~25km horizontal resolution tropical cyclone permitting atmospheric model (GFDL AM2.5-C360) with identical sea surface temperature and radiative forcing time series was analyzed in conjunction with historical hurricane track observations. While a frequency increase was recovered by all the simulations, the observed multidecadal eastward shift in tracks was not robust across the ensemble members, indicating that it included a substantial contribution from weather-scale variability. A statistical model was developed to simulate expected storm tracks based on genesis location and steering flow, and it was used to conduct experiments testing the roles of changing genesis location and changing steering flow in producing the multidecadal weather-driven shifts in storm tracks. These experiments indicated that shifts in genesis location were a substantially larger driver of these multidecadal track changes than changes in steering flow. The substantial impact of weather on tracks indicates that there may be limited predictability for multidecadal track changes like those observed, though basinwide frequency has greater potential for prediction. Additionally, understanding changes in genesis location appears essential to understanding changes in track location.
Abstract
This study investigates the relative roles of sea surface temperature-forced climate changes and weather variability in driving the observed eastward shift of Atlantic hurricane tracks over the period from 1970 to 2021. A ten-member initial condition ensemble with a ~25km horizontal resolution tropical cyclone permitting atmospheric model (GFDL AM2.5-C360) with identical sea surface temperature and radiative forcing time series was analyzed in conjunction with historical hurricane track observations. While a frequency increase was recovered by all the simulations, the observed multidecadal eastward shift in tracks was not robust across the ensemble members, indicating that it included a substantial contribution from weather-scale variability. A statistical model was developed to simulate expected storm tracks based on genesis location and steering flow, and it was used to conduct experiments testing the roles of changing genesis location and changing steering flow in producing the multidecadal weather-driven shifts in storm tracks. These experiments indicated that shifts in genesis location were a substantially larger driver of these multidecadal track changes than changes in steering flow. The substantial impact of weather on tracks indicates that there may be limited predictability for multidecadal track changes like those observed, though basinwide frequency has greater potential for prediction. Additionally, understanding changes in genesis location appears essential to understanding changes in track location.
Abstract
The attribution of 2022’s high temperatures in Japan to human-induced climate change exhibits significant regional variability, with FARs of 0.33–1, influenced by large-scale wind changes and regional topography.
Abstract
The attribution of 2022’s high temperatures in Japan to human-induced climate change exhibits significant regional variability, with FARs of 0.33–1, influenced by large-scale wind changes and regional topography.
Airborne Radar Doppler Spectrum Width as a Scale-Dependent Turbulence MetricAbstract
High-resolution airborne cloud Doppler radars such as the W-band Wyoming Cloud Radar (WCR) have, since the 1990s, investigated cloud microphysical, kinematic, and precipitation structures down to 30-m resolution. These measurements revolutionized our understanding of fine-scale cloud structure and the scales at which cloud processes occur. Airborne cloud Doppler radars may also resolve cloud turbulent eddy structure directly at 10-m scales. To date, cloud turbulence has been examined as variances and dissipation rates at coarser resolution than individual pulse volumes. The present work advances the potential of near-vertical pulse-pair Doppler spectrum width as a metric for turbulent air motion. Doppler spectrum width has long been used to investigate turbulent motions from ground-based remote sensors. However, complexities of airborne Doppler radar and spectral broadening resulting from platform and hydrometeor motions have limited airborne radar spectrum width measurements to qualitative interpretation only. Here we present the first quantitative validation of spectrum width from an airborne cloud radar. Echoes with signal-to-noise ratio greater than 10 dB yield spectrum width values that strongly correlate with retrieved mean Doppler variance for a range of nonconvective cloud conditions. Further, Doppler spectrum width within turbulent regions of cloud also shows good agreement with in situ eddy dissipation rate (EDR) and gust probe variance. However, the use of pulse-pair estimated spectrum width as a metric for turbulent air motion intensity is only suitable for turbulent air motions more energetic than the magnitude of spectral broadening, estimated to be <0.4 m s−1 for the WCR in these cases.
Significance Statement
Doppler spectrum width is a widely available airborne radar measurement previously considered too uncertain to attribute to atmospheric turbulence. We validate, for the first time, the response of spectrum width to turbulence at and away from research aircraft flight level and demonstrate that under certain conditions, spectrum width can be used to diagnose atmospheric turbulence down to scales of tens of meters. These high-resolution turbulent air motion intensity measurements may better connect to cloud hydrometeor process and growth response seen in coincident radar reflectivity structures proximate to turbulent eddies.
Abstract
High-resolution airborne cloud Doppler radars such as the W-band Wyoming Cloud Radar (WCR) have, since the 1990s, investigated cloud microphysical, kinematic, and precipitation structures down to 30-m resolution. These measurements revolutionized our understanding of fine-scale cloud structure and the scales at which cloud processes occur. Airborne cloud Doppler radars may also resolve cloud turbulent eddy structure directly at 10-m scales. To date, cloud turbulence has been examined as variances and dissipation rates at coarser resolution than individual pulse volumes. The present work advances the potential of near-vertical pulse-pair Doppler spectrum width as a metric for turbulent air motion. Doppler spectrum width has long been used to investigate turbulent motions from ground-based remote sensors. However, complexities of airborne Doppler radar and spectral broadening resulting from platform and hydrometeor motions have limited airborne radar spectrum width measurements to qualitative interpretation only. Here we present the first quantitative validation of spectrum width from an airborne cloud radar. Echoes with signal-to-noise ratio greater than 10 dB yield spectrum width values that strongly correlate with retrieved mean Doppler variance for a range of nonconvective cloud conditions. Further, Doppler spectrum width within turbulent regions of cloud also shows good agreement with in situ eddy dissipation rate (EDR) and gust probe variance. However, the use of pulse-pair estimated spectrum width as a metric for turbulent air motion intensity is only suitable for turbulent air motions more energetic than the magnitude of spectral broadening, estimated to be <0.4 m s−1 for the WCR in these cases.
Significance Statement
Doppler spectrum width is a widely available airborne radar measurement previously considered too uncertain to attribute to atmospheric turbulence. We validate, for the first time, the response of spectrum width to turbulence at and away from research aircraft flight level and demonstrate that under certain conditions, spectrum width can be used to diagnose atmospheric turbulence down to scales of tens of meters. These high-resolution turbulent air motion intensity measurements may better connect to cloud hydrometeor process and growth response seen in coincident radar reflectivity structures proximate to turbulent eddies.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations.
Significance Statement
High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations.
Significance Statement
High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.
Abstract
The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes’s theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes’s theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.
Significance Statement
Atmospheric circulation regimes are recurrent and persistent patterns that characterize the atmospheric circulation on time scales of 1–3 weeks. They are relevant for predictability on these time scales as mediators of weather. In this study we propose a novel approach to assigning atmospheric states to six predefined wintertime circulation regimes over the North Atlantic and Europe, which can be applied in real time. This approach introduces a probabilistic, instead of deterministic, regime assignment and uses prior knowledge on the regime dynamics. It allows us to better identify the regime persistence and indicates when a state does not clearly belong to one regime. Making use of an ensemble of model simulations, we can identify more pronounced interannual variability by using the full ensemble to inform prior knowledge on the regimes.
Abstract
The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes’s theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes’s theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.
Significance Statement
Atmospheric circulation regimes are recurrent and persistent patterns that characterize the atmospheric circulation on time scales of 1–3 weeks. They are relevant for predictability on these time scales as mediators of weather. In this study we propose a novel approach to assigning atmospheric states to six predefined wintertime circulation regimes over the North Atlantic and Europe, which can be applied in real time. This approach introduces a probabilistic, instead of deterministic, regime assignment and uses prior knowledge on the regime dynamics. It allows us to better identify the regime persistence and indicates when a state does not clearly belong to one regime. Making use of an ensemble of model simulations, we can identify more pronounced interannual variability by using the full ensemble to inform prior knowledge on the regimes.
Abstract
The demand for effective methods to augment precipitation over arid regions of India has been increasing over the past several decades as the changing climate brings warmer average temperatures. In the fourth phase of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX IV), a scientific investigation was conducted over a rain-shadow region of the Western Ghats mountains in India. The primary objective was to investigate the efficacy of hygroscopic seeding in convective clouds and to develop a cloud seeding protocol. CAIPEEX IV followed the World Meteorological Organization (WMO) recommendations in a peer-reviewed report with physical, statistical, and numerical investigations. The initial results of the campaign in the monsoon period of 2018 and 2019 with two instrumented aircraft, a ground-based dual-polarization C-band radar, a network of rain gauges, radiosondes, and surface aerosol measurements are reported here. The hygroscopic seeding material was detected in cloud droplets and key cloud microphysical processes in the seeding hypothesis were tracked. The formidable challenges of assessing seeding impacts in convective clouds and the results from 150 seed and 122 no-seed samples of randomized experiments are illustrated. Over 5,000 cloud passes from the airborne campaign provided details about the convective cloud properties as the key indicators for a seeding strategy and the evaluation protocol. The experimental results suggest that cloud seeding can be approached scientifically to reduce uncertainty. The results from this study should interest the scientific community and policymakers concerned with climate change’s impact on precipitation and how to mitigate rainfall deficiencies.
Abstract
The demand for effective methods to augment precipitation over arid regions of India has been increasing over the past several decades as the changing climate brings warmer average temperatures. In the fourth phase of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX IV), a scientific investigation was conducted over a rain-shadow region of the Western Ghats mountains in India. The primary objective was to investigate the efficacy of hygroscopic seeding in convective clouds and to develop a cloud seeding protocol. CAIPEEX IV followed the World Meteorological Organization (WMO) recommendations in a peer-reviewed report with physical, statistical, and numerical investigations. The initial results of the campaign in the monsoon period of 2018 and 2019 with two instrumented aircraft, a ground-based dual-polarization C-band radar, a network of rain gauges, radiosondes, and surface aerosol measurements are reported here. The hygroscopic seeding material was detected in cloud droplets and key cloud microphysical processes in the seeding hypothesis were tracked. The formidable challenges of assessing seeding impacts in convective clouds and the results from 150 seed and 122 no-seed samples of randomized experiments are illustrated. Over 5,000 cloud passes from the airborne campaign provided details about the convective cloud properties as the key indicators for a seeding strategy and the evaluation protocol. The experimental results suggest that cloud seeding can be approached scientifically to reduce uncertainty. The results from this study should interest the scientific community and policymakers concerned with climate change’s impact on precipitation and how to mitigate rainfall deficiencies.
Abstract
Ocean surface currents introduce variations into the surface wind stress that can change the component of the stress aligned with the thermal wind shear at fronts. This modifies the Ekman buoyancy flux, such that the current feedback on the stress tends to generate an effective flux of buoyancy and potential vorticity to the mixed layer. Scaling arguments and idealized simulations resolving both mesoscale and submesoscale turbulence suggest that this pathway for air–sea interaction can be important both locally at individual submesoscale fronts with strong surface currents—where it can introduce equivalent advective heat fluxes exceeding several hundred watts per square meter—and in the spatial mean where it reduces the integrated Ekman buoyancy flux by approximately 50%. The accompanying source of surface potential vorticity injection suggests that at some fronts the current feedback modification of the Ekman buoyancy flux may be significant in terms of both submesoscale dynamics and boundary layer energetics, with an implied modification of symmetric instability growth rates and dissipation that scales similarly to the energy lost through the negative wind work generated by the current feedback. This provides an example of how the shift of dynamical regimes into the submesoscale may promote the importance of air–sea interaction mechanisms that differ from those most active at larger scale.
Abstract
Ocean surface currents introduce variations into the surface wind stress that can change the component of the stress aligned with the thermal wind shear at fronts. This modifies the Ekman buoyancy flux, such that the current feedback on the stress tends to generate an effective flux of buoyancy and potential vorticity to the mixed layer. Scaling arguments and idealized simulations resolving both mesoscale and submesoscale turbulence suggest that this pathway for air–sea interaction can be important both locally at individual submesoscale fronts with strong surface currents—where it can introduce equivalent advective heat fluxes exceeding several hundred watts per square meter—and in the spatial mean where it reduces the integrated Ekman buoyancy flux by approximately 50%. The accompanying source of surface potential vorticity injection suggests that at some fronts the current feedback modification of the Ekman buoyancy flux may be significant in terms of both submesoscale dynamics and boundary layer energetics, with an implied modification of symmetric instability growth rates and dissipation that scales similarly to the energy lost through the negative wind work generated by the current feedback. This provides an example of how the shift of dynamical regimes into the submesoscale may promote the importance of air–sea interaction mechanisms that differ from those most active at larger scale.
Abstract
As the global research enterprise grapples with the challenge of a low carbon future, a key challenge is the future of international conferences. An emerging initiative which combines elements of the traditional in-person and virtual conference is a multi-hub approach. Here we report on a real-world trial of a multi-hub approach, the World Climate Research Programme/Stratosphere-troposphere Processes And their Role in Climate (WCRP/SPARC) General Assembly held in Qingdao-Reading-Boulder during the last week of October 2022 with more than 400 participants. While there are other examples of conferences run in dual-hub or hybrid online and in-person formats, we are not aware of other large atmospheric science conferences held in this format.
Based on travel surveys of participants, we estimate that the multi-hub approach reduced the carbon footprint from travel by between a factor of 2.3 and 4.1 times the footprint when hosting the conference in a single location. This resulted in a saving of at least 288 tonnes of carbon dioxide equivalent (tCO2eq) and perhaps as much as 683 tCO2eq, compared to having the conference in one location only. Feedback from participants, collected immediately after the conference, showed that the majority (85%) would again attend another conference in a similar format. There are many ways that the format of the SPARC General Assembly could have been improved, but this proof-of-concept provides an inspiration to other groups to give the multi-hub format a try.
Abstract
As the global research enterprise grapples with the challenge of a low carbon future, a key challenge is the future of international conferences. An emerging initiative which combines elements of the traditional in-person and virtual conference is a multi-hub approach. Here we report on a real-world trial of a multi-hub approach, the World Climate Research Programme/Stratosphere-troposphere Processes And their Role in Climate (WCRP/SPARC) General Assembly held in Qingdao-Reading-Boulder during the last week of October 2022 with more than 400 participants. While there are other examples of conferences run in dual-hub or hybrid online and in-person formats, we are not aware of other large atmospheric science conferences held in this format.
Based on travel surveys of participants, we estimate that the multi-hub approach reduced the carbon footprint from travel by between a factor of 2.3 and 4.1 times the footprint when hosting the conference in a single location. This resulted in a saving of at least 288 tonnes of carbon dioxide equivalent (tCO2eq) and perhaps as much as 683 tCO2eq, compared to having the conference in one location only. Feedback from participants, collected immediately after the conference, showed that the majority (85%) would again attend another conference in a similar format. There are many ways that the format of the SPARC General Assembly could have been improved, but this proof-of-concept provides an inspiration to other groups to give the multi-hub format a try.
Abstract
Heat is the leading cause of weather-related death in the United States. Wet bulb globe temperature (WBGT) is a heat stress index commonly used among active populations for activity modification, such as outdoor workers and athletes. Despite widespread use globally, WBGT forecasts have been uncommon in the United States until recent years. This research assesses the accuracy of WBGT forecasts developed by NOAA’s Southeast Regional Climate Center (SERCC) and the Carolinas Integrated Sciences and Assessments (CISA). It also details efforts to refine the forecast by accounting for the impact of surface roughness on wind using satellite imagery. Comparisons are made between the SERCC/CISA WBGT forecast and a WBGT forecast modeled after NWS methods. Additionally, both of these forecasts are compared with in situ WBGT measurements (during the summers of 2019-2021) and estimates from weather stations to assess forecast accuracy. The SERCC/CISA WBGT forecast was within 0.6°C of observations on average and showed less bias than the forecast based on NWS methods across North Carolina. Importantly, the SERCC/CISA WBGT forecast was more accurate for the most dangerous conditions (WBGT > 31°C), although this resulted in higher false alarms for these extreme conditions compared to the NWS method. In particular, this work improved the forecast for sites more sheltered from wind by better accounting for the influences of land cover on 2-meter wind speed. Accurate forecasts are more challenging for sites with complex microclimates. Thus, appropriate caution is necessary when interpreting forecasts and onsite, real-time WBGT measurements remain critical.
Abstract
Heat is the leading cause of weather-related death in the United States. Wet bulb globe temperature (WBGT) is a heat stress index commonly used among active populations for activity modification, such as outdoor workers and athletes. Despite widespread use globally, WBGT forecasts have been uncommon in the United States until recent years. This research assesses the accuracy of WBGT forecasts developed by NOAA’s Southeast Regional Climate Center (SERCC) and the Carolinas Integrated Sciences and Assessments (CISA). It also details efforts to refine the forecast by accounting for the impact of surface roughness on wind using satellite imagery. Comparisons are made between the SERCC/CISA WBGT forecast and a WBGT forecast modeled after NWS methods. Additionally, both of these forecasts are compared with in situ WBGT measurements (during the summers of 2019-2021) and estimates from weather stations to assess forecast accuracy. The SERCC/CISA WBGT forecast was within 0.6°C of observations on average and showed less bias than the forecast based on NWS methods across North Carolina. Importantly, the SERCC/CISA WBGT forecast was more accurate for the most dangerous conditions (WBGT > 31°C), although this resulted in higher false alarms for these extreme conditions compared to the NWS method. In particular, this work improved the forecast for sites more sheltered from wind by better accounting for the influences of land cover on 2-meter wind speed. Accurate forecasts are more challenging for sites with complex microclimates. Thus, appropriate caution is necessary when interpreting forecasts and onsite, real-time WBGT measurements remain critical.
