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Abstract
A 2020 metastudy by Knutson et al. gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. The results form a great resource for those who model the impacts of tropical cyclones. However, a number of steps of processing may be required to use the results in practice. These include interpolation in time, distribution fitting, and reverse engineering of correlations. In this paper we study another processing step that may be required, which is adjusting the frequency change results so that they apply to landfalling frequencies. An adjustment is required because the metastudy results give frequency adjustments as a function of storm lifetime maximum intensity rather than landfall intensity. Increases in the frequency of category-4 and category-5 storms, by lifetime maximum intensity, then contribute to increases in the frequencies of storms of all intensities at landfall. We consider North Atlantic Ocean storms and use historical storm information to quantify this effect as a function of landfall intensity and region. Whereas the original metastudy results suggest that the mean frequency of category-3 storms will decrease, our analysis suggests that the mean frequency of landfalling category-3 storms will increase. Our results are highly uncertain, particularly because we assume that tracks and genesis locations of storms will not change, even though some recent climate model results suggest otherwise. However, making the adjustments we describe is likely to be a better way to model future landfall risk than applying the original metastudy frequency changes directly at landfall.
Significance Statement
A recent metastudy gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. For the North Atlantic Ocean, we show how to convert these results to changes at landfall. This conversion increases the changes in the frequencies of storms in intensity categories 0–3, and, in particular, the mean frequency change of storms in category 3 flips from decreasing to increasing in most regions.
Abstract
A 2020 metastudy by Knutson et al. gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. The results form a great resource for those who model the impacts of tropical cyclones. However, a number of steps of processing may be required to use the results in practice. These include interpolation in time, distribution fitting, and reverse engineering of correlations. In this paper we study another processing step that may be required, which is adjusting the frequency change results so that they apply to landfalling frequencies. An adjustment is required because the metastudy results give frequency adjustments as a function of storm lifetime maximum intensity rather than landfall intensity. Increases in the frequency of category-4 and category-5 storms, by lifetime maximum intensity, then contribute to increases in the frequencies of storms of all intensities at landfall. We consider North Atlantic Ocean storms and use historical storm information to quantify this effect as a function of landfall intensity and region. Whereas the original metastudy results suggest that the mean frequency of category-3 storms will decrease, our analysis suggests that the mean frequency of landfalling category-3 storms will increase. Our results are highly uncertain, particularly because we assume that tracks and genesis locations of storms will not change, even though some recent climate model results suggest otherwise. However, making the adjustments we describe is likely to be a better way to model future landfall risk than applying the original metastudy frequency changes directly at landfall.
Significance Statement
A recent metastudy gave distributions for possible changes in the frequency and intensity of tropical cyclones under climate change. For the North Atlantic Ocean, we show how to convert these results to changes at landfall. This conversion increases the changes in the frequencies of storms in intensity categories 0–3, and, in particular, the mean frequency change of storms in category 3 flips from decreasing to increasing in most regions.
Abstract
The heat index is a widely used measure of apparent temperature that accounts for the effects of humidity using Steadman’s model of human thermoregulation. Steadman’s model, however, gives unphysical results when the air is too hot and humid or too cold and dry, leading to an undefined heat index. For example, at a relative humidity of 80%, the heat index is only defined for temperatures in the range of 288–304 K (59°–88°F). Here, Steadman’s thermoregulation model is extended to define the heat index for all combinations of temperature and humidity, allowing for an assessment of Earth’s future habitability. The extended heat index can be mapped onto physiological responses of an idealized human, such as heat exhaustion, heat stroke, and even heat death, providing an indication of regional health outcomes for different degrees of global warming.
Significance Statement
The existing heat index is well-defined for most combinations of high temperature and humidity experienced on Earth in the preindustrial climate, but global warming is increasingly generating conditions for which the heat index is undefined. Therefore, an extension of the original heat index is needed. When extending the heat index, we use the same physiological model as in the original work of Steadman to ensure backward compatibility. Following Steadman, each value of the heat index is mapped onto a measurable physiological variable, which can be useful for assessing the health impacts of various combinations of temperature and humidity, especially for outdoor workers.
Abstract
The heat index is a widely used measure of apparent temperature that accounts for the effects of humidity using Steadman’s model of human thermoregulation. Steadman’s model, however, gives unphysical results when the air is too hot and humid or too cold and dry, leading to an undefined heat index. For example, at a relative humidity of 80%, the heat index is only defined for temperatures in the range of 288–304 K (59°–88°F). Here, Steadman’s thermoregulation model is extended to define the heat index for all combinations of temperature and humidity, allowing for an assessment of Earth’s future habitability. The extended heat index can be mapped onto physiological responses of an idealized human, such as heat exhaustion, heat stroke, and even heat death, providing an indication of regional health outcomes for different degrees of global warming.
Significance Statement
The existing heat index is well-defined for most combinations of high temperature and humidity experienced on Earth in the preindustrial climate, but global warming is increasingly generating conditions for which the heat index is undefined. Therefore, an extension of the original heat index is needed. When extending the heat index, we use the same physiological model as in the original work of Steadman to ensure backward compatibility. Following Steadman, each value of the heat index is mapped onto a measurable physiological variable, which can be useful for assessing the health impacts of various combinations of temperature and humidity, especially for outdoor workers.
Abstract
Temperature profiles of the lower atmosphere (<3 km) over complex urban areas are related to health risks, including heat stress and respiratory illness. This complexity leads to uncertainty in numerical simulations, and many studies call for more observations of the lower atmosphere over cities. Using 20 years of observations from the Aircraft Meteorological Data Relay (AMDAR) program over Dallas–Fort Worth, Texas, average profiles every 0.5 h are created from the 1.5 million individual soundings. Dallas–Fort Worth is ideal because it is a large urban area in the central Great Plains, has no major topographic or coastal influences, and has two major airports near the center of the urban heat island. With frequent and high-quality measurements over the city, we investigate the evolution of the lower atmosphere around sunrise to quantify the stability, boundary layer height, and duration of the morning transition when there are southerly winds, few clouds, and no precipitation so as to eliminate transient synoptic events. Characteristics of the lower atmosphere are separated by season and maximum wind speed because the the Great Plains low-level jet contributes to day-to-day variability. In all seasons, stronger wind over the city leads to a weaker nocturnal temperature inversion at sunrise and a shorter morning transition period, with the greatest difference during autumn and the smallest difference during summer. During summer, the boundary layer height at sunrise is higher on average, deepens the most as wind strengthens, and has the fewest days exhibiting a surface temperature inversion over the city.
Significance Statement
Cities impact health by creating an urban heat island caused by more heating at the surface, less evaporative cooling, and increased anthropogenic waste heat, and they can have high pollution. Cooling overnight stabilizes the lower atmosphere and traps pollutants near the surface until surface heating after sunrise mixes them away. Inadequate pollution observations make it difficult to study these issues. The greatest mixing occurs about 2 h after sunrise but can be modulated by wind speed. Observations from 1.5 million aircraft landing and taking off over Dallas–Fort Worth, Texas, reveal that strong low-level wind leads to morning transitions ending 0.84 h earlier on average than with light wind. Details from this vast dataset contribute to improved understanding of the lower atmosphere over cities and provide a baseline for simulations.
Abstract
Temperature profiles of the lower atmosphere (<3 km) over complex urban areas are related to health risks, including heat stress and respiratory illness. This complexity leads to uncertainty in numerical simulations, and many studies call for more observations of the lower atmosphere over cities. Using 20 years of observations from the Aircraft Meteorological Data Relay (AMDAR) program over Dallas–Fort Worth, Texas, average profiles every 0.5 h are created from the 1.5 million individual soundings. Dallas–Fort Worth is ideal because it is a large urban area in the central Great Plains, has no major topographic or coastal influences, and has two major airports near the center of the urban heat island. With frequent and high-quality measurements over the city, we investigate the evolution of the lower atmosphere around sunrise to quantify the stability, boundary layer height, and duration of the morning transition when there are southerly winds, few clouds, and no precipitation so as to eliminate transient synoptic events. Characteristics of the lower atmosphere are separated by season and maximum wind speed because the the Great Plains low-level jet contributes to day-to-day variability. In all seasons, stronger wind over the city leads to a weaker nocturnal temperature inversion at sunrise and a shorter morning transition period, with the greatest difference during autumn and the smallest difference during summer. During summer, the boundary layer height at sunrise is higher on average, deepens the most as wind strengthens, and has the fewest days exhibiting a surface temperature inversion over the city.
Significance Statement
Cities impact health by creating an urban heat island caused by more heating at the surface, less evaporative cooling, and increased anthropogenic waste heat, and they can have high pollution. Cooling overnight stabilizes the lower atmosphere and traps pollutants near the surface until surface heating after sunrise mixes them away. Inadequate pollution observations make it difficult to study these issues. The greatest mixing occurs about 2 h after sunrise but can be modulated by wind speed. Observations from 1.5 million aircraft landing and taking off over Dallas–Fort Worth, Texas, reveal that strong low-level wind leads to morning transitions ending 0.84 h earlier on average than with light wind. Details from this vast dataset contribute to improved understanding of the lower atmosphere over cities and provide a baseline for simulations.
Abstract
Water resources severely constrain high-quality development in the Yellow River basin (YRB). Predicting the trend of precipitation on the basis of satisfying precision has important guiding significance for future regional development. Using the projected precipitation in 12 CMIP6 models, this study applied the most appropriate correction method for each model from four quantile-mapping methods and projected future changes of annual precipitation in the YRB and three key regions. The projection uncertainty was quantitatively assessed by addressing model spread (MS) and range. The precipitation anomaly under all four scenarios would increase for the YRB and key regions. The increasing rates (the linear coefficient) from Shared Socioeconomic Pathway 126 (SSP126) to SSP585 were 30–62, 60–103, 84–122, and 134–204 mm (100 yr)−1, respectively. The largest increase was the sediment-yielding region, which reached about 40–60 mm in 2031–60 and 70–125 mm in 2061–90. The 400-mm isohyet was projected to move continuously to the northwest in the future. The uncertainty quantified by MS was reduced by 85.9%–94.6%, and projection ranges were less than 50 mm (about 10% of climatology) in most parts of YRB. From the increasing trend of future precipitation in the YRB, it can be inferred that the arid region will shrink. It may be a good opportunity to implement ecological conservation and high-quality development of the YRB successfully.
Significance Statement
We want to understand the spatial–temporal evolution pattern of future precipitation in the Yellow River basin (YRB) under climate change scenarios. In the future, the precipitation in the YRB and the three key regions will increase, with the sediment-yielding region increasing the most, and the arid region will shrink. Our findings confirm that the spatial–temporal patterns of precipitation in the YRB will change significantly under climate change scenarios. These findings will guide ecological protection and regional social and economic development in the YRB. Future research should focus on adaptation strategies of agricultural production patterns to climate change.
Abstract
Water resources severely constrain high-quality development in the Yellow River basin (YRB). Predicting the trend of precipitation on the basis of satisfying precision has important guiding significance for future regional development. Using the projected precipitation in 12 CMIP6 models, this study applied the most appropriate correction method for each model from four quantile-mapping methods and projected future changes of annual precipitation in the YRB and three key regions. The projection uncertainty was quantitatively assessed by addressing model spread (MS) and range. The precipitation anomaly under all four scenarios would increase for the YRB and key regions. The increasing rates (the linear coefficient) from Shared Socioeconomic Pathway 126 (SSP126) to SSP585 were 30–62, 60–103, 84–122, and 134–204 mm (100 yr)−1, respectively. The largest increase was the sediment-yielding region, which reached about 40–60 mm in 2031–60 and 70–125 mm in 2061–90. The 400-mm isohyet was projected to move continuously to the northwest in the future. The uncertainty quantified by MS was reduced by 85.9%–94.6%, and projection ranges were less than 50 mm (about 10% of climatology) in most parts of YRB. From the increasing trend of future precipitation in the YRB, it can be inferred that the arid region will shrink. It may be a good opportunity to implement ecological conservation and high-quality development of the YRB successfully.
Significance Statement
We want to understand the spatial–temporal evolution pattern of future precipitation in the Yellow River basin (YRB) under climate change scenarios. In the future, the precipitation in the YRB and the three key regions will increase, with the sediment-yielding region increasing the most, and the arid region will shrink. Our findings confirm that the spatial–temporal patterns of precipitation in the YRB will change significantly under climate change scenarios. These findings will guide ecological protection and regional social and economic development in the YRB. Future research should focus on adaptation strategies of agricultural production patterns to climate change.
Abstract
Cities develop a specific climate related to their morphology and the materials that constitute them. The addition of vegetation in urban areas induces cooling and shading effects that can modify local climate and thermal comfort conditions. The Town Energy Balance (TEB) urban canopy model offers several configurations for a more or less fine-tuned consideration of natural covers and associated physical processes in the urban environment. This study aims to evaluate the sensitivity of TEB to the representation of vegetation and the resolution of the chosen databases in the simulation of microclimatic variables, at the scale of a heterogeneous urban neighborhood located in Toulouse, France. First, the effect of the improved description of the vegetation input to the model is highlighted by comparing the results obtained with a readily available national database and then with a very-high-resolution satellite-derived vegetation database. Second, the two vegetation parameterizations, with or without explicit tree stratum, that are available in the TEB model are evaluated and compared. Measurements carried out on specific routes and stop points in a neighborhood of Toulouse allowed microclimatic variables to be evaluated. Results show that refining the vegetation database can somehow improve the modeling of air temperature. As a result of enhancing the vegetation description in the model, that is, physical processes associated with the presence of trees in urban canyons, the air temperature, but also the wind and the thermal comfort index, are better simulated. These results are encouraging for the use of TEB as a decision support tool for urban planning purposes.
Abstract
Cities develop a specific climate related to their morphology and the materials that constitute them. The addition of vegetation in urban areas induces cooling and shading effects that can modify local climate and thermal comfort conditions. The Town Energy Balance (TEB) urban canopy model offers several configurations for a more or less fine-tuned consideration of natural covers and associated physical processes in the urban environment. This study aims to evaluate the sensitivity of TEB to the representation of vegetation and the resolution of the chosen databases in the simulation of microclimatic variables, at the scale of a heterogeneous urban neighborhood located in Toulouse, France. First, the effect of the improved description of the vegetation input to the model is highlighted by comparing the results obtained with a readily available national database and then with a very-high-resolution satellite-derived vegetation database. Second, the two vegetation parameterizations, with or without explicit tree stratum, that are available in the TEB model are evaluated and compared. Measurements carried out on specific routes and stop points in a neighborhood of Toulouse allowed microclimatic variables to be evaluated. Results show that refining the vegetation database can somehow improve the modeling of air temperature. As a result of enhancing the vegetation description in the model, that is, physical processes associated with the presence of trees in urban canyons, the air temperature, but also the wind and the thermal comfort index, are better simulated. These results are encouraging for the use of TEB as a decision support tool for urban planning purposes.
Abstract
Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely, CloudSat’s Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (GPM-DPR). In this paper, a direct comparison of the CPR’s 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate R immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15°C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR-measured reflectivity shows median reduction of 2–3 dB from −15° to −2.5°C, likely the reason for the 2CSP retrieval reduction of R. Two case studies from NASA field campaigns [i.e., Olympic Mountains Experiment (OLYMPEX) and Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS)] provide analogs to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential negative, or low, bias in R. Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation.
Abstract
Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely, CloudSat’s Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (GPM-DPR). In this paper, a direct comparison of the CPR’s 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate R immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15°C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR-measured reflectivity shows median reduction of 2–3 dB from −15° to −2.5°C, likely the reason for the 2CSP retrieval reduction of R. Two case studies from NASA field campaigns [i.e., Olympic Mountains Experiment (OLYMPEX) and Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS)] provide analogs to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential negative, or low, bias in R. Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation.
Abstract
Dynamic and thermodynamic factors involved in future changes in local-scale short-term extreme summertime precipitation on the mesoscale and hourly time scale in Japan were examined using convection-permitting regional climate model simulations under the representative concentration pathway 8.5 scenario. The change in the dynamic component primarily contributes to the total change in vertically integrated moisture flux convergence over the analysis domain that is located off Okinawa Island, whereas the thermodynamic component is dominant over the analysis domain that is located off Kyushu Island. Differences in the amount of the dynamic and thermodynamic components are noticeable in these two domains. These results are explained by the difference in the vertical profiles of the convergence term, and hence the convergence itself between the two specific domains. A mesoscale low pressure system on the seasonal rain front—termed the baiu front—is a key factor underlying the difference in the magnitudes and vertical profiles of convergence between the two specific domains. In the vicinity of the domain off Okinawa Island, a mesoscale low pressure system on the baiu front enhances low-level convergence in the future climate when compared with the present climate. This atmospheric state is attributable to the location of the baiu front itself, which is located relatively southward in the future climate and is affected by the domain off Okinawa Island. In the domain where the dynamic component is dominant such as the domain off Okinawa Island, the total moisture flux convergence follows a super–Clausius–Clapeyron scaling.
Abstract
Dynamic and thermodynamic factors involved in future changes in local-scale short-term extreme summertime precipitation on the mesoscale and hourly time scale in Japan were examined using convection-permitting regional climate model simulations under the representative concentration pathway 8.5 scenario. The change in the dynamic component primarily contributes to the total change in vertically integrated moisture flux convergence over the analysis domain that is located off Okinawa Island, whereas the thermodynamic component is dominant over the analysis domain that is located off Kyushu Island. Differences in the amount of the dynamic and thermodynamic components are noticeable in these two domains. These results are explained by the difference in the vertical profiles of the convergence term, and hence the convergence itself between the two specific domains. A mesoscale low pressure system on the seasonal rain front—termed the baiu front—is a key factor underlying the difference in the magnitudes and vertical profiles of convergence between the two specific domains. In the vicinity of the domain off Okinawa Island, a mesoscale low pressure system on the baiu front enhances low-level convergence in the future climate when compared with the present climate. This atmospheric state is attributable to the location of the baiu front itself, which is located relatively southward in the future climate and is affected by the domain off Okinawa Island. In the domain where the dynamic component is dominant such as the domain off Okinawa Island, the total moisture flux convergence follows a super–Clausius–Clapeyron scaling.
Abstract
A significant part of the uncertainty in satellite-based precipitation products stems from differing assumptions about drop size distributions (DSDs). Satellite radar-based retrieval algorithms rely on DSD assumptions that may be overly simplistic, whereas radiometers further struggle to distinguish cloud water from rain. We utilize the Ocean Rainfall and Ice-phase Precipitation Measurement Network (OceanRAIN), version 1.0, dataset to examine the impact of DSD variability on the ability of satellite measurements to accurately estimate rates of warm rainfall. We use the binned disdrometer counts and a simple model of the atmosphere to simulate observations for three satellite architectures. Two are similar to existing instrument combinations on the GPM Core Observatory and CloudSat, and the third is a theoretical triple-frequency radar–radiometer architecture. Using an optimal estimation framework, we find that the assumed DSD shape can have a large impact on retrieved rain rate. A three-parameter normalized gamma DSD model is sufficient for describing and retrieving the DSDs observed in the OceanRAIN dataset. Assuming simpler single-moment DSD models can lead to significant biases in retrieved rain rate, on the order of 100%. Differing DSD assumptions could thus plausibly explain a large portion of the disagreement in satellite-based precipitation estimates.
Abstract
A significant part of the uncertainty in satellite-based precipitation products stems from differing assumptions about drop size distributions (DSDs). Satellite radar-based retrieval algorithms rely on DSD assumptions that may be overly simplistic, whereas radiometers further struggle to distinguish cloud water from rain. We utilize the Ocean Rainfall and Ice-phase Precipitation Measurement Network (OceanRAIN), version 1.0, dataset to examine the impact of DSD variability on the ability of satellite measurements to accurately estimate rates of warm rainfall. We use the binned disdrometer counts and a simple model of the atmosphere to simulate observations for three satellite architectures. Two are similar to existing instrument combinations on the GPM Core Observatory and CloudSat, and the third is a theoretical triple-frequency radar–radiometer architecture. Using an optimal estimation framework, we find that the assumed DSD shape can have a large impact on retrieved rain rate. A three-parameter normalized gamma DSD model is sufficient for describing and retrieving the DSDs observed in the OceanRAIN dataset. Assuming simpler single-moment DSD models can lead to significant biases in retrieved rain rate, on the order of 100%. Differing DSD assumptions could thus plausibly explain a large portion of the disagreement in satellite-based precipitation estimates.
Abstract
Harvesting of crops in a weakly sloping Midwestern field during the Stable Atmospheric Variability and Transport (SAVANT) observation campaign allowed for a systematic investigation of the influence of surface roughness and static stability magnitude on the applicability of the Monin–Obukhov similarity (MOST) and hockey-stick transition (HOST) theories during stable boundary layer periods. We analyze momentum flux and turbulent velocity scale V TKE in three regimes, defined using the gradient Richardson number Ri and flux Richardson number Ri f as regime 1 (0 < Ri ≤ 0.1 and 0 < Ri f ≤ 0.1), regime 2 (0.1 < Ri ≤ 0.23 and 0.1 < Ri f ≤ 0.23), and regime 3 (both Ri and Ri f > 0.23). After harvest, in regime 1, stability varied from near-neutral to weakly stable and both MOST and HOST were applicable to estimate the momentum fluxes and V TKE as a function of mean wind speed. In regime 2, the momentum flux deviated from the MOST linear relationship as stability increased. In regimes 1 and 2, a HOST-defined threshold wind speed Vs was identified beyond which V TKE increased linearly with wind speed at a rate of 0.26 for all observation heights. Below this threshold wind speed, V TKE behaved independent of mean wind and observation heights. Alternatively, for preharvest periods, MOST was applicable in regimes 1 and 2 for all heights and HOST was applicable with reduced Vs for heights above the crop layer. Regime 3 during pre- and postharvest consisted of strongly stable periods and very weak to weak winds, where MOST was found to be invalid and V TKE remained low and independent of wind speed. The results suggest that roughness due to crops enhances the turbulence generation at lower wind speeds.
Abstract
Harvesting of crops in a weakly sloping Midwestern field during the Stable Atmospheric Variability and Transport (SAVANT) observation campaign allowed for a systematic investigation of the influence of surface roughness and static stability magnitude on the applicability of the Monin–Obukhov similarity (MOST) and hockey-stick transition (HOST) theories during stable boundary layer periods. We analyze momentum flux and turbulent velocity scale V TKE in three regimes, defined using the gradient Richardson number Ri and flux Richardson number Ri f as regime 1 (0 < Ri ≤ 0.1 and 0 < Ri f ≤ 0.1), regime 2 (0.1 < Ri ≤ 0.23 and 0.1 < Ri f ≤ 0.23), and regime 3 (both Ri and Ri f > 0.23). After harvest, in regime 1, stability varied from near-neutral to weakly stable and both MOST and HOST were applicable to estimate the momentum fluxes and V TKE as a function of mean wind speed. In regime 2, the momentum flux deviated from the MOST linear relationship as stability increased. In regimes 1 and 2, a HOST-defined threshold wind speed Vs was identified beyond which V TKE increased linearly with wind speed at a rate of 0.26 for all observation heights. Below this threshold wind speed, V TKE behaved independent of mean wind and observation heights. Alternatively, for preharvest periods, MOST was applicable in regimes 1 and 2 for all heights and HOST was applicable with reduced Vs for heights above the crop layer. Regime 3 during pre- and postharvest consisted of strongly stable periods and very weak to weak winds, where MOST was found to be invalid and V TKE remained low and independent of wind speed. The results suggest that roughness due to crops enhances the turbulence generation at lower wind speeds.
Abstract
Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events.
Significance Statement
A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.
Abstract
Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events.
Significance Statement
A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.
