Abstract
Climate models project a significant intensification of the sea surface temperature (SST) seasonal cycle over the subpolar North Pacific due to global warming, with the shallower mixed layer widely recognized as the dominant factor. However, employing slab ocean experiments with only ocean–atmosphere thermal coupling, we find a substantial contribution from changes in surface heat flux to this seasonal cycle intensification. In particular, the stronger Newtonian cooling effect in winter acts as a more potent damping than in summer. This differential damping inhibits the warming in colder seasons, significantly contributing to the intensified SST seasonal cycle in the subpolar North Pacific. In addition, consistent phase shifts in the North Pacific are identified across CMIP6 models. In the northwest North Pacific, a phase advance is associated with anomalous heating in early spring, driven by enhanced warm atmospheric advection from lower latitudes and sea ice melting in marginal seas. In contrast, the southeast North Pacific exhibits a phase delay attributed to the anomalous cooling in spring relative to autumn. This cooling is due to weakened trade winds and increased presence of high clouds. The former leads to stronger evaporative cooling in spring, while the latter impedes shortwave radiation from reaching the ocean.
Abstract
Climate models project a significant intensification of the sea surface temperature (SST) seasonal cycle over the subpolar North Pacific due to global warming, with the shallower mixed layer widely recognized as the dominant factor. However, employing slab ocean experiments with only ocean–atmosphere thermal coupling, we find a substantial contribution from changes in surface heat flux to this seasonal cycle intensification. In particular, the stronger Newtonian cooling effect in winter acts as a more potent damping than in summer. This differential damping inhibits the warming in colder seasons, significantly contributing to the intensified SST seasonal cycle in the subpolar North Pacific. In addition, consistent phase shifts in the North Pacific are identified across CMIP6 models. In the northwest North Pacific, a phase advance is associated with anomalous heating in early spring, driven by enhanced warm atmospheric advection from lower latitudes and sea ice melting in marginal seas. In contrast, the southeast North Pacific exhibits a phase delay attributed to the anomalous cooling in spring relative to autumn. This cooling is due to weakened trade winds and increased presence of high clouds. The former leads to stronger evaporative cooling in spring, while the latter impedes shortwave radiation from reaching the ocean.
Development of a Precipitation Climate Record from Spaceborne Precipitation Radar Data. Part II: Temporal Adjustment of the Calibration Change Using the Ocean Normalized Radar Cross SectionAbstract
We analyze the calibration stability of the 17-yr precipitation radar (PR) data on board the Tropical Rainfall Measuring Mission (TRMM) satellite to develop a precipitation climate record from the spaceborne precipitation radar data of the TRMM and following satellite missions. Since the PR measures the normalized radar cross section (NRCS) over the ocean surface, the temporal change in the NRCS whose variability is insensitive to the sea surface wind is regarded as the temporal change of the PR calibration. The temporal change of the PR calibration in TRMM, version 7, is found to be 0.19 dB decade−1 from 1998 to 2013. The calibration change is simply adjusted to evaluate the NRCS time series and the near-surface precipitation trend analysis within the latitudinal band between 35°S and 35°N. The NRCS time series at nadir and off-nadir are uncorrelated before the calibration adjustment, but they are correlated after the adjustment. The 0.19 dB decade−1 change of the PR calibration causes an overestimation of 0.08 mm day−1 decade−1 or 2.9% decade−1 for the linear trend of the near-surface precipitation. Even after the adjustment, agreement of the results among the satellite products depends on the analysis period. The temporal stability of the data quality is also important to evaluate the plausible trend analysis. The reprocessing of the PR data in TRMM, version 8 (or later), takes into account the temporal adjustment of the calibration change based upon the results of this study, which can provide more credible data for a long-term precipitation analysis.
Significance Statement
The stability of long-term data is very important for climate research so that an account of temporal calibration changes in the sensor must be made. In this study, we investigate the calibration stability of the TRMM PR data and evaluate its impact on the precipitation trend analysis. The temporal change of the PR calibration is estimated to be 0.19 dB decade−1. Compensating for this change improves the consistency of precipitation trend analysis between the PR and other precipitation datasets. The reprocessed PR data provide more probable data for long-term precipitation analysis.
Abstract
We analyze the calibration stability of the 17-yr precipitation radar (PR) data on board the Tropical Rainfall Measuring Mission (TRMM) satellite to develop a precipitation climate record from the spaceborne precipitation radar data of the TRMM and following satellite missions. Since the PR measures the normalized radar cross section (NRCS) over the ocean surface, the temporal change in the NRCS whose variability is insensitive to the sea surface wind is regarded as the temporal change of the PR calibration. The temporal change of the PR calibration in TRMM, version 7, is found to be 0.19 dB decade−1 from 1998 to 2013. The calibration change is simply adjusted to evaluate the NRCS time series and the near-surface precipitation trend analysis within the latitudinal band between 35°S and 35°N. The NRCS time series at nadir and off-nadir are uncorrelated before the calibration adjustment, but they are correlated after the adjustment. The 0.19 dB decade−1 change of the PR calibration causes an overestimation of 0.08 mm day−1 decade−1 or 2.9% decade−1 for the linear trend of the near-surface precipitation. Even after the adjustment, agreement of the results among the satellite products depends on the analysis period. The temporal stability of the data quality is also important to evaluate the plausible trend analysis. The reprocessing of the PR data in TRMM, version 8 (or later), takes into account the temporal adjustment of the calibration change based upon the results of this study, which can provide more credible data for a long-term precipitation analysis.
Significance Statement
The stability of long-term data is very important for climate research so that an account of temporal calibration changes in the sensor must be made. In this study, we investigate the calibration stability of the TRMM PR data and evaluate its impact on the precipitation trend analysis. The temporal change of the PR calibration is estimated to be 0.19 dB decade−1. Compensating for this change improves the consistency of precipitation trend analysis between the PR and other precipitation datasets. The reprocessed PR data provide more probable data for long-term precipitation analysis.
DINEOF Interpolation of Global Ocean Color Data: Error Analysis and MaskingAbstract
The Data Interpolation Empirical Orthogonal Function (DINEOF) algorithm is used to reconstruct datasets of geophysical and biological variables such as sea surface temperature (SST) and chlorophyll a (Chl a). In this study, we analyze the impact of both the quantity and distribution of missing data on the performance of DINEOF demonstrating how DINEOF plus a connectivity mask can be used for future data reconstruction tasks. We propose an enhanced version of DINEOF (DINEOF+) by adding two steps: 1) Using a 75% threshold of missing data for reconstructing incomplete datasets and 2) masking interpolated points that lack sufficient space–time observations in the original dataset. We successfully apply DINEOF+ to the Ocean Color Climate Change Initiative (OC-CCI) global daily Chl a dataset and validate the results using in situ datasets. We find that the recovery rate varies across ocean basins and years. In oligotrophic waters, the daily data coverage increased by 40%–50% during the period from 2003 to 2020. Using DINEOF+ allows us to obtain a significantly higher temporal resolution of global Chl a data, which will improve understanding of marine phytoplankton dynamics in response to changing environments.
Significance Statement
We perform an error analysis on the application of DINEOF for reconstructing a global Chl a dataset. The results of this analysis illustrate the impact of missing data—both in terms of quantity and distribution—on the performance of DINEOF. We propose using DINEOF+, an enhanced version of DINEOF that adds an editing step to mask out interpolated points based on the number of surrounding observations in the original input. The performance of DINEOF+ was validated using both simulated and in situ datasets. The results indicate that employing this masking technique effectively reduces biased estimates of missing data. DINEOF+ can be applied to other biogeochemical variables. However, caution is advised when dealing with observations characterized by high variance.
Abstract
The Data Interpolation Empirical Orthogonal Function (DINEOF) algorithm is used to reconstruct datasets of geophysical and biological variables such as sea surface temperature (SST) and chlorophyll a (Chl a). In this study, we analyze the impact of both the quantity and distribution of missing data on the performance of DINEOF demonstrating how DINEOF plus a connectivity mask can be used for future data reconstruction tasks. We propose an enhanced version of DINEOF (DINEOF+) by adding two steps: 1) Using a 75% threshold of missing data for reconstructing incomplete datasets and 2) masking interpolated points that lack sufficient space–time observations in the original dataset. We successfully apply DINEOF+ to the Ocean Color Climate Change Initiative (OC-CCI) global daily Chl a dataset and validate the results using in situ datasets. We find that the recovery rate varies across ocean basins and years. In oligotrophic waters, the daily data coverage increased by 40%–50% during the period from 2003 to 2020. Using DINEOF+ allows us to obtain a significantly higher temporal resolution of global Chl a data, which will improve understanding of marine phytoplankton dynamics in response to changing environments.
Significance Statement
We perform an error analysis on the application of DINEOF for reconstructing a global Chl a dataset. The results of this analysis illustrate the impact of missing data—both in terms of quantity and distribution—on the performance of DINEOF. We propose using DINEOF+, an enhanced version of DINEOF that adds an editing step to mask out interpolated points based on the number of surrounding observations in the original input. The performance of DINEOF+ was validated using both simulated and in situ datasets. The results indicate that employing this masking technique effectively reduces biased estimates of missing data. DINEOF+ can be applied to other biogeochemical variables. However, caution is advised when dealing with observations characterized by high variance.
Abstract
Western Central Africa is atypical of the equatorial domain as the main dry season is cloudier than the rainy seasons. To understand this cloud cover's diurnal evolution, we set-up an infrared camera and acquired measurements of the total cloud cover fraction (TCF) and cloud optical depth at Bambidie, Gabon (0°44’30.5” S,12°58’12.4” O) from May to October 2022. Diurnal variations in TCF can be summarized into four types, mostly discretized through the timing and duration of clouds clearing in the afternoon (Early afternoon Clearing: EaC, Late afternoon Clearing: LaC and Clear Night: CNi) while one type (No Clearing: NoC) shows overcast conditions all day long.
Meteorological measurements show that NoC days record 50W/m2 less shortwave incoming surface radiation resulting in daytime temperatures 1°C lower than the seasonal norm, but 20% more diffuse light and 0.5mm/day less ETo. Conversely, EaC days record 50W/m2 more shortwave incoming surface radiation leading to temperatures 1.5°C higher than the seasonal norm, but 40% more direct light. The larger water demand (0.5mm/day more ETo) is partly compensated by more frequent rainfall at night-time.
The SAFNWC satellite estimates well capture the TCF variations for most of the 4 types. They confirm that TCF is dominated by very low and low clouds whose dissipation in the afternoon and evolution into fractional and cumuliform convective clouds explains the clearings on EaC and LaC days. Satellite estimates also show that the 4 types of days extracted at Bambidie are representative of a larger-scale cloud cover evolution in Western Central Africa, with a W-E gradient in the timing of afternoon cloud dissipation.
Abstract
Western Central Africa is atypical of the equatorial domain as the main dry season is cloudier than the rainy seasons. To understand this cloud cover's diurnal evolution, we set-up an infrared camera and acquired measurements of the total cloud cover fraction (TCF) and cloud optical depth at Bambidie, Gabon (0°44’30.5” S,12°58’12.4” O) from May to October 2022. Diurnal variations in TCF can be summarized into four types, mostly discretized through the timing and duration of clouds clearing in the afternoon (Early afternoon Clearing: EaC, Late afternoon Clearing: LaC and Clear Night: CNi) while one type (No Clearing: NoC) shows overcast conditions all day long.
Meteorological measurements show that NoC days record 50W/m2 less shortwave incoming surface radiation resulting in daytime temperatures 1°C lower than the seasonal norm, but 20% more diffuse light and 0.5mm/day less ETo. Conversely, EaC days record 50W/m2 more shortwave incoming surface radiation leading to temperatures 1.5°C higher than the seasonal norm, but 40% more direct light. The larger water demand (0.5mm/day more ETo) is partly compensated by more frequent rainfall at night-time.
The SAFNWC satellite estimates well capture the TCF variations for most of the 4 types. They confirm that TCF is dominated by very low and low clouds whose dissipation in the afternoon and evolution into fractional and cumuliform convective clouds explains the clearings on EaC and LaC days. Satellite estimates also show that the 4 types of days extracted at Bambidie are representative of a larger-scale cloud cover evolution in Western Central Africa, with a W-E gradient in the timing of afternoon cloud dissipation.
Abstract
Drought is one of the most complicated and challenging natural hazards, which occurs nearly in every part of the world and poses recurring challenges to agriculture, food security, livestock, human health, and water management. Pakistan has a long history of drought; however, this study focuses on drought analysis and projection in the province of Punjab, Pakistan, as it provides around 60% of the country’s food product, significantly contributing to the national food supply and economy. This study utilized the previous 56 years (1962–2017) of climate data to calculate the reconnaissance drought index (RDI) and then extracted the drought variables of durations and severity for each meteorological station. The best-fit marginal probability distribution and copula models were chosen for the stations based on numerical as well as graphical evaluation. Lognormal and exponential probability distributions, as well as Gumbel, are selected as the best-fit probability distributions for both drought characteristics and bivariate copula model, respectively, for projections. From the projections, we can infer that the smaller return periods indicate high vulnerability while longer return periods with low vulnerability. The results suggest that Faisalabad, Bahawalpur, Bahawalnagar, and Multan stations have the lowest return periods, indicating high vulnerability, and may experience drought more frequently in the future. Mianwali, Khanpur, Lahore, and Sialkot stations may have an intermediate vulnerability to drought events. The stations of Jhelum, Murree, and Sargodha have larger return periods, implying lower susceptibility to drought events in the future. The projected results provide insights for policymakers and stakeholders to optimize the risk of droughts on agriculture production, livestock, water management, human health, and food security in Punjab, Pakistan.
Abstract
Drought is one of the most complicated and challenging natural hazards, which occurs nearly in every part of the world and poses recurring challenges to agriculture, food security, livestock, human health, and water management. Pakistan has a long history of drought; however, this study focuses on drought analysis and projection in the province of Punjab, Pakistan, as it provides around 60% of the country’s food product, significantly contributing to the national food supply and economy. This study utilized the previous 56 years (1962–2017) of climate data to calculate the reconnaissance drought index (RDI) and then extracted the drought variables of durations and severity for each meteorological station. The best-fit marginal probability distribution and copula models were chosen for the stations based on numerical as well as graphical evaluation. Lognormal and exponential probability distributions, as well as Gumbel, are selected as the best-fit probability distributions for both drought characteristics and bivariate copula model, respectively, for projections. From the projections, we can infer that the smaller return periods indicate high vulnerability while longer return periods with low vulnerability. The results suggest that Faisalabad, Bahawalpur, Bahawalnagar, and Multan stations have the lowest return periods, indicating high vulnerability, and may experience drought more frequently in the future. Mianwali, Khanpur, Lahore, and Sialkot stations may have an intermediate vulnerability to drought events. The stations of Jhelum, Murree, and Sargodha have larger return periods, implying lower susceptibility to drought events in the future. The projected results provide insights for policymakers and stakeholders to optimize the risk of droughts on agriculture production, livestock, water management, human health, and food security in Punjab, Pakistan.
Abstract
The in situ generation and characteristics of planetary waves (PWs) in the mesosphere and lower thermosphere (MLT) during the January 2021 sudden stratospheric warming (SSW) are investigated using the Navy Global Environmental Model. During a SSW, upward-propagating PWs can be absorbed, refracted, or amplified, the latter implying in situ PW generation. Additionally, asymmetric dissipation of GW drag in the MLT due to varying stratospheric winds may also seed PW generation. Ultimately, the interaction of waves with instability can enhance westward-propagating, quasi-stationary, or eastward-propagating components of PWs (WPW, QSPWs, and EPWs, respectively). The propagation pathway of a wave is diagnosed by its refractive index and is dependent on factors such as the baroclinic/barotropic stability of the atmosphere, the wave’s relative phase velocity, and the wavenumber. Through these pathways waves are able to transfer heat and momentum throughout the atmosphere. Under particular conditions, the waves can amplify in situ by extracting energy from the background wind. Our study identifies two scenarios in which WPWs and EPWs were amplified successively. Amplified WPWs propagated upwards and influenced the MLT while amplified EPWs propagated downwards and influenced the upper troposphere.
Abstract
The in situ generation and characteristics of planetary waves (PWs) in the mesosphere and lower thermosphere (MLT) during the January 2021 sudden stratospheric warming (SSW) are investigated using the Navy Global Environmental Model. During a SSW, upward-propagating PWs can be absorbed, refracted, or amplified, the latter implying in situ PW generation. Additionally, asymmetric dissipation of GW drag in the MLT due to varying stratospheric winds may also seed PW generation. Ultimately, the interaction of waves with instability can enhance westward-propagating, quasi-stationary, or eastward-propagating components of PWs (WPW, QSPWs, and EPWs, respectively). The propagation pathway of a wave is diagnosed by its refractive index and is dependent on factors such as the baroclinic/barotropic stability of the atmosphere, the wave’s relative phase velocity, and the wavenumber. Through these pathways waves are able to transfer heat and momentum throughout the atmosphere. Under particular conditions, the waves can amplify in situ by extracting energy from the background wind. Our study identifies two scenarios in which WPWs and EPWs were amplified successively. Amplified WPWs propagated upwards and influenced the MLT while amplified EPWs propagated downwards and influenced the upper troposphere.
Abstract
The effect of warming on severe convective storm potential is commonly explained in terms of changes in vertically integrated (“bulk”) environmental parameters, such as CAPE and 0–6-km shear. However, such events are known to depend on the details of the vertical structure of the thermodynamic and kinematic environment that can change independently of these bulk parameters. This work examines how warming may affect the complete vertical structure of these environments for fixed ranges of values of high CAPE and bulk shear, using data over the central Great Plains from two high-performing climate models (CNRM and MPI). To first order, projected changes in the vertical sounding structure are consistent between the two models: the environment warms approximately uniformly with height at constant relative humidity, and the shear profile remains relatively constant. The boundary layer becomes slightly drier (−2% to 6% relative humidity) while the free troposphere becomes slightly moister (+1% to 3%), with a slight increase in moist static energy deficit aloft with stronger magnitude in CNRM. CNRM indicates enhanced low-level shear and storm-relative helicity associated with stronger hodograph curvature in the lowest 2 km, whereas MPI shows near-zero change. Both models strongly underestimate shear below 1 km compared to ERA5, indicating large uncertainty in projecting subtle changes in the low-level flow structure in climate models. The evaluation of the net effect of these modest thermodynamic and kinematic changes on severe convective storm outcomes cannot be ascertained here but could be explored in simulation experiments.
Significance Statement
Severe thunderstorms and tornadoes cause substantial damage and loss of life each year, which raise concerns about how they may change as the world warms. We typically use a small number of common atmospheric parameters to understand how these localized events may change with climate change. However, climate change may alter the weather patterns that produce these events in ways not captured by these parameters. This work examines how climate change may alter the complete vertical structure of temperature, moisture, and wind and discusses the potential implications of these changes for future severe thunderstorms and tornadoes.
Abstract
The effect of warming on severe convective storm potential is commonly explained in terms of changes in vertically integrated (“bulk”) environmental parameters, such as CAPE and 0–6-km shear. However, such events are known to depend on the details of the vertical structure of the thermodynamic and kinematic environment that can change independently of these bulk parameters. This work examines how warming may affect the complete vertical structure of these environments for fixed ranges of values of high CAPE and bulk shear, using data over the central Great Plains from two high-performing climate models (CNRM and MPI). To first order, projected changes in the vertical sounding structure are consistent between the two models: the environment warms approximately uniformly with height at constant relative humidity, and the shear profile remains relatively constant. The boundary layer becomes slightly drier (−2% to 6% relative humidity) while the free troposphere becomes slightly moister (+1% to 3%), with a slight increase in moist static energy deficit aloft with stronger magnitude in CNRM. CNRM indicates enhanced low-level shear and storm-relative helicity associated with stronger hodograph curvature in the lowest 2 km, whereas MPI shows near-zero change. Both models strongly underestimate shear below 1 km compared to ERA5, indicating large uncertainty in projecting subtle changes in the low-level flow structure in climate models. The evaluation of the net effect of these modest thermodynamic and kinematic changes on severe convective storm outcomes cannot be ascertained here but could be explored in simulation experiments.
Significance Statement
Severe thunderstorms and tornadoes cause substantial damage and loss of life each year, which raise concerns about how they may change as the world warms. We typically use a small number of common atmospheric parameters to understand how these localized events may change with climate change. However, climate change may alter the weather patterns that produce these events in ways not captured by these parameters. This work examines how climate change may alter the complete vertical structure of temperature, moisture, and wind and discusses the potential implications of these changes for future severe thunderstorms and tornadoes.
Abstract
River ice changes due to climate change significantly impact river hydrology, economies, and societies. This study employed the CMIP6 data and a river ice model to predict global river ice changes in response to climate change. Results indicate significant declines in global river ice due to global warming. With each 1°C increase in surface air temperature (SAT) in the future, river ice extent of ice-affected rivers decrease by 2.11 percentage points, and ice duration shorten by 8.4 days. Under the shared socioeconomic pathways 2-4.5 (SSP2-4.5) scenario, the long-term mean SAT is 1.2°–2.5°C higher than in the near term. This leads to a 1.9–4.4-percentage-point decrease in global river ice extent, a 6.8–15.1-day decrease in river ice duration, and ice-free rivers increasing by up to 4.02%. The SSP5-8.5 scenario predicts a warmer long-term mean SAT, leading to greater reductions in river ice. Geographically, river ice loss is most notable in North America, Europe, Siberia, and the Tibetan Plateau (TIB), particularly in peninsular, coastal, and mountainous regions due to the combined effects of initial temperatures and temperature increases. Overall, the eastern Europe (EEU) shows the greatest loss of river ice on average. Monthly analyses show the most substantial decreases from October to June, indicating pronounced seasonal variability. This study provides valuable insights for addressing challenges related to river ice changes in high-latitude and high-elevation regions.
Significance Statement
River ice has a significant impact on various aspects, including hydrology, ecology, and the economy. The ongoing global warming phenomenon has resulted in a decline in river ice. This ice acts as a barrier, affecting river gas exchange and influencing the metabolism of the river, which is crucial for regulating greenhouse gas (GHG) emissions. The primary objective of this research is to examine the response of river ice to future climate change. The outcomes of this study will play a role in estimating future GHG emissions and understanding river metabolism, as well as providing a valuable reference for tackling emerging challenges in resource acquisition in high-latitude and high-altitude regions.
Abstract
River ice changes due to climate change significantly impact river hydrology, economies, and societies. This study employed the CMIP6 data and a river ice model to predict global river ice changes in response to climate change. Results indicate significant declines in global river ice due to global warming. With each 1°C increase in surface air temperature (SAT) in the future, river ice extent of ice-affected rivers decrease by 2.11 percentage points, and ice duration shorten by 8.4 days. Under the shared socioeconomic pathways 2-4.5 (SSP2-4.5) scenario, the long-term mean SAT is 1.2°–2.5°C higher than in the near term. This leads to a 1.9–4.4-percentage-point decrease in global river ice extent, a 6.8–15.1-day decrease in river ice duration, and ice-free rivers increasing by up to 4.02%. The SSP5-8.5 scenario predicts a warmer long-term mean SAT, leading to greater reductions in river ice. Geographically, river ice loss is most notable in North America, Europe, Siberia, and the Tibetan Plateau (TIB), particularly in peninsular, coastal, and mountainous regions due to the combined effects of initial temperatures and temperature increases. Overall, the eastern Europe (EEU) shows the greatest loss of river ice on average. Monthly analyses show the most substantial decreases from October to June, indicating pronounced seasonal variability. This study provides valuable insights for addressing challenges related to river ice changes in high-latitude and high-elevation regions.
Significance Statement
River ice has a significant impact on various aspects, including hydrology, ecology, and the economy. The ongoing global warming phenomenon has resulted in a decline in river ice. This ice acts as a barrier, affecting river gas exchange and influencing the metabolism of the river, which is crucial for regulating greenhouse gas (GHG) emissions. The primary objective of this research is to examine the response of river ice to future climate change. The outcomes of this study will play a role in estimating future GHG emissions and understanding river metabolism, as well as providing a valuable reference for tackling emerging challenges in resource acquisition in high-latitude and high-altitude regions.
Abstract
With the rising global demand for renewable energy sources, a great number of offshore wind farms are being built worldwide, as well as in the northern South China Sea. There is, however, limited research on the impact of offshore wind farms on the atmospheric and marine environment, particularly tropical cyclones, which frequently occur in summertime in the South China Sea. In this paper, we employ the Weather Research and Forecasting (WRF) Model to investigate the impacts of large-scale offshore wind farms on tropical cyclones, using the case of Typhoon Hato, which caused severe damage in 2017. Model results reveal that maximum wind speeds in coastal areas decrease by 3–5 m s−1 and can reach a maximum of 8 m s−1. Furthermore, the wind farms change low-level moisture convergence, causing a shift in the precipitation center toward the wind farm area and causing a significant overall reduction (up to 16%) in precipitation. Model sensitivity experiments on the area and layout of the wind farm have been carried out. The results show that larger wind farm areas and denser turbine layouts cause a more substantial decrease in the wind speed over the coast and accumulated precipitation reduction, further corroborating our findings.
Significance Statement
This study holds significant implications for developing offshore wind farms in tropical cyclone-prone regions like the South China Sea. By focusing on Typhoon Hato as a case study, the research sheds light on the previously understudied relationship between large-scale offshore wind farms and tropical cyclones. The observed decrease in coastal wind speeds and altered precipitation patterns due to wind farm presence highlights the potential for mitigating cyclone-related risks in these regions. Additionally, the study’s sensitivity experiments underscore the importance of careful planning and design in optimizing wind farm layouts for maximum impact reduction. This research contributes vital insights into sustainable energy infrastructure development while minimizing environmental and meteorological risks in cyclone-prone areas.
Abstract
With the rising global demand for renewable energy sources, a great number of offshore wind farms are being built worldwide, as well as in the northern South China Sea. There is, however, limited research on the impact of offshore wind farms on the atmospheric and marine environment, particularly tropical cyclones, which frequently occur in summertime in the South China Sea. In this paper, we employ the Weather Research and Forecasting (WRF) Model to investigate the impacts of large-scale offshore wind farms on tropical cyclones, using the case of Typhoon Hato, which caused severe damage in 2017. Model results reveal that maximum wind speeds in coastal areas decrease by 3–5 m s−1 and can reach a maximum of 8 m s−1. Furthermore, the wind farms change low-level moisture convergence, causing a shift in the precipitation center toward the wind farm area and causing a significant overall reduction (up to 16%) in precipitation. Model sensitivity experiments on the area and layout of the wind farm have been carried out. The results show that larger wind farm areas and denser turbine layouts cause a more substantial decrease in the wind speed over the coast and accumulated precipitation reduction, further corroborating our findings.
Significance Statement
This study holds significant implications for developing offshore wind farms in tropical cyclone-prone regions like the South China Sea. By focusing on Typhoon Hato as a case study, the research sheds light on the previously understudied relationship between large-scale offshore wind farms and tropical cyclones. The observed decrease in coastal wind speeds and altered precipitation patterns due to wind farm presence highlights the potential for mitigating cyclone-related risks in these regions. Additionally, the study’s sensitivity experiments underscore the importance of careful planning and design in optimizing wind farm layouts for maximum impact reduction. This research contributes vital insights into sustainable energy infrastructure development while minimizing environmental and meteorological risks in cyclone-prone areas.
Abstract
Lightning has killed and injured many people in recent years in the Qinghai–Tibetan Plateau area of China when they were collecting a rare product used for medical applications. The fungus that grows on dead caterpillars at high altitudes in this region demands a high price when sold, so it attracts collectors that unfortunately become at risk from lightning during this process. A total of 12 lightning-related events during 2004–22 resulted in 29 deaths and 53 injuries. All cases occurred at high elevations in rugged terrain with no available lightning-safe structures or vehicles. The fungus collection occurred during the daytime hours in late spring and early summer, which is also when lightning is frequent. Maps of lightning for the cases and information gained from the Chinese-language reports are summarized. It is apparent that this is a unique high-risk, high-reward occupation that is similar in terms of exposure to other situations around the world that result in lightning deaths and injuries.
Significance Statement
Lightning deaths and injuries often occur when people push the limits of safety due to occupational or recreational demands. Most of these decisions are on short time scales such as while tending agricultural fields, working on roofs, running a competitive race, or walking home from school when a thunderstorm is approaching. In the scenario presented here, groups of people with few income alternatives spend weeks or months in mountainous regions where lightning is common, but safety is elusive. This situation is an unusual version of high lightning risk occurring while pursuing a high reward.
Abstract
Lightning has killed and injured many people in recent years in the Qinghai–Tibetan Plateau area of China when they were collecting a rare product used for medical applications. The fungus that grows on dead caterpillars at high altitudes in this region demands a high price when sold, so it attracts collectors that unfortunately become at risk from lightning during this process. A total of 12 lightning-related events during 2004–22 resulted in 29 deaths and 53 injuries. All cases occurred at high elevations in rugged terrain with no available lightning-safe structures or vehicles. The fungus collection occurred during the daytime hours in late spring and early summer, which is also when lightning is frequent. Maps of lightning for the cases and information gained from the Chinese-language reports are summarized. It is apparent that this is a unique high-risk, high-reward occupation that is similar in terms of exposure to other situations around the world that result in lightning deaths and injuries.
Significance Statement
Lightning deaths and injuries often occur when people push the limits of safety due to occupational or recreational demands. Most of these decisions are on short time scales such as while tending agricultural fields, working on roofs, running a competitive race, or walking home from school when a thunderstorm is approaching. In the scenario presented here, groups of people with few income alternatives spend weeks or months in mountainous regions where lightning is common, but safety is elusive. This situation is an unusual version of high lightning risk occurring while pursuing a high reward.
