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Abstract
New simulations at 12-km grid spacing with the Weather and Research Forecasting (WRF) Model nested in the MPI Earth System Model (ESM) are used to quantify possible changes in wind power generation potential as a result of global warming. Annual capacity factors (CF; measures of electrical power production) computed by applying a power curve to hourly wind speeds at wind turbine hub height from this simulation are also used to illustrate the pitfalls in seeking to infer changes in wind power generation directly from low-spatial-resolution and time-averaged ESM output. WRF-derived CF are evaluated using observed daily CF from operating wind farms. The spatial correlation coefficient between modeled and observed mean CF is 0.65, and the root-mean-square error is 5.4 percentage points. Output from the MPI-WRF Model chain also captures some of the seasonal variability and the probability distribution of daily CF at operating wind farms. Projections of mean annual CF (CF A ) indicate no change to 2050 in the southern Great Plains and Northeast. Interannual variability of CF A increases in the Midwest, and CF A declines by up to 2 percentage points in the northern Great Plains. The probability of wind droughts (extended periods with anomalously low production) and wind bonus periods (high production) remains unchanged over most of the eastern United States. The probability of wind bonus periods exhibits some evidence of higher values over the Midwest in the 2040s, whereas the converse is true over the northern Great Plains.
Significance Statement
Wind energy is playing an increasingly important role in low-carbon-emission electricity generation. It is a “weather dependent” renewable energy source, and thus changes in the global atmosphere may cause changes in regional wind power production (PP) potential. We use PP data from operating wind farms to demonstrate that regional simulations exhibit skill in capturing actual power production. Projections to the middle of this century indicate that over most of North America east of the Rocky Mountains annual expected PP is largely unchanged, as is the probability of extended periods of anomalously high or low production. Any small declines in annual PP are of much smaller magnitude than changes due to technological innovation over the last two decades.
Abstract
New simulations at 12-km grid spacing with the Weather and Research Forecasting (WRF) Model nested in the MPI Earth System Model (ESM) are used to quantify possible changes in wind power generation potential as a result of global warming. Annual capacity factors (CF; measures of electrical power production) computed by applying a power curve to hourly wind speeds at wind turbine hub height from this simulation are also used to illustrate the pitfalls in seeking to infer changes in wind power generation directly from low-spatial-resolution and time-averaged ESM output. WRF-derived CF are evaluated using observed daily CF from operating wind farms. The spatial correlation coefficient between modeled and observed mean CF is 0.65, and the root-mean-square error is 5.4 percentage points. Output from the MPI-WRF Model chain also captures some of the seasonal variability and the probability distribution of daily CF at operating wind farms. Projections of mean annual CF (CF A ) indicate no change to 2050 in the southern Great Plains and Northeast. Interannual variability of CF A increases in the Midwest, and CF A declines by up to 2 percentage points in the northern Great Plains. The probability of wind droughts (extended periods with anomalously low production) and wind bonus periods (high production) remains unchanged over most of the eastern United States. The probability of wind bonus periods exhibits some evidence of higher values over the Midwest in the 2040s, whereas the converse is true over the northern Great Plains.
Significance Statement
Wind energy is playing an increasingly important role in low-carbon-emission electricity generation. It is a “weather dependent” renewable energy source, and thus changes in the global atmosphere may cause changes in regional wind power production (PP) potential. We use PP data from operating wind farms to demonstrate that regional simulations exhibit skill in capturing actual power production. Projections to the middle of this century indicate that over most of North America east of the Rocky Mountains annual expected PP is largely unchanged, as is the probability of extended periods of anomalously high or low production. Any small declines in annual PP are of much smaller magnitude than changes due to technological innovation over the last two decades.
Abstract
Capacity factors (CFs) derived from daily expected power at 22 operating wind farms in different regions of North America are used as predictands to train statistical downscaling algorithms using output from ERA5. The statistical downscaling models are then used to make CF projections for a suite of CMIP6 Earth System Models (ESMs). Downscaling is performed using a hybrid statistical approach that employs synoptic types derived using k-means clustering applied to sea level pressure fields with variance corrections applied as a function of the pressure gradient intensity. ESMs exhibit marked variability in terms of the skill with which the frequency of synoptic types and pressure gradients are reproduced relative to ERA5, and that differential skill is used to infer differential credibility in the associated CF projections. Projections of median annual mean CF [P50(CF)] in each 20-yr period from 1980 to 2099 show evidence of declines at most wind farms except in parts of the southern Great Plains, although the magnitude of the changes is strongly dependent on the ESM. For example, P50(CF) in 2080–99 deviate from those in 1980–99 by from −3.1 to +0.2 percentage points in the Northeast. The largest-magnitude declines in P50(CF) ranging from −3.9 to −2 percentage points are projected for the southern West Coast. CF trends exhibit marked seasonality and are strongly linked to changes in the relative intensity of future synoptic patterns, with much less impact from shifts in the occurrence of synoptic types over time. Internal climate modes continue to play a significant role in inducing interannual variability in wind power production, even under high radiative forcing scenarios.
Significance Statement
We describe how future climate changes may affect wind resources and wind power generation. Near-term changes in projected wind power electricity generation potential at operating wind farms over North America are small, but by the end of the current century electricity production is projected to decrease in many areas but may increase in parts of the southern Great Plains. The amount of change in projected wind power production is a strong function of the Earth system model that is downscaled and also depends on the continued presence of internally forced climate variability. An additional dependence on the amount of greenhouse gas–induced global warming indicates the transition of the energy sector to low-carbon sources may assist in maintaining the abundant U.S. wind resource.
Abstract
Capacity factors (CFs) derived from daily expected power at 22 operating wind farms in different regions of North America are used as predictands to train statistical downscaling algorithms using output from ERA5. The statistical downscaling models are then used to make CF projections for a suite of CMIP6 Earth System Models (ESMs). Downscaling is performed using a hybrid statistical approach that employs synoptic types derived using k-means clustering applied to sea level pressure fields with variance corrections applied as a function of the pressure gradient intensity. ESMs exhibit marked variability in terms of the skill with which the frequency of synoptic types and pressure gradients are reproduced relative to ERA5, and that differential skill is used to infer differential credibility in the associated CF projections. Projections of median annual mean CF [P50(CF)] in each 20-yr period from 1980 to 2099 show evidence of declines at most wind farms except in parts of the southern Great Plains, although the magnitude of the changes is strongly dependent on the ESM. For example, P50(CF) in 2080–99 deviate from those in 1980–99 by from −3.1 to +0.2 percentage points in the Northeast. The largest-magnitude declines in P50(CF) ranging from −3.9 to −2 percentage points are projected for the southern West Coast. CF trends exhibit marked seasonality and are strongly linked to changes in the relative intensity of future synoptic patterns, with much less impact from shifts in the occurrence of synoptic types over time. Internal climate modes continue to play a significant role in inducing interannual variability in wind power production, even under high radiative forcing scenarios.
Significance Statement
We describe how future climate changes may affect wind resources and wind power generation. Near-term changes in projected wind power electricity generation potential at operating wind farms over North America are small, but by the end of the current century electricity production is projected to decrease in many areas but may increase in parts of the southern Great Plains. The amount of change in projected wind power production is a strong function of the Earth system model that is downscaled and also depends on the continued presence of internally forced climate variability. An additional dependence on the amount of greenhouse gas–induced global warming indicates the transition of the energy sector to low-carbon sources may assist in maintaining the abundant U.S. wind resource.
Abstract
Atmospheric rivers (ARs) are a frequently studied phenomenon along the West Coast of the United States, where they are typically associated with the heaviest local flooding events and almost one-half of the annual precipitation totals. By contrast, ARs in the northeastern United States have received considerably less attention. The purpose of this study is to utilize a unique visual inspection methodology to create a 30-yr (1988–2017) climatology of ARs in the northeastern United States. Consistent with its formal definition, ARs are defined as corridors with integrated vapor transport (IVT) values greater than 250 kg m−1 s−1 over an area at least 2000 km long but less than 1000 km wide in association with an extratropical cyclone. Using MERRA2 reanalysis data, this AR definition is used to determine the frequency, duration, and spatial distribution of ARs across the northeastern United States. Approximately 100 ARs occur in the northeastern United States per year, with these ARs being quasi-uniformly distributed throughout the year. On average, northeastern U.S. ARs have a peak IVT magnitude between 750 and 999 kg m−1 s−1, last less than 48 h, and arrive in the region from the west to southwest. Average AR durations are longer in summer and shorter in winter. Further, ARs are typically associated with lower IVT in winter and higher IVT in summer. Spatially, ARs more frequently occur over the Atlantic Ocean coastline and adjacent Gulf Stream waters; however, the frequency with which large IVT values are associated with ARs is highest over interior New England.
Abstract
Atmospheric rivers (ARs) are a frequently studied phenomenon along the West Coast of the United States, where they are typically associated with the heaviest local flooding events and almost one-half of the annual precipitation totals. By contrast, ARs in the northeastern United States have received considerably less attention. The purpose of this study is to utilize a unique visual inspection methodology to create a 30-yr (1988–2017) climatology of ARs in the northeastern United States. Consistent with its formal definition, ARs are defined as corridors with integrated vapor transport (IVT) values greater than 250 kg m−1 s−1 over an area at least 2000 km long but less than 1000 km wide in association with an extratropical cyclone. Using MERRA2 reanalysis data, this AR definition is used to determine the frequency, duration, and spatial distribution of ARs across the northeastern United States. Approximately 100 ARs occur in the northeastern United States per year, with these ARs being quasi-uniformly distributed throughout the year. On average, northeastern U.S. ARs have a peak IVT magnitude between 750 and 999 kg m−1 s−1, last less than 48 h, and arrive in the region from the west to southwest. Average AR durations are longer in summer and shorter in winter. Further, ARs are typically associated with lower IVT in winter and higher IVT in summer. Spatially, ARs more frequently occur over the Atlantic Ocean coastline and adjacent Gulf Stream waters; however, the frequency with which large IVT values are associated with ARs is highest over interior New England.
Abstract
Over the past century, precipitation totals in Australia’s southeastern state of Victoria have shown multidecadal variability without clear trends. This has impacted agriculture, water security, ecosystem services, and flood hazards. Hydrological and meteorological evidence suggests that Victorian precipitation regimes have changed since the beginning of the Millennium Drought in 1997. Until now, Victorian precipitation intensity distributions have not been assessed in detail. We assess the time-varying aspect of observed precipitation intensity distributions by identifying temporal shifts in Victorian precipitation and using those different epochs to assess multidecadal changes in precipitation characteristics. We used 788 manual rain gauges and 49 automatic weather stations to analyze subdaily-to-multiday precipitation distributions from 1900 to 2020 for three Victorian regions and four seasons. Distributions are significantly different for the three epochs (1900–45, 1946–96, and 1997–2020). We summarized precipitation distributions by categorizing precipitation intensities, calculating histograms, and fitting gamma distributions. This study provides evidence that Victorian precipitation distributions have shifted over decades and that distributions depend on regional and seasonal differences. Recent precipitation declines are mostly due to decreasing light and moderate precipitation, despite increasing heavy precipitation. Heavy precipitation has shown a tendency to increase in frequency since 1997. Increases were greatest for 6-h springtime and summertime precipitation in northern Victoria and wintertime precipitation in southern and eastern Victoria. Observed precipitation distributions show changes that are consistent with climate projections. To better understand processes driving observed and projected changes to precipitation distributions globally, interdecadal shifts, seasonal variations, and regional climates need to be considered.
Significance Statement
Our research investigated how different rainfall intensities have contributed to changing rainfall totals over the last century in Victoria, Australia. This is important because different rainfall intensities have various impacts on farms, rivers, catchments, and infrastructure. In Victoria, we found three multidecade periods with different average rainfall intensity distributions. Early-twentieth-century rainfall is close to the observed average, 1946–96 was very wet, and 1997–2020 was drier. Recent years were drier because of fewer light and moderate rainfall events. Changes in heavy rainfall depend on the season and subregional factors. This may indicate that weather processes have changed. Decreasing light-to-moderate rainfall intensities will affect stakeholders by decreasing soil moisture, runoff, and streamflow.
Abstract
Over the past century, precipitation totals in Australia’s southeastern state of Victoria have shown multidecadal variability without clear trends. This has impacted agriculture, water security, ecosystem services, and flood hazards. Hydrological and meteorological evidence suggests that Victorian precipitation regimes have changed since the beginning of the Millennium Drought in 1997. Until now, Victorian precipitation intensity distributions have not been assessed in detail. We assess the time-varying aspect of observed precipitation intensity distributions by identifying temporal shifts in Victorian precipitation and using those different epochs to assess multidecadal changes in precipitation characteristics. We used 788 manual rain gauges and 49 automatic weather stations to analyze subdaily-to-multiday precipitation distributions from 1900 to 2020 for three Victorian regions and four seasons. Distributions are significantly different for the three epochs (1900–45, 1946–96, and 1997–2020). We summarized precipitation distributions by categorizing precipitation intensities, calculating histograms, and fitting gamma distributions. This study provides evidence that Victorian precipitation distributions have shifted over decades and that distributions depend on regional and seasonal differences. Recent precipitation declines are mostly due to decreasing light and moderate precipitation, despite increasing heavy precipitation. Heavy precipitation has shown a tendency to increase in frequency since 1997. Increases were greatest for 6-h springtime and summertime precipitation in northern Victoria and wintertime precipitation in southern and eastern Victoria. Observed precipitation distributions show changes that are consistent with climate projections. To better understand processes driving observed and projected changes to precipitation distributions globally, interdecadal shifts, seasonal variations, and regional climates need to be considered.
Significance Statement
Our research investigated how different rainfall intensities have contributed to changing rainfall totals over the last century in Victoria, Australia. This is important because different rainfall intensities have various impacts on farms, rivers, catchments, and infrastructure. In Victoria, we found three multidecade periods with different average rainfall intensity distributions. Early-twentieth-century rainfall is close to the observed average, 1946–96 was very wet, and 1997–2020 was drier. Recent years were drier because of fewer light and moderate rainfall events. Changes in heavy rainfall depend on the season and subregional factors. This may indicate that weather processes have changed. Decreasing light-to-moderate rainfall intensities will affect stakeholders by decreasing soil moisture, runoff, and streamflow.
Abstract
The effect of temperature on flow and pollutant dispersion around an isolated building was investigated by computational fluid dynamics. First, the accuracy of the standard k–ε turbulence model in simulating the thermal effect on the flow and dispersion was assessed. The results showed that the reattachment of the numerical simulation behind the building was longer than that in the experiment because it could not reproduce the periodic fluctuations in the wake region and that the momentum transfer in the lateral direction was underestimated. Despite this, the temperature and concentration of the numerical simulation were in good agreement with the experimental results. Then, the standard k–ε turbulence model was adopted to investigate the effect of the ground temperature on flow and dispersion. The result indicated that, with the increase in temperature, the reattachment length behind the building significantly decreased and the vertical upward velocity increased, suggesting that rising temperature changed the flow. As the flow changed, the pollutant dispersion also changed. The pollutant plume depth increased while its width decreased with increasing ground temperature. It can be seen from the pollutant flux analysis that both convective transport and turbulent transport play important roles in vertical dispersion. The influence of ground temperature on convective motion was more obvious than that on turbulent motion because of the changed airflow.
Significance Statement
(i) The accuracy of the standard k–ε turbulence model in simulating the thermal effect on the flow and dispersion was assessed. The results showed that the reattachment of the numerical simulation behind the building was longer than that in the experiment because it could not reproduce the periodic fluctuations in the wake region and that the momentum transfer in the lateral direction was underestimated. Despite this, the temperature and concentration of the numerical simulation were in good agreement with the experimental results. (ii) Rising temperature not only increases turbulent motion but also alters airflow and pollutant plume morphology.
Abstract
The effect of temperature on flow and pollutant dispersion around an isolated building was investigated by computational fluid dynamics. First, the accuracy of the standard k–ε turbulence model in simulating the thermal effect on the flow and dispersion was assessed. The results showed that the reattachment of the numerical simulation behind the building was longer than that in the experiment because it could not reproduce the periodic fluctuations in the wake region and that the momentum transfer in the lateral direction was underestimated. Despite this, the temperature and concentration of the numerical simulation were in good agreement with the experimental results. Then, the standard k–ε turbulence model was adopted to investigate the effect of the ground temperature on flow and dispersion. The result indicated that, with the increase in temperature, the reattachment length behind the building significantly decreased and the vertical upward velocity increased, suggesting that rising temperature changed the flow. As the flow changed, the pollutant dispersion also changed. The pollutant plume depth increased while its width decreased with increasing ground temperature. It can be seen from the pollutant flux analysis that both convective transport and turbulent transport play important roles in vertical dispersion. The influence of ground temperature on convective motion was more obvious than that on turbulent motion because of the changed airflow.
Significance Statement
(i) The accuracy of the standard k–ε turbulence model in simulating the thermal effect on the flow and dispersion was assessed. The results showed that the reattachment of the numerical simulation behind the building was longer than that in the experiment because it could not reproduce the periodic fluctuations in the wake region and that the momentum transfer in the lateral direction was underestimated. Despite this, the temperature and concentration of the numerical simulation were in good agreement with the experimental results. (ii) Rising temperature not only increases turbulent motion but also alters airflow and pollutant plume morphology.
Abstract
Cold-air pools can have several different impacts on viticulture, including final grape quality and yields. This study focuses on cold pools in the upper Douro Valley, which is one of the most important viticultural regions of northern Portugal. First, digital elevation model data were analyzed to identify pixels corresponding to the valley floors of the Douro and selected side valleys. Next, the topographic amplification factor was calculated for each of these pixels. Down-valley gradients in the topographic amplification factor were used to identify locations where cold air in the valley was likely to pool. High-time-resolution meteorological data recorded between January 2011 and December 2017 were analyzed to identify cold-pool events at one location in the main Douro Valley. The cold pools were assigned to seven different categories on the basis of their temporal behavior. There was a clear seasonal cycle in numbers of cold pools, with most observed during winter and the fewest in summer. The maximum strengths of the cold pools could occur at any time during the night, although the majority peaked around the middle of the night. This study is believed to be the first to examine cold pools in the upper Douro Valley.
Abstract
Cold-air pools can have several different impacts on viticulture, including final grape quality and yields. This study focuses on cold pools in the upper Douro Valley, which is one of the most important viticultural regions of northern Portugal. First, digital elevation model data were analyzed to identify pixels corresponding to the valley floors of the Douro and selected side valleys. Next, the topographic amplification factor was calculated for each of these pixels. Down-valley gradients in the topographic amplification factor were used to identify locations where cold air in the valley was likely to pool. High-time-resolution meteorological data recorded between January 2011 and December 2017 were analyzed to identify cold-pool events at one location in the main Douro Valley. The cold pools were assigned to seven different categories on the basis of their temporal behavior. There was a clear seasonal cycle in numbers of cold pools, with most observed during winter and the fewest in summer. The maximum strengths of the cold pools could occur at any time during the night, although the majority peaked around the middle of the night. This study is believed to be the first to examine cold pools in the upper Douro Valley.
Abstract
Near-surface air temperature lapse rate (NSATLR) is vital for hydrological simulation and mountain climate research in snowmelt-dominated regions. In this study, NSATLRs of two vertical zones (i.e., mountain grassland–coniferous forest belt and alpine meadow belt) of the Manasi River basin on the northern slope of the Tianshan Mountains were calculated using the near-surface air temperature data from 18 observation stations. Furthermore, temporal variations of NSATLRs of these two vertical zones at seasonal, monthly, and daily scales were analyzed, combined with altitudinal differences of local environments. The results show that the temporal variations of NSATLRs are different between these two vertical zones. The steepest monthly NSATLR occurs in July in the mountain grassland–coniferous forest belt and in April in the alpine meadow belt. In spring, summer, and autumn, the hourly NSATLRs in the mountain grassland–coniferous forest belt generally steepen with increasing solar radiation and vice versa, contrary to those in the alpine meadow belt. During winter, the hourly NSATLRs on sunny days are overall positive at night but negative during the day in the mountain grassland–coniferous forest belt. The findings of this study indicate that it is necessary to divide mountains with similar local environments to the study area into different vertical zones to accurately estimate NSATLR, and the use of a fixed NSATLR for different months and vertical zones is not suitable for snowmelt runoff modeling in snow-dominated regions such as the northern slope of the Tianshan Mountains.
Significance Statement
This study aims to investigate the altitudinal and temporal variations of near-surface air temperature lapse rate (NSATLR) on the northern slope of the Tianshan Mountains and how mountain environments affect NSATLR. This is important because altitudinal differences of mountain environments lead to different NSATLRs, and these altitudinal variations on the northern slope of the Tianshan Mountains are different from those on the Alps at the same latitude. Our results explain how altitudinal differences of mountain environments affect NSATLRs; hence, using a fixed NSATLR for different months and vertical zones is inappropriate, and estimating NSATLRs for different vertical zones is necessary.
Abstract
Near-surface air temperature lapse rate (NSATLR) is vital for hydrological simulation and mountain climate research in snowmelt-dominated regions. In this study, NSATLRs of two vertical zones (i.e., mountain grassland–coniferous forest belt and alpine meadow belt) of the Manasi River basin on the northern slope of the Tianshan Mountains were calculated using the near-surface air temperature data from 18 observation stations. Furthermore, temporal variations of NSATLRs of these two vertical zones at seasonal, monthly, and daily scales were analyzed, combined with altitudinal differences of local environments. The results show that the temporal variations of NSATLRs are different between these two vertical zones. The steepest monthly NSATLR occurs in July in the mountain grassland–coniferous forest belt and in April in the alpine meadow belt. In spring, summer, and autumn, the hourly NSATLRs in the mountain grassland–coniferous forest belt generally steepen with increasing solar radiation and vice versa, contrary to those in the alpine meadow belt. During winter, the hourly NSATLRs on sunny days are overall positive at night but negative during the day in the mountain grassland–coniferous forest belt. The findings of this study indicate that it is necessary to divide mountains with similar local environments to the study area into different vertical zones to accurately estimate NSATLR, and the use of a fixed NSATLR for different months and vertical zones is not suitable for snowmelt runoff modeling in snow-dominated regions such as the northern slope of the Tianshan Mountains.
Significance Statement
This study aims to investigate the altitudinal and temporal variations of near-surface air temperature lapse rate (NSATLR) on the northern slope of the Tianshan Mountains and how mountain environments affect NSATLR. This is important because altitudinal differences of mountain environments lead to different NSATLRs, and these altitudinal variations on the northern slope of the Tianshan Mountains are different from those on the Alps at the same latitude. Our results explain how altitudinal differences of mountain environments affect NSATLRs; hence, using a fixed NSATLR for different months and vertical zones is inappropriate, and estimating NSATLRs for different vertical zones is necessary.
Abstract
The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, the environment, and water resources. In this study, the innovative trend pivot analysis method (ITPAM) and trend polygon star concept method were applied for precipitation trend detection at 11 stations located in the Soan River basin (SRB), Potohar region, Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting, and calculating monthly shifts under different trend behaviors, that is, increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows a complex nature of behavior, and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first dataset and the second dataset in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behavior in the water resources and agricultural sectors.
Significance Statement
The monthly polygonal trends with risk graphs of total monthly precipitation data depicted a clear picture of climate change effects in the Potohar region of Pakistan. The monsoonal rainfall showed a significant decreasing trend in highland stations and an increasing trend in lowland stations, indicating a shifting pattern of monsoonal rainfall from highland to lowland areas.
Abstract
The trend analysis approach is adopted for the prediction of future climatological behavior and climate change impact on agriculture, the environment, and water resources. In this study, the innovative trend pivot analysis method (ITPAM) and trend polygon star concept method were applied for precipitation trend detection at 11 stations located in the Soan River basin (SRB), Potohar region, Pakistan. Polygon graphics of total monthly precipitation data were created and trends length and slope were calculated separately for arithmetic mean and standard deviation. As a result, the innovative methods produced useful scientific information and helped in identifying, interpreting, and calculating monthly shifts under different trend behaviors, that is, increase in some stations and decrease in others of precipitation data. This increasing and decreasing variability emerges from climate change. The risk graphs of the total monthly precipitation and monthly polygonal trends appear to show changes in the trend of meteorological data in the Potohar region of Pakistan. The monsoonal rainfall of all stations shows a complex nature of behavior, and monthly distribution is uneven. There is a decreasing trend of rainfall in high land stations of SRB with a significant change between the first dataset and the second dataset in July and August. It was examined that monsoon rainfall is increasing in lowland stations indicating a shifting pattern of monsoonal rainfall from highland to lowland areas of SRB. The increasing and decreasing trends in different periods with evidence of seasonal variations may cause irregular behavior in the water resources and agricultural sectors.
Significance Statement
The monthly polygonal trends with risk graphs of total monthly precipitation data depicted a clear picture of climate change effects in the Potohar region of Pakistan. The monsoonal rainfall showed a significant decreasing trend in highland stations and an increasing trend in lowland stations, indicating a shifting pattern of monsoonal rainfall from highland to lowland areas.
Abstract
Recent idealized modeling studies have highlighted the importance of explicitly simulating realistic convective boundary layer (CBL) structures to assess and represent their influence on mesoscale phenomena. The choice of lateral boundary conditions (LBCs) has a substantial impact on these turbulent structures, including the distribution of kinematic and thermodynamic properties within the CBL. While use of periodic LBCs is ideal, open LBCs are required for nonuniform domains (e.g., multiple air masses or land surface types). However, open LBCs result in an unrealistic, laminar CBL structure near the upstream boundary that undoubtedly impacts the evolution of any simulated phenomena. Therefore, there is a need for a modified open LBC option to mitigate this unrealistic structure, while still permitting users to simulate phenomena in nonuniform domains. The Pennsylvania State University–NCAR Cloud Model 1 (CM1), version 19.8, includes an optional inflow-nudging technique to nudge inflow to the base-state wind profile. For the present study, the authors modified this method to one that nudges toward a continually updated, horizontally averaged profile so that the technique may be used for phenomena under evolving conditions. Simulations using LBC choices, including nudging to either the base state or horizontal average, were evaluated relative to respective dual-periodic LBC control simulations with or without vertical wind shear. The horizontal average nudging technique outperformed the traditional open LBCs and nudging to the base state, as demonstrated using a histogram matching technique applied to grid points within the CBL. Ultimately, this work can be used to assist modelers in assessing which LBCs are appropriate for their intended use.
Abstract
Recent idealized modeling studies have highlighted the importance of explicitly simulating realistic convective boundary layer (CBL) structures to assess and represent their influence on mesoscale phenomena. The choice of lateral boundary conditions (LBCs) has a substantial impact on these turbulent structures, including the distribution of kinematic and thermodynamic properties within the CBL. While use of periodic LBCs is ideal, open LBCs are required for nonuniform domains (e.g., multiple air masses or land surface types). However, open LBCs result in an unrealistic, laminar CBL structure near the upstream boundary that undoubtedly impacts the evolution of any simulated phenomena. Therefore, there is a need for a modified open LBC option to mitigate this unrealistic structure, while still permitting users to simulate phenomena in nonuniform domains. The Pennsylvania State University–NCAR Cloud Model 1 (CM1), version 19.8, includes an optional inflow-nudging technique to nudge inflow to the base-state wind profile. For the present study, the authors modified this method to one that nudges toward a continually updated, horizontally averaged profile so that the technique may be used for phenomena under evolving conditions. Simulations using LBC choices, including nudging to either the base state or horizontal average, were evaluated relative to respective dual-periodic LBC control simulations with or without vertical wind shear. The horizontal average nudging technique outperformed the traditional open LBCs and nudging to the base state, as demonstrated using a histogram matching technique applied to grid points within the CBL. Ultimately, this work can be used to assist modelers in assessing which LBCs are appropriate for their intended use.
Abstract
This work examines the diurnal and seasonal variability of near-surface temperature and humidity at several large areas with high population density within the Maritime Continent using the Bureau of Meteorology Atmospheric Regional Reanalysis (BARRA) 12-km-resolution dataset that covers the period 1990–2019. The diurnal cycle is examined in detail, with a key feature being the relatively small diurnal variation of the wet-bulb temperature T WB when compared with the temperature and dewpoint temperature TD . The diurnal variability is strongly modulated by the monsoons with their increased rainfall and cloud cover. The near-surface signals associated with the Madden–Julian oscillation across the domains are relatively weak. Dry and humid temperature extremes are examined for regional and seasonal variability. The dry and moist variable extremes occur at different times of year, but each have spatially coherent structure.
Significance Statement
This paper examines the climatological variations of near-surface temperature and humidity and their extremes in four locations in the “Maritime Continent.” This is important because there are significant variations potentially affecting human and ecosystem health and its resilience to climate change.
Abstract
This work examines the diurnal and seasonal variability of near-surface temperature and humidity at several large areas with high population density within the Maritime Continent using the Bureau of Meteorology Atmospheric Regional Reanalysis (BARRA) 12-km-resolution dataset that covers the period 1990–2019. The diurnal cycle is examined in detail, with a key feature being the relatively small diurnal variation of the wet-bulb temperature T WB when compared with the temperature and dewpoint temperature TD . The diurnal variability is strongly modulated by the monsoons with their increased rainfall and cloud cover. The near-surface signals associated with the Madden–Julian oscillation across the domains are relatively weak. Dry and humid temperature extremes are examined for regional and seasonal variability. The dry and moist variable extremes occur at different times of year, but each have spatially coherent structure.
Significance Statement
This paper examines the climatological variations of near-surface temperature and humidity and their extremes in four locations in the “Maritime Continent.” This is important because there are significant variations potentially affecting human and ecosystem health and its resilience to climate change.
