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- Author or Editor: Roger A. Pielke Sr. x
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Abstract
On several occasions, winter freezes have wrought severe destruction on Florida agriculture. A series of devastating freezes around the turn of the twentieth century, and again during the 1980s, were related to anomalies in the large-scale flow of the ocean–atmosphere system. During the twentieth century, substantial areas of wetlands in south Florida were drained and converted to agricultural land for winter fresh vegetable and sugarcane production. During this time, much of the citrus industry also was relocated to those areas to escape the risk of freeze farther to the north. The purpose of this paper is to present a modeling study designed to investigate whether the conversion of the wetlands to agriculture itself could have resulted in or exacerbated the severity of recent freezes in those agricultural areas of south Florida.
For three recent freeze events, a pair of simulations was undertaken with the Regional Atmospheric Modeling System. One member of each pair employed land surface properties that represent pre-1900s (near natural) land cover, whereas the other member of each pair employed data that represent near-current land-use patterns as derived from analysis of Landsat data valid for 1992/93. These two different land cover datasets capture well the conversion of wetlands to agriculture in south Florida during the twentieth century. Use of current land surface properties resulted in colder simulated minimum temperatures and temperatures that remained below freezing for a longer period at locations of key agricultural production centers in south Florida that were once natural wetlands. Examination of time series of the surface energy budget from one of the cases reveals that when natural land cover is used, a persistent moisture flux from the underlying wetlands during the nighttime hours served to prevent the development of below-freezing temperatures at those same locations. When the model results were subjected to an important sensitivity factor, the depth of standing water in the wetlands, the outcome remained consistent. These results provide another example of the potential for humans to perturb the climate system in ways that can have severe socioeconomic consequences by altering the land surface alone.
Abstract
On several occasions, winter freezes have wrought severe destruction on Florida agriculture. A series of devastating freezes around the turn of the twentieth century, and again during the 1980s, were related to anomalies in the large-scale flow of the ocean–atmosphere system. During the twentieth century, substantial areas of wetlands in south Florida were drained and converted to agricultural land for winter fresh vegetable and sugarcane production. During this time, much of the citrus industry also was relocated to those areas to escape the risk of freeze farther to the north. The purpose of this paper is to present a modeling study designed to investigate whether the conversion of the wetlands to agriculture itself could have resulted in or exacerbated the severity of recent freezes in those agricultural areas of south Florida.
For three recent freeze events, a pair of simulations was undertaken with the Regional Atmospheric Modeling System. One member of each pair employed land surface properties that represent pre-1900s (near natural) land cover, whereas the other member of each pair employed data that represent near-current land-use patterns as derived from analysis of Landsat data valid for 1992/93. These two different land cover datasets capture well the conversion of wetlands to agriculture in south Florida during the twentieth century. Use of current land surface properties resulted in colder simulated minimum temperatures and temperatures that remained below freezing for a longer period at locations of key agricultural production centers in south Florida that were once natural wetlands. Examination of time series of the surface energy budget from one of the cases reveals that when natural land cover is used, a persistent moisture flux from the underlying wetlands during the nighttime hours served to prevent the development of below-freezing temperatures at those same locations. When the model results were subjected to an important sensitivity factor, the depth of standing water in the wetlands, the outcome remained consistent. These results provide another example of the potential for humans to perturb the climate system in ways that can have severe socioeconomic consequences by altering the land surface alone.
Abstract
The North American monsoon is a seasonal shift of upper- and low-level pressure and wind patterns that brings summertime moisture into the southwest United States and ends the late spring wet period in the Great Plains. The interannual variability of the North American monsoon is examined using the NCEP–NCAR reanalysis (1948–98). The diurnal and seasonal evolution of 500-mb geopotential height, integrated moisture flux, and integrated moisture flux convergence are constructed using a 5-day running mean for the months May through September. All of the years are used to calculate an average daily Z score that removes the diurnal, seasonal, and intraseasonal variability. The 30-day average Z score centered about the date is correlated with Pacific sea surface temperature anomaly (SSTA) indices associated with the El Niño–Southern Oscillation (ENSO) and the North Pacific oscillation (NPO). These indices are Niño-3, a North Pacific index, and a Pacific index that combines the previous two. Regional time-evolving precipitation indices for the Southwest and Great Plains, which consider the total number of wet or dry stations in a region, are also correlated with the SSTA indices. The use of nonnormally distributed point source precipitation data is avoided.
Teleconnections are computed relative to the climatological evolution of the North American monsoon, rather than to calendar months, thus more accurately accounting for the climatological changes in the large-scale circulation. Tropical and North Pacific SSTs are related to the occurrence of the Pacific Transition and East Pacific teleconnection patterns, respectively, in June and July. A high (low) NPO phase and El Niño (La Niña) conditions favor a weaker (stronger) and southward (northward) displaced monsoon ridge. These teleconnection patterns affect the timing and large-scale distribution of monsoon moisture. In the Great Plains, the spring wet season is lengthened (shortened) and early summer rainfall and integrated moisture flux convergence are above (below) average. In the Southwest, monsoon onset is late (early) and early summer rainfall and integrated moisture flux convergence are below (above) average. Relationships with Pacific SSTA indices decay in the later part of the monsoon coincident with weakening of the jet stream across the Pacific and strengthening of the monsoon ridge over North America. The most coherent summer climate patterns occur over the entire western United States when the Pacific index is substantially high or low, such as during the Midwest flood of 1993 and drought of 1988. The Pacific index in spring is a good predictor of early summer height anomalies over the western United States when the time evolution of the North Pacific SST dipole is considered.
Abstract
The North American monsoon is a seasonal shift of upper- and low-level pressure and wind patterns that brings summertime moisture into the southwest United States and ends the late spring wet period in the Great Plains. The interannual variability of the North American monsoon is examined using the NCEP–NCAR reanalysis (1948–98). The diurnal and seasonal evolution of 500-mb geopotential height, integrated moisture flux, and integrated moisture flux convergence are constructed using a 5-day running mean for the months May through September. All of the years are used to calculate an average daily Z score that removes the diurnal, seasonal, and intraseasonal variability. The 30-day average Z score centered about the date is correlated with Pacific sea surface temperature anomaly (SSTA) indices associated with the El Niño–Southern Oscillation (ENSO) and the North Pacific oscillation (NPO). These indices are Niño-3, a North Pacific index, and a Pacific index that combines the previous two. Regional time-evolving precipitation indices for the Southwest and Great Plains, which consider the total number of wet or dry stations in a region, are also correlated with the SSTA indices. The use of nonnormally distributed point source precipitation data is avoided.
Teleconnections are computed relative to the climatological evolution of the North American monsoon, rather than to calendar months, thus more accurately accounting for the climatological changes in the large-scale circulation. Tropical and North Pacific SSTs are related to the occurrence of the Pacific Transition and East Pacific teleconnection patterns, respectively, in June and July. A high (low) NPO phase and El Niño (La Niña) conditions favor a weaker (stronger) and southward (northward) displaced monsoon ridge. These teleconnection patterns affect the timing and large-scale distribution of monsoon moisture. In the Great Plains, the spring wet season is lengthened (shortened) and early summer rainfall and integrated moisture flux convergence are above (below) average. In the Southwest, monsoon onset is late (early) and early summer rainfall and integrated moisture flux convergence are below (above) average. Relationships with Pacific SSTA indices decay in the later part of the monsoon coincident with weakening of the jet stream across the Pacific and strengthening of the monsoon ridge over North America. The most coherent summer climate patterns occur over the entire western United States when the Pacific index is substantially high or low, such as during the Midwest flood of 1993 and drought of 1988. The Pacific index in spring is a good predictor of early summer height anomalies over the western United States when the time evolution of the North Pacific SST dipole is considered.
Abstract
Fifty-three years of the NCEP–NCAR Reanalysis I are dynamically downscaled using the Regional Atmospheric Modeling System (RAMS) to generate a regional climate model (RCM) climatology of the contiguous United States and Mexico. Data from the RAMS simulations are compared to the recently released North American Regional Reanalysis (NARR), as well as observed precipitation and temperature data. The RAMS simulations show the value added by using a RCM in a process study framework to represent North American summer climate beyond the driving global atmospheric reanalysis. Because of its enhanced representation of the land surface topography, the diurnal cycle of convective rainfall is present. This diurnal cycle largely governs the transitions associated with the evolution of the North American monsoon with regards to rainfall, the surface energy budget, and surface temperature. The lower frequency modes of convective rainfall, though weaker, account for rainfall variability at a remote distance from elevated terrain. As in previous studies with other RCMs, RAMS precipitation is overestimated compared to observations. The Great Plains low-level jet (LLJ) is also well represented in both RAMS and NARR, but the Baja LLJ and associated gulf surges are not.
Abstract
Fifty-three years of the NCEP–NCAR Reanalysis I are dynamically downscaled using the Regional Atmospheric Modeling System (RAMS) to generate a regional climate model (RCM) climatology of the contiguous United States and Mexico. Data from the RAMS simulations are compared to the recently released North American Regional Reanalysis (NARR), as well as observed precipitation and temperature data. The RAMS simulations show the value added by using a RCM in a process study framework to represent North American summer climate beyond the driving global atmospheric reanalysis. Because of its enhanced representation of the land surface topography, the diurnal cycle of convective rainfall is present. This diurnal cycle largely governs the transitions associated with the evolution of the North American monsoon with regards to rainfall, the surface energy budget, and surface temperature. The lower frequency modes of convective rainfall, though weaker, account for rainfall variability at a remote distance from elevated terrain. As in previous studies with other RCMs, RAMS precipitation is overestimated compared to observations. The Great Plains low-level jet (LLJ) is also well represented in both RAMS and NARR, but the Baja LLJ and associated gulf surges are not.
Abstract
The Global Historical Climate Network version 2 (GHCNv.2) surface temperature dataset is widely used for reconstructions such as the global average surface temperature (GAST) anomaly. Because land use and land cover (LULC) affect temperatures, it is important to examine the spatial distribution and the LULC representation of GHCNv.2 stations. Here, nightlight imagery, two LULC datasets, and a population and cropland historical reconstruction are used to estimate the present and historical worldwide occurrence of LULC types and the number of GHCNv.2 stations within each. Results show that the GHCNv.2 station locations are biased toward urban and cropland (>50% stations versus 18.4% of the world’s land) and past century reclaimed cropland areas (35% stations versus 3.4% land). However, widely occurring LULC such as open shrubland, bare, snow/ice, and evergreen broadleaf forests are underrepresented (14% stations versus 48.1% land), as well as nonurban areas that have remained uncultivated in the past century (14.2% stations versus 43.2% land). Results from the temperature trends over the different landscapes confirm that the temperature trends are different for different LULC and that the GHCNv.2 stations network might be missing on long-term larger positive trends. This opens the possibility that the temperature increases of Earth’s land surface in the last century would be higher than what the GHCNv.2-based GAST analyses report.
Abstract
The Global Historical Climate Network version 2 (GHCNv.2) surface temperature dataset is widely used for reconstructions such as the global average surface temperature (GAST) anomaly. Because land use and land cover (LULC) affect temperatures, it is important to examine the spatial distribution and the LULC representation of GHCNv.2 stations. Here, nightlight imagery, two LULC datasets, and a population and cropland historical reconstruction are used to estimate the present and historical worldwide occurrence of LULC types and the number of GHCNv.2 stations within each. Results show that the GHCNv.2 station locations are biased toward urban and cropland (>50% stations versus 18.4% of the world’s land) and past century reclaimed cropland areas (35% stations versus 3.4% land). However, widely occurring LULC such as open shrubland, bare, snow/ice, and evergreen broadleaf forests are underrepresented (14% stations versus 48.1% land), as well as nonurban areas that have remained uncultivated in the past century (14.2% stations versus 43.2% land). Results from the temperature trends over the different landscapes confirm that the temperature trends are different for different LULC and that the GHCNv.2 stations network might be missing on long-term larger positive trends. This opens the possibility that the temperature increases of Earth’s land surface in the last century would be higher than what the GHCNv.2-based GAST analyses report.
Abstract
Dominant spatiotemporal patterns of precipitation, modeled soil moisture, and vegetation are determined in North America within the recent observational record (late twentieth century onward). These data are from a gridded U.S.–Mexico precipitation product, retrospective long-term integrations of two land surface models, and satellite-derived vegetation greenness. The analysis procedure uses three statistical techniques. First, all the variables are normalized according to the standardized precipitation index procedure. Second, dominant patterns of spatiotemporal variability are determined using multitaper method–singular value decomposition for interannual and longer time scales. The dominant spatiotemporal patterns of precipitation generally conform to known and distinct Pacific SST forcing in the cool and warm seasons. Two specific time scales in precipitation at 9 and 6–7 yr correspond to significant variability in soil moisture and vegetation, respectively. The 9-yr signal is related to precipitation in late fall to early winter, whereas the 6–7-yr signal is related to earlysummer precipitation. Canonical correlation analysis is finally used to confirm that strong covariability between land surface variables and precipitation exists at these specific times of the year. Both signals are strongest in the central and western United States and are consistent with prior global modeling and paleoclimate studies that have investigated drought in North America.
Abstract
Dominant spatiotemporal patterns of precipitation, modeled soil moisture, and vegetation are determined in North America within the recent observational record (late twentieth century onward). These data are from a gridded U.S.–Mexico precipitation product, retrospective long-term integrations of two land surface models, and satellite-derived vegetation greenness. The analysis procedure uses three statistical techniques. First, all the variables are normalized according to the standardized precipitation index procedure. Second, dominant patterns of spatiotemporal variability are determined using multitaper method–singular value decomposition for interannual and longer time scales. The dominant spatiotemporal patterns of precipitation generally conform to known and distinct Pacific SST forcing in the cool and warm seasons. Two specific time scales in precipitation at 9 and 6–7 yr correspond to significant variability in soil moisture and vegetation, respectively. The 9-yr signal is related to precipitation in late fall to early winter, whereas the 6–7-yr signal is related to earlysummer precipitation. Canonical correlation analysis is finally used to confirm that strong covariability between land surface variables and precipitation exists at these specific times of the year. Both signals are strongest in the central and western United States and are consistent with prior global modeling and paleoclimate studies that have investigated drought in North America.
Abstract
Recent numerical modeling studies indicate the importance of radiation in the transformation from a tropical disturbance to a tropical depression, a process known as tropical cyclogenesis. This paper employs a numerical modeling framework to examine the sensitivity to radiation in idealized simulations for different initial vortex strengths, and in doing so highlights when during tropical cyclogenesis radiation is most important. It is shown that all else being equal, a stronger initial vortex reduces the impact that radiation has on accelerating tropical cyclogenesis. We find that radiation’s primary role is to moisten the core of a disturbance through nocturnal differential radiative forcing between the disturbance and its cloud-free surroundings, and after sufficient moistening occurs over a deep layer and the winds are sufficiently strong at the surface, radiation no longer plays as significant a role in tropical cyclogenesis.
Abstract
Recent numerical modeling studies indicate the importance of radiation in the transformation from a tropical disturbance to a tropical depression, a process known as tropical cyclogenesis. This paper employs a numerical modeling framework to examine the sensitivity to radiation in idealized simulations for different initial vortex strengths, and in doing so highlights when during tropical cyclogenesis radiation is most important. It is shown that all else being equal, a stronger initial vortex reduces the impact that radiation has on accelerating tropical cyclogenesis. We find that radiation’s primary role is to moisten the core of a disturbance through nocturnal differential radiative forcing between the disturbance and its cloud-free surroundings, and after sufficient moistening occurs over a deep layer and the winds are sufficiently strong at the surface, radiation no longer plays as significant a role in tropical cyclogenesis.
Abstract
Snow cover can significantly suppress daytime temperatures by increasing the surface albedo and limiting the surface temperature to 0°C. The strength of this effect is dependent upon how well the snow can cover, or mask, the underlying surface. In regions where tall vegetation protrudes through a shallow layer of snow, the temperature-reducing effects of the snow will be suppressed since the protruding vegetation will absorb solar radiation and emit an upward turbulent heat flux. This means that an atmospheric model must have a reasonable representation of the land cover, as well as be able to correctly calculate snow depth, if an accurate simulation of surface heat fluxes, air temperatures, and boundary layer structure is to be made. If too much vegetation protrudes through the snow, then the surface sensible heat flux will be too large and the air temperatures will be too high.
In this study four simulations are run with the Regional Atmospheric Modeling System (RAMS 4.30) for a snow event that occurred in 1988 over the Texas Panhandle. The first simulation, called the control, is run with the most realistic version of the current land cover and the results verified against both ground stations and aircraft data. Simulations 2 and 3 use the default methods of specifying land cover in RAMS 4.29 and RAMS 4.30, respectively. The significance of these variations in land-cover definition is then examined by comparing with the control run. Finally, the last simulation is run with the land cover defined as all short grass, the natural cover for the region. The results of this study indicate that variations in the land-cover specification can lead to differences in sensible heat flux over snow as large as 80 W m−2. These differences in sensible heat flux can then lead to differences in daytime temperatures of as much as 6°C. Also, the height of the afternoon boundary layer can vary by as much as 200–300 m.
In addition, the results suggest that daytime temperatures are cooler over snow in the regions where short grass has been converted to cropland, while they appear to be warmer over regions where shrubs have increased.
Abstract
Snow cover can significantly suppress daytime temperatures by increasing the surface albedo and limiting the surface temperature to 0°C. The strength of this effect is dependent upon how well the snow can cover, or mask, the underlying surface. In regions where tall vegetation protrudes through a shallow layer of snow, the temperature-reducing effects of the snow will be suppressed since the protruding vegetation will absorb solar radiation and emit an upward turbulent heat flux. This means that an atmospheric model must have a reasonable representation of the land cover, as well as be able to correctly calculate snow depth, if an accurate simulation of surface heat fluxes, air temperatures, and boundary layer structure is to be made. If too much vegetation protrudes through the snow, then the surface sensible heat flux will be too large and the air temperatures will be too high.
In this study four simulations are run with the Regional Atmospheric Modeling System (RAMS 4.30) for a snow event that occurred in 1988 over the Texas Panhandle. The first simulation, called the control, is run with the most realistic version of the current land cover and the results verified against both ground stations and aircraft data. Simulations 2 and 3 use the default methods of specifying land cover in RAMS 4.29 and RAMS 4.30, respectively. The significance of these variations in land-cover definition is then examined by comparing with the control run. Finally, the last simulation is run with the land cover defined as all short grass, the natural cover for the region. The results of this study indicate that variations in the land-cover specification can lead to differences in sensible heat flux over snow as large as 80 W m−2. These differences in sensible heat flux can then lead to differences in daytime temperatures of as much as 6°C. Also, the height of the afternoon boundary layer can vary by as much as 200–300 m.
In addition, the results suggest that daytime temperatures are cooler over snow in the regions where short grass has been converted to cropland, while they appear to be warmer over regions where shrubs have increased.
Abstract
The presence of snow and its relationship to surrounding vegetation significantly impacts the surface energy balance. For accurate atmospheric model simulations, the degree to which a snowpack can cover vegetation must be realistically represented. Both vegetation height and snow depth must be reasonably known to determine the amount of masking.
The Regional Atmospheric Modeling System/Land Ecosystem–Atmosphere Feedback, version two (RAMS/ LEAF-2) snow model was modified to simulate snow depth in addition to snow water equivalent and was driven offline with observed atmospheric forcing data. The model was run for five of the Boreal Ecosystem–Atmosphere Study (BOREAS) surface mesonet stations over the 1995/96 winter. The time evolution of simulated snow depth was compared with the observed snow depth. Averaged over the winter, the modeled snow depth at the four low-wind stations was within 0.09 m of the observations, and the average percent error was 27%, while the one wind-blown station was considerably worse. The average depth error at all five stations was ±0.08 m. This is shown to be sufficient to reasonably account for the surface energy balance effects of vegetation protruding through the snow.
Abstract
The presence of snow and its relationship to surrounding vegetation significantly impacts the surface energy balance. For accurate atmospheric model simulations, the degree to which a snowpack can cover vegetation must be realistically represented. Both vegetation height and snow depth must be reasonably known to determine the amount of masking.
The Regional Atmospheric Modeling System/Land Ecosystem–Atmosphere Feedback, version two (RAMS/ LEAF-2) snow model was modified to simulate snow depth in addition to snow water equivalent and was driven offline with observed atmospheric forcing data. The model was run for five of the Boreal Ecosystem–Atmosphere Study (BOREAS) surface mesonet stations over the 1995/96 winter. The time evolution of simulated snow depth was compared with the observed snow depth. Averaged over the winter, the modeled snow depth at the four low-wind stations was within 0.09 m of the observations, and the average percent error was 27%, while the one wind-blown station was considerably worse. The average depth error at all five stations was ±0.08 m. This is shown to be sufficient to reasonably account for the surface energy balance effects of vegetation protruding through the snow.
Abstract
During the twentieth century, the natural landscape of the Florida peninsula was transformed extensively by agriculture, urbanization, and the diversion of surface water features. The purpose of this paper is to present a numerical modeling study in which the possible impacts of this transformation on the warm season climate of the region were investigated. For three separate July–August periods (1973, 1989, and 1994), a pair of simulations was performed with the Regional Atmospheric Modeling System. Within each pair, the simulations differed only in the specification of land-cover class. The two different classes were specified using highly detailed datasets that were constructed to represent pre-1900 natural land cover and 1993 land-use patterns, thus capturing the landscape transformation within each pair of simulations.
When the pre-1900 natural cover was replaced with the 1993 land-use dataset, the simulated spatial patterns of the surface sensible and latent heat flux were altered significantly, resulting in changes in the structure and strength of climatologically persistent, surface-forced mesoscale circulations—particularly the afternoon sea-breeze fronts. This mechanism was associated with marked changes in the spatial distribution of convective rainfall totals over the peninsula. When averaged over the model domain, this redistribution was reflected as an overall decrease in the 2-month precipitation total. In addition, the domain average of the diurnal cycle of 2-m temperature was amplified, with a noted increase in the daytime maximum. These results were consistent among all three simulated periods, and largely unchanged when subjected to a number of model sensitivity factors. Furthermore, the model results are in reasonable agreement with an analysis of observational data that indicates decreasing regional precipitation and increasing daytime maximum temperature during the twentieth century.
These results could have important implications for water resource and land-use management issues in south Florida, including efforts to restore and preserve the natural hydroclimate of the Everglades ecosystem. This study also provides more evidence for the need to consider anthropogenic land-cover change when evaluating climate trends.
Abstract
During the twentieth century, the natural landscape of the Florida peninsula was transformed extensively by agriculture, urbanization, and the diversion of surface water features. The purpose of this paper is to present a numerical modeling study in which the possible impacts of this transformation on the warm season climate of the region were investigated. For three separate July–August periods (1973, 1989, and 1994), a pair of simulations was performed with the Regional Atmospheric Modeling System. Within each pair, the simulations differed only in the specification of land-cover class. The two different classes were specified using highly detailed datasets that were constructed to represent pre-1900 natural land cover and 1993 land-use patterns, thus capturing the landscape transformation within each pair of simulations.
When the pre-1900 natural cover was replaced with the 1993 land-use dataset, the simulated spatial patterns of the surface sensible and latent heat flux were altered significantly, resulting in changes in the structure and strength of climatologically persistent, surface-forced mesoscale circulations—particularly the afternoon sea-breeze fronts. This mechanism was associated with marked changes in the spatial distribution of convective rainfall totals over the peninsula. When averaged over the model domain, this redistribution was reflected as an overall decrease in the 2-month precipitation total. In addition, the domain average of the diurnal cycle of 2-m temperature was amplified, with a noted increase in the daytime maximum. These results were consistent among all three simulated periods, and largely unchanged when subjected to a number of model sensitivity factors. Furthermore, the model results are in reasonable agreement with an analysis of observational data that indicates decreasing regional precipitation and increasing daytime maximum temperature during the twentieth century.
These results could have important implications for water resource and land-use management issues in south Florida, including efforts to restore and preserve the natural hydroclimate of the Everglades ecosystem. This study also provides more evidence for the need to consider anthropogenic land-cover change when evaluating climate trends.
Abstract
A cloud-resolving model coupled to an ocean model with high vertical resolution is used to investigate air–sea interactions in 10-day long simulations. Modeled fields showed good agreement with two different convective regimes during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Research Experiment (TOGA COARE) Intensive Observing Period. The model simulates the formation of precipitation-produced, stable freshwater lenses at the top of the ocean mixed layer, with a variety of horizontal dimensions and lifetimes. The simulated fresh anomalies show realistic features, such as a positive correlation between salinity and temperature, the development of a surface jet in the direction of the wind, and, as a consequence, downwelling (upwelling) on its downwind (upwind) edge. The dataset generated by the coupled model is used to evaluate the contribution from several factors (ocean currents, gustiness, and correlations between wind speed and air temperature, wind speed and water vapor mixing ratio, and wind speed and SST) to the surface heat fluxes. Gustiness was shown to be a major contribution to the simulated surface heat fluxes, especially when convection is active. In a multiday average, the contributions from the other effects (currents and wind speed–air temperature, wind speed–water vapor mixing ratio, and wind speed–SST correlations) are small; however, they cannot be neglected under certain circumstances.
Abstract
A cloud-resolving model coupled to an ocean model with high vertical resolution is used to investigate air–sea interactions in 10-day long simulations. Modeled fields showed good agreement with two different convective regimes during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Research Experiment (TOGA COARE) Intensive Observing Period. The model simulates the formation of precipitation-produced, stable freshwater lenses at the top of the ocean mixed layer, with a variety of horizontal dimensions and lifetimes. The simulated fresh anomalies show realistic features, such as a positive correlation between salinity and temperature, the development of a surface jet in the direction of the wind, and, as a consequence, downwelling (upwelling) on its downwind (upwind) edge. The dataset generated by the coupled model is used to evaluate the contribution from several factors (ocean currents, gustiness, and correlations between wind speed and air temperature, wind speed and water vapor mixing ratio, and wind speed and SST) to the surface heat fluxes. Gustiness was shown to be a major contribution to the simulated surface heat fluxes, especially when convection is active. In a multiday average, the contributions from the other effects (currents and wind speed–air temperature, wind speed–water vapor mixing ratio, and wind speed–SST correlations) are small; however, they cannot be neglected under certain circumstances.
