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- Author or Editor: Guiling Wang x
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Abstract
Representation of the canopy hydrological processes has been challenging in land surface modeling due to the subgrid heterogeneity in both precipitation and surface characteristics. The Shuttleworth dynamic–statistical method is widely used to represent the impact of the precipitation subgrid variability on canopy hydrological processes but shows unwanted sensitivity to temporal resolution when implemented into land surface models. This paper presents a canopy hydrology scheme that is robust at different temporal resolutions. This scheme is devised by applying two physically based treatments to the Shuttleworth scheme: 1) the canopy hydrological processes within the rain-covered area are treated separately from those within the nonrain area, and the scheme tracks the relative rain location between adjacent time steps; and 2) within the rain-covered area, the canopy interception is so determined as to sustain the potential evaporation from the wetted canopy or is equal to precipitation, whichever is less, to maintain somewhat wet canopy during any rainy time step. When applied to the Amazon region, the new scheme establishes interception loss ratios of 0.3 at a 10-min time step and 0.23 at a 2-h time step. Compared to interception loss ratios of 0.45 and 0.09 at the corresponding time steps established by the original Shuttleworth scheme, the new scheme is much more stable under different temporal resolutions.
Abstract
Representation of the canopy hydrological processes has been challenging in land surface modeling due to the subgrid heterogeneity in both precipitation and surface characteristics. The Shuttleworth dynamic–statistical method is widely used to represent the impact of the precipitation subgrid variability on canopy hydrological processes but shows unwanted sensitivity to temporal resolution when implemented into land surface models. This paper presents a canopy hydrology scheme that is robust at different temporal resolutions. This scheme is devised by applying two physically based treatments to the Shuttleworth scheme: 1) the canopy hydrological processes within the rain-covered area are treated separately from those within the nonrain area, and the scheme tracks the relative rain location between adjacent time steps; and 2) within the rain-covered area, the canopy interception is so determined as to sustain the potential evaporation from the wetted canopy or is equal to precipitation, whichever is less, to maintain somewhat wet canopy during any rainy time step. When applied to the Amazon region, the new scheme establishes interception loss ratios of 0.3 at a 10-min time step and 0.23 at a 2-h time step. Compared to interception loss ratios of 0.45 and 0.09 at the corresponding time steps established by the original Shuttleworth scheme, the new scheme is much more stable under different temporal resolutions.
Abstract
Observational evidence has demonstrated that heat extremes consistently coincide with droughts in tropical South America. However, the underlying causes and associated physical mechanisms remain relatively unexplored. In this study, we employ numerical experiments using CESM2 with prescribed oceanic forcing to assess how extreme soil temperature and moisture affect subsequent hydrometeorological conditions during the pre-monsoon season in tropical South America, a season when land can play an important role in regional climate. The results reveal a different diurnal pattern in the strength of the surface air temperature response, with a stronger response at night to soil temperature anomalies and a stronger response at noon to soil moisture anomalies. Elevated initial soil temperature or reduced initial soil moisture tends to yield warmer and drier hydrometeorological conditions and increase the occurrence of both drought and heat extremes, with stronger and longer-lasting responses to soil moisture anomalies (than soil temperature anomalies). The simulated responses to initial soil moisture anomalies alone closely resemble those in the experiment with combined soil temperature and moisture anomalies. These findings underscore the dominant role of soil moisture feedback in shaping compound drought-heat extremes, in regions where evaporation plays a significant role, highlighting its importance in the modeling and prediction of hydrometeorological extremes.
Abstract
Observational evidence has demonstrated that heat extremes consistently coincide with droughts in tropical South America. However, the underlying causes and associated physical mechanisms remain relatively unexplored. In this study, we employ numerical experiments using CESM2 with prescribed oceanic forcing to assess how extreme soil temperature and moisture affect subsequent hydrometeorological conditions during the pre-monsoon season in tropical South America, a season when land can play an important role in regional climate. The results reveal a different diurnal pattern in the strength of the surface air temperature response, with a stronger response at night to soil temperature anomalies and a stronger response at noon to soil moisture anomalies. Elevated initial soil temperature or reduced initial soil moisture tends to yield warmer and drier hydrometeorological conditions and increase the occurrence of both drought and heat extremes, with stronger and longer-lasting responses to soil moisture anomalies (than soil temperature anomalies). The simulated responses to initial soil moisture anomalies alone closely resemble those in the experiment with combined soil temperature and moisture anomalies. These findings underscore the dominant role of soil moisture feedback in shaping compound drought-heat extremes, in regions where evaporation plays a significant role, highlighting its importance in the modeling and prediction of hydrometeorological extremes.
Abstract
To investigate the impact of anomalous soil moisture conditions on subsequent precipitation over North America, a series of numerical experiments is performed using a modified version of the Community Atmosphere Model version 3 and the Community Land Model version 3 (CAM3–CLM3). First, the mechanisms underlying the impact of spring and summer soil moisture on subsequent precipitation are examined based on simulations starting on 1 April and 1 June, respectively. How the response of precipitation to initial soil moisture anomalies depends on the characteristics of such anomalies, including the timing, magnitude, spatial coverage, and vertical depth, is then investigated. There are five main findings. First, the impact of spring soil moisture anomalies is not evident until early summer although their impact on the large-scale circulation results in slight changes in precipitation during spring. Second, precipitation increases with initial soil moisture almost linearly within a certain range of soil moisture. Beyond this range, precipitation is less responsive. Third, during the first month following the onset of summer soil moisture anomalies, the precipitation response to wet anomalies is larger in magnitude than that to dry anomalies. However, the resulting wet anomalies in precipitation quickly dissipate within a month or so, while the resulting dry anomalies in precipitation remain at a considerable magnitude for a longer period. Consistently, wet spring anomalies are likely to be ameliorated before summer, and thus have a smaller impact (in magnitude) on summer precipitation than dry spring anomalies. Fourth, soil moisture anomalies of smaller spatial coverage lead to precipitation anomalies that are smaller and less persistent, compared to anomalies at the continental scale. Finally, anomalies in shallow soil can persist long enough to influence the subsequent precipitation at the seasonal time scale. Dry anomalies in deep soils last much longer than those in shallow soils.
Abstract
To investigate the impact of anomalous soil moisture conditions on subsequent precipitation over North America, a series of numerical experiments is performed using a modified version of the Community Atmosphere Model version 3 and the Community Land Model version 3 (CAM3–CLM3). First, the mechanisms underlying the impact of spring and summer soil moisture on subsequent precipitation are examined based on simulations starting on 1 April and 1 June, respectively. How the response of precipitation to initial soil moisture anomalies depends on the characteristics of such anomalies, including the timing, magnitude, spatial coverage, and vertical depth, is then investigated. There are five main findings. First, the impact of spring soil moisture anomalies is not evident until early summer although their impact on the large-scale circulation results in slight changes in precipitation during spring. Second, precipitation increases with initial soil moisture almost linearly within a certain range of soil moisture. Beyond this range, precipitation is less responsive. Third, during the first month following the onset of summer soil moisture anomalies, the precipitation response to wet anomalies is larger in magnitude than that to dry anomalies. However, the resulting wet anomalies in precipitation quickly dissipate within a month or so, while the resulting dry anomalies in precipitation remain at a considerable magnitude for a longer period. Consistently, wet spring anomalies are likely to be ameliorated before summer, and thus have a smaller impact (in magnitude) on summer precipitation than dry spring anomalies. Fourth, soil moisture anomalies of smaller spatial coverage lead to precipitation anomalies that are smaller and less persistent, compared to anomalies at the continental scale. Finally, anomalies in shallow soil can persist long enough to influence the subsequent precipitation at the seasonal time scale. Dry anomalies in deep soils last much longer than those in shallow soils.
Abstract
Previous studies support a positive soil moisture–precipitation feedback over a major fraction of North America; that is, initial soil moisture anomalies lead to precipitation anomalies of the same sign. To investigate how vegetation feedback modifies the sensitivity of precipitation to initial soil moisture conditions over North America, a series of ensemble simulations are carried out using a modified version of the coupled Community Atmosphere Model–Community Land Model (CAM–CLM). The modified CLM includes a predictive vegetation phenology scheme so that the coupled model can represent interactions between soil moisture, vegetation, and precipitation at the seasonal time scale. The focus of this study is on how the impact of vegetation feedback varies with the timing and direction of initial soil moisture anomalies. During summer, wet soil moisture anomalies lead to increase in leaf area index and, consequently, increase in evapotranspiration and surface heating. Such increases tend to favor precipitation. Therefore, under wet summer soil moisture anomalies, the soil moisture–induced precipitation increase is reinforced when predictive phenology is included. That is, the vegetation feedback to precipitation is positive. The response of vegetation to dry soil moisture anomalies in the summer months, however, is not significant due probably to a dry bias in the model, so the resulting vegetation feedback on precipitation is minimal. To soil moisture anomalies in spring, the leaf area index (LAI) response is delayed since LAI is still limited by cold temperature at that time of the year. During the summer following wet spring soil moisture anomalies, vegetation feedback is negative; that is, it tends to suppress the response of precipitation through the depletion of soil moisture by vegetation.
Abstract
Previous studies support a positive soil moisture–precipitation feedback over a major fraction of North America; that is, initial soil moisture anomalies lead to precipitation anomalies of the same sign. To investigate how vegetation feedback modifies the sensitivity of precipitation to initial soil moisture conditions over North America, a series of ensemble simulations are carried out using a modified version of the coupled Community Atmosphere Model–Community Land Model (CAM–CLM). The modified CLM includes a predictive vegetation phenology scheme so that the coupled model can represent interactions between soil moisture, vegetation, and precipitation at the seasonal time scale. The focus of this study is on how the impact of vegetation feedback varies with the timing and direction of initial soil moisture anomalies. During summer, wet soil moisture anomalies lead to increase in leaf area index and, consequently, increase in evapotranspiration and surface heating. Such increases tend to favor precipitation. Therefore, under wet summer soil moisture anomalies, the soil moisture–induced precipitation increase is reinforced when predictive phenology is included. That is, the vegetation feedback to precipitation is positive. The response of vegetation to dry soil moisture anomalies in the summer months, however, is not significant due probably to a dry bias in the model, so the resulting vegetation feedback on precipitation is minimal. To soil moisture anomalies in spring, the leaf area index (LAI) response is delayed since LAI is still limited by cold temperature at that time of the year. During the summer following wet spring soil moisture anomalies, vegetation feedback is negative; that is, it tends to suppress the response of precipitation through the depletion of soil moisture by vegetation.
Abstract
This study examines the land–atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)–Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)–Community Land Model, version 3 (CLM3) and CAM4–CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land–atmosphere coupling; 2) soil moisture–precipitation coupling is weaker than soil moisture–surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4–CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4–CLM4 is weaker than in CAM3–CLM3, which is further supported by Global Land–Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM–CLM models do not seem to overestimate the land–atmosphere coupling strength.
Abstract
This study examines the land–atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)–Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)–Community Land Model, version 3 (CLM3) and CAM4–CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land–atmosphere coupling; 2) soil moisture–precipitation coupling is weaker than soil moisture–surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4–CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4–CLM4 is weaker than in CAM3–CLM3, which is further supported by Global Land–Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM–CLM models do not seem to overestimate the land–atmosphere coupling strength.
Abstract
This study examines the impact of sea surface temperature (SST) and soil moisture on summer precipitation over two regions of the United States (the upper Mississippi River basin and the Great Plains) based on data from observation and observation-forced model simulations (in the case of soil moisture). Results from SST–precipitation correlation analysis show that spatially averaged SST of identified oceanic areas are better predictors than derived SST patterns from the EOF analysis and that both predictors are strongly associated with the Pacific Ocean. Results from conditioned soil moisture–precipitation correlation analysis show that the impact of soil moisture on precipitation differs between the outer-quartiles years (with summer precipitation amount in the first and fourth quartiles) and inner-quartiles years (with summer precipitation amount in the second and third quartiles), and also between the high- and low-skill SST years (categorized according to the skill of SST-based precipitation prediction). Specifically, the soil moisture–precipitation feedback is more likely to be positive and significant in the outer-quartiles years and in the years when the skill of precipitation prediction based on SST alone is low. This study indicates that soil moisture should be included as a useful predictor in precipitation prediction, and the resulting improvement in prediction skills will be especially substantial during years of large precipitation anomalies. It also demonstrates the complexity of the impact of SST and soil moisture on precipitation, and underlines the important complementary roles both SST and soil moisture play in determining precipitation.
Abstract
This study examines the impact of sea surface temperature (SST) and soil moisture on summer precipitation over two regions of the United States (the upper Mississippi River basin and the Great Plains) based on data from observation and observation-forced model simulations (in the case of soil moisture). Results from SST–precipitation correlation analysis show that spatially averaged SST of identified oceanic areas are better predictors than derived SST patterns from the EOF analysis and that both predictors are strongly associated with the Pacific Ocean. Results from conditioned soil moisture–precipitation correlation analysis show that the impact of soil moisture on precipitation differs between the outer-quartiles years (with summer precipitation amount in the first and fourth quartiles) and inner-quartiles years (with summer precipitation amount in the second and third quartiles), and also between the high- and low-skill SST years (categorized according to the skill of SST-based precipitation prediction). Specifically, the soil moisture–precipitation feedback is more likely to be positive and significant in the outer-quartiles years and in the years when the skill of precipitation prediction based on SST alone is low. This study indicates that soil moisture should be included as a useful predictor in precipitation prediction, and the resulting improvement in prediction skills will be especially substantial during years of large precipitation anomalies. It also demonstrates the complexity of the impact of SST and soil moisture on precipitation, and underlines the important complementary roles both SST and soil moisture play in determining precipitation.
Abstract
This paper presents a new index to quantify the strength of soil moisture–precipitation coupling in AGCMs and explores how the soil moisture–precipitation coupling in Community Atmosphere Model version 3 (CAM3)–Community Land Model version 3 (CAM3–CLM3) responds to parameterization-induced surface water budget changes. Specifically, this study (a) compares the regions of strong coupling identified by the newly proposed index and the index currently used in the Global Land–Atmosphere Coupling Experiment (GLACE); (b) examines how the surface water budget changes influence the strength of soil moisture–precipitation coupling as measured by the two indexes, respectively; and (c) examines how these changes influence the memory of the coupled land–atmosphere system as measured by the correlation between soil moisture and subsequent precipitation. The new index and the GLACE index are consistent in identifying central North America and West Africa as major regions of strong coupling during June–August (JJA). However, in some areas of western Europe and of subtropical South America where the GLACE index is low, the new index suggests a modest significant coupling during JJA. In response to the surface water budget changes that presumably favor a stronger soil moisture–precipitation coupling, the new index increases, but the GLACE index decreases in a majority of the regions of modest-to-strong coupling, although both show some mixed response. Changes in the land–atmosphere system memory suggest an increase of coupling strength, consistent with results from the new index. The strong dependence of the GLACE index on the relative importance of atmospheric internal variability is identified as a potential cause for the differences between the two indexes. The two indexes emphasize different aspects of soil moisture–precipitation coupling, and one might be more suitable than the other depending on the purpose of individual studies.
Abstract
This paper presents a new index to quantify the strength of soil moisture–precipitation coupling in AGCMs and explores how the soil moisture–precipitation coupling in Community Atmosphere Model version 3 (CAM3)–Community Land Model version 3 (CAM3–CLM3) responds to parameterization-induced surface water budget changes. Specifically, this study (a) compares the regions of strong coupling identified by the newly proposed index and the index currently used in the Global Land–Atmosphere Coupling Experiment (GLACE); (b) examines how the surface water budget changes influence the strength of soil moisture–precipitation coupling as measured by the two indexes, respectively; and (c) examines how these changes influence the memory of the coupled land–atmosphere system as measured by the correlation between soil moisture and subsequent precipitation. The new index and the GLACE index are consistent in identifying central North America and West Africa as major regions of strong coupling during June–August (JJA). However, in some areas of western Europe and of subtropical South America where the GLACE index is low, the new index suggests a modest significant coupling during JJA. In response to the surface water budget changes that presumably favor a stronger soil moisture–precipitation coupling, the new index increases, but the GLACE index decreases in a majority of the regions of modest-to-strong coupling, although both show some mixed response. Changes in the land–atmosphere system memory suggest an increase of coupling strength, consistent with results from the new index. The strong dependence of the GLACE index on the relative importance of atmospheric internal variability is identified as a potential cause for the differences between the two indexes. The two indexes emphasize different aspects of soil moisture–precipitation coupling, and one might be more suitable than the other depending on the purpose of individual studies.
Abstract
Using the Connecticut River basin as an example, this study assesses the extent to which remote sensing data can help improve hydrological modeling and how it may influence projected future hydrological trends. The dynamic leaf area index (LAI) derived from satellite remote sensing was incorporated into the Variable Infiltration Capacity model (VIC) to enable an interannually varying seasonal cycle of vegetation (VICVEG); the evapotranspiration (ET) data based on remote sensing were combined with ET from a default VIC simulation to develop a simple bias-correction algorithm, and the simulation was then repeated with the bias-corrected ET replacing the simulated ET in the model (VICET). VICET performs significantly better in simulating the temporal variability of river discharge at daily, biweekly, monthly, and seasonal time scales, while VICVEG better captures the interannual variability of discharge, particularly in the winter and spring, and shows slight improvements to soil moisture estimates. The methodology of incorporating ET data into VIC as a bias-correction tool also influences the modeled future hydrological trends. Compared to the default VIC, VICET portrays a future characterized by greater drought risk and a stronger decreasing trend of minimum river flows. Integrating remote sensing data with hydrological modeling helps characterize the range of model-related uncertainties and more accurately reconstruct historic river flow estimates, leading to a better understanding and prediction of hydrological response to future climate changes.
Abstract
Using the Connecticut River basin as an example, this study assesses the extent to which remote sensing data can help improve hydrological modeling and how it may influence projected future hydrological trends. The dynamic leaf area index (LAI) derived from satellite remote sensing was incorporated into the Variable Infiltration Capacity model (VIC) to enable an interannually varying seasonal cycle of vegetation (VICVEG); the evapotranspiration (ET) data based on remote sensing were combined with ET from a default VIC simulation to develop a simple bias-correction algorithm, and the simulation was then repeated with the bias-corrected ET replacing the simulated ET in the model (VICET). VICET performs significantly better in simulating the temporal variability of river discharge at daily, biweekly, monthly, and seasonal time scales, while VICVEG better captures the interannual variability of discharge, particularly in the winter and spring, and shows slight improvements to soil moisture estimates. The methodology of incorporating ET data into VIC as a bias-correction tool also influences the modeled future hydrological trends. Compared to the default VIC, VICET portrays a future characterized by greater drought risk and a stronger decreasing trend of minimum river flows. Integrating remote sensing data with hydrological modeling helps characterize the range of model-related uncertainties and more accurately reconstruct historic river flow estimates, leading to a better understanding and prediction of hydrological response to future climate changes.
Abstract
This study investigates the land–atmosphere coupling strength during summer over the United States using the Regional Climate Model version 4 (RegCM4)–Community Land Model version 3.5 (CLM3.5). First, a 10-yr simulation driven with reanalysis lateral boundary conditions (LBCs) is conducted to evaluate the model performance. The model is then used to quantify the land–atmosphere coupling strength, predictability, and added forecast skill (for precipitation and 2-m air temperature) attributed to realistic land surface initialization following the Global Land–Atmosphere Coupling Experiment (GLACE) approaches. Similar to previous GLACE results using global climate models (GCMs), GLACE-type experiments with RegCM4 identify the central United States as a region of strong land–atmosphere coupling, with soil moisture–temperature coupling being stronger than soil moisture–precipitation coupling, and confirm that realistic soil moisture initialization is more promising in improving temperature forecasts than precipitation forecasts. At a 1–15-day lead, the added forecast skill reflects predictability (or land–atmosphere coupling strength) indicating that that model can capture the realistic land–atmosphere coupling at a short time scale. However, at a 16–30-day lead, predictability cannot translate to added forecast skill, implying that the coupling at the longer time scale may not be represented well in the model. In addition, comparison of results from GLACE2-type experiments with RegCM4 driven by reanalysis LBCs and those driven by GCM LBCs suggest that the intrinsic land–atmosphere coupling strength within the regional model is the dominant factor for the added forecast skill at a 1–15-day lead, while the impact of LBCs from the GCM may play a dominant role in determining the signal of added forecast skill in the regional model at a 16–30-day lead. It demonstrates the complexities of using regional climate model for GLACE-type studies.
Abstract
This study investigates the land–atmosphere coupling strength during summer over the United States using the Regional Climate Model version 4 (RegCM4)–Community Land Model version 3.5 (CLM3.5). First, a 10-yr simulation driven with reanalysis lateral boundary conditions (LBCs) is conducted to evaluate the model performance. The model is then used to quantify the land–atmosphere coupling strength, predictability, and added forecast skill (for precipitation and 2-m air temperature) attributed to realistic land surface initialization following the Global Land–Atmosphere Coupling Experiment (GLACE) approaches. Similar to previous GLACE results using global climate models (GCMs), GLACE-type experiments with RegCM4 identify the central United States as a region of strong land–atmosphere coupling, with soil moisture–temperature coupling being stronger than soil moisture–precipitation coupling, and confirm that realistic soil moisture initialization is more promising in improving temperature forecasts than precipitation forecasts. At a 1–15-day lead, the added forecast skill reflects predictability (or land–atmosphere coupling strength) indicating that that model can capture the realistic land–atmosphere coupling at a short time scale. However, at a 16–30-day lead, predictability cannot translate to added forecast skill, implying that the coupling at the longer time scale may not be represented well in the model. In addition, comparison of results from GLACE2-type experiments with RegCM4 driven by reanalysis LBCs and those driven by GCM LBCs suggest that the intrinsic land–atmosphere coupling strength within the regional model is the dominant factor for the added forecast skill at a 1–15-day lead, while the impact of LBCs from the GCM may play a dominant role in determining the signal of added forecast skill in the regional model at a 16–30-day lead. It demonstrates the complexities of using regional climate model for GLACE-type studies.
Abstract
Precipitation exhibits significant spatial variability at scales much smaller than the typical size of climate model grid cells. Neglecting such subgrid-scale variability in climate models causes unrealistic representation of land–atmosphere flux exchanges. It is especially problematic over densely vegetated land. This paper addresses this issue by incorporating satellite-based precipitation observations into the representation of canopy interception processes in land surface models. Rainfall data derived from passive microwave (PM) observations are used to obtain realistic estimates of 1) conditional mean rain rates, which together with the modeled rain rate are used to estimate the rainfall coverage fraction at each model grid cell in this study, and 2) the probability density function (pdf) of rain rates within the rain-covered areas. Both of these properties significantly impact the land–atmosphere water vapor exchanges. Based on the above information, a statistical–dynamical approach is taken to incorporate the representation of precipitation subgrid variability into canopy interception processes in land surface models. The results reveal that incorporation of precipitation subgrid variability significantly alters the partitioning between runoff and total evapotranspiration as well as the partitioning among the three components of evapotranspiration (i.e., canopy interception loss, ground evaporation, and plant transpiration). This further influences soil water, surface temperature, and surface heat fluxes. It is shown that the choice of the rain-rate pdf within rain-covered areas has an effect on the model simulation of land–atmosphere flux exchanges. This study demonstrates that land surface and climate models can substantially benefit from the fine-resolution remotely sensed rainfall observations.
Abstract
Precipitation exhibits significant spatial variability at scales much smaller than the typical size of climate model grid cells. Neglecting such subgrid-scale variability in climate models causes unrealistic representation of land–atmosphere flux exchanges. It is especially problematic over densely vegetated land. This paper addresses this issue by incorporating satellite-based precipitation observations into the representation of canopy interception processes in land surface models. Rainfall data derived from passive microwave (PM) observations are used to obtain realistic estimates of 1) conditional mean rain rates, which together with the modeled rain rate are used to estimate the rainfall coverage fraction at each model grid cell in this study, and 2) the probability density function (pdf) of rain rates within the rain-covered areas. Both of these properties significantly impact the land–atmosphere water vapor exchanges. Based on the above information, a statistical–dynamical approach is taken to incorporate the representation of precipitation subgrid variability into canopy interception processes in land surface models. The results reveal that incorporation of precipitation subgrid variability significantly alters the partitioning between runoff and total evapotranspiration as well as the partitioning among the three components of evapotranspiration (i.e., canopy interception loss, ground evaporation, and plant transpiration). This further influences soil water, surface temperature, and surface heat fluxes. It is shown that the choice of the rain-rate pdf within rain-covered areas has an effect on the model simulation of land–atmosphere flux exchanges. This study demonstrates that land surface and climate models can substantially benefit from the fine-resolution remotely sensed rainfall observations.
