Browse
Abstract
Anthropogenic climate change–induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the Variable Infiltration Capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a testbed ensemble streamflow prediction system by treating VIC-simulated snow water equivalent (SWE) as a known predictor and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041–70 and 2071–2100 future periods against the1981–2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability.
Significance Statement
The purpose of this study is to evaluate potential changes in seasonal streamflow predictability in relation to snowpack change under future climate. This is highly relevant because snowpack storage provides a means of predicting available freshet water supply, as well as peak flow events in cold regions. We use a machine learning model as an emulator of a hydrologic model in a testbed ensemble prediction system. Our results provide insights on hydroclimatic controls and interactions that affect future streamflow predictability across two river basins in western Canada. We conclude that besides snowpack, predictability depends on a number of other factors (basin/subbasin characteristics, streamflow variables, and future periods), and the loss of snowpack alone is not a sufficient condition for the reduction in streamflow predictability.
Abstract
Anthropogenic climate change–induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the Variable Infiltration Capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a testbed ensemble streamflow prediction system by treating VIC-simulated snow water equivalent (SWE) as a known predictor and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041–70 and 2071–2100 future periods against the1981–2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability.
Significance Statement
The purpose of this study is to evaluate potential changes in seasonal streamflow predictability in relation to snowpack change under future climate. This is highly relevant because snowpack storage provides a means of predicting available freshet water supply, as well as peak flow events in cold regions. We use a machine learning model as an emulator of a hydrologic model in a testbed ensemble prediction system. Our results provide insights on hydroclimatic controls and interactions that affect future streamflow predictability across two river basins in western Canada. We conclude that besides snowpack, predictability depends on a number of other factors (basin/subbasin characteristics, streamflow variables, and future periods), and the loss of snowpack alone is not a sufficient condition for the reduction in streamflow predictability.
Abstract
Urbanization effects on rainfall induced by landfalling tropical cyclones have rarely been studied. Here high-resolution numerical simulations with the Weather Research and Forecasting/Noah/single-layer urban canopy model system (WRF/SLUCM) are conducted to investigate impacts of urban land cover and building heights on heavy rainfall induced by landfalling Typhoon Lekima (2019) over the megacity Shanghai. The default single urban category in WRF was updated to a new land cover data with three urban categories. Results indicate that WRF/SLUCM captures the typhoon intensity, track, and total rainfall amount quite well. Urbanization has a small positive effect on rainfall amount for this event. However, urbanization has a significant impact on the spatial distribution of the accumulated rainfall with enhancement not confined over the urban area but mainly to the southwest of Shanghai possibly due to the changes of the typhoon tracks. With the impact of Typhoon Lekima, the urban heat island disappears, indicating that the thermal effect of urbanization has limited influence on the rainfall processes. The model performance is very sensitive to the building height. More realistic building height values can noticeably improve simulations of the diurnal patterns of rainfall, urban heat island, and the urban wind speed stilling effect. With the rising of building heights, the surface frictional dynamic effect and vertical uplift is enhanced, but seems not enough to evidently intensify the rainfall. The simulated lower-level large moisture flux convergence corresponds well to rainfall peaks. This study has important scientific significance for the accuracy of rainfall forecasts of landfalling typhoons and disaster mitigation in cities.
Abstract
Urbanization effects on rainfall induced by landfalling tropical cyclones have rarely been studied. Here high-resolution numerical simulations with the Weather Research and Forecasting/Noah/single-layer urban canopy model system (WRF/SLUCM) are conducted to investigate impacts of urban land cover and building heights on heavy rainfall induced by landfalling Typhoon Lekima (2019) over the megacity Shanghai. The default single urban category in WRF was updated to a new land cover data with three urban categories. Results indicate that WRF/SLUCM captures the typhoon intensity, track, and total rainfall amount quite well. Urbanization has a small positive effect on rainfall amount for this event. However, urbanization has a significant impact on the spatial distribution of the accumulated rainfall with enhancement not confined over the urban area but mainly to the southwest of Shanghai possibly due to the changes of the typhoon tracks. With the impact of Typhoon Lekima, the urban heat island disappears, indicating that the thermal effect of urbanization has limited influence on the rainfall processes. The model performance is very sensitive to the building height. More realistic building height values can noticeably improve simulations of the diurnal patterns of rainfall, urban heat island, and the urban wind speed stilling effect. With the rising of building heights, the surface frictional dynamic effect and vertical uplift is enhanced, but seems not enough to evidently intensify the rainfall. The simulated lower-level large moisture flux convergence corresponds well to rainfall peaks. This study has important scientific significance for the accuracy of rainfall forecasts of landfalling typhoons and disaster mitigation in cities.
Abstract
Increasingly, satellite-derived rainfall data are used for climate research and action in Africa. In this study, we use 6 years of rain gauge data from 596 stations operated by the Trans-African Hydrometeorological Observatory (TAHMO) to validate three gauge-calibrated satellite rainfall products—CHIRPS, TAMSAT, and GSMaP_wGauge—and one satellite-only rainfall product, GSMaP. Validations are stratified to evaluate performance across the continent and in East Africa, southern Africa, and West Africa at daily, pentadal, and monthly time scales. For daily mean rainfall over Africa, CHIRPS has the highest bias at 15.5% (0.5 mm) whereas GSMaP_wGauge has the lowest bias at 0.02 mm (0.7%). We find higher daily rainfall event detection scores in the GSMaP products than in CHIRPS or TAMSAT. Generally, for every two rainfall events predicted by CHIRPS and TAMSAT, the GSMaP products predict three or more events. The highest mean monthly biases are produced by CHIRPS in East Africa (29%; wet bias of 26.3 mm), TAMSAT in southern Africa (13%; dry bias of 10.4 mm), and GSMaP in West Africa (23%; wet bias of 19.6 mm). Considerable biases in seasonal rainfall are observed in all subregions for every satellite product. There is an increase of 0.6–1.3 mm in satellite rainfall RMSE for a 1-km increase in elevation revealing the influence of elevation on rainfall estimation by satellite models. Overall, satellite-derived rainfall products have notable errors, while GSMaP products produce comparable or better results at multiple time scales relative to CHIRPS and TAMSAT.
Abstract
Increasingly, satellite-derived rainfall data are used for climate research and action in Africa. In this study, we use 6 years of rain gauge data from 596 stations operated by the Trans-African Hydrometeorological Observatory (TAHMO) to validate three gauge-calibrated satellite rainfall products—CHIRPS, TAMSAT, and GSMaP_wGauge—and one satellite-only rainfall product, GSMaP. Validations are stratified to evaluate performance across the continent and in East Africa, southern Africa, and West Africa at daily, pentadal, and monthly time scales. For daily mean rainfall over Africa, CHIRPS has the highest bias at 15.5% (0.5 mm) whereas GSMaP_wGauge has the lowest bias at 0.02 mm (0.7%). We find higher daily rainfall event detection scores in the GSMaP products than in CHIRPS or TAMSAT. Generally, for every two rainfall events predicted by CHIRPS and TAMSAT, the GSMaP products predict three or more events. The highest mean monthly biases are produced by CHIRPS in East Africa (29%; wet bias of 26.3 mm), TAMSAT in southern Africa (13%; dry bias of 10.4 mm), and GSMaP in West Africa (23%; wet bias of 19.6 mm). Considerable biases in seasonal rainfall are observed in all subregions for every satellite product. There is an increase of 0.6–1.3 mm in satellite rainfall RMSE for a 1-km increase in elevation revealing the influence of elevation on rainfall estimation by satellite models. Overall, satellite-derived rainfall products have notable errors, while GSMaP products produce comparable or better results at multiple time scales relative to CHIRPS and TAMSAT.
Abstract
We perform a worldwide analysis of the raindrop size distribution using 166 disdrometer datasets from 76 distinct sites for a total of 1 527 963 one-minute drop counts, 428 410 two-minute drop counts, and a total of 988 922 720 drops. Following data science tenets, we adopt a functional-agnostic description of the raindrop size distribution. In this way, we uncover the presence of an invariant structure of statistical relationship among the distribution parameters, not depending on location, synoptic origin, or type of disdrometer. The features of this structure are 1) count–shape independence (there is no dependence between the drop count N and the shape of raindrop spectra), 2) mean–skewness prominence (the variability of the shape of raindrop spectra can be fully captured by its mean μ and skewness γ), and 3) mean–skewness invariant parameterization [we derive empirical invariant functional forms expressing all other shape describing parameters in terms of the free parameters (μ, γ)]. The presented analysis reveals the global and local properties of the raindrop size distribution offering a coherent and universally applicable methodology to describe the raindrop size distribution.
Abstract
We perform a worldwide analysis of the raindrop size distribution using 166 disdrometer datasets from 76 distinct sites for a total of 1 527 963 one-minute drop counts, 428 410 two-minute drop counts, and a total of 988 922 720 drops. Following data science tenets, we adopt a functional-agnostic description of the raindrop size distribution. In this way, we uncover the presence of an invariant structure of statistical relationship among the distribution parameters, not depending on location, synoptic origin, or type of disdrometer. The features of this structure are 1) count–shape independence (there is no dependence between the drop count N and the shape of raindrop spectra), 2) mean–skewness prominence (the variability of the shape of raindrop spectra can be fully captured by its mean μ and skewness γ), and 3) mean–skewness invariant parameterization [we derive empirical invariant functional forms expressing all other shape describing parameters in terms of the free parameters (μ, γ)]. The presented analysis reveals the global and local properties of the raindrop size distribution offering a coherent and universally applicable methodology to describe the raindrop size distribution.
Abstract
This paper assesses the water vapor flux performance of three reanalysis datasets (ERA5, JRA55, NCEP-2) on the South China Sea. The radiosonde data were from the South China Sea Scientific Expedition organized by Sun Yat-sen University in the 2019 summer (SCSEX2019). The comparison shows that all reanalyses underestimate the temperature and specific humidity under 500 hPa. As for the wind profile, the most significant difference appeared at 1800 UTC when there was no conventional radiosonde observation around the experiment area. As for the water vapor flux, ERA5 seems to give the best zonal flux but the worst meridional one. A deeper analysis shows that the bias in the wind mainly caused the difference in water vapor flux from ERA5. As for JRA55 and NCEP-2, the humidity and wind field bias coincidentally canceled each other, inducing a much smaller bias, especially in meridional water vapor flux. Therefore, to get a more realistic water vapor flux, a correction in the wind profile was most needed for ERA5. In contrast, the simultaneous improvement on both wind and humidity fields might produce a better water vapor flux for JRA55 and NCEP-2.
Significance Statement
This paper mainly aims to assess three atmospheric reanalyses from the viewpoint of the water vapor flux over the South China Sea during the monsoon period. The observation data contain more than 120 radiosonde profiles. Our work has given an objective comparison among the reanalyses and observations. We also tried to explain the bias in the water vapor flux over the ocean from the reanalyses. The results of our work might help understand the monsoon precipitation given by atmospheric reanalyses or regional climate models and enlighten the development of atmospheric assimilation products.
Abstract
This paper assesses the water vapor flux performance of three reanalysis datasets (ERA5, JRA55, NCEP-2) on the South China Sea. The radiosonde data were from the South China Sea Scientific Expedition organized by Sun Yat-sen University in the 2019 summer (SCSEX2019). The comparison shows that all reanalyses underestimate the temperature and specific humidity under 500 hPa. As for the wind profile, the most significant difference appeared at 1800 UTC when there was no conventional radiosonde observation around the experiment area. As for the water vapor flux, ERA5 seems to give the best zonal flux but the worst meridional one. A deeper analysis shows that the bias in the wind mainly caused the difference in water vapor flux from ERA5. As for JRA55 and NCEP-2, the humidity and wind field bias coincidentally canceled each other, inducing a much smaller bias, especially in meridional water vapor flux. Therefore, to get a more realistic water vapor flux, a correction in the wind profile was most needed for ERA5. In contrast, the simultaneous improvement on both wind and humidity fields might produce a better water vapor flux for JRA55 and NCEP-2.
Significance Statement
This paper mainly aims to assess three atmospheric reanalyses from the viewpoint of the water vapor flux over the South China Sea during the monsoon period. The observation data contain more than 120 radiosonde profiles. Our work has given an objective comparison among the reanalyses and observations. We also tried to explain the bias in the water vapor flux over the ocean from the reanalyses. The results of our work might help understand the monsoon precipitation given by atmospheric reanalyses or regional climate models and enlighten the development of atmospheric assimilation products.
Abstract
Despite the potential and increasing interest in physically based hydrological models for streamflow forecasting applications, they are constrained in terms of agility to generate ensembles. Hence, we develop and test a Bayesian hierarchical model (BHM) to postprocess physically based hydrologic model simulations at multiple sites on a river network, with the aim to generate probabilistic information (i.e., ensembles) and improve raw model skill. We apply our BHM framework to daily summer (July–August) streamflow simulations at five stations located in the Narmada River basin in central India, forcing the Variable Infiltration Capacity (VIC) model with observed rainfall. In this approach, daily observed streamflow at each station is modeled with a conditionally independent probability density function with time varying distribution parameters, which are modeled as a linear function of potential covariates that include VIC outputs and meteorological variables. Using suitable priors on the parameters, posterior parameters and predictive posterior distributions—and thus ensembles—of daily streamflow are obtained. The best BHM model considers a gamma distribution and uses VIC streamflow and a nonlinear covariate formulated as the product of VIC streamflow and 2-day precipitation spatially averaged across the area between the current and upstream station. The second covariate enables correcting the time delay in flow peaks and nonsystematic biases in VIC streamflow. The results show that the BHM postprocessor increases probabilistic skill in 60% compared to raw VIC simulations, providing reliable ensembles for most sites. This modeling approach can be extended to combine forecasts from multiple sources and provide skillful multimodel ensemble forecasts.
Abstract
Despite the potential and increasing interest in physically based hydrological models for streamflow forecasting applications, they are constrained in terms of agility to generate ensembles. Hence, we develop and test a Bayesian hierarchical model (BHM) to postprocess physically based hydrologic model simulations at multiple sites on a river network, with the aim to generate probabilistic information (i.e., ensembles) and improve raw model skill. We apply our BHM framework to daily summer (July–August) streamflow simulations at five stations located in the Narmada River basin in central India, forcing the Variable Infiltration Capacity (VIC) model with observed rainfall. In this approach, daily observed streamflow at each station is modeled with a conditionally independent probability density function with time varying distribution parameters, which are modeled as a linear function of potential covariates that include VIC outputs and meteorological variables. Using suitable priors on the parameters, posterior parameters and predictive posterior distributions—and thus ensembles—of daily streamflow are obtained. The best BHM model considers a gamma distribution and uses VIC streamflow and a nonlinear covariate formulated as the product of VIC streamflow and 2-day precipitation spatially averaged across the area between the current and upstream station. The second covariate enables correcting the time delay in flow peaks and nonsystematic biases in VIC streamflow. The results show that the BHM postprocessor increases probabilistic skill in 60% compared to raw VIC simulations, providing reliable ensembles for most sites. This modeling approach can be extended to combine forecasts from multiple sources and provide skillful multimodel ensemble forecasts.
Abstract
Large-scale changes in the state of the land surface affect the circulation of the atmosphere and the structure and function of ecosystems alike. As global temperatures increase and regional climates change, the timing of key plant phenophase changes are likely to shift as well. Here we evaluate a suite of phenometrics designed to facilitate an “apples to apples” comparison between remote sensing products and climate model output. Specifically, we derive day-of-year (DOY) thresholds of leaf area index (LAI) from both remote sensing and the Community Land Model (CLM) over the Northern Hemisphere. This systematic approach to comparing phenologically relevant variables reveals appreciable differences in both LAI seasonal cycle and spring onset timing between model simulated phenology and satellite records. For example, phenological spring onset in the model occurs on average 30 days later than observed, especially for evergreen plant functional types. The disagreement in phenology can result in a mean bias of approximately 5% of the total estimated Northern Hemisphere NPP. Further, while the more recent version of CLM (v5.0) exhibits seasonal mean LAI values that are in closer agreement with satellite data than its predecessor (CLM4.5), LAI seasonal cycles in CLM5.0 exhibit poorer agreement. Therefore, despite broad improvements for a range of states and fluxes from CLM4.5 to CLM5.0, degradation of plant phenology occurs in CLM5.0. Therefore, any coupling between the land surface and the atmosphere that depends on vegetation state might not be fully captured by the existing generation of the model. We also discuss several avenues for improving the fidelity between observations and model simulations.
Abstract
Large-scale changes in the state of the land surface affect the circulation of the atmosphere and the structure and function of ecosystems alike. As global temperatures increase and regional climates change, the timing of key plant phenophase changes are likely to shift as well. Here we evaluate a suite of phenometrics designed to facilitate an “apples to apples” comparison between remote sensing products and climate model output. Specifically, we derive day-of-year (DOY) thresholds of leaf area index (LAI) from both remote sensing and the Community Land Model (CLM) over the Northern Hemisphere. This systematic approach to comparing phenologically relevant variables reveals appreciable differences in both LAI seasonal cycle and spring onset timing between model simulated phenology and satellite records. For example, phenological spring onset in the model occurs on average 30 days later than observed, especially for evergreen plant functional types. The disagreement in phenology can result in a mean bias of approximately 5% of the total estimated Northern Hemisphere NPP. Further, while the more recent version of CLM (v5.0) exhibits seasonal mean LAI values that are in closer agreement with satellite data than its predecessor (CLM4.5), LAI seasonal cycles in CLM5.0 exhibit poorer agreement. Therefore, despite broad improvements for a range of states and fluxes from CLM4.5 to CLM5.0, degradation of plant phenology occurs in CLM5.0. Therefore, any coupling between the land surface and the atmosphere that depends on vegetation state might not be fully captured by the existing generation of the model. We also discuss several avenues for improving the fidelity between observations and model simulations.
Abstract
The physical processes of heat exchange between lakes and the surrounding atmosphere are important in simulating and predicting terrestrial surface energy balance. Latent and sensible heat fluxes are the dominant physical process controlling ice growth and decay on the lake surface, as well as having influence on regional climate. While one-dimensional lake models have been used in simulating environmental changes in ice dynamics and water temperature, understanding the seasonal to daily cycles of lake surface energy balance and its relationship to lake thermal properties, atmospheric conditions, and how those are represented in models is still an open area of research. We evaluated a pair of one-dimensional lake models, Freshwater Lake (FLake) and the General Lake Model (GLM), to compare modeled latent and sensible heat fluxes against observed data collected by an eddy covariance tower during a 1-yr period in 2017, using Lake Mendota in Madison, Wisconsin, as our study site. We hypothesized transitional periods of ice cover as a leading source of model uncertainty, and we instead found that the models failed to simulate accurate values for large positive heat fluxes that occurred from late August into late December. Our results ultimately showed that one-dimensional models are effective in simulating sensible heat fluxes but are considerably less sensitive to latent heat fluxes than the observed relationships of latent heat flux to environmental drivers. These results can be used to focus future improvement of these lake models especially if they are to be used for surface boundary conditions in regional numerical weather models.
Significance Statement
While lakes consist of a small amount of Earth’s surface, they have a large impact on local climate and weather. A large amount of energy is stored in lakes during the spring and summer, and then removed from lakes before winter. The effect is particularly noticeable in high latitudes, when the seasonal temperature difference is larger. Modeling this lake energy exchange is important for weather models and measuring this energy exchange is challenging. Here we compare modeled and observed energy exchange, and we show there are large amounts of energy exchange happening in the fall, which models struggle to capture well. During periods of partial ice coverage in early winter, lake behavior can change rapidly.
Abstract
The physical processes of heat exchange between lakes and the surrounding atmosphere are important in simulating and predicting terrestrial surface energy balance. Latent and sensible heat fluxes are the dominant physical process controlling ice growth and decay on the lake surface, as well as having influence on regional climate. While one-dimensional lake models have been used in simulating environmental changes in ice dynamics and water temperature, understanding the seasonal to daily cycles of lake surface energy balance and its relationship to lake thermal properties, atmospheric conditions, and how those are represented in models is still an open area of research. We evaluated a pair of one-dimensional lake models, Freshwater Lake (FLake) and the General Lake Model (GLM), to compare modeled latent and sensible heat fluxes against observed data collected by an eddy covariance tower during a 1-yr period in 2017, using Lake Mendota in Madison, Wisconsin, as our study site. We hypothesized transitional periods of ice cover as a leading source of model uncertainty, and we instead found that the models failed to simulate accurate values for large positive heat fluxes that occurred from late August into late December. Our results ultimately showed that one-dimensional models are effective in simulating sensible heat fluxes but are considerably less sensitive to latent heat fluxes than the observed relationships of latent heat flux to environmental drivers. These results can be used to focus future improvement of these lake models especially if they are to be used for surface boundary conditions in regional numerical weather models.
Significance Statement
While lakes consist of a small amount of Earth’s surface, they have a large impact on local climate and weather. A large amount of energy is stored in lakes during the spring and summer, and then removed from lakes before winter. The effect is particularly noticeable in high latitudes, when the seasonal temperature difference is larger. Modeling this lake energy exchange is important for weather models and measuring this energy exchange is challenging. Here we compare modeled and observed energy exchange, and we show there are large amounts of energy exchange happening in the fall, which models struggle to capture well. During periods of partial ice coverage in early winter, lake behavior can change rapidly.
Abstract
The arid and semiarid areas of the Loess Plateau are extremely sensitive to climate change. Land–atmosphere interactions of these regions play an important role in the regional climate. However, most present land surface models (LSMs) are not reasonable and accurate enough to describe the surface characteristics in these regions. In this study, we investigate the effects of three key land surface parameters including surface albedo, soil thermal conductivity, and additional damping on the Noah LSM in simulating the land surface characteristics. The observational data from June to September from 2007 to 2009 collected at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) station in northwest China are used to validate the Noah LSM simulations. The results suggest that the retrieved values of surface albedo, soil thermal conductivity, and additional damping based on observations are in closer agreement with those of the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping, respectively. Furthermore, the model performance is not obviously affected by surface albedo parameterization schemes, while the scheme of soil thermal conductivity is vital to simulations of latent heat flux and soil temperature and the scheme of additional damping is crucial for simulating net radiation flux, sensible heat flux, and surface soil temperature. A set of optimal parameterizations is proposed for the offline Noah LSM at the SACOL station when the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping are combined simultaneously, especially in the case of sensible heat flux and surface soil temperature simulations.
Abstract
The arid and semiarid areas of the Loess Plateau are extremely sensitive to climate change. Land–atmosphere interactions of these regions play an important role in the regional climate. However, most present land surface models (LSMs) are not reasonable and accurate enough to describe the surface characteristics in these regions. In this study, we investigate the effects of three key land surface parameters including surface albedo, soil thermal conductivity, and additional damping on the Noah LSM in simulating the land surface characteristics. The observational data from June to September from 2007 to 2009 collected at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) station in northwest China are used to validate the Noah LSM simulations. The results suggest that the retrieved values of surface albedo, soil thermal conductivity, and additional damping based on observations are in closer agreement with those of the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping, respectively. Furthermore, the model performance is not obviously affected by surface albedo parameterization schemes, while the scheme of soil thermal conductivity is vital to simulations of latent heat flux and soil temperature and the scheme of additional damping is crucial for simulating net radiation flux, sensible heat flux, and surface soil temperature. A set of optimal parameterizations is proposed for the offline Noah LSM at the SACOL station when the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping are combined simultaneously, especially in the case of sensible heat flux and surface soil temperature simulations.
Abstract
Initialization methods are needed for geophysical components of Earth system prediction models. These methods are needed from medium-range to decadal predictions and also for short-range Earth system forecasts in support of safety (e.g., severe weather), economic (e.g., energy), and other applications. Strongly coupled land–atmosphere data assimilation (SCDA), producing balanced initial conditions across the land–atmosphere components, has not yet been introduced to operational numerical weather prediction (NWP) systems. Most NWP systems have evolved separate data assimilation (DA) procedures for the atmosphere versus land/snow system components. This separated method has been classified as a weakly coupled DA system (WCDA). In the NOAA operational short-range weather models, a moderately coupled land–snow–atmosphere assimilation method (MCLDA) has been implemented, a step forward from WCDA toward SCDA. The atmosphere and land (including snow) variables are both updated within the DA using the same set of observations (aircraft, radiosonde, satellite radiances, surface, etc.). Using this assimilation method, land surface state variables have cycled continuously for 6 years since 2015 for the 3-km NOAA HRRR model and with CONUS cycling since 1997. Month-long experiments were conducted with and without MCLDA for both winter and summer seasons using the 13-km Rapid Refresh model with atmosphere (50 levels), soil (9 levels), and snow (up to 2 layers if present) on the same horizontal grid. Improvements were evident for 2-m temperature for all times of day out to 6–12 h for both seasons but stronger in winter. Better temperature forecasts were also shown in the 1000–900-hPa layer corresponding roughly to the boundary layer.
Significance Statement
Accuracy of weather models depends on accurate initial conditions for soil temperature and moisture as well as for the atmosphere itself. This paper describes a moderately coupled data assimilation method that modifies soil conditions based on forecast error corrections indicated by atmospheric observations. This method has been tested for a month-long period in summer and winter and shown to consistently improve short-range forecasts of 2-m temperature and moisture. This coupled data assimilation method is used already in NOAA operational short-range models to improve its prediction skill for clouds, convective storms, and general weather conditions.
Abstract
Initialization methods are needed for geophysical components of Earth system prediction models. These methods are needed from medium-range to decadal predictions and also for short-range Earth system forecasts in support of safety (e.g., severe weather), economic (e.g., energy), and other applications. Strongly coupled land–atmosphere data assimilation (SCDA), producing balanced initial conditions across the land–atmosphere components, has not yet been introduced to operational numerical weather prediction (NWP) systems. Most NWP systems have evolved separate data assimilation (DA) procedures for the atmosphere versus land/snow system components. This separated method has been classified as a weakly coupled DA system (WCDA). In the NOAA operational short-range weather models, a moderately coupled land–snow–atmosphere assimilation method (MCLDA) has been implemented, a step forward from WCDA toward SCDA. The atmosphere and land (including snow) variables are both updated within the DA using the same set of observations (aircraft, radiosonde, satellite radiances, surface, etc.). Using this assimilation method, land surface state variables have cycled continuously for 6 years since 2015 for the 3-km NOAA HRRR model and with CONUS cycling since 1997. Month-long experiments were conducted with and without MCLDA for both winter and summer seasons using the 13-km Rapid Refresh model with atmosphere (50 levels), soil (9 levels), and snow (up to 2 layers if present) on the same horizontal grid. Improvements were evident for 2-m temperature for all times of day out to 6–12 h for both seasons but stronger in winter. Better temperature forecasts were also shown in the 1000–900-hPa layer corresponding roughly to the boundary layer.
Significance Statement
Accuracy of weather models depends on accurate initial conditions for soil temperature and moisture as well as for the atmosphere itself. This paper describes a moderately coupled data assimilation method that modifies soil conditions based on forecast error corrections indicated by atmospheric observations. This method has been tested for a month-long period in summer and winter and shown to consistently improve short-range forecasts of 2-m temperature and moisture. This coupled data assimilation method is used already in NOAA operational short-range models to improve its prediction skill for clouds, convective storms, and general weather conditions.
