Search Results
Abstract
The operational coupled land–atmosphere forecast model from the National Centers for Environmental Prediction (NCEP) is evaluated for the strength and characteristics of its coupling in the water cycle between land and atmosphere. Following the protocols of the Global Land–Atmosphere Coupling Experiment (GLACE) it is found that the Global Forecast System (GFS) atmospheric model coupled to the Noah land surface model exhibits extraordinarily weak land–atmosphere coupling, much as its predecessor, the GFS–Oregon State University (OSU) coupled system. The coupling strength is evaluated by the ability of subsurface soil wetness to affect locally the time series of precipitation. The surface fluxes in Noah are also found to be rather insensitive to subsurface soil wetness. Comparison to another atmospheric model coupled to Noah as well as a different land surface model show that Noah is responsible for some of the lack of sensitivity, primarily because its thick (10 cm) surface layer dominates the variability in surface latent heat fluxes. Noah is found to be as responsive as other land surface models to surface soil wetness and temperature variations, suggesting the design of the GLACE sensitivity experiment (based only on subsurface soil wetness) handicapped the Noah model. Additional experiments, in which the parameterization of evapotranspiration is altered, as well as experiments where surface soil wetness is also constrained, isolate the GFS atmospheric model as the principal source of the weak sensitivity of precipitation to land surface states.
Abstract
The operational coupled land–atmosphere forecast model from the National Centers for Environmental Prediction (NCEP) is evaluated for the strength and characteristics of its coupling in the water cycle between land and atmosphere. Following the protocols of the Global Land–Atmosphere Coupling Experiment (GLACE) it is found that the Global Forecast System (GFS) atmospheric model coupled to the Noah land surface model exhibits extraordinarily weak land–atmosphere coupling, much as its predecessor, the GFS–Oregon State University (OSU) coupled system. The coupling strength is evaluated by the ability of subsurface soil wetness to affect locally the time series of precipitation. The surface fluxes in Noah are also found to be rather insensitive to subsurface soil wetness. Comparison to another atmospheric model coupled to Noah as well as a different land surface model show that Noah is responsible for some of the lack of sensitivity, primarily because its thick (10 cm) surface layer dominates the variability in surface latent heat fluxes. Noah is found to be as responsive as other land surface models to surface soil wetness and temperature variations, suggesting the design of the GLACE sensitivity experiment (based only on subsurface soil wetness) handicapped the Noah model. Additional experiments, in which the parameterization of evapotranspiration is altered, as well as experiments where surface soil wetness is also constrained, isolate the GFS atmospheric model as the principal source of the weak sensitivity of precipitation to land surface states.
Abstract
The Noah land surface model (LSM) has recently been implemented into the Experimental Climate Prediction Center’s (ECPC’s) global Seasonal Forecast Model (SFM). Its performance is compared to the older ECPC SFM with the Oregon State University (OSU) LSM using two sets of 10-member 50-yr Atmospheric Model Intercomparison Project (AMIP) runs. The climatological biases of several fields tend to increase with the Noah LSM. The differences in near-surface temperature bias are traced to changes in the energy budget. In addition to climatology, the variability and skill (anomaly correlation with observations) of the two ensembles are considered. Unlike the climatology, the near-surface temperature skill of the ECPC SFM generally improves with the Noah LSM. Other climatological fields, such as precipitation, show little change in skill.
While the global results are mixed, there are however significant regional improvements over Africa both in terms of climatological bias and skill. In the central African Congo River basin, the Noah LSM removed a warm-dry bias and improved upon the near-surface temperature skill of the OSU LSM. In the African Sahel, the Noah LSM greatly enhanced the climatology, variability, and skill of the ECPC SFM as well as improving the location of the African easterly jet.
Abstract
The Noah land surface model (LSM) has recently been implemented into the Experimental Climate Prediction Center’s (ECPC’s) global Seasonal Forecast Model (SFM). Its performance is compared to the older ECPC SFM with the Oregon State University (OSU) LSM using two sets of 10-member 50-yr Atmospheric Model Intercomparison Project (AMIP) runs. The climatological biases of several fields tend to increase with the Noah LSM. The differences in near-surface temperature bias are traced to changes in the energy budget. In addition to climatology, the variability and skill (anomaly correlation with observations) of the two ensembles are considered. Unlike the climatology, the near-surface temperature skill of the ECPC SFM generally improves with the Noah LSM. Other climatological fields, such as precipitation, show little change in skill.
While the global results are mixed, there are however significant regional improvements over Africa both in terms of climatological bias and skill. In the central African Congo River basin, the Noah LSM removed a warm-dry bias and improved upon the near-surface temperature skill of the OSU LSM. In the African Sahel, the Noah LSM greatly enhanced the climatology, variability, and skill of the ECPC SFM as well as improving the location of the African easterly jet.
Abstract
This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture characteristics well in all aspects. Sometimes R-1 provides a better soil moisture analysis on monthly time scales, which is likely a consequence of the deficiencies in the R-2 surface water balance.
Abstract
This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture characteristics well in all aspects. Sometimes R-1 provides a better soil moisture analysis on monthly time scales, which is likely a consequence of the deficiencies in the R-2 surface water balance.
Abstract
Soil moisture memory is a key aspect of land–atmosphere interaction and has major implications for seasonal forecasting. Because of a severe lack of soil moisture observations on most continents, existing analyses of global-scale soil moisture memory have relied previously on atmospheric general circulation model (AGCM) experiments, with derived conclusions that are probably model dependent. The present study is the first survey examining and contrasting global-scale (near) monthly soil moisture memory characteristics across a broad range of AGCMs. The investigated simulations, performed with eight different AGCMs, were generated as part of the Global Land–Atmosphere Coupling Experiment.
Overall, the AGCMs present relatively similar global patterns of soil moisture memory. Outliers are generally characterized by anomalous water-holding capacity or biases in radiation forcing. Water-holding capacity is highly variable among the analyzed AGCMs and is the main factor responsible for intermodel differences in soil moisture memory. Therefore, further studies on this topic should focus on the accurate characterization of this parameter for present AGCMs. Despite the range in the AGCMs’ behavior, the average soil moisture memory characteristics of the models appear realistic when compared to available in situ soil moisture observations. An analysis of the processes controlling soil moisture memory in the AGCMs demonstrates that it is mostly controlled by two effects: evaporation’s sensitivity to soil moisture, which increases with decreasing soil moisture content, and runoff’s sensitivity to soil moisture, which increases with increasing soil moisture content. Soil moisture memory is highest in regions of medium soil moisture content, where both effects are small.
Abstract
Soil moisture memory is a key aspect of land–atmosphere interaction and has major implications for seasonal forecasting. Because of a severe lack of soil moisture observations on most continents, existing analyses of global-scale soil moisture memory have relied previously on atmospheric general circulation model (AGCM) experiments, with derived conclusions that are probably model dependent. The present study is the first survey examining and contrasting global-scale (near) monthly soil moisture memory characteristics across a broad range of AGCMs. The investigated simulations, performed with eight different AGCMs, were generated as part of the Global Land–Atmosphere Coupling Experiment.
Overall, the AGCMs present relatively similar global patterns of soil moisture memory. Outliers are generally characterized by anomalous water-holding capacity or biases in radiation forcing. Water-holding capacity is highly variable among the analyzed AGCMs and is the main factor responsible for intermodel differences in soil moisture memory. Therefore, further studies on this topic should focus on the accurate characterization of this parameter for present AGCMs. Despite the range in the AGCMs’ behavior, the average soil moisture memory characteristics of the models appear realistic when compared to available in situ soil moisture observations. An analysis of the processes controlling soil moisture memory in the AGCMs demonstrates that it is mostly controlled by two effects: evaporation’s sensitivity to soil moisture, which increases with decreasing soil moisture content, and runoff’s sensitivity to soil moisture, which increases with increasing soil moisture content. Soil moisture memory is highest in regions of medium soil moisture content, where both effects are small.
Abstract
The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.
Abstract
The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.
Abstract
The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.
Abstract
The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.
