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- Author or Editor: Gordon Bonan x
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Abstract
The land surface models used with atmospheric models typically characterize landscapes in terms of generalized biome types. However, the advent of high–spatial resolution satellite-derived data products such as land cover and leaf area index (LAI) allow for more accurate specification of landscape patterns. In this paper, the authors report on the use of 1-km land-cover [converted to plant functional type (PFT)] and LAI datasets developed from the Boreal Ecosystem–Atmosphere Study (BOREAS) to develop and to test a methodology for incorporating satellite data into the National Center for Atmospheric Research (NCAR) land surface model. In this approach, the landscape is composed of patches of PFTs, each with its own LAI, rather than as biomes. Large differences in PFT fractional cover between the remotely sensed and standard model representations were found for the BOREAS region. Changes in the needleleaf evergreen PFT fraction were the most extensive both in terms of spatial distribution and magnitude (up to ±40%). Large differences in LAI were also found (up to ±3 m2 m−2). Although the response of the model to these differences was somewhat small in terms of regionally averaged changes in surface fluxes, the spatial variability of the model response was substantial. The PFT and LAI data were generally of equal importance in modifying the surface fluxes and were most useful for improving the description of spatial variability due to mixtures of recently burned, regrowth, and mature-growth areas.
Abstract
The land surface models used with atmospheric models typically characterize landscapes in terms of generalized biome types. However, the advent of high–spatial resolution satellite-derived data products such as land cover and leaf area index (LAI) allow for more accurate specification of landscape patterns. In this paper, the authors report on the use of 1-km land-cover [converted to plant functional type (PFT)] and LAI datasets developed from the Boreal Ecosystem–Atmosphere Study (BOREAS) to develop and to test a methodology for incorporating satellite data into the National Center for Atmospheric Research (NCAR) land surface model. In this approach, the landscape is composed of patches of PFTs, each with its own LAI, rather than as biomes. Large differences in PFT fractional cover between the remotely sensed and standard model representations were found for the BOREAS region. Changes in the needleleaf evergreen PFT fraction were the most extensive both in terms of spatial distribution and magnitude (up to ±40%). Large differences in LAI were also found (up to ±3 m2 m−2). Although the response of the model to these differences was somewhat small in terms of regionally averaged changes in surface fluxes, the spatial variability of the model response was substantial. The PFT and LAI data were generally of equal importance in modifying the surface fluxes and were most useful for improving the description of spatial variability due to mixtures of recently burned, regrowth, and mature-growth areas.
Abstract
Although the global partitioning of evapotranspiration (ET) into transpiration, soil evaporation, and canopy evaporation is not well known, most current land surface schemes and the few available observations indicate that transpiration is the dominant component on the global scale, followed by soil evaporation and canopy evaporation. The Community Land Model version 3 (CLM3), however, does not reflect this global view of ET partitioning, with soil evaporation and canopy evaporation far outweighing transpiration. One consequence of this unrealistic ET partitioning in CLM3 is that photosynthesis, which is linked to transpiration through stomatal conductance, is significantly underestimated on a global basis. A number of modifications to CLM3 vegetation and soil hydrology parameterizations are described that improve ET partitioning and reduce an apparent dry soil bias in CLM3. The modifications reduce canopy interception and evaporation, reduce soil moisture stress on transpiration, increase transpiration through a more realistic canopy integration scheme, reduce within-canopy soil evaporation, eliminate lateral drainage of soil water, increase infiltration of water into the soil, and increase the vertical redistribution of soil water. The partitioning of ET is improved, with notable increases seen in transpiration (13%–41% of global ET) and photosynthesis (65–148 Pg C yr−1). Soils are wetter and exhibit a far more distinct soil moisture annual cycle and greater interseasonal soil water storage, which permits plants to sustain transpiration through the dry season.
The broader influences of improved ET partitioning on land–atmosphere interaction are diverse. Stronger transpiration and reduced canopy evaporation yield an extended ET response to rain events and a shift in the precipitation distribution toward more frequent small- to medium-size rain events. Soil moisture memory time scales decrease particularly at deeper soil levels. Subsurface soil moisture exerts a slightly greater influence on precipitation. These results indicate that partitioning of ET is an important responsibility for land surface schemes, a responsibility that will gain in relevance as GCMs evolve to incorporate ever more complex treatments of the earth’s carbon and hydrologic cycles.
Abstract
Although the global partitioning of evapotranspiration (ET) into transpiration, soil evaporation, and canopy evaporation is not well known, most current land surface schemes and the few available observations indicate that transpiration is the dominant component on the global scale, followed by soil evaporation and canopy evaporation. The Community Land Model version 3 (CLM3), however, does not reflect this global view of ET partitioning, with soil evaporation and canopy evaporation far outweighing transpiration. One consequence of this unrealistic ET partitioning in CLM3 is that photosynthesis, which is linked to transpiration through stomatal conductance, is significantly underestimated on a global basis. A number of modifications to CLM3 vegetation and soil hydrology parameterizations are described that improve ET partitioning and reduce an apparent dry soil bias in CLM3. The modifications reduce canopy interception and evaporation, reduce soil moisture stress on transpiration, increase transpiration through a more realistic canopy integration scheme, reduce within-canopy soil evaporation, eliminate lateral drainage of soil water, increase infiltration of water into the soil, and increase the vertical redistribution of soil water. The partitioning of ET is improved, with notable increases seen in transpiration (13%–41% of global ET) and photosynthesis (65–148 Pg C yr−1). Soils are wetter and exhibit a far more distinct soil moisture annual cycle and greater interseasonal soil water storage, which permits plants to sustain transpiration through the dry season.
The broader influences of improved ET partitioning on land–atmosphere interaction are diverse. Stronger transpiration and reduced canopy evaporation yield an extended ET response to rain events and a shift in the precipitation distribution toward more frequent small- to medium-size rain events. Soil moisture memory time scales decrease particularly at deeper soil levels. Subsurface soil moisture exerts a slightly greater influence on precipitation. These results indicate that partitioning of ET is an important responsibility for land surface schemes, a responsibility that will gain in relevance as GCMs evolve to incorporate ever more complex treatments of the earth’s carbon and hydrologic cycles.
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.
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.
