Abstract

A land surface model that includes a subgrid parameterization for inland water (lake, swamp, marsh) was coupled to a modified version of the NCAR CCM2. The coupled model was run for 5 yr with and without inland water subgrid points to determine the importance of inland water for global climate simulation. In July, the inclusion of these water bodies resulted in a spatially consistent signal in which high inland water regions were 2°–3°C cooler, had increased latent heat flux (10–45 W m⁻²), and decreased sensible heat flux (5–30 W m⁻²) compared to the simulation without these water bodies. These changes were statistically significant in the lake region of northwest Canada, the Great Lakes region of North America, the swamp and marsh region of the Siberian lowlands, and the lake region of East Africa, but were not significantly different in the swamp and marsh region of Finland and northwest Russia. The effect on Northern Hemisphere January air temperature was difficult to interpret due to large interannual variability. In tropical lake regions (East Africa), the response to lakes was less in the rainy season (January) than in the dry season (July). Precipitation was unchanged in both months except for the Great Lakes region where precipitation increased in January. These changes in temperature, precipitation, and surface fluxes were consistent with mesoscale modeling studies of the effects of lakes on climate and tended to bring the model closer to observations. In particular, the summer cooling in North America helped reduce a large warm temperature bias in the model, but did not eliminate the bias. The lakes had little effect on atmospheric moisture, radiation, or zonal circulation. These results show that subgrid-scale inland water bodies can be successfully added to global land surface models for use with GCMS.

Abstract

Climate model simulations have shown that conversion of natural forest vegetation to croplands in the United States cooled climate. The cooling was greater for daily maximum temperature than for daily minimum temperature, resulting in a reduced diurnal temperature range. This paper presents analyses of observed daily maximum and minimum temperatures that are consistent with the climate simulations. Daily maximum temperature in the croplands of the Midwest United States is reduced relative to forested land in the Northeast, resulting in a decreased diurnal temperature range. The cooling is regional rather than local and is likely created by the contrast between extensive cropland in the Midwest and forest in the Northeast. Seasonal patterns of this cooling are correlated with seasonal changes in crop growth. Analyses of historical temperatures since 1900 and reconstructed cropland extent show a temporal correlation between land use and cooling. The cooling created by the forest–cropland contrast is much more prominent now, when much of the Northeast farmland has been abandoned and reforested, than in the early 1900s when farmlands were more extensive in the Northeast. These results show that human uses of land, especially clearing of forest for agriculture and reforestation of abandoned farmland, are an important cause of regional climate change. Analyses of historical temperature records must consider this “land use” forcing.

Abstract

The National Center for Atmospheric Research (NCAR) Land Surface Model (LSM, version 1.0) provides a comprehensive treatment of land surface processes for the NCAR Community Climate Model version 3 (CCM3). It replaces the prescribed surface wetness, prescribed snow cover, surface albedo, and surface flux parameterizations used in the CCM2. A 15-yr simulation of the coupled atmosphere (CCM3) and land (LSM1.0) models using observed sea surface temperatures for the period December 1978–September 1993 is used to document the model’s land surface climate. The model simulates many of the observed geographic and seasonal patterns of surface air temperature, precipitation, and soil water. In general, the transition seasons (spring, autumn) are better simulated than winter and summer. Annual precipitation and runoff are well simulated for some river basins and poorly simulated for others. In general, precipitation is better simulated than runoff. The inclusion of net land–atmosphere CO₂ exchange is an important component of the land model, allowing it to be used for studies of the global carbon cycle. The model simulates annual net primary production that is consistent with other estimates of annual production. The model also simulates a clearly defined growing season based on temperature and soil water.

Abstract

Observations show that emergence of foliage in springtime slows surface air temperature warming as a result of greater transpiration. Model simulations with the Community Atmosphere Model coupled to the Community Land Model confirm that evapotranspiration contributes to this pattern and that this pattern occurs more reliably with prognostic leaf area as opposed to prescribed leaf area. With prescribed leaf area, leaves emerge independent of prevailing environmental conditions, which may preclude photosynthesis from occurring. In contrast, prognostic leaf area ensures that leaves emerge when conditions are favorable for photosynthesis, and thus transpiration. These results reveal a dynamic coupling between the atmosphere and vegetation in which the observed reduction in the springtime warming trend only occurs when photosynthesis, stomatal conductance, and leaf emergence are synchronized with the surface climate.

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 Community Land Model version 3 (CLM3) Dynamic Global Vegetation Model (CLM–DGVM) is used diagnostically to identify land and atmospheric model biases that lead to biases in the simulated vegetation. The CLM–DGVM driven with observed atmospheric data (offline simulation) underestimates global forest cover, overestimates grasslands, and underestimates global net primary production. These results are consistent with earlier findings that the soils in CLM3 are too dry. In the offline simulation an increase in simulated transpiration by changing this variable's soil moisture dependence and by decreasing canopy-intercepted precipitation results in better global plant biogeography and global net primary production. When CLM–DGVM is coupled to the Community Atmosphere Model version 3 (CAM3), the same modifications do not improve simulated vegetation in the eastern United States and Amazonia where the most serious vegetation biases appear. The dry bias in eastern U.S. precipitation is so severe that the simulated vegetation is insensitive to changes in the hydrologic cycle. In Amazonia, strong coupling among soil moisture, vegetation, evapotranspiration, and precipitation produces a highly complex hydrologic cycle in which small perturbations in precipitation are accentuated by vegetation. These interactions in Amazonia lead to a dramatic precipitation decrease and a collapse of the forest. These results suggest that the accurate parameterization of convection poses a complex and challenging scientific issue for climate models that include dynamic vegetation. The results also emphasize the difficulties that may arise when coupling any two highly nonlinear systems that have only been tested uncoupled.

Abstract

The effects of increased soil moisture on wet season (October–March) precipitation in southern Africa are investigated using the Community Climate System Model version 3 (CCSM3). In the CTRL case, soil moisture is allowed to interact dynamically with the atmosphere. In the MOIST case, soil moisture is set so that evapotranspiration is not limited by the supply of water. The MOIST scenario actually results in decreased precipitation over the region of perturbed soil moisture, compared to CTRL. The increased soil moisture alters the surface energy balance, resulting in a shift from sensible to latent heating. This manifests in two ways relevant for precipitation processes. First, the shift from sensible to latent heating cools the surface, causing a higher surface pressure, a reduced boundary layer height, and an increased vertical gradient in equivalent potential temperature. These changes are indicative of an increase in atmospheric stability, inhibiting vertical movement of air parcels and decreasing the ability of precipitation to form. Second, the surface changes induce anomalous surface divergence and increased subsidence. This causes a reduction in cloud cover and specific humidity above 700 hPa and results in a net decrease of column-integrated precipitable water, despite the increased surface water flux, indicating a reduction in moisture convergence. Based on this and a previous study, soil moisture may act as a negative feedback to precipitation in southern Africa, helping to buffer the system against any external forcing of precipitation (e.g., ENSO).

Abstract

Changes in the land surface can drive large responses in the atmosphere on local, regional, and global scales. Surface properties control the partitioning of energy within the surface energy budget to fluxes of shortwave and longwave radiation, sensible and latent heat, and ground heat storage. Changes in surface energy fluxes can impact the atmosphere across scales through changes in temperature, cloud cover, and large-scale atmospheric circulation. We test the sensitivity of the atmosphere to global changes in three land surface properties: albedo, evaporative resistance, and surface roughness. We show the impact of changing these surface properties differs drastically between simulations run with an offline land model, compared to coupled land–atmosphere simulations that allow for atmospheric feedbacks associated with land–atmosphere coupling. Atmospheric feedbacks play a critical role in defining the temperature response to changes in albedo and evaporative resistance, particularly in the extratropics. More than 50% of the surface temperature response to changing albedo comes from atmospheric feedbacks in over 80% of land areas. In some regions, cloud feedbacks in response to increased evaporative resistance result in nearly 1 K of additional surface warming. In contrast, the magnitude of surface temperature responses to changes in vegetation height are comparable between offline and coupled simulations. We improve our fundamental understanding of how and why changes in vegetation cover drive responses in the atmosphere, and develop understanding of the role of individual land surface properties in controlling climate across spatial scales—critical to understanding the effects of land-use change on Earth’s climate.

Abstract

The statistical representation of multiple land surfaces within a grid cell has received attention as a means to parameterize the nonlinear effects of subgrid-scale heterogeneity on land-atmosphere energy exchange. However, previous analyses have not identified the critical land-surface parameters to which energy exchanges are sensitive; the appropriate number of within-grid-cell classes for a particular parameter, or the effects of interactions among several parameters on the nonlinearity of energy exchanges. The analyses reported here used a land-surface scheme for climate models to examine the effects of subgrid variability in leaf area index, minimum and maximum stomatal resistances, and soil moisture on grid-scale fluxes. Comparisons between energy fluxes obtained using parameter values for the average of 100 subgrid points and the average fluxes for the 100 subgrid points showed minor differences for emitted infrared radiation and reflected solar radiation, but large differences for sensible heat and evapotranspiration. Leaf area index was the most important parameter; stomatal resistances were only important on wet soils. Interactions among parameters increased the nonlinearity of land-atmosphere energy exchange. When considered separately, six to ten values of each parameter greatly reduced the deviation between the two flux estimates. However, this approach became cumbersome when all four parameters varied independently. These analyses suggest that the debate over how to best parameterize the nonlinear effects of subgrid-scale heterogeneity on land-atmosphere interactions will continue.

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.