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Abstract
Human-induced land-use change (LUC) alters the biogeophysical characteristics of the land surface influencing the surface energy balance. The level of atmospheric CO2 is expected to increase in the coming century and beyond, modifying temperature and precipitation patterns and altering the distribution and physiology of natural vegetation. It is important to constrain how CO2-induced climate and vegetation change may influence the regional extent to which LUC alters climate. This sensitivity study uses the HadCM3 coupled climate model under a range of equilibrium forcings to show that the impact of LUC declines under increasing atmospheric CO2, specifically in temperate and boreal regions. A surface energy balance analysis is used to diagnose how these changes occur. In Northern Hemisphere winter this pattern is attributed in part to the decline in winter snow cover and in the summer due to a reduction in latent cooling with higher levels of CO2. The CO2-induced change in natural vegetation distribution is also shown to play a significant role. Simulations run at elevated CO2, yet present-day vegetation show a significantly increased sensitivity to LUC, driven in part by an increase in latent cooling. This study shows that modeling the impact of LUC needs to accurately simulate CO2-driven changes in precipitation and snowfall and incorporate accurate, dynamic vegetation distribution.
Abstract
Human-induced land-use change (LUC) alters the biogeophysical characteristics of the land surface influencing the surface energy balance. The level of atmospheric CO2 is expected to increase in the coming century and beyond, modifying temperature and precipitation patterns and altering the distribution and physiology of natural vegetation. It is important to constrain how CO2-induced climate and vegetation change may influence the regional extent to which LUC alters climate. This sensitivity study uses the HadCM3 coupled climate model under a range of equilibrium forcings to show that the impact of LUC declines under increasing atmospheric CO2, specifically in temperate and boreal regions. A surface energy balance analysis is used to diagnose how these changes occur. In Northern Hemisphere winter this pattern is attributed in part to the decline in winter snow cover and in the summer due to a reduction in latent cooling with higher levels of CO2. The CO2-induced change in natural vegetation distribution is also shown to play a significant role. Simulations run at elevated CO2, yet present-day vegetation show a significantly increased sensitivity to LUC, driven in part by an increase in latent cooling. This study shows that modeling the impact of LUC needs to accurately simulate CO2-driven changes in precipitation and snowfall and incorporate accurate, dynamic vegetation distribution.
Abstract
Regional land surface and remote ocean variables have been considered as primary forcings altering the variability of summer rainfall over the Sahel. However, previous studies usually examined the two components separately. In this study, the authors apply statistical methods including correlation, multivariate linear regression, and Granger causality analyses to investigate the relative roles of spring–summer sea surface temperature (SST) and vegetation activity in explaining the Sahel summer rainfall variability from 1982 to 2006. The remotely sensed normalized difference vegetation index (NDVI) is used as an indicator of land surface forcing. This study shows that spring and summer SSTs over the subtropical North Atlantic have significant positive correlations with summer rainfall. The spring and summer NDVIs over the Sahel have significant negative and positive correlations, respectively, with summer rainfall. Based on the multivariate linear regression analysis, the adjusted R 2 for the integrated model with both the land and ocean variables is 0.70. It is around 2 times larger than the model with SST alone (adjusted R 2 = 0.36). To further investigate the causal relationships of summer rainfall with the SST and NDVI variables selected in the integrated multivariate model, the authors perform a Granger causality test. This study finds that summer NDVI over the Sahel does Granger cause summer rainfall over the Sahel, while the summer SST over the subtropical North Atlantic does not Granger cause the summer rainfall. The results indicate that the regional land surface forcing has a relatively strong contribution to Sahel summer rainfall, compared to the remote ocean forcing, during the recent decades.
Abstract
Regional land surface and remote ocean variables have been considered as primary forcings altering the variability of summer rainfall over the Sahel. However, previous studies usually examined the two components separately. In this study, the authors apply statistical methods including correlation, multivariate linear regression, and Granger causality analyses to investigate the relative roles of spring–summer sea surface temperature (SST) and vegetation activity in explaining the Sahel summer rainfall variability from 1982 to 2006. The remotely sensed normalized difference vegetation index (NDVI) is used as an indicator of land surface forcing. This study shows that spring and summer SSTs over the subtropical North Atlantic have significant positive correlations with summer rainfall. The spring and summer NDVIs over the Sahel have significant negative and positive correlations, respectively, with summer rainfall. Based on the multivariate linear regression analysis, the adjusted R 2 for the integrated model with both the land and ocean variables is 0.70. It is around 2 times larger than the model with SST alone (adjusted R 2 = 0.36). To further investigate the causal relationships of summer rainfall with the SST and NDVI variables selected in the integrated multivariate model, the authors perform a Granger causality test. This study finds that summer NDVI over the Sahel does Granger cause summer rainfall over the Sahel, while the summer SST over the subtropical North Atlantic does not Granger cause the summer rainfall. The results indicate that the regional land surface forcing has a relatively strong contribution to Sahel summer rainfall, compared to the remote ocean forcing, during the recent decades.
Abstract
Stand-clearing disturbances, which remove most of the tree cover but are followed by forest regrowth, affect extensive areas annually, yet each event is usually much smaller than a typical grid cell in Earth system climate models. This study argues that the approach taken to account for the resulting subgrid cell dynamic heterogeneity substantially affects the computation of land–atmosphere exchanges. The authors investigated in a simplified model the effects of three such approaches on the computation of albedo over boreal forests. It was found that the simplest approach—in which any new disturbance-created patch was immediately merged with the rest of the grid cell—underestimated the annual reflected solar radiation by ~3 W m−2 on average (a relative error of 15%) compared with the most accurate approach—in which albedo computations were performed for each individual subgrid patch. This study also investigated an intermediate approach, in which each patch was tracked individually, but albedo was estimated from a much smaller number of subgrid tiles grouping patches having a similar amount of tree cover. Results from this third approach converged quickly toward the most accurate results as the number of tiles increased and were robust to changes in the thresholds used to assign patches to specific tiles. When computing time prevents implementing the most accurate approach in Earth system climate models, the results advocate for using strategies similar to the intermediate approach in order to avoid biasing the net radiative forcing of stand-clearing disturbances toward a warming impact, at least over boreal forests.
Abstract
Stand-clearing disturbances, which remove most of the tree cover but are followed by forest regrowth, affect extensive areas annually, yet each event is usually much smaller than a typical grid cell in Earth system climate models. This study argues that the approach taken to account for the resulting subgrid cell dynamic heterogeneity substantially affects the computation of land–atmosphere exchanges. The authors investigated in a simplified model the effects of three such approaches on the computation of albedo over boreal forests. It was found that the simplest approach—in which any new disturbance-created patch was immediately merged with the rest of the grid cell—underestimated the annual reflected solar radiation by ~3 W m−2 on average (a relative error of 15%) compared with the most accurate approach—in which albedo computations were performed for each individual subgrid patch. This study also investigated an intermediate approach, in which each patch was tracked individually, but albedo was estimated from a much smaller number of subgrid tiles grouping patches having a similar amount of tree cover. Results from this third approach converged quickly toward the most accurate results as the number of tiles increased and were robust to changes in the thresholds used to assign patches to specific tiles. When computing time prevents implementing the most accurate approach in Earth system climate models, the results advocate for using strategies similar to the intermediate approach in order to avoid biasing the net radiative forcing of stand-clearing disturbances toward a warming impact, at least over boreal forests.
Abstract
The rapid expansion of irrigation since the 1950s has significantly depleted the Ogallala Aquifer. This study examines the warm-season climate impacts of irrigation over the Ogallala using high-resolution (6.33 km) simulations of a version of the Weather Research and Forecasting (WRF) Model that has been coupled to the Community Land Model with dynamic crop growth (WRF-CLM4crop). To examine how dynamic crops influence the simulated impact of irrigation, the authors compare simulations with dynamic crops to simulations with a fixed annual cycle of crop leaf area index (static crops). For each crop scheme, simulations were completed with and without irrigation for 9 years that represent the range of observed precipitation. Reduced temperature and precipitation biases occur with dynamic versus static crops. Fundamental differences in the precipitation response to irrigation occur with dynamic crops, as enhanced surface roughness weakens low-level winds, enabling more water from irrigation to remain over the region. Greater simulated rainfall increases (12.42 mm) occur with dynamic crops compared to static crops (9.08 mm), with the greatest differences during drought years (+20.1 vs +5.9 mm). Water use for irrigation significantly impacts precipitation with dynamic crops (R 2 = 0.29), but no relationship exists with static crops. Dynamic crop growth has the largest effect on the simulated impact of irrigation on precipitation during drought years, with little impact during nondrought years, highlighting the need to simulate the dynamic response of crops to environmental variability within Earth system models to improve prediction of the agroecosystem response to variations in climate.
Abstract
The rapid expansion of irrigation since the 1950s has significantly depleted the Ogallala Aquifer. This study examines the warm-season climate impacts of irrigation over the Ogallala using high-resolution (6.33 km) simulations of a version of the Weather Research and Forecasting (WRF) Model that has been coupled to the Community Land Model with dynamic crop growth (WRF-CLM4crop). To examine how dynamic crops influence the simulated impact of irrigation, the authors compare simulations with dynamic crops to simulations with a fixed annual cycle of crop leaf area index (static crops). For each crop scheme, simulations were completed with and without irrigation for 9 years that represent the range of observed precipitation. Reduced temperature and precipitation biases occur with dynamic versus static crops. Fundamental differences in the precipitation response to irrigation occur with dynamic crops, as enhanced surface roughness weakens low-level winds, enabling more water from irrigation to remain over the region. Greater simulated rainfall increases (12.42 mm) occur with dynamic crops compared to static crops (9.08 mm), with the greatest differences during drought years (+20.1 vs +5.9 mm). Water use for irrigation significantly impacts precipitation with dynamic crops (R 2 = 0.29), but no relationship exists with static crops. Dynamic crop growth has the largest effect on the simulated impact of irrigation on precipitation during drought years, with little impact during nondrought years, highlighting the need to simulate the dynamic response of crops to environmental variability within Earth system models to improve prediction of the agroecosystem response to variations in climate.
Abstract
The role of land–atmosphere coupling in modulating the impact of land-use change (LUC) on regional climate extremes remains uncertain. Using the Weather and Research Forecasting Model, this study combines the Global Land–Atmosphere Coupling Experiment with regional LUC to assess the combined impact of land–atmosphere coupling and LUC on simulated temperature extremes. The experiment is applied to an ensemble of planetary boundary layer (PBL) and cumulus parameterizations to determine the sensitivity of the results to model physics. Results show a consistent weakening in the soil moisture–maximum temperature coupling strength with LUC irrespective of the model physics. In contrast, temperature extremes show an asymmetric response to LUC dependent on the choice of PBL scheme, which is linked to differences in the parameterization of vertical transport. This influences convective precipitation, contributing a positive feedback on soil moisture and consequently on the partitioning of the surface turbulent fluxes. The results suggest that the impact of LUC on temperature extremes depends on the land–atmosphere coupling that in turn depends on the choice of PBL. Indeed, the sign of the temperature change in hot extremes resulting from LUC can be changed simply by altering the choice of PBL. The authors also note concerns over the metrics used to measure coupling strength that reflect changes in variance but may not respond to LUC-type perturbations.
Abstract
The role of land–atmosphere coupling in modulating the impact of land-use change (LUC) on regional climate extremes remains uncertain. Using the Weather and Research Forecasting Model, this study combines the Global Land–Atmosphere Coupling Experiment with regional LUC to assess the combined impact of land–atmosphere coupling and LUC on simulated temperature extremes. The experiment is applied to an ensemble of planetary boundary layer (PBL) and cumulus parameterizations to determine the sensitivity of the results to model physics. Results show a consistent weakening in the soil moisture–maximum temperature coupling strength with LUC irrespective of the model physics. In contrast, temperature extremes show an asymmetric response to LUC dependent on the choice of PBL scheme, which is linked to differences in the parameterization of vertical transport. This influences convective precipitation, contributing a positive feedback on soil moisture and consequently on the partitioning of the surface turbulent fluxes. The results suggest that the impact of LUC on temperature extremes depends on the land–atmosphere coupling that in turn depends on the choice of PBL. Indeed, the sign of the temperature change in hot extremes resulting from LUC can be changed simply by altering the choice of PBL. The authors also note concerns over the metrics used to measure coupling strength that reflect changes in variance but may not respond to LUC-type perturbations.
