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- Author or Editor: Guiling Wang x
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Abstract
This paper presents a conceptual modeling study on the behaviors of terrestrial biosphere–atmosphere systems as they relate to multiple equilibrium states and climate variability, and emphasizes their implications for physically based climate modeling. The conceptual biosphere–atmosphere model consists of equilibrium responses of vegetation and precipitation to each other, dynamics of the vegetation system, and stochastic forcing of precipitation representing the impact of atmospheric internal variability. Using precipitation as the atmospheric variable in describing the biosphere–atmosphere interactions, this model pertains to regions where vegetation growth is limited by water. Low moisture convergence in the atmosphere combined with high sensitivity of the atmospheric climate to vegetation changes provides the most favorable condition for the existence of multiple equilibrium states. In a coupled biosphere–atmosphere system with multiple equilibria, experiments varying the stochastic forcing indicate that atmospheric internal variability is an important factor in the long-term variability of the model climate and in its sensitivity to initial conditions. Specifically, the enhancement of low-frequency rainfall variability by vegetation dynamics is most pronounced with a moderate magnitude of atmospheric internal variability and is less pronounced if internal variability is either too large or too small; detecting the existence of multiple equilibria by examining the sensitivity of the coupled model climate to initial conditions is not always reliable since too large an internal variability reduces or even eliminates the model sensitivity to initial conditions. Findings from the conceptual model are confirmed using results from a physically based, synchronously coupled biosphere–atmosphere model. This study provides guidance for interpreting and understanding the model dependence of biosphere–atmosphere interaction studies using complex climate system models.
Abstract
This paper presents a conceptual modeling study on the behaviors of terrestrial biosphere–atmosphere systems as they relate to multiple equilibrium states and climate variability, and emphasizes their implications for physically based climate modeling. The conceptual biosphere–atmosphere model consists of equilibrium responses of vegetation and precipitation to each other, dynamics of the vegetation system, and stochastic forcing of precipitation representing the impact of atmospheric internal variability. Using precipitation as the atmospheric variable in describing the biosphere–atmosphere interactions, this model pertains to regions where vegetation growth is limited by water. Low moisture convergence in the atmosphere combined with high sensitivity of the atmospheric climate to vegetation changes provides the most favorable condition for the existence of multiple equilibrium states. In a coupled biosphere–atmosphere system with multiple equilibria, experiments varying the stochastic forcing indicate that atmospheric internal variability is an important factor in the long-term variability of the model climate and in its sensitivity to initial conditions. Specifically, the enhancement of low-frequency rainfall variability by vegetation dynamics is most pronounced with a moderate magnitude of atmospheric internal variability and is less pronounced if internal variability is either too large or too small; detecting the existence of multiple equilibria by examining the sensitivity of the coupled model climate to initial conditions is not always reliable since too large an internal variability reduces or even eliminates the model sensitivity to initial conditions. Findings from the conceptual model are confirmed using results from a physically based, synchronously coupled biosphere–atmosphere model. This study provides guidance for interpreting and understanding the model dependence of biosphere–atmosphere interaction studies using complex climate system models.
Abstract
Although the intensity of extreme precipitation is predicted to increase with climate warming, at the weather scale precipitation extremes over most of the globe decrease when temperature exceeds a certain threshold, and the spatial extent of this negative scaling is projected to increase as the climate warms. The nature and cause of the negative scaling at high temperature and its implications remain poorly understood. Based on subdaily data from observations, a reanalysis product, and output from a coarse-resolution (∼200 km) global model and a fine-resolution (4 km) convection-permitting regional model, we show that the negative scaling is primarily a reflection of high temperature suppressing precipitation over land and storm-induced temperature variations over the ocean. We further identify the high temperature–induced increase of saturation deficit as a critical condition for the negative scaling of extreme precipitation over land. A large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation. The heat-induced suppression of precipitation, both for its mean and extremes, provides one mechanism for the co-occurrence of drought and heatwaves. As the saturation deficit over land is expected to increase in a warmer climate, our results imply a growing prevalence of negative scaling, potentially increasing the frequency of compound drought and heat events. Understanding the physical mechanisms underlying the negative scaling of precipitation at high temperature is, therefore, essential for assessing future risks of extreme events, including not only flood due to extreme precipitation but also drought and heatwaves.
Significance Statement
Negative scaling, a decrease of extreme precipitation at high local temperature, is a poorly understood phenomenon. It was suggested that the negative scaling may be a reflection of precipitation’s influence on temperature. Here we show based on observational data, a reanalysis product, and climate models that the negative scaling results primarily from the impact of the high temperature–induced saturation deficit on precipitation over land and from storm-induced temperature variations over the ocean. In hot weather when moisture is limited (as is over land), a large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation, leading to a negative scaling. The same mechanism can also contribute to increased compound drought and heat events.
Abstract
Although the intensity of extreme precipitation is predicted to increase with climate warming, at the weather scale precipitation extremes over most of the globe decrease when temperature exceeds a certain threshold, and the spatial extent of this negative scaling is projected to increase as the climate warms. The nature and cause of the negative scaling at high temperature and its implications remain poorly understood. Based on subdaily data from observations, a reanalysis product, and output from a coarse-resolution (∼200 km) global model and a fine-resolution (4 km) convection-permitting regional model, we show that the negative scaling is primarily a reflection of high temperature suppressing precipitation over land and storm-induced temperature variations over the ocean. We further identify the high temperature–induced increase of saturation deficit as a critical condition for the negative scaling of extreme precipitation over land. A large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation. The heat-induced suppression of precipitation, both for its mean and extremes, provides one mechanism for the co-occurrence of drought and heatwaves. As the saturation deficit over land is expected to increase in a warmer climate, our results imply a growing prevalence of negative scaling, potentially increasing the frequency of compound drought and heat events. Understanding the physical mechanisms underlying the negative scaling of precipitation at high temperature is, therefore, essential for assessing future risks of extreme events, including not only flood due to extreme precipitation but also drought and heatwaves.
Significance Statement
Negative scaling, a decrease of extreme precipitation at high local temperature, is a poorly understood phenomenon. It was suggested that the negative scaling may be a reflection of precipitation’s influence on temperature. Here we show based on observational data, a reanalysis product, and climate models that the negative scaling results primarily from the impact of the high temperature–induced saturation deficit on precipitation over land and from storm-induced temperature variations over the ocean. In hot weather when moisture is limited (as is over land), a large saturation deficit reduces precipitation intensity by slowing down the convective updraft condensation rate and accelerating condensate evaporation, leading to a negative scaling. The same mechanism can also contribute to increased compound drought and heat events.
Abstract
Subgrid variability in rainfall distribution has been widely recognized as an important factor to include in the representation of land surface hydrology within climate models. In this paper, using West Africa as a case study, the impact of the subgrid variability in rainfall interception on the modeling of the biosphere–atmosphere system is investigated. According to the authors’ results, when neglecting the rainfall spatial variability, even if the impact on the total evapotranspiration is negligible, significant errors may result in the representation of surface hydrological processes and surface energy balance. These findings are consistent with the results of previous studies. However, in this paper, this issue is further explored and it is demonstrated that the extent of the resulting errors is not limited to the land surface processes. They extend to the atmosphere via the low-level cloud feedback to impact solar radiation, boundary layer energy, atmospheric circulation, and the distribution of precipitation. The same errors also propagate into the biosphere through vegetation dynamics and can eventually lead to a significantly different biosphere–atmosphere equilibrium state. This study provides a good example for the need to have physical realism in modeling the subgrid variability and most other details of the complex biosphere–atmosphere–ocean system.
Abstract
Subgrid variability in rainfall distribution has been widely recognized as an important factor to include in the representation of land surface hydrology within climate models. In this paper, using West Africa as a case study, the impact of the subgrid variability in rainfall interception on the modeling of the biosphere–atmosphere system is investigated. According to the authors’ results, when neglecting the rainfall spatial variability, even if the impact on the total evapotranspiration is negligible, significant errors may result in the representation of surface hydrological processes and surface energy balance. These findings are consistent with the results of previous studies. However, in this paper, this issue is further explored and it is demonstrated that the extent of the resulting errors is not limited to the land surface processes. They extend to the atmosphere via the low-level cloud feedback to impact solar radiation, boundary layer energy, atmospheric circulation, and the distribution of precipitation. The same errors also propagate into the biosphere through vegetation dynamics and can eventually lead to a significantly different biosphere–atmosphere equilibrium state. This study provides a good example for the need to have physical realism in modeling the subgrid variability and most other details of the complex biosphere–atmosphere–ocean system.
Abstract
The natural variability in the annual flow of the Nile is significantly regulated by the El Niño–Southern Oscillation (ENSO). In this paper, several sources of information are combined, including ENSO, rainfall over Ethiopia, and the recent history of river flow in the Nile, in order to obtain accurate forecasts of the Nile flood at Aswan. The Bayesian theorem is used in developing the discriminant forecasting algorithm. Conditional categoric probabilities are used to describe the flood forecasts, and a synoptic index is defined to measure the forecasts’ skill. The results presented show that ENSO information is the only valuable predictor for the long-range forecasts (lead time longer than the hydrological response timescale, which is 2–3 months in this study). However, the incorporation of the rainfall and river flow information in addition to the ENSO information significantly improves the quality of the medium-range forecasts (lead time shorter than the hydrological response timescale).
Abstract
The natural variability in the annual flow of the Nile is significantly regulated by the El Niño–Southern Oscillation (ENSO). In this paper, several sources of information are combined, including ENSO, rainfall over Ethiopia, and the recent history of river flow in the Nile, in order to obtain accurate forecasts of the Nile flood at Aswan. The Bayesian theorem is used in developing the discriminant forecasting algorithm. Conditional categoric probabilities are used to describe the flood forecasts, and a synoptic index is defined to measure the forecasts’ skill. The results presented show that ENSO information is the only valuable predictor for the long-range forecasts (lead time longer than the hydrological response timescale, which is 2–3 months in this study). However, the incorporation of the rainfall and river flow information in addition to the ENSO information significantly improves the quality of the medium-range forecasts (lead time shorter than the hydrological response timescale).
Abstract
Using the Regional Climate Model (RegCM) coupled with the Community Land Model (CLM) including modules of carbon–nitrogen cycling (CN) and vegetation dynamics (DV), this study evaluates the performance of the model with different capacity of representing vegetation processes in simulating the present-day climate over China based on three 21-yr simulations driven with boundary conditions from the ERA-Interim reanalysis data during 1989–2009. For each plant functional type (PFT), the plant pheonology, density, and fractional coverage in RegCM-CLM are all prescribed as static from year to year; RegCM-CLM-CN prescribes static fractional coverage but predicts plant phenology and density, and RegCM-CLM-CN-DV predicts plant phenology, density, and fractional coverage. Compared against the observational data, all three simulations reproduce the present-day climate well, including the wind fields, temperature and precipitation seasonal cycles, extremes, and interannual variabilities. Relative to RegCM-CLM, both RegCM-CLM-CN and RegCM-CLM-CN-DV perform better in simulating the interannual variability of temperature and spatial distribution of mean precipitation, but produce larger biases in the mean temperature field. RegCM-CLM-CN overestimates leaf area index (LAI), which enhances the cold biases and alleviates the dry biases found in RegCM-CLM. RegCM-CLM-CN-DV underestimates vegetation cover and/or stature, and hence overestimates surface albedo, which enhances the wintertime cold and dry biases found in RegM-CLM. During summer, RegCM-CLM-CN-DV overestimates LAI in south and east China, which enhances the cold biases through increased evaporative cooling; in the west where evaporation is low, the albedo effect of the underestimated vegetation cover is still dominant, leading to enhanced cold biases relative to RegCM-CLM.
Abstract
Using the Regional Climate Model (RegCM) coupled with the Community Land Model (CLM) including modules of carbon–nitrogen cycling (CN) and vegetation dynamics (DV), this study evaluates the performance of the model with different capacity of representing vegetation processes in simulating the present-day climate over China based on three 21-yr simulations driven with boundary conditions from the ERA-Interim reanalysis data during 1989–2009. For each plant functional type (PFT), the plant pheonology, density, and fractional coverage in RegCM-CLM are all prescribed as static from year to year; RegCM-CLM-CN prescribes static fractional coverage but predicts plant phenology and density, and RegCM-CLM-CN-DV predicts plant phenology, density, and fractional coverage. Compared against the observational data, all three simulations reproduce the present-day climate well, including the wind fields, temperature and precipitation seasonal cycles, extremes, and interannual variabilities. Relative to RegCM-CLM, both RegCM-CLM-CN and RegCM-CLM-CN-DV perform better in simulating the interannual variability of temperature and spatial distribution of mean precipitation, but produce larger biases in the mean temperature field. RegCM-CLM-CN overestimates leaf area index (LAI), which enhances the cold biases and alleviates the dry biases found in RegCM-CLM. RegCM-CLM-CN-DV underestimates vegetation cover and/or stature, and hence overestimates surface albedo, which enhances the wintertime cold and dry biases found in RegM-CLM. During summer, RegCM-CLM-CN-DV overestimates LAI in south and east China, which enhances the cold biases through increased evaporative cooling; in the west where evaporation is low, the albedo effect of the underestimated vegetation cover is still dominant, leading to enhanced cold biases relative to RegCM-CLM.
Abstract
In arid and semiarid regions most of the solar radiation penetrates through the canopy and reaches the ground, and hence the turbulent exchange coefficient under canopy Cs becomes important. The use of a constant Cs that is only appropriate for thick canopies is found to be primarily responsible for the excessive warm bias of around 10 K in monthly mean ground temperature over these regions in version 2 of the Community Climate System Model (CCSM2). New Cs formulations are developed for the consistent treatment of undercanopy turbulence for both thick and thin canopies in land models, and provide a preliminary solution of this problem.
Abstract
In arid and semiarid regions most of the solar radiation penetrates through the canopy and reaches the ground, and hence the turbulent exchange coefficient under canopy Cs becomes important. The use of a constant Cs that is only appropriate for thick canopies is found to be primarily responsible for the excessive warm bias of around 10 K in monthly mean ground temperature over these regions in version 2 of the Community Climate System Model (CCSM2). New Cs formulations are developed for the consistent treatment of undercanopy turbulence for both thick and thin canopies in land models, and provide a preliminary solution of this problem.
Abstract
This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.
Abstract
This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.
