Search Results
You are looking at 1 - 10 of 28 items for
- Author or Editor: A. J. Pitman x
- Refine by Access: All Content x
Abstract
The sensitivity of a land-surface scheme (the Biosphere Atmosphere Transfer Scheme, BATS) to its parameter values was investigated using a single column model. Identifying which parameters were important in controlling the turbulent energy fluxes, temperature, soil moisture, and runoff was dependent upon many factors. In the simulation of a nonmoisture-stressed tropical forest, results were dependent on a combination of reservoir terms (soil depth, root distribution), flux efficiency terms (roughness length, stomatal resistance), and available energy (albedo). If moisture became limited, the reservoir terms increased in importance because the total fluxes predicted depended on moisture availability and not on the rate of transfer between the surface and the atmosphere. The sensitivity shown by BATS depended on which vegetation type was being simulated, which variable was used to determine sensitivity, the magnitude and sign of the parameter change, the climate regime (precipitation amount and frequency), and soil moisture levels and proximity to wilting. The interactions between these factors made it difficult to identify the most important parameters in BATS. Therefore, this paper does not argue that a particular set of parameters is important in BATS, rather it shows that no general ranking of parameters is possible. It is also emphasized that using “stand-alone” forcing to examine the sensitivity of a land-surface scheme to perturbations, in either parameters or the atmosphere, is unreliable due to the lack of surface-atmospheric feedbacks.
Abstract
The sensitivity of a land-surface scheme (the Biosphere Atmosphere Transfer Scheme, BATS) to its parameter values was investigated using a single column model. Identifying which parameters were important in controlling the turbulent energy fluxes, temperature, soil moisture, and runoff was dependent upon many factors. In the simulation of a nonmoisture-stressed tropical forest, results were dependent on a combination of reservoir terms (soil depth, root distribution), flux efficiency terms (roughness length, stomatal resistance), and available energy (albedo). If moisture became limited, the reservoir terms increased in importance because the total fluxes predicted depended on moisture availability and not on the rate of transfer between the surface and the atmosphere. The sensitivity shown by BATS depended on which vegetation type was being simulated, which variable was used to determine sensitivity, the magnitude and sign of the parameter change, the climate regime (precipitation amount and frequency), and soil moisture levels and proximity to wilting. The interactions between these factors made it difficult to identify the most important parameters in BATS. Therefore, this paper does not argue that a particular set of parameters is important in BATS, rather it shows that no general ranking of parameters is possible. It is also emphasized that using “stand-alone” forcing to examine the sensitivity of a land-surface scheme to perturbations, in either parameters or the atmosphere, is unreliable due to the lack of surface-atmospheric feedbacks.
Abstract
The Regional Atmospheric Modeling System (RAMS) was run at a 1-km grid spacing over the Sydney basin in Australia to assess the impact of land cover change on a simulated storm event. The simulated storm used NCEP–NCAR reanalysis data, first with natural (i.e., pre-European settlement in 1788) land cover and then with satellite-derived land cover representing Sydney's current land use pattern. An intense convective storm develops in the model in close proximity to Sydney's dense urban central business district under current land cover. The storm is absent under natural land cover conditions. A detailed investigation of why the change in land cover generates a storm was performed using factorial analysis, which revealed the storm to be sensitive to the presence of agricultural land in the southwest of the domain. This area interacts with the sea breeze and affects the horizontal divergence and moisture convergence—the triggering mechanisms of the storm. The existence of the storm over the dense urban area of Sydney is therefore coincidental. The results herein support efforts to develop parameterization of urban surfaces in high-resolution simulations of Sydney's meteorological environment but also highlight the need to improve the parameterization of other types of land cover change at the periphery of the urban area, given that these types dominate the explanation of the results.
Abstract
The Regional Atmospheric Modeling System (RAMS) was run at a 1-km grid spacing over the Sydney basin in Australia to assess the impact of land cover change on a simulated storm event. The simulated storm used NCEP–NCAR reanalysis data, first with natural (i.e., pre-European settlement in 1788) land cover and then with satellite-derived land cover representing Sydney's current land use pattern. An intense convective storm develops in the model in close proximity to Sydney's dense urban central business district under current land cover. The storm is absent under natural land cover conditions. A detailed investigation of why the change in land cover generates a storm was performed using factorial analysis, which revealed the storm to be sensitive to the presence of agricultural land in the southwest of the domain. This area interacts with the sea breeze and affects the horizontal divergence and moisture convergence—the triggering mechanisms of the storm. The existence of the storm over the dense urban area of Sydney is therefore coincidental. The results herein support efforts to develop parameterization of urban surfaces in high-resolution simulations of Sydney's meteorological environment but also highlight the need to improve the parameterization of other types of land cover change at the periphery of the urban area, given that these types dominate the explanation of the results.
Abstract
A comparison of three global reanalyses is conducted based on probability density functions of daily maximum and minimum temperature at 2-m and 1000-hPa levels. The three reanalyses compare very favorably in both maximum and minimum temperatures at 1000 hPa, in both the mean and the 99.7th and 0.3rd percentiles of both quantities in most regions. At 2 m, there are large and widespread differences in the mean and 99.7th percentiles in maximum temperature between the three reanalyses over land commonly exceeding ±5°C and regionally exceeding ±10°C. The 2-m minimum temperatures compare unfavorably between the three reanalyses over virtually all continental surfaces with differences exceeding ±10°C over widespread areas. It is concluded that the three reanalyses are generally interchangeable in 1000-hPa temperatures. The three reanalyses of 2-m temperatures are very different owing to the methods used to diagnose these quantities. At this time, the probability distribution functions of the 2-m temperatures from the three reanalyses are sufficiently different that either the 2-m air temperatures should not be used or all three products should be used independently in any application and the differences highlighted.
Abstract
A comparison of three global reanalyses is conducted based on probability density functions of daily maximum and minimum temperature at 2-m and 1000-hPa levels. The three reanalyses compare very favorably in both maximum and minimum temperatures at 1000 hPa, in both the mean and the 99.7th and 0.3rd percentiles of both quantities in most regions. At 2 m, there are large and widespread differences in the mean and 99.7th percentiles in maximum temperature between the three reanalyses over land commonly exceeding ±5°C and regionally exceeding ±10°C. The 2-m minimum temperatures compare unfavorably between the three reanalyses over virtually all continental surfaces with differences exceeding ±10°C over widespread areas. It is concluded that the three reanalyses are generally interchangeable in 1000-hPa temperatures. The three reanalyses of 2-m temperatures are very different owing to the methods used to diagnose these quantities. At this time, the probability distribution functions of the 2-m temperatures from the three reanalyses are sufficiently different that either the 2-m air temperatures should not be used or all three products should be used independently in any application and the differences highlighted.
Abstract
Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection.
Abstract
Daily data from climate models submitted to the Fourth Assessment of the Intergovernmental Panel on Climate Change are compared with daily data from observations over Australia by measuring the overlap of the probability density functions (PDFs). The capacity of these models to simulate maximum temperature, minimum temperature, and precipitation is assessed. The resulting skill score is then used to exclude models with relatively poor skill region by region over Australia. The remaining sample of coupled climate models is then used to determine the seasonal changes in these three variables under a high- (A2) and low- (B1) emission scenario for 2050 and 2100. The authors demonstrate that some projected phenomena, such as the projected drying over southwest Western Australia, are robust and not caused by the inclusion of some weak models in earlier assessments. Some other results, such as the projected change in the monsoon, are more consistent among the good climate models. Consistent with earlier work, a consistent pattern of mean warming is identified in the projections. The amount of warming in the 99.7th percentile is not dramatically higher than the warming in the mean. However, while the mean warming is generally least in the south, the amount of warming in the 99.7th percentile is substantially higher along the southern coast of Australia. This is due to a coupling of the temperature response with reduced rainfall, which causes drying and allows extreme maximum temperatures to increase dramatically. The authors show that, in general, the amount of rainfall is projected to change relatively little, but the frequency of rainfall decreases and the intensity of rainfall at the upper tail of the distribution increases. However, the scale of the increase in extreme rainfall is not large on the time scales analyzed here. The range in projected temperature changes among those climate models with skill in simulating the observations is at least twice as large for the 99.7th/0.3rd percentiles as for the mean. For rainfall, the range among the good models is of order 10 times greater in the 99.7th percentile than in the mean. Since the impact of changes in extremes is increasingly recognized as societally important, this result strongly limits the use of climate model data to explore sectors that are vulnerable to extremes. This suggests an evaluation strategy that focuses on model capacity to simulate whole PDFs since capacity to simulate the mean is a necessary but insufficient criterion for determining a model’s value for future projection.
Abstract
The potential role of the impacts of land-cover changes (LCCs) in the Australian climate is investigated within the context of increasing CO2 concentrations and temperature. Specifically, it is explored if possible scenarios for LCC can moderate or amplify CO2-induced changes in climate over Australia. The January climate of Australia is simulated under three different land-cover-change scenarios using a high-resolution regional climate model. The land-cover-change scenarios include a steady-state land cover that is equivalent to current land cover, a low-reforestation scenario that recovers approximately 25% of the trees replaced by grasslands within the last 200 yr, and a high-reforestation scenario that recovers at least 75% of the deforested regions. The model was driven by boundary conditions taken from transitory climate simulations from a general circulation model that included two climate scenarios based on two projected scenarios of CO2 concentration increase. The results show that reforestation has the potential to reduce the projected increase in Australian temperatures in 2050 and 2100 by as much as 40% and 20%, respectively. This cooling effect, however, is highly localized and occurs only in regions of reforestation. The results therefore hint that the potential of reforestation to moderate the impact of global warming may be significantly limited by the spatial scale of reforestation. In terms of deforestation, results show that any future land clearing can exacerbate the projected warming in certain regions of Australia. Carbon-related variables are also analyzed and results show that changes in net CO2 flux may be influenced more by soil respiration than by photosynthesis. The results herein encourage studies on the inclusion of land-cover-change scenarios in future climate change projection simulations of the Australian climate.
Abstract
The potential role of the impacts of land-cover changes (LCCs) in the Australian climate is investigated within the context of increasing CO2 concentrations and temperature. Specifically, it is explored if possible scenarios for LCC can moderate or amplify CO2-induced changes in climate over Australia. The January climate of Australia is simulated under three different land-cover-change scenarios using a high-resolution regional climate model. The land-cover-change scenarios include a steady-state land cover that is equivalent to current land cover, a low-reforestation scenario that recovers approximately 25% of the trees replaced by grasslands within the last 200 yr, and a high-reforestation scenario that recovers at least 75% of the deforested regions. The model was driven by boundary conditions taken from transitory climate simulations from a general circulation model that included two climate scenarios based on two projected scenarios of CO2 concentration increase. The results show that reforestation has the potential to reduce the projected increase in Australian temperatures in 2050 and 2100 by as much as 40% and 20%, respectively. This cooling effect, however, is highly localized and occurs only in regions of reforestation. The results therefore hint that the potential of reforestation to moderate the impact of global warming may be significantly limited by the spatial scale of reforestation. In terms of deforestation, results show that any future land clearing can exacerbate the projected warming in certain regions of Australia. Carbon-related variables are also analyzed and results show that changes in net CO2 flux may be influenced more by soil respiration than by photosynthesis. The results herein encourage studies on the inclusion of land-cover-change scenarios in future climate change projection simulations of the Australian climate.
Abstract
The effect of land cover change on the Australian regional-scale climate is investigated using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). Four ensemble simulations are performed consisting of January and July experiments for eight different years with a 50-km grid spacing using natural (1788) and current (1988) vegetation cover. The statistical significance of changes that occurred following the replacement of natural vegetation with current vegetation on air temperature, rainfall, latent heat flux, and other related quantities is explored. Results show that the impact of land cover change on local air temperature is statistically significant at a 99% confidence level. Furthermore, there are indications that the observed increase in local maximum air temperatures in certain regions of Australia can be partially attributed to land cover change. The results are evidence of statistically significant changes in rainfall, and the sign of these changes over Western Australia in July, and the lack of any simulated changes in January, agree with observations. These results provide further evidence of large-scale reductions in rainfall following land cover change. Changes in wind speed are also simulated and are consistent with those expected following land cover change. The results indicate that attempts to identify greenhouse-related warming in Australian air temperature records should account for the effects of both land cover change and increasing CO2 concentrations since both types of anthropogenic forcing exist in long-term observational records. Since further land cover change will occur in the future, directly via human impact and indirectly via CO2 fertilization, the results support efforts to include land surface schemes that allow the vegetation to interact with changes in climate in climate models.
Abstract
The effect of land cover change on the Australian regional-scale climate is investigated using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). Four ensemble simulations are performed consisting of January and July experiments for eight different years with a 50-km grid spacing using natural (1788) and current (1988) vegetation cover. The statistical significance of changes that occurred following the replacement of natural vegetation with current vegetation on air temperature, rainfall, latent heat flux, and other related quantities is explored. Results show that the impact of land cover change on local air temperature is statistically significant at a 99% confidence level. Furthermore, there are indications that the observed increase in local maximum air temperatures in certain regions of Australia can be partially attributed to land cover change. The results are evidence of statistically significant changes in rainfall, and the sign of these changes over Western Australia in July, and the lack of any simulated changes in January, agree with observations. These results provide further evidence of large-scale reductions in rainfall following land cover change. Changes in wind speed are also simulated and are consistent with those expected following land cover change. The results indicate that attempts to identify greenhouse-related warming in Australian air temperature records should account for the effects of both land cover change and increasing CO2 concentrations since both types of anthropogenic forcing exist in long-term observational records. Since further land cover change will occur in the future, directly via human impact and indirectly via CO2 fertilization, the results support efforts to include land surface schemes that allow the vegetation to interact with changes in climate in climate models.
Abstract
This paper presents a methodology for examining land–atmosphere coupling in a regional climate model by examining how the resistances to moisture transfer from the land to the atmosphere control the surface turbulent energy fluxes. Perturbations were applied individually to the aerodynamic resistance from the soil surface to the displacement height, the aerodynamic resistance from the displacement height to the reference level, the stomatal resistance, and the leaf boundary layer resistance. Only perturbations to the aerodynamic resistance from the soil surface to the displacement height systematically affected 2-m air temperature for the shrub and evergreen boreal forest plant functional types (PFTs). This was associated with this resistance systematically increasing the terrestrial and atmospheric components of the land–atmosphere coupling strength through changes in the partitioning of the surface energy balance. Perturbing the other resistances did contribute to changing the partitioning of the surface energy balance but did not lead to systematic changes in the 2-m air temperature. The results suggest that land–atmosphere coupling in the modeling system presented here acts mostly through the aerodynamic resistance from the soil surface to the displacement height, which is a function of both the friction velocity and vegetation height and cover. The results show that a resistance pathway framework can be used to examine how changes in the resistances affect the partitioning of the surface energy balance and how this subsequently influences surface climate through land–atmosphere coupling. Limitations in the present analysis include grid-scale rather than PFT-scale analysis, the exclusion of resistance dependencies, and the linearity assumption of how temperature responds to a resistance perturbation.
Abstract
This paper presents a methodology for examining land–atmosphere coupling in a regional climate model by examining how the resistances to moisture transfer from the land to the atmosphere control the surface turbulent energy fluxes. Perturbations were applied individually to the aerodynamic resistance from the soil surface to the displacement height, the aerodynamic resistance from the displacement height to the reference level, the stomatal resistance, and the leaf boundary layer resistance. Only perturbations to the aerodynamic resistance from the soil surface to the displacement height systematically affected 2-m air temperature for the shrub and evergreen boreal forest plant functional types (PFTs). This was associated with this resistance systematically increasing the terrestrial and atmospheric components of the land–atmosphere coupling strength through changes in the partitioning of the surface energy balance. Perturbing the other resistances did contribute to changing the partitioning of the surface energy balance but did not lead to systematic changes in the 2-m air temperature. The results suggest that land–atmosphere coupling in the modeling system presented here acts mostly through the aerodynamic resistance from the soil surface to the displacement height, which is a function of both the friction velocity and vegetation height and cover. The results show that a resistance pathway framework can be used to examine how changes in the resistances affect the partitioning of the surface energy balance and how this subsequently influences surface climate through land–atmosphere coupling. Limitations in the present analysis include grid-scale rather than PFT-scale analysis, the exclusion of resistance dependencies, and the linearity assumption of how temperature responds to a resistance perturbation.
Abstract
Six modes of complexity of the Chameleon land surface model (CHASM) are used to explore the relationship between the complexity of the surface energy balance (SEB) formulation and the capacity of the model to explain intermodel variations in results from the Rhône-Aggregation Intercomparison Project (Rhône-AGG). At an annual time scale, differences between models identified in the Rhône-AGG experiments in the partitioning of available energy and water at the spatial scale of the Rhône Basin can be reproduced by CHASM via variations in the SEB complexity. Only two changes in the SEB complexity in the model generate statistically significant differences in the mean latent heat flux. These are the addition of a constant surface resistance to the simplest mode of CHASM and the addition of tiling and temporally and spatially variable surface resistance to produce the most complex model. Further, the only statistically significant differences in runoff occur following the addition of a constant surface resistance to the simplest mode of CHASM. As the time scale is reduced from annual to monthly, specific mechanisms begin to dominate the simulations produced by each Rhône-AGG model and introduce parameterization-specific behavior that depends on the time evolution of processes operating on longer time scales. CHASM cannot capture all this behavior by varying the SEB complexity, demonstrating the contribution to intermodel differences by hydrology and snow-related processes. Despite the increasing role of hydrology and snow in simulating processes at finer time scales, provided the constant surface resistance is included, CHASM's modes perform within the range of uncertainty illustrated by other Rhône-AGG models on seasonal and annual time scales.
Abstract
Six modes of complexity of the Chameleon land surface model (CHASM) are used to explore the relationship between the complexity of the surface energy balance (SEB) formulation and the capacity of the model to explain intermodel variations in results from the Rhône-Aggregation Intercomparison Project (Rhône-AGG). At an annual time scale, differences between models identified in the Rhône-AGG experiments in the partitioning of available energy and water at the spatial scale of the Rhône Basin can be reproduced by CHASM via variations in the SEB complexity. Only two changes in the SEB complexity in the model generate statistically significant differences in the mean latent heat flux. These are the addition of a constant surface resistance to the simplest mode of CHASM and the addition of tiling and temporally and spatially variable surface resistance to produce the most complex model. Further, the only statistically significant differences in runoff occur following the addition of a constant surface resistance to the simplest mode of CHASM. As the time scale is reduced from annual to monthly, specific mechanisms begin to dominate the simulations produced by each Rhône-AGG model and introduce parameterization-specific behavior that depends on the time evolution of processes operating on longer time scales. CHASM cannot capture all this behavior by varying the SEB complexity, demonstrating the contribution to intermodel differences by hydrology and snow-related processes. Despite the increasing role of hydrology and snow in simulating processes at finer time scales, provided the constant surface resistance is included, CHASM's modes perform within the range of uncertainty illustrated by other Rhône-AGG models on seasonal and annual time scales.
Abstract
A land surface scheme with and without groundwater–vegetation interactions is used to explore the impact of rainfall variability on transpiration over drought-vulnerable regions of southeastern Australia. The authors demonstrate that if groundwater is included in the simulations, there is a low correlation between rainfall variability and the response of transpiration to this variability over forested regions. Groundwater reduces near-surface water variability, enabling forests to maintain transpiration through several years of low rainfall, in agreement with independent observations of vegetation greenness. If groundwater is not included, the transpiration variability matches the rainfall variability independent of land cover type. The authors’ results suggest that omitting groundwater in regions where groundwater sustains forests will 1) probably overestimate the likelihood of forest dieback during drought, 2) overestimate a positive feedback linked with declining transpiration and a drying boundary layer, and 3) underestimate the impact of land cover change due to inadequately simulating the different responses to drought for different land cover types.
Abstract
A land surface scheme with and without groundwater–vegetation interactions is used to explore the impact of rainfall variability on transpiration over drought-vulnerable regions of southeastern Australia. The authors demonstrate that if groundwater is included in the simulations, there is a low correlation between rainfall variability and the response of transpiration to this variability over forested regions. Groundwater reduces near-surface water variability, enabling forests to maintain transpiration through several years of low rainfall, in agreement with independent observations of vegetation greenness. If groundwater is not included, the transpiration variability matches the rainfall variability independent of land cover type. The authors’ results suggest that omitting groundwater in regions where groundwater sustains forests will 1) probably overestimate the likelihood of forest dieback during drought, 2) overestimate a positive feedback linked with declining transpiration and a drying boundary layer, and 3) underestimate the impact of land cover change due to inadequately simulating the different responses to drought for different land cover types.
