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- Author or Editor: Naota Hanasaki x
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Quantification of sources and sinks of carbon at global and regional scales requires not only a good description of the land sources and sinks of carbon, but also of the synoptic and mesoscale meteorology. An experiment was performed in Les Landes, southwest France, during May–June 2005, to determine the variability in concentration gradients and fluxes of CO2 The CarboEurope Regional Experiment Strategy (CERES; see also http://carboregional.mediasfrance.org/index) aimed to produce aggregated estimates of the carbon balance of a region that can be meaningfully compared to those obtained from the smallest downscaled information of atmospheric measurements and continental-scale inversions. We deployed several aircraft to sample the CO2 concentration and fluxes over the whole area, while fixed stations observed the fluxes and concentrations at high accuracy. Several (mesoscale) meteorological modeling tools were used to plan the experiment and flight patterns.
Results show that at regional scale the relation between profiles and fluxes is not obvious, and is strongly influenced by airmass history and mesoscale flow patterns. In particular, we show from an analysis of data for a single day that taking either the concentration at several locations as representative of local fluxes or taking the flux measurements at those sites as representative of larger regions would lead to incorrect conclusions about the distribution of sources and sinks of carbon. Joint consideration of the synoptic and regional flow, fluxes, and land surface is required for a correct interpretation. This calls for an experimental and modeling strategy that takes into account the large spatial gradients in concentrations and the variability in sources and sinks that arise from different land use types. We briefly describe how such an analysis can be performed and evaluate the usefulness of the data for planning of future networks or longer campaigns with reduced experimental efforts.
Quantification of sources and sinks of carbon at global and regional scales requires not only a good description of the land sources and sinks of carbon, but also of the synoptic and mesoscale meteorology. An experiment was performed in Les Landes, southwest France, during May–June 2005, to determine the variability in concentration gradients and fluxes of CO2 The CarboEurope Regional Experiment Strategy (CERES; see also http://carboregional.mediasfrance.org/index) aimed to produce aggregated estimates of the carbon balance of a region that can be meaningfully compared to those obtained from the smallest downscaled information of atmospheric measurements and continental-scale inversions. We deployed several aircraft to sample the CO2 concentration and fluxes over the whole area, while fixed stations observed the fluxes and concentrations at high accuracy. Several (mesoscale) meteorological modeling tools were used to plan the experiment and flight patterns.
Results show that at regional scale the relation between profiles and fluxes is not obvious, and is strongly influenced by airmass history and mesoscale flow patterns. In particular, we show from an analysis of data for a single day that taking either the concentration at several locations as representative of local fluxes or taking the flux measurements at those sites as representative of larger regions would lead to incorrect conclusions about the distribution of sources and sinks of carbon. Joint consideration of the synoptic and regional flow, fluxes, and land surface is required for a correct interpretation. This calls for an experimental and modeling strategy that takes into account the large spatial gradients in concentrations and the variability in sources and sinks that arise from different land use types. We briefly describe how such an analysis can be performed and evaluate the usefulness of the data for planning of future networks or longer campaigns with reduced experimental efforts.
Abstract
Evaluation of Chinese precipitation extremes is conducted based on large ensemble projections of the present climate and 4-K-warmer climates derived from a high-resolution atmospheric general circulation model. The model reproduced the overall trend and magnitude of total precipitation and extreme precipitation events for China reasonably well, revealing that this dataset can represent localized precipitation extremes. Precipitation extremes are more frequent and more severe in future projections under 4-K-warmer climates than in the representative concentration pathway 8.5 (RCP8.5) scenario of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Our results show that using a large-ensemble simulation can improve the ability to estimate with high precision both the precipitation mean and the precipitation extremes compared with small numbers of simulations, and the averaged maximum yearly precipitation will be likely to increase by approximately 18% under a +4-K future in southern China compared with the past. Finally, uncertainty evaluation in future precipitation projections indicates that the component caused by the difference in six ΔSST patterns is more important in southern China compared with the component due to the atmospheric internal variability. All these results could provide valuable insights in simulating and predicting precipitation extremes in China.
Abstract
Evaluation of Chinese precipitation extremes is conducted based on large ensemble projections of the present climate and 4-K-warmer climates derived from a high-resolution atmospheric general circulation model. The model reproduced the overall trend and magnitude of total precipitation and extreme precipitation events for China reasonably well, revealing that this dataset can represent localized precipitation extremes. Precipitation extremes are more frequent and more severe in future projections under 4-K-warmer climates than in the representative concentration pathway 8.5 (RCP8.5) scenario of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Our results show that using a large-ensemble simulation can improve the ability to estimate with high precision both the precipitation mean and the precipitation extremes compared with small numbers of simulations, and the averaged maximum yearly precipitation will be likely to increase by approximately 18% under a +4-K future in southern China compared with the past. Finally, uncertainty evaluation in future precipitation projections indicates that the component caused by the difference in six ΔSST patterns is more important in southern China compared with the component due to the atmospheric internal variability. All these results could provide valuable insights in simulating and predicting precipitation extremes in China.
Abstract
Anthropogenic activities have been significantly perturbing global freshwater flows and groundwater reserves. Despite numerous advances in the development of land surface models (LSMs) and global terrestrial hydrological models (GHMs), relatively few studies have attempted to simulate the impacts of anthropogenic activities on the terrestrial water cycle using the framework of LSMs. From the comparison of simulated terrestrial water storage with the Gravity Recovery and Climate Experiment (GRACE) satellite observations it is found that a process-based LSM, the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), outperforms the bucket-model-based GHM called H08 in simulating hydrologic variables, particularly in water-limited regions. Therefore, the water regulation modules of H08 are incorporated into MATSIRO. Further, a new irrigation scheme based on the soil moisture deficit is developed. Incorporation of anthropogenic water regulation modules significantly improves river discharge simulation in the heavily regulated global river basins. Simulated irrigation water withdrawal for the year 2000 (2462 km3 yr−1) agrees well with the estimates provided by the Food and Agriculture Organization (FAO). Results indicate that irrigation changes surface energy balance, causing a maximum increase of ~50 W m−2 in latent heat flux averaged over June–August. Moreover, unsustainable anthropogenic water use in 2000 is estimated to be ~450 km3 yr−1, which corresponds well with documented records of groundwater overdraft, representing an encouraging improvement over the previous modeling studies. Globally, unsustainable water use accounts for ~40% of blue water used for irrigation. The representation of anthropogenic activities in MATSIRO makes the model a suitable tool for assessing potential anthropogenic impacts on global water resources and hydrology.
Abstract
Anthropogenic activities have been significantly perturbing global freshwater flows and groundwater reserves. Despite numerous advances in the development of land surface models (LSMs) and global terrestrial hydrological models (GHMs), relatively few studies have attempted to simulate the impacts of anthropogenic activities on the terrestrial water cycle using the framework of LSMs. From the comparison of simulated terrestrial water storage with the Gravity Recovery and Climate Experiment (GRACE) satellite observations it is found that a process-based LSM, the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO), outperforms the bucket-model-based GHM called H08 in simulating hydrologic variables, particularly in water-limited regions. Therefore, the water regulation modules of H08 are incorporated into MATSIRO. Further, a new irrigation scheme based on the soil moisture deficit is developed. Incorporation of anthropogenic water regulation modules significantly improves river discharge simulation in the heavily regulated global river basins. Simulated irrigation water withdrawal for the year 2000 (2462 km3 yr−1) agrees well with the estimates provided by the Food and Agriculture Organization (FAO). Results indicate that irrigation changes surface energy balance, causing a maximum increase of ~50 W m−2 in latent heat flux averaged over June–August. Moreover, unsustainable anthropogenic water use in 2000 is estimated to be ~450 km3 yr−1, which corresponds well with documented records of groundwater overdraft, representing an encouraging improvement over the previous modeling studies. Globally, unsustainable water use accounts for ~40% of blue water used for irrigation. The representation of anthropogenic activities in MATSIRO makes the model a suitable tool for assessing potential anthropogenic impacts on global water resources and hydrology.
Abstract
A pattern scaling approach allows projection of regional climate changes under a wide range of emission scenarios. A basic assumption of this approach is that the spatial response pattern to global warming (scaling pattern) is the same for all emission scenarios. Precipitation minus evapotranspiration (PME) over land can be considered to be a measure of the maximum available renewable freshwater resource, and estimation of PME is fundamentally important for the assessment of water resources. The authors assessed the basic assumption of pattern scaling for PME by the use of five global climate models. A significant scenario dependency (SD) of the scaling pattern of PME was found over some regions. This SD of the scaling pattern of PME was mainly due to the SD and the nonlinear response of large-scale atmospheric and oceanic changes. When the SD of the scaling pattern of PME is significant in a target area, projections of the impact of climate change need to carefully take into consideration the SD. Although the SD of the anthropogenic aerosol scaling patterns tended to induce SDs of precipitation and evapotranspiration scaling patterns, the SDs of precipitation and evapotranspiration tended to cancel each other out. As a result, the SD of the PME scaling pattern tended to be insignificant over most regions where the SD of anthropogenic aerosol scaling patterns were significant. The authors could not find large impacts of land use change on PME scaling pattern, but the former may influence the latter on different time scales or spatial scales.
Abstract
A pattern scaling approach allows projection of regional climate changes under a wide range of emission scenarios. A basic assumption of this approach is that the spatial response pattern to global warming (scaling pattern) is the same for all emission scenarios. Precipitation minus evapotranspiration (PME) over land can be considered to be a measure of the maximum available renewable freshwater resource, and estimation of PME is fundamentally important for the assessment of water resources. The authors assessed the basic assumption of pattern scaling for PME by the use of five global climate models. A significant scenario dependency (SD) of the scaling pattern of PME was found over some regions. This SD of the scaling pattern of PME was mainly due to the SD and the nonlinear response of large-scale atmospheric and oceanic changes. When the SD of the scaling pattern of PME is significant in a target area, projections of the impact of climate change need to carefully take into consideration the SD. Although the SD of the anthropogenic aerosol scaling patterns tended to induce SDs of precipitation and evapotranspiration scaling patterns, the SDs of precipitation and evapotranspiration tended to cancel each other out. As a result, the SD of the PME scaling pattern tended to be insignificant over most regions where the SD of anthropogenic aerosol scaling patterns were significant. The authors could not find large impacts of land use change on PME scaling pattern, but the former may influence the latter on different time scales or spatial scales.
Abstract
Large-scale hydrological models describing the terrestrial water balance at continental and global scales are increasingly being used in earth system modeling and climate impact assessments. However, because of incomplete process understanding and limits of the forcing data, model simulations remain uncertain. To quantify this uncertainty a multimodel ensemble of nine large-scale hydrological models was compared to observed runoff from 426 small catchments in Europe. The ensemble was built within the framework of the European Union Water and Global Change (WATCH) project. The models were driven with the same atmospheric forcing data. Models were evaluated with respect to their ability to capture the interannual variability of spatially aggregated annual time series of five runoff percentiles—derived from daily time series—including annual low and high flows. Overall, the models capture the interannual variability of low, mean, and high flows well. However, errors in the mean and standard deviation, as well as differences in performance between the models, became increasingly pronounced for low runoff percentiles, reflecting the uncertainty associated with the representation of hydrological processes, such as the depletion of soil moisture stores. The large spread in model performance implies that any single model should be applied with caution as there is a great risk of biased conclusions. However, this large spread is contrasted by the good overall performance of the ensemble mean. It is concluded that the ensemble mean is a pragmatic and reliable estimator of spatially aggregated time series of annual low, mean, and high flows across Europe.
Abstract
Large-scale hydrological models describing the terrestrial water balance at continental and global scales are increasingly being used in earth system modeling and climate impact assessments. However, because of incomplete process understanding and limits of the forcing data, model simulations remain uncertain. To quantify this uncertainty a multimodel ensemble of nine large-scale hydrological models was compared to observed runoff from 426 small catchments in Europe. The ensemble was built within the framework of the European Union Water and Global Change (WATCH) project. The models were driven with the same atmospheric forcing data. Models were evaluated with respect to their ability to capture the interannual variability of spatially aggregated annual time series of five runoff percentiles—derived from daily time series—including annual low and high flows. Overall, the models capture the interannual variability of low, mean, and high flows well. However, errors in the mean and standard deviation, as well as differences in performance between the models, became increasingly pronounced for low runoff percentiles, reflecting the uncertainty associated with the representation of hydrological processes, such as the depletion of soil moisture stores. The large spread in model performance implies that any single model should be applied with caution as there is a great risk of biased conclusions. However, this large spread is contrasted by the good overall performance of the ensemble mean. It is concluded that the ensemble mean is a pragmatic and reliable estimator of spatially aggregated time series of annual low, mean, and high flows across Europe.
Abstract
Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).
Abstract
Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).
