Temperature scaling pattern dependence on representative concentration pathway emission scenarios A letter

To preserve consistency among developed emission scenarios, the scenarios used in climate modeling, and the climate scenarios available for impact research, the pattern scaling technique is useful technique. The basic assumption of pattern scaling is that the spatial response pattern per 1 K increase in the global mean surface air temperature (SAT) (scaling pattern) is the same among emission scenarios, but this assumption requires further validation. We therefore investigated the dependence of the scaling pattern of the annual mean SAT on GHGs emission scenarios of representative concentration pathways (RCP) and the causes of that dependence using the Model for Interdisciplinary research on Climate 5 developed by Japanese research community. In particular, we focused on the relationships of the dependency with effects of aerosols and Atlantic meridional overturning circulation. We found significant dependencies of the scaling pattern on emission scenarios at middle and high latitudes of the Northern Hemisphere, with differences of >15 % over parts of East Asia, North America, and Europe. Impact researchers should take into account those dependencies that seriously affect their research. The mid-latitude dependence is caused by differences in sulfate aerosol emissions per 1 K increase in the global mean SAT, and the high-latitude dependence is mainly caused by nonlinear responses of sea ice and ocean heat transport to global warming. Long-term trends in land-use and land-cover changes did not significantly affect the scaling pattern of annual mean SAT, but they might have an effect at different timescales.     

径流对气候变化的敏感性分析

全球变暖愈来愈引起社会各界的关注 ,本文利用月水文模型 ,采取假定气候方案 ,以黄河流域为例 ,分析了径流对气候变化的敏感性。结果表明 ,径流对降水变化的响应较气温变化显著 ;一般情况下 ,半干旱地区径流较半湿润地区对气候变化敏感 ,人类活动的影响可在一定程度上削弱径流对气候变化的敏感性     

Creating regional climate change scenarios over southern South America for the 2020’s and 2050’s using the pattern scaling technique: validity and limitations

Dynamical downscaling of global climate simulations is the most adequate tool to generate regional projections of climate change. This technique involves at least a present climate simulation and a simulation of a future scenario, usually at the end of the twenty first century. However, regional projections for a variety of scenarios and periods, the 2020s or the 2050s, are often required by the impact community. The pattern scaling technique is used to estimate information on climate change for periods and scenarios not simulated by the regional model. We based our study on regional simulations performed over southern South America for present climate conditions and two emission scenarios at the end of the twenty first century. We used the pattern scaling technique to estimate mean seasonal changes of temperature and precipitation for the 2020s and the 2050s. The validity of the scalability assumptions underlying the pattern scaling technique for estimating near future regional climate change scenarios over southern South America is assessed. The results show that the pattern scaling works well for estimating mean temperature changes for which the regional changes are linearly related to the global mean temperature changes. For precipitation changes, the validity of the scalability assumption is weaker. The errors of estimating precipitation changes are comparable to those inherent to the regional model and to the projected changes themselves.     

A GCM study of future climate response to aerosol pollution reductions

We use the global atmospheric GCM aerosol model ECHAM5-HAM to asses possible impacts of future air pollution mitigation strategies on climate. Air quality control strategies focus on the reduction of aerosol emissions. Here we investigate the extreme case of a maximum feasible end-of-pipe abatement of aerosols in the near term future (2030) in combination with increasing greenhouse gas (GHG) concentrations. The temperature response of increasing GHG concentrations and reduced aerosol emissions leads to a global annual mean equilibrium temperature response of 2.18 K. When aerosols are maximally abated only in the Industry and Powerplant sector, while other sectors stay with currently enforced regulations, the temperature response is 1.89 K. A maximum feasible abatement applied in the Domestic and Transport sector, while other sectors remain with the current legislation, leads to a temperature response of 1.39 K. Increasing GHG concentrations alone lead to a temperature response of 1.20 K. We also simulate 2–5% increases in global mean precipitation among all scenarios considered, and the hydrological sensitivity is found to be significantly higher for aerosols than for GHGs. Our study, thus highlights the huge potential impact of future air pollution mitigation strategies on climate and supports the need for urgent GHG emission reductions. GHG and aerosol forcings are not independent as both affect and are influenced by changes in the hydrological cycle. However, within the given range of changes in aerosol emissions and GHG concentrations considered in this study, the climate response towards increasing GHG concentrations and decreasing aerosols emissions is additive.     

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm^–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.     

A global assessment of the impact of climate change on water scarcity

This paper presents a global scale assessment of the impact of climate change on water scarcity. Patterns of climate change from 21 Global Climate Models (GCMs) under four SRES scenarios are applied to a global hydrological model to estimate water resources across 1339 watersheds. The Water Crowding Index (WCI) and the Water Stress Index (WSI) are used to calculate exposure to increases and decreases in global water scarcity due to climate change. 1.6 (WCI) and 2.4 (WSI) billion people are estimated to be currently living within watersheds exposed to water scarcity. Using the WCI, by 2050 under the A1B scenario, 0.5 to 3.1 billion people are exposed to an increase in water scarcity due to climate change (range across 21 GCMs). This represents a higher upper-estimate than previous assessments because scenarios are constructed from a wider range of GCMs. A substantial proportion of the uncertainty in the global-scale effect of climate change on water scarcity is due to uncertainty in the estimates for South Asia and East Asia. Sensitivity to the WCI and WSI thresholds that define water scarcity can be comparable to the sensitivity to climate change pattern. More of the world will see an increase in exposure to water scarcity than a decrease due to climate change but this is not consistent across all climate change patterns. Additionally, investigation of the effects of a set of prescribed global mean temperature change scenarios show rapid increases in water scarcity due to climate change across many regions of the globe, up to 2 °C, followed by stabilisation to 4 °C.     

Climatic changes associated with a global “2°C-stabilization” scenario simulated by the ECHAM5/MPI-OM coupled climate model

In this study, concentrations of the well-mixed greenhouse gases as well as the anthropogenic sulphate aerosol load and stratospheric ozone concentrations are prescribed to the ECHAM5/MPI-OM coupled climate model so that the simulated global warming does not exceed 2°C relative to pre-industrial times. The climatic changes associated with this so-called “2°C-stabilization” scenario are assessed in further detail, considering a variety of meteorological and oceanic variables. The climatic changes associated with such a relatively weak climate forcing supplement the recently published fourth assessment report by the IPCC in that such a stabilization scenario can only be achieved by mitigation initiatives. Also, the impact of the anthropogenic sulphate aerosol load and stratospheric ozone concentrations on the simulated climatic changes is investigated. For this particular climate model, the 2°C-stabilization scenario is characterized by the following atmospheric concentrations of the well-mixed greenhouse gases: 418 ppm (CO₂), 2,026 ppb (CH₄), and 331 ppb (N₂O), 786 ppt (CFC-11) and 486 ppt (CFC-12), respectively. These greenhouse gas concentrations correspond to those for 2020 according to the SRES A1B scenario. At the same time, the anthropogenic sulphate aerosol load and stratospheric ozone concentrations are changed to the level in 2100 (again, according to the SRES A1B scenario), with a global anthropogenic sulphur dioxide emission of 28 TgS/year leading to a global anthropogenic sulphate aerosol load of 0.23 TgS. The future changes in climate associated with the 2°C-stabilization scenario show many of the typical features of other climate change scenarios, including those associated with stronger climatic forcings. That are a pronounced warming, particularly at high latitudes accompanied by a marked reduction of the sea-ice cover, a substantial increase in precipitation in the tropics as well as at mid- and high latitudes in both hemispheres but a marked reduction in the subtropics, a significant strengthening of the meridional temperature gradient between the tropical upper troposphere and the lower stratosphere in the extratropics accompanied by a pronounced intensification of the westerly winds in the lower stratosphere, and a strengthening of the westerly winds in the Southern Hemisphere extratropics throughout the troposphere. The magnitudes of these changes, however, are somewhat weaker than for the scenarios associated with stronger global warming due to stronger climatic forcings, such as the SRES A1B scenario. Some of the climatic changes associated with the 2°C-stabilization are relatively strong with respect to the magnitude of the simulated global warming, i.e., the pronounced warming and sea-ice reduction in the Arctic region, the strengthening of the meridional temperature gradient at the northern high latitudes and the general increase in precipitation. Other climatic changes, i.e., the El Niño like warming pattern in the tropical Pacific Ocean and the corresponding changes in the distribution of precipitation in the tropics and in the Southern Oscillation, are not as markedly pronounced as for the scenarios with a stronger global warming. A higher anthropogenic sulphate aerosol load (for 2030 as compared to the level in 2100 according to the SRES A1B scenario) generally weakens the future changes in climate, particularly for precipitation. The most pronounced effects occur in the Northern Hemisphere and in the tropics, where also the main sources of anthropogenic sulphate aerosols are located.     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate

The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers.     

On the tropical origin of uncertainties in the global land precipitation response to global warming

Understanding the response of the global hydrological cycle to recent and future anthropogenic emissions of greenhouse gases and aerosols is a major challenge for the climate modelling community. Recent climate scenarios produced for the fourth assessment report of the Intergovernmental Panel on Climate Change are analysed here to explore the geographical origin of, and the possible reasons for, uncertainties in the hydrological model response to global warming. Using the twentieth century simulations and the SRES-A2 scenarios from eight different coupled ocean–atmosphere models, it is shown that the main uncertainties originate from the tropics, where even the sign of the zonal mean precipitation change remains uncertain over land. Given the large interannual fluctuations of tropical precipitation, it is then suggested that the El Niño Southern Ocillation (ENSO) variability can be used as a surrogate of climate change to better constrain the model reponse. While the simulated sensitivity of global land precipitation to global mean surface temperature indeed shows a remarkable similarity between the interannual and climate change timescales respectively, the model ability to capture the ENSO-precipitation relationship is not a major constraint on the global hydrological projections. Only the model that exhibits the highest precipitation sensitivity clearly appears as an outlier. Besides deficiencies in the simulation of the ENSO-tropical rainfall teleconnections, the study indicates that uncertainties in the twenty-first century evolution of these teleconnections represent an important contribution to the model spread, thus emphasizing the need for improving the simulation of the tropical Pacific variability to provide more reliable scenarios of the global hydrological cycle. It also suggests that validating the mean present-day climate is not sufficient to assess the reliability of climate projections, and that interannual variability is another suitable and possibly more useful candidate for constraining the model response. Finally, it is shown that uncertainties in precipitation change are, like precipitation itself, very unevenly distributed over the globe, the most vulnerable countries sometimes being those where the anticipated precipitation changes are the most uncertain.     

A flexible climate model for use in integrated assessments

 Because of significant uncertainty in the behavior of the climate system, evaluations of the possible impact of an increase in greenhouse gas concentrations in the atmosphere require a large number of long-term climate simulations. Studies of this kind are impossible to carry out with coupled atmosphere ocean general circulation models (AOGCMs) because of their tremendous computer resource requirements. Here we describe a two dimensional (zonally averaged) atmospheric model coupled with a diffusive ocean model developed for use in the integrated framework of the Massachusetts Institute of Technology (MIT) Joint Program on the Science and Policy of Global Change. The 2-D model has been developed from the Goddard Institute for Space Studies (GISS) GCM and includes parametrizations of all the main physical processes. This allows it to reproduce many of the nonlinear interactions occurring in simulations with GCMs. Comparisons of the results of present-day climate simulations with observations show that the model reasonably reproduces the main features of the zonally averaged atmospheric structure and circulation. The model’s sensitivity can be varied by changing the magnitude of an inserted additional cloud feedback. Equilibrium responses of different versions of the 2-D model to an instantaneous doubling of atmospheric CO₂ are compared with results of similar simulations with different AGCMs. It is shown that the additional cloud feedback does not lead to any physically inconsistent results. On the contrary, changes in climate variables such as precipitation and evaporation, and their dependencies on surface warming produced by different versions of the MIT 2-D model are similar to those shown by GCMs. By choosing appropriate values of the deep ocean diffusion coefficients, the transient behavior of different AOGCMs can be matched in simulations with the 2-D model, with a unique choice of diffusion coefficients allowing one to match the performance of a given AOGCM for a variety of transient forcing scenarios. Both surface warming and sea level rise due to thermal expansion of the deep ocean in response to a gradually increasing forcing are reasonably reproduced on time scales of 100–150 y. However a wide range of diffusion coefficients is needed to match the behavior of different AOGCMs. We use results of simulations with the 2-D model to show that the impact on climate change of the implied uncertainty in the rate of heat penetration into the deep ocean is comparable with that of other significant uncertainties. Received: 10 March 1997 / Accepted: 20 October 1997     

Impact of climate change on the hydrogeology of two basins in northern France

This study presents an analysis of climate-change impacts on the water resources of two basins located in northern France, by integrating four sources of uncertainty: climate modelling, hydrological modelling, downscaling methods, and emission scenarios. The analysis focused on the evolution of the water budget, the river discharges and piezometric heads. Seven hydrological models were used, from lumped rainfall-discharge to distributed hydrogeological models, and led to quite different estimates of the water-balance components. One of the hydrological models, CLSM, was found to be unable to simulate the increased water stress and was, thus, considered as an outlier even though it gave fair results for the present day compared to observations. Although there were large differences in the results between the models, there was a marked tendency towards a decrease of the water resource in the rivers and aquifers (on average in 2050 about ?14 % and ?2.5 m, respectively), associated with global warming and a reduction in annual precipitation (on average in 2050 +2.1 K and ?3 %, respectively). The uncertainty associated to climate models was shown to clearly dominate, while the three others were about the same order of magnitude and 3–4 times lower. In terms of impact, the results found in this work are rather different from those obtained in a previous study, even though two of the hydrological models and one of the climate models were used in both studies. This emphasizes the need for a survey of the climatic-change impact on the water resource.     

全球1.5℃和2.0℃升温下潮白河流域气候和径流量变化预估

潮白河流域为北京主要供水源,其水资源量对北京用水保障至关重要,因此开展该流域在全球1.5℃和2.0℃升温下的径流预估研究具有现实意义。利用1961—2001年WATCH数据对SWAT水文模型进行率定和验证,在此基础上,应用第五次耦合模式比较计划(CMIP5)中5个全球气候模式在典型浓度路径(RCP4.5、RCP6.0和RCP8.5)下预估的全球1.5℃和2.0℃升温下的数据驱动SWAT模型,开展了潮白河流域气温、降水及径流量的变化预估研究,并量化评估由气候模式和RCPs导致的水文效应的不确定性。结果表明：(1) SWAT模型基本能较好地模拟潮白河流域的月径流特征,应用该模型进行气候变化对径流量的影响评估是可行的。(2)在全球1.5℃和2.0℃升温下,潮白河流域年平均温度较基准期(1976—2005年)分别增加1.5℃和2.2℃,年平均降水量也增加4.9%和7.0%。预估的年径流量在全球1.5℃升温下总体略有增加,盛夏和秋初的径流量占全年的比例也有所增加;在全球2.0℃升温下,年径流量增幅达30%以上,但夏季径流量占全年的比例明显减少。(3)在全球2.0℃升温下,潮白河流域极端丰水流量明显增加,洪涝发生风险增大。(4)未来气温、降水量和径流量的预估都存在一定的不确定性,在全球2.0℃升温下不确定性更大;相对而言,径流量的不确定性要远大于降水量的不确定性;无论是全球1.5℃升温下还是2.0℃升温下,预估不确定性主要来源于全球气候模式。     

Integrated assessment of changes in flooding probabilities due to climate change

An approach to considering changes in flooding probability in the integrated assessment of climate change is introduced. A reduced-form hydrological model for flood prediction and a downscaling approach suitable for integrated assessment modeling are presented. Based on these components, the fraction of world population living in river basins affected by changes in flooding probability in the course of climate change is determined. This is then used as a climate impact response function in order to derive emission corridors limiting the population affected. This approach illustrates the consideration of probabilistic impacts within the framework of the tolerable windows approach. Based on the change in global mean temperature, as calculated by the simple climate models used in integrated assessment, spatially resolved changes in climatic variables are determined using pattern scaling, while natural variability in these variables is considered using twentieth century deviations from the climatology. Driven by the spatially resolved climate change, the hydrological model then aggregates these changes to river basin scale. The hydrological model is subjected to a sensitivity analysis with regard to the water balance, and the uncertainty arising through the different projections of changes in mean climate by differing climate models is considered by presenting results based on different models. The results suggest that up to 20% of world population live in river basins that might inevitably be affected by increased flood events in the course of global warming, depending on the climate model used to estimate the regional distribution of changes in climate. This article is dedicated to the memory of the late Gerhard Petschel-Held. He was an inspiring colleague, as well as a good friend. His sudden departure leaves me deeply shocked, and I am sure he will sorely be missed by all who had the pleasure of meeting him. Thomas Kleinen     

An analysis of global model projections over Italy, with particular attention to the Italian Greater Alpine Region (GAR)

This study surveys the most recent projections of future climate change provided by 20 Atmospheric-Ocean General Circulation Models (AOGCMs) participating in the Coupled Model Intercomparison Project 3 (CMIP3) with focus on the Italian region and in particular on the Italian Greater Alpine Region (GAR). We analyze historical and future simulations of monthly-mean surface air temperature (T) and total precipitation (P). We first compare simulated T and P from the AOGCMs with observations over Italy for the period 1951–2000, using bias indices as a metric for estimating the performance of each model. Using these bias indices and different ensemble averaging methods, we construct ensemble mean projections of future climate change over these regions under three different IPCC emission scenarios (A2, A1B, and B1). We find that the emissions pathway chosen has a greater impact on future simulated climate than the criteria used to obtain the ensemble means. Across all averaging methods and emission scenarios, the models project annual mean increase in T of 2–4°C over the period 1990–2100, with more pronounced increases in summer and warming of similar magnitude at high and low elevations areas (according to a threshold of 400 m). The models project decreases in annual-mean P over this same time period both over the Italian and GAR regions. This decrease is more pronounced over Italy, since a small increase in precipitation over the GAR is projected in the winter season.     

辽河流域气候变化及对径流影响预估与评估分析

辽河流域属于气候变暖较为显著区域,增温幅度比全球和全国的增温幅度都要高。同时辽河流域也是水资源较为匮乏且需求量大的地区,因此气候变化对水资源影响问题也更值得关注。基于长期历史观测气象水文数据和未来不同情景下气候变化预估资料,建立评估气候变化与径流量的关系,预估未来气候变化对径流量的可能影响,为辽河流域应对气候变化决策提供科学依据。结果表明:1961—2020年,辽河流域气温为持续上升趋势,降水没有明显的增减趋势,但存在阶段性变化;辽河流域降水量与径流量有较好的相关关系,具有较为一致的长期变化趋势与特征,年降水量与径流量相关数达到0.6以上。日降水量与径流量相关分析表明,降水发生后次日且为大雨降水等级(即日降水量≥25 mm)时,两者相关系数可高达0.85;敏感性试验和模式模拟试验表明,径流量对气候变化有明显的响应,降水增加(减少)、气温降低(升高),则径流量增加(减少);在未来RCP8.5排放情景下气温升高趋势最为明显,未来径流量也为显著增加趋势;RCP2.6排放情景下气温增加的幅度最小,未来径流量也表现为无明显增减趋势;RCP4.5情景下,气温增加的幅度居中,未来径流量则为减少趋势。     

Localized climate change scenarios of mean temperature and precipitation over Switzerland

There is a growing need of the climate change impact modeling and adaptation community to have more localized climate change scenario information available over complex topography such as in Switzerland. A gridded dataset of expected future climate change signals for seasonal averages of daily mean temperature and precipitation in Switzerland is presented. The basic scenarios are taken from the CH2011 initiative. In CH2011, a Bayesian framework was applied to obtain probabilistic scenarios for three regions within Switzerland. Here, the results for two additional Alpine sub-regions are presented. The regional estimates have then been downscaled onto a regular latitude-longitude grid with a resolution of 0.02° or roughly 2 km. The downscaling procedure is based on the spatial structure of the climate change signals as simulated by the underlying regional climate models and relies on a Kriging with external drift using height as auxiliary predictor. The considered emission scenarios are A1B, A2 and the mitigation scenario RCP3PD. The new dataset shows an expected warming of about 1 to 6 °C until the end of the 21st century, strongly depending on the scenario and the lead time. Owing to a large vertical gradient, the warming is about 1 °C stronger in the Alps than in the Swiss lowlands. In case of precipitation, the projection uncertainty is large and in most seasons precipitation can increase or decrease. In summer a distinct decrease of precipitation can be found, again strongly depending on the emission scenario.     

Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.     

Future African Water Resources: Interactions between Soil Degradation and Global Warming

This study uses a well-established water balance methodology to evaluate the relative impact of global warming and soil degradation due to desertification on future African water resources. Using a baseline climatology, a GCM global warming scenario, a newly derived soil water-holding capacity data set, and a worldwide survey of soil degradation between 1950 and 1980, four climate and soil degradation scenarios are created to simulate the potential impact of global warming and soil degradation on African water resources for the 2010–2039 time period. Results indicate that, on a continental scale, the impact of global warming will be significantly greater than the impact of soil degradation. However, when only considering the locations where desertification is an issue (wet and dry climate regions), the potential effects of these two different human impacts on local water resources can be expected to be on the same order of magnitude. Drying associated with global warming is primarily the result of increased water demand (potential evapotranspiration) across the entire continent. While there are small increases in precipitation under global warming conditions, they are inadequate to meet the increased water demand. Soil degradation is most severe in highly populated, wet and dry climate regions and results in decreased water-holding capacities in these locations. This results in increased water surplus conditions during wet seasons when the soil's ability to absorb precipitation is reduced. At the same time, water deficits in these locations increase because of reduced soil water availability in the dry seasons. The net result of the combined scenarios is an intensification and extension of drought conditions during dry seasons.     

A regional and global analysis of carbon dioxide physiological forcing and its impact on climate

An increase in atmospheric carbon dioxide concentration has both a radiative (greenhouse) effect and a physiological effect on climate. The physiological effect forces climate as plant stomata do not open as wide under enhanced CO₂ levels and this alters the surface energy balance by reducing the evapotranspiration flux to the atmosphere, a process referred to as ‘carbon dioxide physiological forcing’. Here the climate impact of the carbon dioxide physiological forcing is isolated using an ensemble of twelve 5-year experiments with the Met Office Hadley Centre HadCM3LC fully coupled atmosphere–ocean model where atmospheric carbon dioxide levels are instantaneously quadrupled and thereafter held constant. Fast responses (within a few months) to carbon dioxide physiological forcing are analyzed at a global and regional scale. Results show a strong influence of the physiological forcing on the land surface energy budget, hydrological cycle and near surface climate. For example, global precipitation rate reduces by ~3% with significant decreases over most land-regions, mainly from reductions to convective rainfall. This fast hydrological response is still evident after 5 years of model integration. Decreased evapotranspiration over land also leads to land surface warming and a drying of near surface air, both of which lead to significant reductions in near surface relative humidity (~6%) and cloud fraction (~3%). Patterns of fast responses consistently show that results are largest in the Amazon and central African forest, and to a lesser extent in the boreal and temperate forest. Carbon dioxide physiological forcing could be a source of uncertainty in many model predicted quantities, such as climate sensitivity, transient climate response and the hydrological sensitivity. These results highlight the importance of including biological components of the Earth system in climate change studies.     

基于统计降尺度模型的江淮流域极端气候的模拟与预估

利用江淮流域29个代表站点1961--2000年逐日最高温度、最低温度和逐日降水资料,以及NCEP逐日大尺度环流场资料,引入基于多元线性回归与随机天气发生器相结合的统计降尺度模型SDSM（statistical downscalingmodel）,通过对每个站点建模,确立SDSM参数,并将该模型应用于SRESA2排放情景下HadCM3和cGcM3模式,得到了江淮流域各代表台站21世纪的逐日最高、最低温度和降水序列以及热浪、霜冻、强降水等极端气候指数。结果表明,当前气候下,统计降尺度方法模拟的极端温度指数与观测值有很好的一致性,能有效纠正耦合模式的“冷偏差”,如SDSM对江淮平均的冬季最高、最低温度的模拟偏差较CGCM3模式分别减少3℃和4．5℃。对于极端降水则能显著纠正耦合模式模拟的降水强度偏低的问题,如CGCM3对江淮流域夏季降水强度的模拟偏差为-60．6％,但降尺度后SDSM—CGCM3的偏差仅为-6％,说明降尺度模型SDSM的确有“增加值”的作用。21世纪末期在未来SRESA2情景下,对于极端温度,无论Had．CM3还是CGCM3模式驱动统计模型,江淮流域所有代表台站,各个季节的最高、最低温度都显著增加,且以夏季最为显著,增幅在2—4℃;与之相应霜冻天数将大幅减少,热浪天数大幅增多,各站点冬季霜冻天数减少幅度为5—25d,夏季热浪天数增加幅度为4～14d;对于极端降水指数,在两个不同耦合模式HadCM3和CGCM3驱动下的变化尤其是变化幅度的一致性比温度差,但大部分站点各个季节极端强降水事件将增多,强度增强,SDSM—HadCM3和SDSM-CGCM3预估的夏季极端降水贡献率将分别增加26％和27％。