A method to study the impact of climate change on variability of river flow: an example from the Guadalupe River in Texas

This work introduced a method to study river flow variability in response to climate change by using remote sensing precipitation data, downscaled climate model outputs with bias corrections, and a land surface model. A meteorological forcing dataset representing future climate was constructed via the delta change method in which the modeled change was added to the present-day conditions. The delta change was conducted at a fine spatial and temporal scale to contain the signals of weather events, which exhibit substantial responses to climate change. An empirical transformation technique was further applied to the constructed forcing to ensure a realistic range. The meteorological forcing was then used to drive the land surface model to simulate the future river flow. The results show that preserving fine-scale processes in response to climate change is a necessity to assess climatic impacts on the variability of river flow events.     

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     

Response of a Grassland Ecosystem to Climate Change in a Theoretical Model

The response of a grassland ecosystem to climate change is discussed within the context of a theoretical model.An optimization approach,a conditional nonlinear optimal perturbation related to parameter(CNOP-P) approach,was employed in this study.The CNOP-P,a perturbation of moisture index in the theoretical model,represents a nonlinear climate perturbation.Two kinds of linear climate perturbations were also used to study the response of the grassland ecosystem to different types of climate changes.The results show that the extent of grassland ecosystem variation caused by the CNOP-P-type climate change is greater than that caused by the two linear types of climate change.In addition,the grassland ecosystem affected by the CNOP-P-type climate change evolved into a desert ecosystem,and the two linear types of climate changes failed within a specific amplitude range when the moisture index recovered to its reference state.Therefore,the grassland ecosystem response to climate change was nonlinear.This study yielded similar results for a desert ecosystem seeded with both living and wilted biomass litter.The quantitative analysis performed in this study also accounted for the role of soil moisture in the root zone and the shading effect of wilted biomass on the grassland ecosystem through nonlinear interactions between soil and vegetation.The results of this study imply that the CNOP-P approach is a potentially effective tool for assessing the impact of nonlinear climate change on grassland ecosystems.     

Uncertainties linked to land-surface processes in climate change simulations

 The impact of climate change on the hydrology of continental surfaces is critical for human activities but the response of the surface to this perturbation may also affect the sensitivity of the climate. This complex feedback is simulated in general circulation models (GCMs) used for climate change predictions by their land-surface schemes. The present study attempts to quantify the uncertainty associated with these schemes and what impact it has on our confidence in the simulated climate anomalies. Four GCMs, each coupled to two different land-surface schemes, are used to explore the spectrum of uncertainties. It is shown that, in this sample, surface processes have a significant contribution to our ability to predict surface temperature changes and perturbations of the hydrological cycle in an environment with doubled greenhouse gas concentration. The results reveal that the uncertainty introduced by land-surface processes in the simulated climate is different from its impact on the sensitivity of GCMs to climate change, indeed an alteration of the surface parametrization with little impact on model climate can affect sensitivity significantly. This result leads us to believe that the validation of land-surface schemes should not be limited to the current climate but should also cover their sensitivity to variations in climatic forcing. Received: 24 June 1999 / Accepted: 20 April 2000     

Numerical study for potential predictability of short— term anomalous climate change caused by El nino

In this paper, the two-layer IAP model with sea surface temperature anomalies in the equatorial central-eastern Pacific is used to investigate potential predictability of global short-term anomalous climate change caused by El Nino via the “switching” experiments. The experimental results show that short-term anomalous climate change in the tropics is mainly caused by instantaneous response of tropical atmosphere to SSTA in the tropics. The effective period of this kind of anomalous climate is shorter and about monthly scale. In the high latitudes, the anomalous cli-mate is mainly caused by the lag response of atmosphere to SSTA in the tropics. The strongest influence appears in the month after a half year when the SSTA in the tropics disappears. Therefore, potential predictability of short-term anomalous climate change may be reached to one year; anomalous climate change in the middle-latitudes is not only affected by instantaneous response to SSTA in the tropics, but also by lag response to that. Therefore, short-term climate change prediction with monthly time scale can be not only done by using SSTA in the tropics, but also predic-tion of short-term climate after a half year can be done and its effective predictable period may be reached to one year.     

Using climate impacts indicators to evaluate climate model ensembles: temperature suitability of premium winegrape cultivation in the United States

We explore the potential to improve understanding of the climate system by directly targeting climate model analyses at specific indicators of climate change impact. Using the temperature suitability of premium winegrape cultivation as a climate impacts indicator, we quantify the inter- and intra-ensemble spread in three climate model ensembles: a physically uniform multi-member ensemble consisting of the RegCM3 high-resolution climate model nested within the NCAR CCSM3 global climate model; the multi-model NARCCAP ensemble consisting of single realizations of multiple high-resolution climate models nested within multiple global climate models; and the multi-model CMIP3 ensemble consisting of realizations of multiple global climate models. We find that the temperature suitability for premium winegrape cultivation is substantially reduced throughout the high-value growing areas of California and the Columbia Valley region (eastern Oregon and Washington) in all three ensembles in response to changes in temperature projected for the mid-twenty first century period. The reductions in temperature suitability are driven primarily by projected increases in mean growing season temperature and occurrence of growing season severe hot days. The intra-ensemble spread in the simulated climate change impact is smaller in the single-model ensemble than in the multi-model ensembles, suggesting that the uncertainty arising from internal climate system variability is smaller than the uncertainty arising from climate model formulation. In addition, the intra-ensemble spread is similar in the NARCCAP nested climate model ensemble and the CMIP3 global climate model ensemble, suggesting that the uncertainty arising from the model formulation of fine-scale climate processes is not smaller than the uncertainty arising from the formulation of large-scale climate processes. Correction of climate model biases substantially reduces both the inter- and intra-ensemble spread in projected climate change impact, particularly for the multi-model ensembles, suggesting that—at least for some systems—the projected impacts of climate change could be more robust than the projected climate change. Extension of this impacts-based analysis to a larger suite of impacts indicators will deepen our understanding of future climate change uncertainty by focusing on the climate phenomena that most directly influence natural and human systems.     

Evaluation of a component of the cloud response to climate change in an intercomparison of climate models

Most of the uncertainty in the climate sensitivity of contemporary general circulation models (GCMs) is believed to be connected with differences in the simulated radiative feedback from clouds. Traditional methods of evaluating clouds in GCMs compare time–mean geographical cloud fields or aspects of present-day cloud variability, with observational data. In both cases a hypothetical assumption is made that the quantity evaluated is relevant for the mean climate change response. Nine GCMs (atmosphere models coupled to mixed-layer ocean models) from the CFMIP and CMIP model comparison projects are used in this study to demonstrate a common relationship between the mean cloud response to climate change and present-day variability. Although atmosphere–mixed-layer ocean models are used here, the results are found to be equally applicable to transient coupled model simulations. When changes in cloud radiative forcing (CRF) are composited by changes in vertical velocity and saturated lower tropospheric stability, a component of the local mean climate change response can be related to present-day variability in all of the GCMs. This suggests that the relationship is not model specific and might be relevant in the real world. In this case, evaluation within the proposed compositing framework is a direct evaluation of a component of the cloud response to climate change. None of the models studied are found to be clearly superior or deficient when evaluated, but a couple appear to perform well on several relevant metrics. Whilst some broad similarities can be identified between the 60°N–60°S mean change in CRF to increased CO₂ and that predicted from present-day variability, the two cannot be quantitatively constrained based on changes in vertical velocity and stability alone. Hence other processes also contribute to the global mean cloud response to climate change.     

A nonlinear impulse response model of the coupled carbon cycle-climate system (NICCS)

 Impulse-response-function (IRF) models are designed for applications requiring a large number of climate change simulations, such as multi-scenario climate impact studies or cost-benefit integrated-assessment studies. The models apply linear response theory to reproduce the characteristics of the climate response to external forcing computed with sophisticated state-of-the-art climate models like general circulation models of the physical ocean-atmosphere system and three-dimensional oceanic-plus-terrestrial carbon cycle models. Although highly computer efficient, IRF models are nonetheless capable of reproducing the full set of climate-change information generated by the complex models against which they are calibrated. While limited in principle to the linear response regime (less than about 3 ^∘C global-mean temperature change), the applicability of the IRF model presented has been extended into the nonlinear domain through explicit treatment of the climate system's dominant nonlinearities: CO₂ chemistry in ocean water, CO₂ fertilization of land biota, and sublinear radiative forcing. The resultant nonlinear impulse-response model of the coupled carbon cycle-climate system (NICCS) computes the temporal evolution of spatial patterns of climate change for four climate variables of particular relevance for climate impact studies: near-surface temperature, cloud cover, precipitation, and sea level. The space-time response characteristics of the model are derived from an EOF analysis of a transient 850-year greenhouse warming simulation with the Hamburg atmosphere-ocean general circulation model ECHAM3-LSG and a similar response experiment with the Hamburg carbon cycle model HAMOCC. The model is applied to two long-term CO₂ emission scenarios, demonstrating that the use of all currently estimated fossil fuel resources would carry the Earth's climate far beyond the range of climate change for which reliable quantitative predictions are possible today, and that even a freezing of emissions to present-day levels would cause a major global warming in the long term. Received: 28 January 2000 / Accepted: 9 March 2001     

Climate change impacts on wildfires in a Mediterranean environment

We analyse the observed climate-driven changes in summer wildfires and their future evolution in a typical Mediterranean environment (NE Spain). By analysing observed climate and fire data from 1970 to 2007, we estimate the response of fire number (NF) and burned area (BA) to climate trends, disentangling the drivers responsible for long-term and interannual changes by means of a parsimonious Multi Linear Regression model (MLR). In the last forty years, the observed NF trend was negative. Here we show that, if improvements in fire management were not taken into account, the warming climate forcing alone would have led to a positive trend in NF. On the other hand, for BA, higher fuel flammability is counterbalanced by the indirect climate effects on fuel structure (i.e. less favourable conditions for fine-fuel availability and fuel connectivity), leading to a slightly negative trend. Driving the fire model with A1B climate change scenarios based on a set of Regional Climate Models from the ENSEMBLES project indicates that increasing temperatures promote a positive trend in NF if no further improvements in fire management are introduced.     

陆面模式中土壤呼吸的研究概况

土壤呼吸是陆地植被吸收的CO2返回大气的基本途径,土壤呼吸速度轻微变化也会引起大气中CO2浓度的明显改变,进而影响气候变化。陆面过程模式中更好地描述土壤呼吸过程对于预测未来气候变化是至关重要的。对于土壤呼吸的模拟研究,介绍了经验模型和以过程为基础的机理模型,以及国内外陆面过程模式中土壤呼吸的研究情况,并讨论了土壤呼吸模式中需要改进的问题。       

Urban Water Management vs. Climate Change: Impacts on Cold Region Waste Water Inflows

Failure to account for non-climatic changes to water systems, such as design and operation, within climate change impact assessments leads to misconceptions because these activities buffer the human built enviroment from bio-physical impacts. Urban drainage in cold regions, which is dominated by snowmelt, is especially vulnerable to climate change and is investigated in this paper within the context of future rehabilitation of the sewer network. The objectives are to illustrate the relative response of urban drainage to changes in both the pipe network and climate and demonstrate the use of response surfaces for climate change studies. An incremental climate scenario approach is used to generate two sensitivity analyses for waste water inflows to the Lycksele waste water treatment plant in north-central Sweden. Air temperature and precipitation data (1984–1993) are altered incrementally between –5 and +15 °C and –10 and +40% respectively. These data are then used to drive a hydrological transformation model to obtain estimates of sewer infiltration from groundwater. The results are presented as winter and spring response surfaces – these are graphical representations of a response matrix where each point relates to a single model run. Climate scenario envelopes which summarise a series of GCM runs (ACACIA; Carter, 2002, pers. comm.) are overlaid to indicate the range of plausible waste water inflows. Estimates of natural multi-decadal variability are also included. The first sensitivity analysis assumes no change to the drainage system while the second simulates sewer renovation in which the system is fully separated and sewer infiltration is reduced. The main conclusions are that innovations in drainage network technology have a greater potential to alter waste water inflows than climate change and that, while the direction of climate change is fairly certain, there is great uncertainty surrounding magnitude of those changes and their impacts.     

Climate change and river flooding: Part 2 sensitivity characterisation for british catchments and example vulnerability assessments

This paper is the second of a series describing a scenario-neutral methodology to assess the sensitivity and vulnerability of British catchments to changes in flooding due to climate change. In paper one, nine flood sensitivity types were identified from response surfaces generated for 154 catchments. The response surfaces describe changes in 20-year return period flood peaks (RP20) in response to a large set of changes in precipitation, temperature and potential evapotranspiration. In this paper, a recursive partitioning algorithm is used to link families of sensitivity types to catchment properties, via a decision tree. The tree shows 85 % success characterising the four sensitivity families, using five properties and nine paths. Catchment annual average rainfall is the primary partitioning factor, with drier catchments having a more variable response to climate (precipitation) change than wetter catchments and higher catchment losses and permeability being aggravating factors. The full sensitivity-exposure-vulnerability methodology is illustrated for two catchments: sensitivity is estimated by using the decision tree to identify the sensitivity family (and its associated average response surface); exposure is defined from a set of climate model projections and combined with the response surface to estimate the resulting impacts (changes in RP20); vulnerability under a range of adaptive capacity thresholds is estimated from the set of impacts. Even though they are geographically close, the two catchments show differing vulnerability to climate change, due to their differing properties. This demonstrates that generalised response surfaces characterised by catchment properties are useful screening tools to quantify the vulnerability of catchments to climate change without the need to undertake a full climate change impact study.     

Towards evaluating cloud response to climate change using clustering technique identification of cloud regimes

Most of the discrepancies in the climate sensitivity of general circulation models (GCMs) are believed to be due to differences in cloud radiative feedback. Analysis of cloud response to climate change in different ‘regimes’ may offer a more detailed understanding of how the cloud response differs between GCMs. In which case, evaluation of simulated cloud regimes against observations in terms of both their cloud properties and frequency of occurrence will assist in assessing confidence in the cloud response to climate change in a particular GCM. In this study, we use a clustering technique on International Satellite Cloud Climatology Project (ISCCP) data and on ISCCP-like diagnostics from two versions of the Hadley Centre GCM to identify cloud regimes over four different geographical regions. The two versions of the model are evaluated against observational data and their cloud response to climate change compared within the cloud regime framework. It is found that cloud clusters produced by the more recent GCM, HadSM4, compare more favourably with observations than HadSM3. In response to climate change, although the net cloud response over particular regions is often different in the two models, in several instances the same basic processes may be seen to be operating. Overall, both changes in the frequency of occurrence of cloud regimes and changes in the properties (optical depth and cloud top height) of the cloud regimes contribute to the cloud response to climate change.     

A New Economic Assessment Index for the Impact of Climate Change on Grain Yield

The impact of climate change on agriculture has received wide attention by the scientific community. This paper studies how to assess the grain yield impact of climate change, according to the climate change over a long time period in the future as predicted by a climate system model. The application of the concept of a traditional ＂yield impact of meteorological factor （YIMF）＂ or ＂yield impact of weather factor＂ to the grain yield assessment of a decadal or even a longer timescale would be suffocated at the outset because the YIMF is for studying the phenomenon on an interannual timescale, and it is difficult to distinguish between the trend caused by climate change and the one resulting from changes in non-climatic factors. Therefore, the concept of the yield impact of climatic change （YICC）, which is defined as the difference in the per unit area yields （PUAY） of a grain crop under a changing and an envisaged invariant climate conditions, is presented in this paper to assess the impact of global climate change on grain yields. The climatic factor has been introduced into the renowned economic Cobb-Douglas model, yielding a quantitative assessment method of YICC using real data. The method has been tested using the historical data of Northeast China, and the results show that it has an encouraging application outlook.     

The modification of greenhouse gas warming by the direct effect of sulphate aerosols

 The Canadian Centre for Climate Modelling and Analysis (CCCma) second generation climate model (GCMII) consists of an atmospheric GCM coupled to mixed layer ocean. It is used to investigate the climate response to a doubling of the CO₂ concentration together with the direct effect of scattering by sulphate aerosols. As expected, the aerosols offset some of the greenhouse gas (GHG) warming; the global annual mean screen temperature change due to doubled CO₂ is 3.4 °C in this model and this is reduced to 2.7 °C when an estimate of the direct effect of anthropogenic sulphate aerosols is included. The pattern of climate response to the comparatively localized aerosol forcing is not itself localized, and it bears a striking resemblance to the response pattern that arises from the globally distributed change in GHG forcing. This “non-local” response to “localized” forcing indicates that the pattern of climate response is determined, to first order, by the overall magnitude of the change in forcing rather than its detailed nature or structure. Feedback processes operating in the system apparently determine this pattern by locally amplifying and suppressing the response to the magnitude of the change in forcing. The influence of the location of the change in forcing is relatively small. These “non-local” and “local” effects of aerosol forcing are characterized and displayed and some of their consequences discussed. Effects on the moisture budget and on the energetics of the global climate are also examined. Received: 10 June 1997 / Accepted: 8 January 1998     

Interpretation of the positive low-cloud feedback predicted by a climate model under global warming

The response of low-level clouds to climate change has been identified as a major contributor to the uncertainty in climate sensitivity estimates among climate models. By analyzing the behaviour of low-level clouds in a hierarchy of models (coupled ocean-atmosphere model, atmospheric general circulation model, aqua-planet model, single-column model) using the same physical parameterizations, this study proposes an interpretation of the strong positive low-cloud feedback predicted by the IPSL-CM5A climate model under climate change. In a warmer climate, the model predicts an enhanced clear-sky radiative cooling, stronger surface turbulent fluxes, a deepening and a drying of the planetary boundary layer, and a decrease of tropical low-clouds in regimes of weak subsidence. We show that the decrease of low-level clouds critically depends on the change in the vertical advection of moist static energy from the free troposphere to the boundary-layer. This change is dominated by variations in the vertical gradient of moist static energy between the surface and the free troposphere just above the boundary-layer. In a warmer climate, the thermodynamical relationship of Clausius-Clapeyron increases this vertical gradient, and then the import by large-scale subsidence of low moist static energy and dry air into the boundary layer. This results in a decrease of the low-level cloudiness and in a weakening of the radiative cooling of the boundary layer by low-level clouds. The energetic framework proposed in this study might help to interpret inter-model differences in low-cloud feedbacks under climate change.     

Climate change hotspots in the CMIP5 global climate model ensemble

We use a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5 climate model ensemble. Our hotspot metric extends previous work through the inclusion of extreme seasonal temperature and precipitation, which exert critical influence on climate change impacts. The results identify areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau as persistent regional climate change hotspots throughout the 21st century of the RCP8.5 and RCP4.5 forcing pathways. In addition, areas of southern Africa, the Mediterranean, the Arctic, and Central America/western North America also emerge as prominent regional climate change hotspots in response to intermediate and high levels of forcing. Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2 °C of global warming (relative to the late-20th-century baseline), but not at the higher levels of global warming that occur in the late-21st-century period of the RCP8.5 pathway, with areas of southern Africa, the Mediterranean, and the Arctic exhibiting particular intensification of relative aggregate climate change in response to high levels of forcing. Although specific impacts will clearly be shaped by the interaction of climate change with human and biological vulnerabilities, our identification of climate change hotspots can help to inform mitigation and adaptation decisions by quantifying the rate, magnitude and causes of the aggregate climate response in different parts of the world.     

On the relationship between cloud–radiation interaction, atmospheric stability and Atlantic tropical cyclones in a variable-resolution climate model

We compare two 28-year simulations performed with two versions of the Global Environmental Multiscale model run in variable-resolution mode. The two versions differ only by small differences in their radiation scheme. The most significant modification introduced is a reduction in the ice effective radius, which is observed to increase absorption of upwelling infrared radiation and increase temperature in the upper troposphere. The resulting change in vertical lapse rate is then observed to drive a resolution-dependent response of convection, which in turn modifies the zonal circulation and induces significant changes in simulated Atlantic tropical cyclone activity. The resulting change in vertical lapse rate and its implication in the context of anthropogenic climate change are discussed.     

Coupled ocean-atmosphere models with flux correction

A method is proposed for removing the drift of coupled atmosphere-ocean models, which in the past has often hindered the application of coupled models in climate response and sensitivity experiments. The ocean-atmosphere flux fields exhibit inconsistencies when evaluated separately for the individual sub-systems in independent, uncoupled mode equilibrium climate computations. In order to balance these inconsistencies a constant ocean-atmosphere flux correction field is introduced in the boundary conditions coupling the two sub-systems together. The method ensures that the coupled model operates at the reference climate state for which the individual model subsystems were designed without affecting the dynamical response of the coupled system in climate variability experiments. The method is illustrated for a simple two component box model and an ocean general circulation model coupled to a two layer diagnostic atmospheric model.     

MODELLING CLIMATE CHANGE IMPACTS ON ECOSYSTEMS USING LINKED MODELS AND A GIS

Future climate change scenarios have been applied to a linked model at five UK sites. Results indicate a decrease in grassland productivity under a changed climate, resulting from lower soil water content, with possible large consequences to future UK water resource planning. The effect of running the grassland sub-model in isolation, compared with running it as part of a linked model showed that linked models should be used for climate change impacts studies. The location specific response to climate change was also highlighted. The model is mechanistic, consists of four sub-models (grassland, water balance, nitrate and evapotranspiration) and is spatially explicit.