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1.
The Implications of Climate Change on Floods of the Ganges, Brahmaputra and Meghna Rivers in Bangladesh 总被引：4，自引：0，他引：4
Climate change in the future would have implications for river discharges in Bangladesh. In this article, possible changes in the magnitude, extent and depth of floods of the Ganges, Brahmaputra and Meghna (GBM) rivers in Bangladesh were assessed using a sequence of empirical models and the MIKE11-GIS hydrodynamic model. Climate change scenarios were constructed from the results of four General Circulation Models (GCMs) –CSIRO9, UKTR, GFDL and LLNL, which demonstrate a range of uncertainties. Changes in magnitude, depth and extent of flood discharge vary considerably between the GCMs. Future changes in the peak discharge of the Ganges River are expected to be higher than those for the Brahmaputra River. Peak discharge of the Meghna River may also increase considerably. As a result, significant changes in the spatial extent and depths of inundation in Bangladesh may occur. Faster changes in inundation are expected at low temperature increases than of higher temperature changes. Changes in land inundation categories may introduce substantial changes in rice agriculture and cropping patterns in Bangladesh. Reduction of increased flood hazard due to climate change requires strengthening of flood management policies and adaptation measures in Bangladesh. 相似文献
2.
This study uses empirical agricultural impact models to compare the U.S. climate change predictions of 16 General Circulation Models (GCMs). The impact analysis provides a policy-relevant index by which to judge complex climate predictions. National aggregate impacts vary widely across the 16 GCMs because of varying regional and seasonal patterns of predicted climate change. Examining the predicted impacts from the full set of GCMs reveals that the seasonal detail in the GCM predictions is so noisy that it is not significantly different from a constant annual change. However, a consistent regional pattern does emerge across the set of models. Nonetheless, aggregating climate change across seasons and regions within the United States, using a national-annual climate change provides a reasonable and efficient approximation to the expected impact predicted by the 16 GCM models. 相似文献
3.
Tobias Lung Alessandro Dosio William Becker Carlo Lavalle Laurens M. Bouwer 《Climatic change》2013,120(1-2):211-227
Despite an increasing understanding of potential climate change impacts in Europe, the associated uncertainties remain a key challenge. In many impact studies, the assessment of uncertainties is underemphasised, or is not performed quantitatively. A key source of uncertainty is the variability of climate change projections across different regional climate models (RCMs) forced by different global circulation models (GCMs). This study builds upon an indicator-based NUTS-2 level assessment that quantified potential changes for three climate-related hazards: heat stress, river flood risk, and forest fire risk, based on five GCM/RCM combinations, and non-climatic factors. First, a sensitivity analysis is performed to determine the fractional contribution of each single input factor to the spatial variance of the hazard indicators, followed by an evaluation of uncertainties in terms of spread in hazard indicator values due to inter-model climate variability, with respect to (changes in) impacts for the period 2041–70. The results show that different GCM/RCM combinations lead to substantially varying impact indicators across all three hazards. Furthermore, a strong influence of inter-model variability on the spatial patterns of uncertainties is revealed. For instance, for river flood risk, uncertainties appear to be particularly high in the Mediterranean, whereas model agreement is higher for central Europe. The findings allow for a hazard-specific identification of areas with low vs. high model agreement (and thus confidence of projected impacts) within Europe, which is of key importance for decision makers when prioritising adaptation options. 相似文献
4.
Kirsti Jylhä Stefan Fronzek Heikki Tuomenvirta Timothy R. Carter Kimmo Ruosteenoja 《Climatic change》2008,86(3-4):441-462
Changes in indices related to frost and snow in Europe by the end of the twenty-first century were analyzed based on experiments
performed with seven regional climate models (RCMs). All the RCMs regionalized information from the same general circulation
model (GCM), applying the IPCC-SRES A2 radiative forcing scenario. In addition, some simulations used SRES B2 radiative forcing
and/or boundary conditions provided by an alternative GCM. Ice cover over the Baltic Sea was examined using a statistical
model that related the annual maximum extent of ice to wintertime coastal temperatures. Fewer days with frost and snow, shorter
frost seasons, a smaller liquid water equivalent of snow, and milder sea ice conditions were produced by all model simulations,
irrespective of the forcing scenario and the driving GCM. The projected changes have implications across a diverse range of
human activities. Details of the projections were subject to differences in RCM design, deviations between the boundary conditions
of the driving GCMs, uncertainties in future emissions and random effects due to internal climate variability. A larger number
of GCMs as drivers of the RCMs would most likely have resulted in somewhat wider ranges in the frost, snow and sea ice estimates
than those presented in this paper. 相似文献
5.
Comparison of uncertainty sources for climate change impacts: flood frequency in England 总被引：6，自引：3，他引：3
This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology. 相似文献
6.
Juan-Carlos Ciscar László Szabó Denise van Regemorter Antonio Soria 《Climatic change》2012,112(1):127-142
The PESETA project has estimated the physical effects of climate change in Europe for the following impact categories with
a market valuation: agriculture, river floods, coastal systems and tourism. Four alternative scenarios of future climate change
have been considered. The computable general equilibrium (CGE) GEM-E3 model for Europe has been used to integrate the PESETA
damages under a consistent economic framework. The approach followed has been to assess the effects of future climate (as
of 2080s) on today’s economy. This article details the way each sectoral impact has been integrated into the CGE model. The
EU welfare loss is estimated to be in a range of 0.2% to 1%, depending on the climate future and the projected sea level rise.
Results show that the Southern Europe region appears as the most vulnerable area to climate change. Impacts in coastal systems,
agriculture and river floods determine the overall and regional pattern of impacts within Europe. 相似文献
7.
J. E. Olesen T. R. Carter C. H. Díaz-Ambrona S. Fronzek T. Heidmann T. Hickler T. Holt M. I. Minguez P. Morales J. P. Palutikof M. Quemada M. Ruiz-Ramos G. H. Rubæk F. Sau B. Smith M. T. Sykes 《Climatic change》2007,81(1):123-143
The uncertainties and sources of variation in projected impacts of climate change on agriculture and terrestrial ecosystems depend not only on the emission scenarios and climate models used for projecting future climates, but also on the impact models used, and the local soil and climatic conditions of the managed or unmanaged ecosystems under study. We addressed these uncertainties by applying different impact models at site, regional and continental scales, and by separating the variation in simulated relative changes in ecosystem performance into the different sources of uncertainty and variation using analyses of variance. The crop and ecosystem models used output from a range of global and regional climate models (GCMs and RCMs) projecting climate change over Europe between 1961–1990 and 2071–2100 under the IPCC SRES scenarios. The projected impacts on productivity of crops and ecosystems included the direct effects of increased CO2 concentration on photosynthesis. The variation in simulated results attributed to differences between the climate models were, in all cases, smaller than the variation attributed to either emission scenarios or local conditions. The methods used for applying the climate model outputs played a larger role than the choice of the GCM or RCM. The thermal suitability for grain maize cultivation in Europe was estimated to expand by 30–50% across all SRES emissions scenarios. Strong increases in net primary productivity (NPP) (35–54%) were projected in northern European ecosystems as a result of a longer growing season and higher CO2 concentrations. Changing water balance dominated the projected responses of southern European ecosystems, with NPP declining or increasing only slightly relative to present-day conditions. Both site and continental scale models showed large increases in yield of rain-fed winter wheat for northern Europe, with smaller increases or even decreases in southern Europe. Site-based, regional and continental scale models showed large spatial variations in the response of nitrate leaching from winter wheat cultivation to projected climate change due to strong interactions with soils and climate. The variation in simulated impacts was smaller between scenarios based on RCMs nested within the same GCM than between scenarios based on different GCMs or between emission scenarios. 相似文献
8.
9.
基于旋转经验正交函数分解 (REOF) 方法探讨淮河流域1961—2010年夏季降水与厄尔尼诺/南方涛动 (ENSO)、北大西洋涛动 (NAO)、印度洋偶极子 (IOD)、太平洋年代际振荡 (PDO) 之间的关系,并进一步分析各气候因子不同位相单独以及联合对淮河流域夏季降水的影响。结果表明:淮河流域夏季降水与ENSO,PDO,NAO,IOD等气候因子具有较稳定的相关性,其中,PDO和IOD是影响淮河流域夏季降水的关键因子,且PDO与夏季降水呈显著负相关关系;各气候因子的冷暖位相单独及联合对淮河流域夏季降水的影响不同,PDO的冷期以及NAO,IOD冷位相使流域北部的夏季降水量呈显著增加趋势,PDO分别联合ENSO,NAO和IOD的冷、暖位相对流域北部地区和淮河上游地区的夏季降水影响显著。 相似文献
10.
Roy Darwin 《Climatic change》2004,66(1-2):191-238
Because of many uncertainties, quantitative estimates of agriculturally related economic impacts of greenhouse gas emissions are often given low confidence. A major source of uncertainty is our inability to accurately project future changes in economic activity, emissions, and climate. This paper focuses on two issues. First, to what extent do variable projections of climate generate uncertainty in agriculturally related economic impacts? Second, to what extent do agriculturally related economic impacts of greenhouse gas emissions depend on economic conditions at the time of impacts? Results indicate that uncertainty due to variable projections of climate is fairly large for most of the economic effects evaluated in this analysis. Results also indicate that economic conditions at the time of impact influence the direction and size of as well as the confidence in the economic effects of identical projections of greenhouse gas impacts. The economic variable that behaves most consistently in this analysis is world crop production. Increases in mean global temperature, for example, cause world crop production to decrease on average under both 1990 and improved economic conditions and in both instances the confidence with respect to variable projections of climate is medium (e.g.,67%) or greater. In addition and as expected, CO2 fertilization causesworld crop production to increase on average under 1990 and improved economic conditions. These results suggest that crop production may be a fairly robust indicator of the potential impacts of greenhouse gas emissions.A somewhat unexpected finding is that improved economic conditions are not necessarily a panacea to potential greenhouse-gas-induced damages, particularly at the region level. In fact, in some regions, impacts of climate change or CO2 fertilization that are beneficial undercurrent economic conditions may be detrimental under improved economic conditions (relative to the new economic base). Australia plus New Zealand suffer from this effect in this analysis because under improved economic conditions they are assumed to obtain a relatively large share of income from agricultural exports. When the climate-change and CO2-fertilization scenariosin this analysis are also included, agricultural exports from Australia plus New Zealand decline on average. The resultant declines in agricultural income in Australia plus New Zealand are too large to be completely offset by rising incomes in other sectors. This indicates that regions that rely on agricultural exports for relatively large shares of their income may be vulnerable not only to direct climate-induced agricultural damages, but also to positive impacts induced by greenhouse gas emissions elsewhere. 相似文献
11.
We investigate the effect of climate change on crop productivity in Africa using satellite derived data on land use and net primary productivity (NPP) at a small river basin scale, distinguishing between the impact of local and upper-catchment weather. Regression results show that both of these are determining factors of local cropland productivity. These estimates are then combined with climate change predictions obtained from two general circulation models (GCMs) under two greenhouse gas emissions (GHG) assumptions to evaluate the impact of climate change by 2100. For some scenarios significant decreases are predicted over the northern and southern parts of Africa. 相似文献
12.
Going to the Extremes 总被引：8，自引：1，他引：8
Claudia Tebaldi Katharinec Hayhoe Julie M. Arblaster Gerald A. Meehl 《Climatic change》2006,79(3-4):185-211
Projections of changes in climate extremes are critical to assessing the potential impacts of climate change on human and
natural systems. Modeling advances now provide the opportunity of utilizing global general circulation models (GCMs) for projections
of extreme temperature and precipitation indicators. We analyze historical and future simulations of ten such indicators as
derived from an ensemble of 9 GCMs contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change (IPCC-AR4), under a range of emissions scenarios. Our focus is on the consensus from the GCM ensemble, in terms of
direction and significance of the changes, at the global average and geographical scale. The climate extremes described by
the ten indices range from heat-wave frequency to frost-day occurrence, from dry-spell length to heavy rainfall amounts. Historical
trends generally agree with previous observational studies, providing a basic sense of reliability for the GCM simulations.
Individual model projections for the 21st century across the three scenarios examined are in agreement in showing greater
temperature extremes consistent with a warmer climate. For any specific temperature index, minor differences appear in the
spatial distribution of the changes across models and across scenarios, while substantial differences appear in the relative
magnitude of the trends under different emissions rates. Depictions of a wetter world and greater precipitation intensity
emerge unequivocally in the global averages of most of the precipitation indices. However, consensus and significance are
less strong when regional patterns are considered. This analysis provides a first overview of projected changes in climate
extremes from the IPCC-AR4 model ensemble, and has significant implications with regard to climate projections for impact
assessments.
An erratum to this article is available at .
An erratum to this article can be found at 相似文献
13.
This paper investigates how using different regional climate model (RCM) simulations affects climate change impacts on hydrology in northern Europe using an offline hydrological model. Climate change scenarios from an ensemble of seven RCMs, two global climate models (GCMs), two global emissions scenarios and two RCMs of varying resolution were used. A total of 15 climate change simulations were included in studies on the Lule River basin in Northern Sweden. Two different approaches to transfer climate change from the RCMs to hydrological models were tested. A rudimentary estimate of change in hydropower potential on the Lule River due to climate change was also made. The results indicate an overall increase in river flow, earlier spring peak flows and an increase in hydropower potential. The two approaches for transferring the signal of climate change to the hydrological impacts model gave similar mean results, but considerably different seasonal dynamics, a result that is highly relevant for other types of climate change impacts studies. 相似文献
14.
Hydrologic Sensitivity of Global Rivers to Climate Change 总被引：12，自引：1，他引：12
Bart Nijssen Greg M. O'Donnell Alan F. Hamlet Dennis P. Lettenmaier 《Climatic change》2001,50(1-2):143-175
Climate predictions from four state-of-the-art general circulation models (GCMs) were used to assess the hydrologic sensitivity to climate change of nine large, continental river basins (Amazon, Amur, Mackenzie, Mekong, Mississippi, Severnaya Dvina, Xi, Yellow, Yenisei). The four climate models (HCCPR-CM2, HCCPR-CM3, MPI-ECHAM4, and DOE-PCM3) all predicted transient climate response to changing greenhouse gas concentrations, and incorporated modern land surface parameterizations. Model-predicted monthly average precipitation and temperature changes were downscaled to the river basin level using model increments (transient minus control) to adjust for GCM bias. The variable infiltration capacity (VIC) macroscale hydrological model (MHM) was used to calculate the corresponding changes in hydrologic fluxes (especially streamflow and evapotranspiration) and moisture storages. Hydrologic model simulations were performed for decades centered on 2025 and 2045. In addition, a sensitivity study was performed in which temperature and precipitation were increased independently by 2 °C and 10%, respectively, during each of four seasons. All GCMs predict a warming for all nine basins, with the greatest warming predicted to occur during the winter months in the highest latitudes. Precipitation generally increases, but the monthly precipitation signal varies more between the models than does temperature. The largest changes in the hydrological cycle are predicted for the snow-dominated basins of mid to higher latitudes. This results in part from the greater amount of warming predicted for these regions, but more importantly, because of the important role of snow in the water balance. Because the snow pack integrates the effects of climate change over a period of months, the largest changes occur in early to mid spring when snow melt occurs. The climate change responses are somewhat different for the coldest snow dominated basins than for those with more transitional snow regimes. In the coldest basins, the response to warming is an increase of the spring streamflow peak, whereas for the transitional basins spring runoff decreases. Instead, the transitional basins have large increases in winter streamflows. The hydrological response of most tropical and mid-latitude basins to the warmer and somewhat wetter conditions predicted by the GCMs is a reduction in annual streamflow, although again, considerable disagreement exists among the different GCMs. In contrast, for the high-latitude basins increases in annual flow volume are predicted in most cases. 相似文献
15.
Climate change impacts, adaptation and vulnerability studies tend to confine their attention to impacts and responses within the same geographical region. However, this approach ignores cross-border climate change impacts that occur remotely from the location of their initial impact and that may severely disrupt societies and livelihoods. We propose a conceptual framework and accompanying nomenclature for describing and analysing such cross-border impacts. The conceptual framework distinguishes an initial impact that is caused by a climate trigger within a specific region. Downstream consequences of that impact propagate through an impact transmission system while adaptation responses to deal with the impact propagate through a response transmission system. A key to understanding cross-border impacts and responses is a recognition of different types of climate triggers, categories of cross-border impacts, the scales and dynamics of impact transmission, the targets and dynamics of responses and the socio-economic and environmental context that also encompasses factors and processes unrelated to climate change. These insights can then provide a basis for identifying relevant causal relationships. We apply the framework to the floods that affected industrial production in Thailand in 2011, and to projected Arctic sea ice decline, and demonstrate that the framework can usefully capture the complex system dynamics of cross-border climate impacts. It also provides a useful mechanism to identify and understand adaptation strategies and their potential consequences in the wider context of resilience planning. The cross-border dimensions of climate impacts could become increasingly important as climate changes intensify. We conclude that our framework will allow for these to be properly accounted for, help to identify new areas of empirical and model-based research and thereby support climate risk management. 相似文献
16.
Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 1: baseline climate 总被引：1，自引：0，他引：1
Assessing future climate and its potential implications on river flows is a key challenge facing water resource planners. Sound, scientifically-based advice to decision makers also needs to incorporate information on the uncertainty in the results. Moreover, existing bias in the reproduction of the ‘current’ (or baseline) river flow regime is likely to transfer to the simulations of flow in future time horizons, and it is thus critical to undertake baseline flow assessment while undertaking future impacts studies. This paper investigates the three main sources of uncertainty surrounding climate change impact studies on river flows: uncertainty in GCMs, in downscaling techniques and in hydrological modelling. The study looked at four British catchments’ flow series simulated by a lumped conceptual rainfall–runoff model with observed and GCM-derived rainfall series representative of the baseline time horizon (1961–1990). A block-resample technique was used to assess climate variability, either from observed records (natural variability) or reproduced by GCMs. Variations in mean monthly flows due to hydrological model uncertainty from different model structures or model parameters were also evaluated. Three GCMs (HadCM3, CCGCM2, and CSIRO-mk2) and two downscaling techniques (SDSM and HadRM3) were considered. Results showed that for all four catchments, GCM uncertainty is generally larger than downscaling uncertainty, and both are consistently greater than uncertainty from hydrological modelling or natural variability. No GCM or downscaling technique was found to be significantly better or to have a systematic bias smaller than the others. This highlights the need to consider more than one GCM and downscaling technique in impact studies, and to assess the bias they introduce when modelling river flows. 相似文献
17.
The majority of climate change impacts assessments account for climate change uncertainty by adopting the scenario-based approach.
This typically involves assessing the impacts for a small number of emissions scenarios but neglecting the role of climate
model physics uncertainty. Perturbed physics ensemble (PPE) climate simulations offer a unique opportunity to explore this
uncertainty. Furthermore, PPEs mean it is now possible to make risk-based impacts estimates because they allow for a range
of estimates to be presented to decision-makers, which spans the range of climate model physics uncertainty inherent from
a given climate model and emissions scenario, due to uncertainty associated with the understanding of physical processes in
the climate model. This is generally not possible with the scenario-based approach. Here, we present the first application
of a PPE to estimate the impact of climate change on heat-related mortality. By using the estimated impacts of climate change
on heat-related mortality in six cities, we demonstrate the benefits of quantifying climate model physics uncertainty in climate
change impacts assessment over the more common scenario-based approach. We also show that the impacts are more sensitive to
climate model physics uncertainty than they are to emissions scenario uncertainty, and least sensitive to whether the climate
change projections are from a global climate model or a regional climate model. The results demonstrate the importance of
presenting model uncertainties in climate change impacts assessments if the impacts are to be placed within a climate risk
management framework. 相似文献
18.
Climate Change Impacts on the Potential Productivity of Corn and Winter Wheat in Their Primary United States Growing Regions 总被引：2，自引：0，他引：2
We calculate the impacts of climate effects inferred from three atmospheric general circulation models (GCMs) at three levels of climate change severity associated with change in global mean temperature (GMT) of 1.0, 2.5 and 5.0 °C and three levels of atmospheric CO2 concentration ([CO2]) – 365 (no CO2 fertilization effect), 560 and 750 ppm – on the potential production of dryland winter wheat (Triticum aestivum L.) and corn (Zea mays L.) for the primary (current) U.S. growing regions of each crop. This analysis is a subset of the Global Change Assessment Model (GCAM) which has the goal of integrating the linkages and feedbacks among human activities and resulting greenhouse gas emissions, changes in atmospheric composition and resulting climate change, and impacts on terrestrial systems. A set of representative farms was designed for each of the primary production regions studied and the Erosion Productivity Impact Calculator (EPIC) was used to simulate crop response to climate change. The GCMs applied were the Goddard Institute of Space Studies (GISS), the United Kingdom Meteorological Transient (UKTR) and the Australian Bureau of Meteorological Research Center (BMRC), each regionalized by means of a scenario generator (SCENGEN). The GISS scenarios have the least impact on corn and wheat production, reducing national potential production for corn by 6% and wheat by 7% at a GMT of 2.5 °C and no CO2 fertilization effect; the UKTR scenario had the most severe impact on wheat, reducing production by 18% under the same conditions; BMRC had the greatest negative impact on corn, reducing production by 20%. A GMT increase of 1.0°C marginally decreased corn and wheat production. Increasing GMT had a detrimental impact on both corn and wheat production, with wheat production suffering the greatest losses. Decreases for wheat production at GMT 5.0 and [CO2] = 365 ppm range from 36% for the GISS to 76% for the UKTR scenario. Increases in atmospheric [CO2] had a positive impact on both corn and wheat production. AT GMT 1.0, an increase in [CO2] to 560 ppm resulted in a net increase in corn and wheat production above baseline levels (from 18 to 29% for wheat and 2 to 5% for corn). Increases in [CO2] help to offset yield reductions at higher GMT levels; in most cases, however, these increases are not sufficient to return crop production to baseline levels. 相似文献
19.
Cambodia is one of the most vulnerable countries to climate change impacts such as floods and droughts. Study of future climate change and drought conditions in the upper Siem Reap River catchment is vital because this river plays a crucial role in maintaining the Angkor Temple Complex and livelihood of the local population since 12th century. The resolution of climate data from Global Circulation Models (GCM) is too coarse to employ effectively at the watershed scale, and therefore downscaling of the dataset is required. Artificial neural network (ANN) and Statistical Downscaling Model (SDSM) models were applied in this study to downscale precipitation and temperatures from three Representative Concentration Pathways (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios) from Global Climate Model data of the Canadian Earth System Model (CanESM2) on a daily and monthly basis. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were adopted to develop criteria for dry and wet conditions in the catchment. Trend detection of climate parameters and drought indices were assessed using the Mann-Kendall test. It was observed that the ANN and SDSM models performed well in downscaling monthly precipitation and temperature, as well as daily temperature, but not daily precipitation. Every scenario indicated that there would be significant warming and decreasing precipitation which contribute to mild drought. The results of this study provide valuable information for decision makers since climate change may potentially impact future water supply of the Angkor Temple Complex (a World Heritage Site). 相似文献
20.
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 相似文献