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1.
Differences in the radiative feedback from clouds account for much of the variation in climate sensitivity amongst General
Circulation Models (GCMs). Therefore metrics of model performance which are demonstrated to be relevant to the cloud response
to climate change form an important contribution to the overall evaluation of GCMs. In this paper we demonstrate an alternative
method for assigning model data to observed cloud regimes obtained from clustering histograms of cloud amount in joint cloud
optical depth—cloud top pressure classes. The method removes some of the subjectivity that exists in previous GCM cloud clustering
studies. We apply the method to ten GCMs submitted to the Cloud Feedback Model Intercomparison Project (CFMIP), evaluate the
simulated cloud regimes and analyse the climate change response in the context of these regimes. We also propose two cloud
regime metrics, one of which is specifically targeted at assessing GCMs for the purpose of obtaining the global cloud radiative
response to climate change. Most of the global variance in the cloud radiative response between GCMs is due to low clouds,
with 47% arising from the stratocumulus regime and 18% due to the regime characterised by clouds undergoing transition from
stratocumulus to cumulus. This result is found to be dominated by two structurally similar GCMs. The shallow cumulus regime,
though widespread, has a smaller contribution and reduces the variance. For the stratocumulus and transition regimes, part
of the variance results from a large model spread in the radiative properties of the regime in the control simulation. Comparison
with observations reveals a systematic bias for both the stratocumulus and transition regimes to be overly reflective. If
this bias was corrected with all other aspects of the response unchanged, the variance in the low cloud response would reduce.
The response of some regimes with high cloud tops differ between the GCMs. These regimes are simulated too infrequently in
a few of the models. If the frequency in the control simulation were more realistic and changes within the regimes were unaltered,
the variance in the cloud radiative response from high-top clouds would increase. As a result, use of observations of the
mean present-day cloud regimes suggests that whilst improvements in the simulation of the cloud regimes would impact the climate
sensitivity, the inter-model variance may not reduce. When the cloud regime metric is calculated for the GCMs analysed here,
only one model is on average consistent with observations within their uncertainty (and even this model is not consistent
with the observations for all regimes), indicating scope for improvement in the simulation of cloud regimes.
Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. 相似文献
2.
The radiative feedback from clouds remains the largest source of variation in climate sensitivity amongst general circulation
models (GCMs). A cloud clustering methodology is applied to six contemporary GCMs in order to provide a detailed intercomparison
and evaluation of the simulated cloud regimes. By analysing GCMs in the context of cloud regimes, processes related to particular
cloud types are more likely to be evaluated. In this paper, the mean properties of the global cloud regimes are evaluated,
and the cloud response to climate change is analysed in the cloud-regime framework. Most of the GCMs are able to simulate
the principal cloud regimes, however none of the models analysed have a good representation of trade cumulus in the tropics.
The models also share a difficulty in simulating those regimes with cloud tops at mid-levels, with only ECHAM5 producing a
regime of tropical cumulus congestus. Optically thick, high top cloud in the extra-tropics, typically associated with the
passage of frontal systems, is simulated considerably too frequently in the ECHAM5 model. This appears to be a result of the
cloud type persisting in the model after the meteorological conditions associated with frontal systems have ceased. The simulation
of stratocumulus in the MIROC GCMs is too extensive, resulting in the tropics being too reflective. Most of the global-mean
cloud response to doubled CO2 in the GCMs is found to be a result of changes in the cloud radiative properties of the regimes, rather than changes in the
relative frequency of occurrence (RFO) of the regimes. Most of the variance in the global cloud response between the GCMs
arises from differences in the radiative response of frontal cloud in the extra-tropics and from stratocumulus cloud in the
tropics. This variance is largely the result of excessively high RFOs of specific regimes in particular GCMs. It is shown
here that evaluation and subsequent improvement in the simulation of the present-day regime properties has the potential to
reduce the variance of the global cloud response, and hence climate sensitivity, amongst GCMs. For the ensemble of models
considered in this study, the use of observations of the mean present-day cloud regimes suggests a potential reduction in
the range of climate sensitivity of almost a third.
Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. 相似文献
3.
Matthew C. Wyant Christopher S. Bretherton Julio T. Bacmeister Jeffrey T. Kiehl Isaac M. Held Ming Zhao Stephen A. Klein Brian J. Soden 《Climate Dynamics》2006,27(2-3):261-279
Low-latitude cloud distributions and cloud responses to climate perturbations are compared in near-current versions of three leading U.S. AGCMs, the NCAR CAM 3.0, the GFDL AM2.12b, and the NASA GMAO NSIPP-2 model. The analysis technique of Bony et al. (Clim Dyn 22:71–86, 2004) is used to sort cloud variables by dynamical regime using the monthly mean pressure velocity ω at 500 hPa from 30S to 30N. All models simulate the climatological monthly mean top-of-atmosphere longwave and shortwave cloud radiative forcing (CRF) adequately in all ω-regimes. However, they disagree with each other and with ISCCP satellite observations in regime-sorted cloud fraction, condensate amount, and cloud-top height. All models have too little cloud with tops in the middle troposphere and too much thin cirrus in ascent regimes. In subsidence regimes one model simulates cloud condensate to be too near the surface, while another generates condensate over an excessively deep layer of the lower troposphere. Standardized climate perturbation experiments of the three models are also compared, including uniform SST increase, patterned SST increase, and doubled CO2 over a mixed layer ocean. The regime-sorted cloud and CRF perturbations are very different between models, and show lesser, but still significant, differences between the same model simulating different types of imposed climate perturbation. There is a negative correlation across all general circulation models (GCMs) and climate perturbations between changes in tropical low cloud cover and changes in net CRF, suggesting a dominant role for boundary layer cloud in these changes. For some of the cases presented, upper-level clouds in deep convection regimes are also important, and changes in such regimes can either reinforce or partially cancel the net CRF response from the boundary layer cloud in subsidence regimes. This study highlights the continuing uncertainty in both low and high cloud feedbacks simulated by GCMs. 相似文献
4.
K. D. Williams M. A. Ringer C. A. Senior M. J. Webb B. J. McAvaney N. Andronova S. Bony J. -L. Dufresne S. Emori R. Gudgel T. Knutson B. Li K. Lo I. Musat J. Wegner A. Slingo J. F. B. Mitchell 《Climate Dynamics》2006,26(2-3):145-165
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. 相似文献
5.
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
6.
Shortwave cloud radiative forcing on major stratus cloud regions in AMIP-type simulations of CMIP3 and CMIP5 models 总被引：1，自引：0，他引：1
Cloud and its radiative effects are major sources of uncertainty that lead to simulation discrepancies in climate models. In this study, shortwave cloud radiative forcing (SWCF) over major stratus regions is evaluated for Atmospheric Models Intercomparison Project (AMIP)-type simulations of models involved in the third and fifth phases of the Coupled Models Intercomparison Project (CMIP3 and CMIP5). Over stratus regions, large deviations in both climatological mean and seasonal cycle of SWCF are found among the models. An ambient field sorted by dynamic (vertical motion) and thermodynamic (inversion strength or stability) regimes is constructed and used to measure the response of SWCF to large-scale controls. In marine boundary layer regions, despite both CMIP3 and CMIP5 models being able to capture well the center and range of occurrence frequency for the ambient field, most of the models fail to simulate the dependence of SWCF on boundary layer inversion and the insensitivity of SWCF to vertical motion. For eastern China, there are large differences even in the simulated ambient fields. Moreover, almost no model can reproduce intense SWCF in rising motion and high stability regimes. It is also found that models with a finer grid resolution have no evident superiority than their lower resolution versions. The uncertainties relating to SWCF in state-of-the-art models may limit their performance in IPCC experiments. 相似文献
7.
The contribution of anthropogenic forcings to regional changes in temperature during the last decade
Nikolaos Christidis Peter A. Stott Francis W. Zwiers Hideo Shiogama Toru Nozawa 《Climate Dynamics》2012,39(6):1259-1274
Regional distributions of the mean annual temperature in the 2000s are computed with and without the effect of anthropogenic influences on the climate in several sub-continental regions. Simulated global patterns of the temperature response to external forcings are regressed against observations using optimal fingerprinting. The global analysis provides constraints which are then used to construct the regional temperature distributions. A similar approach was also employed in previous work, but here the methodology is extended to examine changes in any region, including areas with a poor observational coverage that were omitted in the earlier study. Two different General Circulation Models (GCMs) are used in the analysis. Anthropogenic forcings are found to have at least quadrupled the likelihood of occurrence of a year warmer than the warmest year since 1900 in 23 out of the 24 regions. The temperature distributions computed with the two models are very similar. While a more detailed assessment of model dependencies remains to be made once additional suitable GCM simulations become available, the present study introduces the statistical methodology and demonstrates its first application. The derived information concerning the effect of human influences on the regional climate is useful for adaptation planning. Moreover, by pre-computing the change in the likelihood of exceeding a temperature threshold over a range of thresholds, this kind of analysis enables a near real-time assessment of the anthropogenic impact on the observed regional temperatures. 相似文献
8.
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
9.
Present and future climatologies in the phase I CREMA experiment 总被引：1，自引：0，他引：1
Erika Coppola Filippo Giorgi Francesca Raffaele Ramon Fuentes-Franco Graziano Giuliani Marta LLopart-Pereira Ashu Mamgain Laura Mariotti Gulilat Tefera Diro Csaba Torma 《Climatic change》2014,125(1):23-38
We provide an overall assessment of the surface air temperature and precipitation present day (1976–2005) and future (2070–2099) ensemble climatologies in the Phase I CREMA experiment. This consists of simulations performed with different configurations (physics schemes) of the ICTP regional model RegCM4 over five CORDEX domains (Africa, Mediterranean, Central America, South America, South Asia), driven by different combinations of three global climate models (GCMs) and two greenhouse gas (GHG) representative concentration pathways (RCP8.5 and RCP4.5). The biases (1976–2005) in the driving and nested model ensembles compared to observations show a high degree of spatial variability and, when comparing GCMs and RegCM4, similar magnitudes and more similarity for precipitation than for temperature. The large scale patterns of change (2070–2099 minus 1976–2005) are broadly consistent across the GCM and RegCM4 ensembles and with previous analyses of GCM projections, indicating that the GCMs selected in the CREMA experiment are representative of the more general behavior of current GCMs. The RegCM4, however, shows a lower climate sensitivity (reduced warming) than the driving GCMs, especially when using the CLM land surface scheme. While the broad patterns of precipitation change are consistent across the GCM and RegCM4 ensembles, greater differences are found at sub-regional scales over the various domains, evidently tied to the representation of local processes. This paper serves to provide a reference view of the behavior of the CREMA ensemble, while more detailed and process-based analysis of individual domains is left to companion papers of this special issue. 相似文献
10.
Use of general circulation model output in the creation of climate change scenarios for impact analysis 总被引：1，自引：0，他引：1
Alan Robock Richard P. Turco Mark A. Harwell Thomas P. Ackerman Rigoberto Andressen Hsin-Shih Chang M. V. K. Sivakumar 《Climatic change》1993,23(4):293-335
Many scientific studies warn of a rapid global climate change during the next century. These changes are understood with much less certainty on a regional scale than on a global scale, but effects on ecosystems and society will occur at local and regional scales. Consequently, in order to study the true impacts of climate change, regional scenarios of future climate are needed. One of the most important sources of information for creating scenarios is the output from general circulation models (GCMs) of the climate system. However, current state-of-the-art GCMs are unable to simulate accurately even the current seasonal cycle of climate on a regional basis. Thus the simple technique of adding the difference between 2 × CO2 and 1 × CO2 GCM simulations to current climatic time series cannot produce scenarios with appropriate spatial and temporal details without corrections for model deficiencies. In this study a technique is developed to allow the information from GCM simulations to be used, while accommodating for the deficiencies. GCM output is combined with knowledge of the regional climate to produce scenarios of the equilibrium climate response to a doubling of the atmospheric CO2 concentration for three case study regions, China, Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general climate change calculated with several GCMs with the observed patterns of interannual climate variability, reasonable scenarios of temperature and precipitation variations can be created. Generalizations of this procedure to other regions of the world are discussed. 相似文献
11.
Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area. 相似文献
12.
Yoko Tsushima Mark A. Ringer Mark J. Webb Keith D. Williams 《Climate Dynamics》2013,41(9-10):2679-2696
An extended cloud-clustering method to assess the seasonal variation of clouds is applied to five CMIP5 models. The seasonal variation of the total cloud radiative effect (CRE) is dominated by variations in the relative frequency of occurrence of the different cloud regimes. Seasonal variations of the CRE within the individual regimes contribute much less. This is the case for both observations, models and model errors. The error in the seasonal variation of cloud regimes, and its breakdown into mean amplitude and time varying components, are quantified with a new metric. The seasonal variation of the CRE of the cloud regimes is relatively well simulated by the models in the tropics, but less well in the extra-tropics. The stratocumulus regime has the largest seasonal variation of shortwave CRE in the tropics, despite having a small magnitude in the climatological mean. Most of the models capture the temporal variation of the CRE reasonably well, with the main differences between models coming from the variation in amplitude. In the extra-tropics, most models fail to correctly represent both the amplitude and time variation of the CRE of congestus, frontal and stratocumulus regimes. The annual mean climatology of the CRE and its amplitude in the seasonal variation are both underestimated for the anvil regime in the tropics, the cirrus regime and the congestus regime in the extra-tropics. The models in this study that best capture the seasonal variation of the cloud regimes tend to have higher climate sensitivities. 相似文献
13.
M. Déqué R. G. Jones M. Wild F. Giorgi J. H. Christensen D. C. Hassell P. L. Vidale B. Rockel D. Jacob E. Kjellström M. de. Castro F. Kucharski B. van den Hurk 《Climate Dynamics》2005,25(6):653-670
Four high resolution atmospheric general circulation models (GCMs) have been integrated with the standard forcings of the
PRUDENCE experiment: IPCC-SRES A2 radiative forcing and Hadley Centre sea surface temperature and sea-ice extent. The response
over Europe, calculated as the difference between the 2071–2100 and the 1961–1990 means is compared with the same diagnostic
obtained with nine Regional Climate Models (RCM) all driven by the Hadley Centre atmospheric GCM. The seasonal mean response
for 2m temperature and precipitation is investigated. For temperature, GCMs and RCMs behave similarly, except that GCMs exhibit
a larger spread. However, during summer, the spread of the RCMs—in particular in terms of precipitation—is larger than that
of the GCMs. This indicates that the European summer climate is strongly controlled by parameterized physics and/or high-resolution
processes. The temperature response is larger than the systematic error. The situation is different for precipitation. The
model bias is twice as large as the climate response. The confidence in PRUDENCE results comes from the fact that the models
have a similar response to the IPCC-SRES A2 forcing, whereas their systematic errors are more spread. In addition, GCM precipitation
response is slightly but significantly different from that of the RCMs. 相似文献
14.
The general circulation model (GCM) used in this study includes a prognostic cloud scheme and a rather detailed radiation scheme. In a preceding paper, we showed that this model was more sensitive to a global perturbation of the sea surface temperatures than most other models with similar physical parametrization. The experiments presented here show how this feature might depend on some of the cloud modelling assumptions. We have changed the temperature at which the water clouds are allowed to become ice clouds and analyzed separately the feedbacks associated with the variations of cloud cover and cloud radiative properties. We show that the feedback effect associated with cloud radiative properties is positive in one case and negative in the other. This can be explained by the elementary cloud radiative forcing and has implications concerning the use of the GCMs for climate sensitivity studies. 相似文献
15.
Tropical cloud regimes defined by cluster analysis of International Satellite Cloud Climatology Project (ISCCP) cloud top pressure (CTP)–optical thickness distributions and ISCCP-like Goddard Institute for Space Studies (GISS) general circulation model (GCM) output are analyzed in this study. The observations are evaluated against radar–lidar cloud-top profiles from the atmospheric radiation measurement (ARM) Program active remote sensing of cloud layers (ARSCL) product at two tropical locations and by placing them in the dynamical context of the Madden–Julian oscillation (MJO). ARSCL highest cloud-top profiles indicate that differences among some of the six ISCCP regimes may not be as prominent as suggested by ISCCP at the ARM tropical sites. An experimental adjustment of the ISCCP CTPs to produce cloud-top height profiles consistent with ARSCL eliminates the independence between those regimes. Despite these ambiguities, the ISCCP regime evolution over different phases of the MJO is consistent with existing MJO mechanisms, but with a greater mix of cloud types in each phase than is usually envisioned. The GISS Model E GCM produces two disturbed and two suppressed regimes when vertical convective condensate transport is included in the model’s cumulus parameterization. The primary model deficiencies are the absence of an isolated cirrus regime, a lack of mid-level cloud relative to ARSCL, and a tendency for occurrences of specific parameterized processes such as deep and shallow convection and stratiform low cloud formation to not be associated preferentially with any single cloud regime. 相似文献
16.
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
17.
Climate scenarios for the Netherlands are constructed by combining information from global and regional climate models employing
a simplified, conceptual framework of three sources (levels) of uncertainty impacting on predictions of the local climate.
In this framework, the first level of uncertainty is determined by the global radiation balance, resulting in a range of the
projected changes in the global mean temperature. On the regional (1,000–5,000 km) scale, the response of the atmospheric
circulation determines the second important level of uncertainty. The third level of uncertainty, acting mainly on a local
scale of 10 (and less) to 1,000 km, is related to the small-scale processes, like for example those acting in atmospheric
convection, clouds and atmospheric meso-scale circulations—processes that play an important role in extreme events which are
highly relevant for society. Global climate models (GCMs) are the main tools to quantify the first two levels of uncertainty,
while high resolution regional climate models (RCMs) are more suitable to quantify the third level.
Along these lines, results of an ensemble of RCMs, driven by only two GCM boundaries and therefore spanning only a rather
narrow range in future climate predictions, are rescaled to obtain a broader uncertainty range. The rescaling is done by first
disentangling the climate change response in the RCM simulations into a part related to the circulation, and a residual part
which is related to the global temperature rise. Second, these responses are rescaled using the range of the predictions of
global temperature change and circulation change from five GCMs. These GCMs have been selected on their ability to simulate
the present-day circulation, in particular over Europe. For the seasonal means, the rescaled RCM results obey the range in
the GCM ensemble using a high and low emission scenario. Thus, the rescaled RCM results are consistent with the GCM results
for the means, while adding information on the small scales and the extremes. The method can be interpreted as a combined
statistical–dynamical downscaling approach, with the statistical relations based on regional model output. 相似文献
18.
Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP) 总被引：2，自引：1，他引：1
L. O. Mearns S. Sain L. R. Leung M. S. Bukovsky S. McGinnis S. Biner D. Caya R. W. Arritt W. Gutowski E. Takle M. Snyder R. G. Jones A. M. B. Nunes S. Tucker D. Herzmann L. McDaniel L. Sloan 《Climatic change》2013,120(4):965-975
We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs. 相似文献
19.
A flexible climate model for use in integrated assessments 总被引：2，自引：0，他引：2
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 相似文献
20.
Krijn P. Paaijmans Justine I. Blanford Robert G. Crane Michael E. Mann Liang Ning Kathleen V. Schreiber Matthew B. Thomas 《Climatic change》2014,125(3-4):479-488
The potential impact of climate warming on patterns of malaria transmission has been the subject of keen scientific and policy debate. Standard climate models (GCMs) characterize climate change at relatively coarse spatial and temporal scales. However, malaria parasites and the mosquito vectors respond to diurnal variations in conditions at very local scales. Here we bridge this gap by downscaling a series of GCMs to provide high-resolution temperature data for four different sites and show that although outputs from both the GCM and the downscaled models predict diverse but qualitatively similar effects of warming on the potential for adult mosquitoes to transmit malaria, the predicted magnitude of change differs markedly between the different model approaches. Raw GCM model outputs underestimate the effects of climate warming at both hot (3-fold) and cold (8–12 fold) extremes, and overestimate (3-fold) the change under intermediate conditions. Thus, downscaling could add important insights to the standard application of coarse-scale GCMs for biophysical processes driven strongly by local microclimatic conditions. 相似文献