Frequency distributions of transient regional climate change from perturbed physics ensembles of general circulation model simulations

Large ensembles of coupled atmosphere–ocean general circulation model (AOGCM) simulations are required to explore modelling uncertainty and make probabilistic predictions of future transient climate change at regional scales. These are not yet computationally feasible so we have developed a technique to emulate the response of such an ensemble by scaling equilibrium patterns of climate change derived from much cheaper “slab” model ensembles in which the atmospheric component of an AOGCM is coupled to a mixed-layer ocean. Climate feedback parameters are diagnosed for each member of a slab model ensemble and used to drive an energy balance model (EBM) to predict the time-dependent response of global surface temperature expected for different combinations of uncertain AOGCM parameters affecting atmospheric, land and sea-ice processes. The EBM projections are then used to scale normalised patterns of change derived for each slab member, and hence emulate the response of the relevant atmospheric model version when coupled to a dynamic ocean, in response to a 1% per annum increase in CO₂. The emulated responses are validated by comparison with predictions from a 17 member ensemble of AOGCM simulations, constructed from variants of HadCM3 using the same parameter combinations as 17 members of the slab model ensemble. Cross-validation permits estimation of the spatial and temporal dependence of emulation error, and also allows estimation of a correction field to correct discrepancies between the scaled equilibrium patterns and the transient response, reducing the emulation error. Emulated transient responses and their associated errors are obtained from the slab ensemble for 129 pseudo-HadCM3 versions containing multiple atmospheric parameter perturbations. These are combined to produce regional frequency distributions for the transient response of annual surface temperature change and boreal winter precipitation change. The technique can be extended to any surface climate variable demonstrating a scaleable, approximately linear response to forcing.     

Towards quantifying uncertainty in transient climate change

Ensembles of coupled atmosphere–ocean global circulation model simulations are required to make probabilistic predictions of future climate change. “Perturbed physics” ensembles provide a new approach in which modelling uncertainties are sampled systematically by perturbing uncertain parameters. The aim is to provide a basis for probabilistic predictions in which the impact of prior assumptions and observational constraints can be clearly distinguished. Here we report on the first perturbed physics coupled atmosphere–ocean model ensemble in which poorly constrained atmosphere, land and sea-ice component parameters are varied in the third version of the Hadley Centre model (the variation of ocean parameters will be the subject of future study). Flux adjustments are employed, both to reduce regional sea surface temperature (SST) and salinity biases and also to admit the use of combinations of model parameter values which give non-zero values for the global radiation balance. This improves the extent to which the ensemble provides a credible basis for the quantification of uncertainties in climate change, especially at a regional level. However, this particular implementation of flux-adjustments leads to a weakening of the Atlantic overturning circulation, resulting in the development of biases in SST and sea ice in the North Atlantic and Arctic Oceans. Nevertheless, model versions are produced which are of similar quality to the unperturbed and un-flux-adjusted version. The ensemble is used to simulate pre-industrial conditions and a simple scenario of a 1% per year compounded increase in CO₂. The range of transient climate response (the 20 year averaged global warming at the time of CO₂ doubling) is 1.5–2.6°C, similar to that found in multi-model studies. Measures of global and large scale climate change from the coupled models show simple relationships with associated measures computed from atmosphere-mixed-layer-ocean climate change experiments, suggesting that recent advances in computing the probability density function of climate change under equilibrium conditions using the perturbed physics approach may be extended to the transient case.     

The impact of perturbations to ocean-model parameters on climate and climate change in a coupled model

Assessments of the impacts of uncertainties in parameters on mean climate and climate change in complex climate models have, to date, largely focussed on perturbations to parameters in the atmosphere component of the model. Here we expand on a previously published study which found the global impacts of perturbed ocean parameters on the rate of transient climate change to be small compared to perturbed atmosphere parameters. By separating the climate-change-induced ocean vertical heat transport in each perturbed member into components associated with the resolved flow and each parameterisation scheme, we show that variations in global mean heat uptake in different perturbed versions are an order of magnitude smaller than the average heat uptake. The lack of impact of the perturbations is attributed to (1) the relatively small impact of the perturbation on the direct vertical heat transport associated with the perturbed process and (2) a compensation between those direct changes and indirect changes in heat transport from other processes. Interactions between processes and changes appear to combine in complex ways to limit ensemble spread and uncertainty in the rate of warming. We also investigate regional impacts of the perturbations that may be important for climate change predictions. We find variations across the ensemble that are significant when measured against natural variability. In terms of the experimental set-up used here (models without flux adjustments) we conclude that perturbed physics ensembles with ocean parameter perturbations are an important component of any probabilistic estimate of future climate change, despite the low spread in global mean quantities. Hence, careful consideration should be given to assessing uncertainty in ocean processes in future probabilistic assessments of regional climate change.     

Intercomparsion of regional biases and doubled CO2-sensitivity of coupled atmosphere-ocean general circulation model experiments

 We compared regional biases and transient doubled CO₂ sensitivities of nine coupled atmosphere-ocean general circulation models (GCMs) from six international climate modeling groups. We evaluated biases and responses in winter and summer surface air temperatures and precipitation for seven subcontinental regions, including those in the 1990 Intergovernmental Panel on Climate Change (IPCC) Scientific Assessment. Regional biases were large and exceeded the variance among four climatological datasets, indicating that model biases were not primarily due to uncertainty in observations. Model responses to altered greenhouse forcing were substantial (average temperature change=2.7±0.9 ^°C, range of precipitation change =−35 to +120% of control). While coupled models include more climate system feedbacks than earlier GCMs implemented with mixed-layer ocean models, inclusion of a dynamic ocean alone did not improve simulation of long-term mean climatology nor increase convergence among model responses to altered greenhouse gas forcing. On the other hand, features of some of the coupled models including flux adjustment (which may have simply masked simulation errors), high horizontal resolution, and estimation of screen height temperature contributed to improved simulation of long-term surface climate. The large range of model responses was partly accounted for by inconsistencies in forcing scenarios and transient-simulation averaging periods. Nonetheless, the models generally had greater agreement in their sensitivities than their controls did with observations. This suggests that consistent, large-scale response features from an ensemble of model sensitivity experiments may not depend on details of their representation of present-day climate. Received: 9 September 1996 / Revised: 31 July 1997     

Future changes in tropical cyclone genesis in fully dynamic ocean- and mixed layer ocean-coupled climate models: a low-resolution model study

The global and regional projected changes in tropical cyclone (TC) genesis due to increased CO₂ concentrations has been investigated through a large-scale TC genesis parameter (convective seasonal genesis parameter, ConvGP) in two perturbed physics ensembles. The ensembles are based on the third generation Hadley Centre atmosphere?Cocean general circulation model with the first ensemble using a coupled fully dynamic ocean (HadCM3) and the second coupled to a simplified mixed layer thermodynamic ocean (HadSM3) both consisting of 17 members. In each ensemble, parameters are identically perturbed to provide a wide range of climate sensitivity whilst retaining a credible present-day climate simulation. It is found, by comparing the ConvGP climatology from reanalysis data with the best track genesis, that it is possible to reproduce the observed genesis distribution. Future changes in the spatial ConvGP distribution are explored with respect to each tropical ocean basin. Whilst there is a similarity in the gross pattern of the ensemble-mean projected ConvGP change between HadCM3 and HadSM3, there is a non-trivial difference in the tropical Pacific Ocean, arising from different patterns of tropical Pacific sea surface temperature change. This indicates that ocean representation can be important for regional scale projections. The quantitative contribution of individual constituent parameters (i.e. vorticity parameter, shear parameter and convective potential) to the projected ConvGP change is estimated. It is found that all three large-scale parameters generally contribute constructively, but with different magnitude, in the regions where a large doubled CO₂ response is found.     

Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM

We analyze ensembles (four realizations) of historical and future climate transient experiments carried out with the coupled atmosphere-ocean general circulation model (AOGCM) of the Hadley Centre for Climate Prediction and Research, version HADCM2, with four scenarios of greenhouse gas (GHG) and sulfate forcing. The analysis focuses on the regional scale, and in particular on 21 regions covering all land areas in the World (except Antarctica). We examine seasonally averaged surface air temperature and precipitation for the historical period of 1961–1990 and the future climate period of 2046–2075. Compared to previous AOGCM simulations, the HADCM2 model shows a good performance in reproducing observed regional averages of summer and winter temperature and precipitation. The model, however, does not reproduce well observed interannual variability. We find that the uncertainty in regional climate change predictions associated with the spread of different realizations in an ensemble (i.e. the uncertainty related to the internal model variability) is relatively low for all scenarios and regions. In particular, this uncertainty is lower than the uncertainty due to inter-scenario variability and (by comparison with previous regional analyses of AOGCMs) with inter-model variability. The climate biases and sensitivities found for different realizations of the same ensemble were similar to the corresponding ensemble averages and the averages associated with individual realizations of the same ensemble did not differ from each other at the 5% confidence level in the vast majority of cases. These results indicate that a relatively small number of realizations (3 or 4) is sufficient to characterize an AOGCM transient climate change prediction at the regional scale. Received: 12 January 1998 / Accepted: 7 July 1999     

Identifying uncertainties in Arctic climate change projections

Wide ranging climate changes are expected in the Arctic by the end of the 21st century, but projections of the size of these changes vary widely across current global climate models. This variation represents a large source of uncertainty in our understanding of the evolution of Arctic climate. Here we systematically quantify and assess the model uncertainty in Arctic climate changes in two CO₂ doubling experiments: a multimodel ensemble (CMIP3) and an ensemble constructed using a single model (HadCM3) with multiple parameter perturbations (THC-QUMP). These two ensembles allow us to assess the contribution that both structural and parameter variations across models make to the total uncertainty and to begin to attribute sources of uncertainty in projected changes. We find that parameter uncertainty is an major source of uncertainty in certain aspects of Arctic climate. But also that uncertainties in the mean climate state in the 20th century, most notably in the northward Atlantic ocean heat transport and Arctic sea ice volume, are a significant source of uncertainty for projections of future Arctic change. We suggest that better observational constraints on these quantities will lead to significant improvements in the precision of projections of future Arctic climate change.     

Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations

This paper describes an approach to computing probabilistic assessments of future climate, using a climate model. It clarifies the nature of probability in this context, and illustrates the kinds of judgements that must be made in order for such a prediction to be consistent with the probability calculus. The climate model is seen as a tool for making probabilistic statements about climate itself, necessarily involving an assessment of the model’s imperfections. A climate event, such as a 2^{^}C increase in global mean temperature, is identified with a region of ‘climate-space’, and the ensemble of model evaluations is used within a numerical integration designed to estimate the probability assigned to that region.     

Applying probabilistic projections of climate change with impact models: a case study for sub-arctic palsa mires in Fennoscandia

A comparison of two approaches for determining probabilistic climate change impacts is presented. In the first approach, ensemble climate projections are applied directly as inputs to an impact model and the risk of impact is computed from the resulting ensemble of outcomes. As this can involve large numbers of projections, the approach may prove to be impractical when applied to complex impact models with demanding input requirements. The second approach is to construct an impact response surface based on a sensitivity analysis of the impact model with respect to changes in key climatic variables, and then to superimpose probabilistic projections of future climate onto the response surface to assess the risk of impact. To illustrate this comparison, an impact model describing the spatial distribution of palsas in Fennoscandia was applied to estimate the risk of palsa disappearance. Palsas are northern mire complexes with permanently frozen peat hummocks, located at the outer limit of the permafrost zone and susceptible to rapid decline due to regional warming. Probabilities of climate changes were derived from an ensemble of coupled atmosphere–ocean general circulation model (AOGCM) projections using a re-sampling method. Results indicated that the response surface approach, though introducing additional uncertainty, gave risk estimates of area decline for palsa suitability that were comparable to those obtained using multiple simulations with the original palsa model. It was estimated as very likely (>90% probability) that a decline of area suitable for palsas to less than half of the baseline distribution will occur by the 2030s and likely (>66%) that all suitable areas will disappear by the end of the twenty-first century under scenarios of medium (A1B) and moderately high (A2) emissions. For a low emissions (B1) scenario, it was more likely than not (>50%) that conditions over a small fraction of the current palsa distribution would remain suitable until the end of the twenty-first century.     

Transient climate changes in a perturbed parameter ensemble of emissions-driven earth system model simulations

We describe results from a 57-member ensemble of transient climate change simulations, featuring simultaneous perturbations to 54 parameters in the atmosphere, ocean, sulphur cycle and terrestrial ecosystem components of an earth system model (ESM). These emissions-driven simulations are compared against the CMIP3 multi-model ensemble of physical climate system models, used extensively to inform previous assessments of regional climate change, and also against emissions-driven simulations from ESMs contributed to the CMIP5 archive. Members of our earth system perturbed parameter ensemble (ESPPE) are competitive with CMIP3 and CMIP5 models in their simulations of historical climate. In particular, they perform reasonably well in comparison with HadGEM2-ES, a more sophisticated and expensive earth system model contributed to CMIP5. The ESPPE therefore provides a computationally cost-effective tool to explore interactions between earth system processes. In response to a non-intervention emissions scenario, the ESPPE simulates distributions of future regional temperature change characterised by wide ranges, and warm shifts, compared to those of CMIP3 models. These differences partly reflect the uncertain influence of global carbon cycle feedbacks in the ESPPE. In addition, the regional effects of interactions between different earth system feedbacks, particularly involving physical and ecosystem processes, shift and widen the ESPPE spread in normalised patterns of surface temperature and precipitation change in many regions. Significant differences from CMIP3 also arise from the use of parametric perturbations (rather than a multimodel ensemble) to represent model uncertainties, and this is also the case when ESPPE results are compared against parallel emissions-driven simulations from CMIP5 ESMs. When driven by an aggressive mitigation scenario, the ESPPE and HadGEM2-ES reveal significant but uncertain impacts in limiting temperature increases during the second half of the twenty-first century. Emissions-driven simulations create scope for development of errors in properties that were previously prescribed in coupled ocean–atmosphere models, such as historical CO₂ concentrations and vegetation distributions. In this context, historical intra-ensemble variations in the airborne fraction of CO₂ emissions, and in summer soil moisture in northern hemisphere continental regions, are shown to be potentially useful constraints, subject to uncertainties in the relevant observations. Our results suggest that future climate-related risks can be assessed more comprehensively by updating projection methodologies to support formal combination of emissions-driven perturbed parameter and multi-model earth system model simulations with suitable observational constraints. This would provide scenarios underpinned by a more complete representation of the chain of uncertainties from anthropogenic emissions to future climate outcomes.     

Northern African climate at the end of the twenty-first century: an integrated application of regional and global climate models

A method for simulating future climate on regional space scales is developed and applied to northern Africa. Simulation with a regional model allows for the horizontal resolution needed to resolve the region’s strong meridional gradients and the optimization of parameterizations and land-surface model. The control simulation is constrained by reanalysis data, and realistically represents the present day climate. Atmosphere–ocean general circulation model (AOGCM) output provides SST and lateral boundary condition anomalies for 2081–2100 under a business-as-usual emissions scenario, and the atmospheric CO₂ concentration is increased to 757 ppmv. A nine-member ensemble of future climate projections is generated by using output from nine AOGCMs. The consistency of precipitation projections for the end of the twenty-first century is much greater for the regional model ensemble than among the AOGCMs. More than 77% of ensemble members produce the same sign rainfall anomaly over much of northern Africa. For West Africa, the regional model projects wetter conditions in spring, but a mid-summer drought develops during June and July, and the heat stoke risk increases across the Sahel. Wetter conditions resume in late summer, and the likelihood of flooding increases. The regional model generally projects wetter conditions over eastern Central Africa in June and drying during August through September. Severe drought impacts parts of East Africa in late summer. Conditions become wetter in October, but the enhanced rainfall does not compensate for the summertime deficit. The risk of heat stroke increases over this region, although the threat is not projected to be as great as in the Sahel.     

Ecosystem Responses to Abrupt Climate Change: Teleconnections, Scale and the Hydrological Cycle

The occurrence of past and future abrupt climate change, such as could occur under thermohaline circulation (THC) weakening, is increasingly evident in the paleoclimate record and model experiments. We examine potential responses of ecosystem structure and function to abrupt climate change using temperature and precipitation patterns generated by HadCM3 in response to forced THC weakening. The large changes in potential ecosystem structure and function that occur are not focused in the North Atlantic region where temperature sensitivity to THC is highest but occur throughout the world in response to climate system teleconnections. Thus, THC weakening, which is often viewed as a European problem, has globally distributed ecosystem implications. Although temperature changes associated with THC weakening affect the extent of several high latitude biomes, the distribution of ecosystem change results primarily from changes in the hydrological cycle. Currently there remains large uncertainty in climate model projections of the hydrological cycle. Therefore, the predictions of the magnitude andlocation of ecosystem perturbations will also be characterized by large uncertainty, making impact assessment, and thus adaptation, more difficult. Finally, these results illustrate the importance of scale and disaggregation in assessing ecosystem responses. Small globally aggregated ecosystem responses to THC weakening, approximately five percent for NPP and biomass, mask large local and regional changes.     

Climate projections of future extreme events accounting for modelling uncertainties and historical simulation biases

A methodology is presented for providing projections of absolute future values of extreme weather events that takes into account key uncertainties in predicting future climate. This is achieved by characterising both observed and modelled extremes with a single form of non-stationary extreme value (EV) distribution that depends on global mean temperature and which includes terms that account for model bias. Such a distribution allows the prediction of future “observed” extremes for any period in the twenty-first century. Uncertainty in modelling future climate, arising from a wide range of atmospheric, oceanic, sulphur cycle and carbon cycle processes, is accounted for by using probabilistic distributions of future global temperature and EV parameters. These distributions are generated by Bayesian sampling of emulators with samples weighted by their likelihood with respect to a set of observational constraints. The emulators are trained on a large perturbed parameter ensemble of global simulations of the recent past, and the equilibrium response to doubled CO₂. Emulated global EV parameters are converted to the relevant regional scale through downscaling relationships derived from a smaller perturbed parameter regional climate model ensemble. The simultaneous fitting of the EV model to regional model data and observations allows the characterisation of how observed extremes may change in the future irrespective of biases that may be present in the regional models simulation of the recent past climate. The clearest impact of a parameter perturbation in this ensemble was found to be the depth to which plants can access water. Members with shallow soils tend to be biased hot and dry in summer for the observational period. These biases also appear to have an impact on the potential future response for summer temperatures with some members with shallow soils having increases for extremes that reduce with extreme severity. We apply this methodology for London, using the A1B future emissions scenario to obtain projections of the 50 year return values for the 20 year period centred on 2050. We obtain 10th to 90th percentile ranges of 35.9–42.1 °C for summer daily maximum temperature, 35.5–52.4 mm for summer daily rainfall and 79.2, 97.0 mm for autumn 5 day total rainfall, compared to observed estimates for 1961–1990 of 35.7 °C, 42.1 and 78.4 mm respectively.     

Climate change scenarios for global impacts studies

We describe a set of global climate change scenarios that have been used in a series of studies investigating the global impacts of climate change on several environmental systems and resources — ecosystems, food security, water resources, malaria and coastal flooding. These scenarios derive from modelling experiments completed by the Hadley Centre over the last four years using successive versions of their coupled ocean–atmosphere global climate model. The scenarios benefit from ensemble simulations (made using HadCM2) and from an un-flux-corrected experiment (made using HadCM3), but consider only the effects of increasing greenhouse gas concentrations. The effects of associated changes in sulphate aerosol concentrations are not considered. The scenarios are presented for three future time periods — 30-year means centred on the 2020s, the 2050s and the 2080s — and are expressed with respect to the mean 1961–1990 climate. A global land observed climatology at 0.5° latitude/longitude resolution is used to describe current climate. Other scenario variables — atmospheric CO₂ concentrations, global-mean sea-level rise and non-climatic assumptions relating to population and economy — are also provided. We discuss the limitations of the created scenarios and in particular draw attention to sources of uncertainty that we have not fully sampled.     

The sensitivity of the meridional overturning circulation to modelling uncertainty in a perturbed physics ensemble without flux adjustment

Ocean dynamics play a key role in the climate system, by redistributing heat and freshwater. The uncertainty of how these processes are represented in climate models, and how this uncertainty affects future climate projections can be investigated using perturbed physics ensembles of global circulation models (GCMs). Techniques such as flux adjustments should be avoided since they can impact the sensitivity of the ensemble to the imposed forcing. In this study a method for developing an coupled ensemble with a GCM that does not use flux adjustment is presented. The ensemble is constrained by using information from a prior ensemble with a mixed layer ocean coupled to an atmosphere GCM, to reduce drifts in the coupled ensemble. Constraints on parameter perturbations are derived by using observational constraints on surface temperature, and top of the atmosphere radiative fluxes. As an example of such an ensemble developed with this methodology, uncertainty in response of the meridional overturning circulation (MOC) to increased CO₂ concentrations is investigated. The ensemble mean MOC strength is 17.1?Sv and decreases by 2.1?Sv when greenhouse gas concentrations are doubled. No rapid changes or shutdown of the MOC are seen in any of the ensemble members. There is a strong negative relationship between global mean temperature and MOC strength across the ensemble which is not seen in a multimodel ensemble. A positive relationship between climate sensitivity and the decrease of MOC strength is also seen.     

Sub-Saharan Northern African climate at the end of the twenty-first century: forcing factors and climate change processes

A regional climate model, the Weather Research and Forecasting (WRF) Model, is forced with increased atmospheric CO₂ and anomalous SSTs and lateral boundary conditions derived from nine coupled atmosphere–ocean general circulation models to produce an ensemble set of nine future climate simulations for northern Africa at the end of the twenty-first century. A well validated control simulation, agreement among ensemble members, and a physical understanding of the future climate change enhance confidence in the predictions. The regional model ensembles produce consistent precipitation projections over much of northern tropical Africa. A moisture budget analysis is used to identify the circulation changes that support future precipitation anomalies. The projected midsummer drought over the Guinean Coast region is related partly to weakened monsoon flow. Since the rainfall maximum demonstrates a southward bias in the control simulation in July–August, this may be indicative of future summer drying over the Sahel. Wetter conditions in late summer over the Sahel are associated with enhanced moisture transport by the West African westerly jet, a strengthening of the jet itself, and moisture transport from the Mediterranean. Severe drought in East Africa during August and September is accompanied by a weakened Indian monsoon and Somali jet. Simulations with projected and idealized SST forcing suggest that overall SST warming in part supports this regional model ensemble agreement, although changes in SST gradients are important over West Africa in spring and fall. Simulations which isolate the role of individual climate forcings suggest that the spatial distribution of the rainfall predictions is controlled by the anomalous SST and lateral boundary conditions, while CO₂ forcing within the regional model domain plays an important secondary role and generally produces wetter conditions.     

Interpreting observed northern hemisphere snow trends with large ensembles of climate simulations

Simulated variability and trends in Northern Hemisphere seasonal snow cover are analyzed in large ensembles of climate integrations of the National Center for Atmospheric Research’s Community Earth System Model. Two 40-member ensembles driven by historical radiative forcings are generated, one coupled to a dynamical ocean and the other driven by observed sea surface temperatures (SSTs) over the period 1981–2010. The simulations reproduce many aspects of the observed climatology and variability of snow cover extent as characterized by the NOAA snow chart climate data record. Major features of the simulated snow water equivalent (SWE) also agree with observations (GlobSnow Northern Hemisphere SWE data record), although with a lesser degree of fidelity. Ensemble spread in the climate response quantifies the impact of natural climate variability in the presence and absence of coupling to the ocean. Both coupled and uncoupled ensembles indicate an overall decrease in springtime snow cover that is consistent with observations, although springtime trends in most climate realizations are weaker than observed. In the coupled ensemble, a tendency towards excessive warming in wintertime leads to a strong wintertime snow cover loss that is not found in observations. The wintertime warming bias and snow cover reduction trends are reduced in the uncoupled ensemble with observed SSTs. Natural climate variability generates widely different regional patterns of snow trends across realizations; these patterns are related in an intuitive way to temperature, precipitation and circulation trends in individual realizations. In particular, regional snow loss over North America in individual realizations is strongly influenced by North Pacific SST trends (manifested as Pacific Decadal Oscillation variability) and by sea level pressure trends in the North Pacific/North Atlantic sectors.     

Climate sensitivity due to increased CO2: experiments with a coupled atmosphere and ocean general circulation model

A version of the National Center for Atmospheric Research community climate model — a global, spectral (R15) general circulation model — is coupled to a coarse-grid (5° latitude-] longitude, four-layer) ocean general circulation model to study the response of the climate system to increases of atmospheric carbon dioxide (CO₂). Three simulations are run: one with an instantaneous doubling of atmospheric CO₂ (from 330 to 660 ppm), another with the CO₂ concentration starting at 330 ppm and increasing linearly at a rate of 1% per year, and a third with CO₂ held constant at 330 pm. Results at the end of 30 years of simulation indicate a globally averaged surface air temperature increase of 1.6° C for the instantaneous doubling case and 0.7°C for the transient forcing case. Inherent characteristics of the coarse-grid ocean model flow sea-surface temperatures (SSTs) in the tropics and higher-than-observed SSTs and reduced sea-ice extent at higher latitudes] produce lower sensitivity in this model after 30 years than in earlier simulations with the same atmosphere coupled to a 50-m, slab-ocean mixed layer. Within the limitations of the simulated meridional overturning, the thermohaline circulation weakens in the coupled model with doubled CO₂ as the high-latitude ocean-surface layer warms and freshens and westerly wind stress is decreased. In the transient forcing case with slowly increasing CO₂ (30% increase after 30 years), the zonal mean warming of the ocean is most evident in the surface layer near 30°–50° S. Geographical plots of surface air temperature change in the transient case show patterns of regional climate anomalies that differ from those in the instantaneous CO₂ doubling case, particularly in the North Atlantic and northern European regions. This suggests that differences in CO₂ forcing in the climate system are important in CO₂ response in regard to time-dependent climate anomaly regimes. This confirms earlier studies with simple climate models that instantaneous CO₂ doubling simulations may not be analogous in all respects to simulations with slowly increasing CO₂.A portion of this study is supported by the US Department of Energy as part of its Carbon Dioxide Research Program     

Evaluation of ocean and climate models using present‐day observations and forcing

Abstract

The most common method used to evaluate climate models involves spinning them up under perpetual present‐day forcing and comparing the model results with present‐day observations. This approach clearly ignores any potential long‐term memory of the model ocean to past climatic conditions. Here we examine the validity of this approach through the 6000‐year integration of a coupled atmosphere–ocean–sea‐ice model. The coupled model is initially spun‐up with atmospheric CO₂ concentrations and orbital parameters applicable for 6KBP. The model is then integrated forward in time to 2100. Results from this transient coupled model simulation are compared with the results from two additional simulations, in which the model is spun up with perpetual 1850 (preindustrial) and 1998 (present‐day) atmospheric CO₂ concentrations and orbital parameters. This comparison leads to substantial differences between the equilibrium climatologies and the transient simulation, even at 1850 (in weakly ventilated regions), prior to any significant changes in atmospheric CO₂. When compared to the present‐day equilibrium climatology, differences are very large: the global mean surface air and sea surface temperatures are ,0.5°C and ,0.4°C colder, respectively, deep ocean temperatures are substantially cooler, Southern Hemisphere sea‐ice cover is 38% larger, and the North Atlantic conveyor 16% weaker in the transient case. These differences are due to the long timescale memory of the deep ocean to climatic conditions which prevailed throughout the late Holocene, as well as to its large thermal inertia. It is also demonstrated that a ‘cold start’ global warming simulation (one that starts from a 1998 equilibrium climatology) underestimates the global temperature increase at 2100 by ,10%. Our results question the accuracy of current techniques for climate model evaluation and underline the importance of using paleoclimatic simulations in parallel with present‐day simulations in this evaluation process.     

A study on combining global and regional climate model results for generating climate scenarios of temperature and precipitation for the Netherlands

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.