Sensitivity of climate change projections to uncertainties in the estimates of observed changes in deep-ocean heat content

The MIT 2D climate model is used to make probabilistic projections for changes in global mean surface temperature and for thermosteric sea level rise under a variety of forcing scenarios. The uncertainties in climate sensitivity and rate of heat uptake by the deep ocean are quantified by using the probability distributions derived from observed twentieth century temperature changes. The impact on climate change projections of using the smallest and largest estimates of twentieth century deep ocean warming is explored. The impact is large in the case of global mean thermosteric sea level rise. In the MIT reference (“business as usual”) scenario the median rise by 2100 is 27 and 43 cm in the respective cases. The impact on increases in global mean surface air temperature is more modest, 4.9 and 3.9 C in the two respective cases, because of the correlation between climate sensitivity and ocean heat uptake required by twentieth century surface and upper air temperature changes. The results are also compared with the projections made by the IPCC AR4’s multi-model ensemble for several of the SRES scenarios. The multi-model projections are more consistent with the MIT projections based on the largest estimate of ocean warming. However, the range for the rate of heat uptake by the ocean suggested by the lowest estimate of ocean warming is more consistent with the range suggested by the twentieth century changes in surface and upper air temperatures, combined with the expert prior for climate sensitivity.     

A Bayesian Statistical Analysis of the Enhanced Greenhouse Effect

This paper demonstrates that there is a robust statistical relationship between the records of the global mean surface air temperature and the atmospheric concentration of carbon dioxide over the period 1870–1991. As such, the enhanced greenhouse effect is a plausible explanation for the observed global warming. Long term natural variability is another prime candidate for explaining the temperature rise of the last century. Analysis of natural variability from paleo-reconstructions, however, shows that human activity is so much more likely an explanation that the earlier conclusion is not refuted. But, even if one believes in large natural climatic variability, the odds are invariably in favour of the enhanced greenhouse effect. The above conclusions hold for a range of statistical models, including one that is capable of describing the stabilization of the global mean temperature from the 1940s to the 1970s onwards. This model is also shown to be otherwise statistically adequate. The estimated climate sensitivity is about 3.8 °C with a standard deviation of 0.9 °C, but depends slightly on which model is preferred and how much natural variability is allowed. These estimates neglect, however, the fact that carbon dioxide is but one of a number of greenhouse gases and that sulphate aerosols may well have dampened warming. Acknowledging the fact that carbon dioxide is used as a proxy for all human induced changes in radiative forcing brings a lot of additional uncertainty. Prior knowledge on both climate sensitivity and radiative forcing is needed to say anything about the respective sizes. A fully Bayesian approach is used to combine expert knowledge with information from the observations. Prior knowledge on the climate sensitivity plays a dominant role. The data largely exclude climate sensitivity to be small, but cannot exclude climate sensitivity to be large, because of the possibility of strong negative sulphate forcing. The posterior of climate sensitivity has a strong positive skewness. Moreover, its mode (again 3.8 °C; standard deviation 2.4 °C) is higher than the best guess of the IPCC.     

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

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

Contributions of carbon cycle uncertainty to future climate projection spread

We have characterized the relative contributions to uncertainty in predictions of global warming amount by year 2100 in the C4MIP model ensemble ( Friedlingstein et al., 2006 ) due to both carbon cycle process uncertainty and uncertainty in the physical climate properties of the Earth system. We find carbon cycle uncertainty to be important. On average the spread in transient climate response is around 40% of that due to the more frequently debated uncertainties in equilibrium climate sensitivity and global heat capacity.
This result is derived by characterizing the influence of different parameters in a global climate-carbon cycle 'box' model that has been calibrated against the 11 General Circulation models (GCMs) and Earth system Models of Intermediate Complexity (EMICs) in the C4MIP ensemble; a collection of current state-of-the-art climate models that include an explicit representation of the global carbon cycle.     

Climate forcings and climate sensitivities diagnosed from atmospheric global circulation models

Understanding the historical and future response of the global climate system to anthropogenic emissions of radiatively active atmospheric constituents has become a timely and compelling concern. At present, however, there are uncertainties in: the total radiative forcing associated with changes in the chemical composition of the atmosphere; the effective forcing applied to the climate system resulting from a (temporary) reduction via ocean-heat uptake; and the strength of the climate feedbacks that subsequently modify this forcing. Here a set of analyses derived from atmospheric general circulation model simulations are used to estimate the effective and total radiative forcing of the observed climate system due to anthropogenic emissions over the last 50 years of the twentieth century. They are also used to estimate the sensitivity of the observed climate system to these emissions, as well as the expected change in global surface temperatures once the climate system returns to radiative equilibrium. Results indicate that estimates of the effective radiative forcing and total radiative forcing associated with historical anthropogenic emissions differ across models. In addition estimates of the historical sensitivity of the climate to these emissions differ across models. However, results suggest that the variations in climate sensitivity and total climate forcing are not independent, and that the two vary inversely with respect to one another. As such, expected equilibrium temperature changes, which are given by the product of the total radiative forcing and the climate sensitivity, are relatively constant between models, particularly in comparison to results in which the total radiative forcing is assumed constant. Implications of these results for projected future climate forcings and subsequent responses are also discussed.     

Probabilistic projections of transient climate change

This paper describes a Bayesian methodology for prediction of multivariate probability distribution functions (PDFs) for transient regional climate change. The approach is based upon PDFs for the equilibrium response to doubled carbon dioxide, derived from a comprehensive sampling of uncertainties in modelling of surface and atmospheric processes, and constrained by multiannual mean observations of recent climate. These PDFs are sampled and scaled by global mean temperature predicted by a Simple Climate Model (SCM), in order to emulate corresponding transient responses. The sampled projections are then reweighted, based upon the likelihood that they correctly replicate observed historical changes in surface temperature, and combined to provide PDFs for 20 year averages of regional temperature and precipitation changes to the end of the twenty-first century, for the A1B emissions scenario. The PDFs also account for modelling uncertainties associated with aerosol forcing, ocean heat uptake and the terrestrial carbon cycle, sampled using SCM configurations calibrated to the response of perturbed physics ensembles generated using the Hadley Centre climate model HadCM3, and other international climate model simulations. Weighting the projections using observational metrics of recent mean climate is found to be as effective at constraining the future transient response as metrics based on historical trends. The spread in global temperature response due to modelling uncertainty in the carbon cycle feedbacks is determined to be about 65–80 % of the spread arising from uncertainties in modelling atmospheric, oceanic and aerosol processes of the climate system. Early twenty-first century aerosol forcing is found to be extremely unlikely to be less than ?1.7 W m^?2. Our technique provides a rigorous and formal method of combining several lines of evidence used in the previous IPCC expert assessment of the Transient Climate Response. The 10th, 50th and 90th percentiles of our observationally constrained PDF for the Transient Climate Response are 1.6, 2.0 and 2.4 °C respectively, compared with the 10–90 % range of 1.0–3.0 °C assessed by the IPCC.     

The role of ecosystem-atmosphere interactions in simulated Amazonian precipitation decrease and forest dieback under global climate warming

Summary A suite of simulations with the HadCM3LC coupled climate-carbon cycle model is used to examine the various forcings and feedbacks involved in the simulated precipitation decrease and forest dieback. Rising atmospheric CO₂ is found to contribute 20% to the precipitation reduction through the physiological forcing of stomatal closure, with 80% of the reduction being seen when stomatal closure was excluded and only radiative forcing by CO₂ was included. The forest dieback exerts two positive feedbacks on the precipitation reduction; a biogeophysical feedback through reduced forest cover suppressing local evaporative water recycling, and a biogeochemical feedback through the release of CO₂ contributing to an accelerated global warming. The precipitation reduction is enhanced by 20% by the biogeophysical feedback, and 5% by the carbon cycle feedback from the forest dieback. This analysis helps to explain why the Amazonian precipitation reduction simulated by HadCM3LC is more extreme than that simulated in other GCMs; in the fully-coupled, climate-carbon cycle simulation, approximately half of the precipitation reduction in Amazonia is attributable to a combination of physiological forcing and biogeophysical and global carbon cycle feedbacks, which are generally not included in other GCM simulations of future climate change. The analysis also demonstrates the potential contribution of regional-scale climate and ecosystem change to uncertainties in global CO₂ and climate change projections. Moreover, the importance of feedbacks suggests that a human-induced increase in forest vulnerability to climate change may have implications for regional and global scale climate sensitivity.     

Projected increased risk of water deficit over major West African river basins under future climates

Despite decades of research, large multi-model uncertainty remains about the Earth’s equilibrium climate sensitivity to carbon dioxide forcing as inferred from state-of-the-art Earth system models (ESMs). Statistical treatments of multi-model uncertainties are often limited to simple ESM averaging approaches. Sometimes models are weighted by how well they reproduce historical climate observations. Here, we propose a novel approach to multi-model combination and uncertainty quantification. Rather than averaging a discrete set of models, our approach samples from a continuous distribution over a reduced space of simple model parameters. We fit the free parameters of a reduced-order climate model to the output of each member of the multi-model ensemble. The reduced-order parameter estimates are then combined using a hierarchical Bayesian statistical model. The result is a multi-model distribution of reduced-model parameters, including climate sensitivity. In effect, the multi-model uncertainty problem within an ensemble of ESMs is converted to a parametric uncertainty problem within a reduced model. The multi-model distribution can then be updated with observational data, combining two independent lines of evidence. We apply this approach to 24 model simulations of global surface temperature and net top-of-atmosphere radiation response to abrupt quadrupling of carbon dioxide, and four historical temperature data sets. Our reduced order model is a 2-layer energy balance model. We present probability distributions of climate sensitivity based on (1) the multi-model ensemble alone and (2) the multi-model ensemble and observations.     

Sensitivity of direct global warming potentials to key uncertainties

The concept of global warming potential was developed as a relative measure of the potential effects on climate of a greenhouse gas as compared to CO₂. In this paper a series of sensitivity studies examines several uncertainties in determination of Global Warming Potentials (GWPs). For example, the original evaluation of GWPs for the Intergovernmental Panel on Climate Change (IPCC, 1990) did not attempt to account for the possible sinks of carbon dioxide (CO₂) that could balance the carbon cycle and produce atmospheric concentrations of CO₂ that match observations. In this study, a balanced carbon cycle model is applied in calculation of the radiative forcing from CO₂. Use of the balanced model produces up to 21% enhancement of the GWPs for most trace gases compared with the IPCC (1990) values for time horizons up to 100 years, but a decreasing enhancement with longer time horizons. Uncertainty limits of the fertilization feedback parameter contribute a 20% range in GWP values. Another systematic uncertainty in GWPs is the assumption of an equilibrium atmosphere (one in which the concentration of trace gases remains constant) versus a disequilibrium atmosphere (one in which the concentration of trace gases varies with time). The latter gives GWPs that are 19 to 32% greater than the former for a 100 year time horizons, depending upon the carbon dioxide emission scenario chosen. Five scenarios are employed: constant-concentration, constant-emission past 1990 and the three IPCC (1992) emission scenarios. For the analysis of uncertainties in atmospheric lifetime (τ) the GWP changes in direct proportion toτ for short-lived gases, but to a lesser extent for gases withτ greater than the time horizontal for the GWP calculation.     

Interactions between perturbations to different Earth system components simulated by a fully-coupled climate model

We examine the global mean surface temperature and carbon cycle responses to the A1B emissions scenario for a new 57 member perturbed-parameter ensemble of simulations generated using the fully coupled atmosphere-ocean-carbon cycle climate model HadCM3C. The model variants feature simultaneous perturbation to parameters that control atmosphere, ocean, land carbon cycle and sulphur cycle processes in this Earth system model, and is the first experiment of its kind. The experimental design, based on four earlier ensembles with parameters varied within each individual Earth system component, allows the effects of interactions between uncertainties in the different components to be explored. A large spread in response is obtained, with atmospheric CO₂ at the end of the twenty-first century ranging from 615 to 1,100 ppm. On average though, the mean effect of the parameter perturbations is to significantly reduce the amount of atmospheric CO₂ compared to that seen in the standard HadCM3C model. Global temperature change for 2090–2099 relative to the pre-industrial period ranges from 2.2 to 7.5 °C, with large temperature responses occurring when atmospheric model versions with high climate sensitivities are combined with carbon cycle components that emit large amounts of CO₂ to the atmosphere under warming. A simple climate model, tuned to reproduce the responses of the separate Earth system component ensembles, is used to demonstrate that interactions between uncertainties in the different components play a significant role in determining the spread of responses in global mean surface temperature. This ensemble explores a wide range of interactions and response, and therefore provides a useful resource for the provision of regional climate projections and associated uncertainties.     

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.     

气候变化的归因与预估模拟研究

本文总结了近五年来中国科学院大气物理研究所在气候变暖的归因模拟与预估研究上的主要进展。研究表明,利用海温、太阳辐射和温室气体等实际强迫因子驱动大气环流模式,能够较为合理地模拟全球平均地表气温在20世纪的演变,但是难以模拟出包括北大西洋涛动／北极涛动和南极涛动在内的高纬度环流的长期变化趋势。利用温室气体和硫酸盐气溶胶等“历史资料”驱动气候系统模式,能够较好地模拟出20世纪后期的全球增暖,但如果要再现20世纪前期(1940年代)的变暖,还需同时考虑太阳辐射等自然外强迫因子。20世纪中国气温演变的耦合模式模拟技巧,较之全球平均情况要低;中国气候在1920年代的变暖机理目前尚不清楚。对于近50年中国东部地区“南冷北暖”、“南涝北旱”的气候变化,基于大气环流模式特别是区域气候模式的数值试验表明,夏季硫酸盐气溶胶的负辐射效应超过了温室气体的增暖效应,从而对变冷产生贡献。但现有的数值模拟证据,不足以说明气溶胶增加对“南涝北旱”型降水异常有贡献。20世纪中期以来,青藏高原主体存在明显增温趋势,温室气体浓度的增加对这种增暖有显著贡献。多模式集合预估的未来气候变化表明,21世纪全球平均温度将继续增暖,增温幅度因不同排放情景而异;中国大陆年均表面气温的增暖与全球同步,但增幅在东北、西部和华中地区较大,冬季升温幅度高于夏季、日最低温度升幅要强于日最高温度;全球增暖有可能对我国中东部植被的地理分布产生影响。伴随温室气体增加所导致的夏季平均温度升高,极端温度事件增多;在更暖的气候背景下,中国大部分地区总降水将增多,极端降水强度加大且更频繁发生,极端降水占总降水的比例也将增大。全球增暖有可能令大洋热盐环流减弱,但是减弱的幅度因模式而异。全球增暖可能不是导致北太平洋副热带－热带经圈环流自20世纪70年代以来变弱的原因。文章同时指出了模式预估结果中存在的不确定性。     

On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates

This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO₂) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO₂ radiative forcing. By considering tropospheric adjustments to CO₂ as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO₂ is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO₂), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.     

Quantifying uncertainties in projections of extremes��a perturbed land surface parameter experiment

Uncertainties in the climate response to a doubling of atmospheric CO₂ concentrations are quantified in a perturbed land surface parameter experiment. The ensemble of 108 members is constructed by systematically perturbing five poorly constrained land surface parameters of global climate model individually and in all possible combinations. The land surface parameters induce small uncertainties at global scale, substantial uncertainties at regional and seasonal scale and very large uncertainties in the tails of the distribution, the climate extremes. Climate sensitivity varies across the ensemble mainly due to the perturbation of the snow albedo parameterization, which controls the snow albedo feedback strength. The uncertainty range in the global response is small relative to perturbed physics experiments focusing on atmospheric parameters. However, land surface parameters are revealed to control the response not only of the mean but also of the variability of temperature. Major uncertainties are identified in the response of climate extremes to a doubling of CO₂. During winter the response both of temperature mean and daily variability relates to fractional snow cover. Cold extremes over high latitudes warm disproportionately in ensemble members with strong snow albedo feedback and large snow cover reduction. Reduced snow cover leads to more winter warming and stronger variability decrease. As a result uncertainties in mean and variability response line up, with some members showing weak and others very strong warming of the cold tail of the distribution, depending on the snow albedo parametrization. The uncertainty across the ensemble regionally exceeds the CMIP3 multi-model range. Regarding summer hot extremes, the uncertainties are larger than for mean summer warming but smaller than in multi-model experiments. The summer precipitation response to a doubling of CO₂ is not robust over many regions. Land surface parameter perturbations and natural variability alter the sign of the response even over subtropical regions.     

Climate sensitivity estimated from ensemble simulations of glacial climate

The concentration of greenhouse gases (GHGs) in the atmosphere continues to rise, hence estimating the climate system’s sensitivity to changes in GHG concentration is of vital importance. Uncertainty in climate sensitivity is a main source of uncertainty in projections of future climate change. Here we present a new approach for constraining this key uncertainty by combining ensemble simulations of the last glacial maximum (LGM) with paleo-data. For this purpose we used a climate model of intermediate complexity to perform a large set of equilibrium runs for (1) pre-industrial boundary conditions, (2) doubled CO₂ concentrations, and (3) a complete set of glacial forcings (including dust and vegetation changes). Using proxy-data from the LGM at low and high latitudes we constrain the set of realistic model versions and thus climate sensitivity. We show that irrespective of uncertainties in model parameters and feedback strengths, in our model a close link exists between the simulated warming due to a doubling of CO₂, and the cooling obtained for the LGM. Our results agree with recent studies that annual mean data-constraints from present day climate prove to not rule out climate sensitivities above the widely assumed sensitivity range of 1.5–4.5°C (Houghton et al. 2001). Based on our inferred close relationship between past and future temperature evolution, our study suggests that paleo-climatic data can help to reduce uncertainty in future climate projections. Our inferred uncertainty range for climate sensitivity, constrained by paleo-data, is 1.2–4.3°C and thus almost identical to the IPCC estimate. When additionally accounting for potential structural uncertainties inferred from other models the upper limit increases by about 1°C.     

Temperature stabilization, ocean heat uptake and radiative forcing overshoot profiles

Political leaders in numerous nations argue for an upper limit of the global average surface temperature of 2 K above the pre-industrial level, in order to attempt to avoid the most serious impacts of climate change. This paper analyzes what this limit implies in terms of radiative forcing, emissions pathways and abatement costs, for a range of assumptions on rate of ocean heat uptake and climate sensitivity. The primary aim is to analyze the importance of ocean heat uptake for radiative forcing pathways that temporarily overshoot the long-run stabilization forcing, yet keep the temperature increase at or below the 2 K limit. In order to generate such pathways, an integrated climate-economy model, MiMiC, is used, in which the emissions pathways generated represent the least-cost solution of stabilizing the global average surface temperature at 2 K above the pre-industrial level. We find that the level of overshoot can be substantial. For example, the level of overshoot in radiative forcing in 2100 ranges from about 0.2 to 1 W/m2, where the value depends strongly and positively on the effective diffusivity of heat in the oceans. Measured in relative terms, the level of radiative forcing overshoot above its longrun equilibrium level in 2100 is 20% to 60% for high values of climate sensitivity (i.e., about 4.5 K) and 8% to 30% for low values of climate sensitivity (i.e., about 2 K). In addition, for cases in which the radiative forcing level can be directly stabilized at the equilibrium level associated with a specific climate sensitivity and the 2 K limit, the net present value abatement cost is roughly cut by half if overshoot pathways are considered instead of stabilization of radiative forcing at the equilibrium level without an overshoot.     

An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter

We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus ocean temperature/salinity climatology, and NCEP/NCAR atmospheric temperature/humidity reanalysis data. The resulting ensemble of tuned model states is validated by comparing various diagnostics, such as mass and heat transports, to observational estimates and other model results. We show that this ensemble has a very reasonable climatology, with the 3-D ocean in particular having comparable realism to much more expensive coupled numerical models, at least in respect of these averaged indicators. A simple global warming experiment is performed to investigate the response and predictability of the climate to a change in radiative forcing, due to 100 years of 1% per annum atmospheric CO₂ increase. The equilibrium surface air temperature rise for this CO₂ increase is 4.2±0.1°C, which is approached on a time scale of 1,000 years. The simple atmosphere in this version of the model is missing several factors which, if included, would substantially increase the uncertainty of this estimate. However, even within this ensemble, there is substantial regional variability due to the possibility of collapse of the North Atlantic thermohaline circulation (THC), which switches off in more than one third of the ensemble members. For these cases, the regional temperature is not only 3–5°C colder than in the warmed worlds where the THC remains switched on, but is also 1–2°C colder than the current climate. Our results, which illustrate how objective probabilistic projections of future climate change can be efficiently generated, indicate a substantial uncertainty in the long-term future of the THC, and therefore the regional climate of western Europe. However, this uncertainty is only apparent in long-term integrations, with the initial transient response being similar across the entire ensemble. Application of this ensemble Kalman filtering technique to more complete climate models would improve the objectivity of probabilistic forecasts and hence should lead to significantly increased understanding of the uncertainty of our future climate.     

Assessing the robustness of adaptation decisions to climate change uncertainties: A case study on water resources management in the East of England

Projections of future climate change are plagued with uncertainties, causing difficulties for planners taking decisions on adaptation measures. This paper presents an assessment framework that allows the identification of adaptation strategies that are robust (i.e. insensitive) to climate change uncertainties. The framework is applied to a case study of water resources management in the East of England, more specifically to the Anglian Water Services’ 25 year Water Resource Plan (WRP). The paper presents a local sensitivity analysis (a ‘one-at-a-time’ experiment) of the various elements of the modelling framework (e.g., emissions of greenhouse gases, climate sensitivity and global climate models) in order to determine whether or not a decision to adapt to climate change is sensitive to uncertainty in those elements.Water resources are found to be sensitive to uncertainties in regional climate response (from general circulation models and dynamical downscaling), in climate sensitivity and in climate impacts. Aerosol forcing and greenhouse gas emissions uncertainties are also important, whereas uncertainties from ocean mixing and the carbon cycle are not. Despite these large uncertainties, Anglian Water Services’ WRP remains robust to the climate change uncertainties sampled because of the adaptation options being considered (e.g. extension of water treatment works), because the climate model used for their planning (HadCM3) predicts drier conditions than other models, and because ‘one-at-a-time’ experiments do not sample the combination of different extremes in the uncertainty range of parameters. This research raises the question of how much certainty is required in climate change projections to justify investment in adaptation measures, and whether such certainty can be delivered.     

Elicitation of Expert Judgments of Aerosol Forcing

A group of twenty-four leading atmospheric and climate scientists provided subjective probability distributions that represent their current judgment about the value of planetary average direct and indirect radiative forcing from anthropogenic aerosols at the top of the atmosphere. Separate estimates were obtained for the direct aerosol effect, the semi-direct aerosol effect, cloud brightness (first aerosol indirect effect), and cloud lifetime/distribution (second aerosol indirect effect). Estimates were also obtained for total planetary average forcing at the top of the atmosphere and for surface forcing. Consensus was strongest among the experts in their assessments of the direct aerosol effect and the cloud brightness indirect effect. Forcing from the semi-direct effect was thought to be small (absolute values of all but one of the experts' best estimates were ≤0.5 W/m²). There was not agreement about the sign of the best estimate of the semi-direct effect, and the uncertainty ranges some experts gave for this effect did not overlap those given by others. All best estimates of total aerosol forcing were negative, with values ranging between −0.25 W/m² and −2.1 W/m². The range of uncertainty that a number of experts associated with their estimates, especially those for total aerosol forcing and for surface forcing, was often much larger than that suggested in 2001 by the IPCC Working Group 1 summary figure (IPCC, 2001).     

Conceptual understanding of climate change with a globally resolved energy balance model

The future climate change projections are essentially based on coupled general circulation model (CGCM) simulations, which give a distinct global warming pattern with arctic winter amplification, an equilibrium land-sea warming contrast and an inter-hemispheric warming gradient. While these simulations are the most important tool of the Intergovernmental Panel on Climate Change (IPCC) predictions, the conceptual understanding of these predicted structures of climate change and the causes of their uncertainties is very difficult to reach if only based on these highly complex CGCM simulations. In the study presented here we will introduce a very simple, globally resolved energy balance (GREB) model, which is capable of simulating the main characteristics of global warming. The model shall give a bridge between the strongly simplified energy balance models and the fully coupled 4-dimensional complex CGCMs. It provides a fast tool for the conceptual understanding and development of hypotheses for climate change studies, which shall build a basis or starting point for more detailed studies of observations and CGCM simulations. It is based on the surface energy balance by very simple representations of solar and thermal radiation, the atmospheric hydrological cycle, sensible turbulent heat flux, transport by the mean atmospheric circulation and heat exchange with the deeper ocean. Despite some limitations in the representations of the basic processes, the models climate sensitivity and the spatial structure of the warming pattern are within the uncertainties of the IPCC models simulations. It is capable of simulating aspects of the arctic winter amplification, the equilibrium land-sea warming contrast and the inter-hemispheric warming gradient with good agreement to the IPCC models in amplitude and structure. The results give some insight into the understanding of the land-sea contrast and the polar amplification. The GREB model suggests that the regional inhomogeneous distribution of atmospheric water vapor and the non-linear sensitivity of the downward thermal radiation to changes in the atmospheric water vapor concentration partly cause the land-sea contrast and may also contribute to the polar amplification. The combination of these characteristics causes, in general, dry and cold regions to warm more than other regions.