David M. H. Sexton

Quantification of modelling uncertainties in a large ensemble of climate change simulations.

Comprehensive global climate models are the only tools that account for the complex set of processes which will determine future climate change at both a global and regional level. Planners are typically faced with a wide range of predicted changes from different models of unknown relative quality, owing to large but unquantified uncertainties in the modelling process. Here we report a systematic attempt to determine the range of climate changes consistent with these uncertainties, based on a 53-member ensemble of model versions constructed by varying model parameters. We estimate a probability density function for the sensitivity of climate to a doubling of atmospheric carbon dioxide levels, and obtain a 5–95 per cent probability range of 2.4–5.4 °C. Our probability density function is constrained by objective estimates of the relative reliability of different model versions, the choice of model parameters that are varied and their uncertainty ranges, specified on the basis of expert advice. Our ensemble produces a range of regional changes much wider than indicated by traditional methods based on scaling the response patterns of an individual simulation.

Projected increase in continental runoff due to plant responses to increasing carbon dioxide

In addition to influencing climatic conditions directly through radiative forcing, increasing carbon dioxide concentration influences the climate system through its effects on plant physiology. Plant stomata generally open less widely under increased carbon dioxide concentration, which reduces transpiration and thus leaves more water at the land surface. This driver of change in the climate system, which we term ‘physiological forcing’, has been detected in observational records of increasing average continental runoff over the twentieth century. Here we use an ensemble of experiments with a global climate model that includes a vegetation component to assess the contribution of physiological forcing to future changes in continental runoff, in the context of uncertainties in future precipitation. We find that the physiological effect of doubled carbon dioxide concentrations on plant transpiration increases simulated global mean runoff by 6 per cent relative to pre-industrial levels; an increase that is comparable to that simulated in response to radiatively forced climate change (11 ± 6 per cent). Assessments of the effect of increasing carbon dioxide concentrations on the hydrological cycle that only consider radiative forcing will therefore tend to underestimate future increases in runoff and overestimate decreases. This suggests that freshwater resources may be less limited than previously assumed under scenarios of future global warming, although there is still an increased risk of drought. Moreover, our results highlight that the practice of assessing the climate-forcing potential of all greenhouse gases in terms of their radiative forcing potential relative to carbon dioxide does not accurately reflect the relative effects of different greenhouse gases on freshwater resources.

The New Hadley Centre Climate Model (HadGEM1): Evaluation of Coupled Simulations

Abstract A new coupled general circulation climate model developed at the Met Offices Hadley Centre is presented, and aspects of its performance in climate simulations run for the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) documented with reference to previous models. The Hadley Centre Global Environmental Model version 1 (HadGEM1) is built around a new atmospheric dynamical core; uses higher resolution than the previous Hadley Centre model, HadCM3; and contains several improvements in its formulation including interactive atmospheric aerosols (sulphate, black carbon, biomass burning, and sea salt) plus their direct and indirect effects. The ocean component also has higher resolution and incorporates a sea ice component more advanced than HadCM3 in terms of both dynamics and thermodynamics. HadGEM1 thus permits experiments including some interactive processes not feasible with HadCM3. The simulation of present-day mean climate in HadGEM1 is significantly better overall ...

Global temperature change and its uncertainties since 1861

We present the first analysis of global and hemispheric surface warming trends that attempts to quantify the major sources of uncertainty. We calculate global and hemispheric annual temperature anomalies by combining land surface air temperature and sea surface temperature (SST) through an optimal averaging technique. The technique allows estimation of uncertainties in the annual anomalies resulting from data gaps and random errors. We add independent uncertainties due to urbanisation, changing land-based observing practices and SST bias corrections. We test the accuracy of the SST bias corrections, which represent the largest source of uncertainty in the data, through a suite of climate model simulations. These indicate that the corrections are likely to be fairly accurate on an annual average and on large space scales. Allowing for serial correlation and annual uncertainties, the best linear fit to annual global surface temperature gives an increase of 0.61 ± 0.16°C between 1861 and 2000.

A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles

A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean–atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25 km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales. The methodology is necessarily dependent on a number of expert choices, which are highlighted throughout the paper.

Comparison of Modeled and Observed Trends in Indices of Daily Climate Extremes

Abstract Gridded trends of annual values of various climate extreme indices were estimated for 1950 to 1995, presenting a clearer picture of the patterns of trends in climate extremes than has been seen with raw station data. The gridding also allows one, for the first time, to compare these observed trends with those simulated by a suite of climate model runs forced by observed changes in sea surface temperatures, sea ice extent, and various combinations of human-induced forcings. Bootstrapping techniques are used to assess the uncertainty in the gridded trend estimates and the field significance of the patterns of observed trends. The findings mainly confirm earlier, less objectively derived, results based on station data. There have been significant decreases in the number of frost days and increases in the number of very warm nights over much of the Northern Hemisphere. Regions of significant increases in rainfall extremes and decreases in the number of consecutive dry days are smaller in extent. Howe...

A new global gridded radiosonde temperature data base and recent temperature trends

We present a new analysis of global radiosonde temperature data. From 1979 onwards, the data from the Australasian region have been corrected for instrument-related discontinuities with the help of comparisons with collocated retrievals from satellite-based Microwave Sounding Units (MSU) and metadata: in future work, adjustments will be applied worldwide and extended to earlier years. The data are stored as monthly anomalies from a 1971–1990 reference period on a 5° latitude × 10° longitude grid at 8 levels from 50 hPa to 850 hPa. Seasonal and annual temperature anomalies have also been created on a 10° × 20° grid using an eigenvector reconstruction method to filter noise. Latitude-height profiles of zonal-mean temperature changes since the 1960s show significant cooling in the lower stratosphere, especially in middle and high latitudes of the Southern Hemisphere, but the cooling over Australasia is less than shown by unadjusted data. Warming dominates the troposphere but is not a maximum in the tropical upper troposphere. In the annual mean, tropospheric warming is greatest around 45°N and possibly in the data-sparse high latitudes of the Southern Hemisphere.

The expanding genetic overlap between multiple sclerosis and type I diabetes

Familial clustering of autoimmune disease is well recognized and raises the possibility that some susceptibility genes may predispose to autoimmunity in general. In light of this observation, it might be expected that some of the variants of established relevance in one autoimmune disease may also be relevant in other related conditions. On the basis of this hypothesis, we tested seven single nucleotide polymorphisms (SNPs) that are known to be associated with type I diabetes in a large multiple sclerosis data set consisting of 2369 trio families, 5737 cases and 10 296 unrelated controls. Two of these seven SNPs showed evidence of association with multiple sclerosis; that is rs12708716 from the CLEC16A gene (P=1.6 × 10−16) and rs763361 from the CD226 gene (P=5.4 × 10−8). These findings thereby identify two additional multiple sclerosis susceptibility genes and lend support to the notion of autoimmune susceptibility genes.

Analyzing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments

Global climate models (GCMs) contain imprecisely defined parameters that account, approximately, for subgrid-scale physical processes. The response of a GCM to perturbations in its parameters, which is crucial for quantifying uncertainties in simulations of climate change, can—in principle—be assessed by simulating the GCM many times. In practice, however, such “perturbed physics” ensembles are small because GCMs are so expensive to simulate. Statistical tools can help in two ways. First, they can be used to combine ensembles from different but related experiments, increasing the effective number of simulations. Second, they can be used to describe the GCM’s response in ways that cannot be extracted directly from the ensemble(s). The authors combine two experiments to learn about the response of the Hadley Centre Slab Climate Model version 3 (HadSM3) climate sensitivity to 31 model parameters. A Bayesian statistical framework is used in which expert judgments are required to quantify the relationship between the two experiments; these judgments are validated by detailed diagnostics. The authors identify the entrainment rate coefficient of the convection scheme as the most important single parameter and find that this interacts strongly with three of the large-scale-cloud parameters.

Replication analysis identifies TYK2 as a multiple sclerosis susceptibility factor

In a recent genome-wide association study (GWAS) based on 12 374 non-synonymous single nucleotide polymorphisms we identified a number of candidate multiple sclerosis susceptibility genes. Here, we describe the extended analysis of 17 of these loci undertaken using an additional 4234 patients, 2983 controls and 2053 trio families. In the final analysis combining all available data, we found that evidence for association was substantially increased for one of the 17 loci, rs34536443 from the tyrosine kinase 2 (TYK2) gene (P=2.7 × 10−6, odds ratio=1.32 (1.17–1.47)). This single nucleotide polymorphism results in an amino acid substitution (proline to alanine) in the kinase domain of TYK2, which is predicted to influence the levels of phosphorylation and therefore activity of the protein and so is likely to have a functional role in multiple sclerosis.