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Climate models can produce accurate representations of the most important modes of climate variability, but they cannot be expected to follow their observed time evolution. This makes direct comparison of simulated and observed variability difficult and creates uncertainty in estimates of forced change. We investigate the role of three modes of climate variability, the North Atlantic Oscillation, El Niño–Southern Oscillation and the Southern Annular Mode, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. We use particle filter data assimilation to constrain the observed variability in a global climate model without nudging, producing a near-free-running model simulation with the time evolution of these modes similar to those observed. Since the climate model also contains external forcings, these simulations, in combination with model experiments with identical forcing but no assimilation, can be used to compare the forced response to the effect of the three modes assimilated and evaluate the extent to which these are confounded with the forced response. The assimilated model is significantly closer than the “forcing only” simulations to annual temperature and precipitation observations over many regions, in particular the tropics, the North Atlantic and Europe. The results indicate where initialised simulations that track these modes could be expected to show additional skill. Assimilating the three modes cannot explain the large discrepancy previously found between observed and modelled variability in the southern extra-tropics but constraining the El Niño–Southern Oscillation reconciles simulated global cooling with that observed after volcanic eruptions.

Climate models can produce accurate representations of the most important modes of climate variability, but they cannot be expected to follow their observed time-evolution. This makes direct comparison of simulated and observed variability difficult, and creates uncertainty in estimates of forced change. We investigate the role of three modes of climate variability, the North Atlantic Oscillation, El-Niño Southern Oscillation and the Southern Annular Mode, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. We use particle filter data assimilation to constrain the observed variability in a global climate model without nudging, producing a near free running model simulation with the time-evolution of these modes similar to those observed. Since the climate model also contains external forcings, these simulations, in combination with model experiments with identical forcing but no assimilation, can be used to compare the forced response to the effect of the three modes assimilated, and evaluate to what extent these are confounded with the forced response. The assimilated model is significantly closer than the “forcing only” simulations to annual temperature and precipitation observations over many regions, in particular the tropics, the north Atlantic and Europe. The results indicate where initialized simulations that track these modes could be expected to show additional skill. Assimilating the three modes cannot explain the large discrepancy previously found between observed and modelled variability in the southern extra-tropics but constraining the El-Niño Southern Oscillation reconciles simulated global cooling with that observed after volcanic eruptions.

Uncertainty in climate projections is large as shown by the likely uncertainty ranges in equilibrium climate sensitivity (ECS) of 2.5–4 K and in the transient climate response (TCR) of 1.4–2.2 K. Uncertainty in model projections could arise from the way in which unresolved processes are represented, the parameter values used, or the targets for model calibration. We show that, in two climate model ensembles that were objectively calibrated to minimize differences from observed large-scale atmospheric climatology, uncertainties in ECS and TCR are about 2–6 times smaller than in the CMIP5 or CMIP6 multimodel ensemble. We also find that projected uncertainties in surface temperature, precipitation, and annual extremes are relatively small. Residual uncertainty largely arises from unconstrained sea ice feedbacks. The more than 20-year-old HadAM3 standard model configuration simulates observed hemispheric-scale observations and preindustrial surface temperatures about as well as the median CMIP5 and CMIP6 ensembles while the optimized configurations simulate these better than almost all the CMIP5 and CMIP6 models. Hemispheric-scale observations and preindustrial temperatures are not systematically better simulated in CMIP6 than in CMIP5 although the CMIP6 ensemble seems to better simulate patterns of large-scale observations than the CMIP5 ensemble and the optimized HadAM3 configurations. Our results suggest that most CMIP models could be improved in their simulation of large-scale observations by systematic calibration. However, the uncertainty in climate projections (for a given scenario) likely largely arises from the choice of parameterization schemes for unresolved processes (“structural uncertainty”), with different tuning targets another possible contributor. Significance Statement Climate models represent unresolved phenomena controlled by uncertain parameters. Changes in these parameters impact how well a climate model simulates current climate and its climate projections. Multiple calibrations of a single climate model, using an objective method, to large-scale atmospheric observations are performed. These models produce very similar climate projections at both global and regional scales. An analysis that combines uncertainties in observations with simulated sensitivity to observations and climate response also has small uncertainty showing that, for this model, current observations constrain climate projections. Recently developed climate models have a broad range of abilities to simulate large-scale climate with only some improvement in their ability to simulate this despite a decade of model development.

This study investigates the potential influences of anthropogenic forcings and natural variability on the risk of summer extreme temperatures over China. We use three multi-thousand-member ensemble simulations with different forcings (with or without anthropogenic greenhouse gases and aerosol emissions) to evaluate the human impact, and with sea surface temperature patterns from three different years around the El Niño–Southern Oscillation (ENSO) 2015/16 event (years 2014, 2015 and 2016) to evaluate the impact of natural variability. A generalized extreme value (GEV) distribution is used to fit the ensemble results. Based on these model results, we find that, during the peak of ENSO (2015), daytime extreme temperatures are smaller over the central China region compared to a normal year (2014). During 2016, the risk of nighttime extreme temperatures is largely increased over the eastern coastal region. Both anomalies are of the same magnitude as the anthropogenic influence. Thus, ENSO can amplify or counterbalance (at a regional and annual scale) anthropogenic effects on extreme summer temperatures over China. Changes are mainly due to changes in the GEV location parameter. Thus, anomalies are due to a shift in the distributions and not to a change in temperature variability.

Optimisation methods were successfully used to calibrate parameters in an atmospheric component of a climate model using two variants of the Gauss-Newton line-search algorithm. 1) A standard Gauss-Newton algorithm in which, in each iteration, all parameters were perturbed. 2) A randomized block-coordinate variant in which, in each iteration, a random sub-set of parameters was perturbed. The cost function to be minimized used multiple large-scale, multi-annual average observations and was constrained to produce net radiative fluxes close to those observed. These algorithms were used to calibrate the HadAM3 (3rd Hadley Centre Atmospheric Model) model at N48 resolution and the HadAM3P model at N96 resolution. For the HadAM3 model, cases with seven and fourteen parameters were tried. All ten 7-parameter cases using HadAM3 converged to cost function values similar to that of the standard configuration. For the 14-parameter cases several failed to converge, with the random variant in which 6 parameters were perturbed being most successful. Multiple sets of parameter values were found that produced multiple models very similar to the standard configuration. HadAM3 cases that converged were coupled to an ocean model and ran for 20 years starting from a pre-industrial HadCM3 (3rd Hadley Centre Coupled model) state resulting in several models whose global-average temperatures were consistent with pre-industrial estimates. For the 7-parameter cases the Gauss-Newton algorithm converged in about 70 evaluations. For the 14-parameter algorithm with 6 parameters being randomly perturbed about 80 evaluations were needed for convergence. However, when 8 parameters were randomly perturbed algorithm performance was poor. Our results suggest the computational cost for the Gauss-Newton algorithm scales between P and P2 where P is the number of parameters being calibrated. For the HadAM3P model three algorithms were tested. Algorithms in which seven parameters were perturbed and three out of seven parameters randomly perturbed produced final configurations comparable to the standard hand tuned configuration. An algorithm in which six out of thirteen parameters were randomly perturbed failed to converge. These results suggest that automatic parameter calibration using atmosphericmodels is feasible and that the resulting coupled models are stable. Thus, automatic calibration could replace human driven trial and error. However, convergence and costs are, likely, sensitive to details of the algorithm.

This study investigates the hypothesis that a significant portion of spread in climate model projections of sea ice is due to poorly-constrained model parameters. New automated methods for optimization are applied to historical sea ice in a global coupled climate model (HadCM3) in order to calculate the combination of parameters required to reduce the difference between simulation and observations to within the range of model noise. The optimized parameters result in a simulated sea-ice time series which is more consistent with Arctic observations throughout the satellite record (1980-present), particularly in the September minimum, than the standard configuration of HadCM3. Divergence from observed Antarctic trends and mean regional sea ice distribution reflects broader structural uncertainty in the climate model. We also find that the optimized parameters do not cause adverse effects on the model climatology. This simple approach provides evidence for the contribution of parameter uncertainty to spread in sea ice extent trends and could be customized to investigate uncertainties in other climate variables.

We identify three key issues to be taken into account when designing the next generation of software environments for agricultural modelling. There is a burgeoning need to support collaborative research in a search for answers to big research questions, to integrate the work of data providers and model developers and to provide more generic systems. We describe the concepts of software design of a framework, designed with these points in mind, which facilitates the integration of point-based agricultural models with methods to interpolate climate data. Our approach allows the inter-working of model and interpolation through Fortran functions that are invoked from a central framework. We advocate that the framework code remains open to collaborators, such that it may be adapted to different classes of application, whilst recognising that some module developers need to retain their control on individual elements of the software. The rationale presented within the paper continues a major move away from the stand-alone programs that still dominate agricultural models and interpolation methods. Secondly, the paper considers how these approaches are extendable to exploit opportunities in the emerging Web Service and Grid context. The emerging technology of the Grid allows geographically distributed resources in hardware, software, data and network to be co-ordinated to meet the needs of “virtual organisations.” We explore how the modularity of our existing code can be exploited in the Grid environment, whilst noting the pre-requisite of a co-operative culture in which both software developers and data providers seek to deliver services to the widest possible community of users.

Parallel processing comprises the concurrent use of multiple processors to speed execution of one operation. Although techniques suited to most of the common geographical data models have been prototyped, the prominent exception has been vector-topology. This paper explores whether operations that create a vector-topological dataset can benefit from parallelisation. It describes techniques for using multiple processors concurrently to create vector-topology for multiple sub-areas, and for stitching these sub-areas together to form the resultant dataset. To achieve performance gains over sequential processing, the overhead of the stitching must be less than the gains from the parallel processing of sub-areas. These methods are tested in the context of the conversion of raster data to polygonal vector-topology. Speed-up in comparison to single-processor performance is achieved on both a 4-processor shared-memory Sun server and using up to 15 processors of a Cray T3E. The approach taken hides the parallelism...

Five stakeholder-relevant indices of agro-meteorological change were analysed for the UK, over past (1961-1990) and future (2061-2090) periods. Accumulated Frosts, Dry Days, Growing Season Length, Plant Heat Stress and Start of Field Operations were calculated from the E-Obs (European Observational) and HadRM3 (Hadley Regional Climate Model) PPE (perturbed physics ensemble) data sets. Indices were compared directly and examined for current and future uncertainty. Biases are quantified in terms of ensemble member climate sensitivity and regional aggregation. Maps of spatial change then provide an appropriate metric for end-users both in terms of their requirements and statistical robustness. A future UK is described with fewer frosts, fewer years with a large number of frosts, an earlier start to field operations (e.g., tillage), fewer occurrences of sporadic rainfall, more instances of high temperatures (in both the mean and upper range), and a much longer growing season.

Perturbed physics configurations of version 3 of the Hadley Centre Atmosphere Model (HadAM3) driven with observed sea surface temperatures (SST) and sea ice were tuned to outgoing radiation observations using a Gauss–Newton line search optimization algorithm to adjust the model parameters. Four key parameters that previous research found affected climate sensitivity were adjusted to several different target values including two sets of observations. The observations used were the global average reflected shortwave radiation (RSR) and outgoing longwave radiation (OLR) from the Clouds and the Earth's Radiant Energy System instruments combined with observations of ocean heat content. Using the same method, configurations were also generated that were consistent with the earlier Earth Radiation Budget Experiment results. Many, though not all, tuning experiments were successful, with about 2500 configurations being generated and the changes in simulated outgoing radiation largely due to changes in clouds. Clear-sky radiation changes were small, largely due to a cancellation between changes in upper-tropospheric relative humidity and temperature. Changes in other climate variables are strongly related to changes in OLR and RSR particularly on large scales. There appears to be some equifinality with different parameter configurations producing OLR and RSR values close to observed values. These models have small differences in their climatology with the one group being similar to the standard configuration and the other group drier in the tropics and warmer everywhere.

A large number of perturbed-physics simulations of version 3 of the Hadley Centre Atmosphere Model (HadAM3) were compared with the Clouds and the Earth's Radiant Energy System (CERES) estimates of outgoing longwave radiation (OLR) and reflected shortwave radiation (RSR) as well as OLR and RSR from the earlier Earth Radiation Budget Experiment (ERBE) estimates. The model configurations were produced from several independent optimization experiments in which four parameters were adjusted. Model–observation uncertainty was estimated by combining uncertainty arising from satellite measurements, observational radiation imbalance, total solar irradiance, radiative forcing, natural aerosol, internal climate variability, and sea surface temperature and that arising from parameters that were not varied. Using an emulator built from 14 001 “slab” model evaluations carried out using the climateprediction.net ensemble, the climate sensitivity for each configuration was estimated. Combining different prior probabilities for model configurations with the likelihood for each configuration and taking account of uncertainty in the emulated climate sensitivity gives, for the HadAM3 model, a 2.5%–97.5% range for climate sensitivity of 2.7–4.2 K if the CERES observations are correct. If the ERBE observations are correct, then they suggest a larger range, for HadAM3, of 2.8–5.6 K. Amplifying the CERES observational covariance estimate by a factor of 20 brings CERES and ERBE estimates into agreement. In this case the climate sensitivity range is 2.7–5.4 K. The results rule out, at the 2.5% level for HadAM3 and several different prior assumptions, climate sensitivities greater than 5.6 K.

This paper lays out a framework, based on the emerging Open GIS standards, which will allow the integration of parallel computing technology such that it becomes a viable component of a new generation of geographical information system (GIS) software. The significant costs of parallel re-implementation have thus far acted as a major disincentive to software vendors taking advantage of parallel technology to solve performance problems. These problems will be thrown into sharp focus by the demands of web-based geographical information services. Designs for a series of software libraries, which are subject to a prototype implementation involving the use of a sophisticated data format (Neutral Transfer Format Level 4), are examined with a view to re-implementation making use of the Open GIS Abstract Specification Model. A range of services are envisaged, which can provide functions at various levels from data retrieval, spatial analysis and map generation to specialist environmental models, which are made available over the Internet. Parallelism is seen as an important route for accelerating individual transactions. These services can equally be based on large specialised parallel servers or a co-operating set of under-used workstations. The implementation strategy involves insulating standard serial algorithms from parallelism through support libraries. These libraries handle, for example, the decomposition of the data, thus effectively encapsulating the parallelism within one component of the software and allowing the creation of high-performance software components which are compatible with the Open GIS service architecture.

Significant trends in the processing of geographical data require increasingly powerful software and hardware, consistent with the exploitation of parallel computing. Despite recent progress in technology, exploiting parallel processing is still difficult so that few applications have been developed in the environmental and geographical domains.

Geographical Information Systems (GIS) are able to manipulate spatial data. Such spatial data can be available in a variety of formats, one of the most important of which is the vector-topological. This format retains the topological relationships between geographical features and is commonly used in a range of geographical data analyses. This paper describes the implementation and performance of a parallel data partitioning algorithm for the input of vector-topological data to parallel processes.

The emerging technology of the grid will enable distributed "virtual organisations" to flexibly share resources including data, software, hardware and expertise. Potential applications in urban air quality illustrate that in addition to underpinning scientific research, grid technology can contribute strongly to environmental monitoring, decision-making and policy-making. Amongst factors that determine the extent to which this will happen are a) the investment made in establishing standards within the air quality communities for metadata, data and models, and b) the creation of toolkits that simplify the development of applications, isolating the application from grid middleware.