Quarterly Journal of the Royal Meteorological Society (2012)
High-Resolution Global Climate Simulations with the ECMWF Model in Project Athena: Experimental Design, Model Climate, and Seasonal Forecast Skill
JOURNAL OF CLIMATE 25 (2012) 3155-3172
Journal of Computational Physics 231 (2012)
Towards the probabilistic Earth-system simulator: A vision for the future of climate and weather prediction
Quarterly Journal of the Royal Meteorological Society 138 (2012) 841-861
There is no more challenging problem in computational science than that of estimating, as accurately as science and technology allows, the future evolution of Earth's climate; nor indeed is there a problem whose solution has such importance and urgency. Historically, the simulation tools needed to predict climate have been developed, somewhat independently, at a number of weather and climate institutes around the world. While these simulators are individually deterministic, it is often assumed that the resulting diversity provides a useful quantification of uncertainty in global or regional predictions. However, this notion is not well founded theoretically and corresponding 'multi-simulator' estimates of uncertainty can be prone to systemic failure. Separate to this, individual institutes are now facing considerable challenges in finding the human and computational resources needed to develop more accurate weather and climate simulators with higher resolution and full Earth-system complexity. A new approach, originally designed to improve reliability in ensemble-based numerical weather prediction, is introduced to help solve these two rather different problems. Using stochastic mathematics, this approach recognizes uncertainty explicitly in the parametrized representation of unresolved climatic processes. Stochastic parametrization is shown to be more consistent with the underlying equations of motion and, moreover, provides more skilful estimates of uncertainty when compared with estimates from traditional multi-simulator ensembles, on time-scales where verification data exist. Stochastic parametrization can also help reduce long-term biases which have bedevilled numerical simulations of climate from the earliest days to the present. As a result, it is suggested that the need to maintain a large 'gene pool' of quasi-independent deterministic simulators may be obviated by the development of probabilistic Earth-system simulators. Consistent with the conclusions of the World Summit on Climate Modelling, this in turn implies that individual institutes will be able to pool human and computational resources in developing future-generation simulators, thus benefitting from economies of scale; the establishment of the Airbus consortium provides a useful analogy here. As a further stimulus for such evolution, discussion is given to a potential new synergy between the development of dynamical cores, and stochastic processing hardware. However, it is concluded that the traditional challenge in numerical weather prediction, of reducing deterministic measures of forecast error, may increasingly become an obstacle to the seamless development of probabilistic weather and climate simulators, paradoxical as that may appear at first sight. Indeed, going further, it is argued that it may be time to consider focusing operational weather forecast development entirely on high-resolution ensemble prediction systems. Finally, by considering the exceptionally challenging problem of quantifying cloud feedback in climate change, it is argued that the development of the probabilistic Earth-system simulator may actually provide a route to reducing uncertainty in climate prediction. © 2012 Royal Meteorological Society.
The Intra-Seasonal Oscillation and its control of tropical cyclones simulated by high-resolution global atmospheric models
CLIMATE DYNAMICS 39 (2012) 2185-2206
Geophysical Research Letters 39 (2012)
The Antarctic polar night jet has intensified during spring in recent decades due to stratospheric ozone depletion and rising greenhouse gas (GHG) concentrations and this has had substantial effects on the region's climate. GHG concentrations will rise over the 21st century whereas stratospheric ozone is expected to recover and there is uncertainty in future southern hemisphere (SH) circulation trends. We examine sensitivity to the physics parameterisation of the 21st century SH circulation projection of a coupled atmosphere-ocean General Circulation Model and the sensitivity of the contribution from stratospheric ozone recovery. Different model parameterizations give a greater range of future trends in the position of the tropospheric jet than has been found in previous multi-model comparisons. Ozone recovery causes a weakening and northward shift of the DJF tropospheric jet. Varying the physics parameterization affects the zonal wind response to ozone recovery of the SON stratosphere by ∼10% and that of the DJF troposphere by ∼25%. The projected future SAM index changes with and without ozone recovery and the SAM index response to ozone recovery alone are found to be strongly positively correlated with projected 21st century global warming. © 2012. American Geophysical Union. All Rights Reserved.
Useful decadal climate prediction at regional scales? A look at the ENSEMBLES stream 2 decadal hindcasts
Environmental Research Letters 7 (2012)
Decadal climate prediction is a branch of climate modelling with the theoretical potential to anticipate climate impacts years in advance. Here we present analysis of the ENSEMBLES decadal simulations, the first multi-model decadal hindcasts, focusing on the skill in prediction of temperature and precipitation - important for impact prediction. Whilst previous work on this dataset has focused on the skill in multi-year averages, we focus here on the skill in prediction at smaller timescales. Considering annual and seasonal averages, we look at correlations, potential predictability and multi-year trend correlations. The results suggest that the prediction skill for temperature comes from the long-term trend, and that precipitation predictions are not skilful. The potential predictability of the models is higher for annual than for seasonal means and is largest over the tropics, though it is low everywhere else and is much lower for precipitation than for temperature. The globally averaged temperature trend correlation is significant at the 99% level for all models and is higher for annual than for seasonal averages; however, for smaller spatial regions the skill is lower. For precipitation trends, the correlations are not skilful on either annual or seasonal scales. Whilst climate models run in decadal prediction mode may be useful by other means, the hindcasts studied here have limited predictive power on the scales at which climate impacts and the results presented suggest that they do not yet have sufficient skill to drive impact models on decadal timescales. © 2012 IOP Publishing Ltd.
Monthly weather review American Meteorological Society 139 (2011) 2455-2470
Atmospheric blocking occurred over the Rocky Mountains at 1200 UTC 15 December 2005. The operational medium-range ensemble forecasts of the Canadian Meteorological Center (CMC), the Japan Meteorological Agency (JMA), and the National Centers for Environmental Prediction (NCEP), as initialized at 1200 UTC 10 December 2005, showed remarkable differences regarding this event. All of the NCEP members failed to predict the correct location of the blocking, whereas almost all of the JMA members and most of the CMC members were successful in predicting the correct location. The present study investigated the factors that caused NCEP to incorrectly predict the blocking location, based on an ensemble-based sensitivity analysis and the JMA global spectral model (GSM) multianalysis ensemble forecasts with NCEP, regionally amplified NCEP, and globally amplified NCEP analyses.A sensitive area for the blocking formation was detected over the central North Pacific. In this area, the NCEP control analysis experienced problems in the handling of a cutoff cyclone, and the NCEP initial perturbations were ineffective in reducing uncertainties in the NCEP control analysis. The JMA GSM multianalysis ensemble forecasts revealed that regional amplification of initial perturbations over the sensitive area could lead to further improvements in forecasts over the blocking region without degradation of forecasts over the Northern Hemisphere (NH), whereas the global amplification of initial perturbations could lead to improved forecasts over the blocking region and degraded forecasts over the NH. This finding may suggest that excessive amplification of initial perturbations over nonsensitive areas is undesirable, and that case-dependent rescaling of initial perturbations may be of value compared with climatology-based rescaling, which is widely used in current operational ensemble prediction systems.
Geophysical Research Letters 38 (2011)
The European summer 2003 is a prominent example for an extreme hot and dry season. The main mechanisms that contributed to the growth of the heat wave are still disputed and state-of-the-art climate models have difficulty to realistically simulate the extreme conditions. Here we analyse simulations using recent versions of the European Centre for Medium-Range Weather Forecasts seasonal ensemble forecasting system and present, for the first time, retrospective forecasts which simulate accurately not only the abnormal warmth but also the observed precipitation and mid-tropospheric circulation patterns. It is found that while the land surface hydrology plays a crucial role, the successful simulations also required revised formulations of the radiative and convective parameterizations. We conclude that the predictability of the event was less due to remote teleconnections effects and more due to in situ processes which helped maintain the dry surface anomalies occurring at the beginning of the summer. Copyright 2011 by the American Geophysical Union.
Geophysical Research Letters 38 (2011)
How accurate are predictions of climate change from a model which is biased against contemporary observations? If a model bias can be thought of as a state-independent linear offset, then the signal of climate change derived from a biased climate model should not be affected substantially by that model's bias. By contrast, if the processes which cause model bias are highly nonlinear, we could expect the accuracy of the climate change signal to degrade with increasing bias. Since we do not yet know the late 21st Century climate change signal, we cannot say at this stage which of these two paradigms describes best the role of model bias in studies of climate change. We therefore study this question using time-slice projections from a global climate model run at two resolutions - a resolution typical of contemporary climate models and a resolution typical of contemporary numerical weather prediction - and treat the high-resolution model as a surrogate of truth, for both 20th and 21st Century climate. We find that magnitude of the regionally varying model bias is a partial predictor of the accuracy of the regional climate change signal for both wind and precipitation. This relationship is particularly apparent for the 850 mb wind climate change signal. Our analysis lends some support to efforts to weight multi-model ensembles of climate change according to 20th Century bias, though note that the optimal weighting appears to be a nonlinear function of bias. Copyright © 2011 by the American Geophysical Union.
Future changes in the East Asian rain band projected by global atmospheric models with 20-km and 60-km grid size
CLIMATE DYNAMICS 37 (2011) 2481-2493
Evaluation of probabilistic quality and value of the ENSEMBLES multimodel seasonal forecasts: Comparison with DEMETER
Monthly Weather Review 139 (2011) 581-607
The performance of the new multimodel seasonal prediction system developed in the framework of the European Commission FP7 project called ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) is compared with the results from the previous project [i.e., Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER)]. The comparison is carried out over the five seasonal prediction systems (SPSs) that participated in both projects. Since DEMETER, the contributing SPSs have improved in all aspects with the main advancements including the increase in resolution, the better representation of subgrid physical processes, land, sea ice, and greenhouse gas boundary forcing, and the more widespread use of assimilation for ocean initialization. The ENSEMBLES results show an overall enhancement for the prediction of anomalous surface temperature conditions. However, the improvement is quite small and with considerable space-time variations. In the tropics, ENSEMBLES systematically improves the sharpness and the discrimination attributes of the forecasts. Enhancements of the ENSEMBLES resolution attribute are also reported in the tropics for the forecasts started 1 February, 1 May, and 1 November. Our results indicate that, in ENSEMBLES, an increased portion of prediction signal from the single-models effectively contributes to amplify the multimodel forecasts skill. On the other hand, a worsening is shown for the multimodel calibration over the tropics compared to DEMETER. Significant changes are also shown in northern midlatitudes, where the ENSEMBLES multimodel discrimination, resolution, and reliability improve for February, May, and November starting dates. However, the ENSEMBLES multimodel decreases the capability to amplify the performance with respect to the contributing single models for the forecasts started in February, May, and August. This is at least partly due to the reduced overconfidence of the ENSEMBLES single models with respect to the DEMETER counterparts. Provided that they are suitably calibrated beforehand, it is shown that the ENSEMBLES multimodel forecasts represent a step forward for the potential economical value they can supply. A warning for all potential users concerns the need for calibration due to the degraded tropical reliability compared to DEMETER. In addition, the superiority of recalibrating the ENSEMBLES predictions through the discrimination information is shown. Concerning the forecasts started inAugust, ENSEMBLES exhibitsmixed results over both tropics and northernmidlatitudes. In this case, the increased potential predictability compared to DEMETER appears to be balanced by the reduction in the independence of the SPSs contributing to ENSEMBLES. Consequently, for the August start dates no clear advantage of using one multimodel system instead of the other can be evidenced. © 2011 American Meteorological Society.
Analyzing precipitation projections: A comparison of different approaches to climate model evaluation
Journal of Geophysical Research 116 (2011)
Extended warming of the northern high latitudes due to an overshoot of the Atlantic meridional overturning circulation
Geophysical Research Letters 38 (2011) n/a-n/a
GEOPHYSICAL RESEARCH LETTERS 38 (2011) ARTN L06801
JOURNAL OF CLIMATE 24 (2011) 6456-6470
Decadal climate prediction with the European Centre for Medium-Range Weather Forecasts coupled forecast system: Impact of ocean observations
Journal of Geophysical Research: Atmospheres 116 (2011)
Three 10 year ensemble decadal forecast experiments have been performed with the European Centre for Medium-Range Weather Forecasts coupled forecast system using an initialization strategy common in seasonal forecasting with realistic initial conditions. One experiment initializes the ocean in a standard way using an ocean-only simulation forced with an atmospheric reanalysis and with strong relaxation to observed sea surface temperatures. The other two experiments initialize the ocean from a similar ocean-only run that, in addition, assimilates subsurface observations. This is the first time that these experiments were performed. The system drifts from the realistic initial conditions toward the model climate, the drift being of the same order as, if not larger than, the interannual signal. There are small drift differences in the three experiments that reflect mainly the influence of dynamical ocean processes in controlling the adjustment between the initialized state and the model climate in the extratropics. In spite of the drift, the predictions show that the system is able to skillfully predict some of the interannual variability of the global and regional air and ocean temperature. No significant forecast quality benefit of the assimilation of ocean observations is found over the extratropics, although a negative impact of the assimilation of incorrect expendable bathythermograph profiles has been found for the global mean upper ocean heat content and the Atlantic multidecadal oscillation. The results illustrate the importance of reducing the important model drift and the ocean analysis uncertainty. Copyright 2011 by the American Geophysical Union.
GEOPHYSICAL RESEARCH LETTERS 38 (2011) ARTN L16703
Geophysical Research Letters 38 (2011)
The probabilistic skill of ensemble forecasts for the first month and the first season of the forecasts is assessed, where model uncertainty is represented by the a) multi-model, b) perturbed parameters, and c) stochastic parameterisation ensembles. The main foci of the assessment are the Brier Skill Score for near-surface temperature and precipitation over land areas and the spread-skill relationship of sea surface temperature in the tropical equatorial Pacific. On the monthly timescale, the ensemble forecast system with stochastic parameterisation provides overall the most skilful probabilistic forecasts. On the seasonal timescale the results depend on the variable under study: for near surface temperature the multi-model ensemble is most skilful for most land regions and for global land areas. For precipitation, the ensemble with stochastic parameterisation most often produces the highest scores on global and regional scales. Our results indicate that stochastic parameterisations should now be developed for multi-decadal climate predictions using earth-system models. Copyright 2011 by the American Geophysical Union.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369 (2011) 4751-4767
Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation. This journal is © 2011 The Royal Society.