JOURNAL OF CLIMATE 24 (2011) 6456-6470
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.
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.
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.
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.
Geophysical and Astrophysical Fluid Dynamics 105 (2011) 351-365
The equations of climate are, in principle, known. Why then is it so hard to formulate a biasfree model of climate? Here, some ideas in nonlinear dynamics are explored to try to answer this question. Specifically it is suggested that the climatic response to physically different forcings shows a tendency to project onto structures corresponding to the systems natural internal modes of variability. This is shown using results from complex climate models and from the relatively simple Lorenz three-component model. It is suggested that this behaviour is consistent with what might be expected from the fluctuation-dissipation theorem. Based on this, it is easy to see how climate models can easily suffer from having errors in the representation of two or more different physical processes, whose responses compensate one another and hence make individual error diagnosis difficult. A proposal is made to try to overcome these problems and advance the science needed to develop a bias-free climate model. The proposal utilises powerful diagnostics from data assimilation. The key point here is that these diagnostics derive from short-range forecast tendencies, estimated long before the model has asymptotically settled down to its (biased) climate attractor. However, it is shown that these diagnostics will not identify all sources of model error, and a so-called "bias of the second kind" is discussed. This latter bias may be alleviated by recently developed stochastic parametrisations. © 2011 Taylor & Francis.
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
Impact of 2007 and 2008 Arctic ice anomalies on the atmospheric circulation: Implications for long-range predictions
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 136 (2010) 1655-1664
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 91 (2010) 1493-U121
Forecast quality assessment of the ENSEMBLES seasonal-to-decadal Stream 2 hindcasts. ECMWF Tech Memo.
ECMWF (2010) 621
Decadal climate prediction with the ECMWF coupled forecast system: Impact of ocean observations. ECMWF Tech Memo.
Bulletin of the American Meteorological Society 91 (2010) 1377-1388
Some scientists have proposed the Earth-System Prediction Initiative (EPI) at the 2007 GEO Summit in Cape Town, South Africa. EPI will draw upon coordination between international programs for Earth system observations, prediction, and warning, such as the WCRP, WWRP, GCOS, and hence contribute to GEO and the GEOSS. It will link with international organizations, such as the International Council for Science (ICSU), Intergovernmental Oceanographic Commission (IOC), UNEP, WMO, and World Health Organization (WHO). The proposed initiative will provide high-resolution climate models that capture the properties of regional high-impact weather events, such as tropical cyclones, heat wave, and sand and dust storms associated within multi-decadal climate projections of climate variability and change. Unprecedented international collaboration and goodwill are necessary for the success of EPI.
Bulletin of the American Meteorological Society 91 (2010) 1407-1412
National climate research facilities must be enhanced and dedicated multi-national facilities should be established to accelerate progress in understanding and predicting regional climate change. In addition to the merits of running climate models at a resolution comparable with that of NWP models, the continual confrontation of an NWP model with observations can provide important constraints when the same model is used for much longer-time-scale climate predictions. Short-range forecast models give encouraging results using grid lengths of close to 1 km, without parameterizing deep convection. Prediction uncertainty, a key variable can be estimated by making an ensemble of forecasts with varying initial conditions, model equations, and other input fields such as greenhouse gas concentrations. The new generation of models will yield improved statistics of daily weather and, therefore, better predictions of regional climate variations on seasonal time scales.
Future change in Southern Hemisphere summertime and wintertime atmospheric blockings simulated using a 20-km-mesh AGCM
GEOPHYSICAL RESEARCH LETTERS 37 (2010) ARTN L02803
ECMWF Newsletter ECMWF 122 (2010) 21-26
Is science fiction a genre for communicating scientific research? A case study in climate prediction
Bulletin of the American Meteorological Society 91 (2010) 1413-1415
The author, T. N. Palmer describes a book by Isaac Asimov titled Nightfall, which describes a civilization's first encounter with darkness for thousands of years. The civilization inhabits the planet Lagash, which orbits one of six gravitationally-bound suns. Nightfall occurs during a total eclipse, when only one of the suns is above the horizon. Although in this sense climate change is inherently predictable, the author is not confirm whether how reliable the predictions of climate change are in practice. The first message of the story is that reliable predictions of regional climate change are crucially important to guide decisions on infrastructure investment for societies to adapt to future climate change. The second message of the story is that if current climate models can systematically misrepresent the regional effects of the annual cycle, they can also misrepresent the regional effects of climate change. One way to reduce these systematic deficiencies would be to simulate more of the climate system with the proper equations of motion.
Bulletin of the American Meteorological Society 91 (2010) 1357-1363
The EC-Earth consortium is a grouping of meteorologists and Earth-system scientists from 10 European countries, put together to face the challenges of climate and weather forecasting. The NWP system of the European Centre for Medium-Range Weather Forecasts (ECWMF) forms the basis of the EC-Earth Earth-system model. NWP models are designed to accurately capture short-term atmospheric fluctuations. They are used for forecasts at daily-to-seasonal time scales and include data assimilation capabilities. Climate models are designed to represent the global coupled ocean-atmosphere system. The atmospheric model of EC-Earth version 2, is based on ECMWF's Integrated Forecasting System (IFS), cycle 31R1, corresponding to the current seasonal forecast system of ECMWF. The EC-Earth consortium and ECMWF are collaborating on development of initialization procedures to improve long-term predictions. The EC-Earth model displays good performance from daily up to inter-annual time scales and for long-term mean climate.
MONTHLY WEATHER REVIEW 138 (2010) 3157-3174
Quarterly Journal of the Royal Meteorological Society 135 (2009) 1538-1559
The relative merits of three forecast systems addressing the impact of model uncertainty on seasonal/annual forecasts are described. One system consists of a multi-model, whereas two other systems sample uncertainties by perturbing the parametrization of reference models through perturbed parameter and stochastic physics techniques. Ensemble reforecasts over 1991 to 2001 were performed with coupled climate models started from realistic initial conditions. Forecast quality varies due to the different strategies for sampling uncertainties, but also to differences in initialisation methods and in the reference forecast system. Both the stochastic-physics and perturbed-parameter ensembles improve the reliability with respect to their reference forecast systems, but not the discrimination ability. Although the multi-model experiment has an ensemble size larger than the other two experiments, most of the assessment was done using equally-sized ensembles. The three ensembles show similar levels of skill: significant differences in performance typically range between 5 and 20%. However, a nine-member multi-model shows better results for seasonal predictions with lead times shorter than five months, followed by the stochastic-physics and perturbed-parameter ensembles. Conversely, for seasonal predictions with lead times longer than four months, the perturbed-parameter ensemble gives more often better results. All systems suggest that spread cannot be considered a useful predictor of skill. Annual-mean predictions showed lower forecast quality than seasonal predictions. Only small differences between the systems were found. The full multi-model ensemble has improved quality with respect to all other systems, mainly from the larger ensemble size for lead times longer than four months and annual predictions. © 2009 Royal Meteorological Society and Crown Copyright.