Anca Brookshaw

Skillful long‐range prediction of European and North American winters

This work was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101), the UK Public Weather Service research program, and the European Union Framework 7 SPECS project. Leon Hermanson was funded as part of his Research Fellowship by Willis as part of Willis Research Network (WRN).

A performance comparison of coupled and uncoupled versions of the Met Office seasonal prediction general circulation model

Wecompare the performance of the MetOffice’s ocean’atmosphere coupled general circulation model (CGCM) seasonal prediction system with that of an atmosphere-only system (AGCM). The CGCM and AGCM systems share the same atmospheric component and the performance comparison therefore provides insight into the skill benefits available from coupling atmosphere and ocean models. In this study, the AGCM is forced with predicted sea surface temperature (SST) based on persistence of prior observed SST anomalies. The analysis uses 43-yr, nine-member ensemble hindcast data sets generated with both systems as part of the European Union project DEMETER. Results are focused on global and regional comparisons of long-term skill for probabilistic prediction of 2-m temperature in the upper tercile, and on selected case studies for the tropics and Europe. Performance assessments using relative operating characteristic scores, Brier skill scores and the resolution and reliability terms of the Brier score decomposition are contrasted. The largest CGCM benefits are found in tropical regions, where benefits to both resolution (essentially ‘event detection’) and to reliability (essentially ‘calibration’ of the forecast probabilities) are demonstrated. Improvements to reliability are found to be substantially greater than improvements to resolution. Regional assessments show benefits, as expected, in the tropical east Pacific, from improved prediction of SST variability associated with the El Niño Southern Oscillation (ENSO). However, substantial benefits are also seen throughout the tropical belt in seasons associated with the peak and decay of ENSO activity. Such benefits appear associated with representation of lagged teleconnection responses to ENSO in the tropical Atlantic and Indian Oceans. In the extratropics, CGCM improvements to reliability are also substantial, although benefits to resolution (assessed over large regions) appear negligible. Two classes of benefit are described. First, advantages from improved ENSO predictions appear to benefit skill in the North Pacific and North American regions, through teleconnection responses. Secondly, there is evidence of benefits from representation of coupled processes over the North Atlantic. In particular, CGCM skill benefits for prediction of spring season temperature in the European region appear to derive, in part, from coupled model representation of linkage between a well-documented tripole pattern in North Atlantic SST anomalies and the North Atlantic oscillation. This result provides encouraging evidence that use of CGCMs offers prospects for improving seasonal prediction in the extratropics through representation of coupled ocean’atmosphere processes in extratropical ocean basins, as well as through indirect impacts from improved prediction of ENSO and associated teleconnections.

Long-range forecasts of UK winter hydrology

Seasonal river flow forecasts are beneficial for planning agricultural activities, river navigation, and for management of reservoirs for public water supply and hydropower generation. In the United Kingdom (UK), skilful seasonal river flow predictions have previously been limited to catchments in lowland (southern and eastern) regions. Here we show that skilful long-range forecasts of winter flows can now be achieved across the whole of the UK. This is due to a remarkable geographical complementarity between the regional geological and meteorological sources of predictability for river flows. Forecast skill derives from the hydrogeological memory of antecedent conditions in southern and eastern parts of the UK and from meteorological predictability in northern and western areas. Specifically, it is the predictions of the atmospheric circulation over the North Atlantic that provides the skill at the seasonal timescale. In addition, significant levels of skill in predicting the frequency of winter high flow events is demonstrated, which has the potential to allow flood adaptation measures to be put in place.

Skillful Seasonal Forecasts of Winter Disruption to the U.K. Transport System

ABSTRACTThe impacts of winter weather on transport networks have been highlighted by various high-profile disruptions to road, rail, and air transport in the United Kingdom during recent winters. Recent advances in the predictability of the winter North Atlantic Oscillation (NAO) at seasonal time scales, using the Met Office Global Seasonal forecasting system, version 5 (GloSea5), present a timely opportunity for assessing the long-range predictability of a variety of winter-weather impacts on transport. This study examines the relationships between the observed and forecast NAO and a variety of U.K. winter impacts on transport in the road, rail, and aviation sectors. The results of this preliminary study show statistically significant relationships between both observed and forecast NAO index and quantities such as road-accident numbers in certain weather conditions, weather-related delays to flights leaving London Heathrow Airport, and weather-related incidents on the railway network. This supports the ...

Skillful seasonal prediction of Yangtze river valley summer rainfall

China suffers from frequent summer floods and droughts, but seasonal forecast skill of corresponding summer rainfall remains a key challenge. In this study, we demonstrate useful levels of prediction skill over the Yangtze river valley for summer rainfall and river flows using a new high resolution forecast system. Further analysis of the sources of predictability suggests that the predictability of Yangtze river valley summer rainfall corresponds to skillful prediction of rainfall in the deep tropics and around the Maritime Continent. The associated dynamical signals favor increased poleward water vapor transport from South China and hence Yangtze river valley summer rainfall and river flow. The predictability and useful level of skill demonstrated by this study imply huge potential for flooding and drought related disaster mitigation and economic benefits for the region based on early warning of extreme climate events.

Multi-Scale Projections Of Weather And Climate At The Uk Met Office

Due to the availability of unprecedented computational power, national meteorological and hydrological services have had the opportunity to push the limit of predictability beyond the 2 weeks Lorenz suggested in 1963. This has been largely possible through the use of ensemble modelling. The adoption of such a technique has had a twofold effect: by averaging out the most unpredictable scales an ensemble average could directly increase forecast skill; ensembles also provide an estimate of uncertainty. This paper analyses the sources of predictability at different time scales and shows how the ensemble technique has been successfully used to inform decisions on time scales ranging from days to centuries.

An Evaluation of the Simulation of Monthly to Seasonal Summer Monsoon Rainfall over India with a Coupled Ocean Atmosphere General Circulation Model (GloSea)

The performance of the UK Met Office’s coupled ocean-atmosphere General Circulation Model (GCM) is evaluated in simulation of summer monsoon rainfall over Indian monsoon region. The UK Met Office’s Global Seasonal (GloSea) forecasting model is initialized at 0000 UTC of 1st May and integrated for a period of 6 month with 15 ensemble members to generate the model forecast. These experiments have been conducted in similar approach from 1987 to 2002 (16 years) to have monthly as well as seasonal forecast of individual year. The model simulated rainfall is compared with the verification analysis (Xie-Arkin) during the monsoon season from June to September (JJAS). The monthly forecast climatology from June to September separately and the seasonal forecast climatology (June to September; JJAS) of rainfall are well simulated by the model with two maxima viz., one over the west coast of India and other over the head Bay of Bengal region. However, the rainfall magnitude over the west-coast of India is less in the model simulation for monthly as well as in seasonal simulation. The model has shown good skill in simulation of seasonal (JJAS) mean rainfall over the Indian monsoon region. However, a little overestimation in rainfall is noted (approximately 4%) when considered the Indian monsoon region covering the land region and surrounding oceanic regions. The pattern correlation during JJAS shows highly significant correlation coefficients (CCs) over the global tropics (0.91) and Indian monsoon region (0.82). Similarly the Root Mean Square Error (RMSE) during JJAS is found to be less (1.01) over the global tropics than the Indian monsoon region (1.68). The interannual variability of forecast ensemble mean rainfall over the Indian monsoon region shows similar behaviour with that of verification rainfall variability with Correlation Coefficient of about 0.43 during the 16 years period from 1987 to 2002. The Anomaly Correlation Coefficients (ACCs) between verification and simulated rainfall during 1987–2002 over the Indian monsoon region is quite significant (more than 0.6 during some years). Overall, it can be stated that the performance of the UK Met Office’s seasonal mean simulation is reasonably good.