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

Graham, Richard; Gordon, Margaret; McLean, P.; Ineson, Sarah; Huddleston, M. R.; Davey, Michael; Brookshaw, Anca; Barnes, R. T. H.

doi:10.3402/tellusa.v57i3.14666

Cited by 38 publications

(21 citation statements)

References 19 publications

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“…It is difficult to compare the operational predictive skill of IRI's forecast system with that of other systems such as a single-tiered dynamical system [e.g., Palmer et al 2004 (and references therein); Graham et al 2005;Saha et al 2006;Kug et al 2008;Wang et al 2008] or a purely empirical system (Van den Dool 2007). Improvement in ENSO prediction has obvious value toward improvement of climate prediction, and the potential predictability of ENSO is an open question but believed not fully realized (Chen and Cane 2008).…”

Section: Discussionmentioning

confidence: 99%

Verification of the First 11 Years of IRI’s Seasonal Climate Forecasts

Barnston

Mason

et al. 2010

Journal of Applied Meteorology and Climatology

140

118

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This paper examines the quality of seasonal probabilistic forecasts of near-global temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from late 1997 through 2008, using mainly a two-tiered multimodel dynamical prediction system. Skill levels, while modest when globally averaged, depend markedly on season and location and average higher in the tropics than extratropics. To first order, seasons and regions of useful skill correspond to known direct effects as well as remote teleconnections from anomalies of tropical sea surface temperature in the Pacific Ocean (e.g., ENSO related) and in other tropical basins. This result is consistent with previous skill assessments by IRI and others and suggests skill levels beneficial to informed clients making climate risk management decisions for specific applications. Skill levels for temperature are generally higher, and less seasonally and regionally dependent, than those for precipitation, partly because of correct forecasts of enhanced probabilities for above-normal temperatures associated with warming trends. However, underforecasting of above-normal temperatures suggests that the dynamical forecast system could be improved through inclusion of time-varying greenhouse gas concentrations. Skills of the objective multimodel probability forecasts, used as the primary basis for the final forecaster-modified issued forecasts, are comparable to those of the final forecasts, but their probabilistic reliability is somewhat weaker. Automated recalibration of the multimodel output should permit improvements to their reliability, allowing them to be issued as is. IRI is currently developing single-tier prediction components.

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Section: Discussionmentioning

confidence: 99%

Verification of the First 11 Years of IRI’s Seasonal Climate Forecasts

Barnston

Mason

et al. 2010

Journal of Applied Meteorology and Climatology

140

118

View full text Add to dashboard Cite

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“…The first type consisted of ''potential predictability'' or ''reference'' runs, obtained by driving the malaria model with the correct ERA-40 data for both spinup and the duration of the forecast period. The second consisted of ''best-guess'' runs (referred to henceforth as control runs), obtained using a similar methodology to ensemble streamflow prediction (ESP) used in operational hydrology to predict likely future flows (Carpenter and Georgakakos 2004;Wood et al 2005), and analogous to the ''persistence'' forecasts often used as a baseline in seasonal forecasting-where a model is driven by observed conditions up to the forecast origin and then constant conditions such as persisted SST anomalies for the forecast period (e.g., Graham et al 2005). Here, the malaria model was driven with the correct spinup data for a given year, followed by data for the forecast period taken in turn from ERA-40 for each of the other 19 (wrong) yr.…”

Section: A Production Of Malaria Reforecastsmentioning

confidence: 99%

Application and Validation of a Seasonal Ensemble Prediction System Using a Dynamic Malaria Model

Jones

Morse

2010

Journal of Climate

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Seasonal multimodel forecasts from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project are used to drive a malaria model and create reforecasts of malaria incidence for Botswana, in southern Africa, in a unique integration of a fully dynamic, process-based malaria model with an ensemble forecasting system. The forecasts are verified against a 20-yr malaria index and compared against reference simulations obtained by driving the malaria model with data from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40). Performance assessment reveals skill in the DEMETER-driven malaria forecasts for prediction of low (below the lower tercile), above-average (above the median), and high (above the upper tercile) malaria events, with the best results obtained for low malaria events [relative operating characteristics (ROC) area 5 0.84, 95% confidence interval 5 0.63-1.0]. For high malaria events, the DEMETER-driven malaria forecasts are skillful, but the forecasting system performs poorly for those years that it predicts the highest probabilities of a high malaria event. Potential economic value analysis demonstrates the potential value for the DEMETER-driven malaria forecasts over a wide range of user cost-loss ratios, which is primarily due to the ability of the system to save on the cost of action in low malaria years.

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“…However, numerical models are complex and required high computing power for a full ensemble running. The choice of whether to use a numerical or statistical model for seasonal prediction ultimately depends on the focus because the prediction skill strongly depends on the location and the season (Goddard et al 2001;Anderson et al 2003;Graham et al 2005;Jin et al 2008) Fig. 14 a Time series of 1,000 hPa AT anomalies for January, averaged over the central pacific (|r|>0.70, Fig.…”

Section: Comparison With Coupled Gcm Prediction Skillsmentioning

confidence: 99%

“…ENSO is the most successfully predicted large-scale phenomenon on seasonal to inter-annual scales, and the ENSO prediction is thus the starting point for assessment of seasonal prediction models (Goddard et al 2001;Graham et al 2005). ENSO prediction skills for the most characteristics coupled GCMs are shown in Table 5.…”

Section: Comparison With Coupled Gcm Prediction Skillsmentioning

confidence: 99%