Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts
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...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
2009
|
| _version_ | 1797097515412094976 |
|---|---|
| author | Doblas-Reyes, F Weisheimer, A Deque, M Keenlyside, N McVean, M Murphy, J Rogel, P Smith, D Palmer, T |
| author_facet | Doblas-Reyes, F Weisheimer, A Deque, M Keenlyside, N McVean, M Murphy, J Rogel, P Smith, D Palmer, T |
| author_sort | Doblas-Reyes, F |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T04:56:38Z |
| format | Journal article |
| id | oxford-uuid:d6d49ed8-5c0d-4f31-94d8-8d8888a49dd7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:56:38Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:d6d49ed8-5c0d-4f31-94d8-8d8888a49dd72022-03-27T08:36:28ZAddressing model uncertainty in seasonal and annual dynamical ensemble forecastsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:d6d49ed8-5c0d-4f31-94d8-8d8888a49dd7EnglishSymplectic Elements at Oxford2009Doblas-Reyes, FWeisheimer, ADeque, MKeenlyside, NMcVean, MMurphy, JRogel, PSmith, DPalmer, TThe 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. |
| spellingShingle | Doblas-Reyes, F Weisheimer, A Deque, M Keenlyside, N McVean, M Murphy, J Rogel, P Smith, D Palmer, T Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts |
| title | Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts |
| title_full | Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts |
| title_fullStr | Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts |
| title_full_unstemmed | Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts |
| title_short | Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts |
| title_sort | addressing model uncertainty in seasonal and annual dynamical ensemble forecasts |
| work_keys_str_mv | AT doblasreyesf addressingmodeluncertaintyinseasonalandannualdynamicalensembleforecasts AT weisheimera addressingmodeluncertaintyinseasonalandannualdynamicalensembleforecasts AT dequem addressingmodeluncertaintyinseasonalandannualdynamicalensembleforecasts AT keenlysiden addressingmodeluncertaintyinseasonalandannualdynamicalensembleforecasts AT mcveanm addressingmodeluncertaintyinseasonalandannualdynamicalensembleforecasts AT murphyj addressingmodeluncertaintyinseasonalandannualdynamicalensembleforecasts AT rogelp addressingmodeluncertaintyinseasonalandannualdynamicalensembleforecasts AT smithd addressingmodeluncertaintyinseasonalandannualdynamicalensembleforecasts AT palmert addressingmodeluncertaintyinseasonalandannualdynamicalensembleforecasts |