ENSEMBLES: A new multi‐model ensemble for seasonal‐to‐annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs

Weisheimer, Antje; Doblas‐Reyes, F. J.; Palmer, Tim; Alessandri, Andrea; Arribas, Alberto; Déqué, Michel; Keenlyside, Noel; MacVean, M. K.; Navarra, Antonio; Rogel, P.

doi:10.1029/2009gl040896

Cited by 250 publications

(245 citation statements)

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“…The atmosphere and ocean were initialized using realistic estimates of their observed states and each model was run from an ensemble of nine initial conditions. Further details on the initial condition perturbations can be found in Weisheimer et al (2009). Some recent studies (Weisheimer et al, 2009;Rajeevan et al, 2011) examined the skill of ENSEMBLES hindcasts.…”

Section: Seasonal Prediction Of the Nemrmentioning

confidence: 99%

“…Further details on the initial condition perturbations can be found in Weisheimer et al (2009). Some recent studies (Weisheimer et al, 2009;Rajeevan et al, 2011) examined the skill of ENSEMBLES hindcasts. Retrospective forecasts or hindcasts that emulate real time seasonal forecast situations for the past were performed in a co-ordinated experiment by the models.…”

Section: Seasonal Prediction Of the Nemrmentioning

confidence: 99%

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Northeast monsoon over India: variability and prediction

Rajeevan

Unnikrishnan

Bhate

et al. 2012

Meteorological Applications

178

161

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South peninsular India experiences a large portion of the annual rainfall during the northeast monsoon season (October to December). In this study, the facets of diurnal, intra-seasonal and inter-annual variability of the northeast monsoon rainfall (the NEMR) over India have been examined. The analysis of satellite derived hourly rainfall reveals that there are distinct features of diurnal variation over the land and oceans during the season. Over the land, rainfall peaks during the late afternoon/evening, while over the oceans an early morning peak is observed. The harmonic analysis of hourly data reveals that the amplitude and variance are the largest over south peninsular India. The NEMR also exhibits significant intra-seasonal variability on a 20-40 day time scale. Analysis also shows significant northward propagation of the maximum cloud zone from south of equator to the south peninsula during the season. The NEMR exhibits large inter-annual variability with the co-efficient of variation (CV) of 25%. The positive phases of ENSO and the Indian Ocean Dipole (IOD) are conducive for normal to above normal rainfall activity during the northeast monsoon. There are multi-decadal variations in the statistical relationship between ENSO and the NEMR. During the period 2001-2010 the statistical relationship between ENSO and the NEMR has significantly weakened. The analysis of seasonal rainfall hindcasts for the period 1960-2005 produced by the state-of-the-art coupled climate models, ENSEMBLES, reveals that the coupled models have very poor skill in predicting the inter-annual variability of the NEMR. This is mainly due to the inability of the ENSEMBLES models to simulate the positive relationship between ENSO and the NEMR correctly.

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Section: Seasonal Prediction Of the Nemrmentioning

confidence: 99%

Section: Seasonal Prediction Of the Nemrmentioning

confidence: 99%

Northeast monsoon over India: variability and prediction

Rajeevan

Unnikrishnan

Bhate

et al. 2012

Meteorological Applications

178

161

View full text Add to dashboard Cite

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“…The ENSEMBLES MME comprises five coupled atmosphere-ocean general circulation models developed quasi-independently in Europe and initialised using realistic estimates of the observed states [Weisheimer et al, 2009a]. Each model was run from an ensemble of nine initial conditions which results in an overall MME size of 45 members.…”

Section: Description Of Models and Experimentsmentioning

confidence: 99%

Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles

Weisheimer

Palmer

Doblas‐Reyes

2011

Geophys. Res. Lett.

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[1] 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. Citation: Weisheimer, A., T. N. Palmer, and F. J. Doblas-Reyes (2011), Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles, Geophys.

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“…[4] Seasonal precipitation forecasts from coupled GCMs are routinely issued by several centers [e.g., Saha et al, 2006;Weisheimer et al, 2009] and can be propagated through hydrological models to obtain seasonal streamflow forecasts with global coverage. This is not yet done on an operational basis, for several possible reasons.…”

Section: Introductionmentioning

confidence: 99%

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

Dijk

Peña‐Arancibia

Wood

et al. 2013

Water Resources Research

138

107

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[1] Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialized with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analyzed theoretical and actual skill in bimonthly streamflow forecasts from a global ensemble streamflow prediction (ESP) system. Forecasts were generated six times per year for 1979-2008 by an initialized hydrological model and an ensemble of 1 resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and 1 of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (<10,000 km 2 ) catchments worldwide. The results show that initial catchment conditions provide the main source of skill. Post-ESP sampling enhanced skill in equatorial South America and Southeast Asia, particularly in terms of tercile probability skill, due to the persistence and influence of the El Niño Southern Oscillation. Actual skill was on average 54% of theoretical skill but considerably more for selected regions and times of year. The realized fraction of the theoretical skill probably depended primarily on the quality of precipitation estimates. Forecast skill could be predicted as the product of theoretical skill and historic model performance. Increases in seasonal forecast skill are likely to require improvement in the observation of precipitation and initial hydrological conditions. Citation: van Dijk, A. I. J. M., J. L. Peña-Arancibia, E. F. Wood, J. Sheffield, and H. E. Beck (2013), Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide, Water Resour.

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ENSEMBLES: A new multi‐model ensemble for seasonal‐to‐annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs

Cited by 250 publications

References 14 publications

Northeast monsoon over India: variability and prediction

Northeast monsoon over India: variability and prediction

Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles

Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide

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