The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction

Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M.; Kinter, James L.; Paolino, D. A.; Zhang, Qin; Dool, Huug van den; Saha, Sujata; Peña, M.; Becker, Emily; Peng, Peitao; Tripp, Patrick; Huang, Jin; DeWitt, David G.; Tippett, Michael K.; Barnston, Anthony G.; Li, Shuhua; Rosati, Anthony; Schubert, Siegfried D.; Rienecker, Michele M.; Suárez, Max J.; Li, Zhao E.; Marshak, Jelena; Lim, Yin Cheng; Tribbia, Joseph; Pegion, Kathy; Merryfield, William J.; Denis, B.; Wood, Eric F.

doi:10.1175/bams-d-12-00050.1

Cited by 836 publications

(765 citation statements)

References 32 publications

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“…2, left panel). This behavior is explained by the fact that CCSM4 shares initial conditions with CFSv2 (Kirtman et al 2014;Infanti and Kirtman 2016), which come from the Climate Forecast System Reanalysis (CFSR; Saha et al 2010).…”

Section: Methodsmentioning

confidence: 99%

“…The models are those of the North American Multi-model ensemble (NMME; Kirtman et al 2014), and include models from both operational forecast producing centers and research institutions in the U.S. and Canada. This model set contains some, but not all, of today's leading state-of-the-science models.…”

Section: Introductionmentioning

confidence: 99%

“…Realtime seasonal predictions in the same format begin in 2011 and continue to the present, as of 2017 (Kirtman et al 2014). In phase I of the NMME project, predictions of SST, 2-m temperature, precipitation and several additional fields were issued as 1-month averages, and in phase II daily prediction data also began being produced (but not in real-time).…”

Section: Introductionmentioning

confidence: 99%

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Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

et al. 2017

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the presence of calibration errors in some of the models. In particular, the amplitudes of some model predictions are too high when predictability is limited by the northern spring ENSO predictability barrier and/or when the interannual variability of the SST is near its seasonal minimum. The skill of the NMME system is compared to that of the MME from the IRI/CPC ENSO prediction plume, both for a comparable hindcast period and also for a set of real-time predictions spanning 2002-2011. Comparisons are made both between the MME predictions of each model group, and between the average of the skills of the respective individual models in each group. Acknowledging a hindcast versus real-time inconcsistency in the 2002-2012 skill comparison, the skill of the NMME is slightly higher than that of the prediction plume models in all cases. This result reflects well on the NMME system, with its large total ensemble size and opportunity for possible complementary contributions to skill.Abstract Hindcasts and real-time predictions of the eastcentral tropical Pacific sea surface temperature (SST) from the North American Multimodel Ensemble (NMME) system are verified for 1982-2015. Skill is examined using two deterministic verification measures: mean squared error skill score (MSESS) and anomaly correlation. Verification of eight individual models shows somewhat differing skills among them, with some models consistently producing more successful predictions than others. The skill levels of MME predictions are approximately the same as the two best performing individual models, and sometimes exceed both of them. A decomposition of the MSESS indicates This paper is a contribution to the special collection on the North American Multi-Model Ensemble (NMME) seasonal prediction experiment. The special collection focuses on documenting the use of the NMME system database for research ranging from predictability studies, to multi-model prediction evaluation and diagnostics, to emerging applications of climate predictability for subseasonal to seasonal predictions

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

et al. 2017

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“…Formatconsistent data and output from numerous ensemble members from eight North American GCMs are archived. (For further details about NMME models, see Kirtman et al, 2014).…”

Section: Winter Forecasts Based On El Niñomentioning

confidence: 99%

Winter 2015/16: A Turning Point in ENSO-Based Seasonal Forecasts

Cohen¹,

Pfeiffer²,

Francis³

2017

Oceanog.

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“…In this study, we mainly consider the effect of model biases and how MME may improve the simulations. MME has been applied to both coupled earth system models (Krishnamurti et al 2000;Barnston et al 2003;Palmer et al 2004;Kirtman et al 2014) and offline LSMs (Guo et al 2007), and it was found that results from MME are generally better than those from most individual models. An unexplored problem is whether a land model ensemble can improve climate simulations in coupled land-atmosphere models and how effective the land model ensemble is compared to the traditional MME across the coupled models.…”

Section: Introductionmentioning

confidence: 99%

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

Wei

Dirmeyer

Yang

et al. 2017

Theor Appl Climatol

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Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

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The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction

Abstract: The North American Multimodel Ensemble prediction experiment is described, and forecast quality and methods for accessing digital and graphical data from the model are discussed.

Cited by 836 publications

References 32 publications

Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

Winter 2015/16: A Turning Point in ENSO-Based Seasonal Forecasts

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

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