Multimodel Ensemble Forecasts for Weather and Seasonal Climate

Krishnamurti, T. N.; Kishtawal, C. M.; Zhang, Zhan; LaRow, Timothy E.; Bachiochi, David; Williford, Eric; Gadgil, Sulochana; Surendran, Sajani

doi:10.1175/1520-0442(2000)013<4196:meffwa>2.0.co;2

Cited by 578 publications

(375 citation statements)

References 34 publications

Supporting

Mentioning

359

Contrasting

Unclassified

Order By: Relevance

“…This result is to be expected as improvement in skill with larger ensemble sizes has been demonstrated (e.g. Krishnamurti et al, 2000). The area-averaged values of Figure 4 show that the ECHAM4.5-RegCM3 system is marginally outscored by the small-ensemble raw GCM and MOS simulations (the nested system has lower correlation values and higher RMSE values than the other two systems).…”

Section: Simulation Skill Levels For the 10-year Periodsupporting

confidence: 54%

See 1 more Smart Citation

Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective

Landman

Kgatuke

Mbedzi

et al. 2008

Intl Journal of Climatology

View full text Add to dashboard Cite

ABSTRACT:The ability of advanced state-of-the-art methods of downscaling large-scale climate predictions to regional and local scale as seasonal rainfall forecasting tools for South Africa is assessed. Various downscaling techniques and raw general circulation model (GCM) output are compared to one another over 10 December-January-February (DJF) seasons from 1991/1992 to 2000/2001 and also to a baseline prediction technique that uses only global sea-surface temperature (SST) anomalies as predictors. The various downscaling techniques described in this study include both an empirical technique called model output statistics (MOS) and a dynamical technique where a finer resolution regional climate model (RCM) is nested into the large-scale fields of a coarser GCM. The study addresses the performance of a number of simulation systems (no forecast lead-time) of varying complexity. These systems' performance is tested for both homogeneous regions and for 963 stations over South Africa, and compared with each other over the 10-year test period. For the most part, the simulations method outscores the baseline method that uses SST anomalies to simulate rainfall, therefore providing evidence that current approaches in seasonal forecasting are outscoring earlier ones. Current operational forecasting approaches involve the use of GCMs, which are considered to be the main tool whereby seasonal forecasting efforts will improve in the future. Advantages in statistically post-processing output from GCMs as well as output from RCMs are demonstrated. Evidence is provided that skill should further improve with an increased number of ensemble members. The demonstrated importance of statistical models in operation capacities is a major contribution to the science of seasonal forecasting. Although RCMs are preferable due to physical consistency, statistical models are still providing similar or even better skill and should still be applied.

show abstract

Section: Simulation Skill Levels For the 10-year Periodsupporting

confidence: 54%

“…Krishnamurti et al, 2000). As raw GCM and GCM-MOS skill improves with an increased number of ensemble members, it can also be expected that the GCM-RCM skill will improve.…”

Section: Discussionmentioning

confidence: 99%

Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective

Landman

Kgatuke

Mbedzi

et al. 2008

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…The superensemble approach is a recent contribution to the general area of weather and climate prediction developed at FSU (Krishnamurti et al, 1999;Krishnamurti et al, 2000a). A variant of the conventional superensemble formulation, the synthetic superensemble (FSUSSE), was created to improve the skill in seasonal climate forecasts Krishnamurti et al, 2005).…”

Section: Florida State University Synthetic Superensemble (Fsusse) Mementioning

confidence: 99%

“…This process was necessary since the data length, i.e. numbers of forecasts, were still quite small for the optimal development of a training phase as discussed in Krishnamurti et al (2000a). In Figure 5, the forecasts produced by the FSUSSE algorithm show lower RMS errors (higher skill) than those of the multi-model ensemble mean and the models comprising the ensemble for the overall average of all years.…”

Section: Precipitation Forecast Skill Of the Fsussementioning

confidence: 99%

Weather and seasonal climate prediction for South America using a multi-model superensemble

2005

Self Cite

View full text Add to dashboard Cite

This work examines the feasibility of weather and seasonal climate predictions for South America using the multi-model synthetic superensemble approach for climate, and the multi-model conventional superensemble approach for numerical weather prediction, both developed at Florida State University (FSU). The effect on seasonal climate forecasts of the number of models used in the synthetic superensemble is investigated. It is shown that the synthetic superensemble approach for climate and the conventional superensemble approach for numerical weather prediction can reduce the errors over South America in seasonal climate prediction and numerical weather prediction.For climate prediction, a suite of 13 models is used. The forecast lead-time is 1 month for the climate forecasts, which consist of precipitation and surface temperature forecasts. The multi-model ensemble is comprised of four versions of the FSU-Coupled Ocean-Atmosphere Model, seven models from the Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER), a version of the Community Climate Model (CCM3), and a version of the predictive Ocean Atmosphere Model for Australia (POAMA). The results show that conditions over South America are appropriately simulated by the Florida State University Synthetic Superensemble (FSUSSE) in comparison to observations and that the skill of this approach increases with the use of additional models in the ensemble. When compared to observations, the forecasts are generally better than those from both a single climate model and the multi-model ensemble mean, for the variables tested in this study.For numerical weather prediction, the conventional Florida State University Superensemble (FSUSE) is used to predict the mass and motion fields over South America. Predictions of mean sea level pressure, 500 hPa geopotential height, and 850 hPa wind are made with a multi-model superensemble comprised of six global models for the period January, February, and December of 2000. The six global models are from the following forecast centers: FSU, Bureau of Meteorology Research Center (BMRC), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), Naval Research Laboratory (NRL), and Recherche en Prevision Numerique (RPN). Predictions of precipitation are made for the period January, February, and December of 2001 with a 'multi-analysis-multi-model' superensemble where, in addition to the six forecast models just mentioned, five additional versions of the FSU model are used in the ensemble, each with a different initialization (analysis) based on different physical initialization procedures. On the basis of observations, the results show that the FSUSE provides the best forecasts of the mass and motion field variables to forecast day 5, when compared to both the models comprising the ensemble and the multi-model ensemble mean during the wet season of December-February over South America. Individual case studies show that the FSUSE provides excellent ...

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Multimodel Ensemble Forecasts for Weather and Seasonal Climate

Cited by 578 publications

References 34 publications

Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective

Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective

Weather and seasonal climate prediction for South America using a multi-model superensemble

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

Contact Info

Product

Resources

About