Ensemble forecasting and data assimilation: two problems with the same solution?

Kalnay, Eugenia; Hunt, Brian R.; Ott, Edward; Szunyogh, Istvan

doi:10.1017/cbo9780511617652.008

Cited by 20 publications

(21 citation statements)

References 71 publications

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“…Even so, uncertainties related to initial boundary conditions, data assimilation, external forcings and model formulation all limit the skill of a single deterministic forecast. Probabilistic approaches overcome these limitations through ensemble prediction (Kalnay et al, 2006) using multiple realizations for a single forecast time and location to sample forecast uncertainty. Ensemble generation is achieved by either perturbations of initial conditions, perturbations introduced at each model integration (stochastic physics) or use of multi-model ensembles (Graham et al, 2000;Tebaldi et al, 2004;Thomson et al, 2006;Shutts et al, 2011;Weisheimer et al, 2011;Doblas-Reyes et al, 2013;Weisheimer and Palmer, 2014).…”

Section: Introductionmentioning

confidence: 99%

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Ogutu

Franssen

Supit

et al. 2016

Intl Journal of Climatology

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This study evaluates the potential use of the ECMWF System-4 seasonal forecasts (S4) for impact analysis over East Africa. For use, these forecasts should have skill and small biases. We used the 15-member ensemble of 7-month forecasts initiated every month, and tested forecast skill of precipitation (tp), near-surface air temperature (tas) and surface downwelling shortwave radiation (rsds). We validated the 30-year (1981-2010) hindcast version of S4 against the WFDEI reanalysis (WATCH Forcing Data ERA-Interim) and to independent relevant observational data sets. Probabilistic skill is assessed using anomaly correlation, ranked probability skill score (RPSS) and the relative operating curve skill score (ROCSS) at both grid cell and over six distinct homogeneous rainfall regions for the three growing seasons of East Africa (i.e. MAM, JJA and OND).S4 exhibits a wet bias in OND, a dry bias in MAM and a mix of both in JJA. Temperature biases are similar in all seasons, constant with lead-time and correlate with elevation. Biases in rsds correlate with cloud/rain patterns. Bias correction clears biases but does not affect probabilistic skills.Predictability of the three variables varies with season, location and lead-time. The choice of validating dataset plays little role in the regional patterns and magnitudes of probabilistic skill scores. The OND tp forecasts show skill over a larger area up to 3 months lead-time compared to MAM and JJA. Upper-and lower-tercile tp forecasts are 20-80% better than climatology. Temperature forecasts are skillful for at least 3 months lead-time and they are 40-100% better than climatology. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher in all lead months against the alternative datasets. The forecast system captures El-Niño Southern Oscillation (ENSO)-related anomalous years with region-dependent skill.

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Section: Introductionmentioning

confidence: 99%

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Ogutu

Franssen

Supit

et al. 2016

Intl Journal of Climatology

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“…Small perturbations of the model system lead to different variations of the models climate system [e.g., Lorenz , ]. Using its ensemble mean helps to reduce errors and increase accuracy of predictions [ Kalnay et al ., ]. This is usually done after model runs.…”

Section: Modeling Methods and Datamentioning

confidence: 99%

“…The application of an ensemble approach is essential for a decadal prediction system [ Sienz et al ., ]—in many ways. Due to nonlinear filtering of errors, the ensemble average is closer to the truth [ Kumar and Hoerling , ; Kalnay et al ., ]. Therefore, the evaluation of the accuracy of a model system with an ensemble mean is likely to be more skillful than using any of its individual members [ Eade et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Decadal climate predictions improved by ocean ensemble dispersion filtering

Kadow

Illing

Kröner

et al. 2017

J Adv Model Earth Syst

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Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere‐ocean interaction plays an important role for such climate forecasts. While short‐term weather forecasts represent an initial value problem and long‐term climate projections represent a boundary condition problem, the decadal climate prediction falls in‐between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.

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“…The weighting scheme of these methods relies on a certain distribution of the errors and other prior assumptions regarding the models; these assumptions are not necessarily valid for climate dynamics and predictions. Many variations of the Bayesian methods were applied to weather forecasting in order to establish the ensemble of models (Kalnay et al, 2006); these methods are less useful for climate predictions in which the variability between different models is larger than the internal variability of each model (Meehl et al, 2009;Hawkins and Sutton, 2009).…”

Section: E Strobach and G Bel: Climate Predictions Using Learning Amentioning

confidence: 99%

Improvement of climate predictions and reduction of their uncertainties using learning algorithms

Strobach

Bel

2015

Atmos. Chem. Phys.

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Abstract. Simulated climate dynamics, initialized with observed conditions, is expected to be synchronized, for several years, with the actual dynamics. However, the predictions of climate models are not sufficiently accurate. Moreover, there is a large variance between simulations initialized at different times and between different models. One way to improve climate predictions and to reduce the associated uncertainties is to use an ensemble of climate model predictions, weighted according to their past performances. Here, we show that skillful predictions, for a decadal time scale, of the 2 m temperature can be achieved by applying a sequential learning algorithm to an ensemble of decadal climate model simulations. The predictions generated by the learning algorithm are shown to be better than those of each of the models in the ensemble, the better performing simple average and a reference climatology. In addition, the uncertainties associated with the predictions are shown to be reduced relative to those derived from an equally weighted ensemble of bias-corrected predictions. The results show that learning algorithms can help to better assess future climate dynamics.

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Ensemble forecasting and data assimilation: two problems with the same solution?

Cited by 20 publications

References 71 publications

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Skill of ECMWF system‐4 ensemble seasonal climate forecasts for East Africa

Decadal climate predictions improved by ocean ensemble dispersion filtering

Improvement of climate predictions and reduction of their uncertainties using learning algorithms

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