A posteriori adjustment of near‐term climate predictions: Accounting for the drift dependence on the initial conditions

Fučkar, Neven S.; Volpi, Danila; Guémas, Virginie; Doblas‐Reyes, F. J.

doi:10.1002/2014gl060815

Cited by 41 publications

(49 citation statements)

References 37 publications

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“…In this case a bias correction that depends on the forecast start date may be necessary (e.g. Kharin et al, 2012;Fučkar et al, 2014). The most appropriate way to achieve this, especially for regional predictions, is a research question, and several methods could be considered (e.g.…”

Section: Trendsmentioning

confidence: 99%

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

et al. 2016

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Abstract. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al

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Section: Trendsmentioning

confidence: 99%

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

et al. 2016

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“…After introduction of two statistical models for reference forecasts we assess the performance of five-member 12-month-long EC-Earth2.3 coupled climate predictions of the Arctic SIT modes in capturing the reconstructed historical SIT mode variability over the 1979-2010 period. ECEarth2.3 monthly predictions of mean SIT in the 32 selected regions in the NH defined in Fučkar et al (2016) are trend bias corrected (Kharin et al 2012;Fučkar et al 2014) to minimize their root mean square error. We use various prediction skill measures, such as accuracy, RPSS, reliability diagram and relative operating characteristic (ROC: hit rate versus false alarm rate) diagram to examine dynamical forecast quality.…”

Section: Apd Csd Catmentioning

confidence: 99%

“…Fučkar et al 2014) is classified into three Arctic SIT modes from the historical reconstruction discussed above. We apply a three-state first-order Markov chain model and climatological probability forecasts of the Arctic SIT modes as statistical benchmarks for our EC-Earth2.3 mode predictions.…”

Section: Summary Conclusion and Future Directionsmentioning

confidence: 99%

Dynamical prediction of Arctic sea ice modes of variability

et al. 2018

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This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. We use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates that the transition from the sea-ice melting season to growing season in the EC-Earth2.3 forecasts, with respect to the Markov chain model, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.

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“…Several driftadjustment procedures, which are mandatory prior to evaluation, exist but are not yet satisfactory in an operational context (Hawkins et al 2014). Difficulties are mostly related to the temporal nonstationarity of the model drifts due to errors in initial conditions (e.g., Fučkar et al 2014;Sanchez-Gomez et al 2016;Pohlmann et al 2016) and interactions with the model representation and responses to external forcings (Kharin et al 2012).…”

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confidence: 99%

Decadal Climate Variability and Predictability: Challenges and Opportunities

Cassou

Kushnir

Hawkins

et al. 2018

Bulletin of the American Meteorological Society

109

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The study of Decadal Climate Variability (DCV) and Predictability is the interdisciplinary endeavor to characterize, understand, attribute, simulate, and predict the slow, multiyear variations of climate at global (e.g., the recent slowdown of global mean temperature rise in the early 2000s) and regional (e.g., decadal modulation of hurricane activity in the Atlantic, ongoing drought in California or in the Sahel in the 1970s–80s, etc.) scales. This study remains very challenging despite decades of research, extensive progress in climate system modeling, and improvements in the availability and coverage of a wide variety of observations. Considerable obstacles in applying this knowledge to actual predictions remain. This short article is a succint review paper about DCV and predictability. Based on listed issues and priorities, it also proposes a unifying theme referred to as “drivers of teleconnectivity” as a backbone to address and structure the core DCV research challenge. This framework goes beyond a preoccupation with changes in the global mean temperature and directly addresses the regional impacts of external (natural and anthropogenic) climate forcing and internal climate interactions; it thus explicitly deals with the societal needs for region-specific climate information. Such a framework also enables the integration of efforts in a large international research community toward advancing the observation, characterization, understanding, and prediction of DCV. Recommendations to make progress are provided as part of the contribution of the CLIVAR “DCVP Research Focus” group.

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A posteriori adjustment of near‐term climate predictions: Accounting for the drift dependence on the initial conditions

Cited by 41 publications

References 37 publications

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

Dynamical prediction of Arctic sea ice modes of variability

Decadal Climate Variability and Predictability: Challenges and Opportunities

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