Decadal Climate Prediction: An Update from the Trenches

Meehl, Gerald A.; Goddard, Lisa; Boer, G. J.; Burgman, Robert; Branstator, Grant; Cassou, Christophe; Corti, Susanna; Danabasoglu, Gökhan; Doblas‐Reyes, F. J.; Hawkins, Ed; Karspeck, Alicia; Kimoto, Masahide; Kumar, Arun; Matei, Daniela; Mignot, Juliette; Msadek, Rym; Navarra, Antonio; Pohlmann, Holger; Rienecker, Michele M.; Rosati, Anthony; Schneider, Edwin K.; Smith, Doug; Sutton, Rowan; Teng, Haiyan; Oldenborgh, Geert Jan van; Vecchi, Gabriel A.; Yeager, Stephen

doi:10.1175/bams-d-12-00241.1

Cited by 494 publications

(499 citation statements)

References 140 publications

Supporting

Mentioning

470

Contrasting

Order By: Relevance

“…We will also identify major science issues and challenges for understanding and extracting the imprints of internal climate modes in observed sea level change patterns, with a hope of contributing to decadal sea level predictions, which emerge as pressing priorities in climate research today (e.g., Goddard et al 2009;Hurrell et al 2009;Meehl et al 2009Meehl et al , 2014Pohlmann et al 2009;Doblas-Reyes et al 2011;Polkova et al 2015). In Sects.…”

Section: Introductionmentioning

confidence: 99%

Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes

et al. 2016

Self Cite

View full text Add to dashboard Cite

Sea level rise (SLR) can exert significant stress on highly populated coastal societies and low-lying island countries around the world. Because of this, there is huge societal demand for improved decadal predictions and future projections of SLR, particularly on a local scale along coastlines. Regionally, sea level variations can deviate considerably from the global mean due to various geophysical processes. These include changes of ocean circulations, which partially can be attributed to natural, internal modes of variability in the complex Earth's climate system. Anthropogenic influence may also contribute to regional sea level variations. Separating the effects of natural climate modes and anthropogenic forcing, however, remains a challenge and requires identification of the imprint of specific climate modes in observed sea level change patterns. In this paper, we review our current state of knowledge about spatial patterns of sea level variability associated with natural climate modes on interannual-to-multidecadal timescales, with particular focus on decadal-to-multidecadal variability. Relevant climate modes and our current state of understanding their associated sea level patterns and driving mechanisms are elaborated separately for the Pacific, the Indian, the Atlantic, and the Arctic and Southern Oceans. We also discuss the issues, challenges and future outlooks for understanding the regional sea level patterns associated with climate modes. Effects of these internal modes have to be taken into account in order to achieve more reliable near-term predictions and future projections of regional SLR.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…The data are downloaded from the Earth System Grid Federation (ESGF) at the address https ://esgf-data.dkrz.de/proje cts/esgf-dkrz/. Information about the 11 models are summarized in Tables 1 and 2, and more details can be found in (Meehl et al 2014). …”

Section: Cmip5 Decadal Hindcast Simulationsmentioning

confidence: 99%

“…However, due to the lack of perfect initial conditions and the inability to perfectly model the climate system, there are unavoidable uncertainty and inadequacy in the model simulations (Palmer 2000;Slingo and Palmer 2011), and even the internal variability is so far not fully captured by current models (Doblas-Reyes et al 2013;Meehl et al 2014). In this situation, are current models able to reproduce the LTM that was detected from observational data, and further capture the memory part M(t)?…”

Section: Introductionmentioning

confidence: 99%

On memory and non-memory parts of surface air temperatures over China: can they be simulated by decadal hindcast experiments in CMIP5?

Xiong

Yuan

et al. 2018

Clim Dyn

View full text Add to dashboard Cite

It has been well recognized that, for most climatic records, their current states are influenced by both past conditions and current dynamical excitations. However, how to properly use this idea to improve the climate predictive skills, is still an open question. In this study, we evaluated the decadal hindcast experiments of 11 models (participating in phase 5 of the Coupled Model Intercomparison Project, CMIP5) in simulating the effects of past conditions (memory part, M(t)) and the current dynamical excitations (non-memory part, (t) ). Poor skills in simulating the memory part of surface air temperatures (SAT) are found in all the considered models. Over most regions of China, the CMIP5 models significantly overestimated the long-term memory (LTM) of SAT. While in the southwest, the LTM was significantly underestimated. After removing the biased memory part from the simulations using fractional integral statistical model (FISM), the remaining non-memory part, however, was found reasonably simulated in the multi-model means. On annual scale, there were high correlations between the simulated and the observed (t) over most regions of the country, and for most cases they had the same sign. These findings indicated that the current errors of dynamical models may be partly due to the unrealistic simulations of the impacts from the past. To improve predictive skills, a new strategy was thus suggested. As FISM is capable of extracting M(t) quantitatively, by combining FISM with dynamical models (which may produce reasonable estimations of (t) ), improved climate predictions with the effects of past conditions properly considered may become possible.

show abstract

“…It is calculated here as the difference between the mean SST anomalies in the North Atlantic and the global mean SST anomalies between 60 • S and 60 • N following the definition of Trenberth and Shea (2006). Previous studies have shown that the predictive skill for AMO can be improved by initialisation (Meehl et al 2014) The positive impact of the initialisation for the AMO index persists for the whole forecast time (Fig. 9).…”

Section: Impact On Some Modes Of Climate Variabilitymentioning

confidence: 99%

Decadal climate prediction with a refined anomaly initialisation approach

et al. 2016

Self Cite

View full text Add to dashboard Cite

An experiment initialised with this refined AI method has been compared with a full field and standard AI experiment. Results show that the use of such refinements enhances the surface temperature skill over part of the North and South Atlantic, part of the South Pacific and the Mediterranean Sea for the first forecast year. However, part of such improvement is lost in the following forecast years. For the tropical Pacific surface temperature, the full field initialised experiment performs the best. The prediction of the Arctic sea-ice volume is improved by the refined AI method for the first three forecast years and the skill of the Atlantic multidecadal oscillation is significantly increased compared to a non-initialised forecast, along the whole forecast time.

show abstract

Decadal Climate Prediction: An Update from the Trenches

Abstract: International audienc

Cited by 494 publications

References 140 publications

Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes

Spatial Patterns of Sea Level Variability Associated with Natural Internal Climate Modes

On memory and non-memory parts of surface air temperatures over China: can they be simulated by decadal hindcast experiments in CMIP5?

Decadal climate prediction with a refined anomaly initialisation approach

Contact Info

Product

Resources

About