Decadal Prediction

Meehl, Gerald A.; Goddard, Lisa; Murphy, James M.; Stouffer, Ronald J.; Boer, G. J.; Danabasoglu, Gökhan; Dixon, Keith W.; Giorgetta, Marco; Greene, Arthur M.; Hawkins, Ed; Hegerl, Gabriele C.; Karoly, David J.; Keenlyside, Noel; Kimoto, Masahide; Kirtman, Ben P.; Navarra, Antonio; Pulwarty, Roger S.; Smith, Doug; Stammer, Detlef; Stockdale, Tim

doi:10.1175/2009bams2778.1

Cited by 676 publications

(269 citation statements)

References 81 publications

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“…For 6-9 years, the predictive skill does not change much compared to 2-5 years ( Figure S2 in Text S1). The relatively long prediction skill appears over the region where the externally forced component and low-frequency climate variability is dominant [Keenlyside et al, 2008;Meehl et al, 2009;Pohlmann et al, 2009;Chikamoto et al, 2012, Mochizuki et al, 2012Oldenborgh et al, 2012]. High prediction skill also occurs in the tropical Atlantic SST which is an important factor in climate variability in that region and beyond [Keenlyside et al, 2008].…”

Section: Global Surface Temperaturementioning

confidence: 99%

“…Not only does natural variability have a large-amplitude influence over broad regions of the globe, it is an integral component of climate variability that modulates low-frequency climate phenomena as well as extreme climate events such as tropical cyclone activity. On decadal timescales, some aspects of internal climate variability may be predictable [Collins and Allen, 2002;Smith et al, 2007;Keenlyside et al, 2008;Meehl et al, 2009Meehl et al, , 2010Pohlmann et al, 2009;Mochizuki et al, 2012]. However, the actual prediction skill of natural climate variability on decadal timescales using various current climate models has received little attention [van Oldenborgh et al, 2012].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of short‐term climate change prediction in multi‐model CMIP5 decadal hindcasts

Kim

Webster

2012

Geophysical Research Letters

194

186

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[1] This study assesses the CMIP5 decadal hindcast/ forecast simulations of seven state-of-the-art oceanatmosphere coupled models. Each decadal prediction consists of simulations over a 10 year period each of which are initialized every five years from climate states of 1960/1961 to 2005/2006. Most of the models overestimate trends, whereby the models predict less warming or even cooling in the earlier decades compared to observations and too much warming in recent decades. All models show high prediction skill for surface temperature over the Indian, North Atlantic and western Pacific Oceans where the externally forced component and low-frequency climate variability is dominant. However, low prediction skill is found over the equatorial and North Pacific Ocean. The Atlantic Multidecadal Oscillation (AMO) index is predicted in most of the models with significant skill, while the Pacific Decadal Oscillation (PDO) index shows relatively low predictive skill. The multi-model ensemble has in general better-forecast quality than the single-model systems for global mean surface temperature, AMO and PDO.

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Section: Global Surface Temperaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of short‐term climate change prediction in multi‐model CMIP5 decadal hindcasts

Kim

Webster

2012

Geophysical Research Letters

194

186

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“…The goal of seasonal-todecadal prediction is generally to provide a statistical summary, or probability distributions, of conditions over the coming months and years given some knowledge of the current climate state, or phase of climate variability. The feasibility of predictions on these timescales arises from components of the climate system that evolve at a slower rate than the atmosphere, such as the ocean and land surface, and the interactions between them (Palmer and Hagedorn 2006; Meehl et al 2009;Boer 2011). Sources of potential prediction skill include both externally forced low-frequency variability due to anthropogenic factors (such as greenhouse gas and aerosol concentrations and land use changes), as well as natural variations in solar activity and volcanic aerosol, and low-frequency variations within the climate system (such as large-scale modes of variability in the atmosphere and oceans).…”

Section: Introduction To Climate Forecastingmentioning

confidence: 99%

Seasonal-to-Decadal Climate Forecasting

Suckling

2018

Weather &Amp; Climate Services for the Energy Industry

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Forecasting climate over the near-term, from a season to decades ahead, has the potential to inform decision-making within the energy sector in a number of ways: from energy trading to scheduling maintenance and resource management. Recent advances in forecasting at these timescales have led to promising levels of skill in predicting the large-scale drivers of seasonal and multi-annual climate variability as well as the consequent local climate impacts of relevance for the energy sector (e.g. seasonal temperatures and wind speeds). This chapter discusses the unique aspects of near-term prediction, how it differs from the task of weather prediction and long-term climate projections, the sources of predictability on these timescales, as well as some of the current climate forecasting tools and products aiming to provide value to the energy sector.

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“…A new group of global climate simulations, referred to as the decadal experiments, was introduced in the Coupled Model Intercomparison Project (CMIP5) multi-model ensemble (Taylor et al, 2012;Meehl et al, 2009). The decadal climate predictions differ from the long-term climate projections in their duration, aims and meaningful output.…”

Section: Introductionmentioning

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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Decadal Prediction

Abstract: A new field called "decadal prediction" will use initialized climate models to produce time-evolving predictions of regional climate that will bridge ENSO forecasting and future climate change projections.

Cited by 676 publications

References 81 publications

Evaluation of short‐term climate change prediction in multi‐model CMIP5 decadal hindcasts

Evaluation of short‐term climate change prediction in multi‐model CMIP5 decadal hindcasts

Seasonal-to-Decadal Climate Forecasting

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

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