Robust skill of decadal climate predictions

Smith, Doug; Eade, Rosie; Scaife, Adam A.; Caron, Louis-Philippe; Danabasoglu, Gökhan; DelSole, Timothy; Delworth, T. L.; Doblas‐Reyes, Francisco J.; Dunstone, Nick; Hermanson, Leon; Kharin, Viatcheslav; Kimoto, Masahide; Merryfield, William J.; Mochizuki, Takashi; Müller, Wolfgang A.; Pohlmann, Holger; Yeager, Stephen; Yang, Xiaosong

doi:10.1038/s41612-019-0071-y

Cited by 194 publications

(262 citation statements)

References 90 publications

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“…There is a significant correlation between the potential predictability variance fraction and the ratio of the persistence and noise variance, with correlation coefficients of 0.94 and 0.42 for seasonal and annual NAO predictability, respectively. This high correlation, especially for seasonal NAO index, indicates that the signal‐to‐noise paradox may also exist at seasonal‐to‐annual and longer timescales (e.g., Smith et al, ), as it is notable that the NAO predictability for the observational estimates presents higher values than most of the CMIP5 model simulations. Similar results are also found in other observational datasets such as the ERA20C data.…”

Section: Application To Nao Indexmentioning

confidence: 96%

“…Uncertainty in the initial condition will evolve with time, lead to uncertainty of the forecast, and inherently limit the prediction skill (Shukla, 1998;Slingo & Palmer, 2011;Zhang & Kirtman, 2019). The idea of ensemble forecasting from different initial conditions, therefore, has been applied to the operational systems for numerical weather prediction (e.g., Buizza et al, 1999;Hamill et al, 2013;Murphy, 1988) and subsequently seasonal-to-decadal climate prediction (e.g., Kirtman et al, 2014;MacLachlan et al, 2015;Smith et al, 2019). Ensemble forecasting can quantify the uncertainty due to imperfect initial states and model formulations and has become routinely used in practice.…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Signal‐to‐Noise Paradox with a Simple Markov Model

Zhang

Kirtman

2019

Geophysical Research Letters

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There is a growing list of examples for the existence of the signal‐to‐noise paradox, where in the ensemble‐based climate prediction, the model ensemble mean forecast generally shows higher correlations with observations than with individual ensemble members. This seems to lead to a paradox that the model makes better predictions for the real world than predicting itself. Here we introduce a Markov model to represent the ensemble forecasts and reproduce the signal‐to‐noise paradox, which we argue is primarily dependent on the magnitude of the persistence and noise variance between the models and the corresponding observations. The monthly North Atlantic Oscillation indices based on uninitialized historical simulations of 40 CMIP5 models have been analyzed, suggesting that the signal‐to‐noise paradox is common in currently available coupled models, and the paradox is not due to problems with initialization processes used in the seasonal‐to‐decadal predictions in previous studies and is instead a general model problem.

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Section: Application To Nao Indexmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Understanding the Signal‐to‐Noise Paradox with a Simple Markov Model

Zhang

Kirtman

2019

Geophysical Research Letters

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“…It is the most commonly used skill measure to assess the contribution of the internally generated climate variability component to the multiannual predictive skill (Siegert et al 2017). However, it has been pointed out by some studies (Siegert et al 2017, Smith et al 2019 that using correlation difference for two highly correlated hindcasts underestimates the impact of initialization. Therefore, in order to determine whether initialization adds any additional information compared to the uninitialized forecast in the areas where both INIT and NoINIT return high correlation values, we apply the residual correlation methodology recently suggested by Smith et al (2019).…”

Section: Forecast Quality Assessmentmentioning

confidence: 99%

“…These decadal predictions are initialized with observation-based data and then run for a decade under the influence of contemporaneous changing external forcings, similarly to climate projections. The evolution of the climate system in this case is impacted by both internally generated and slowly varying natural variability and externally forced components (Meehl et al 2009, Doblas-Reyes et al 2013a, Meehl et al 2014, Smith et al 2019. A clear benefit of this new source of information is the potential to provide reliable and robust climate outlooks on a multi-annual timescale compared to long-term climate projections or climatological forecasts , Hewitt et al 2017.…”

Section: Introductionmentioning

confidence: 99%

Multi-year prediction of European summer drought conditions for the agricultural sector

Solaraju-Murali

Caron

González-Reviriego

et al. 2019

Environ. Res. Lett.

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Decadal climate prediction, where climate models are initialized with the contemporaneous state of the Earth system and run for a decade into the future, represents a new source of near-term climate information to better inform decisions and policies across key climate-sensitive sectors. This paper illustrates the potential usefulness of such predictions for building a climate service for agricultural needs. In particular, we assess the forecast quality of multi-model climate predictions in estimating two user-relevant drought indices, Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI), at multi-annual timescales during European summer. We obtain high skill for predicting five-year average (forecast years 1-5) SPEI across Southern Europe, while for the same forecast period SPI exhibits high and significant skill over Scandinavia and its surrounding regions. In addition, an assessment of the added value of initialized decadal climate information with respect to standard uninitialized climate projections is presented. The model initialization improves the forecast skill over Central Europe, the Balkan region and Southern Scandinavia. Most of the increased skill found with initialization seems to be due to the climate forecast systems ability to improve the extended summer precipitation and potential evapotranspiration forecast, as well as their ability to adequately represent the observed effects of these climate variables on the drought indices.

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“…Recently, studies have shown that several climatic parameters are predictable up to 10 years into the future (e.g., Boer et al, 2016;Marotzke et al, 2016;Yeager & Robson, 2017). High-impact events like tropical (Dunstone et al, 2011) and extratropical (Schuster et al, 2019) storms, Sahel summer rainfall (Sheen et al, 2017), or Eurasian precipitation and sea level pressure (SLP) (Smith et al, 2019) were shown to be predictable on this time scale. Seasonal predictions of temperature extremes were shown to be skillful (e.g., Eade et al, 2012), as well as decadal predictions of warm summer temperature extremes-skill in the latter, however, was attributed to the global warming trend in the analyzed data (Hanlon et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Decadal Predictions of the Probability of Occurrence for Warm Summer Temperature Extremes

Borchert

Pohlmann

Baehr

et al. 2019

Geophysical Research Letters

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We use a decadal prediction system with the Coupled Model Intercomparison Project Phase 6 version of the coupled Max Planck Institute Earth System Model to predict the probability of occurrence for extremely warm summers in the Northern Hemisphere. An assimilation run with Max Planck Institute Earth System Model shows a robust response of summer temperature extremes in northern Europe and northeast Asia to North Atlantic sea surface temperature via a circumglobal Rossby wavetrain. When the North Atlantic is warm, warm summer temperature extremes occur with a probability of 20% and 24% in northern Europe and northeast Asia, respectively. In a cold North Atlantic phase, these probabilities are 0% and 8%. A similar difference in probability of occurrence is found in the initialized climate predictions. Consequently, the likelihood of a warm summer temperature extreme occurring in the examined regions in the next 10 years can be inferred from predictions of North Atlantic temperature. Plain Language Summary Extremely warm summers can have a substantial impact onsociety. Trustworthy predictions of such events several years ahead could help in anticipating the extremes and managing their impacts. In this study, we show that the probability with which a warm summer temperature extreme occurs in northern Europe and northeast Asia can be linked to the surface temperature of the North Atlantic ocean. We further show that North Atlantic ocean surface temperature and the connection between ocean temperature and extreme summer temperature can be predicted. As a result, the probability for extremely warm summers to occur in northern Europe and northeast Asia in the next 10 years can be predicted.

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Robust skill of decadal climate predictions

Cited by 194 publications

References 90 publications

Understanding the Signal‐to‐Noise Paradox with a Simple Markov Model

Understanding the Signal‐to‐Noise Paradox with a Simple Markov Model

Multi-year prediction of European summer drought conditions for the agricultural sector

Decadal Predictions of the Probability of Occurrence for Warm Summer Temperature Extremes

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