Skillful predictions of decadal trends in global mean surface temperature

Fyfe, John C.; Merryfield, William J.; Kharin, Viatcheslav; Boer, G. J.; Lee, W.-S.; Salzen, Knut von

doi:10.1029/2011gl049508

Cited by 41 publications

(31 citation statements)

References 12 publications

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“…Climate predictability arises from both externally forced and internally generated variability (Doblas-Reyes et al 2013;Fyfe et al 2011;Meehl et al 2014). Thus, the variance of climate variables related to external forcings and internal oscillations determines the theoretical limit of predictive skill.…”

Section: Introductionmentioning

confidence: 99%

On climate prediction: how much can we expect from climate memory?

et al. 2018

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Slowing variability in climate system is an important source of climate predictability. However, it is still challenging for current dynamical models to fully capture the variability as well as its impacts on future climate. In this study, instead of simulating the internal multi-scale oscillations in dynamical models, we discussed the effects of internal variability in terms of climate memory. By decomposing climate state x(t) at a certain time point t into memory part M(t) and non-memory part (t) , climate memory effects from the past 30 years on climate prediction are quantified. For variables with strong climate memory, high variance (over 20% ) in x(t) is explained by the memory part M(t), and the effects of climate memory are nonnegligible for most climate variables, but the precipitation. Regarding of multi-steps climate prediction, a power law decay of the explained variance was found, indicating long-lasting climate memory effects. The explained variances by climate memory can remain to be higher than 10% for more than 10 time steps. Accordingly, past climate conditions can affect both short (monthly) and long-term (interannual, decadal, or even multidecadal) climate predictions. With the memory part M(t) precisely calculated from Fractional Integral Statistical Model, one only needs to focus on the non-memory part (t) , which is an important quantity that determines climate predictive skills.

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Section: Introductionmentioning

confidence: 99%

On climate prediction: how much can we expect from climate memory?

et al. 2018

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“…The decadal predictability and prediction skill of individual models have been analyzed separately for multi-year prediction horizons over different time periods and regions [Pohlmann et al, 2009;Fyfe et al, 2011;Chikamoto et al, 2012;Mochizuki et al, 2012]. However, the CMIP5 decadal predictions from different models have not been evaluated and compared using the same evaluation matrix.…”

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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[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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“…Since then, a number of pioneering modeling studies have been carried out, some of them with a focus on global near-surface temperature (Smith et al 2007, DoblasReyes et al 2011, Fyfe et al 2011, Matei et al 2012, others with a more regional focus such as the North Pacific (Mochizuki et al 2010) and the North Atlantic (Keenlyside et al 2008, Pohlmann et al 2009, and a few focusing on variables other than temperature, such as Atlantic hurricane activity ).…”

Section: Introductionmentioning

confidence: 99%