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

Zhang, Wei; Kirtman, Ben P.

doi:10.1029/2019gl085159

Cited by 31 publications

(39 citation statements)

References 81 publications

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“…CMIP GCMs underestimate the multi-decadal variability in the NAO and this appears as a lack of serial correlation compared to the observed NAO. This lack of serial correlation in CMIP GCMs is closely linked to the low signal-to-noise ratio in GCMs (Zhang and Kirtman 2019), whereby the variance of the ensemble mean (signal) in predictions of the NAO is weaker than would be expected given the correlation with observations (Scaife et al 2014;Eade et al 2014;Scaife and Smith 2018;Smith et al 2020). The CMIP 1963-1993 multimodel ensemble mean trend is very weak (0.00183 year −1 ) relative to the observed trend, and there is no consistency in the timing of maximum NAO trends in the individual GCM simulations.…”

Section: Discussionmentioning

confidence: 99%

Quantifying the rarity of extreme multi-decadal trends: how unusual was the late twentieth century trend in the North Atlantic Oscillation?

et al. 2021

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Climate trends over multiple decades are important drivers of regional climate change that need to be considered for climate resilience. Of particular importance are extreme trends that society may not be expecting and is not well adapted to. This study investigates approaches to assess the likelihood of maximum moving window trends in historical records of climate indices by making use of simulations from climate models and stochastic time series models with short- and long-range dependence. These approaches are applied to assess the unusualness of the large positive trend that occurred in the North Atlantic Oscillation (NAO) index between the 1960s to 1990s. By considering stochastic models, we show that the chance of extreme trends is determined by the variance of the trend process, which generally increases when there is more serial correlation in the index series. We find that the Coupled Model Intercomparison Project (CMIP5 + 6) historical simulations have very rarely (around 1 in 200 chance) simulated maximum trends greater than the observed maximum. Consistent with this, the NAO indices simulated by CMIP models were found to resemble white noise, with almost no serial correlation, in contrast to the observed NAO which exhibits year-to-year correlation. Stochastic model best fits to the observed NAO suggest an unlikely chance (around 1 in 20) for there to be maximum 31-year NAO trends as large as the maximum observed since 1860. This suggests that current climate models do not fully represent important aspects of the mechanism for low frequency variability of the NAO.

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Section: Discussionmentioning

confidence: 99%

Quantifying the rarity of extreme multi-decadal trends: how unusual was the late twentieth century trend in the North Atlantic Oscillation?

et al. 2021

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“…Two hypotheses stemming from hindcast experiments are that winter NAO skill is enhanced by skillful prediction of a QBO teleconnection that is too weak in models (O'Reilly et al 2019), and that transient eddy feedbacks are too weak in models . Others based on simple models suggest that the NAO predictable signal is too weak because climate models switch between NAO regimes too rapidly (Strommen and Palmer 2019), or exhibit less persistent NAO variability than is observed (Zhang and Kirtman 2019).…”

Section: Seasonal To Decadalmentioning

confidence: 99%

Current and Emerging Developments in Subseasonal to Decadal Prediction

Merryfield

Baehr

Batté

et al. 2020

Bulletin of the American Meteorological Society

161

113

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Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

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“…Consequently, the real world is more predictable than climate models suggest 10,18 and uncertainties diagnosed from raw model simulations are too large. The cause of this error is not yet known, though there are several hypotheses including weak teleconnections to the quasi-biennial oscillation 40 , lack of persistence in the NAO 41,42 and in weather regimes , unresolved ocean atmosphere interactions and weak transient eddy feedback .…”

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confidence: 99%

North Atlantic climate far more predictable than models imply

Smith

Scaife

Eade

et al. 2020

Nature

201

231

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Quantifying signals and uncertainties in climate models is essential for climate change detection, attribution, prediction and projection [1][2][3] . Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain 4 , leading to low confidence in regional projections especially for precipitation over the coming decades 5, 6 . Furthermore, model simulations with tiny differences in initial conditions suggest that uncertainties may be largely irreducible due to the chaotic nature of the climate system 7-9 . However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate predictions of the last six decades project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Economía y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Skĺodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours

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

Cited by 31 publications

References 81 publications

Quantifying the rarity of extreme multi-decadal trends: how unusual was the late twentieth century trend in the North Atlantic Oscillation?

Quantifying the rarity of extreme multi-decadal trends: how unusual was the late twentieth century trend in the North Atlantic Oscillation?

Current and Emerging Developments in Subseasonal to Decadal Prediction

North Atlantic climate far more predictable than models imply

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