North Atlantic observations sharpen meridional overturning projections

Olson, Roman; An, Soon‐Il; Fan, Yanan; Evans, Jason P.; Caesar, Levke

doi:10.1007/s00382-017-3867-7

Cited by 22 publications

(22 citation statements)

References 64 publications

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“…The difference between the best model and the pseudo-observed trends is then the unscaled error of the jth model. Thus, we obtain samples for unscaled discrepancy εD directly from the differences between each model's trend and the next-closest model trend (see [9] for details). We acknowledge that this parameterization is simplified; model error is an emergent research topic [37].…”

Section: Weighting the Trend Submodelsmentioning

confidence: 99%

“…6). This factor is a new addition to the model presented in previous work [9]. f is a parameter that scales εD to account for potential overconfidence.…”

Section: Weighting the Trend Submodelsmentioning

confidence: 99%

“…Future model projections are implemented largely following previous work [9]. Once the weights are obtained, the statistical model for system change between projection reference and projection periods follows the BMA formula [20,21]:…”

Section: Future Projectionsmentioning

confidence: 99%

“…A common forecasting problem is one of probabilistic multi-model forecasts of a stochastic dynamical system [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Sometimes, when a collection of complex dynamical models is used to provide multi-model forecasts, these forecasts are weighted according to model performance compared to observations [1,5,10,[19][20][21][22][23]. The Bayesian approach to this problem assumes that associated with k dynamical models are k competing statistical models Mi for vector of observations y.…”

Section: Introductionmentioning

confidence: 99%

“…In ensemble modelling, models are usually judged on how well they represent the mean state of the system, its trend, or spatio-temporal fields [1,3,6,14,22,23]. However, it is increasingly being recognized that variability is of utmost importance for future prediction.…”

Section: Introductionmentioning

confidence: 99%

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Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series

Olson

Fan

et al. 2019

PLoS ONE

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We present a novel quasi-Bayesian method to weight multiple dynamical models by their skill at capturing both potentially non-linear trends and first-order autocorrelated variability of the underlying process, and to make weighted probabilistic projections. We validate the method using a suite of one-at-a-time cross-validation experiments involving Atlantic meridional overturning circulation (AMOC), its temperature-based index, as well as Korean summer mean maximum temperature. In these experiments the method tends to exhibit superior skill over a trend-only Bayesian model averaging weighting method in terms of weight assignment and probabilistic forecasts. Specifically, mean credible interval width, and mean absolute error of the projections tend to improve. We apply the method to a problem of projecting summer mean maximum temperature change over Korea by the end of the 21 st century using a multi-model ensemble. Compared to the trend-only method, the new method appreciably sharpens the probability distribution function (pdf) and increases future most likely, median, and mean warming in Korea. The method is flexible, with a potential to improve forecasts in geosciences and other fields.

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Section: Weighting the Trend Submodelsmentioning

confidence: 99%

“…6). This factor is a new addition to the model presented in previous work [9]. f is a parameter that scales εD to account for potential overconfidence.…”

Section: Weighting the Trend Submodelsmentioning

confidence: 99%

Section: Future Projectionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series

Olson

Fan

et al. 2019

PLoS ONE

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Timescale‐dependent AMOC–AMO relationship in an earth system model of intermediate complexity

Kim

2020

Intl Journal of Climatology

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The relationship between Atlantic multi-decadal oscillation (AMO) and Atlantic meridional overturning circulation (AMOC) is examined with respect to two (inter-and multi-decadal) different timescales using a long-term unforced simulation of an earth system model of intermediate complexity. In the inter-decadal timescale, the AMO and the AMOC establish a self-sustaining oscillatory mode; the AMOC induces the positive AMO through meridional heat transport (MHT), but with the time delay of approximately 7 years as the AMOC anomalies propagate southward over time within the Atlantic basin. After then, the AMO reduces the density in the main sinking region and brings the negative phase of the AMOC, which results in the rest half of the cycle. On the other hand, in the multi-decadal timescale, the AMO and the AMOC are almost in phase because the AMOC is spatially stationary, resulting in a pan-Atlantic surface warming. In addition, the Arctic-originated density fluctuations are required for the multi-decadal AMOC to switch its phase. The results obtained in this study suggest that timescale dependency should be considered when investigating the AMOC-AMO relationship.

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Improved probabilistic twenty-first century projections of sea surface temperature over East Asian marginal seas by considering uncertainty owing to model error and internal variability

Shin

Olson

2019

Clim Dyn

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North Atlantic observations sharpen meridional overturning projections

Cited by 22 publications

References 64 publications

Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series

Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series

Timescale‐dependent AMOC–AMO relationship in an earth system model of intermediate complexity

Improved probabilistic twenty-first century projections of sea surface temperature over East Asian marginal seas by considering uncertainty owing to model error and internal variability

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