A verification framework for interannual-to-decadal predictions experiments

Goddard, Lisa; Kumar, Arun; Solomon, Amy; Smith, Douglas M.; Boer, G. J.; Gonzalez, Paula; Kharin, Viatcheslav; Merryfield, William J.; Deser, Clara; Mason, Simon J.; Kirtman, Ben P.; Msadek, Rym; Sutton, Rowan; Hawkins, Ed; Fricker, Thomas E.; Hegerl, Gabriele C.; Ferro, Christopher A. T.; Stephenson, David B.; Meehl, Gerald A.; Stockdale, Tim; Burgman, Robert; Greene, Arthur M.; Kushnir, Yochanan; Newman, Matthew; Carton, James A.; Fukumori, Ichiro; Delworth, Thomas L.

doi:10.1007/s00382-012-1481-2

Cited by 284 publications

(367 citation statements)

References 56 publications

(66 reference statements)

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“…The CRPS is a measure of the integrated squared difference between the cumulative distribution function of the forecasts and the step function located at the observed value (e.g., Goddard et al 2013). A skill score for CRPSs (CRPSS) is calculated in the same way as for MSEs.…”

Section: Methodsmentioning

confidence: 99%

Full-field initialized decadal predictions with the MPI earth system model: an initial shock in the North Atlantic

et al. 2017

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Our decadal climate prediction system, which is based on the Max-Planck-Institute Earth System Model, is initialized from a coupled assimilation run that utilizes nudging to selected state parameters from reanalyses. We apply full-field nudging in the atmosphere and either full-field or anomaly nudging in the ocean. Full fields from two different ocean reanalyses are considered. This comparison of initialization strategies focuses on the North Atlantic Subpolar Gyre (SPG) region, where the transition from anomaly to full-field nudging reveals large differences in prediction skill for sea surface temperature and ocean heat content (OHC). We show that nudging of temperature and salinity in the ocean modifies OHC and also induces changes in mass and heat transports associated with the ocean flow. In the SPG region, the assimilated OHC signal resembles well OHC from observations, regardless of using full fields or anomalies. The resulting ocean transport, on the other hand, reveals considerable differences between full-field and anomaly nudging. In all assimilation runs, ocean heat transport together with net heat exchange at the surface does not correspond to OHC tendencies, the SPG heat budget is not closed. Discrepancies in the budget in the cases of full-field nudging exceed those in the case of anomaly nudging by a factor of 2-3. The nudging-induced changes in ocean transport continue to be present in the free running hindcasts for up to 5 years, a clear expression of memory in our coupled system. In hindcast mode, on annual to inter-annual scales, ocean heat transport is the dominant driver of SPG OHC. Thus, we ascribe a significant reduction in OHC prediction skill when using full-field instead of anomaly initialization to an initialization shock resulting from the poor initialization of the ocean flow.

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Section: Methodsmentioning

confidence: 99%

Full-field initialized decadal predictions with the MPI earth system model: an initial shock in the North Atlantic

et al. 2017

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“…The quality of the observational record is an inherent uncertainty source, particularly pertaining to variability on decadal and longer time scales.. Limited duration, incomplete spatial coverage, and observational errors hinder our ability to characterize past changes and attribute them to anthropogenic forcing, and limit our ability to evaluate models 65 .…”

Section: Observationsmentioning

confidence: 99%

Towards predictive understanding of regional climate change

et al. 2015

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Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared to the forced change, advancing our understanding of the long-term coupling between changes in upper ocean temperature and the atmospheric circulation will help most to narrow uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. We conclude that the current priority is to understand and reduce uncertainties on scales > 100 km to facilitate assessments at finer scales.(5557 words in text, 134 words for preface)

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“…The null hypothesis would state that CE value is equal to one, i.e., RMSE exhibited by hindcast, and historical runs are the same. Readers are encouraged to read Goddard et al [23] for details regarding the non-parametric bootstrap approach in the context of a decadal experiment's verification. The similar performance of historical and hindcast runs as climate predictors are attributed to the efficiency of the ACCA technique because predictor uncertainty is considered as one of the dominant sources of the uncertainty in simulated streamflow [26].…”

Section: Discussionmentioning

confidence: 99%

“…based on the year of SST initialization. Earlier studies found that hindcast runs have the potential of preserving the precipitation and temperature cross-correlation [22] and of predicting long-lead ENSO (El Niño-Southern Oscillation) events [23]. Current study forces BMLS with outputs from multiple hindcast and historical runs to understand the improvement in empirical streamflow simulation from the SST initialization.…”

Section: Introductionmentioning

confidence: 95%

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

Bhowmik

Seo

Sahoo³

2018

Water

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Statistical models for hydrologic simulation are a common choice among researchers particularly when catchment information is limited. In this study, we adopt a new statistical approach, namely Bayesian regression with multivariate linear spline (BMLS) for long-term simulation of streamflow on a Hydroclimate Data Network (HCDN) site in the United States. The study aims to: (i) evaluate the performance of the BMLS model; (ii) compare the performance of climate model outputs as predictors in hydrologic simulation; and (iii) estimate the changes in streamflow caused by anthropogenic climate change which is defined as the projected change in precipitation and temperature under different greenhouse gas emission scenarios. Performance of the BMLS model is compared with climatology for the validation period. Results suggest that the BMLS model forced with observed monthly precipitation and average temperature exhibits information that is not presented in the climatology of the validation period. Later, we consider Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and hindcast runs to simulate streamflow at the HCDN site. The study found that sea-surface temperature-initialized decadal hindcast runs are performing no better than 20th century historical runs regarding hydrologic simulation. Finally, the changes in mean and variability in streamflow at the HCDN site are estimated by forcing the model with CMIP5 future projections for the period 2000-2049.

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A verification framework for interannual-to-decadal predictions experiments

Cited by 284 publications

References 56 publications

Full-field initialized decadal predictions with the MPI earth system model: an initial shock in the North Atlantic

Full-field initialized decadal predictions with the MPI earth system model: an initial shock in the North Atlantic

Towards predictive understanding of regional climate change

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

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