Towards Useful Decadal Climate Services

Dunstone, Nick; Lockwood, Julia; Solaraju-Murali, Balakrishnan; Reinhardt, Katja; Tsartsali, Eirini; Athanasiadis, Panos; Bellucci, Alessio; Brookshaw, Anca; Caron, Louis-Philippe; Doblas‐Reyes, Francisco J.; Früh, Barbara; González-Reviriego, Nube; Gualdi, Silvio; Hermanson, Leon; Materia, Stefano; Nicodemou, Andria; Nicolì, Dario; Pankatz, Klaus; Paxian, Andreas; Scaife, Adam A.; Smith, Doug; Thornton, Hazel

doi:10.1175/bams-d-21-0190.1

Cited by 22 publications

(8 citation statements)

References 28 publications

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“…The decadal timescale is a key planning horizon for many flood risk managers (Done et al., 2021). However, they typically require more fine‐grained information to inform decisions (Dunstone et al., 2022). Our work demonstrates that a dynamical‐statistical approach can bridge these spatial scales to provide skillful predictions of high streamflow at the catchment scale.…”

Section: Discussionmentioning

confidence: 99%

Skillful Decadal Flood Prediction

Moulds

Slater

Dunstone

et al. 2023

Geophysical Research Letters

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Accurate long‐term flood predictions are increasingly needed for flood risk management in a changing climate, but are hindered by the underestimation of climate variability by climate models. Here, we drive a statistical flood model with a large ensemble of dynamical CMIP5‐6 predictions of precipitation and temperature. Predictions of UK winter flooding (95th streamflow percentile) have low skill when using the raw 676‐member ensemble averaged over lead times of 2–5 years from the initialization date. Sub‐selecting 20 ensemble members that adequately represent the multiyear temporal variability in the North Atlantic Oscillation (NAO) significantly improves the flood predictions. Applying this method we show positive skill in 46% of stations compared to 26% using the raw ensemble, primarily in regions most strongly influenced by the NAO. Our findings reveal the potential of decadal predictions to inform flood risk management at long lead times.

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Section: Discussionmentioning

confidence: 99%

Skillful Decadal Flood Prediction

Moulds

Slater

Dunstone

et al. 2023

Geophysical Research Letters

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“…In addition, the initial state of some land surface characteristics, stratosphere, snow cover, and sea ice may also impact the predictability of the climate system (e.g., Bellucci et al, 2015b;Meehl et al, 2021). The aforementioned initialized components provide additional predictability, for the atmospheric circulation and, in particular, for the North Atlantic Oscillation (NAO) affecting boreal winter climate over Europe (Smith et al, 2019;Athanasiadis et al, 2020;Dunstone et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system

Nicolì

Bellucci²,

Ruggieri³

et al. 2023

Geosci. Model Dev.

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Abstract. Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of near-term climate change and are a useful tool to inform decision-makers on future climate-related risks. Here we present results from the CMIP6 (Coupled Model Intercomparison Project Phase 6) Decadal Climate Prediction Project (DCPP) decadal hindcasts produced with the operational CMCC (Euro-Mediterranean Center on Climate Change) decadal prediction system (DPS), based on the fully coupled CMCC-CM2-SR5 dynamical model. A 20-member suite of 10-year retrospective forecasts, initialized every year from 1960 to 2020, is performed using a full-field initialization strategy. The predictive skill for key variables is assessed and compared with the skill of an ensemble of non-initialized historical simulations so as to quantify the added value of the initialization. In particular, the CMCC DPS is able to skillfully reproduce past climate surface and subsurface temperature fluctuations over large parts of the globe. The North Atlantic Ocean is the region that benefits the most from initialization, with the largest skill enhancement occurring over the subpolar region compared to historical simulations. On the other hand, the predictive skill over the Pacific Ocean rapidly decays with forecast time, especially over the North Pacific. In terms of precipitation, the skill of the CMCC DPS is significantly higher than that of the historical simulations over a few specific regions, including the Sahel, northern Eurasia, and over western and central Europe. The Atlantic multidecadal variability is also skillfully predicted, and this likely contributes to the skill found over remote areas through downstream influence, circulation changes, and teleconnections. Considering the relatively small ensemble size, a remarkable prediction skill is also found for the North Atlantic Oscillation, with maximum correlations obtained in the 1–9 lead year range. Systematic errors also affect the forecast quality of the CMCC DPS, featuring a prominent cold bias over the Northern Hemisphere, which is not found in the historical runs, suggesting that, in some areas, the adopted full-field initialization strategy likely perturbs the equilibrium state of the model climate quite significantly. The encouraging results obtained in this study indicate that climate variability over land can be predictable over a multiyear range, and they demonstrate that the CMCC DPS is a valuable addition to the current generation of DPSs. This stresses the need to further explore the potential of the near-term predictions, further improving future decadal systems and initialization methods, with the aim to provide a reliable tool to inform decision-makers on how regional climate will evolve in the next decade.

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“…With the requisite protocols for routine NTCP and data delivery in place, in order to identify where the available forecast skill intersects with the greatest economic and societal benefits (Dunstone et al, 2022), there is a need to inform and refine products based on analysis of carefully chosen case studies. In practise this requires improving the understanding of sectoral needs for prediction information, and to produce and tailor products and services to help meet those needs.…”

Section: Introductionmentioning

confidence: 99%

Recent applications and potential of near-term (interannual to decadal) climate predictions

et al. 2023

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Following efforts from leading centres for climate forecasting, sustained routine operational near-term climate predictions (NTCP) are now produced that bridge the gap between seasonal forecasts and climate change projections offering the prospect of seamless climate services. Though NTCP is a new area of climate science and active research is taking place to increase understanding of the processes and mechanisms required to produce skillful predictions, this significant technical achievement combines advances in initialisation with ensemble prediction of future climate up to a decade ahead. With a growing NTCP database, the predictability of the evolving externally-forced and internally-generated components of the climate system can now be quantified. Decision-makers in key sectors of the economy can now begin to assess the utility of these products for informing climate risk and for planning adaptation and resilience strategies up to a decade into the future. Here, case studies are presented from finance and economics, water management, agriculture and fisheries management demonstrating the emerging utility and potential of operational NTCP to inform strategic planning across a broad range of applications in key sectors of the global economy.

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Towards Useful Decadal Climate Services

Cited by 22 publications

References 28 publications

Skillful Decadal Flood Prediction

Skillful Decadal Flood Prediction

The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system

Recent applications and potential of near-term (interannual to decadal) climate predictions

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