Skillful decadal prediction of unforced southern European summer temperature variations

Borchert, Leonard; Koul, Vimal; Menary, Matthew; Befort, Daniel J.; Swingedouw, Didier; Sgubin, Giovanni; Mignot, Juliette

doi:10.1088/1748-9326/ac20f5

Cited by 15 publications

(10 citation statements)

References 50 publications

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“…Moreover, increasing the ensemble size is expected to further increase the skill by allowing the predictable signal to emerge more clearly from the chaotic variability (Athanasiadis et al, 2020). Interestingly, the results obtained from the CMCC DPS are broadly consistent with similar assessments from other CMIP6 decadal prediction systems (Bethke et al, 2021;Bilbao et al, 2021;Kataoka et al, 2020;Robson et al, 2018;Sospedra-Alfonso et al, 2021;Xin et al, 2019;Yeager et al, 2018) and multimodel studies (Borchert et al, 2021a, b;Delgado-Torres et al, 2022). In particular, most of the DPSs feature high predictive skill over the Atlantic Ocean, the Indian Ocean, and continental areas, where a large fraction of predictability stems from the external forcings.…”

Section: Discussionsupporting

confidence: 75%

“…The core of the DCPP is component A, which includes a set of retrospective forecasts (hindcasts). This framework has laid the groundwork for a number of singlemodel (Bethke et al, 2021;Bilbao et al, 2021;Kataoka et al, 2020;Robson et al, 2018;Sospedra-Alfonso et al, 2021;Xin et al, 2019;Yeager et al, 2018) and multimodel studies (Borchert et al, 2021a, b;Delgado-Torres et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 75%

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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“…Observed global average temperatures showed a slowdown in their warming rate during the early 2000s (Fig. 6), sometimes also termed as the "hiatus" period (Easterling and Wehner, 2009;Cowtan and Way, 2014;Trenberth, 2015;Fyfe et al, 2016). The HadCRUT4.6 time series shows a trend slope that is close to zero during 2003-2013, although the true global warming rate is thought to have been slightly larger when accounting for unsampled regions (Cowton and Way, 2014).…”

Section: Regional Sst Constraints and Attribution Of Skill To Specifi...mentioning

confidence: 99%

Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal timescales – a poor man's initialized prediction system

et al. 2022

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Abstract. Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure, and precipitation on decadal to multi-decadal timescales. We find that the constrained projections show significant skill in predicting the climate of the following 10 to 20 years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first 2 decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.

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“…The literature indicates that there is a link between NA Sea Surface Temperature (SST) and variability in the European climate (e.g. Dong et al, 2013;Ossó et al, 2020;Carvalho-Oliveira et al, 2021;Börgel et al, 2022;Sutton and Dong, 2012;Booth et al, 2012;Borchert et al, 2021a). The link between NA SST and drivers of the European climate is complex and how the atmosphere and NA interact over different timescales has not been fully determined.…”

Section: Ocean Criteriamentioning

confidence: 99%

Performance based sub-selection of CMIP6 models for impact assessments in Europe

Palmer

McSweeney

Booth

et al. 2022

Preprint

Self Cite

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Abstract. We have created a performance-based assessment of CMIP6 models for Europe that can be used to inform the sub-selection of models for this region. Our assessment covers criteria indicative of the ability of individual models to capture a range of large-scale processes that are important for the representation of present-day European climate. We use this study to provide examples of how this performance-based assessment may be applied to multi-model ensemble of CMIP6 models to a) filter the ensemble for performance against these climatological/ processed-based criteria and, b) create a smaller sub-set of models based on performance, that also maintains model diversity and the filtered projection range as far as possible. Filtering by excluding the least realistic models leads to higher sensitivity models remaining in the ensemble as an emergent consequence of the assessment. This results in both the 25th percentile and the median of the projected temperature range being shifted toward greater warming for the filtered set of models. We also weight the unfiltered ensemble against global trends. In contrast this shifts both the distribution of towards less warming. This highlights a tension for regional model selection in terms of selection based on regional climate processes versus the global mean warming trend.

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Skillful decadal prediction of unforced southern European summer temperature variations

Cited by 15 publications

References 50 publications

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

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

Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal timescales – a poor man's initialized prediction system

Performance based sub-selection of CMIP6 models for impact assessments in Europe

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