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Guillaume Evin, Benoit Hingray, Juliette Blanchet, Nicolas Eckert, Samuel Morin, and Deborah Verfaillie

Abstract

The quantification of uncertainty sources in ensembles of climate projections obtained from combinations of different scenarios and climate and impact models is a key issue in climate impact studies. The small size of the ensembles of simulation chains and their incomplete sampling of scenario and climate model combinations makes the analysis difficult. In the popular single-time ANOVA approach for instance, a precise estimate of internal variability requires multiple members for each simulation chain (e.g., each emission scenario–climate model combination), but multiple members are typically available for a few chains only. In most ensembles also, a precise partition of model uncertainty components is not possible because the matrix of available scenario/models combinations is incomplete (i.e., projections are missing for many scenario–model combinations). The method we present here, based on data augmentation and Bayesian techniques, overcomes such limitations and makes the statistical analysis possible for single-member and incomplete ensembles. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. This approach is illustrated for projections of regional precipitation and temperature for four mountain massifs in France. It is applicable for any kind of ensemble of climate projections, including those produced from ad hoc impact models.

Full access
Deborah Verfaillie, Francisco J. Doblas-Reyes, Markus G. Donat, Núria Pérez-Zanón, Balakrishnan Solaraju-Murali, Verónica Torralba, and Simon Wild

Abstract

Decadal climate predictions are being increasingly used by stakeholders interested in the evolution of climate over the coming decade. However, investigating the added value of those initialized decadal predictions over other sources of information typically used by stakeholders generally relies on forecast accuracy, while probabilistic aspects, although crucial to users, are often overlooked. In this study, the quality of the near-surface air temperature from initialized predictions has been assessed in terms of reliability, an essential characteristic of climate simulation ensembles, and compared to the reliability of noninitialized simulations performed with the same model ensembles. Here, reliability is defined as the capability to obtain a true estimate of the forecast uncertainty from the ensemble spread. We show the limited added value of initialization in terms of reliability, the initialized predictions being significantly more reliable than their noninitialized counterparts only for specific regions and the first forecast year. By analyzing reliability for different forecast system ensembles, we further highlight the fact that the combination of models seems to play a more important role than the ensemble size of each individual forecast system. This is due to sampling different model errors related to model physics, numerics, and initialization approaches involved in the multimodel, allowing for a certain level of error compensation. Finally, this study demonstrates that all forecast system ensembles are affected by systematic biases and dispersion errors that affect the reliability. This set of errors makes bias correction and calibration necessary to obtain reliable estimates of forecast probabilities that can be useful to stakeholders.

Open access
Benjamin Pohl, Thomas Saucède, Vincent Favier, Julien Pergaud, Deborah Verfaillie, Jean-Pierre Féral, Ylber Krasniqi, and Yves Richard

Abstract

Daily weather regimes are defined around the Kerguelen Islands (Southern Ocean) on the basis of daily 500-hPa geopotential height anomalies derived from the ERA5 ensemble reanalysis over the period 1979–2018. Ten regimes are retained as significant. Their occurrences are highly consistent across reanalysis ensemble members. Regimes show weak seasonality and nonsignificant long-term trends in their occurrences. Their sequences are usually short (1–3 days), with extreme persistence values above 10 days. Seasonal regime frequency is mostly driven by the phase of the southern annular mode over Antarctica, midlatitude dynamics over the Southern Ocean such as the Pacific–South American mode, and, to a lesser extent, tropical variability, with significant but weaker relationships with El Niño–Southern Oscillation. At the local scale over the Kerguelen Islands, regimes have a strong influence on measured atmospheric and oceanic variables, including minimum and maximum air temperature, mostly driven by horizontal advections, seawater temperature recorded 5 m below the surface, wind speed, and sea level pressure. Relationships are weaker for precipitation amounts. Regimes also modify regional contrasts between observational sites in Kerguelen, highlighting strong exposure contrasts. The regimes allow us to improve our understanding of weather and climate variability and interactions in this region; they will be used in future work to assess past and projected long-term circulation changes in the southern midlatitudes.

Restricted access
Dragana Bojovic, Roberto Bilbao, Leandro B. Díaz, Markus Donat, Pablo Ortega, Yohan Ruprich-Robert, Balakrishnan Solaraju-Murali, Marta Terrado, Deborah Verfaillie, and Francisco Doblas-Reyes
Free access