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Daniel J. Befort
,
Kevin I. Hodges
, and
Antje Weisheimer

Abstract

In this study, tropical cyclones (TCs) over the western North Pacific (WNP) and North Atlantic (NA) basins are analyzed in seasonal forecasting models from five European modeling centers. Most models are able to capture the observed seasonal cycle of TC frequencies over both basins; however, large differences for numbers and spatial track densities are found. In agreement with previous studies, TC numbers are often underestimated, which is likely related to coarse model resolutions. Besides shortcomings in TC characteristics, significant positive skill (deterministic and probabilistic) in predicting TC numbers and accumulated cyclone energy is found over both basins. Whereas the predictions of TC numbers over the WNP basin are mostly unreliable, most seasonal forecast provide reliable predictions for the NA basin. Besides positive skill over the entire NA basin, all seasonal forecasting models are skillful in predicting the interannual TC variability over a region covering the Caribbean and North American coastline, suggesting that the models carry useful information, including for adaptation and mitigation purposes ahead of the upcoming TC season. However, skill in all forecast models over a smaller region centered along the Asian coastline is smaller compared to their skill in the entire WNP basin.

Open access
Lukas Brunner
,
Carol McSweeney
,
Andrew P. Ballinger
,
Daniel J. Befort
,
Marianna Benassi
,
Ben Booth
,
Erika Coppola
,
Hylke de Vries
,
Glen Harris
,
Gabriele C. Hegerl
,
Reto Knutti
,
Geert Lenderink
,
Jason Lowe
,
Rita Nogherotto
,
Chris O’Reilly
,
Saïd Qasmi
,
Aurélien Ribes
,
Paolo Stocchi
, and
Sabine Undorf

Abstract

Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. To provide these estimates, different approaches to constrain, filter, or weight climate model projections into probabilistic distributions have been proposed. However, an assessment of multiple such methods to, for example, expose cases of agreement or disagreement, is often hindered by a lack of coordination, with methods focusing on a variety of variables, time periods, regions, or model pools. Here, a consistent framework is developed to allow a quantitative comparison of eight different methods; focus is given to summer temperature and precipitation change in three spatial regimes in Europe in 2041–60 relative to 1995–2014. The analysis draws on projections from several large ensembles, the CMIP5 multimodel ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. The methods’ key features are summarized, assumptions are discussed, and resulting constrained distributions are presented. Method agreement is found to be dependent on the investigated region but is generally higher for median changes than for the uncertainty ranges. This study, therefore, highlights the importance of providing clear context about how different methods affect the assessed uncertainty—in particular, the upper and lower percentiles that are of interest to risk-averse stakeholders. The comparison also exposes cases in which diverse lines of evidence lead to diverging constraints; additional work is needed to understand how the underlying differences between methods lead to such disagreements and to provide clear guidance to users.

Open access