An enhanced methodology to evaluate natural variability in ClimaMeter

Anthropogenic climate change (ACC) is one of the most demanding challenges facing our society. The intensification and increased frequency of many extreme events due to ACC are among its most impactful consequences, threatening human health, infrastructure, and ecosystems. In this context, raising awareness of the general public of the relationship between ACC, extremes, and associated impacts becomes a crucial task. To address this challenge, the ClimaMeter platform was developed [Faranda et al. 2024]. Its purpose is to provide rapid analyses of specific extreme events within hours of their occurrence, thereby contributing to public discourse and maximising media attention.

ClimaMeter is based on the analogue methodology for extreme events attribution [Yiou, 2014], which emphasises the dynamical processes associated with extremes by identifying weather situations similar to the event of interest, the so-called analogues. ClimaMeter leverages analogues to evaluate how events with the same dynamics as the one examined have evolved from 1950 to the present. Statistically significant changes between past and present analogues are assessed in terms of atmospheric circulation and associated meteorological hazards.

A key component of ClimaMeter’s methodology is the quantification of the relative influence of natural climate variability and ACC in explaining observed changes. Specifically, three modes of Sea Surface Temperature variability are taken into account: the El Niño-Southern Oscillation, the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation. These three modes are considered with equal weight, and changes not explained by them are, by default, assumed to be due to ACC [Faranda et al., 2024]. While the methodology is rapid and easy to communicate, it may have potential limitations. 

In this work, we propose a new method, which we call ClimaMeter 2.0, that generalises and extends the original ClimaMeter approach. We propose two main modifications. First,  the contributions of the three modes of variability are weighted according to the strength of their teleconnections with the event region and the specific hazard under consideration. Second, we explicitly test the assumption that climate change systematically influences the extreme under consideration. This generalised approach expands ClimaMeter’s methodology, increases its methodological flexibility, and provides new insights into the complex mechanisms linking natural variability and extremes.

Faranda, D., Messori, G., Coppola, E., Alberti, T., Vrac, M., Pons, F., Yiou, P., Saint Lu, M., Hisi, A. N. S., Brockmann, P., Dafis, S., Mengaldo, G., and Vautard, R.: ClimaMeter: contextualizing extreme weather in a changing climate, Weather Clim. Dynam., 5, 959–983, https://doi.org/10.5194/wcd-5-959-2024, 2024.

Yiou, P.: AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geosci. Model Dev., 7, 531–543, https://doi.org/10.5194/gmd-7-531-2014, 2014.