Heat extremes in Western Europe increasing faster than simulated due to atmospheric circulation trends.

Over the last 70 years, extreme heat has been increasing at a disproportionate rate in Western Europe, compared to climate model simulations. This mismatch is not well understood. Here, we show that a substantial fraction (0.8 °C [0.2°-1.4 °C] of 3.4 °C per global warming degree) of the heat extremes trend is induced by atmospheric circulation changes, through more frequent southerly flows over Western Europe. In the 170 available simulations from 32 different models that we analyzed, including 3 large model ensembles, none have a circulation-induced heat trend as large as observed. This can be due to underestimated circulation response to external forcing, or to a systematic underestimation of low-frequency variability, or both. The former implies that future projections are too conservative, the latter that we are left with deep uncertainty regarding the pace of future summer heat in Europe. This calls for caution when interpreting climate projections of heat extremes over Western Europe, in view of adaptation to heat waves.

Reducing uncertainty in local temperature projections.

Planning for adaptation to climate change requires accurate climate projections. Recent studies have shown that the uncertainty in global mean surface temperature projections can be considerably reduced using historical observations. However, the transposition of these new results to the local scale is not yet available. Here, we adapt an innovative statistical method that combines the latest generation of climate model simulations, global observations, and local observations to reduce uncertainty in local temperature projections. By taking advantage of the tight links between local and global temperature, we can derive the local implications of global constraints. The model uncertainty is reduced by 30% up to 70% at any location worldwide, allowing to substantially improve the quantification of risks associated with future climate change. A rigorous evaluation of these results within a perfect model framework indicates a robust skill, leading to a high confidence in our constrained climate projections.

Black carbon and dust alter the response of mountain snow cover under climate change.

By darkening the snow surface, mineral dust and black carbon (BC) deposition enhances snowmelt and triggers numerous feedbacks. Assessments of their long-term impact at the regional scale are still largely missing despite the environmental and socio-economic implications of snow cover changes. Here we show, using numerical simulations, that dust and BC deposition advanced snowmelt by 17 ± 6 days on average in the French Alps and the Pyrenees over the 1979-2018 period. BC and dust also advanced by 10-15 days the peak melt water runoff, a substantial effect on the timing of water resources availability. We also demonstrate that the decrease in BC deposition since the 1980s moderates the impact of current warming on snow cover decline. Hence, accounting for changes in light-absorbing particles deposition is required to improve the accuracy of snow cover reanalyses and climate projections, that are crucial for better understanding the past and future evolution of mountain social-ecological systems.

Making climate projections conditional on historical observations.

Many studies have sought to constrain climate projections based on recent observations. Until recently, these constraints had limited impact, and projected warming ranges were driven primarily by model outputs. Here, we use the newest climate model ensemble, improved observations, and a new statistical method to narrow uncertainty on estimates of past and future human-induced warming. Cross-validation suggests that our method produces robust results and is not overconfident. We derive consistent observationally constrained estimates of attributable warming to date and warming rate, the response to a range of future scenarios, and metrics of climate sensitivity. We find that historical observations narrow uncertainty on projected future warming by about 50%. Our results suggest that using an unconstrained multimodel ensemble is no longer the best choice for global mean temperature projections and that the lower end of previous estimates of 21st century warming can now be excluded.

Local sea level trends, accelerations and uncertainties over 1993-2019.

Satellite altimetry missions provide a quasi-global synoptic view of sea level variations over more than 25 years and provide regional sea level (SL) indicators such as trends and accelerations. Estimating realistic uncertainties on these quantities is crucial to address current climate science questions. While uncertainty estimates are available for the global mean sea level (GMSL), information is not available at local scales so far. We estimate a local satellite altimetry error budget and use it to derive local error variance-covariance matrices, and estimate confidence intervals on trends and accelerations at the 90% confidence level. Over 1993-2019, we find that the average local sea level trend uncertainty is 0.83 mm.yr-1 with values ranging from 0.78 to 1.22 mm.yr-1. For accelerations, uncertainties range from 0.057 to 0.12 mm.yr-1, with a mean value of 0.062. We also perform a sensitivity study to investigate a range of plausible error budgets. Local error levels, error variance-covariance matrices, SL trends and accelerations, along with corresponding uncertainties are provided.