Over the first half of 2020, Siberia experienced the warmest period from January to June since records began and on the 20th of June the weather station at Verkhoyansk reported 38 °C, the highest daily maximum temperature recorded north of the Arctic Circle. We present a multi-model, multi-method analysis on how anthropogenic climate change affected the probability of these events occurring using both observational datasets and a large collection of climate models, including state-of-the-art higher-resolution simulations designed for attribution and many from the latest generation of coupled ocean-atmosphere models, CMIP6. Conscious that the impacts of heatwaves can span large differences in spatial and temporal scales, we focus on two measures of the extreme Siberian heat of 2020: January to June mean temperatures over a large Siberian region and maximum daily temperatures in the vicinity of the town of Verkhoyansk. We show that human-induced climate change has dramatically increased the probability of occurrence and magnitude of extremes in both of these (with lower confidence for the probability for Verkhoyansk) and that without human influence the temperatures widely experienced in Siberia in the first half of 2020 would have been practically impossible.
The breaking of the United Kingdom's daily rainfall record in October 2020 made a striking addition to the list of recent heavy precipitation events in the country. Mounting evidence from attribution research suggests that such extremes become more frequent and intense in a warming climate. Although most studies consider extreme events in specific months or seasons, here we investigate for the first time how extremes of the wettest day of the year may be influenced by anthropogenic forcings. Data from large multimodel ensembles indicate that the moderate historical trend towards wetter conditions will emerge more strongly in coming decades, while a notable anthropogenic influence on the variability of the wettest day may be identified as early as the 1900s. Experiments with different forcings are employed to estimate the changing probability of extremes due to anthropogenic climate change in a riskbased attribution framework. We introduce a new methodology of estimating probabilities of extremes in the present and future that calibrates data from long simulations of the preindustrial climate to the mean state and variability of the reference climatic period. The new approach utilises larger samples of rainfall data than alternative methods, which is a major advantage when analysing extremely rare events. The record rainfall of the wettest day in year 2020 is estimated to have become about 2.5 times more likely because of human influence, while its return time, currently about 100 years, will decrease to only about 30 years by 2100. Compared to a hypothetical natural climate, we estimate a 10-fold increase in the chances of such extreme rainfall events in the United Kingdom by the end of this century, which underlines the need for effective adaptation planning. 1 | INTRODUCTION On October 3, 2020, the United Kingdom received a provisionally estimated average 31.7 mm of rainfall, setting a new record for the country's wettest day according to observations since 1891. To appreciate the enormity of this extreme rainfall amount, it was pointed out that the water which fell on that single day was enough to fill Loch Ness, the United Kingdom's largest lake by volume (Met Office Press Office, 2020). The event developed following the passage of storm Alex, which brought strong winds and prolonged heavy rainfall across the United
Research into weather circulation changes over the UK for future climate has mainly focused on changes in the Summer and Winter seasons, with less analysis on seasonality and the transition seasons. Using the 30 Met Office weather patterns we examine the influence of climate change on seasonality through atmospheric circulation using a number of climate models. Changes in seasonality are important as they can have large impacts on many sectors including agriculture, energy and tourism. This paper finds a noticeable increase in Autumn over the UK in the frequency of drier summer-type regimes and a decrease in stormy winter types that emerge as early as the 2020s. The change in circulation signal once isolated from the overall signal is responsible for a 4–12% decrease in Autumn mean rainfall on average for England by the end of this century (where the values in the range are dependent on the emissions scenario). This change is projected over English regions that are already experiencing water stress, and with predictions of drier summers over the UK in future, this could further increase drought risk. The change in circulation in Autumn also moderates the large increase in the number of large-scale extreme daily rainfall events over the same regions predicted due to climate change. While this future circulation change is replicated across all the climate models used, large differences remain in the strength of the signal between models. The climate models used replicate the frequency of the 30 weather patterns well for all seasons.
<p>We investigate the attribution of the flooding in Northern England that saw at least 500 homes flooded and over 1000 properties evacuated in flooded areas in 2019. This occurred during the wettest Autumn on record in some areas and also contained some very high daily rainfall totals. In the light of climate change, it is expected that intense rainfall events are to become more intense as a result of increased global average temperatures and the Clausius-Clapeyron relationship, but here we investigate quantitatively how much climate change has increased the risk of such an event to date.</p><p>We use results from the 2.2km convective permitting high resolution local UK Climate Projections (UKCP) and observations to show that more intense rainfall events may already be occurring in Autumn in the UK. This work shows using this high resolution UKCP data that a heavy rainfall event exceeding 50mm in one day in Autumn was 33-40% more likely to occur in 2019 than 1985. Further work that looks at the HadGEM3-A simulations shows that these heavy rainfall days are more likely to occur in a climate impacted by human activity than one with just natural climate forcings.</p>
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