Peter A. Stott

Spatially synchronous extreme warm events can amplify environmental and societal impacts by affecting multiple regions simultaneously. However, previous studies have largely focused on June–August (JJA) events during the instrumental period, limiting understanding of their seasonal prevalence and long-term evolution. Here, we examine globally synchronous seasonal warm events (GSSWEs) using seasonal mean near-surface air temperature (SAT) and introduce an Extremity Index (EI) that combines normalized local land SAT anomaly intensity with the spatial extent of land areas exceeding a specified SAT threshold. Using instrumental observations, paleo-reanalysis products and climate model simulations, we assess changes in global-land GSSWE extremity since 850 Common Era (CE). We find that GSSWEs have intensified sharply since the 1970s across all four seasons, reaching levels not found earlier in the multi-dataset records. Detection and attribution analyses indicate that anthropogenic forcing, dominated by greenhouse-gas forcing, is the main contributor to this recent intensification. Spatially, the tropics contribute greatly to the recent extremity of GSSWEs due to their lower intrinsic variability. These findings highlight that climate-risk assessment and adaptation planning need to consider the growing spatial coherence and intensity of seasonal warm extremes.

Understanding the extent to which human activities have influenced regional climate is a key scientific and policy challenge. The UK is one of the world's best observed regions climatically, with a long and reliable temperature record that makes it an important test case for regional detection and attribution. Here, for the first time, we apply optimal fingerprinting to UK mean 2‐m air temperature changes using the Estimating Equations method, HadUK‐Grid observations, and CMIP6 simulations. We assess the extent to which observed UK temperature changes can be explained by natural internal variability, anthropogenic forcings, and natural external forcings. We detect a significant anthropogenic influence on warming in recent decades and identify greenhouse gases as the main driver. We also detect a cooling contribution from other anthropogenic influences in the mid‐twentieth century, likely dominated by sulphate aerosols. These results update earlier UK‐focused work and demonstrate that human influences, both warming and cooling, are detectable even at the national scale.

In 2022 large parts of Pakistan suffered devastating flooding, with the southern provinces of Balochistan and Sindh particularly badly impacted. These regions received record-breaking rainfall totals during August, following a very wet July over the summer monsoon season. In this attribution study we combine the forecasting attribution technique developed by Leach et al. 2021 with flood inundation modelling to estimate the influence of anthropogenic climate change on the 2022 floods. This combined storyline and probabilistic approach uses the European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts, and perturbed counterfactual forecasts with the same synoptic setup. These are fed into the 2D hydrodynamic flood inundation model LISFLOOD-FP over the worst affected regions to produce flood maps at 90m resolution.

It will be important to know when global warming has reached 1.5°C, as this will be a key marker in global policy given the ambition to pursue efforts to limit warming to this level. But how should the temperature increase be defined in this context? The Global Stocktake agreed at COP28 in Dubai noted “global warming of about 1.1 °C” based on the IPCC 6th Assessment Report, but this number applies to the average of 2011-2020 and hence is already out of date. We propose that the metric for current global warming should allow immediate of identification of passing particular levels of global warming, such as 1.5°C, to avoid unnecessary delays in responding to the exceedance. We also propose that the metric should be consistent with the definition of future Global Warming Levels in the IPCC 6th Assessment Report, which uses 20-year means of projected temperature anomalies with an exceedance year defined as the mid-point of the 20-year period. Without this consistency, the apparent time of reaching 1.5°C could differ from the time previously projected by the IPCC merely because of differences in the definition, which could be misinterpreted as indicting that global warming had reached 1.5°C either earlier or later than projected. This could either undermine confidence in projections or misinform discussions on action to address climate change.While various indicators are already in use that provide a more instantaneous measure of global warming, none are consistent with the IPCC definition of future GWLs nor are suitable for use as a baseline for impacts assessments. To address this, we propose a new metric, the Current Global Warming Level (CGWL), which uses a 20-year average over the previous 10 years from observations and the next 10 years from a forecast or projections. Here we compare the CGWL with the various indicators currently in use for quantifying the current level of global mean temperature change, and compare their indications of global temperature change over recent decades and of the current level of global warming. We also compare the year of exceeding past global warming levels of 0.5°C, 1.0°C and 1.2°C for each indicator. We use a combined observational dataset following IPCC methods and process the indicators from this. For each indicator, we explain potential difficulties that could arise from its use to assess when global warming reaches 1.5°C relative to pre-industrial, and explain the rationale for our proposed indicator, the Current Global Warming Level.

We will present findings from a comprehensive review of the detection and attribution of climate change in the UK, including both recent and past events within the observation record. We will highlight where there are notable gaps, including those that can and cannot be closed with existing data and/or attribution techniques.This systematic review of detection and attribution literature will feed into a report, with a database of supporting evidence, to inform the Climate Change Committee’s upcoming UK Climate Change Risk Assessment. The first part of the review will cover the detection and attribution of weather and climate changes in the UK, relevant to specific Climate Impact Drivers, while the second will cover societal, infrastructural, economic, and biodiversity impacts associated with these. As part of this, we will identify variables which are key drivers of multiple impacts, and, importantly, where further attribution analysis is needed, especially when the impacts are critical for UK risk.

The risk of flash flooding is likely to increase with the intensification of short-duration rainfall extremes due to Climate Change. Using the latest convective-permitting resolution climate model data for the UK and the LISFLOOD-FP flood inundation model, we adopt a trend detection approach to attribute flash flooding impacts over the UK city, Leeds. Our study is based on an extreme rainfall event in August 2014, where over 400 properties were flooded after 80mm of rainfall fell in five hours in parts of the city. This research will be the first attribution study for UK pluvial flood impacts, using pure convective-permitting resolution climate model data. The flood inundation model simulates flood maps for over 12 000 events using soil moisture and rainfall data as inputs.

The science of event attribution has developed considerably in recent years. There is now a growing interest in making this science operational. This perspective considers the challenges involved in doing this and suggests some priorities for further developments. It concludes that there is a requirement to deepen understanding of user needs for operational attribution, that further research will be needed to enable attribution of a wider variety of extreme events and their impacts, that there will have to be a greater underpinning of operational capability for such activities to be achievable, and that improved strategies for communicating results are needed for successful uptake by users.

It sounds straightforward. As the Earth warms due to the increased concentration of greenhouse gases in the atmosphere, global temperatures rise and so heatwaves become warmer as well. This means that a fixed temperature threshold is passed more often: the probability of extreme heat increases. However, land use changes, vegetation change, irrigation, air pollution, and other changes also drive local and regional trends in heatwaves. Sometimes they enhance heatwave intensity, but they can also counteract the effects of climate change, and in some regions, the mechanisms that impact on trends in heatwaves have not yet been fully identified. Climate models simulate heatwaves and the increased intensity and probability of extreme heat reasonably well on large scales. However, changes in annual daily maximum temperatures do not follow global warming over some regions, including the Eastern United States and parts of Asia, reflecting the influence of local drivers as well as natural variability. Also, temperature variability is unrealistic in many models, and can fail standard quality checks. Therefore, reliable attribution and projection of change in heatwaves remain a major scientific challenge in many regions, particularly where the moisture budget is not well simulated, and where land surface changes, changes in short‐lived forcers, and soil moisture interactions are important.

Probabilistic event attribution aims to quantify the role of anthropogenic climate change in altering the intensity or probability of extreme climate and weather events. It was originally conceived to calculate the costs associated with any increased likelihood of the meteorological event in question. However, only recently have such studies attempted to divide liability between polluting nations and ascribe a cost. Recent protests indicate a perception that older generations have the greater responsibility for climate change. In this paper, we examine how a portion of the cost of an event can be attributed to any individual person, according to their age and nationality. We demonstrate that this is quantitatively feasible using the example of the 2018 summer heatwave in eastern China and its impact on aquaculture. A relatively simple technique finds sample individuals responsible for between 0.53 and 18.10 yuan, increasing with their age and their country’s emissions over their adult lifetime since the first international consensus on carbon emissions was reached. This provides an illustration of the scale of such responsibilities, and how it is affected by national development and demographics. Such data can support decisions, at national and international levels, on how to fund recovery from climate impacts. It offers a simple quantitative approach for individuals to know their impact on the climate, or for governments to use in making policy decisions about how best to distribute costs of climate change.