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High impact weather events such as extreme temperatures or rainfall can cause significant disruption across the UK affecting sectors such as health, transport, agriculture and energy. In this study we draw on the latest set of UK climate projections, UKCP, to examine metrics relating to high-impact weather over the UK and how these change with different levels of future global warming from 1.5 °C to 4 °C above pre-industrial. The changes to these hazards show increases in the frequency of extremely hot days and nights, with a UK average increase in hot days of between 5 and 39 days per year between 1.5 °C and 4 °C of global warming. Projections indicate an increase in cooling degree days of 134–627% and an increase in growing degree days of 19–60% between 1.5 °C and 4 °C of global warming. Extremely hot nights, which are currently rare, are emerging as more common occurrences. The frequency of high daily temperatures and rainfall increase systematically, while the frequency of very cold conditions (based on days where temperatures fall below 0 °C) is shown to decrease by 10 to 49 days per year. A reduction in heating degree days, of 11–32% between 1.5 °C and 4 °C of warming, is projected. Levels of daily rainfall, which currently relate to increased risk of river flooding, are shown to increase across the country, with increases of days with high impact levels of rainfall occurring by 1 to 8 days per year between 1.5 °C and 4 °C of warming. Average drought severity is projected to increase for 3-, 6-, 12- and 36-month-long droughts. The largest changes in the severity of the 12-month drought are between −3 and +19% between 1.5 °C and 4 °C of warming and for 36-month drought between −2 and +54% between 1.5 °C and 4 °C of warming. The projected future changes in high impact weather from this study will enable the characterization of climate risks and ultimately be able to better inform adaptation planning in different sectors to support the increase in resilience of the UK to future climate variability and change.

Pattern scaling is widely used to create climate change projections to investigate future impacts. We consider the performance of pattern scaling for emulating the HadGEM2-ES general circulation model (GCM) paying particular attention to “high end” warming scenarios and to different choices of GCM simulations used to diagnose the climate change patterns. We demonstrate that evaluating pattern-scaling projections by comparing them with GCM simulations containing unforced variability gives a significantly less favorable view of the actual performance of pattern scaling. Using a four-member initial-condition ensemble of HadGEM2-ES simulations, we infer that the root-mean-square errors of pattern-scaled monthly temperature changes over land are less than 0.25°C for global warming up to approximately 3.5°C. Some regional errors are larger than this and, for this GCM, there is a tendency for pattern scaling to underestimate warming over land. For warming above 3.5°C, the pattern-scaled projection errors grow but remain small relative to the climate change signal. We investigate whether patterns diagnosed by pooling GCM experiments from several scenarios are suitable for emulating the GCM under a high-end warming scenario. For global warming up to 3.5°C, pattern scaling using this pooled pattern closely emulates GCM simulations. For warming beyond 3.5°C, pattern-scaling performance is notably improved by using patterns diagnosed only from the high-forcing representative concentration pathway 8.5 (RCP8.5) scenario. Assessments of climate change impacts under high-end warming using pattern-scaling projections could be improved by using change patterns diagnosed from pooled scenarios for projections up to 3.5°C above preindustrial levels and patterns diagnosed from only strong forcing simulations for projecting beyond that. Similar findings are obtained for five other GCMs.

The scenarios generated by energy systems models provide a picture of the range of possible pathways to a low-carbon future. However, in order to be truly useful, these scenarios should not only be possible but also plausible. In this paper, we have used lessons from historical energy transitions to create a set of diagnostic tests to assess the feasibility of an example 2 °C scenario (generated using the least cost optimization model, TIAM-Grantham). The key assessment criteria included the rate of deployment of low carbon technologies and the rate of transition between primary energy resources. The rates of deployment of key low-carbon technologies were found to exceed the maximum historically observed rate of deployment of 20% per annum. When constraints were added to limit the scenario to within historically observed rates of change, the model no longer solved for 2 °C. Under these constraints, the lowest median 2100 temperature change for which a solution was found was about 2.1 °C and at more than double the cumulative cost of the unconstrained scenario. The analysis in this paper highlights the considerable challenge of meeting 2 °C, requiring rates of energy supply technology deployment and rates of declines in fossil fuels which are unprecedented.

Understanding how 1.5 °C pathways could adjust in light of new adverse information, such as a reduced 1.5 °C carbon budget, or slower-than-expected low-carbon technology deployment, is critical for planning resilient pathways. We use an integrated assessment model to explore potential pathway adjustments starting in 2025 and 2030, following the arrival of new information. The 1.5 °C target remains achievable in the model, in light of some adverse information, provided a broad portfolio of technologies and measures is still available. If multiple pieces of adverse information arrive simultaneously, average annual emissions reductions near 3 GtCO 2 /yr for the first five years following the pathway adjustment, compared to 2 GtCO 2 /yr in 2020 when the Covid-19 pandemic began. Moreover, in these scenarios of multiple simultaneous adverse information, by 2050 mitigation costs are 4-5 times as high as a no adverse information scenario, highlighting the criticality of developing a wide range of mitigation options, including energy demand reduction options. Emerging limitations on climate and low-carbon technology would require adjusting our 15.C climate change mitigation pathways. However, this could increase average annual emissions reductions to around 3GtCO 2 /year using a broad portfolio of mitigation measures.

The increasing frequency and intensity of heavy rainfall events driven by climate change poses challenges for flood risk management. In this study, we use a high-resolution, convection-permitting ensemble from the UK climate projections local dataset to explore how the spatiotemporal characteristics of heavy rainfall events may evolve across the UK. Adopting an event-based framework, we analyse 5 km hourly rainfall data from 12 ensemble members and compare changes in future rainfall events to those derived from applying intensity-based scaling factors alone. This comparison allows us to identify aspects of rainfall change that are not captured by shifts in intensity distributions. Our results show that short-duration winter events become increasingly localised, with peak intensities increasing by up to 47%, amplifying flash flood potential. In summer, rainfall events exhibit expanded spatial extents—expanding by 25%–40%—magnifying total precipitation volumes. While we find small changes in the number of clustered events (i.e. heavy rainfall events that occur within a 21 day window), there are large changes to the contribution these have to seasonal precipitation, particularly in summer (7%–11% in the baseline to 11%–16% in future period). These findings highlight new insights into how heavy rainfall may change under future climate conditions, identifying aspects of change beyond intensity increases alone that are relevant for informing current practice for flood risk estimation.

Climate change in Brazil is expected to increase the occurrences of heat related conditions hazardous to human health. Thresholds in the environmental conditions leading to heat stress in humans are projected to be exceeded for long periods of the year across large parts of the country. We analyse future changes in the frequency of exceeding heat stress related thresholds during the hottest part of the day as measured by the Wet Bulb Globe Temperature (WBGT) and using the CMIP6 climate projections. Thresholds that require significant reduction in physical activity are estimated to be exceeded for most of northern Brazilian if the increase in global temperature reaches 2 °C. These exceedances are projected to occur for the hottest part of the day for at least four months of the year. Reducing global temperature rise to 1.5 °C would lessen the impact seen in the northern states. If the temperature rise exceeds 3 °C, then almost the entire country at some point in the year will have levels of WBGT that would pose a high risk to health for people undertaking physical activity. Furthermore, 8% of the population will be affected for almost half of the year. The states of Amazonas, Amapa, Acre, Maranhao, Para and Roraima are most prone to experiencing high levels of WBGT and will be the first to experience WBGT levels that are too high for intense physical activity for more than 9 months if global temperature reaches 3 °C. High levels of WBGT will have significant impact on workers in rural areas. Adaptive policies for the agricultural areas of Brazil will need to consider the impact of heat stress rendering large regions of the country unsuitable for outdoor work for large parts of the year. This will be true even at global warming levels of 2 °C for northern and central Brazil.

This study explores the critical notion of how feasible it is to achieve long-term mitigation goals to limit global temperature change. It uses a model inter-comparison of three integrated assessment models (TIAM-Grantham, MESSAGE-GLOBIOM and WITCH) harmonized for socio-economic growth drivers using one of the new shared socio-economic pathways (SSP2), to analyse multiple mitigation scenarios aimed at different temperature changes in 2100, in order to assess the model outputs against a range of indicators developed so as to systematically compare the feasibility across scenarios. These indicators include mitigation costs and carbon prices, rates of emissions reductions and energy efficiency improvements, rates of deployment of key low-carbon technologies, reliance on negative emissions, and stranding of power generation assets. The results highlight how much more challenging the 2 °C goal is, when compared to the 2.5–4 °C goals, across virtually all measures of feasibility. Any delay in mitigation or limitation in technology options also renders the 2 °C goal much less feasible across the economic and technical dimensions explored. Finally, a sensitivity analysis indicates that aiming for less than 2 °C is even less plausible, with significantly higher mitigation costs and faster carbon price increases, significantly faster decarbonization and zero-carbon technology deployment rates, earlier occurrence of very significant carbon capture and earlier onset of global net negative emissions. Such a systematic analysis allows a more in-depth consideration of what realistic level of long-term temperature changes can be achieved and what adaptation strategies are therefore required.

Past changes in global industrial aerosol emissions have played a significant role in historical shifts in African rainfall, and yet assessment of the impact on African rainfall of near-term (10–40 yr) potential aerosol emission pathways remains largely unexplored. While existing literature links future aerosol declines to a northward shift of Sahel rainfall, existing climate projections rely on RCP scenarios that do not explore the range of air quality drivers. Here we present projections from two emission scenarios that better envelop the range of potential aerosol emissions. More aggressive emission cuts result in northward shifts of the tropical rainbands whose signal can emerge from expected internal variability on short, 10–20-yr time horizons. We also show for the first time that this northward shift also impacts East Africa, with evidence of delays to both onset and withdrawal of the short rains. However, comparisons of rainfall impacts across models suggest that only certain aspects of both the West and East African model responses may be robust, given model uncertainties. This work motivates the need for wider exploration of air quality scenarios in the climate science community to assess the robustness of these projected changes and to provide evidence to underpin climate adaptation in Africa. In particular, revised estimates of emission impacts of legislated measures every 5–10 years would have a value in providing near-term climate adaptation information for African stakeholders.

Measurements made by the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) provide a multidecadal record of global atmospheric temperature change, which have been used by several groups to produce long‐term temperature records of thick layers of the atmosphere from the lower troposphere to the lower stratosphere. Here we present an internal uncertainty estimate for the Remote Sensing Systems data sets made using a Monte Carlo approach that includes contributions to the total uncertainty from sampling error, premerge adjustments to each individual satellite, and the merging procedure. The results can be used to estimate uncertainties in this product at all space and time scales of interest to any specific application. On small space and time scales sampling effects dominate. On the longer time scales intersatellite merging is important at all levels and the diurnal adjustment is a critical uncertainty for the two layers that have a significant surface component, particularly over land. A comparison of trends for the globe, tropics, and extratropics between the best estimate data set along with these error estimates and homogenized radiosonde estimates and available MSU/AMSU estimates from other groups is undertaken. This shows consistency between our product and those produced by others within the stated uncertainty for many regions and layers. In almost as many cases, however, the interdata set differences of the estimated trends are too large be accounted for by the internal uncertainty estimates derived herein. Key Points Internal uncertainty in 4 atmospheric temperature datasets is described Often, this uncertainty is less than the differences between datasets Uncertainty estimates are necessary to perform interdataset comparisons

Mounting evidence links air pollution to dementia, the most prevalent cause of cognitive impairment in older people. Here we investigated individual and compound effects of particulate matters (PM 10 , PM 2.5 , PM coarse , PM abs ) and nitric oxides (NO 2 , NO) on risk of all-cause dementia, and its most common subtypes, Alzheimer’s disease (AD) and vascular dementia (VAD), using data from UK Biobank. We addressed factors that hinder causal interpretation of associations previously shown in the literature and their translation into clear public health policies. Specifically: 1) spatial confounding by area-level covariates, 2) collinearity among and identification of the most relevant air pollutants, and 3) the time window for pollution exposure. Furthermore, we used chronic obstructive pulmonary disease (COPD) and frequency of oily fish intake in positive and negative control analyses. We found NO 2 to be the strongest risk factor for dementia, especially when considering participants with longer permanence at residential address as proxy for longer periods ( years) of exposure (all-cause dementia hazard ratio HR=1.06, 1.02-1.11 per 9.86 interquartile range). There was stronger evidence of an effect on risk for AD than VAD. Positive control analysis did not provide any evidence against causality, although the analyses of spatial confounding and negative control analyses revealed the presence of some residual bias, thus warranting care in the interpretation of the results. Together, our results highlight that targeting air pollution, in particular NO 2 levels, could inform preventive public health policies for dementia.