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In the summer of 2022 unprecedented weather conditions in the UK lead to wildfires in many regions. Record breaking temperatures – above 40 °C for the first time - and prolonged dry conditions led to exceptional fire weather severity. On July 19th London Fire Brigade declared a major incident as firefighters battled several significant wildfires across the city. We investigate if climate change is enhancing summertime wildfire risk in the UK. We use reanalysis data from 1960 to the present day to analysis trends in the climatic indicators that influence the fire weather index. A large ensemble of initialised climate model simulations from the same time period are used to support the findings and identify as-yet-unrealised possible fire weather index situations in the UK. In the UK the term ‘wildfire’ is not limited to fires in wildland, but to any uncontrolled vegetation fire which requires action regarding suppression. Wildfires in the UK are considered a semi-natural hazard due to their close link with human activity. Though we investigate only meteorological influences, these are not the sole cause of wildfires – for example fuel availability plays a large role. Better understanding of the trends in the fire weather severity and chance of exceptional conditions for the UK will enable improved understanding of the risks. This information can feed into relevant policy and contingency planning, allowing society to become better prepared for the future as the planet continues to warm.

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.

Rapid Arctic warming and sea-ice loss have been observed in recent decades, and these trends are likely to continue in the future. Research has shown that Arctic changes affect weather and climate across the Northern Hemisphere. These changes, especially those in the extreme ends, can adversely impact communities and ecosystems. Recent development of the Polar Amplification Model Intercomparison Project (PAMIP) means that large ensembles of standardised climate experiments are now available for examining the effects of future Arctic changes on extreme weather in a coordinated way. Based on present-day and future (2°C global mean warming above pre-industrial levels) PAMIP model simulations, I will show the projected responses of winter hot and cold extremes (at the 20-year return period) to future Arctic sea-ice loss and ocean warming separately. I will focus on land areas in the Northern Hemispheric mid- and high latitudes where responses are expected to be largest. I will discuss the importance of considering the effect of sea-ice concentration loss together with that from ocean warming, which is not routinely done in the literature.

Weather and climate variability and trends impact health in a range of different ways. In this work I will cover five hazards, heat/drought, cold, storms/flooding, wildfire, and ‘other’ (e.g. hazards related to infectious diseases). I will give a summary of an expert judgment regarding how much various health outcomes associated with these hazards currently impact the UK population, and how this might change in the future. I will discuss how long exposure needs to be to the hazard before the health outcome is detectable. I will finish with highlighting where there are significant gaps in the literature, and where research groups and funding agencies should focus more, not just in the UK context, but the world. This work was done through the Met Office academic partnership “temperature and health” group, and involves a group of 50+ experts in climate and/or health.

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.

Attributing extreme weather events to human-induced climate change has been a topic of interest for decades. In recent years where unprecedented and high-impact events have occurred all over the world, it is all the more important to not just understand how human influence on the climate system has changed the probability or magnitude of the weather events, but also how it has changed the impacts of these events on human life. In this talk, I will review the climate-epidemiology literature on attributing adverse human health impacts to climate change, with a focus on heat-related mortality, as it is well-established that high temperatures are associated with increased mortality risks. I will include notable heat-mortality events in history such as the 1995 Chicago heatwave, 2006 UK summer, and the 2003 European heatwave. I will also discuss the use of large ensembles of future climate projections to ‘attribute’ heat-related mortality that could occur if global mean warming reached certain levels, keeping other factors unchanged. Finally, I will discuss the use of climate-health attribution information in engaging with the media and communicating with policymakers.

Flood hazards from Tropical cyclones (TCs) are frequently the leading cause of mortality and damages. Current research indicates that TC rainfall will increase by 7 % per degree of ocean warming with a greater proportion of them being extreme . It is vital to understand how this increase in rainfall will translate to flood risk from tropical cyclones by first accurately modelling TC rainfall under present climatic conditions.General circulation models struggle to reproduce TC rainfall fields so downscaling models are often used to generate more realistic TC rainfall data. Increasingly, rainfall downscaling studies have adopted deep learning techniques from the Computer Vision field to achieve comparable results to traditional methods at a fraction of the computational cost. Initially, convolutional neural networks (CNNs), specifically U-NETs, showed promise in precipitation downscaling. But more recently, the use of Generative models has been explored following the success of GANs in classical image super-resolution problems compared to CNNs. Generative approaches have shown potential at reproducing the fine spatial detail and stochastic nature of precipitation.Here, we develop upon the WGAN and Variational Autoencoder GAN (VAEGAN) from Harris et al. (2022) and apply it to rainfall data from TCs to increase the resolution of rainfall measurements from 100 km resolution to 10 km resolution.Overall, the Wasserstein GAN, performed better than other methods, the variational autoencoder GAN, U-Net and bilinear interpolation, across all diagnostics explored. We showed that for regular TCs the WGAN had the most realistic power spectra for all wave numbers, closely followed by the VAEGAN which only deviated for scales of around 5 pixels or fewer. The U-Net and Bilinear Interpolation methods both reproduced power spectra poorly compared to observations, with significant differences present from wave numbers greater than 3. We found that the WGAN had the lowest mean bias overall with errors around the core of the TC within 5 % error, while the VAEGAN had a dry bias of over 5 % outside of the inner core region. Both models had a low negative bias in standard deviation of between 0-5 %.When looking at the 100 most extreme samples, beyond the intensity of storms used in training the models, the WGAN is able to produce results of similar quality to those for TCs of intensities used in training, except for predictions having too low spread. This indicates that if the WGAN were trained on the full observational dataset, it could perform well for storms more intense than those previously observed, which is important for judging the model's robustness. Conversely, the power spectra of the VAEGAN became more unrealistic and predictions more artificial. There were some very large errors present in VAEGAN at the upper end of the extreme test set which demonstrates the importance of evaluating models on the most extreme, unseen, cases. Overall, these results show that generative approaches have the potential to generate TC rainfall fields with a high degree of accuracy.

Background It is unclear if ambient temperature changes affect eczema. It is also unclear if people with worse disease are more susceptible to weather-related flares, or specific types of emollient offer protection. Substantiating these links may help inform action plans and patients self-management. Objective To investigate the effect of short-term temperature variations on eczema symptoms in children. Methods Data from a UK cohort of 519 children (6 months-12 years) with at least mild eczema, participating in a randomised trial comparing four types of emollients on eczema symptoms, were combined with observed temperature data from the Hadley Centre’s Integrated Surface Database. Hot & cold weeks were defined by average regional temperature >75th or Results The baseline mean age was 4.9 years (SD 3.2) and POEM score was 9.2 (SD 5.5), indicating moderate eczema. 90% of participants lived within 20 km of their nearest weather station. From the 519 participants there were 6,796 consecutively paired POEMs and 1,082 flares. Seasonal variation in POEM scores was observed, suggesting symptoms worsening with colder weather in winter and improving with warmer weather in summer. Odds ratios of flares were: 1.15 (p = 0.136, 95%CI 0.96-1.39) in cold weeks, 0.85 (p = 0.045, 95%CI 0.72-1.00) in hot weeks. Likelihood ratio test showed no evidence of this differing by disease severity (p = 0.53) or emollient type used (p = 0.55). Conclusions Our findings are consistent with previous studies demonstrating either improvements in eczema symptoms or reduced flares in hot weather. Worse disease and different emollient types did not increase susceptibility or provide protection against temperature changes. Further work should investigate the role of sunlight, humidity, air pollution and other environmental factors.

Extreme weather attribution, quantifying the role of human influence in specific weather events, is of interest to scientists, adaptation planners and the general public. However, the devastating 2021 Pacific Northwest heatwave challenged conventional statistical approaches to attribution due to the absence of similar events in the historical record, and model-based approaches due to poor representation of key causal processes in current climate models. Here we use state-of-the-art operational medium-range and seasonal weather prediction systems, applied for the first time to this kind of climate question and unequivocally able to simulate the detailed physics of the heatwave in question, to show that human influence on the climate made this event at least 8 [2--30] times more likely to occur. Quantifying the absolute probability of such an unprecedented event is more challenging, but the length of the observational record suggests at least a multi-decade return-time in the current climate, with the likelihood doubling every 17 [10--50] years at the current rate of global warming. Our forecast-based approach synthesises the storyline approach, which examines human influence on the physical drivers of an event in a deterministic manner, and the probabilistic approach, which assesses how the frequency of a class of events has been affected by human influence. If developed as a routine service in a number of forecasting centres, it could provide reliable estimates of the changing probabilities of all extreme events that can be represented in forecast models, which is critical to supporting effective adaptation planning.

In June 2021, western North America experienced a record-breaking heat wave outside the distribution of previously observed temperatures. While it is clear that the event was extreme, it is not obvious whether other areas in the world have also experienced events so far outside their natural variability. Using a novel assessment of heat extremes, we investigate how extreme this event was in the global context. Characterizing the relative intensity of an event as the number of standard deviations from the mean, the western North America heat wave is remarkable, coming in at over four standard deviations. Throughout the globe, where we have reliable data, only five other heat waves were found to be more extreme since 1960. We find that in both reanalyses and climate projections, the statistical distribution of extremes increases through time, in line with the distribution mean shift due to climate change. Regions that, by chance, have not had a recent extreme heat wave may be less prepared for potentially imminent events.

Fluvial flood hazards from tropical cyclones (TCs) are frequently the leading cause of mortality and damages (Rezapour and Baldock, 2014). Accurately modeling TC precipitation is vital for studying the current and future impacts of TCs. However, general circulation models at typical resolution struggle to accurately reproduce TC rainfall, especially for the most extreme storms (Murakami et al., 2015). Increasing horizontal resolution can improve precipitation estimates (Roberts et al., 2020; Zhang et al., 2021), but as these methods are computationally expensive there is a trade-off between accuracy and generating enough ensemble members to generate sufficient high impact, low probability events. Often, downscaling models are used as a computationally cheaper alternative. Here, we downscale TC precipitation data from 100 km to 10 km resolution using a generative adversarial network (GAN). Generative approaches have the potential to well reproduce the fine spatial detail and stochastic nature of precipitation (Ravuri et al., 2021). Using observational products for tracking (IBTrACS) and rainfall (MSWEP), we train our GAN over the historical period 1979 - 2020. We are interested in how well our model reproduces precipitation intensity and structure with a focus on the most extreme events, where models have traditionally struggled. Bibliography Murakami, H., et al., 2015. Simulation and Prediction of Category 4 and 5 Hurricanes in the High-Resolution GFDL HiFLOR Coupled Climate Model*. Journal of Climate, 28(23), pp.9058-9079. Ravuri, S., et al., 2021. Skilful precipitation nowcasting using deep generative models of radar. Nature, 597(7878), pp.672-677. Rezapour, M. and Baldock, T., 2014. Classification of Hurricane Hazards: The Importance of Rainfall. Weather and Forecasting, 29(6), pp.1319-1331. Roberts, M., et al., 2020. Impact of Model Resolution on Tropical Cyclone Simulation Using the HighResMIP–PRIMAVERA Multimodel Ensemble. Journal of Climate, 33(7), pp.2557-2583. Zhang, W., et al., 2021. Tropical cyclone precipitation in the HighResMIP atmosphere-only experiments of the PRIMAVERA Project. Climate Dynamics, 57(1-2), pp.253-273.

Extreme high temperatures are associated with elevated human mortality risks. This is evidenced by a typically U- or J-shaped relationship between daily temperature and mortality found for most places in the world where data exist. However, high temperature is not the only contributor to heat stress. Humidity is also an important factor because it affects evaporation of sweat, which is crucial for cooling the human body in hot environments. Although various heat stress metrics, many of which are a combination of atmospheric temperature and humidity based on different physiological assumptions, have been developed to estimate heat stress, the relationship between these metrics and mortality remains unclear.In this study, the relationships between seven heat stress metrics — wet bulb temperature, apparent temperature, discomfort index and swamp cooler temperatures at four different efficiencies [1] — and mortality are systematically assessed using well-established Distributed Lag Non-linear Models (DLNMs) [2]. The predictive powers of these metrics, as well as that of daily mean temperature, are compared for the summer season at global locations in 39 countries, where sufficient meteorological and health data are available [3]. The results of this study provide new information as to which of these metrics are most indicative of summer mortality in different locations, and whether the ‘best-fit’ heat stress metric for a location gives a substantially different mortality estimate compared to the commonly used daily mean temperature. These results have important implications for heat-health impact monitoring, developing national and international heat-health action plans, as well as for projecting future heat-related mortality under different climate change scenarios.References:[1] Buzan, J. R. et al.: Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5. Geosci. Model Dev., 8, 151–170, 2015.[2] Gasparrini and Armstrong: Reducing and meta-analysing estimates from distributed lag non-linear models. BMC Medical Research Methodology, 13:1, 2013.[3] Vicedo-Cabrera, A. M. et al.: The burden of heat-related mortality attributable to recent human-induced climate change. Nature Climate Change, 11, 492–500, 2021.

Small Island Developing States are some of the most at risk places to flooding caused by tropical cyclone rainfall. However, there is a mismatch between existing flood risk assessment in small islands, and the increasing severity of projected tropical cyclone rainfall under current and future climate change. This research aims to address this gap by presenting the first application of an event-based rainfall-driven hydrodynamic model in a small island, for the Caribbean island of Puerto Rico. Applying an event set of 59,000 synthetic hurricane rainfall events, we represent hurricane rainfall spatially (~10km) and temporally (2-hourly), estimating flood hazard and population exposure at the island scale (9,100km2) at 20m model resolution using hydrodynamic model LISFLOOD-FP. Using this event-based approach, we aim to understand: i) what are the current estimates of population exposure to flooding from hurricane rainfall in Puerto Rico; and ii) how do these risk estimates change under 1.5°C and 2°C climate scenarios. We find that current population exposure to flooding from hurricane rainfall in Puerto Rico is high (8-9.80% of the population every 5 years), with an increase in population exposure of 1.60-15.20% and 0.70-22.30% under 1.5°C and 2°C climate change. This has critical implications for adaptation to more extreme flood risk in Puerto Rico, as well as underlining the important implications of the 1.5°C Paris Agreement target for small islands – a finding that is likely to be applicable to other small islands affected by tropical cyclones.

There is no uniform definition of heat waves and many climate indices can be derived from the surface temperature. When considering the impact of heat on human health, heat stress needs to be considered. Several indicators of heat stress have are commonly used, such as the Heat Index (HI), the Wet-Bulb Globe Temperature (WBGT) or the Wet-Bulb Temperature (Tw), all take into account the temperature and humidity. Each of these indices can be computed from non-linear empirical formula but they all use different scales which make results difficult to compare. Here we performed a comparative study using these 3 indices by defining corresponding levels of heat stress between the different metrics. We analyzed where sever, dangerous and deadly heat stress hazards will become more frequent, using climate model projections from CMIP6, and where the choice of the index makes a difference. For each index, we use a filtering techniques to remove models that cannot reproduce realistic extreme values during the current period (using a set of 4 different reanalyses as a reference). Following, we translated this risk in terms of country exposure and vulnerability, using population and GDP growth scenario.We show that South and East Asia and Middle-East, as previously pointed out by many studies, are highly exposed to heat stress hazards. But more vulnerable countries with less resources for mitigation are also highlighted such as West Africa and Central and South America. For all these regions, about 20 to more than 50% of the population would be exposed to sever heat stress each year no matter the heat stress index chosen. European countries and USA will also be exposed several time per year to conditions of similar heat stress level than the 2003 heat wave. When going to more extreme hazards, especially when considering the “survivability threshold” of 35°C for Tw, different indices lead to more discrepancies in the results but similar regions can be identified as the most vulnerable.

In June 2021 western North America experienced a record-breaking heatwave, outside the distribution of previously observed temperatures. Our research asks whether other regions across the world have experienced so far outside their natural variability - and have there been greater heat extremes. In our novel assessment of heat extremes we characterise the relative intensity of an event as the number of standard deviations from the mean, finding the western North America heatwave is remarkable, outside four single deviations. Throughout the globe, where we have reliable data, only 5 other heatwaves were found to be more extreme since 1960. We can also identify regions which, by chance, have not had a recent extreme heatwave, and may be less prepared for future events. Using extreme value analysis the western North America heat extreme has been shown to be outside the previous distribution of extremes for the region. We can test if this is unique, or if previous events show similar. By assessing the numbers of regions globally exceeding various thresholds, in terms of standard deviation from the mean, we can show that extremes appear to increase in line with changes to the mean-state of the distribution of the climate, and projected increase in extremes aligns with projected warming.

Major disruptions of the winter season, high-latitude, stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, subseasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, and fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.

Geoscientific Model Development, 15 (13), ISSN:1991-9603, ISSN:1991-959X

Precipitation events cause disruption around the world and will be altered by climate change. However, different climate modeling approaches can result in different future precipitation projections. The corresponding “method uncertainty” is rarely explicitly calculated in climate impact studies and major reports but can substantially change estimated precipitation changes. A comparison across five commonly used modeling activities shows that, for changes in mean precipitation, less than half of the regions analyzed had significant changes between the present climate and 1.5°C global warming for the majority of modeling activities. This increases to just over half of the regions for changes between present climate and 2°C global warming. There is much higher confidence in changes in maximum 1-day precipitation than in mean precipitation, indicating the robust influence of thermodynamics in the climate change effect on extremes. We also find that none of the modeling activities captures the full range of estimates from the other methods in all regions. Our results serve as an uncertainty map to help interpret which regions require a multimethod approach. Our analysis highlights the risk of overreliance on any single modeling activity and the need for confidence statements in major synthesis reports to reflect this method uncertainty. Considering multiple sources of climate projections should reduce the risks of policymakers being unprepared for impacts of warmer climates relative to using single-method projections to make decisions.

Urban environments are known to be warmer than their sub-urban or rural surroundings, particularly at night. In summer, urban heat islands exacerbate the occurrence of extreme heat events, posing health risks to urban residents. In the UK where 90% of the population is projected to live in urban areas by 2050, projecting changes in urban heat islands in a warming climate is essential to adaptation and urban planning.With the use of the new UK Climate Projections (UKCP18) in which urban land use is constant, I will show that both summer urban and sub-urban temperatures are projected to increase in the 10 most populous built-up areas in England between 1980 and 2080. However, differential warming rates in urban and sub-urban areas, and during day and at night suggest a trend towards a reduced daytime urban heat island effect but an enhanced night-time urban heat island effect. These changes in urban heat islands have implications on thermal comfort and local atmospheric circulations that impact the dispersion of air pollutants. I will further demonstrate that the opposite trends in daytime and night-time urban heat island effects are projected to emerge from current variability in more than half of the studied cities below a global mean warming of 3°C above pre-industrial levels.

Extreme heat is associated with increased risks of human mortality. In a warming climate, extreme heat events are projected to intensify and become more frequent, potentially adversely affecting human health. The Paris Agreement aims at limiting global mean temperature rise this century to well below 2°C above pre-industrial levels, but mitigation ambition as established in nations’ initial Nationally Determined Contributions still implies ~3°C warming. Quantifying the differences in extreme heat-related mortality between 1.5, 2 and 3°C warming is essential to understanding the public health impacts of climate policies and how societies may adapt to a warming climate.In this talk, I will show a new approach to projecting extreme heat-related mortality using the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) large ensemble and health models. The large ensemble of HAPPI simulations of the 1.5, 2 and 3°C warmer worlds allows extreme heat events and their health impacts in these worlds to be examined, rather than the mean climates. Using published case studies of the United States and Europe; I will demonstrate that limiting global mean warming from 3°C to 2°C or 1.5°C above pre-industrial levels could reduce heat-related mortality associated with extreme heat events, with the 1.5°C limit being substantially more beneficial to public health than 2°C. In addition to climate change, I will discuss the roles of urbanisation, population changes and adaptation in future extreme heat exposure and heat-related mortality.

The Paris Agreement1 aims to ‘pursue efforts to limit the temperature increase to 1.5 °C above pre-industrial levels.’ However, it has been suggested that temperature targets alone are insufficient to limit the risks associated with anthropogenic emissions2,3. Here, using an ensemble of model simulations, we show that atmospheric CO2 increase—an even more predictable consequence of emissions than global temperature increase—has a significant direct impact on Northern Hemisphere summer temperature, heat stress, and tropical precipitation extremes. Hence in an iterative climate mitigation regime aiming solely for a specific temperature goal, an unexpectedly low climate response may have corresponding ‘dangerous’ changes in extreme events. The direct impact of higher CO2 concentrations on climate extremes therefore substantially reduces the upper bound of the carbon budget, and highlights the need to explicitly limit atmospheric CO2 concentration when formulating allowable emissions. Thus, complementing global mean temperature goals with explicit limits on atmospheric CO2 concentrations in future climate policy would limit the adverse effects of high-impact weather extremes. A 1.5 °C temperature target can have varying atmospheric CO2 concentrations associated with it. GCM simulations reveal CO2 increases have a direct impact on climate extremes, highlighting the need for climate policy to complement temperature goals with CO2 targets.

Mortality associated with rising temperatures is one of the clearest and impactful fingerprints of a changing climate. Research now shows an attributable increase in mortality due to climate change is already evident in cities on every continent.

The winter polar vortices on Mars are annular in terms of their potential vorticity (PV) structure, a phenomenon identified in observations, reanalysis and some numerical simulations. Some recent modeling studies have proposed that condensation of atmospheric carbon dioxide at the winter pole is a contributing factor to maintaining the annulus through the release of latent heat. Dust and topographic forcing are also known to be causes of internal and interannual variability in the polar vortices. However, coupling between these factors remains uncertain, and previous studies of their impact on vortex structure and variability have been largely limited to a single Martian global climate model (MGCM). Here, by further developing a novel MGCM, we decompose the relative roles of latent heat and dust as drivers for the variability and structure of the northern Martian polar vortex. We also consider how Martian topography modifies the driving response. By also analyzing a reanalysis dataset we show that there is significant dependence in the polar vortex structure and variability on the observations assimilated. In both model and reanalysis, high atmospheric dust loading (such as that seen during a global dust storm) can disrupt the vortex, cause the destruction of PV in the low-mid altitudes (> 0.1 hPa), and significantly reduce spatial and temporal vortex variability. Through our simulations, we find that the combination of dust and topography primarily drives the eddy activity throughout the Martian year, and that although latent heat release can produce an annular vortex, it has a relatively minor effect on vortex variability., 16 pages, 14 figures, The Planetary Science Journal

In a warming world, temperature extremes are expected to show a distinguishable change over much of the globe1 and in many regions this change has already been detected in observations2,3. Although previous studies predict an increase in heat extreme events, the magnitude of the change varies greatly among different models even for the same mean warming4. This uncertainty has been linked to differences in land-atmosphere feedbacks across models2. Here we show that a significant constraint for future projections can be based on the ability of climate models to accurately simulate the variability of daily atmospheric surface maximum temperature (TX). By applying an emergent constraint (EC) locally on a metric describing TX variability with a large ensemble of CMIP55 and CMIP66 models we demonstrate that the best estimate increase in hot extremes could be worse than previously estimated over a large part of the land, with an increase in extremes of up to 50% larger than based on the multi-model mean. Our findings highlight the importance to correctly simulate TX variability during the historical period. Analysis of models soil moisture suggests that the EC arises because both TX variability and changes in hot extremes are related to land surface humidity processes.

Saturn exhibits an obliquity of 26.7° such that the largest moon, Titan, experiences seasonal variations including the formation of a polar vortex in the winter hemisphere. Titan's polar vortex is characterised by cold stratospheric temperatures due to the lack of insolation over the winter pole, and an increase in trace gas abundance as a result of complex organic chemistry in the upper atmosphere combined with polar subsidence. Meridional variations in temperature and gas abundance across the vortex have previously been investigated, but there has not yet been any in-depth study of the zonal variations in the temperature or composition of the northern vortex. Here we present the first comprehensive two-dimensional seasonal mapping of Titan's northern winter vortex. Using 18 nadir mapping sequences observed by the Composite InfraRed Spectrometer (CIRS) instrument on-board Cassini, we investigate the evolution of the vortex over almost half a Titan year, from late winter through to mid summer (Ls = 326 − 86°, 2007–2017). We find the stratospheric symmetry axis to be tilted from the solid body rotation axis by around 3.5°, although our results for the azimuthal orientation of the tilt are inconclusive. We find that the northern vortex appears to remain zonally uniform in both temperature and composition at all times. A comparison with vortices observed on Earth, Mars, and Venus shows that large-scale wave mechanisms that are important on other terrestrial planets are not as significant in Titan's atmosphere. This allows the northern vortex to be more symmetrical and persist longer throughout the annual cycle compared to other terrestrial planets.

The increasingly inter-connected global food system is becoming more vulnerable to production shocks owing to increasing global mean temperatures and more frequent climate extremes. Little is known, however, about the actual risks of multiple breadbasket failure due to extreme weather events. Motivated by the Paris Climate Agreement, this paper quantifies spatial risks to global agriculture in 1.5 and 2 °C warmer worlds. This paper focuses on climate risks posed to three major crops - wheat, soybean and maize - in five major global food producing areas. Climate data from the atmosphere-only HadAM3P model as part of the “Half a degree Additional warming, Prognosis and Projected Impacts” (HAPPI) experiment are used to analyse the risks of climatic extreme events. Using the copula methodology, the risks of simultaneous crop failure in multiple breadbaskets are investigated. Projected losses do not scale linearly with global warming increases between 1.5 and 2 °C Global Mean Temperature (GMT). In general, whilst the differences in yield at 1.5 versus 2 °C are significant they are not as large as the difference between 1.5 °C and the historical baseline which corresponds to 0.85 °C above pre-industrial GMT. Risks of simultaneous crop failure, however, do increase disproportionately between 1.5 and 2 °C, so surpassing the 1.5 °C threshold will represent a threat to global food security. For maize, risks of multiple breadbasket failures increase the most, from 6% to 40% at 1.5 to 54% at 2 °C warming. In relative terms, the highest simultaneous climate risk increase between the two warming scenarios was found for wheat (40%), followed by maize (35%) and soybean (23%). Looking at the impacts on agricultural production, we show that limiting global warming to 1.5 °C would avoid production losses of up to 2753 million (161,000, 265,000) tonnes maize (wheat, soybean) in the global breadbaskets and would reduce the risk of simultaneous crop failure by 26%, 28% and 19% respectively.

Flood hazard is a global problem, but regions such as south Asia, where people's livelihoods are highly dependent on water resources, can be affected disproportionally. The 2017 monsoon flooding in the Ganges-Brahmaputra-Meghna (GBM) basin, with record river levels observed, resulted in ~1200 deaths, and dramatic loss of crops and infrastructure. The recent Paris Agreement called for research into impacts avoided by stabilizing climate at 1.5?C over 2?C global warming above pre-industrial conditions. Climate model scenarios representing these warming levels were combined with a high-resolution flood hazard model over the GBM region. The simulations of 1.5?C and 2?C warming indicate an increase in extreme precipitation and corresponding flood hazard over the GBM basin compared to the current climate. So, for example, even with global warming limited to 1.5?C, for extreme precipitation events such as the south Asian crisis in 2017 there is a detectable increase in the likelihood in flooding. The additional ~0.6?C warming needed to take us from current climate to 1.5?C highlights the changed flood risk even with low levels of warming. An analysis of other large river basins around the world also showed the majority will see increases in extreme precipitation, but the GBM, with a particularly vulnerable population, shows one of the largest increases.

On June 28, 2019, a temperature of 45·9°C was recorded at a weather station in France, exceeding the country's previous temperature record—set during the infamous 2003 heatwave—by almost 2°C. The heatwave peaked over central and northern Europe, fuelled by a very persistent planetary-scale Rossby wave (giant meanders in upper-tropospheric winds), which turned into an omega block, so named because its shape resembles the Greek letter (Ω). This blocking event led to hot air from northern Africa being transferred to Europe. Given the extraordinary nature of this event, the public and media are now wondering: is such weather the new norm, and how bad could it get in the future?

A severe drought hit the Greater Horn of Africa (GHA) in 2014, but it remains unclear whether this extreme event was attributable to anthropogenic climate change or part of longer‐term natural cycles. Precipitation patterns are known to be changing across the GHA, but trajectories in land surface variables are much less well known. We simulated the GHA land surface environment to assess the balance between natural cycles and human‐induced climate change. Using a new form of event attribution study where we focused on both climate variables and also directly simulated land surface variables, we combined publicly volunteered distributed computing with land surface simulations to quantify land surface responses. Uncertainty was quantified both for climate model and land surface model outputs. We identified two distinct “drought trajectories” in the GHA bimodal seasonality area during the March–May (Long Rains season) of 2014. Human‐induced climate change may have resulted in regions from Lake Nalubaale (Lake Victoria) to Northern Kenya receiving less precipitation in this season and having up to 20% higher probability of drought‐level evapotranspiration rates (increasing drought). In contrast, the simulated anthropogenic climate change signal for this season induced somewhat wetter conditions and up to 20% lower probability of drought‐level evapotranspiration in Eastern Ethiopia, Southern Somalia, and coastal Kenya (decreasing drought). Uncertainties in our modeling system varied by region and variable of focus, but broadly we found that land surface simulation uncertainty neither added significantly to climate model uncertainty nor significantly reduced it., Journal of Geophysical Research: Atmospheres, 124 (8), ISSN:0148-0227, ISSN:2169-897X

Hurricanes are among the most impactful extreme weather events affecting small island states such as the Caribbean and require long-term planning for community and infrastructure resilience. By coupling an offline dynamical hurricane model to the output of a large ensemble of global climate model simulations from the Half a degree of Additional warming Prognosis and Projected Impacts (HAPPI) project, we assess how the impacts of hurricanes may change under the Paris Climate goals. Specifically, we concentrate on hurricane rainfall over particular regions, with both the mobility and intensity of a hurricane being key drivers of local level impacts. For example, Hurricane Dorian (2019) caused widespread devastation when it stalled over Bahamas as a category 5 storm. We show that since 1970 only one other hurricane stalled at this strength: Hurricane Mitch (1998). Due to a combination of increased stalling and precipitation yield under a warmer world, our analysis indicates a greater likelihood of extreme hurricane rainfall occurring in the Caribbean under both Paris Agreement scenarios of 1.5 ∘C and 2 ∘C Global Warming goals, compared to present climate projections. Focusing on specific hurricane events, we show that a rainfall event equal in magnitude to Hurricane Maria is around half as likely to occur under the 1.5 ∘C Paris Agreement goal compared to a 2 ∘C warmer climate. Our results highlight the need for more research into hurricanes in the Caribbean, an area which has traditionally received far less attention than mainland USA and requires more comprehensive infrastructure planning.

Increased aridity and drought risks are significant global concerns. However, there are few comprehensive studies on the related risks with regard to the differences between relatively weak levels of warming, including the recent targets of the United Nations Framework Convention on Climate Change (UNFCCC) of 1.5 °C or 2 °C. The present study investigates the impacts of 1.5 °C and 2 °C warming on aridification and their non-linearity based on the relationship between available water and energy at the Earth’s terrestrial surface. Large multi-model ensembles with a 4000-model-year in total are sourced from the Half a degree Additional warming, Prognosis, and Projected Impacts (HAPPI) project. Results demonstrate that 2 °C warming results in more frequent dry states in the Amazon Basin, western Europe, and southern Africa, and a limited warming to 1.5 °C will mitigate aridification and increase the frequency of extreme dry-year in these regions. In the Mediterranean region, a significant acceleration of aridification is found from the 1.5 °C to 2 °C warming projections, which indicates a need to limit the warming by 1.5 °C. A substantial portion of Asia is projected to become increasingly humid under both 1.5 °C and 2 °C warming scenarios. In some geographic regions, such as Australia, a strong nonlinear shift of aridification is found as 2 °C warming results in shift to wetter state contrast to significant increases in aridity and dry-year frequency at the weaker level of warming. The results suggest that the responses of regional precipitation to global warming cause the aridity changes, but their nonlinear behaviors along with different warming levels should be assessed carefully, in particular, to incorporate the additional 0.5 °C warming.

Previous studies evaluating anthropogenic influences on the meteorological drivers of drought have found mixed results owing to (1) the complex physical mechanisms which lead to the onset of drought, (2) differences in the characteristics and timescales of drought for different regions of the world, and (3) different approaches to the question of attribution. For a mid-latitude, temperate climate like New Zealand, strongly modulated by oceanic influences, summer droughts last on the order of three months, and are less strongly linked to persistent temperature anomalies than continental climates. Here, we demonstrate the utility of a novel approach for characterizing the meteorological conditions conducive to extreme drought over the North Island of New Zealand, using the January-March 2013 event as a case study. Specifically, we consider the use of self-organizing map (SOM) techniques in a multi-member coupled climate model ensemble to capture changes in daily circulation, between two 41-year periods (1861-1901 and 1993-2033). Comparisons are made with seasonal pressure and precipitation indices. Our results demonstrate robust (>99% confidence) increases in the likelihood of observing circulation patterns like those of the 2013 drought in the recent-climate simulations when compared with the early-climate simulations. Best-guess estimates of the fraction of attributable risk range from 0.2 to 0.4, depending on the metric used and threshold considered. Contributions to uncertainty in these attribution statements are discussed.

The Paris Agreement binds all nations to undertake ambitious efforts to combat climate change, with the commitment to hold warming well below 2 degrees C in global mean temperature (GMT), relative to pre-industrial levels, and to pursue efforts to limit warming to 1.5 degrees C. The 1.5 degrees C limit constitutes an ambitious goal for which greater evidence on its benefits for health would help guide policy and potentially increase the motivation for action. Here we contribute to this gap with an assessment on the potential health benefits, in terms of reductions in temperature-related mortality, derived from the compliance to the agreed temperature targets, compared to more extreme warming scenarios. We performed a multi-region analysis in 451 locations in 23 countries with different climate zones, and evaluated changes in heat and cold-related mortality under scenarios consistent with the Paris Agreement targets (1.5 and 2 degrees C) and more extreme GMT increases (3 and 4 degrees C), and under the assumption of no changes in demographic distribution and vulnerability. Our results suggest that limiting warming below 2 degrees C could prevent large increases in temperature-related mortality in most regions worldwide. The comparison between 1.5 and 2 degrees C is more complex and characterized by higher uncertainty, with geographical differences that indicate potential benefits limited to areas located in warmer climates, where direct climate change impacts will be more discernible.

The much awaited and intensely negotiated Paris Agreement was adopted on 12 December 2015 by the Parties to the United Nations Framework Convention on Climate Change. The agreement set out a more ambitious long-term temperature goal than many had anticipated, implying more stringent emissions reductions that have been under-explored by the research community. By its very nature a multidisciplinary challenge, filling the knowledge gap requires not only climate scientists, but the whole Earth system science community, as well as economists, engineers, lawyers, philosophers, politicians, emergency planners and others to step up. To kick start cross-disciplinary discussions, the University of Oxford's Environmental Change Institute focused its 25th anniversary conference upon meeting the challenges of the Paris Agreement for science and society. This theme issue consists of review papers, opinion pieces and original research from some of the presentations within that meeting, covering a wide range of issues underpinning the Paris Agreement. This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty

The half a degree additional warming, prognosis and projected impacts (HAPPI) experimental protocol provides a multi-model database to compare the effects of stabilizing anthropogenic global warming of 1.5 °C over preindustrial levels to 2.0 °C over these levels. The HAPPI experiment is based upon large ensembles of global atmospheric models forced by sea surface temperature and sea ice concentrations plausible for these stabilization levels. This paper examines changes in extremes of high temperatures averaged over three consecutive days. Changes in this measure of extreme temperature are also compared to changes in hot season temperatures. We find that over land this measure of extreme high temperature increases from about 0.5 to 1.5 °C over present-day values in the 1.5 °C stabilization scenario, depending on location and model. We further find an additional 0.25 to 1.0 °C increase in extreme high temperatures over land in the 2.0 °C stabilization scenario. Results from the HAPPI models are consistent with similar results from the one available fully coupled climate model. However, a complicating factor in interpreting extreme temperature changes across the HAPPI models is their diversity of aerosol forcing changes., Earth System Dynamics, 9 (1), ISSN:2190-4987, ISSN:2190-4979