The Earth’s terrain is linked to many physical processes, and gaining the most accurate representation is key to work in many sectors from engineering to natural hazards modeling and ecology. Existing global digital elevation models (DEMs) are widely used, however often suffer from systematic biases caused by trees, buildings and instrumentation error, ultimately limiting their effectiveness. We present here, FathomDEM, a new global 30 m DEM produced using a novel application of a hybrid vision transformer model. This model removes surface artifacts from a global radar DEM, Copernicus DEM, aligning it more closely with true topography. In addition to improving on other global DEMs, FathomDEM also has reduced error compared to coastal-focussed DEMs such as the recent DeltaDTM. This demonstrates its impressive capacity to perform for specific landscapes, while being trained globally to model a wide range of terrain types. FathomDEM has been tested on the downstream task of flood modeling, showing increased accuracy compared to those run with the previous best global DEM, FABDEM, approaching the performance of LiDAR based flood modeling. This improvement is attributed to FathomDEM’s smaller error and substantial reduction in artifacts. This shows the suitability of FathomDEM for applied tasks and strengthens our evaluation compared to one based on vertical error alone.

Single Model Initial-condition Large Ensembles (SMILEs) represent a pivotal progress in climate modeling, offering multiple simulations from a single model to address the inherent uncertainties in climate projections (Maher et al., 2021). However, biases intrinsic to climate models can distort SMILEs' outputs, potentially misrepresenting climate risks and uncertainties. In climate impact studies, bias correction of Earth System Models (ESMs) typically aligns model outputs with observed historical data, using statistical methods to adjust climatic variables. While essential, this correction may suppress the range of climatic conditions, particularly when applied individually to each ensemble member, thus diminishing the ensemble's diversity and its ability to represent varied climate futures. Instead, we explore whether a bulk approach to bias correction is more appropriate for SMILEs. This method involves applying a consistent correction across the entire ensemble, thereby maintaining the relative differences and natural variability among the ensemble members and preserving the unique capacity of SMILEs to represent a broad spectrum of climatic conditions, in particular under current and near-future climate. Our analysis used the 100-member dataset from the Community Earth System Model Large Ensemble Project Phase 2 (CESM-LENS2, Rodgers et al., 2021), covering historical and future climate simulations. We adjusted key climate variables—precipitation, temperature, relative humidity, and surface pressure within the CONUS domain—using the ISIMIP3basd algorithm (Lange, 2019), with MSWX reanalysis data as the historical reference (Beck et al., 2022). Our experiment involved a twofold comparison: We first evaluated the results after adjusting the entire ensemble at once using (the bulk approach) and, secondly, after adjusting each individual ensemble member separately (member-by-member approach). This comparative analysis allowed us to discern the effects of these two different bias correction methodologies on the ensemble's ability to represent climate variability and extremes. Our results show the effect of both bias correction approaches on the variability of crucial climate extreme statistics and the correlation between ENSO and climate variables. Additionally, we discuss how the choice of bias adjustment method can influence the magnitude of projected changes under future climate scenarios, a key consideration in climate impact studies. References: Beck, H. E., Van Dijk, A. I., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., & Miralles, D. G. (2022). MSWX: Global 3-hourly 0.1 bias-corrected meteorological data including near-real-time updates and forecast ensembles. Bulletin of the American Meteorological Society, 103(3), E710-E732. Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). Geoscientific Model Development, 12(7), 3055-3070. Maher, N., Milinski, S., & Ludwig, R. (2021). Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble. Earth System Dynamics, 12(2), 401-418. Rodgers, K. B., Lee, S. S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., ... & Yeager, S. G. (2021). Ubiquity of human-induced changes in climate variability. Earth System Dynamics, 12(4), 1393-1411.

Machine Learning (ML) is playing an increasingly valuable role in statistical downscaling. Capable of leveraging complex, non-linear relationships latent in the training data, the community has demonstrated significant potential for ML to learn a downscaling mapping. Following the perfect-prognosis (PP) approach, ML models can be trained on historical reanalysis data to learn a relationship between coarse predictors and higher resolution (i.e. downscaled) predictands. Once trained, the models can then be evaluated on general circulation model (GCM) outputs to generate regional downscaled results. Due to the relatively low computational cost of training and utilising these models, they can be used to efficiently downscale large ensembles of climate models over regional to global domains.This work employs a novel diffusion algorithm to downscale climate data. Diffusion models have proven highly successful in applications such as natural image generation and super-resolution (the natural image analogue to climate downscaling). Diffusion models have been shown to significantly outperform earlier generative ML models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs); they can produce highly diverse samples, emulate fine details with high fidelity, and exhibit much more stable training than alternative ML models. This work trains and evaluates diffusion models on the Multi-Source Weighted-Ensemble Precipitation (MSWEP) observational dataset over the Colorado River Basin (USA). High resolution (10km x 10km) MSWEP fields are artificially coarsened to generate training data. Once trained, the models are applied to bias-corrected climate model outputs to evaluate their ability to generate realistic downscaled precipitation fields. Performance is compared with several benchmarks, including classical regression techniques as well as alternative ML models.

Digital Elevation Models (DEMs) describe the earth surface’s topography and are an important source of information for applications of physical modelling, engineering and many others. Flood inundation modelling, where water flows are determined by terrain slope, is also highly dependent on DEM quality. The most accurate DEMs currently available are sourced from airborne LiDAR, however these only cover a small fraction of the globe, leaving the majority of the globe sourced from satellite imagery. Satellite based DEMs have limitations and are considered Digital Surface Models (DSMs) which represent the surface of vegetation canopy, buildings and other objects, rather than the bare earth surface which is represented by a Digital Terrain Model (DTM). Due to this, we have developed FathomDEM, a DTM generated from the best global satellite based DSM, Copernicus DEM. FathomDEM uses a novel vision transformer technique to improve on previous attempts to generate a DTM from Copernicus DEM. FathomDEM reduces the Mean Absolute Error and Root Mean Squared Error to half of our previous work, FABDEM, and quarter of Copernicus DEM, while also improving the spatial correlation. Flood simulations of inundation using a given DEM shows its use in a real world application and we present results showing flood inundation maps from different global DEMs and LiDAR. FathomDEM gives similar scores to LiDAR data when compared to benchmark flood extents, tested across multiple sites. FathomDEM therefore provides a significant advance when applied to flood inundation modelling in locations without LiDAR DEMs.

Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation data contains forest and building artifacts that limit its usefulness for applications that require precise terrain heights, in particular flood simulation. Here, we use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (∼30 m) grid spacing. We train our correction algorithm on a unique set of reference elevation data from 12 countries, covering a wide range of climate zones and urban extents. Hence, this approach has much wider applicability compared to previous DEMs trained on data from a single country. Our method reduces mean absolute vertical error in built-up areas from 1.61 to 1.12 m, and in forests from 5.15 to 2.88 m. The new elevation map is more accurate than existing global elevation maps and will strengthen applications and models where high quality global terrain information is required.

Digital Elevation Models (DEMs) depict the elevation of the Earth’s surface and are fundamental to many applications, particularly in the geosciences. To date, global DEMs contain building and forest artifacts that limit its functionality for applications that require precise measurement of terrain elevation, such as flood inundation modeling. Using machine learning techniques, we remove both building and tree height bias from the recently published Copernicus GLO-30 DEM to create a new dataset called FABDEM (Forest And Buildings removed Copernicus DEM). This new dataset is available at 1 arc second grid spacing (~30m) between 60°S-80°N, and is the first global DEM to remove both buildings and trees.Our correction algorithm is trained on a comprehensive and unique set of reference elevation data from 12 countries that covers a wide range of climate zones and urban types. This results in a wider applicability compared to previous DEM correction studies trained on data from a single country. As a result, we reduce mean absolute vertical error from 5.15m to 2.88m in forested areas, and from 1.61m to 1.12m in built-up areas, compared to Copernicus GLO-30 DEM. Further statistical and visual comparisons to other global DEMs suggests FABDEM is the most accurate global DEM with median errors ranging from -0.11m to 0.45m for the different landcover types assessed. The biggest improvements were found in areas of dense canopy coverage (>50%), with FABDEM having a median error of 0.45m compared to 2.95m in MERIT DEM and 12.95m for Copernicus GLO-30 DEM.FABDEM has notable improvements over existing global DEMs, resulting from the use of Copernicus GLO-30 and a powerful machine learning correction of building and tree bias. As such, there will be beneifts in using FABDEM for purposes where depiction of the bare-earth terrain is required, such as in applications in geomorphology, glaciology and hydrology.

Riverine flood hazard is the consequence of meteorological drivers, primarily precipitation, hydrological processes and the interaction of floodwaters with the floodplain landscape. Modeling this can be particularly challenging because of the multiple steps and differing spatial scales involved in the varying processes. As the climate modeling community increases their focus on the risks associated with climate change, it is important to translate the meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. This is due to the complexity and uncertainties of model cascades and the computational cost of flood inundation modeling. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (∼90 m) river flooding (fluvial) hazards. Thus, this framework is designed to be an accessible, computationally efficient tool using freely available data to enable greater uptake of this type of modeling. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data sets and thus can be applied anywhere in the world, but we use the Brahmaputra River in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework. This framework is designed to be driven by meteorology from observational data sets or climate model output. In this study, only observations are used to drive the models, so climate changes are not assessed. However, by comparing current and future simulated climates, this framework can also be used to assess impacts of climate change.

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.

Abstract. There is an urgent need for the climate community to translate their meteorological drivers into relevant hazard estimates. This is especially important for the climate attribution and climate projection communities as we seek to understand how anthropogenic climate change has, and will, impact our society. This can be particularly challenging because there are often multiple specialized steps to model the hazard. Current climate change assessments of flood risk typically neglect key processes, and instead of explicitly modeling flood inundation, they commonly use precipitation or river flow as proxies for flood hazard. Here, we lay out a clear methodology for taking meteorological drivers, e.g., from observations or climate models, through to high-resolution (~ 90 m) river flooding (fluvial) hazards. The meteorological inputs (precipitation and air temperature) are transformed through a series of modeling steps to yield, in turn, surface runoff, river flow, and flood inundation. We explore uncertainties at different modeling steps. The flood inundation estimates can then be directly related to impacts felt at community and household levels to determine exposure and risks from flood events. The approach uses global data-sets and thus can be applied anywhere in the world, but we use the Brahmaputra river in Bangladesh as a case study in order to demonstrate the necessary steps in our hazard framework.

Cloud computing is a mature technology that has already shown benefits for a wide range of academic research domains that, in turn, utilize a wide range of application design models. In this paper, we discuss the use of cloud computing as a tool to improve the range of resources available for climate science, presenting the evaluation of two different climate models. Each was customized in a different way to run in public cloud computing environments (hereafter cloud computing) provided by three different public vendors: Amazon, Google and Microsoft. The adaptations and procedures necessary to run the models in these environments are described. The computational performance and cost of each model within this new type of environment are discussed, and an assessment is given in qualitative terms. Finally, we discuss how cloud computing can be used for geoscientific modelling, including issues related to the allocation of resources by funding bodies. We also discuss problems related to computing security, reliability and scientific reproducibility.

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.

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 (figure).1Kalnay E Kanamitsu M Kistler R et al.The NCEP/NCAR 40-year reanalysis project.Bull Amer Meteor Soc. 1996; 77: 437-470Crossref Scopus (24493) Google Scholar 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? Although heatwaves have always occurred as an intrinsic part of our weather, strong evidence suggests that their frequency and magnitude are increasing because of anthropogenic climate change,2Stott PA Stone DA Allen MR Human contribution to the European heatwave of 2003.Nature. 2004; 432: 610-614Crossref PubMed Scopus (1144) Google Scholar and the 2019 event is no exception. Heatwaves are initiated by weather patterns, and atmospheric patterns similar to those responsible for this temperature record have been identified in many of the major European heatwaves observed over the past 20 years.3Kornhuber K Osprey S Coumou D et al.Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern.Environ Res Lett. 2019; 14054002Crossref Scopus (157) Google Scholar These patterns often result in stationary high-pressure systems over western Europe. For instance, the simultaneous heatwaves over much of the northern hemisphere in the summer of 2018 happened during the occurrence of a high-amplitude and stationary Rossby wave, locked in position and likely to have been further reinforced by latent heating from dry soil regions.3Kornhuber K Osprey S Coumou D et al.Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern.Environ Res Lett. 2019; 14054002Crossref Scopus (157) Google Scholar A detailed analysis of the 2018 event concluded that it was “virtually certain” that the heatwave was enhanced by human-induced climate change,4Vogel MM Zscheischler J Wartenburger R Dee D Seneviratne SI Concurrent 2018 hot extremes across Northern Hemisphere due to human-induced climate change.Earths Future. 2019; (published online June 7.)DOI:10.1029/2019EF001189Crossref PubMed Scopus (132) Google Scholar which prompts the question of whether such a statement applies to all factors that influenced the event. For instance, clusters of persistent high atmospheric pressure (ridges) have been observed over Europe over the past two decades, possibly explained by a slow-down of the mid-latitude summer circulation. However, projections showed that associated blocking is unlikely to change in the future based on traditional blocking metrics.5Woollings T Barriopedro D Methven J Blocking and its response to climate change.Curr Clim Change Rep. 2018; 4: 287-300Crossref PubMed Scopus (198) Google Scholar Thus, the attribution of such circulation changes to anthropogenic greenhouse gases is still under investigation, but, either way, changes in heatwave statistics are probably being driven mainly by the background thermodynamic response of the atmosphere. The climate has warmed by around 1°C globally to date since pre-industrial conditions;6Stocker TF Qin D Plattner GK et al.Climate change 2013: the physical science basis. Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge2013: 1535Google Scholar however, land areas,7Huntingford C Mercado LM High chance that current atmospheric greenhouse concentrations commit to warmings greater than 1.5°C over land.Sci Rep. 2016; 630294Crossref PubMed Scopus (31) Google Scholar particularly urban regions, are warming faster than the oceans, and the locations most relevant to human populations are therefore being more exposed to heat stress than this global average temperature increase implies. No event made this more apparent than the July–August 2003 heatwave, which saw around 15 000 heat-exposure-related deaths in France, and around 70 000 deaths across Europe.8Watts N Amann M Ayeb-Karlsson S et al.The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health.Lancet. 2018; 391: 581-630Summary Full Text Full Text PDF PubMed Scopus (591) Google Scholar Climate change had a significant role to play in 2003, with 70% of the Parisian heatwave deaths attributed to human greenhouse gas emissions;9Mitchell D Heaviside C Vardoulakis S et al.Attributing human mortality during extreme heat waves to anthropogenic climate change.Environ Res Lett. 2016; 11074006Crossref Scopus (190) Google Scholar yet asking the same question of the 2019 heatwave is less straightforward because the heat–mortality relationship is likely to have changed since countries have put in place better heat-response plans. Establishing the present-day heat-mortality relationship for each city requires daily information on different types of deaths, which is hard to collate on timescales of less than a year. As such, the only heat-related deaths we hear about at the time of the heatwave are those obviously associated with heat, such as those reported to be due to cold shock (a sudden change in heat [eg, from a hot beach to cold sea]) during the 2019 heatwave, or deaths in children left in overheated cars during the Chicago 1995 heat event. Therefore, although the extreme temperatures associated with the 2019 heatwave are tending towards becoming the new normal in terms of summer climate, it is less clear how this change will translate into human health. The deaths reported at the time will be a substantial underestimate of the true number, which will only become apparent in the coming months and years. These under-reported deaths will be mainly from cardiovascular or respiratory failure in older and at-risk populations, and such deaths are harder to immediately attribute to the heat. On the question of how bad this situation could get in the future, all climate models project warming as atmospheric greenhouse gases increase, with mean projected global warming at the end of the century ranging from 2°C to 5°C above preindustrial values.6Stocker TF Qin D Plattner GK et al.Climate change 2013: the physical science basis. Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge2013: 1535Google Scholar This range is due to a combination of uncertainties in the emissions scenario and in model physics, especially those related to clouds. To elucidate how future climate change under the Paris Agreement might alter a 2019-like heatwave, we evaluated the probability of recurrence of such a heatwave over France using a set of global climate models specifically designed to study extreme weather.10Mitchell D James R Forster PM Betts RA Shiogama H Allen M Realizing the impacts of a 1.5 °C warmer world.Nat Clim Chang. 2016; 6: 735-737Crossref Scopus (146) Google Scholar The 2019 heatwave is so extreme that our current models could not fully represent the observed temperature magnitude over France under current climate forcing, probably because of a misrepresentation of the soil-moisture feedback, which acts to enhance warming trends, especially in mid-latitude areas such as central Europe.11Seneviratne S Lüthi D Litschi M Schär C Land–atmosphere coupling and climate change in Europe.Nature. 2006; 443: 205-209Crossref PubMed Scopus (1132) Google Scholar Although it is hard to model all the conditions that drove the June 2019 heatwave, the evidence is clear that severe heatwaves will become more frequent as the climate continues to warm.2Stott PA Stone DA Allen MR Human contribution to the European heatwave of 2003.Nature. 2004; 432: 610-614Crossref PubMed Scopus (1144) Google Scholar Future heat-related mortality will depend on country-specific adaptability and mitigation plans, as well as the future emissions scenario; even so, different climate models give different temperature projections, so mortality counts are uncertain.12Guo Y Gasparrini A Li S et al.Quantifying excess deaths related to heatwaves under climate change scenarios: a multicountry time series modelling study.PLoS Med. 2018; 15e1002629Crossref PubMed Scopus (172) Google Scholar Unless we significantly reduce net greenhouse gas emissions, heatwaves will become more extreme in magnitude in the future, and human mortality will increase as a result, unless adequate heat-adaptation plans are put in place.13Lo YTE Mitchell DM Gasparrini A et al.Increasing mitigation ambition to meet the Paris Agreement's temperature goal avoids substantial heat-related mortality in US cities.Sci Adv. 2019; 5eaau4373Crossref PubMed Scopus (32) Google Scholar The concerns implicit in the literature8Watts N Amann M Ayeb-Karlsson S et al.The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health.Lancet. 2018; 391: 581-630Summary Full Text Full Text PDF PubMed Scopus (591) Google Scholar are that incorporation of long-term planning for heatwaves might not be occurring in some countries. Such frustrations to civil preparedness arise because of poor dissemination of climate science findings and inadequate data collection, and, perhaps, because health and climate researchers need to work together more closely, including sharing data.8Watts N Amann M Ayeb-Karlsson S et al.The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health.Lancet. 2018; 391: 581-630Summary Full Text Full Text PDF PubMed Scopus (591) Google Scholar Immediately before and during heatwaves, planned responses often seem to be rather last minute or poorly constrained. This situation affirms the need for climate modellers to continue their efforts towards better understanding and predicting heatwaves, with the aim of providing earlier warning, where possible. We strongly recommend that these efforts should occur alongside tighter collaborations with medical practitioners, researchers, and local officials involved in the design of heat-action plans. Despite the record-breaking temperatures of 2019, the total number of heatwave deaths might be lower than those during previous events. If so, this difference will be partly because of the persistence of the heat, but—to give due credit—also down to improved emergency heatwave plans developed in reaction to the 2003 and other disasters. Hospitals in particular should prepare for patients with increased heat stress and associated physiological conditions. Crucially, if correlations between past heatwaves and associated mortality rates based on previous heatwaves predict more deaths than actually occur during 2019, it will provide encouragement that recent heatwave planning has been effective. We declare no competing interests. Record breakersThe last 22 years contain 20 of the warmest (global average) years on record. This trend is powerfully demonstrated by the popular #Showyourstripes initiative. That most of the warmest years have occurred very recently is hardly surprising given that our planet is being heated by increasing amounts of heat trapping CO2 in the atmosphere—primarily emitted from burning fossil fuels. In this context 2019, which has seen a number of record-breaking heatwaves, is displaying exactly the sorts of conditions that we should expect to see more and more often. Full-Text PDF Open Access

Current greenhouse gas mitigation ambition is consistent with ~3°C global mean warming above preindustrial levels. There is a clear need to strengthen mitigation ambition to stabilize the climate at the Paris Agreement goal of warming of less than 2°C. We specify the differences in city-level heat-related mortality between the 3°C trajectory and warming of 2° and 1.5°C. Focusing on 15 U.S. cities where reliable climate and health data are available, we show that ratcheting up mitigation ambition to achieve the 2°C threshold could avoid between 70 and 1980 annual heat-related deaths per city during extreme events (30-year return period). Achieving the 1.5°C threshold could avoid between 110 and 2720 annual heat-related deaths. Population changes and adaptation investments would alter these numbers. Our results provide compelling evidence for the heat-related health benefits of limiting global warming to 1.5°C in the United States.

Given the Paris Agreement it is imperative there is greater understanding of the consequences of limiting global warming to the target 1.5° and 2°C levels above preindustrial conditions. It is challenging to quantify changes across a small increment of global warming, so a pattern-scaling approach may be considered. Here we investigate the validity of such an approach by comprehensively examining how well local temperatures and warming trends in a 1.5°C world predict local temperatures at global warming of 2°C. Ensembles of transient coupled climate simulations from multiple models under different scenarios were compared and individual model responses were analyzed. For many places, the multimodel forced response of seasonal-average temperatures is approximately linear with global warming between 1.5° and 2°C. However, individual model results vary and large contributions from nonlinear changes in unforced variability or the forced response cannot be ruled out. In some regions, such as East Asia, models simulate substantially greater warming than is expected from linear scaling. Examining East Asia during boreal summer, we find that increased warming in the simulated 2°C world relative to scaling up from 1.5°C is related to reduced anthropogenic aerosol emissions. Our findings suggest that, where forcings other than those due to greenhouse gas emissions change, the warming experienced in a 1.5°C world is a poor predictor for local climate at 2°C of global warming. In addition to the analysis of the linearity in the forced climate change signal, we find that natural variability remains a substantial contribution to uncertainty at these low-warming targets.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).A supplement to this article is available online (10.1175/BAMS-D-17-0088.2)

On 19 May 2016 the afternoon temperature reached 51.0 degrees C in Phalodi in the northwest of India - a new record for the highest observed maximum temperature in India. The previous year, a widely reported very lethal heat wave occurred in the southeast, in Andhra Pradesh and Telangana, killing thousands of people. In both cases it was widely assumed that the probability and severity of heat waves in India are increasing due to global warming, as they do in other parts of the world. However, we do not find positive trends in the highest maximum temperature of the year in most of India since the 1970s (except spurious trends due to missing data). Decadal variability cannot explain this, but both increased air pollution with aerosols blocking sunlight and increased irrigation leading to evaporative cooling have counteracted the effect of greenhouse gases up to now. Current climate models do not represent these processes well and hence cannot be used to attribute heat waves in this area. The health effects of heat are often described better by a combination of temperature and humidity, such as a heat index or wet bulb temperature. Due to the increase in humidity from irrigation and higher sea surface temperatures (SSTs), these indices have increased over the last decades even when extreme temperatures have not. The extreme air pollution also exacerbates the health impacts of heat. From these factors it follows that, from a health impact point of view, the severity of heat waves has increased in India. For the next decades we expect the trend due to global warming to continue but the surface cooling effect of aerosols to diminish as air quality controls are implemented. The expansion of irrigation will likely continue, though at a slower pace, mitigating this trend somewhat. Humidity will probably continue to rise. The combination will result in a strong rise in the temperature of heat waves. The high humidity will make health effects worse, whereas decreased air pollution would decrease the impacts.

On 4-6 December 2015, storm Desmond caused very heavy rainfall in Northern England and Southern Scotland which led to widespread flooding. A week after the event we provided an initial assessment of the influence of anthropogenic climate change on the likelihood of one-day precipitation events averaged over an area encompassing Northern England and Southern Scotland using data and methods available immediately after the event occurred. The analysis was based on three independent methods of extreme event attribution: historical observed trends, coupled climate model simulations and a large ensemble of regional model simulations. All three methods agreed that the effect of climate change was positive, making precipitation events like this about 40% more likely, with a provisional 2.5%-97.5% confidence interval of 5%-80%. Here we revisit the assessment using more station data, an additional monthly event definition, a second global climate model and regional model simulations of winter 2015/16. The overall result of the analysis is similar to the real-time analysis with a best estimate of a 59% increase in event frequency, but a larger confidence interval that does include no change. It is important to highlight that the observational data in the additional monthly analysis does not only represent the rainfall associated with storm Desmond but also that of storms Eve and Frank occurring towards the end of the month.