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Anthropogenic climate change is likely to increase the risk (probability of occurrence of a hazard) of extreme weather events in the future. Previous studies have robustly shown how and where climate change has already changed the risks of weather extremes. However, developing countries have been somewhat underrepresented in these studies, despite high vulnerability and limited capacities to adapt. How additional global warming would affect the future risks of extreme rainfall events in Bangladesh needs to be addressed to limit adverse impacts. Our study focuses on understanding and quantifying the relative risks of extreme rainfall events in Bangladesh under the Paris Agreement temperature goals of 1.5 and 2.0 ∘C warming above pre-industrial levels. In particular, we investigate the influence of anthropogenic aerosols on these risks given their likely future reduction and resulting amplification of global warming. Using large ensemble regional climate model simulations from weather@home under different forcing scenarios, we compare the risks of rainfall events under pre-industrial (natural; NAT), current (actual; ACT), 1.5 and 2.0 ∘C warmer, and greenhouse gas (GHG)-only (with pre-industrial levels of anthropogenic aerosols) conditions. Both GHGs and anthropogenic aerosols have an impact on seasonal mean rainfall over this region. In general, higher global mean temperature levels lead to higher rainfall and higher aerosol concentrations to lower rainfall, however the relative importance of the two factors varies between the regions. For extreme rainfall events, we find that the risk of a 1 in 100 year rainfall episode has already increased significantly compared with pre- industrial levels across parts of Bangladesh, with additional increases likely for 1.5 and 2.0 ∘C warming. Climate change impacts on the probabilities of extreme rainfall episodes are found during both pre-monsoon and monsoon seasons. Results show that reduction in anthropogenic aerosols will exacerbate the effects of GHG-induced warming and thereby increasing the rainfall intensity, which has otherwise attenuated the impacts. We highlight that the net aerosol effect varies from region to region within Bangladesh, which leads to different outcomes of aerosol reduction on extreme rainfall statistics and must therefore be considered in future risk assessments. While there is a substantial reduction in risk at 1.5 ∘C warming when compared to 2 ∘C warming, the difference is spatially and temporally variable too, specifically with respect to seasonal extreme rainfall events.

Analysis suggests that to limit global temperature rise, we must slash emissions and invest now to protect, manage and restore ecosystems and land for the future. Analysis suggests that to limit global temperature rise, we must slash emissions and invest now to protect, manage and restore ecosystems and land for the future.

A potential glacial lake outburst flood from Lake Palcacocha (Cordillera Blanca, Peru) threatens Huaraz, a city of 120,000 people. In 1941, an outburst flood destroyed one-third of the city and caused at least 1,800 fatalities. Since pre-industrial times, Lake Palcacocha has expanded due to the retreat of Palcaraju glacier. Here we used observations and numerical models to evaluate the anthropogenic contribution to the glacier’s retreat and glacial lake outburst flood hazard. We found that the magnitude of human-induced warming equals between 85 and 105% (5–95% confidence interval) of the observed 1 °C warming since 1880 in this region. We conclude that it is virtually certain (>99% probability) that the retreat of Palcaraju glacier to the present day cannot be explained by natural variability alone, and that the retreat by 1941 represented an early impact of anthropogenic greenhouse gas emissions. Our central estimate is that the overall retreat is entirely attributable to the observed temperature trend, and that the resulting change in the geometry of the lake and valley has substantially increased the outburst flood hazard. Human-induced warming is responsible for the retreat of Palcaraju glacier and the associated increase in glacial lake outburst flood hazard, according to an analysis of observations and numerical models.

Biennials, regional hubs and virtual attendance can slash emissions, new calculations show. Biennials, regional hubs and virtual attendance can slash emissions, new calculations show.

Here we explore the use of “counterfactual” weather forecasts, using perturbed initial condition runs of a state-of-the-art high-resolution coupled ocean-atmosphere-sea-ice ensemble NWP system, for the attribution of extreme weather events to anthropogenic climate change. We use the “record-shattering” heatwave experienced by Western North America during summer 2021 as a case study - though our forecast-based approach is applicable to other events.Since we cannot make direct observations of a world without human influence on climate, all approaches to extreme event attribution involve some kind of modelling, either statistical or numerical. Both approaches struggle with the most extreme weather events, which are poorly represented in both observational records and the climate models normally used for attribution studies. Recognising the compromises involved, researchers have traditionally relied on comparing results from several different approaches to assess the robustness of conclusions. We argue that a better approach would be to use initialised numerical models that have demonstrated their ability to simulate the event in question through a successful forecast.This work represents a continuation of a previous EGU talk and published study (https://meetingorganizer.copernicus.org/EGU21/EGU21-5731.html & https://doi.org/10.1073/pnas.2112087118), in which we used demonstrably successful weather forecasts to estimate the direct impact of increased CO2 concentrations (one component, but not the entirety, of human influence) on the 2019 European winter heatwave. In the previous and current work we use the operational ECMWF ensemble prediction system. This state-of-the-art weather forecast system is run at a much higher resolution (Tco639 / 18km) than most climate model simulations - important as even small reductions in resolution often change the representation of extreme events in numerical models. Using a reliable forecast ensemble allows us to quantify the associated uncertainties in our attribution analyses.We have built on this work with the aim of providing a more complete estimate of the human influence on an isolated extreme event. In addition to the reduction of CO2 concentrations back to pre-industrial levels, we now also remove an estimate of the human influence on 3D ocean temperatures since the pre-industrial period from the initial state of the forecast model. These changes allow the model to provide a “counterfactual” picture of what an extreme event might have looked like if it had occurred before human influence on the climate.Using this perturbed initial condition approach, we produce counterfactual forecasts of the Pacific Northwest heatwave at the end of June 2021. This event broke records throughout Western North America, including a new Canadian high temperature record of 49.6°C, shattering the previous record by almost 5°C. The heatwave was driven by a combination of meteorological factors, including an omega block and water vapour transport at the synoptic scale, and high solar irradiation and subsidence at the meso-scale (research into the drivers is ongoing). Crucially, the event was well-predicted by weather forecast models over a week in advance.We estimate the human contribution to this exceptional heatwave by comparing our counterfactual forecasts to the operational forecasts that successfully predicted the event.

Summary In the absence of immediate, rapid, and unprecedented reduction in global demand for carbon-intensive energy and products, the capture and permanent storage of billions of tons of carbon dioxide (CO2) annually will be needed before mid-century to meet Paris Agreement goals. Yet the focus on absolute emission reductions and cheaper, more temporary forms of carbon storage means that permanent CO2 disposal remains starved of investment, currently deployed to capture only about 0.1% of global Energy and Industrial Process (EIP) emissions. This stored fraction, the percentage of fossil EIP emissions that are captured and permanently stored, must reach 100% to stop EIP emissions causing further global warming. Here, we show that a cost-effective transition can occur by mandating an increasing stored fraction through a progressive carbon takeback obligation (CTBO) on fossil carbon producers and importers. By emulating the behavior of an integrated assessment model (IAM) and employing conservative assumptions for the costs of permanent carbon storage, we show that projected economy-wide costs of a CTBO policy are comparable to the costs associated with achieving similarly ambitious climate goals in IAMs employing a global carbon price, or potentially lower if the perceived policy risk cost associated with a CTBO is lower than that associated with a politically determined carbon price. Compared with a global carbon price, an upstream CTBO has advantages of simple governance, speed, and controllability: equivalent carbon prices under a CTBO are reliably capped by the cost of direct air capture and storage, by ensuring deployment keeps pace with continued fossil fuel use, reducing the risk of punitive carbon prices or more draconian measures being needed to drive out the final tranche of emissions. When combined with measures to reduce CO2 production in the near-term, a CTBO could deliver a viable pathway to achieving net-zero emissions consistent with 1.5°C by mid-century.

Between the 21st and 27th February 2019, climatologically exceptional warm temperature anomalies of 10-15 °C were experienced throughout Northern and Western Europe. In particular, the 25th - 27th February saw record-breaking temperatures measured at many weather stations over wide areas of Iberia, France, the British Isles, the Netherlands, Germany and Southern Sweden. This heatwave was well-predicted by the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system. Their forecasts indicated "extreme" heat was possible at a lead time of around two weeks, and likely at a lead time of around ten days. The performance of these forecasts in predicting the surface heat is also reflected in their ability to predict the synoptic situation. We exploit this successful forecast to perform an attribution analysis of the heatwave that differs from "conventional" analyses in several key regards. Firstly, we are not only confident that the model used is able to simulate the event in question; but that we are unequivocally studying the specific winter heatwave that occurred in Europe during February 2019. This analysis is carried out using a state-of-the-art coupled high-resolution forecast model ensemble, as opposed to the prescribed-SST experiments within climate model ensembles traditionally used for attribution.A crucial distinction between the typical climate model simulations used for attribution and the forecasts used here is that the climate model simulations are usually allowed to spin out for a sufficient length of time such that they have no memory of their initial conditions; an ensemble constructed in this way will therefore be representative of the climatology of the model (possibly conditioned on any prescribed-SST patterns). Unlike these climatological simulations, the successful forecasts used here are clearly heavily dependent on the initial conditions used. Within these simulations, the level of dynamical conditioning can therefore be specified by altering the lead time from initialisation to the event in question. We explore the implications of this aspect of forecast-based attribution, attempting to integrate between the "conventional" climatological and "storyline" frameworks of attribution. To simplify the interpretation of our experiments, here we have decided to only change a single feature between our factual and counterfactual experiments. The analysis presented is therefore limited to attributing the impact of diabatic heating due to increased CO2 concentrations above pre-industrial levels just over the days between the model initialisation date and the event. We carry out simulations at four different lead times from the event, allowing us to investigate the balance between the level of conditioning of the ensemble and the relaxation of the ensemble toward a new equilibrium at the lowered CO2 concentrations.

Committed warming describes how much future warming can be expected from historical emissions due to inertia in the climate system. It is usually defined in terms of the level of warming above the present for an abrupt halt of emissions. Owing to socioeconomic constraints, this situation is unlikely, so we focus on the committed warming from present-day fossil fuel assets. Here we show that if carbon-intensive infrastructure is phased out at the end of its design lifetime from the end of 2018, there is a 64% chance that peak global mean temperature rise remains below 1.5 °C. Delaying mitigation until 2030 considerably reduces the likelihood that 1.5 °C would be attainable even if the rate of fossil fuel retirement was accelerated. Although the challenges laid out by the Paris Agreement are daunting, we indicate 1.5 °C remains possible and is attainable with ambitious and immediate emission reduction across all sectors., Power plants, vehicles and industry will continue to produce emissions for as long as they are used. Here, the authors show that retiring existing fossil fuel infrastructure at the end of its expected lifetime provides a good chance that the 1.5 °C Paris Agreement target can still be met.

Interest in carbon offsetting is resurging among companies and institutions, but existing offerings fail to make continued use of fossil fuels compatible with a credible transition to sustainable net zero emissions. A clear definition of what makes an offset net-zero-compliant is needed. We introduce the ‘proset’, a new form of composite offset in which the fraction of carbon allocated to geological-timescale storage options increases progressively, reaching 100% by the target net zero date, generating predictable demand for effectively permanent CO2 storage while making the most of the near-term opportunities provided by nature-based climate solutions, all at an affordable cost to the offset purchaser.

Modified frequencies and magnitudes of extreme events due to climate change can have large impacts on societies and are therefore a key area of current research. Large model ensembles are required to quantify and attribute changes to extreme events. Until now, the large ensembles used for such studies are commonly atmosphere-only models forced with time-varying sea surface temperatures (SST) and sea ice. This approach is very powerful but presents problems of internal physical consistency. In an SST-forced model, the ocean acts as having an infinite heat capacity whereas in the real-world SST emerges dynamically from the interaction of atmospheric and oceanic processes (Dong et al., 2020). This is particularly relevant for the North Atlantic where ocean processes, in particular meridional heat transport, are key drivers of air-sea coupling. A long-standing challenge, however, is the computational cost of spinning up fully coupled atmosphere-ocean models that hinders their application to large-ensemble, high-resolution simulations required to quantify changing hazard frequencies of low-probability events. In this work we combine the HadAM4 (Webb et al., 2001) atmospheric model at N144 resolution with a Slab ocean (Hewitt & Mitchell, 1997; Williams et al., 2003), which includes a simple sea ice model, to yield the atmosphere-Slab Ocean model HadSM4. The Slab Ocean is forced with diagnosed heat convergence (Q-Flux) and surface currents for sea ice advection (a useful model-development finding for this kind of experiment is that including sea ice velocity information from reanalyses in the surface current field yields a substantially improved spatial pattern of sea ice). We are therefore able to directly compare SST-forced atmosphere-only runs with Q-Flux-forced runs where SST is an emergent property of the model, specifically accounting for the passive response of SSTs in the North Atlantic. Using the distributed infrastructure of climateprediction.net (Guillod et al., 2017; Massey et al., 2015) we run large ensembles to compare extreme statistics and quantify the importance of fast ocean-atmosphere coupling for extreme event statistics.We further use this large ensemble setup to investigate the dynamics that drive extreme events from the ocean through air-sea interaction to atmospheric processes. We address is whether and how the slope of a return-time plot (related to the scale parameter of a GEV distribution) is affected by atmosphere-ocean interactions, since this statistic plays a central role in determining relative-risk estimates in event attribution studies. We then investigate how a perturbation to the Q-Flux, representing a change in ocean heat transport, propagates through the system and alters the statistics of extreme events.Dong et al., 2020, Climate Dynamics, 55(5–6), 1225–1245. Guillod et al., 2017, Geoscientific Model Development, 10(5), 1849–1872. Hewitt & Mitchell, 1997, Climate Dynamics, 13(11), 821–834.Massey et al., 2015, Quarterly Journal of the Royal Meteorological Society, 141(690), 1528–1545. Webb et al., 2001, Climate Dynamics, 17(12), 905–922. Williams et al., 2003, Climate Dynamics, 20(7–8), 705–721.

As climate changes – potentially to a warmer state than any time during the evolution of humans – heat extremes threatening human health, global ecosystem and socio-economic fabric of our society are occurring at increasing frequency and intensity in most parts of the world. This study examines changes in global land area and population exposed to both tails of temperature distribution in changing climate since heat and cold exposure is directly associated with a range of health impacts and affects thermal comfort and occupational capacity. We first utilise the latest ECMWF atmospheric reanalysis, ERA5, to examine changes over the satellite era (since 1979), and then we explore the equivalent changes in CMIP6 archive of historical runs and future projections. Besides daily maximum and minimum of dry-bulb surface air temperature (SAT), we also consider daily extremes of the universal thermal climate index (UTCI) that includes the influence of humidity, wind and radiation encapsulating the synergetic heat exchanges between the environment and the human body. Our analysis dissects changes in spatial and temporal exposure to both heat waves and cold waves and presents metrics contrasting changes in the opposite extremes of SAT and UTCI distributions. We assess the significance of the observed, modelled and projected changes and relate them to external drivers of climate change.

Integrated Assessment Model (IAM) design philosophy currently focuses on demand-side global carbon pricing as the principal policy tool to drive mitigation. However, ambitious mitigation scenarios produced with these IAMs rely heavily on the availability of carbon capture and storage (CCS) technologies in the mid-century, at the scale of billions of tonnes. If integrated assessment continues to employ demand-side policies exclusively we risk a gap forming between the requirements of economically-optimal mitigation trajectories in these IAMs and the reality of developed CCS capacity.If CCS capacity fails to keep up with the ambition of mitigation policy, carbon prices could rise well above the cost of direct air capture as markets aim to drive residual emissions down. To avoid this, scenarios could include both supply and demand-side policies in tandem, where supply-side policies are targeted to increase CCS capacity to appropriate levels.One such supply-side policy option is a Carbon Takeback Obligation (CTBO), where suppliers of fossil carbon are required to recapture and store an increasing fraction of the carbon in their products. This ‘stored fraction’ would be increased from near zero at present, up to 100% at the time of net-zero. By applying such a policy suppliers of fossil carbon products are forced to take responsibility for decarbonising their own products and provide the drive to develop the CCS capacity necessary to achieve net-zero emissions in the mid-century. In theory, if a CTBO was enforced globally the costs associated with the production of one tonne of CO2 would be capped around the price for the capture, transport and storage of diffuse, mobile, or otherwise hard-to-abate CO2 emission sources (i.e. the cost of direct air capture).Here, we discuss the implementation of a global CTBO. Using an Integrated Assessment Model emulator, tuned to existing IAM carbon price/abatement rate relationships, we explore the total policy cost of applying a CTBO globally to achieve net-zero by 2050. Using the emulator we harmonise the combined CTBO and demand-side carbon price policies, and show how a SSP2-26 level of ambition can be achieved using these policies with a similar total policy cost. Further, we explore what additional near-term carbon prices can be included to achieve SSP2-19 level policy ambition. These results suggest there are significant benefits to defining climate policy around measures targeting suppliers of fossil carbon, including for long-term planning, implementation and governance of the policy, and overall cost. For further insight, and to provide a greater variety of policy options feeding into IPCC’s WG3, we argue IAMs should look to include CTBO-like policies in future scenario design.

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.

Global aviation operations contribute to anthropogenic climate change via a complex set of processes that lead to a net surface warming. Of importance are aviation emissions of carbon dioxide (CO2), nitrogen oxides (NOx), water vapor, soot and sulfate aerosols, and increased cloudiness due to contrail formation. Aviation grew strongly over the past decades (1960–2018) in terms of activity, with revenue passenger kilometers increasing from 109 to 8269 billion km yr−1, and in terms of climate change impacts, with CO2 emissions increasing by a factor of 6.8–1034 Tg CO2 yr−1. Over the period 2013–2018, the growth rates in both terms show a marked increase. Here, we present a new comprehensive and quantitative approach for evaluating aviation climate forcing terms. Both radiative forcing (RF) and effective radiative forcing (ERF) terms and their sums are calculated for the years 2000–2018. Contrail cirrus, consisting of linear contrails and the cirrus cloudiness arising from them, yields the largest positive net (warming) ERF term followed by CO2 and NOx emissions. The formation and emission of sulfate aerosol yields a negative (cooling) term. The mean contrail cirrus ERF/RF ratio of 0.42 indicates that contrail cirrus is less effective in surface warming than other terms. For 2018 the net aviation ERF is +100.9 mW (mW) m−2 (5–95% likelihood range of (55, 145)) with major contributions from contrail cirrus (57.4 mW m−2), CO2 (34.3 mW m−2), and NOx (17.5 mW m−2). Non-CO2 terms sum to yield a net positive (warming) ERF that accounts for more than half (66%) of the aviation net ERF in 2018. Using normalization to aviation fuel use, the contribution of global aviation in 2011 was calculated to be 3.5 (4.0, 3.4) % of the net anthropogenic ERF of 2290 (1130, 3330) mW m−2. Uncertainty distributions (5%, 95%) show that non-CO2 forcing terms contribute about 8 times more than CO2 to the uncertainty in the aviation net ERF in 2018. The best estimates of the ERFs from aviation aerosol-cloud interactions for soot and sulfate remain undetermined. CO2-warming-equivalent emissions based on global warming potentials (GWP* method) indicate that aviation emissions are currently warming the climate at approximately three times the rate of that associated with aviation CO2 emissions alone. CO2 and NOx aviation emissions and cloud effects remain a continued focus of anthropogenic climate change research and policy discussions., Graphical abstract Image 1, Highlights • Global aviation warms Earth's surface through both CO2 and net non-CO2 contributions. • Global aviation contributes a few percent to anthropogenic radiative forcing. • Non-CO2 impacts comprise about 2/3 of the net radiative forcing. • Comprehensive and quantitative calculations of aviation effects are presented. • Data are made available to analyze past, present and future aviation climate forcing.

Growth in aviation contributes more to global warming than is generally appreciated because of the mix of climate pollutants it generates. Here, we model the CO2 and non-CO2 effects like nitrogen oxide emissions and contrail formation to analyse aviation’s total warming footprint. Aviation contributed approximately 4% to observed human-induced global warming to date, despite being responsible for only 2.4% of global annual emissions of CO2. Aviation is projected to cause a total of about 0.1 °C of warming by 2050, half of it to date and the other half over the next three decades, should aviation’s pre-COVID growth resume. The industry would then contribute a 6%–17% share to the remaining 0.3 °C–0.8 °C to not exceed 1.5 °C–2 °C of global warming. Under this scenario, the reduction due to COVID-19 to date is small and is projected to only delay aviation’s warming contribution by about five years. But the leveraging impact of growth also represents an opportunity: aviation’s contribution to further warming would be immediately halted by either a sustained annual 2.5% decrease in air traffic under the existing fuel mix, or a transition to a 90% carbon-neutral fuel mix by 2050.

Here, we provide a comment in response to a recently published paper ‘Unintentional unfairness when applying new greenhouse gas emissions metrics at country level’ by Rogelj and Schleussner (2019). We note a number of errors in their critique of the use of GWP* to relate cumulative and short-lived climate pollutants, argue that their logic is flawed, their ethical considerations are narrow and their conclusions not justified by the results presented.

Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard Impulse Response model used for greenhouse gas (GHG) metric calculations in the IPCC's fifth assessment report, plus one additional physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth System Models and observations. These six equations are transparent and sufficiently simple that the model is able to be written in standard tabular data analysis packages, such as Excel; increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a one million member ensemble, using the constrained ensemble to make scenario dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased hot or cold: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications; and therefore could be used as a lowest common denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

As the Paris Agreement moves towards implementation, we explore opportunities it presents to move part of the climate change mitigation challenge upstream into supply-side climate policy. Supply-side climate policies have been sparsely employed to date, although interest is growing in approaches that can curtail and end fossil fuel production. Rather than focussing on fully eliminating fossil fuel production, we set out an alternative supply-side approach that involves fossil fuel producers taking progressive action to reduce and eliminate the climate impacts of their products. Our proposal uses the Paris Agreement’s framework for finance and international cooperation to establish supply-side climate action that supplements demand-side climate policies, and thereby enhance climate ambition. The concepts presented rely on large-scale geosequestration technologies that can deposit carbon into geological reservoirs at rates progressively aligned to those at which carbon is being extracted by fossil fuel producers. Convergence of extraction and deposition rates is a necessary condition for achieving net-zero CO2 emissions while still enabling some essential uses of fossil carbon. We propose an evolutionary approach to policy development, starting as a technology-support mechanism using results-based finance before transitioning into systematic measures under, firstly, Article 6 of the Paris Agreement, and, ultimately, decarbonised fuel standards. Collectively these could incentivise fossil fuel resource holders to manage both outflows and inflows of carbon in the geosphere, and in doing so, create, foster, accelerate and maintain an urgently needed market for geological CO2 storage.

Multi-thousand member climate model simulations are highly valuable for showing how extreme weather events will change as the climate changes, using a physically-based approach. However, until now, studies using such an approach have been limited to using models with a resolution much coarser than the most modern systems. We have developed a global atmospheric model with 5/6°x5/9° resolution (~60km in middle latitudes) that can be run in the climateprediction.net distributed computing system to produce such large datasets. This resolution is finer than that of many current global climate models and sufficient for good simulation of extratropical synoptic features such as storms. It will also allow many extratropical extreme weather events to be simulated without requiring regional downscaling. We will show that this model's simulation of extratropical weather is competitive with that in other current models. We will also present results from the first multi-thousand member ensembles produced at this resolution, showing the impact of 1.5°C and 2°C global warming on extreme winter rainfall and extratropical cyclones in Europe.

The atmospheric lifetime and radiative impacts of different climate pollutants can both differ markedly, so metrics that equate emissions using a single scaling factor, such as the 100-year Global Warming Potential (GWP100), can be misleading. An alternative approach is to report emissions as ‘warming-equivalents’ that result in similar warming impacts without requiring a like-for-like weighting per emission. GWP*, an alternative application of GWPs where the CO2-equivalence of short-lived climate pollutant emissions is predominantly determined by changes in their emission rate, provides a straightforward means of generating warming-equivalent emissions. In this letter we illustrate the contrasting climate impacts resulting from emissions of methane, a short-lived greenhouse gas, and CO2, and compare GWP100 and GWP* CO2-equivalents for a number of simple emissions scenarios. We demonstrate that GWP* provides a useful indication of warming, while conventional application of GWP100 falls short in many scenarios and particularly when methane emissions are stable or declining, with important implications for how we consider ‘zero emission’ or ‘climate neutral’ targets for sectors emitting different compositions of gases. We then illustrate how GWP* can provide an improved means of assessing alternative mitigation strategies. GWP* allows warming-equivalent emissions to be calculated directly from CO2-equivalent emissions reported using GWP100, consistent with the Paris Rulebook agreed by the UNFCCC, on condition that short-lived and cumulative climate pollutants are aggregated separately, which is essential for transparency. It provides a direct link between emissions and anticipated warming impacts, supporting stocktakes of progress towards a long-term temperature goal and compatible with cumulative emissions budgets.

For public, scientists and policy-makers, it is important to know to what extent human-induced climate change played (or did not play) a role behind changing risks of extreme weather events. Probabilistic event attribution (PEA) can provide scientific information regarding this association and reveal whether and to what extent external drivers of climate change have influenced the probability of high-impact weather events. To date, most of the PEA-based studies have focused on extreme events of mid-latitudes and predominantly events that have occurred in the developed countries. Developing countries located at the tropical monsoon regions are underrepresented in this field of research, despite that fact that these countries are highly climate vulnerable, often experience extreme weather events that cause severe damages and have the least capacity to adapt. Bangladesh, a South Asian country with tropical monsoon climate, is a hotspot of climate change impacts as it is vulnerable to a combination of increasing challenges from record-breaking temperatures, extreme rainfall events, more intense river floods, tropical cyclones, and rising sea levels. The unique geographical location of this country particularly exposes it to high risks of flooding and landslides caused by heavy rainfall events. Observation based studies indicate that the frequency of high-intensity rainfall events may have already increased, with significant repercussions for agriculture, health, ecosystems and economic development.Using high resolution regional climate model (RCM) simulations from weather@home, here we quantify the risks of extreme rainfall events in Bangladesh under pre-industrial, present-day and future climate scenarios of the Paris Agreement temperature targets of 1.5°C and 2°C warming. Additionally, we assess the risks under greenhouse gas (GHG)-only climate scenario where anthropogenic aerosols are reduced to pre-industrial levels. In order to test the robustness of the RCM results, available four atmosphere only global circulation model (AGCM) simulations from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project are analysed. This enabled for the first time, a multi-model assessment of the changing risks of extreme rainfall events in Bangladesh considering anthropogenic climate change drivers.Findings suggest that both a 1.5°C and 2.0°C warmer world is poised to experience increased seasonal mean and, to a lesser extent, increased extreme rainfall events. The risk of a 1 in 100 year rainfall event under current climate condition has already increased significantly compared with pre-industrial levels. Substantial reduction in the impacts resulting from 1.5°C compared with 2°C warming is reported in this study; however the difference is spatially and temporally variable across Bangladesh. This paper highlights that reduction in the anthropogenic aerosols play an important role in determining the overall future climate change impacts; by exacerbating the effects of GHG induced global warming and thereby increasing the rainfall intensity. The policy-makers therefore need to take stronger climate actions to avoid impacts of 2°C warmer world and consider future changes in the risks of extreme rainfall events in the face of changeable GHG and aerosol impacts.

Here we present a Generalised Impulse Response (GIR) model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and teaching. This model is based on a set of only six equations, which correspond to the standard Impulse Response model used for greenhouse gas metric calculations by the IPCC, plus one physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. These six equations are simple and transparent enough to be easily understood and implemented in other models without reliance on the original source code, but flexible enough to reproduce observed well-mixed greenhouse gas (GHG) concentrations and atmospheric lifetimes, best-estimate effective radiative forcing, and temperature response. We describe the assumptions and methods used in selecting the default parameters, but emphasize that other methods would be equally valid: our focus here is on identifying a minimum level of structural complexity. The tunable nature of the model lends it to use as a fully transparent emulator of complex Earth System Models, such as those participating in CMIP6, while also reproducing the behaviour of other simple climate models. We argue that this GIR model is adequate to reproduce the global temperature response to global emissions and effective radiative forcing, and that it should be used as a lowest-common denominator to provide consistency and continuity between different climate assessments. The model design is such that it can be written in tabular data analysis software, such as Excel, increasing the potential user base considerably.

Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC)−1 (median and 5–95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS∕TCR parameters but less sensitive to the ERF from a doubling of CO2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081–2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS∕TCR distributions.

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.

A very large ensemble is used to identify subgrid-scale parameter settings for the HadCM3 model that are capable of best simulating the ocean state over the recent past (1980–2010). A simple particle filtering technique based upon the agreement of basin mean sea surface temperature (SST) and upper 700-m ocean heat content with EN3 observations is applied to an existing perturbed physics ensemble with initial conditions perturbations. A single set of subgrid-scale parameter values was identified from the wide range of initial parameter sets that gave the best agreement with ocean observations for the period studied. The parameter set, different from the standard model parameters, has a transient climate response of 1.68 K. The selected parameter set shows an improved agreement with EN3 decadal-mean SST patterns and the Atlantic meridional overturning circulation (AMOC) at 26°N as measured by the Rapid Climate Change (RAPID) array. Particle filtering techniques as demonstrated here could have a useful role in improving the starting point for traditional model-tuning exercises in coupled climate models.

In order to mobilize the volume of mitigation required to reach a global emissions path consistent with 1.5°C, policy instruments need to be much more stringent than they have been to date. They wi...

Researchers have quantified the contributions of industrialized and developing nations’ historical emissions to global surface temperature rise. Recent findings that nearly two-thirds of total industrial CO2 and CH4 emissions can be traced to 90 major industrial carbon producers have drawn attention to their potential climate responsibilities. Here, we use a simple climate model to quantify the contribution of historical (1880–2010) and recent (1980–2010) emissions traced to these producers to the historical rise in global atmospheric CO2, surface temperature, and sea level. Emissions traced to these 90 carbon producers contributed ∼57% of the observed rise in atmospheric CO2, ∼42–50% of the rise in global mean surface temperature (GMST), and ∼26–32% of global sea level (GSL) rise over the historical period and ∼43% (atmospheric CO2), ∼29–35% (GMST), and ∼11–14% (GSL) since 1980 (based on best-estimate parameters and accounting for uncertainty arising from the lack of data on aerosol forcings traced to producers). Emissions traced to seven investor-owned and seven majority state-owned carbon producers were consistently among the top 20 largest individual company contributors to each global impact across both time periods. This study lays the groundwork for tracing emissions sourced from industrial carbon producers to specific climate impacts and furthers scientific and policy consideration of their historical responsibilities for climate change.

Carbon budgets, which define the total allowable CO2 emissions associated with a given global climate target, are a useful way of framing the climate mitigation challenge. In this paper, we review the geophysical basis for the idea of a carbon budget, showing how this concept emerges from a linear climate response to cumulative CO2 emissions. We then discuss the difference between a “CO2-only carbon budget” associated with a given level of CO2-induced warming and an “effective carbon budget” associated with a given level of warming caused by all human emissions. We present estimates for the CO2-only and effective carbon budgets for 1.5 and 2 °C, based on both model simulations and updated observational data. Finally, we discuss the key contributors to uncertainty in carbon budget estimates and suggest some implications of this uncertainty for decision-making. Based on the analysis presented here, we argue that while the CO2-only carbon budget is a robust upper bound on allowable emissions for a given climate target, the size of the effective carbon budget is dependent on the how quickly we are able to mitigate non-CO2 greenhouse gas and aerosol emissions. This suggests that climate mitigation efforts could benefit from being responsive to a changing effective carbon budget over time, as well as to potential new information that could narrow uncertainty associated with the climate response to CO2 emissions.

Meeting the ambitious long-term temperature goal of the Paris Agreement necessitates far-reaching changes in the global energy system and economy. Understanding both physical constraints on this transition, as well as economic constraints on the deployment of various technologies, can help to assess what role carbon capture and storage (CCS) can play in achieving this transition over the remainder of the century. This chapter reviews the necessary physical climate science to understand the role of CCS in limiting warming. We describe how the concept of a finite “carbon budget” arises out of interactions between the carbon-cycle and the rest of the climate system. A clear physically-based requirement for achieving the long-term temperature goal of the Paris Agreement is the necessity of reaching global net-zero carbon dioxide emissions into the atmosphere. Based on this insight, we undertake a high-level but informative decomposition of Integrated Assessment Model (IAM) emissions reduction scenarios into contributions arising from the substitution of demand for carbon dioxide, and from the sequestration of carbon dioxide. In all scenarios that allow for the availability of CCS, the cost-effective solution offsets 20% or greater of business-as-usual end-of-century CO2 emissions with CCS and/or negative emissions technologies. We highlight the need for the sequestered fraction of extracted carbon, a potentially useful climate policy measure, to rise to reach unity in order to limit warming to any value and describe a near-term schedule for increases in this fraction compatible with IAM-simulated cost-effective pathways that succeed in meeting the Paris Agreement long-term temperature goal.

Investors will play a major role, whether active or passive, in climate change mitigation. To enable prudent decision-making, we propose three physically based engagement principles that could be used to assess whether an investment is consistent with a long-term climate goal.

Summer weather extremes are often associated with high‐amplitude atmospheric planetary waves (Petoukhov et al., 2013). Such conditions lead to stationary weather patterns, triggering heat waves and sometimes prolonged intense rainfall. These wave events, referred to as periods of Quasi‐Resonant Amplification (QRA), are relatively rare though and hence provide only a few data points in the meteorological record to analyse. Here, we use atmospheric models coupled to boundary conditions that have evolved slowly (i.e., climate), to supplement measurements. Specifically we assess altered probabilities of resonant episodes by employing a unique massive ensemble of atmosphere‐only climate simulations to populate statistical distributions of event occurrence. We focus on amplified waves during the two most extreme European heat waves on record, in years 2003 and 2015 (Russo et al., 2015). These years are compared with other modelled recent years (1987–2011), and critically against a modelled world without climate change. We find that there are differences in the statistical characteristics of wave event likelihood between years, suggesting a strong dependence on the known and prescribed Sea Surface Temperature (SST) patterns. The differences are larger than those projected to have occurred under climate change since the pre‐industrial period. However, this feature of small differences since pre‐industrial is based on single large ensembles, with members consisting of a range of estimates of SST adjustment from pre‐industrial to present. Such SST changes are from projections by a set of coupled atmosphere–ocean (AOGCM) climate models. When instead an ensemble for pre‐industrial estimates is subdivided into simulations according to which AOGCM the SST changes are based on, we find differences in QRA occurrence. These differences suggest that to reliably estimate changes to extremes associated with altered amplification of planetary waves, and under future raised greenhouse gas concentrations, likely requires reductions in any spread of future modelled SST patterns.

The North Atlantic Oscillation (NAO) and eddy-driven jet contain a forced component arising from sea surface temperature (SST) variations. Due to large amounts of internal variability, it is not trivial to determine where and to what extent SSTs force the NAO and jet. A linear statistical–dynamic method is employed with a large climate ensemble to compute the sensitivities of the winter and summer NAO and jet speed and latitude to the SSTs. Key regions of sensitivity are identified in the Indian and Pacific basins, and the North Atlantic tripole. Using the sensitivity maps and a long observational SST dataset, skillful reconstructions of the NAO and jet time series are made. The ability to skillfully forecast both the winter and summer NAO using only SST anomalies is also demonstrated. The linear approach used here allows precise attribution of model forecast signals to SSTs in particular regions. Skill comes from the Atlantic and Pacific basins on short lead times, while the Indian Ocean SSTs may contribute to the longer-term NAO trend. However, despite the region of high sensitivity in the Indian Ocean, SSTs here do not provide significant skill on interannual time scales, which highlights the limitations of the imposed SST approach. Given the impact of the NAO and jet on Northern Hemisphere weather and climate, these results provide useful information that could be used for improved attribution and forecasting.

We investigate the opposing effects of direct radiative forcing and sea surface warming on the atmospheric circulation using a hierarchy of models. In large ensembles of three general circulation models, direct $$\hbox {CO}_2$$ forcing produces a wavenumber 5 stationary wave over the Northern Hemisphere in summer. Sea surface warming produces a similar wave, but with the opposite sign. The waves are also present in the Coupled Model Intercomparison Project phase 5 ensemble with opposite signs due to direct $$\hbox {CO}_2$$ and sea surface warming. Analyses of tropical precipitation changes and equivalent potential temperature changes and the results from a simple barotropic model show that the wave is forced from the tropics. Key forcing locations are the Western Atlantic, Eastern Atlantic and in the Indian Ocean just off the east coast of Africa. The stationary wave has a significant impact on regional temperature anomalies in the Northern Hemisphere summer, explaining some of the direct effect that $$\hbox {CO}_2$$ concentration has on temperature extremes. Ultimately, the climate sensitivity and future changes in the land–sea temperature contrast will dictate the balance between the opposing effects on regional changes in mean and extreme temperature and precipitation under climate change.

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

In June 2013, Uttarakhand experienced a hydro-meteorological disaster due to a 4 d extreme precipitation event of return period more than 100 years, claiming thousands of lives and causing enormous damage to infrastructure. Using the weather@home climate modelling system and its Half a degree Additional warming, Prognosis and Projected Impacts simulations, this study investigates the change in the return period of similar events in a 1.5 °C and 2 °C warmer world, compared to current and pre-industrial levels. We find that the likelihood of such extreme precipitation events will significantly increase under both future scenarios. We also estimate the change in extreme river flow at the Ganges; finding a considerable increase in the risk of flood events. Our results also suggest that until now, anthropogenic aerosols may have effectively counterbalanced the otherwise increased meteorological flood risk due to greenhouse gas (GHG) induced warming. Disentangling the response due to GHGs and aerosols is required to analyses the changes in future rainfall in the South Asia monsoon region. More research with other climate models is also necessary to make sure these results are robust.

The year 2014 broke the record for the warmest yearly average temperature in Europe. Attributing how much this was due to anthropogenic climate change and how much it was due to natural variability is a challenging question but one that is important to address. In this study, we compare four event attribution methods. We look at the risk ratio (RR) associated with anthropogenic climate change for this event, over the whole European region, as well as its spatial distribution. Each method shows a very strong anthropogenic influence on the event over Europe. However, the magnitude of the RR strongly depends on the definition of the event and the method used. Across Europe, attribution over larger regions tended to give greater RR values. This highlights a major source of sensitivity in attribution statements and the need to define the event to analyze on a case-by-case basis.

Discussions on a long-term global goal to limit climate change, in the form of an upper limit to warming, were only partially resolved at the United Nations Framework Convention on Climate Change negotiations in Paris, 2015. Such a political agreement must be informed by scientific knowledge. One way to communicate the costs and benefits of policies is through a mapping that systematically explores the consequences of different choices. Such a multi-disciplinary effort based on the analysis of a set of scenarios helped structure the IPCC AR5 Synthesis Report. This Perspective summarizes this approach, reviews its strengths and limitations, and discusses how decision-makers can use its results in practice. It also identifies research needs that can facilitate integrated analysis of climate change and help better inform policy-makers and the public.

Parties to the United Nations Framework Convention on Climate Change (UNFCCC) have requested guidance on common greenhouse gas metrics in accounting for Nationally determined contributions (NDCs) to emission reductions1. Metric choice can affect the relative emphasis placed on reductions of ‘cumulative climate pollutants’ such as carbon dioxide versus ‘short-lived climate pollutants’ (SLCPs), including methane and black carbon2, 3, 4, 5, 6. Here we show that the widely used 100-year global warming potential (GWP100) effectively measures the relative impact of both cumulative pollutants and SLCPs on realized warming 20–40 years after the time of emission. If the overall goal of climate policy is to limit peak warming, GWP100 therefore overstates the importance of current SLCP emissions unless stringent and immediate reductions of all climate pollutants result in temperatures nearing their peak soon after mid-century7, 8, 9, 10, which may be necessary to limit warming to “well below 2 °C” (ref. 1). The GWP100 can be used to approximately equate a one-off pulse emission of a cumulative pollutant and an indefinitely sustained change in the rate of emission of an SLCP11, 12, 13. The climate implications of traditional CO2-equivalent targets are ambiguous unless contributions from cumulative pollutants and SLCPs are specified separately.

Computing resources donated by volunteers have generated the first superensemble of regional climate model results, in which the Hadley Centre Regional Model, version 3P (HadRM3P), and Hadley Centre Atmosphere Model, version 3P (HadAM3P), were implemented for the western United States at 25-km resolution. Over 136,000 valid and complete 1-yr runs have been generated to date: about 126,000 for 1960–2009 using observed sea surface temperatures (SSTs) and 10,000 for 2030–49 using projected SSTs from a global model simulation. Ensemble members differ in initial conditions, model physics, and (potentially, for future runs) SSTs. This unprecedented confluence of high spatial resolution and large ensemble size allows high signal-to-noise ratio and more robust estimates of uncertainty. This paper describes the experiment, compares model output with observations, shows select results for climate change simulations, and gives examples of the strength of the large ensemble size.

Some of the differences between recent estimates of the remaining budget of carbon dioxide (CO2) emissions consistent with limiting warming to 1.5 °C arise from different estimates of the level of warming to date relative to pre-industrial conditions, but not all. Here we show that, for simple geometrical reasons, the combination of both the level and rate of human-induced warming provides a remarkably accurate prediction of remaining emission budgets to peak warming across a broad range of scenarios, if budgets are expressed in terms of CO2-forcing-equivalent emissions. These in turn predict CO2 emissions budgets if (but only if) the fractional contribution of non-CO2 drivers to warming remains approximately unchanged, as it does in some ambitious mitigation scenarios, indicating a best-estimate remaining budget for 1.5 °C of about 22 years’ current emissions from mid-2017, with a ‘likely’ (1 standard error) range of 13–32 years. This provides a simple, transparent and model-independent metric of progress towards an ambitious temperature stabilization goal that could be used to inform the Paris Agreement stocktake process. It is less applicable to less ambitious goals. Alternative definitions of current warming and scenarios for non-CO2 drivers give lower 1.5 °C budgets. Lower budgets based on the MAGICC simple modelling system widely used in integrated assessment studies reflect its relatively high simulated current warming rates. A combination of the level and rate of human-induced warming allows estimation of remaining emission budgets to peak warming across a broad range of scenarios, suggests an analysis of emissions budgets expressed in terms of CO2-forcing-equivalent emissions.

Anthropogenic climate change is likely to increase the frequency of extreme weather events in future. Previous studies have robustly shown how and where climate change has already changed the risks of weather extremes. However, developing countries have been somewhat underrepresented in these studies, despite high vulnerability and limited capacities to adapt. How additional global warming would affect the future risks of extreme rainfall events in Bangladesh needs to be addressed to limit adverse impacts. Our study focuses on understanding and quantifying the relative risks of seasonal extreme rainfall events in Bangladesh under the Paris Agreement temperature goals of 1.5 °C and 2 °C warming above pre-industrial levels. In particular, we investigate the influence of anthropogenic aerosols on these risks given their likely future reduction and resulting amplification of global warming. Using large ensemble regional climate model simulations from weather@home under different forcing scenarios, we compare the risks of rainfall events under pre-industrial (natural), current (actual), 1.5 °C, and 2.0 °C warmer and greenhouse gas only (anthropogenic aerosols removed) conditions. We find that the risk of a 1 in 100 year rainfall event has already increased significantly compared with pre-industrial levels across parts of Bangladesh, with additional increases likely for 1.5 and 2.0 degree warming (of up to 5.5 times higher, with an uncertainty range of 3.5 to 7.8 times). Impacts were observed during both the pre-monsoon and monsoon periods, but were spatially variable across the country in terms of the level of impact. Results also show that reduction in anthropogenic aerosols plays an important role in determining the overall future climate change impacts; by exacerbating the effects of GHG induced global warming and thereby increasing the rainfall intensity. We highlight that the net aerosol effect varies from region to region within Bangladesh, which leads to different outcomes of aerosol reduction on extreme rainfall statistics, and must therefore be considered in future risk assessments. Whilst there is a substantial reduction in the impacts resulting from 1.5 °C compared with 2 °C warming, the difference is spatially and temporally variable, specifically with respect to seasonal extreme rainfall events.