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Populations around the world have already experienced the increasing severity of extreme weather causing disaster displacement. Anthropogenic climate change can intensify these impacts. Extreme event attribution studies center around the question of whether impactful extreme events could have occurred in a pre-industrial climate. Here, we argue that the next step for attribution science is to focus on those most vulnerable populations to future extremes and impacts from climate change. Up until now, the vulnerability dimension has not been systematically addressed in attribution studies, yet it would add urgently needed context, given the vast differences in adaptive capacity. We propose three integrative points to cascade disaster displacement linked to anthropogenic climate change.

Climate change communication has been and remains to be challenging, even when there is no controversy amongst actual climate science experts anymore. However, it is an uphill battle in cases where the science is less clear or where consensus has emerged only very recently. Exemplified for global and regional (or national for that matter) warming levels to date (with reference to the quasi-preindustrial baseline 1850-1900), I will demonstrate what sci-comm problems we face and which ways there are to resolve it. This includes the discussion of strategies to raise awareness within the expert community as well as suggestions for clear, salient and perhaps emotional communication to the public (or media for that matter). The fact that we get ever close to the 1.5°C warming threshold adds considerable urgency to the issue.I will first introduce the method(s) to estimate the human-induced level of global and regional warming, i.e. how much of the observed warming of the past 150 years is attributable to anthropogenic climate change. I will then highlight the comms failures along the lines of erring on the side of least drama and ask the question when such tendency to 'remain on the safe side' are bordering on actual (unconscious) misinformation. In the second step, I will present results from a questionnaire where we have sampled the climate change knowledge of school students (age 14-19), with particular regard to the attributable warming fraction of global and regional climate. The results will be discussed in light of the unclear messaging from scientists, followed by what I think are the best long-term strategies to improve the situation.

Human-driven climate change has caused a wide range of extreme weather events to become more frequent in recent decades. Although increased and intense periods of extreme weather are expected consequences of anthropogenic climate warming, it remains challenging to rapidly and continuously assess the degree to which human activity alters the probability of specific events. This study introduces a new framework to enable the production and communication of global real-time estimates of how human-driven climate change has changed the likelihood of daily weather events. The framework's multi-method approach implements one model-based and two observation-based methods to provide ensemble attribution estimates with accompanying confidence levels. The framework is designed to be computationally lightweight to allow attributable probability changes to be rapidly calculated using forecasts or the latest observations. The framework is particularly suited for highlighting ordinary weather events that have been altered by human-caused climate change. An example application using daily maximum temperature in Phoenix, AZ, USA, highlights the framework's effectiveness in estimating the attributable human influence on observed daily temperatures (and deriving associated confidence levels). Global analyses show that the framework is capable of producing worldwide complementary observational- and model-based assessments of how human-caused climate change changes the likelihood of daily maximum temperatures. For instance, over 56 % of the Earth's total land area, all three framework methods agree that maximum temperatures greater than the preindustrial 99th percentile have become at least twice as likely in today's human-influenced climate. Additionally, over 52 % of land in the tropics, human-caused climate change is responsible for at least five-fold increases in the likelihood of preindustrial 99th percentile maximum temperatures. By systematically applying this framework to near-term forecasts or daily observations, local attribution analyses can be provided in real time worldwide. These new analyses create opportunities to enhance communication and provide input and/or context for policy, adaptation, human health, and other ecosystem/human system impact studies.

The agricultural region of Marathwada in India incurred significant loss of crops and lives during the summer of 2015, which was characterised by persistent hot and dry conditions. We use observations and large ensembles of regional climate model simulations to understand and attribute this joint occurrence of deficient rainfall and high temperature in a novel, multivariate framework. Highly unlikely in a world without anthropogenic climate change (1-in-256 year), the event is found to be frequent (1-in-38 year) in the actual world. Thus, the risk of this event is found to be atleast quintupled due to anthropogenic factors, implying that the 2015 event is more likely due to anthropogenic climate change. Interestingly, the 2015 dry event is not unprecedented (∼1-in-15 year) based on observed records and for either of the model scenarios, suggesting that risk assessments based on rainfall alone may not be enough to reconcile the observed impacts. Further, such compound drought events are projected to become even more frequent under future end-of-the-century warming targets of 1.5 °C and 2 °C above pre-industrial levels, with expected doubling and tripling of the probability of the 2015 hot-dry conditions, respectively. Our findings highlight the role of human-induced warming on increased incidences of compound extreme events, thereby warranting adaptation strategies that aim at alleviating associated risks.

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.

Die wissenschaftlichen Fakten zu notwendigem Klimaschutz und den bereits spürbaren sowie zukünftig zu erwartenden Folgen des Klimawandels liegen so umfassend und eindeutig vor, wie nie zuvor (IPCC, 2014, 2018). Der anthropogene – also menschengemachte – Klimawandel sowie die damit einhergehende globale Erderwärmung sind real und betreffen jeden. Dies ist unter Klimawissenschaftler:innen unumstritten und wird auch im politischen und gesellschaftlichen Diskurs mittlerweile weithin anerkannt. Doch was genau sind die Ursachen für den bereits spürbaren und zukünftig zu erwartenden Klimawandel? Wo genau stehen wir aktuell in Deutschland?

Wind and windstorms cause severe damage to natural and human-made environments. Thus, wind-related risk assessment is vital for the preparation and mitigation of calamities. However, the cascade of events leading to damage depends on many factors that are environment-specific and the available methods to address wind-related damage often require sophisticated analysis and specialization. Fortunately, simple indices and thresholds are as effective as complex mechanistic models for many applications. Nonetheless, the multitude of indices and thresholds available requires a careful selection process according to the target environment. Here, we first provide a basic background on wind and storm formation and characteristics, followed by a comprehensive collection of both indices and thresholds that can be used to predict the occurrence and magnitude of wind and storm damage. We focused on five key environments: forests, urban, transport, agriculture, and wind-based energy production. For each environment we described indices and thresholds relating to physical properties such as topography and land cover but also to economic aspects (e.g. disruptions in transportation or energy production). In the face of increased climatic variability, the promotion of more effective analysis of wind and storm damage could reduce the impact on society and the environment.

Slides presented at the 102 Annual American Meteorological Society Meeting, as part of the session "Major Weather Events and Impacts of 2021" (paper6.3- It's Getting Hot in Here: Real-Time Climate Fingerprints Applied to the 2021 Extreme Heat Season) For more information, please reach out to Daniel Gilford at dgilford@climatecentral.org. Presentation Abstract: Extreme heat was observed and experienced across large portions of the United States in 2021, including during notable record-breaking events in the Pacific Northwest, the Southwest, and along the East coast. The contiguous US experienced its hottest June on record, and excess heat related deaths stretched into the thousands. While more frequent and intense periods of extreme heat are expected consequences of anthropogenic climate change, rapidly and continuously assessing the degree to which human emissions of greenhouse gases increase the likelihood of a specific event remains a challenging technical process. In this study we introduce the Realtime Climate attribution framework and illustrate its application through an analysis of observed 2021 extreme heat events. The framework implements one model-based and two observation-based approaches to produce three distinct attribution assessments, including best estimates and uncertainties. The framework is designed to be flexible across a range of variables and scales, computationally lightweight, and adaptable for impact studies. Using a suite of global climate models, observed global mean temperatures, and local observed daily temperatures, we quantify the extent to which human-driven climate change made 2021 maximum and minimum daily temperature extremes more likely across the United States. Results confirm the continued and growing influence of human-driven climate change in local weather extremes. For instance, we find that the record-breaking high temperatures in June near Phoenix, AZ, were at least 3.25 times more likely because of human activity. Through this framework, we are building the capacity to produce attribution estimates while an event is unfolding. Furthermore, the ability to estimate attribution levels continuously will enhance studies of extreme heat impacts on human health, along with other socioeconomic or influences.

The role of external (radiative) forcing factors and internal unforced (ocean) low-frequency variations in the instrumental global temperature record are still hotly debated. More recent findings point towards a larger contribution from changes in external forcing, but the jury is still out. While the estimation of the human-induced total global warming fraction since pre-industrial times is fairly robust and mostly independent of multidecadal internal variability, this is not necessarily the case for key regional features such as Arctic amplification or enhanced warming over continental land areas. Accounting for the slow global temperature adjustment after strong volcanic eruptions, the spatially heterogeneous nature of anthropogenic aerosol forcing and known biases in the sea surface temperature record, almost all of the multidecadal fluctuations observed over at least the last 160+ years can be explained without a relevant role for internal variability. Using a two-box response model framework, I will demonstrate that not only multidecadal variability is very likely a forced response, but warming trends over the past 40+ years are entirely attributable to human factors. Repercussions for amplifed European (or D-A-CH for that matter) warming and associated implications for extreme weather events are discussed. Further consideration is given to the communications aspect of such critical results as well as the question of wider societal impacts.

Concerns about water security often inform climate risk-related decisions made by environmentally focused investors (Porritt, The world in context: Beyond the business case for sustainable development. HRH The Prince of Wales’ Business and the Environment Programme, Cambridge Programme for Industry, 2001; Stern, Stern Review executive summary. New Economics Foundation, 2006). Yet, potential liabilities for damage caused by extreme flood and drought events linked to global warming present risks that are not always reflected in share prices (Krosinsky et al., Evolutions in sustainable investing: Strategies, funds and thought leadership. John Wiley & Sons, 2012). Considering the highly destructive nature of such events, we query whether companies, or specific sectors, could and should be held at least partially liable for their emission-releasing business activities. Recent articles (Rayer & Millar, Citywire Wealth Manager®, (429), p. 36, 2018a; Rayer et al., Ecological, societal, and technological risks and the financial sector, Palgrave Macmillan, pp. 39–68, 2020) estimate that under a hypothetical climate liability regime, North Atlantic hurricane seasons might increasingly generate 1–2% losses on market capitalizations (or share prices) for the top seven carbon-emitting, publicly listed companies. In this chapter, we extend the concept of the climate liability regime to estimate the impact of global flood- and drought-related damages on the share prices of nine fossil-fuel firms (including the seven mentioned by Rayer et al. [Ecological, societal, and technological risks and the financial sector, Palgrave Macmillan, pp. 39–68, 2020]). Following Rayer et al. (Global warming and extreme weather investment risks (abstract) [Conference session], 2019; Ecological, societal, and technological risks and the financial sector, Palgrave Macmillan, pp. 39–68, 2020), we use incremental climate impacts and historical corporate emissions to estimate that climate change-related global flood and drought damages for the period of 2012 to 2016 amount to approximately 2–3% of the top nine carbon-emitting companies’ market capitalizations. Quantifying impacts from extreme weather events increase salience and serve as an example of how science can identify and address the important business questions, pertinent to both investors and companies that arise from a changing climate.

The main goal of Climate Services for eXtremes (which in is an integral part of the ClimXtreme framework) is to advance our understanding of the intensity as well as the spatio-temporal distribution of extreme weather and climate events, but tailored to the needs of stakeholders in the agricultural and forestry sector. The project is designed to optimise the communication between scientists and decision makers and thus to maximise the mutual benefit with regard to climate adaption. The scientists involved learn from the interview partners what climate information is actually required on the ground to facilitate the development of adaptation strategies, whereas the sector experts gain insights into the capabilities and limits of state-of-the-art climate information.In order to increase the efficiency of the knowledge transfer between scientists and stakeholders, we introduce a process-chain based approach: (i) the sector-specific identification of the characteristics of extreme weather conditions in close cooperation with partners from forestry and agriculture, (ii) the analysis of past and future weather and climate extremes with various statistical techniques, (iii) the investigation of the effects of these extremes by means of forest and agricultural case studies, and (iv) the development of possible needs-based adaptation strategies to future climatic conditions and extreme events based on this information.The extended summer drought in Germany during the warm seasons 2018 to 2020 is the perfect testbed for the approach, given the wide-ranging consequences this multi-year event had especially on the forestry sector. The event will be analysed from a probabilistic point of view, i.e. what is the return time and what were the causal factors from an atmospheric dynamic and teleconnection point of view. There is also potential to investigate the role of climate change in terms of altered risks. With this information, we can offer initial guidance for the project partners as to what they have to prepare for. But crucially, the interview feedback will help guide our ultimate research strategy. It will be a function of spatial scale, indices of interest as well as scope and complexity of the data and services our partners require. The new insights will serve as a basis to investigate such extreme drought events under potential future climate conditions.

The remaining carbon budget quantifies the future CO2 emissions to limit global warming below a desired level. Carbon budgets are subject to uncertainty in the Transient Climate Response to Cumulative CO2 Emissions (TCRE), as well as to non-CO2 climate influences. Here we estimate the TCRE using observational constraints, and integrate the geophysical and socioeconomic uncertainties affecting the distribution of the remaining carbon budget. We estimate a median TCRE of 0.44 °C and 5–95% range of 0.32–0.62 °C per 1000 GtCO2 emitted. Considering only geophysical uncertainties, our median estimate of the 1.5 °C remaining carbon budget is 440 GtCO2 from 2020 onwards, with a range of 230–670 GtCO2, (for a 67–33% chance of not exceeding the target). Additional socioeconomic uncertainty related to human decisions regarding future non-CO2 emissions scenarios can further shift the median 1.5 °C remaining carbon budget by ±170 GtCO2.

Desert dust aerosols affect Earth’s global energy balance through interactions with radiation(1,2), clouds(3,4), and ecosystems(5). But the magnitudes of these effects are so uncertain that it remains unclear whether atmospheric dust has a net warming or cooling effect on global climate(1,4,6). Consequently, it is still uncertain whether large changes in atmospheric dust loading over the past century have slowed or accelerated anthropogenic climate change(4,7–9), and the climate impact of possible future alterations in dust loading is similarly disputed(9,10). Here we use an integrative analysis of dust aerosol sizes and abundance to constrain the climatic impact of dust through direct interactions with radiation. Using a combination of observational, experimental, and model data, we find that atmospheric dust is substantially coarser than represented in current climate models. Since coarse dust warms global climate, the dust direct radiative effect (DRE) is likely less cooling than the ~0.4 W/m(2) estimated by models in a current ensemble(2,11–13). We constrain the dust DRE to - 0.20 (−0.48 to +0.20) W/m(2), which suggests that the dust DRE produces only about half the cooling that current models estimate, and raises the possibility that dust DRE is actually net warming the planet.

The remaining carbon budget quantifies the allowable future CO2 emissions to keep global mean warming below a desiredlevel. Carbon budget estimates are subject to uncertainty in the Transient Climate Response to Cumulative CO2 Emissions (TCRE), which measures the warming resulting from a given total amount of CO2 emitted. Moreover, other sources of uncertainty linked to non-CO2 emissions have been shown to also strongly affect estimates of the remaining carbon budget. Here we present a new framework that estimates the TCRE using geophysical constraints derived from observations, and integrates the effect of geophysical and socioeconomic pathway uncertainties on the distribution of the remaining carbon budget. We estimate a median TCRE of 0.40 °C and likely range of 0.3 to 0.5 °C (17-83%) per 1000 GtCO2 emitted. Our 1.5 °C remaining carbon budget has a median value of 710 GtCO2 from 2020 onwards, with a range of 470 to 960 GtCO2, (for a 67% to 33% chance of not exceeding the target). Uncertainty in the amount of current warming from non-CO2 forcing is the dominant geophysical contributor to the spread in both the TCRE and remaining carbon budget estimates. The remaining carbon budget distribution is also strongly affected by current and future mitigation decisions, where the range of non-CO2forcing across scenarios has the potential to increase or decrease the median 1.5 °C remaining carbon budget by 740 GtCO2.

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.

The extreme precipitation that resulted in historic flooding in central-northern France began 26 May 2016 and was linked to a large cutoff low. The floods caused some casualties and over a billion euros in damage. To objectively answer the question of whether anthropogenic climate change played a role, a near-real-time “rapid” attribution analysis was performed, using well-established event attribution methods, best available observational data, and as many climate simulations as possible within that time frame. This study confirms the results of the rapid attribution study. We estimate how anthropogenic climate change has affected the likelihood of exceedance of the observed amount of 3-day precipitation in April–June for the Seine and Loire basins. We find that the observed precipitation in the Seine basin was very rare, with a return period of hundreds of years. It was less rare on the Loire—roughly 1 in 20 years. We evaluated five climate model ensembles for 3-day basin-averaged precipitation extremes in April–June. The four ensembles that simulated the statistics agree well. Combining the results reduces the uncertainty and indicates that the probability of such rainfall has increased over the last century by about a factor of 2.2 (>1.4) on the Seine and 1.9 (>1.5) on the Loire due to anthropogenic emissions. These numbers are virtually the same as those in the near-real-time attribution study by van Oldenborgh et al. Together with the evaluation of the attribution of Storm Desmond by Otto et al., this shows that, for these types of events, near-real-time attribution studies are now possible.

In northern and central Ethiopia, 2015 was a very dry year. Rainfall was only from one-half to three-quarters of the usual amount, with both the “belg” (February–May) and “kiremt” rains (June–September) affected. The timing of the rains that did fall was also erratic. Many crops failed, causing food shortages for many millions of people. The role of climate change in the probability of a drought like this is investigated, focusing on the large-scale precipitation deficit in February–September 2015 in northern and central Ethiopia. Using a gridded analysis that combines station data with satellite observations, it is estimated that the return period of this drought was more than 60 years (lower bound 95% confidence interval), with a most likely value of several hundred years. No trend is detected in the observations, but the large natural variability and short time series means large trends could go undetected in the observations. Two out of three large climate model ensembles that simulated rainfall reasonably well show no trend while the third shows an increased probability of drought. Taking the model spread into account the drought still cannot be clearly attributed to anthropogenic climate change, with the 95% confidence interval ranging from a probability decrease between preindustrial and today of a factor of 0.3 and an increase of a factor of 5 for a drought like this one or worse. A soil moisture dataset also shows a nonsignificant drying trend. According to ENSO correlations in the observations, the strong 2015 El Niño did increase the severity of the drought.

Science on the role of anthropogenic influence on extreme weather events, such as heatwaves or droughts, has evolved rapidly in the past years. The approach of “event attribution” compares the occurrence-probability of an event in the present, factual, climate with its probability in a hypothetical, counterfactual, climate without human-induced climate change. Several methods can be used for event attribution, based on climate model simulations and observations, and usually researchers only assess a subset of methods and data sources. Here, we explore the role of methodological choices for the attribution of the 2015 meteorological summer drought in Europe. We present contradicting conclusions on the relevance of human influence as a function of the chosen data source and event attribution methodology. Assessments using the maximum number of models and counterfactual climates with pre-industrial greenhouse gas concentrations point to an enhanced drought risk in Europe. However, other evaluations show contradictory evidence. These results highlight the need for a multi-model and multi-method framework in event attribution research, especially for events with a low signal- to-noise ratio and high model dependency such as regional droughts.

Environmentally focused investors often consider climate risks (Porritt, The world in context: Beyond the business case for sustainable development. Cambridge: HRH The Prince of Wales’ Business and the Environment Programme, Cambridge Programme for Industry, 2001, Stern, Stern review executive summary. London: New Economics Foundation, 2006); however, potential liabilities for damages from extreme weather events due to emissions from carbon-intensive sectors present risks that may not be reflected in current share prices (Krosinsky, Robins, & Viederman, Evolutions in sustainable investing: Strategies, funds and thought leadership. Hoboken, NJ: John Wiley & Sons, 2012). Given the devastation of the 2017 Atlantic hurricane season, it is worth asking: how close are we to some companies or sectors being held liable, at least partially, for their activities around emissions? The answer may be that this is closer than many expect. The evolving field of extreme weather event attribution serves as a good example of how new science can raise important thought-provoking questions regarding the appropriate actions of both investors and companies under a changing climate and answer them with increasing confidence.

The early twentieth-century warming (EW; 1910–45) and the mid-twentieth-century cooling (MC; 1950–80) have been linked to both internal variability of the climate system and changes in external radiative forcing. The degree to which either of the two factors contributed to EW and MC, or both, is still debated. Using a two-box impulse response model, we demonstrate that multidecadal ocean variability was unlikely to be the driver of observed changes in global mean surface temperature (GMST) after AD 1850. Instead, virtually all (97%–98%) of the global low-frequency variability (>30 years) can be explained by external forcing. We find similarly high percentages of explained variance for interhemispheric and land–ocean temperature evolution. Three key aspects are identified that underpin the conclusion of this new study: inhomogeneous anthropogenic aerosol forcing (AER), biases in the instrumental sea surface temperature (SST) datasets, and inadequate representation of the response to varying forcing factors. Once the spatially heterogeneous nature of AER is accounted for, the MC period is reconcilable with external drivers. SST biases and imprecise forcing responses explain the putative disagreement between models and observations during the EW period. As a consequence, Atlantic multidecadal variability (AMV) is found to be primarily controlled by external forcing too. Future attribution studies should account for these important factors when discriminating between externally forced and internally generated influences on climate. We argue that AMV must not be used as a regressor and suggest a revised AMV index instead [the North Atlantic Variability Index (NAVI)]. Our associated best estimate for the transient climate response (TCR) is 1.57 K (±0.70 at the 5%–95% confidence level).

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.

Extreme one-day rainfall caused widespread flooding in Chennai, India, in December 2015. No effect of global warming was detected, likely caused by aerosols counteracting greenhouse gases up to now.

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.

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.

Targets agreed to in Paris in 2015 aim to limit global warming to 'well below 2 °C and to pursue efforts to limit the temperature increase to 1.5 °C above pre-industrial levels'. Despite the far-reaching consequences of this multi-lateral climate change mitigation strategy, the implications for global river flows remain unclear. Here we estimate the impacts of 1.5 °C versus 2.0 °C mitigation scenarios on peak flows by using daily river flow data from a multi-model ensemble which follows the HAPPI Protocol (that is specifically designed to simulate these temperature targets). We find agreement between models with regard to changing risk of river flow extremes. Moreover, we find that the response at 2.0 °C is not a uniform extension of the response at 1.5°, suggesting a non-linear global response of peak flows to the two mitigation levels. Yet committing to the 2.0 °C warming target, rather than 1.5 °C, is projected to lead to an increase in the frequency of occurrence of extreme flows in several large catchments. In the most affected areas, predominantly in South Asia, while region-specific features such as aerosol loads may determine precipitation patterns, we estimate that under our 1.5 °C scenario the historical 1-in-100 year flow occurs with a frequency of 1-in-25 years. At 2.0 °C, similar increases are observed in several global regions. These shifts are also accompanied by changes in the duration of rainy seasons which influence the occurrence of high flows.

In August 2017 Bangladesh faced one of its worst river flooding events in recent history. This paper presents, for the first time, an attribution of this precipitation-induced flooding to anthropogenic climate change from a combined meteorological and hydrological perspective. Experiments were conducted with three observational datasets and two climate models to estimate changes in the extreme 10-day precipitation event frequency over the Brahmaputra basin up to the present and, additionally, an outlook to 2 ∘C warming since pre-industrial times. The precipitation fields were then used as meteorological input for four different hydrological models to estimate the corresponding changes in river discharge, allowing for comparison between approaches and for the robustness of the attribution results to be assessed. In all three observational precipitation datasets the climate change trends for extreme precipitation similar to that observed in August 2017 are not significant, however in two out of three series, the sign of this insignificant trend is positive. One climate model ensemble shows a significant positive influence of anthropogenic climate change, whereas the other large ensemble model simulates a cancellation between the increase due to greenhouse gases (GHGs) and a decrease due to sulfate aerosols. Considering discharge rather than precipitation, the hydrological models show that attribution of the change in discharge towards higher values is somewhat less uncertain than in precipitation, but the 95 % confidence intervals still encompass no change in risk. Extending the analysis to the future, all models project an increase in probability of extreme events at 2 ∘C global heating since pre-industrial times, becoming more than 1.7 times more likely for high 10-day precipitation and being more likely by a factor of about 1.5 for discharge. Our best estimate on the trend in flooding events similar to the Brahmaputra event of August 2017 is derived by synthesizing the observational and model results: we find the change in risk to be greater than 1 and of a similar order of magnitude (between 1 and 2) for both the meteorological and hydrological approach. This study shows that, for precipitation-induced flooding events, investigating changes in precipitation is useful, either as an alternative when hydrological models are not available or as an additional measure to confirm qualitative conclusions. Besides this, it highlights the importance of using multiple models in attribution studies, particularly where the climate change signal is not strong relative to natural variability or is confounded by other factors such as aerosols.

During August 25-30, 2017, Hurricane Harvey stalled over Texas and caused extreme precipitation, particularly over Houston and the surrounding area on August 26-28. This resulted in extensive flooding with over 80 fatalities and large economic costs. It was an extremely rare event: the return period of the highest observed three-day precipitation amount, 1043.4 mm 3dy-1 at Baytown, is more than 9000 years (97.5% one-sided confidence interval) and return periods exceeded 1000 yr (750 mm 3dy-1) over a large area in the current climate. Observations since 1880 over the region show a clear positive trend in the intensity of extreme precipitation of between 12% and 22%, roughly two times the increase of the moisture holding capacity of the atmosphere expected for 1 °C warming according to the Clausius-Clapeyron (CC) relation. This would indicate that the moisture flux was increased by both the moisture content and stronger winds or updrafts driven by the heat of condensation of the moisture. We also analysed extreme rainfall in the Houston area in three ensembles of 25 km resolution models. The first also shows 2 × CC scaling, the second 1 × CC scaling and the third did not have a realistic representation of extreme rainfall on the Gulf Coast. Extrapolating these results to the 2017 event, we conclude that global warming made the precipitation about 15% (8%-19%) more intense, or equivalently made such an event three (1.5-5) times more likely. This analysis makes clear that extreme rainfall events along the Gulf Coast are on the rise. And while fortifying Houston to fully withstand the impact of an event as extreme as Hurricane Harvey may not be economically feasible, it is critical that information regarding the increasing risk of extreme rainfall events in general should be part of the discussion about future improvements to Houston's flood protection system.

Human influence may have enhanced the circulation pattern that drives cold outbreaks and frost risk over southwest Western Australia in September 2016, but larger thermodynamic changes may have still made these events less likely.

Empirical evidence suggests that variations in climate affect economic growth across countries over time. However, little is known about the relative impacts of climate change on economic outcomes when global mean surface temperature (GMST) is stabilized at 1.5°C or 2°C warming relative to pre-industrial levels. Here we use a new set of climate simulations under 1.5°C and 2°C warming from the ‘Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) project to assess changes in economic growth using empirical estimates of climate impacts in a global panel dataset. Panel estimation results that are robust to outliers and breaks suggest that within-year variability of monthly temperatures and precipitation has little effect on economic growth beyond global nonlinear temperature effects. While expected temperature changes under a GMST increase of 1.5°C lead to proportionally higher warming in the Northern Hemisphere, the projected impact on economic growth is larger in the Tropics and Southern Hemisphere. Accounting for econometric estimation and climate uncertainty, the projected impacts on economic growth of 1.5°C warming are close to indistinguishable from current climate conditions, while 2°C warming suggests statistically lower economic growth for a large set of countries (median projected annual growth up to 2% lower). Level projections of gross domestic product (GDP)per capitaexhibit high uncertainties, with median projected global average GDPper capitaapproximately 5% lower at the end of the century under 2°C warming relative to 1.5°C. The correlation between climate-induced reductions inper capitaGDP growth and national income levels is significant at thep This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Atmospheric modes of variability relevant for extreme temperature and precipitation events are evaluated in models currently being used for extreme event attribution. A 100 member initial condition ensemble of the global circulation model HadAM3P is compared with both the multi-model ensemble from the Coupled Model Inter-comparison Project, Phase 5 (CMIP5) and the CMIP5 atmosphere-only counterparts (AMIP5). The use of HadAM3P allows for huge ensembles to be computed relatively fast, thereby providing unique insights into the dynamics of extremes. The analysis focuses on mid Northern Latitudes (primarily Europe) during winter, and is compared with ERA-Interim reanalysis. The tri-modal Atlantic eddy-driven jet distribution is remarkably well captured in HadAM3P, but not so in the CMIP5 or AMIP5 multi-model mean, although individual models fare better. The well known underestimation of blocking in the Atlantic region is apparent in CMIP5 and AMIP5, and also, to a lesser extent, in HadAM3P. Pacific blocking features are well produced in all modeling initiatives. Blocking duration is biased towards models reproducing too many short-lived events in all three modelling systems. Associated storm tracks are too zonal over the Atlantic in the CMIP5 and AMIP5 ensembles, but better simulated in HadAM3P with the exception of being too weak over Western Europe. In all cases, the CMIP5 and AMIP5 performances were almost identical, suggesting that the biases in atmospheric modes considered here are not strongly coupled to SSTs, and perhaps other model characteristics such as resolution are more important. For event attribution studies, it is recommended that rather than taking statistics over the entire CMIP5 or AMIP5 available models, only models capable of producing the relevant dynamical phenomena be employed. ISSN:0930-7575 ISSN:1432-0894

Desert dust aerosols affect Earth's global energy balance through direct interactions with radiation, and through indirect interactions with clouds and ecosystems. But the magnitudes of these effects are so uncertain that it remains unclear whether atmospheric dust has a net warming or cooling effect on global climate. Consequently, it is still uncertain whether large changes in atmospheric dust loading over the past century have slowed or accelerated anthropogenic climate change, or what the effects of potential future changes in dust loading will be. Here we present an analysis of the size and abundance of dust aerosols to constrain the direct radiative effect of dust. Using observational data on dust abundance, in situ measurements of dust optical properties and size distribution, and climate and atmospheric chemical transport model simulations of dust lifetime, we find that the dust found in the atmosphere is substantially coarser than represented in current global climate models. Since coarse dust warms climate, the global dust direct radiative effect is likely to be less cooling than the ~-0.4 W/m2 estimated by models in a current global aerosol model ensemble. Instead, we constrain the dust direct radiative effect to a range between -0.48 and +0.20 W/m2, which includes the possibility that dust causes a net warming of the planet., Comment: Published as article in Nature Geoscience

Dust production models (DPM) used to estimate vertical fluxes of mineral dust aerosols over arid regions need accurate data on soil and surface properties. The Laboratoire Inter-Universitaire des Systemes Atmospheriques (LISA) data set was developed for Northern Africa, the Middle East, and East Asia. This regional data set was built through dedicated field campaigns and include, among others, the aerodynamic roughness length, the smooth roughness length of the erodible fraction of the surface, and the dry (undisturbed) soil size distribution. Recently, satellite-derived roughness length and high-resolution soil texture data sets at the global scale have emerged and provide the opportunity for the use of advanced schemes in global models. This paper analyzes the behavior of the ERS satellite-derived global roughness length and the State Soil Geographic data base-Food and Agriculture Organization of the United Nations (STATSGO-FAO) soil texture data set (based on wet techniques) using an advanced DPM in comparison to the LISA data set over Northern Africa and the Middle East. We explore the sensitivity of the drag partition scheme (a critical component of the DPM) and of the dust vertical fluxes (intensity and spatial patterns) to the roughness length and soil texture data sets. We also compare the use of the drag partition scheme to a widely used preferential source approach in global models. Idealized experiments with prescribed wind speeds show that the ERS and STATSGO-FAO data sets provide realistic spatial patterns of dust emission and friction velocity thresholds in the region. Finally, we evaluate a dust transport model for the period of March to July 2011 with observed aerosol optical depths from Aerosol Robotic Network sites. Results show that ERS and STATSGO-FAO provide realistic simulations in the region.

The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the Half a degree Additional warming, Projections, Prognosis and Impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 °C and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of integrated assessment models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slice experiments are proposed, each a decade in length; the first being the most recent observed 10-year period (2006–2015), the second two being estimates of the a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the Representative Concentration Pathways 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.

The extreme precipitation that would result in historic flooding across areas of northeastern France and southern Germany began on May 26th when a large cut-off low spurred the development of several slow moving low pressure disturbances. The precipitation took different forms in each country. Warm and humid air from the south fueled sustained, large-scale, heavy rainfall over France resulting in significant river flooding on the Seine and Loire (and their tributaries), whereas the rain came from smaller clusters of intense thunderstorms in Germany triggering flash floods in mountainous areas. The floods left tens of thousands without power, caused over a billion Euros in damage in France alone, and are reported to have killed at least 18 people in Germany, France, Romania, and Belgium. The extreme nature of this event left many asking whether anthropogenic climate change may have played a role. To answer this question objectively, a rapid attribution analysis was performed in near-real time, using the best available observational data and climate models. In this rapid attribution study, where results were completed and released to the public in one week and an additional week to finalise this article, we present a first estimate of how anthropogenic climate change affected the likelihood of meteorological variables corresponding to the event, 3-day precipitation averaged over the Seine and Loire basins and the spatial maximum of 1-day precipitation over southern Germany (excluding the Alps). We find that the precipitation in the Seine basin was very rare in April–June, with a return time of hundreds of years in this season. It was less rare on the Loire, roughly 1 in 50 years in April–June. At a given location the return times for 1-day precipitation as heavy as the highest observed in southern Germany is 1 in 3000 years in April–June. This translates to once roughly every 20 years somewhere in this region and season. The probability of 3-day extreme rainfall in this season has increased by about a factor 2.3 (> 1.6) on the Seine a factor 2.0 (> 1.4) on the Loire, with all four climate models that simulated the statistical properties of the extremes agreeing. The observed trend of heavy 1-day precipitation in southern Germany is significantly negative, whereas the one model that has the correct distribution simulates a significant positive trend, making an attribution statement for these thunderstorms impossible at this time.

In this study, aerosol optical depth (AOD) measurements, from a MFR sun photometer operating in Athens, were compared with columnar dust loading estimations, from the BSC-DREAM model, during identified dust events, in order to extract the typical specific extinction cross-section for dust over the area. The selected urban environment of Athens provided us with the opportunity to investigate the mixing of dust and urban pollution and to estimate the contribution of the latter. The specific extinction cross-section for dust at 500 nm was found to be equal to σ500*=0.64±0.04 m2 g, typical for medium to large distances from dust sources, with weak wavelength dependence in the visible and near infrared band (0.4–0.9 μm). The model showed a tendency to underpredict AOD levels for increasing values of the Ångström exponent, indicative of fine particles of anthropogenic origin inside the boundary layer. On average we found an AOD under-prediction of 10–15% for Ångström exponents in the range of 0 to 1 and 30–40% in the range of 1 to 2. Additionally, modelled surface concentrations were evaluated against surface PM10 measurements. Model values were lower than measured surface concentrations by 30% which, in conjunction with large scatter, indicated that the effect of the boundary layer anthropogenic contribution to columnar dust loadings is amplified near the ground.

A succession of storms reaching Southern England in the winter of 2013/2014 caused severe floods and £451 million insured losses. In a large ensemble of climate model simulations, we find that, as well as increasing the amount of moisture the atmosphere can hold, anthropogenic warming caused a small but significant increase in the number of January days with westerly flow, both of which increased extreme precipitation. Hydrological modelling indicates this increased extreme 30-day-average Thames river flows, and slightly increased daily peak flows, consistent with the understanding of the catchment’s sensitivity to longer-duration precipitation and changes in the role of snowmelt. Consequently, flood risk mapping shows a small increase in properties in the Thames catchment potentially at risk of riverine flooding, with a substantial range of uncertainty, demonstrating the importance of explicit modelling of impacts and relatively subtle changes in weather-related risks when quantifying present-day effects of human influence on climate.

On 4–6 December 2015, the storm "Desmond" caused very heavy rainfall in northern England and Scotland, which led to widespread flooding. Here we provide 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 is 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 agree that the effect of climate change is positive, making precipitation events like this about 40 % more likely, with a provisional 2.5–97.5 % confidence interval of 5–80 %.

Southeast Brazil experienced profound water shortages in 2014/15. Anthropogenic climate change is not found to be a major influence on the hazard, whereas increasing population and water consumption increased vulnerability.

Within the framework of the Dust Observations for Models (DO4Models) project, the performance of three commonly used dust emission schemes is investigated in this paper using a box model environment. We constrain the model with field data (surface and dust particle properties as well as meteorological parameters) obtained from a dry lake bed with a crusted surface in Botswana during a 3 month period in 2011. Our box model results suggest that all schemes fail to reproduce the observed horizontal dust flux. They overestimate the magnitude of the flux by several orders of magnitude. The discrepancy is much smaller for the vertical dust emission flux, albeit still overestimated by up to an order of magnitude. The key parameter for this mismatch is the surface crusting which limits the availability of erosive material, even at higher wind speeds. The second-most important parameter is the soil size distribution. Direct dust entrainment was inferred to be important for several dust events, which explains the smaller gap between modelled and measured vertical dust fluxes. We conclude that both features, crusted surfaces and direct entrainment, need to be incorporated into dust emission schemes in order to represent the entire spectra of source processes. We also conclude that soil moisture exerts a key control on the threshold shear velocity and hence the emission threshold of dust in the model. In the field, the state of the crust is the controlling mechanism for dust emission. Although the crust is related to the soil moisture content to some extent, we are not as yet able to deduce a robust correlation between state of crust and soil moisture.

The NMMB/BSC Chemical Transport Model (NMMB/BSC-CTM) is a new air quality modeling system under development at the Earth Sciences Department of BSC in collaboration with several research institutions. It is an on-line model based on the NCEP new global/regional Nonhydrostatic Multiscale Model on the B grid (NMMB). NMMB is an evolution of the WRF-NMMe model extending from meso to global scales. Its unified nonhydrostatic dynamical core allows regional and global simulations and forecasts. NMMB/BSC-CTM incorporates an aerosol module that simulates the global life cycle of mineral dust, sea salt, black carbon and organic carbon, and sulfate. Additionally, a gas-phase chemistry module has been implemented.