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Land surface air temperature products have been essential for monitoring the evolution of the climate system. Before a temperature dataset is included in such analyses, it is important that nonclimatic influences be removed or changed so that the dataset is considered to be homogenous. These inhomogeneities include changes in station location, instrumentation, and observing practices. Many homogenized products exist on the monthly time scale, but few daily and weekly products exist. Recently, a submonthly homogenized dataset has been developed using data and software from NOAA’s National Centers for Environmental Information. Homogeneous daily data are useful for identification and attribution of extreme heat events. Projections of increasing temperatures are expected to result in corresponding increases in the frequency, duration, and intensity of such events. It is also established that heat events can have significant public health impacts, including increases in mortality and morbidity. The method to identify extreme heat events using daily homogeneous temperature data is described and used to develop a climatology of heat event onset, length, and severity. This climatology encompasses nearly 3000 extreme maximum and minimum temperature events across the United States since 1901. A sizeable number of events occurred during the Dust Bowl period of the 1930s; however, trend analysis shows an increase in heat event number and length since 1951. Overnight extreme minimum temperature events are increasing more than daytime maximum temperatures, and regional analysis shows that events are becoming much more prevalent in the western and southeastern parts of the United States.

Increasing likelihoods of extreme weather events are the most noticeable and damaging manifestation of anthropogenic climate change. In the aftermath of an extreme event, policy makers are often called upon to make timely and sensitive decisions about rebuilding and managing present and future risks. Information regarding whether, where, and how present day and future risks are changing is needed to adequately inform these decisions. But, this information is often not available and when it is, it is often not presented in a systematic way. Here, we demonstrate a seamless approach to the science of extreme event attribution and future risk assessment by using the same set of model ensembles to provide such information on past, present and future hazard risks in four case studies on different types of events. Given the current relevance, we focus on estimating the change in future hazard risk under 1.5°C and 2°C of global mean temperature rise. We find that this approach not only addresses important decision-making gaps, but also improves the robustness of future risk assessment and attribution statements alike.

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.

Findings from the most recent surveys of TV weathercasters—which are methodologically superior to prior surveys in a number of important ways—suggest that weathercasters’ views of climate change may be rapidly evolving. In contrast to prior surveys, which found many weathercasters who were unconvinced of climate change, newer results show that approximately 80% of weathercasters are convinced of human-caused climate change. A majority of weathercasters now indicate that climate change has altered the weather in their media markets over the past 50 years, and many feel there have also been harmful impacts to water resources, agriculture, transportation resources, and human health. Nearly all weathercasters—89%—believe their viewers are at least slightly interested in learning about local impacts. The majority of weathercasters are interested in reporting on local impacts, including extreme precipitation and flooding, drought and water shortages, extreme heat events, air quality, and harm to local wildlife, crops and livestock, and human health; and nearly half had reported on the local impacts in at least one channel over the past 12 months. Thus, it appears that a strong majority of weathercasters are now convinced that human-caused climate change is happening, many feel they are already witnessing harmful impacts in their communities, and many are beginning to explore ways of educating their viewers about these local impacts of global climate change. We believe that the role of local climate educator will soon become a normative practice for broadcast meteorologists—adding a significant and important new role to their job descriptions.

As American Meteorological Society (AMS) members who study Americans’ understanding of climate change and who are engaged in programs to educate Americans about climate change, we want our AMS colleagues to realize their key role in public education. In this article we make the case that 1) AMS members are well positioned to play important leadership roles in educational outreach on climate change, 2) the public wants to learn more about climate change, 3) there is a need for more effective public engagement efforts, and 4) we have successful outreach and educational models that we can start using today.

A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (US), starting around 10 August 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a 3-day maximum found at a station in Livingston, LA (east of Baton Rouge), from 12 to 14 August 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30 000 people had been rescued, nearly 10 600 people had slept in shelters on the night of 14 August and at least 60 600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least 13 people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the US since Super Storm Sandy made landfall in New Jersey on 24 October 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real time using the best readily available observational data and high-resolution global climate model simulations. The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data and analysis methods are readily available upon the start. It is the authors' aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here, we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the central US Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region of 29–31° N, 85–95° W, which we refer to as the central US Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (CI): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (CI 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A central US Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story; in the most accurate analyses, the regional probability of 3-day extreme precipitation increases by more than a factor of 1.4 due to anthropogenic climate change. The magnitude of the shift in probabilities is greater in the 25 km (higher-resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.

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.

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.

This sixth edition of explaining extreme events of the previous year (2016) from a climate perspective is the first of these reports to find that some extreme events were not possible in a preindustrial climate. The events were the 2016 record global heat, the heat across Asia, as well as a marine heat wave off the coast of Alaska. While these results are novel, they were not unexpected. Climate attribution scientists have been predicting that eventually the influence of human-caused climate change would become sufficiently strong as to push events beyond the bounds of natural variability alone. It was also predicted that we would first observe this phenomenon for heat events where the climate change influence is most pronounced. Additional retrospective analysis will reveal if, in fact, these are the first events of their kind or were simply some of the first to be discovered. Last year, the editors emphasized the need for additional papers in the area of “impacts attribution” that investigate whether climate change’s influence on the extreme event can subsequently be directly tied to a change in risk of the socio-economic or environmental impacts. Several papers in this year’s report address this challenge, including Great Barrier Reef bleaching, living marine resources in the Pacific, and ecosystem productivity on the Iberian Peninsula. This is an increase over the number of impact attribution papers than in the past, and are hopefully a sign that research in this area will continue to expand in the future. Other extreme weather event types in this year’s edition include ocean heat waves, forest fires, snow storms, and frost, as well as heavy precipitation, drought, and extreme heat and cold events over land. There were a number of marine heat waves examined in this year’s report, and all but one found a role for climate change in increasing the severity of the events. While humancaused climate change caused China’s cold winter to be less likely, it did not influence U.S. storm Jonas which hit the mid-Atlantic in winter 2016. As in past years, the papers submitted to this report are selected prior to knowing the final results of whether human-caused climate change influenced the event. The editors have and will continue to support the publication of papers that find no role for human-caused climate change because of their scientific value in both assessing attribution methodologies and in enhancing our understanding of how climate change is, and is not, impacting extremes. In this report, twenty-one of the twenty-seven papers in this edition identified climate change as a significant driver of an event, while six did not. Of the 131 papers now examined in this report over the last six years, approximately 65% have identified a role for climate change, while about 35% have not found an appreciable effect. Looking ahead, we hope to continue to see improvements in how we assess the influence of human-induced climate change on extremes and the continued inclusion of stakeholder needs to inform the growth of the field and how the results can be applied in decision making. While it represents a considerable challenge to provide robust results that are clearly communicated for stakeholders to use as part of their decision-making processes, these annual reports are increasingly showing their potential to help meet such growing needs.

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

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

Augmenting previous papers about the exceptional 2011-15 California drought, we offer new perspectives on the ‘snow drought’ that extended into Oregon in 2014 and Washington in 2015. Over 80% of measurement sites west of 115°W experienced record low snowpack in 2015, and we estimate a return period of 400-1000 years for California’s snowpack under the questionable assumption of stationarity. Hydrologic modeling supports the conclusion that 2015 was the most severe on record by a wide margin. Using a crowd-sourced superensemble of regional climate model simulations, we show that both human influence and sea surface temperature anomalies contributed strongly to the risk of snow drought in Oregon and Washington: the contribution of SST anomalies was about twice that of human influence. By contrast, SSTs and humans appear to have played a smaller role in creating California’s snow drought. In all three states, the anthropogenic effect on temperature exacerbated the snow drought.

A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (U.S.) starting around 10 August, 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a three-day maximum found at a station in Livingston, LA (east of Baton Rouge) from 12–14 August, 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30,000 people had been rescued, nearly 10,600 people had slept in shelters on the night of 14 August, and at least 60,600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least thirteen people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the U.S. since Super Storm Sandy made landfall in New Jersey on 24 October, 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real-time using the best readily available observational data and high-resolution global climate model simulations. The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data, and analysis methods are readily available upon the start. It is the authors aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the Central U.S. Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region: 29–31º N, 85–95º W, which we refer to as the Central U.S. Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (C.I.): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (C.I. 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A Central U.S. Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story, with the regional probability of 3-day extreme precipitation increasing due to anthropogenic climate change by a factor of more than a factor 1.4 in the most accurate analyses. The magnitude of the shift in probabilities is greater in the 25 km (higher resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.

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.

The response of the Atlantic Ocean to North Atlantic Oscillation (NAO)-like wind forcing was investigated using an ocean-only general circulation model coupled to an atmospheric boundary layer model. A series of idealized experiments was performed to investigate the interannual to multi-decadal frequency response of the ocean to a winter wind anomaly pattern. Overall, the strength of the SST response increased slightly with longer forcing periods. In the subpolar gyre, however, the model showed a broad response maximum in the decadal band (12-16 years).