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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.

TV weathercasters are well positioned to educate Americans about the relationships among weather, climate, and climate change. Through a collaboration involving TV meteorologists, climatologists, and social scientists, we produced a series of educational segments to assess the impact of such an education. The educational segments were branded “Climate Matters” and aired over one year during the nightly weather segment on WLTX TV (Columbia, South Carolina). Prior to airing, we conducted a telephone survey of adult TV news viewers in the Columbia media market using random digit dialing (n = 1,068) to establish baseline measures; respondent screening was used to sample approximately equal numbers of WLTX viewers and viewers of competing stations. Approximately one year later, we resurveyed all available members of the baseline cohort (n = 502) and an independent sample of randomly selected residents (n = 910). The longitudinal data showed that—after controlling for baseline measures, demographics, and politi...

Local television (TV) weathercasters are a potentially promising source of climate education, in that weather is the primary reason viewers watch local TV news, large segments of the public trust TV weathercasters as a source of information about global warming, and extreme weather events are increasingly common (Leiserowitz et al.; U.S. Global Change Research Program). In an online experiment conducted in two South Carolina cities (Greenville, n = 394; Columbia, n = 352) during and immediately after a summer heat wave, the effects on global warming risk perceptions were examined following exposure to a TV weathercast in which a weathercaster explained the heat wave as a local manifestation of global warming versus exposure to a 72-h forecast of extreme heat. No main effect of the global warming video on learning was found. However, a significant interaction effect was found: subjects who evaluated the TV weathercaster more positively were positively influenced by the global warming video, and viewers who evaluated the weathercaster less positively were negatively influenced by the video. This effect was strongest among politically conservative viewers. These results suggest that weathercaster-delivered climate change education can have positive, albeit nuanced, effects on TV-viewing audiences.

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.

Global climate change is influencing the weather in every region of the United States, often in harmful ways. Yet, like people in many countries, most Americans view climate change as a threat that is distant in space (i.e., not here), time (i.e., not now), and species (i.e., not us). To manage risk and avoid harm, it is imperative that the public, professionals, and policy-makers make decisions with an informed understanding of our changing climate. In the United States, broadcast meteorologists are ideally positioned to educate Americans about the current and projected impacts of climate change in their community. They have tremendous reach, are trusted sources of climate information, and are highly skilled science communicators. When our project began in 2009, we learned that many U.S.-based TV weathercasters were potentially interested in reporting on climate change, but few actually were, citing significant barriers including a lack of time to prepare and air stories, and lack of access to high-quality content that can be rapidly used in their broadcasts, social media, and community presentations. To test the premise that TV weathercasters can be effective climate educators—if supported with high-quality localized climate communication content—in 2010 George Mason University, Climate Central, and WLTX-TV (Columbia, SC) developed and pilot-tested Climate Matters, a series of short on-air (and online) segments about the local impacts of climate change, delivered by the station’s chief meteorologist. During the first year, more than a dozen stories aired. To formally evaluate Climate Matters, we conducted pre- and post-test surveys of local TV news viewers in Columbia. After one year, WLTX viewers had developed a more science-based understanding of climate change than viewers of other local news stations, confirming our premise that when TV weathercasters report on the local implications of climate change, their viewers learn. Through a series of expansions, including the addition of important new partners—the American Meteorological Society, National Aeronautical and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and Yale University—Climate Matters has become a comprehensive nationwide climate communication resource program for American broadcast meteorologists. As of March 2016, 313 local weathercasters nationwide (at 202 stations in 111 media markets) are participating in the program, receiving new content on a weekly basis. Some leaders in the World Meteorological Organization are now promoting the concept of “TV weather presenters as climate change communicators,” and collaborative discussions are underway with Climate Central. In this article, we review the theoretical basis of the program, detail its development and national scale-up, and conclude with insights for how to develop climate communication initiatives for other professional communities of practice in the U.S. and other countries.

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.

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.

Analysis of daily observations shows that wintertime (November–April) precipitation over Southwest Asia is modulated by Madden–Julian oscillation (MJO) activity in the eastern Indian Ocean, with strength comparable to the interannual variability. Daily outgoing longwave radiation (OLR) for 1979–2001 is used to provide a long and consistent, but indirect, estimate of precipitation, and daily records from 13 stations in Afghanistan reporting at least 50% of the time for 1979–85 are used to provide direct, but shorter and irregularly reported, precipitation data. In the station data, for the average of all available stations, there is a 23% increase in daily precipitation relative to the mean when the phase of the MJO is negative (suppressed tropical convection in the eastern Indian Ocean), and a corresponding decrease when the MJO is positive. The distribution of extremes is also affected such that the 10 wettest days all occur during the negative MJO phase. The longer record of OLR data indicates that the effect of the MJO is quite consistent from year to year, with the anomalies averaged over Southwest Asia more negative (indicating more rain) for the negative phase of the MJO for each of the 22 yr in the record. Additionally, in 9 of the 22 yr the average influence of the MJO is larger than the interannual variability (e.g., the relationship results in anomalously wet periods even in dry years and vice versa). Examination of NCEP–NCAR reanalysis data shows that the MJO modifies both the local jet structure and, through changes to the thermodynamic balance, the vertical motion field over Southwest Asia, consistent with the observed modulation of the associated synoptic precipitation. A simple persistence scheme for forecasting the sign of the MJO suggests that the modulation of Southwest Asia precipitation may be predictable for 3-week periods. Finally, analysis of changes in storm evolution in Southwest Asia due to the influence of the MJO shows a large difference in strength as the storms move over Afghanistan, with apparent relevance for the flooding event of 12–13 April 2002.

Severe drought over the past three years (1998‐2001), in combination with the effects of protracted sociopolitical disruption, has led to widespread famine affecting over 60 million people in central and southwest (CSW) Asia. Here both a regional and a large-scale mode of climate variability are documented that, together, suggest a possible forcing mechanism for the drought. During the boreal cold season, an inverse relationship exists between precipitation anomalies in the eastern Indian Ocean and CSW Asia. Suppression of precipitation over CSW Asia is consistent with interaction between local synoptic storms and wave energy generated by enhanced tropical rainfall in the eastern Indian Ocean. This regional out-of-phase precipitation relationship is

Interannual to decadal variations in Middle Eastern temperature, precipitationand streamflow reflect the far-field influence of the North Atlantic Oscillation (NAO), a dominant mode of Atlantic sector climate variability. Using a new sea surface temperature (SST) based index of the NAO and availablestreamflow data from five Middle Eastern rivers, we show that the first principal component of December through March streamflow variability reflects changes in the NAO. However, Middle East rivers have two primary flooding periods.The first is rainfall-driven runoff from December through March, regulated on interannual to decadal timescales by the NAO as reflected in local precipitation and temperature.The second period, from April through June, reflects spring snowmelt and contributes in excess of 50% of annual runoff.This period, known locally as the khamsin, displays no significantNAO connections and a less direct relationship with local climatic factors, suggesting that streamflow variability during this period reflects land-cover change, possibly related to agriculture and hydropower generation, and snowmelt.

Multiproxy composite reconstructions of North Atlantic Oscillation (NAO) indices are presented spanning the last three centuries. These composite time series draw on four nearly independent reconstructions derived from different combinations of global and North Atlantic sector climate proxy data (e.g., tree rings and ice cores) as well as long instrumental records. A composite reconstruction based on all four series (R4) extends from 1750 to 1979, while another version based on only three series (R3) extends from 1701 to 1979. Both composite reconstructions outperform the individual series based on correlation and verification statistics. These results suggest that each individual series reflects different aspects of the NAO, such that when combined, they yield more robust reconstructions. As to be expected, these composite reconstructions, based on both proxy and long instrumental data, yield more optimal results than individual or composite reconstructions based on proxy data alone. The reconstructions presented here provide an improved means of addressing features evident in the instrumental record with respect to those of prior centuries. One such feature evident in the reconstructions is an apparent increase in variance in the instrumental period relative to the past record.

Numerical experiments are performed to examine the causes of variability of Atlantic Ocean SST during the period covered by the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis (1958-98). Three ocean models are used. Two are mixed layer models: one with a 75-m-deep mixed layer and the other with a variable depth mixed layer. For both mixed layer models the ocean heat transports are assumed to remain at their diagnosed climatological values. The third model is a full dynamical ocean general circulation model (GCM). All models are coupled to a model of the subcloud atmospheric mixed layer (AML). The AML model computes the air temperature and humidity by balancing surface fluxes, radiative cooling, entrainment at cloud base, advection and eddy heat, and moisture transports. The models are forced with NCEP-NCAR monthly mean winds from 1958 to 1998. The ocean mixed layer models adequately reproduce the dominant pattern of Atlantic Ocean climate variability in both its spatial pattern and time dependence. This pattern is the familiar tripole of alternating zonal bands of SST anomalies stretching between the subpolar gyre and the subtropics. This SST pattern goes along with a wind pattern that corresponds to the North Atlantic Oscillation (NAO). Analysis of the results reveals that changes in wind speed create the subtropical SST anomalies while at higher latitudes changes in advection of temperature and humidity and changes in atmospheric eddy fluxes are important. An observational analysis of the boundary layer energy balance is also performed. Anomalous atmospheric eddy heat fluxes are very closely tied to the SST anomalies. Anomalous horizontal eddy fluxes damp the SST anomalies while anomalous vertical eddy fluxes tend to cool the entire midlatitude North Atlantic during the NAO's high-index phase with the maximum cooling exactly where the SST gradient is strengthened the most. The SSTs simulated by the ocean mixed layer model are compared with those simulated by the dynamic ocean GCM. In the far North Atlantic Ocean anomalous ocean heat transports are equally important as surface fluxes in generating SST anomalies and they act constructively. The anomalous heat transports are associated with anomalous Ekman drifts and are consequently in phase with the changing surface fluxes. Elsewhere changes in surface fluxes dominate over changes in ocean heat transport. These results suggest that almost all of the variability of the North Atlantic SST in the last four decades can be explained as a response to changes in surface fluxes caused by changes in the atmospheric circulation. Changes in the mean atmospheric circulation force the SST while atmospheric eddy fluxes dampen the SST. Both the interannual variability and the longer timescale changes can be explained in this way. While the authors were unable to find evidence for changes in ocean heat transport systematically leading or lagging development of SST anomalies, this leaves open the problem of explaining the causes of the low-frequency variability. Possible causes are discussed with reference to the modeling results.

Changes in streamflow of the Tigris and the Euphrates Rivers are shown to be associated with the North Atlantic Oscillation (NAO), a large-scale mode of natural climate variability which governs the path of Atlantic mid-latitude storm tracks and precipitation in the eastern Mediterranean. Composite indices of Turkish winter (December‐March, DJFM) temperature and precipitation are developed which capture the interannual‐decadal climate variability for the Tigris‐Euphrates headwater region, a significant source of freshwater for Turkey, Syria and Iraq. These indices are significantly correlated with the NAO, with 27% of the variance in precipitation accounted for by this natural mechanism. As evidenced by the recent widespread drought events of 1984, 1989 and 1990, the Tigris‐Euphrates streamflow also exhibits significant, 940% variability, associated with extrema. Copyright © 2000 Royal Meteorological Society.

Evidence from North Atlantic deep sea cores reveals that abrupt shifts punctuated what is conventionally thought to have been a relatively stable Holocene climate. During each of these episodes, cool, ice-bearing waters from north of Iceland were advected as far south as the latitude of Britain. At about the same times, the atmospheric circulation above Greenland changed abruptly. Pacings of the Holocene events and of abrupt climate shifts during the last glaciation are statistically the same; together, they make up a series of climate shifts with a cyclicity close to 1470 ± 500 years. The Holocene events, therefore, appear to be the most recent manifestation of a pervasive millennial-scale climate cycle operating independently of the glacial-interglacial climate state. Amplification of the cycle during the last glaciation may have been linked to the North Atlantic's thermohaline circulation.

(2003). Commentary: Zimbabwe's Food Crisis. Environment: Science and Policy for Sustainable Development: Vol. 45, No. 1, pp. 9-11.

In 2014, Central England experienced its warmest year in a record extending back to 1659. Using both state-of-the-art climate models and empirical techniques, our analysis shows a substantial and significant increase in the likelihood of record-breaking warm years, such as 2014, due to human influences on climate. With 90% confidence we find that anthropogenic forcings on the climate have increased the chances of record warm years in Central England by at least 13-fold. This study points to a large influence of human activities on extreme warm years despite the small region of study and the variable climate of Central England. Our analysis shows that climate change is clearly visible on the local-scale in this case.

The climate of the Atlantic sector exhibits considerable variability on a wide range of time scales. A substantial portion is associated with the North Atlantic Oscillation (NAO), a hemispheric meridional oscillation in atmospheric mass with centers of action near Iceland and over the subtropical Atlantic. NAO-related impacts on winter climate extend from Florida to Greenland and from northwestern Africa over Europe far into northern Asia. Over the last 3 decades, the phase of the NAO has been shifting from mostly negative to mostly positive index values. Much remains to be learned about the mechanisms that produce such low frequency changes in the North Atlantic climate, but it seems increasingly likely that human activities are playing a significant role.

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).

Recent research has suggested that there is significant predictability of hydrologic extremes, on timescales from synoptic to interannual, in a region encompassing the central Asian republics and a southern tier of countries ranging from Iraq to northwestern India (approximately 40°–80°E, 25°–55°N). Water scarcity irrigation demand, and a combination of other factors make the population of this semiarid region particularly vulnerable to hydrologic variability and predictions of this variability would be of great societal benefit. The investigation of the hydroclimatic variability of the region poses a number of scientific and practical challenges due to data scarcity, high mountains, complex land surface characteristics, and key physical processes that remain poorly understood.To review progress in understanding and predicting hydrologic variability, survey available data, consider societal applications, and identify key challenges, a U.S. National Science Foundation (NSF) workshop,“Hydrology of Central-Southwest Asia,” recently was held in San Diego, California.

The Akkadian empire ruled Mesopotamia from the headwaters of the Tigris-Euphrates Rivers to the Persian Gulf during the late third millennium B.C. Archeological evidence has shown that this highly developed civilization collapsed abruptly near 4170 ± 150 calendar yr B.P., perhaps related to a shift to more arid conditions. Detailed paleoclimate records to test this assertion from Mesopotamia are rare, but changes in regional aridity are preserved in adjacent ocean basins. We document Holocene changes in regional aridity using mineralogic and geochemical analyses of a marine sediment core from the Gulf of Oman, which is directly downwind of Mesopotamian dust source areas and archeological sites. Our results document a very abrupt increase in eolian dust and Mesopotamian aridity, accelerator mass spectrometer radiocarbon dated to 4025 ± 125 calendar yr B.P., which persisted for ~300 yr. Radiogenic (Nd and Sr) isotope analyses confirm that the observed increase in mineral dust was derived from Mesopotamian source areas. Geochemical correlation of volcanic ash shards between the archeological site and marine sediment record establishes a direct temporal link between Mesopotamian aridification and social collapse, implicating a sudden shift to more arid conditions as a key factor contributing to the collapse of the Akkadian empire.