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Corrigendum: Human contribution to the European heatwave of 2003
... The main goal of this study is to quantify the effect of the 2010 SM conditions on annual maximum temperatures in the region of the RHW with the framework of event attribution [e.g. Allen, 2003; Stott et al., 2004]. For this purpose we run ensemble simulations with a global climate model, with and without prescribed " observed " 2010 SM conditions and compare the resulting temperature distributions. ...
... We conduct five sets of ensemble simulations that are summarized inTable 1 [2012]. Whereas other studies use a no-climate-change scenario as reference [e.g. Stott et al., 2004], which potentially leads to a larger climate change signal, we chose observed climate of the past, where climate change had less effect as today, as a reference. This allows us to compare our model simulations with observed temperature data, and to employ observed SSTs. ...
... To The investigated region was specifically chosen to be " the region of highest heat wave intensity " [Dole et al., 2011] and is thus subject to a selection bias for our analysis. Therefore, we do not consider the magnitude of the 2010 heat wave directly but compute probabilities to exceed the second warmest event on the historical record [as in Stott et al., 2004]. We use the risk ratio (RR) as the main diagnostic to express the influence of SM and climate change on heat waves. ...
The severe 2010 heat wave in western Russia was found to be influenced by anthropogenic climate change. Additionally, soil moisture-temperature feedbacks were deemed important for the build-up of the exceptionally high temperatures. We quantify the relative role of both factors by applying the probabilistic event attribution framework and analyse ensemble simulations to distinguish the effect of climate change and the 2010 soil moisture conditions for annual maximum temperatures. The dry 2010 soil moisture alone has increased the risk of a severe heat wave in western Russia six-fold, while climate change from 1960 to 2000 has approximately tripled it. The combined effect of climate change and 2010 soil moisture yields a 13 times higher heat wave risk. We conclude that internal climate variability causing the dry 2010 soil moisture conditions formed a necessary basis for the extreme heat wave.
... As with traditional detection and attribution of trends in climate variables (Bindoff et al. 2013), climate models must play an important role in the methodology due to the absence of extremely long observational records. The fraction of attributable risk (F AR) or the risk ratio (RR) are commonly-used measures that quantify this potential human influence (Palmer 1999; Allen 2003; Stott et al. 2004; Jaeger et al. 2008; Pall et al. 2011; Wolski et al. 2014). Following the notation used in Stott et al. (2004) , let p A be the probability in a simulation using all external (anthropogenic plus natural) forcings of an event of similar magnitude, location and season to the actual event and p C be the probability of such an event under natural forcings. ...
... The fraction of attributable risk (F AR) or the risk ratio (RR) are commonly-used measures that quantify this potential human influence (Palmer 1999; Allen 2003; Stott et al. 2004; Jaeger et al. 2008; Pall et al. 2011; Wolski et al. 2014). Following the notation used in Stott et al. (2004) , let p A be the probability in a simulation using all external (anthropogenic plus natural) forcings of an event of similar magnitude, location and season to the actual event and p C be the probability of such an event under natural forcings. The F AR is defined as F AR = 1 − p C /p A while the RR is defined as RR = p A /p C , with each quantity a simple mathematical transformation of the other. ...
... We note that the commonly used term " risk ratio " is more precisely a " probability ratio " (Fischer and Knutti 2015) but we will stick to the RR nomenclature in this study—in part because RR is well-established terminology. In the seminal study of the 2003 European heat wave by Stott et al. (2004), their climate model did remarkably well in simulating both European mean summer temperature and its interannual standard deviation. However, this is not generally the case for the entirety of available climate model outputs nor for the wide range of extreme events of current interest (Peterson et al. 2012Peterson et al. , 2013 Herring et al. 2014). ...
Extreme event attribution characterizes how anthropogenic climate change may have influenced the probability and magnitude of selected individual extreme weather and climate events. Attribution statements often involve quantification of the fraction of attributable risk (FAR) or the risk ratio (RR) and associated confidence intervals. Many such analyses use climate model output to characterize extreme event behavior with and without anthropogenic influence. However, such climate models may have biases in their representation of extreme events. To account for discrepancies in the probabilities of extreme events between observational datasets and model datasets, we demonstrate an appropriate rescaling of the model output based on the quantiles of the datasets to estimate an adjusted risk ratio. Our methodology accounts for various components of uncertainty in estimation of the risk ratio. In particular, we present an approach to construct a one-sided confidence interval on the lower bound of the risk ratio when the estimated risk ratio is infinity. We demonstrate the methodology using the summer 2011 central US heatwave and output from the Community Earth System Model. In this example, we find that the lower bound of the risk ratio is relatively insensitive to the magnitude and probability of the actual event.
... Les courbes nous renseignent d'une part sur le potentiel correspondant à une perte de conductivité hydraulique de 50 % (Ø 50 ), et la pente en ce point. La comparaison des courbes de vulnérabilité de branches pour le hêtre (Cochard et al. 1999 ;Lemoine et al. 2002 ;Zapater 2009) à celles d'autres espèces permet de dire que cette espèce a un bon contrôle de l'emballement de l'embolie des vaisseaux aux vues de la pente douce de la courbe au Ø 50 (Sperry 2000, Zapater 2009 (Stott et al. 2004 ;Ciais et al. 2005 ;Bréda et al. 2006 ;Granier et al. 2007 ;Gartner et al. 2009). Ces évènements ne devraient pas être considérés comme isolés, mais pourraient se répéter de plus en plus fréquemment dans un futur assez proche (Bréda et al. 2006 ;International Panel on Climate Change, IPCC 2007). ...
... Global change is expected to increase the frequency and severity of soil drought events in the northern hemisphere, especially during the spring and summer (International Panel on Climate Change, IPCC 2007). Thus, the exceptional soil drought and heat wave that occurred in Europe in summer 2003 (Stott et al., 2004;Ciais et al., 2005;Bréda et al., 2006;Granier et al., 2007;Gartner et al., 2009) should not be considered as an isolated extreme event, but representative of what might occur with increasing frequency in the near future. The influence of such extreme events on the dynamics and functioning of forest ecosystems is still poorly documented because of their limited occurrence under past and present climates Bréda et al., 2006). ...
... Moreover, several studies have indicated that anthropogenic forcing has contributed to specific European events (e.g. Stott et al. 2004), while others indicate increases in the frequency of future heatwaves under greenhouse conditions (Orlowsky and Seneviratne 2012). However, a unified approach in understanding and characterizing atmospheric heatwaves in Australia is currently missing. ...
... Classically, studies analysing the role of human influence on observed extreme temperature events are based on monthly/seasonal anomalies for large spatial domains (e.g. Stott et al. 2004). In Australia, the intensity of the 2012/2013 summer was five times more likely to occur in a climate under the influence of anthropogenic greenhouse gases, compared to a climate without these influences (Lewis and Karoly 2013). ...
As part of a special issue on natural hazards, this paper reviews the current state of scientific knowledge of Australian heatwaves. Over recent years, progress has been made in understanding both the causes of and changes to heatwaves. Relationships between atmospheric heatwaves and large-scale and synoptic variability have been identified, with increasing trends in heatwave intensity, frequency and duration projected to continue throughout the 21st century. However, more research is required to further our understanding of the dynamical interactions of atmospheric heatwaves, particularly with the land surface. Research into marine heatwaves is still in its infancy, with little known about driving mechanisms, and observed and future changes. In order to address these knowledge gaps, recommendations include: focusing on a comprehensive assessment of atmospheric heatwave dynamics; understanding links with droughts; working towards a unified measurement framework; and investigating observed and future trends in marine heatwaves. Such work requires comprehensive and long-term collaboration activities. However, benefits will extend to the international community, thus addressing global grand challenges surrounding these extreme events.
... The influence that the UHI has on indoor temperatures is difficult to quantify, and requires greater understanding, as people spend the majority of their time indoors and the UHI can exacerbate building overheating during heatwaves, in particular , reducing cooling rates at night [18, 19]. Since the frequency of heatwaves is likely to increase in future , and it has even been estimated that events as severe as 2003 may become as frequent as once every 2 years by 2040202122 , it is likely that with constant or increasing urbanisation , the associated health risks will continue to be a cause for concern for the UK public health sector in future decades, and quantification of potential impacts is required. Calculations of the potential health effects of heatwaves can be based on a variety of methods. ...
... As well as a change in mean temperatures, research has shown that heatwaves are likely to become more common in future. Analysis of the 2003 European heatwave suggests that heatwaves of this scale are likely to occur every two years by the year 2040202122. The surface scheme used in the UKCP09 regional climate model does not include the UHI effect, and heat storage and release by urban materials is not modelled. ...
Background:
The Urban Heat Island (UHI) effect describes the phenomenon whereby cities are generally warmer than surrounding rural areas. Traditionally, temperature monitoring sites are placed outside of city centres, which means that point measurements do not always reflect the true air temperature of urban centres, and estimates of health impacts based on such data may under-estimate the impact of heat on public health. Climate change is likely to exacerbate heatwaves in future, but because climate projections do not usually include the UHI, health impacts may be further underestimated. These factors motivate a two-dimensional analysis of population weighted temperature across an urban area, for heat related health impact assessments, since populations are typically densest in urban centres, where ambient temperatures are highest and the UHI is most pronounced. We investigate the sensitivity of health impact estimates to the use of population weighting and the inclusion of urban temperatures in exposure data.
Methods:
We quantify the attribution of the UHI to heat related mortality in the West Midlands during the heatwave of August 2003 by comparing health impacts based on two modelled temperature simulations. The first simulation is based on detailed urban land use information and captures the extent of the UHI, whereas in the second simulation, urban land surfaces have been replaced by rural types.
Results and conclusions:
The results suggest that the UHI contributed around 50 % of the total heat-related mortality during the 2003 heatwave in the West Midlands. We also find that taking a geographical, rather than population-weighted, mean of temperature across the regions under-estimates the population exposure to temperatures by around 1 °C, roughly equivalent to a 20 % underestimation in mortality. We compare the mortality contribution of the UHI to impacts expected from a range of projected temperatures based on the UKCP09 Climate Projections. For a medium emissions scenario, a typical heatwave in 2080 could be responsible for an increase in mortality of around 3 times the rate in 2003 (278 vs. 90 deaths) when including changes in population, population weighting and the UHI effect in the West Midlands, and assuming no change in population adaptation to heat in future.
... The field of event attribution has expanded greatly in recent years as scientists attempt to answer whether specific high-impact extreme weather events have become more likely due to anthropogenic climate change. These results are then used as a communication tool for journalists and the media to convey the effects climate change is having (or not having) on the likelihood of extremes such as heatwaves and hot spells (Stott et al., 2004; Christidis et al., 2015; Perkins and Gibson, 2015; Min et al., 2014), cold spells (Christidis et al., 2014), droughts (King et al., 2014; Williams et al., 2015), and heavy rain events (Pall et al., 2011; King et al., 2013; Singh et al., 2014). Evidence for the great interest in event attribution from scientists and the wider public may be seen in the dedicated special issues of the Bulletin of the American Meteorological Society to this topic (Petersen et al., 2012Petersen et al., , 2013 Herring et al., 2014 Herring et al., , 2015). ...
... The field of event attribution has expanded greatly in recent years as scientists attempt to answer whether specific high-impact extreme weather events have become more likely due to anthropogenic climate change. These results are then used as a communication tool for journalists and the media to convey the effects climate change is having (or not having) on the likelihood of extremes such as heat waves and hot spells [Stott et al., 2004; Christidis et al., 2015; Perkins and Gibson, 2015; Min et al., 2014], cold spells [Christidis et al., 2014], droughts [King et al., 2014; Williams et al., 2015], and heavy rain events [Pall et al., 2011; King et al., 2013; Singh et al., 2014]. Evidence for the great interest in event attribution from scientists and the wider public may be seen in the dedicated special issues of the Bulletin of the American Meteorological Society to this topic [Peterson et al., 2012 [Peterson et al., , 2013 Herring et al., 2014 Herring et al., , 2015. ...
Climate scientists have demonstrated that a substantial fraction of the probability of numerous recent extreme events may be attributed to human-induced climate change. However, it is likely that for temperature extremes occurring over previous decades a fraction of their probability was attributable to anthropogenic influences. We identify the first record-breaking warm summers and years for which a discernible contribution can be attributed to human influence. We find a significant human contribution to the probability of record-breaking global temperature events as early as the 1930s. Since then, all the last 16 record-breaking hot years globally had an anthropogenic contribution to their probability of occurrence. Aerosol-induced cooling delays the timing of a significant human contribution to record-breaking events in some regions. Without human-induced climate change recent hot summers and years would be very unlikely to have occurred.
... The fraction of attributable risk (FAR) can quantify the human influence on the occurrence of individual recent historical events, such as heat waves, droughts (Lewis and Karoly 2013;Otto et al. 2012;Stott et al. 2005), heavy precipitation (Min et al. 2011;Sippel and Otto 2014), and floods (Pall et al. 2011). We calculated FAR as (P A −P N )/P A , where P A and P N represent the probability of a flood exceeding the magnitude of the flood (10-year flood in ALL-LNG experiment) for the ALL and NAT experiments, respectively. ...
The ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead to more intense precipitation extremes and associated floods. However, given the limitations of observations and model simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected by anthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90% likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicating that the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area of northern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part of northeastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in the occurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal and extreme precipitation, and because a climate change signal is often not detectable from limited observation records, large ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.
... Hallegatte et al., 2018). Without such knowledge, policy-makers are ill-prepared to address the challenges posed by the potential increased occurrence and severity of natural disasters (Emanuel, 2005;Webster et al., 2005;Scott et al., 2004). ...
This paper examines the response of firms to capital destruction, using a new measure of firm exposure to tropical storms as a negative exogenous shock on firms’ capital stock. Drawing on a panel of Indian manufacturing firms between 1995 and 2006, we establish that, depending on their strength, storms destroy up to 75.3% of the fixed assets of the median firm (in terms of its productivity and industry performance). We quantify the response of firm sales within and across industries and find effects akin to Schumpeterian creative destruction, where surviving firms build back better. Within an industry, the sales of less productive firms decrease disproportionately more, while across industries capital destruction leads to a shift in sales towards more performing industries. This build-back better effect is driven by firms active in multiple industries and, to a large extent, by shifts in the firm-level production mix within a firm’s active set of industries. Finally, while there is no evidence that firms adjust by investing in new industry lines, firms tend to abandon production in industries that exhibit lower comparative advantage.
... On another hand, we know the evolution of the farmers community in France. According to French "Institut (Scott, Stone, & Allen, 2005), the extreme HW that occurred in 2003 throughout Europe could become unexceptional events by 2040. ...
2019 was marked by several heat waves (HW) and wildfires. July was considered to be the worst in 100 years. In trying to better understand how heat impacts the subsoils and affect the ecosystems, researches were conducted in France. Temperatures were recorded at different depths into two types of grounds; sedimentary and pebbly alluvium. During the days of July 24th and 27th, August 6th and 27th 2019 temperatures were collected every 1 ½ hour, each 10cm, from -10 cm until -50 cm. The high temperature was recorded in the two holes, with extreme degrees until 50cm depth, closed to 27°C. Important temperatures were also recorded from the surface of the soil 0cm “SlST”, until +150cm in the Sun (Su) and in shadow (Sh). Records over 49°C were measured in the surface and important temperatures were collected in SU and SH, respectively 42°C and 37°C. Results showed that subsoils are storing heat differently, depends on the nature of the soil and remained high until the 27th July. Heatwaves phenomena are the source of many disasters in the agroforestry, the building sector, and caused diseases and deaths in 2003 in France, UK, and in 2019 in Australia and the northern hemisphere. According to recent research, HW would be the cause of the emergence of the epidemic sources
... On another hand, we know the evolution of the farmers community in France. According to French "Institut (Scott, Stone, & Allen, 2005), the extreme HW that occurred in 2003 throughout Europe could become unexceptional events by 2040. ...
2019 was marked by several heat waves (HW) and wildfires around the world; and particularly intense in the USA, China and Europe. The one of July was considered to be the worst in 100 years. At first, in trying to better understand how heat impact the subsoil and affect the ecosystems, researches were conducted in France. Temperatures were recorded at different depth into two types of grounds; sedimentary and pebbly alluvium ground and at different dates, with a digital thermometer "Otio". During the days of July 24th and 27th, August 6th and 27th 2019 temperatures were collected every 1 ½ hour, each 10cm, from-10 cm until-50 cm. High temperature was record in the two holes, with extreme degrees until 50cm depth, closed to 27°C. Important temperatures were also recorded from the surface of the soil 0cm "SlST", until +150cm in the Sun (Su) and in shadow (Sh). Records over 49°C were measured in the surface and important temperatures were collected in SU and SH, respectively 42°C and 37°C. Results showed that subsoils are storing heat differently, depends of the nature of soil and remained high until the 27th July. Secondly, studies suggest that those events could sometimes be due to abnormal warm winters. Results showed that, the subsoil is working like a thermal battery, who delivers the heat all the night under surface. Heat waves phenomena are the source of many disasters in the agriculture, the building sector, and caused diseases and deaths in 2003 in France and United Kingdom. Furthermore, studies point to relations between HW diseases in the USA and epidemic around the world. In fact, they suggest that those events could be due to abnormal warm winter. In May 16 th , the WHO declares the COVID-19 outbreak a worldwide severe pandemic. This phenomenon would become more common in the next decades, and may be due to abnormal warm, dry summers and winters, due by climate changes.
... A small number of studies have analyzed the probability ratio (PR) of temperatures [31,32] to study the subregions which are severely affected by extreme events. The defining formula of the PR is ...
Recently, NCAR (the National Center for Atmospheric Research) released the Community Earth System Model’s low-warming simulations, which provided long-term climate data for stabilization pathways at 1.5 °C and 2.0 °C above pre-industrial levels. Based on these data, six extreme low temperature indices—TXn (coldest day), TNn (coldest night), TX10p (cool days), TN10p (cool nights), CSDI (cold spell duration indicator), and DTR (diurnal temperature range)—were calculated to assess the changes in extreme low temperature over Northern China under 1.5 °C and 2.0 °C warmer future. The results indicate that compared to the preindustrial level, the whole of China will experience 0.32–0.46 °C higher minimum surface air temperature (SAT) warming than the global average, and the winter temperature increase in Northern China will be the most pronounced over the country. In almost all the regions of Northern China, especially Northeast and Northwest China, extreme low temperature events will occur with lower intensity, frequency, and duration. Compared with the present day, the intensity of low temperature events will decrease most in Northeast China, with TXn increasing by 1.9 °C/2.0 °C and TNn increasing by 2.0 °C/2.5 °C under 1.5 °C/2.0 °C global warming, respectively. The frequency of low temperature events will decrease relatively more in North China, with TX10p decreasing by 8 days/11 days and TN10p decreasing by 7 days/9 days under 1.5 °C/2.0 °C warming. CSDI will decrease most in Northwest China, with decreases of 7 days/10 days with 1.5 °C/2.0 °C warming. DTR will decrease in the Northwest and Northeast but increase in North China, with −0.9 °C/−2.0 °C in the Northwest, −0.4 °C/−1.5 °C in the Northeast, and 1.7 °C/2.0 °C in North China in the 1.5 °C/2.0 °C warming scenarios. For temperatures lower than the 5th percentile, the PRs (probability ratios) will be 0.68 and 0.55 of that of the present day under 1.5 °C and 2.0 °C warmer futures, respectively. Global warming of 2.0 °C instead of 1.5 °C will lead to extreme low temperature events decreasing by 6–56% in regard to intensity, frequency, and duration over Northern China, and the maximal values of decrease (24–56%) will be seen in Northeast China.
... A large amount of evidence has shown that global warming has increased (reduced) the probability of global heat (cold) waves (Stott et al. 2004;Peterson et al. 2012Peterson et al. , 2013Herring et al. 2014Herring et al. , 2015Herring et al. , 2016Screen et al. 2015;Ma et al. 2017;Yeh et al. 2018). However, a series of unusual extreme cold events, such as the bitterly cold waves in Europe, the eastern and central United States, and southern Canada, occurred during the winter of 2010/11, 2013/14, and 2014/15, and an all-time record low temperature in New York occurred on 7 January 2014 (Peterson et al. 2012;Herring et al. 2016). ...
It is argued that anthropogenic global warming may decrease the global occurrence of cold waves. However, a historical record-extreme cold wave, popularly called the ''boss level'' cold wave, attacked East Asia in January 2016, which gives rise to the discussion of why this boss-level cold wave occurred during the winter with the warmest recorded global mean surface air temperature (SAT). To explore the impacts of human-induced global warming and natural internal atmosphere variability, we investigated the coldwave- related circulation regime (i.e., the large-scale atmospheric circulation pattern) and compared the observation with the large ensemble simulations of the MIROC5 model. Our results showed that this East Asian extreme cold-wave-related atmospheric circulation regime mainly exhibited an extremely strong anomaly of the Ural blocking high (UBH) and a record-breaking anomaly of the surface Siberian high (SH), and it largely originated from the natural internal atmosphere variability. However, because of the dynamic effect of Arctic amplification, anthropogenic global warming may increase the likelihood of extreme cold waves through shifting the responsible natural atmospheric circulation regime toward a stronger amplitude. The probability of occurrence of extreme anomalies of UBH, SH, and the East Asia area mean SAT have been increased by 58%, 57%, and 32%, respectively, as a consequence of anthropogenic global warming. Therefore, extreme cold waves in East Asia, such as the one in January 2016, may be an enhanced response to the larger internal atmospheric variability modulated by human-induced global warming.
... HadCM3 is a coupled climate model that has been used extensively for climate prediction, detection, attribution and sensitivity studies (e.g. Hulme et al., 1999;Johns et al., 2003;Stott et al., 2004;De Silva et al., 2007; R. K. STENNETT-BROWN et al. Lei et al., 2013). ...
The Statistical Downscaling Model (SDSM) is used to investigate future projections of daily minimum and maximum temperature extremes for 45 stations and rainfall extremes for 39 stations across the Caribbean and neighbouring regions. Models show good skill in reproducing the monthly climatology of the mean daily temperatures and the frequencies of warm days, warm nights, cool days and cool nights between 1961 and 2001. Models for rainfall exhibit lower skill but generally capture the monthly climatology of mean daily rainfall and the spatial distribution of the mean annual maximum number of consecutive dry days (CDD) and mean annual count of days with daily rainfall above 10 mm (R10). Future projections suggest an increase (decrease) in warm (cool) days and nights by 2071–2099 under the A2 and B2 scenarios relative to 1961–1990. An increase in CDD is suggested for most stations except some eastern Caribbean stations and Bahamas. Decreases in RX1 (monthly maximum 1-day precipitation), R10 and R95p (annual total rainfall above the 95th percentile) are also suggested for some northern Caribbean locations and Belize under the A2 scenario, compared to a mixture of increases and decreases for the eastern Caribbean. Atmospheric predictors used in SDSM correlate well with known oceanic and atmospheric drivers of Caribbean climate, e.g. the Atlantic Multidecadal Oscillation (AMO) on a seasonal timescale. Atlantic sea surface temperatures and the Caribbean low level jet appear to have significant influence on Caribbean temperature and rainfall extremes.
... Global change is expected to increase the frequency and severity of soil drought events in the northern hemisphere, especially during spring and summer (International Panel on Climate Change, IPCC, 2007). Thus, the exceptional soil drought and heat wave that occurred in Europe in summer 2003 (Stott et al., 2004;Ciais et al., 2005;Bréda et al., 2006;Granier et al., 2007;Gartner et al., 2009) should not be considered as an isolated extreme event, but representative of what might occur with increasing frequency in the near future. The influence of such extreme events on the dynamics and functioning of forest ecosystems is still poorly documented because of their low occurrence (Saxe et al., 2001;Bréda et al., 2006). ...
... In particular, intense human activity/traffic and tall buildings that obstruct air movement contribute to the urban heat island (UHI) effect [3], and the intensity of the UHI effect is likely to increase in the context of global warming [1,4]. Previous studies have shown that the UHI effect significantly increases cooling energy in the summer [5][6][7], worsens air pollution [8][9][10], raises risks for early mortality [11], and deteriorates the living conditions of urban dwellers [12][13][14]. Accordingly, policy makers have attempted to mitigate the UHI effect with social, spatial, and environmental interventions. ...
The rapid increase of impervious surfaces and the dense development that accompanies urban growth has reduced the amount of green space in urban landscapes and increased urban surface temperatures. Accordingly, the greening of urban spaces has been proposed as one approach to mitigating urban heat island (UHI) effects. To find the most practical green space design for reducing land surface temperatures (LSTs), we explored the effects of the physical characteristics of green spaces on cooling intensity and distance. The physical characteristics of green spaces were defined as shape, size, Normalized Difference Vegetation Index (NDVI), and the land-use type of their surroundings. LANDSAT 8 images were used to examine 30 green spaces in Ulsan, Korea. The analytical results showed that the cooling effect was mainly observed within 120 m of a green area and that the intensity of the cooling effects did not exceed 3.0 K. A belt-shaped green space had a greater cooling distance compared to a compact green space. We also found that the NDVI and size of a green space had a positive but non-linear association with cooling intensity.
... Abstract: Under the exacerbation of climate change, climate extreme events, especially for 31 drought, happened frequently and intensively across the globe with greater spatial differences. Stott et al., 2004; Elsner et al., 2008; Min et al., 71 2011; Sheffield et al., 2012; Dai, 2004 Dai, , 2013 Trenberth et al., 2013; IPCC, 2014). Among all 72 the extremes, droughts are believed to be the most damaging natural disasters [1995; Romm, 2011], which affects more people 74 than any other forms of devastating meteorological hazards (Wilhite, 2000; Yu et al., 2014Barriopedro et al., 2012). ...
Under the exacerbation of climate change, climate extreme events, especially for drought, happened frequently and intensively across the globe with greater spatial differences. We used the Standardized Precipitation-Evapotranspiration Index (SPEI) computed from the routine meteorological observations at 269 sites in Southwest China (SWC) to study the drought characteristics (e.g., extent, duration and intensity) and their decadal variations during 1971-2012. It was revealed that the drought, in responses to the coupling between decadal precipitation and potential evapotranspiration (PET) anomalies, differed among regions and periods. For the entire SWC, droughts in 1970s and 2000s+ was generally stronger than in 1980s and 1990s with respect to their spatial extent, duration and intensity, especially in 2000s+. It was well-known that drought was closely related with a lack of precipitation; however, the impact of atmospheric demand of evaporation [reflected by PET here] on drought (e.g., duration and intensity) was rarely paid enough attentions. To that end, a spatial multi-linear regression approach was proposed in this study for quantifying the contributions of decadal PET and precipitation variations to drought duration and intensity. We have found that the contributions of decadal PET anomalies to drought duration and intensity could exceed those of precipitation, e.g., during 1980s and 1990s in SWC. Additionally, despite the strongest droughts in 2000s+, it was suggested that PET could exert comparable impacts on drought anomalies as precipitation. All these findings implied that PET plays a critical role in drought event, which acts to amplify drought duration and intensity. To sum up, this study stressed the need for enough attentions for PET processes in drought studies.
... The temperature estimates for Switzerland from a great number of coherent qualitative documentary evidence confirm exceptional heat; Wetter and Pfister (2013) considered the spring–summer temperatures for 1540 probably more extreme in neighboring regions of western and central Europe than those of 2003 when between 22 000 and 35 000 heatrelated deaths were recorded across Europe (e.g. Beniston, 2004; Schär and Jendritzky, 2004; Stott et al., 2004; Fischer et al., 2007). More recently, Wetter et al. (2014) employed the term " megadrought " to the 1540 weather patterns in Europe , a coinage that led to some discussion in the tree-ring (Büntgen et al., 2015) and documentary data (Pfister et al., 2015) communities. ...
Viticulture has long been essential to the commercial and social well-being
of parts of the Czech Lands (now the Czech Republic), and detailed
records have been kept for centuries of the timing and relative success of
the grape crop. Using such documentary data from the Bohemian wine-growing
region (mainly northwest of the capital, Prague), series of grape-harvest
dates (GHDs) were created for the 1499–2015 period. Because the link between
harvest dates and temperatures is strong, GHD series, together with
instrumental mean temperature series starting in 1801, were used to
reconstruct mean April–August temperatures for the region from 1499 to 2015.
Linear regression (LR) and variance scaling (VS) methods were used for
calibration and compared in terms of explained variance and their ability to
capture extreme values. It emerged that LR does not significantly
underestimate temperature variability. However, VS shows far greater capacity
to capture extremes. GHDs explain 64 % of temperature variability over
the full calibration period. The 1986–2015 period was identified as the
warmest 30-year period of the past 514 years, an observation consistent with
recent global warming. The highest April–August temperatures appeared in
a reconstruction for the year 1540, which was warmer than the next two very
warm, and far more recent, seasons in 2003 and 2015. The coldest period
occurred at the beginning of the 20th century (1900–1929). The series
reconstructed for the Czech Lands is in close agreement with other (central)
European reconstructions based on other proxies. The series created here
makes an important contribution to a better understanding of long-term
spatiotemporal temperature variability in central Europe.
... คลื ่ นความร้ อน นั บเป็ นภั ยคุ กคามทางภู มิ อากาศที ่ สามารถส่ งผลกระทบทั ้ งทางตรงและทางอ้ อมต่ อสั งคม โดย ส่ วนใหญ่ รู ้ จั กกั นถึ งผลกระทบต่ อสุ ขภาพของมนุ ษย์ (Kovats and Hajat, 2008) (Beniston, 2004;Schär et al., 2004;Stott et al., 2004 .83783 x 10-3*T2 -5.481717 x 10-2* RH2 +1.22874 x 10-3*T2*RH +8.5282 x 10-4*T*RH2 -1.99 x 10-6 * T2 *RH2 1 C = 5/9 * ( o F -32) 2 HI: heat index ( o F), T: air temperature ( o F), RH: relative humidity (%) F: Fahrenheit, C: Celsius สมการข้ างต้ น เป็ นอั ลกอรึ ทึ มถดถอยพหุ คู ณ ประกอบด้ วย 9-term multiple regression model ที ่ สามารถประยุ กต์ ใช้ ค� านวณ HI ได้ ดี เมื ่ ออุ ณหภู มิ และความชื ้ นสั มพั ทธ์ สู งกว่ า 26 o C และ 39% ตามล� าดั บ ดั งนั ้ น มี ข้ อแนะน� าให้ ปรั บความถู กต้ อง ของค่ า HI = T ในกรณี ที ่ อุ ณหภู มิ และความชื ้ นสั มพั ทธ์ ต� ่ ากว่ าค่ าดั งกล่ าว (Patricola and Cook 2010;Zahid and Rasul, 2010;Oka, 2011;Rajib et al., 2011) ...
... In light of this challenge, an emerging field of climate science (known as event attribution) is seeking to quantify how 25 the risk of weather and climate-related extremes has changed as a consequence of particular forcings acting on the climate system (Allen, 2003; Stott et al., 2004; Pall et al., 2011; National Academies of Science, Engineering and Medicine, 2016). This is typically achieved by comparing the probability of such events under the current (observed) climate against that for counterfactual worlds in which particular forcing factors (such as human-induced climate change) are absent. ...
A new climate modelling project has been developed for regional climate simulation and the attribution of weather and climate extremes over Australia and New Zealand. The project, known as weather@home Australia-New Zealand, uses public volunteers' home computers to run a moderate-resolution global atmospheric model with a nested regional model over the Australasian region. By harnessing the aggregated computing power of home computers, weather@home is able to generate an unprecedented number of simulations of possible weather under various climate scenarios. This combination of large ensemble sizes with high spatial resolution allows extreme events to be examined with more robust estimates of uncertainty. This paper provides an overview of the weather@home Australia-New Zealand project, including initial evaluation of the regional model performance. The model is seen to be capable of resolving many climate features that are important for the Australian and New Zealand regions, including the influence of El Niño-Southern Oscillation on driving natural climate variability. To date, 75 model simulations of the observed climate have been successfully integrated over the period 1985–2014 in a time-slice manner. In addition, multi-thousand member ensembles have also been generated for the years 2013, 2014 and 2015 under climate scenarios with and without the effect of human influences. All data generated by the project is freely available to the broader research community.
... However, it is the superposition of the three climate timescales that can lead to changes or trends in the frequency of adverse years. Extreme examples are potentially the protracted drought conditions in the western United States from the mid-1990s to the early twenty-first century [6], the 2003 European heat wave [7], or the extremely active hurricane season 2005 [8], which was accompanied by many land-falling hurricanes in the United States such as Katrina. The primary difference between prediction of climate variability on different timescales is the drivers, or phenomena, associated with those impacts. ...
... Such hot summers were repeatedly accompanied by severe drought conditions. The record-breaking summers of 2003 in the western (e.g., Luterbacher et al., 2004; Schär et al., 2004; Stott et al., 2004) and 2010 in the eastern parts of Europe (e.g., Barriopedro et al., 2011; Dole et al., 2011; Rahmstorf and Coumou, 2011; Valeriánová et al., 2015) are the most prominent examples of this change, mainly due to their extremely large-scale spatial relevance and the very long duration of accompanying heat waves. Other summers with high temperature extremes received less attention, yet were regionally severe. ...
In 2015, large parts of Europe were characterized by extraordinary high summer temperatures, accompanied by very dry conditions, particularly in central‐eastern Europe. Several major heat episodes occurred from the end of June until mid‐September. We provide an ad‐hoc evaluation of the observed climatological extremes in a secular context, by using a set of long station time series in daily resolution. Our data set comprises 42 temperature and 43 precipitation records, predominantly starting already in the 19th century. To investigate local record values, the individual full record length is analysed for each station, while regionally averaged analyses are presented for the core study period of 1901–2015. The study area covers Europe's central latitudes (44° to 52°N), extending from England in the west up to the central Ukraine in the east.
During summer 2015, various indices representing extremely high maximum and minimum temperatures (strongly) exceeded previous record high values, mainly in an area extending from eastern Germany to western Ukraine. Additionally, severely to extremely dry conditions with unusually frequent dry days were prevailing particularly in the (central‐) eastern part of the study area. Drought indices combining temperature and precipitation revealed drought conditions comparable or even worse than those of former extreme summers like 2003.
... 770 and 1100 CE (Hodell et al. 2005 ). Contemporary events, such as Australia's 1997–2009 'Millennium Drought' (Murphy and Timbal 2008 ; Heberger 2012 ) and the 2003 European heatwave (Stott et al. 2004 ; Ciais et al. 2005 ) clearly demonstrate that extreme climatic or weather conditions can be potent agents of change in anthropogenic and natural systems alike. One reason why few studies have attempted to untangle the complex linkages that exist between climate variability, climatic trends and biotic change is that it is logistically diffi cult to quantify the impact of statistically improbable climatic or meteorological events on single populations, let alone entire ecosystems. ...
Despite growing evidence that species and ecosystems are responding to broad climatic trends globally, relatively little is known about the role that extreme climatic or weather events (ECEs) play in driving population and ecosystem change. The objective of this chapter is to provide an overview of the nature of ECEs and their impacts on the demography of wild plant populations in both terrestrial and aquatic ecosystems. We do this by drawing out some of the main lessons that have been learned from the past and contemporary study of ECEs, focusing primarily on case studies involving Australian vegetation, and then use these to identify potential phytosociological and evolutionary roles of extreme events within the context of anthropogenic climate change. We then discuss the contribution that genomics can make to our understanding of the demographic and evolutionary impact of historical ECEs on plant populations, and propose four key questions that are likely to shape future research in this field.
... Since changes in the frequency of occurrence and in intensity of extreme events tend to have a greater impact on the environment and the society than changes in meteorological averages (WATSON ET ), many of these studies are devoted to the occurrence of extreme events and their relationships with atmospheric circulation. Among the most popular topics in climatological literature published in recent years has been a strong increase in temperatures during the 20 th and 21 st centuries and an accompanying increase in the occurrence of heatwaves (HEINO ET AL., 1999; MEEHL & TEBALDI, 2004; STOTT ET AL., 2004; WIBIG, 2007; KYSELÝ & HUTH, 2008; WIBIG ET AL., 2009a; IPCC, 2013), including their direct impact on the society and health (VANDENTORREN ET BŁAŻEJCZYK & MCGREGOR, 2007; DELLA-MARTA ET AL., 2007; BARRIOPEDRO ET AL., 2011). These changes are accompanied by an increase in minimum temperatures and the trend towards milder weather in winter (WIBIG & GŁOWICKI, 2002; PIOTROWICZ, 2003; GIORGI ET AL., 2004; ROWELL, 2005; VOSE ET AL., 2005;). ...
The paper discusses the spatial and temporal variability in the occurrence of strong highs over Poland in the period 1951-2015. It focuses in particular on the persistence of the systems in question and the changes in their long-term variability. The study was based on the average daily sea-level air pressure values obtained from NCEP/NCAR Reanalyses for 12 grid points. A day with a strong high was defined as a day with daily average pressure equal to or higher than 1030 hPa. Over the study period, a minor increase in the annual air pressure values (0.17-0.32 hPa/10 years) was identified, as well as evidence of an increase in the number of days with strong highs. These changes were the most distinct in December and in southern Poland. A majority of strong high occurrences were recorded during the cool half of the year, they covered less than the whole territory of the country and typically persisted for no more than two days. The longest spells with pressure equal to or greater than 1030 hPa lasted between 15 and 22 days depending on the region. Such long sequences of days with strong highs coincided with years when strong highs were particularly frequent, especially in the 1980s and 1990s. No specific trends in persistence or seasonality were identified.
... Understanding the causes that have led to event is crucial, particularly as studies are increasingly showing that certain extreme events are becoming more frequent under climate change [Allen, 2003]. While it is not possible to entirely attribute a single extreme weather and climate event to either anthropogenic or natural causes, it is possible to evaluate how the odds to experience an extreme event have changed due to the influence of an external driver [Allen, 2003; Stott and Allen, 2004]. Addressing this question has been an active area of recent research using different approaches [Pall et al., 2011; Van Oldenborgh et al., 2014; Yiou and Cattiaux, 2013; King et al., 2013; Christidis et al., 2013; Schaller et al., 2014], commonly carried out on recent extreme weather events (e.g., extreme precipitation and flooding), which are also equally applicable to extreme climate events (e.g., hot summers season or a warming hiatus). ...
Event attribution aims to estimate the role of an external driver after the occurrence of an extreme weather and climate event by comparing the probability that the event occurs in two counterfactual worlds. These probabilities are typically computed using ensembles of climate simulations whose simulated probabilities are known to be imperfect. The implications of using imperfect models in this context are largely unknown, limited by the number of observed extreme events in the past to conduct a robust evaluation. Using an idealized framework, this model limitation is studied by generating large number of simulations with variable reliability in simulated probability. The framework illustrates that unreliable climate simulations are prone to overestimate the attributable risk to climate change. Climate model ensembles tend to be overconfident in their representation of the climate variability which leads to systematic increase in the attributable risk to an extreme event. Our results suggest that event attribution approaches comprising of a single climate model would benefit from ensemble calibration in order to account for model inadequacies similarly as operational forecasting systems.
... Temperature extremes are expected to change in terms of their severity, frequency, and duration as a result of anthropogenic global warming (Easterling et al. 2000; Meehl and Tebaldi 2004; Tebaldi et al. 2006; Meehl et al. 2007). Unusual extreme heat events, such as those which occurred in Chicago in 1995, western Europe in 2003, Russia in 2010, and India in 2015 are expected to become more commonplace in a warmer world (e.g., Beniston 2004; Schär et al. 2004; Stott et al. 2004; Dole et al. 2011; Rahmstorf and Coumou 2011; Gao et al. 2012; Murari et al. 2015). Thus, evaluations of the ability of RCMs in downscaled temperature extremes and their changes are of clear value. ...
The weather research and forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximum temperature through comparison with North American regional reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting a novel bootstrap procedure that makes no assumption of temporal or spatial independence within a year, which is especially important for climate data. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.
... Based on the temperature estimates for Switzerland from a great number of coherent qualitative documentary evidence, confirm 15 exceptional heat; Wetter and Pfister (2013) considered the spring–summer temperatures for 1540 probably more extreme in neighbouring regions of western and central Europe than those of 2003 when between 22,000 and 35,000 heat-related deaths were recorded across Europe (e.g. Beniston, 2004; Schär and Jendritzky, 2004; Stott et al., 2004; Fischer et al., 2007). More recently, Wetter et al. (2014) employed the term " megadrought " to the 1540 weather patterns in Europe, a coinage that led to some discussion in the tree-ring (Büntgen et al., 2015) and documentary data (Pfister et al., 2015) communities. ...
Viticulture has long been essential to the commercial and social well-being of parts of the Czech Lands (recently the Czech Republic), and detailed records have been kept for centuries of the timing and relative success of the grape crop. Using such documentary data from the Bohemian wine-growing region (mainly north-west of the capital, Prague), series of grape-harvest dates (GHDs) were created for the 1499–2012 period. Because warmer temperatures lead to earlier harvest dates and vice versa, GHD series, together with instrumental mean temperature series starting in 1801, were used to reconstruct mean April–August temperatures for the region from 1499 to 2012. Linear regression (LR) and variance scaling (VS) methods were used for calibration and compared in terms of explained variance and their ability to capture extreme values. It emerged that LR does not significantly underestimate temperature variability. However, VS shows far greater capacity to capture extremes. GHDs explain 64 % of temperature variability over the full calibration period. The 1971–2012 period was identified as the warmest of the past 514 years, an observation consistent with recent global warming. The highest April–August temperatures appeared in reconstruction for the year 1540, which was warmer than the next two very warm, and far more recent, seasons in 2000 and 2003. The coldest period occurred at the beginning of the 20th century (1900–1929). The series reconstructed for the Czech Lands is in close agreement with other (central) European reconstructions based on other proxies. The series created here makes an important contribution to a better understanding of long-term spatio-temporal temperature variability in central Europe.
... Major concern about wildfires and their effects in the region began in the 1960s, 1970s and 1980s (Shakesby, 2011 ) as a consequence of an exponential increase in fire activity (Moreno et al., 1998; Pausas, 2004). This increment is commonly due to a decreasing of total rainfall and an increasing of temperature over recent decades (Stott et al., 2004; Harding et al., 2009). However, human influences, including land use change brought about by widespread socio-economic change and urban expansion, have been viewed as the main drivers of the dramatic increase in wildfire activity (Pausas et al., 2008; Shakesby, 2011; Bodí et al., 2012; Carreiras et al., 2014; Pereira et al., 2015). ...
This study deals with the experimental fire effects in overland flow and soil erosion at plot scale and considering rainfall erosivity. The study was conducted in from May 2011 to Dec 2013. Six plots of 12-m length and 2-m width are considered: four of them were burned, whilst two of them remained in natural conditions. Overland flow was collected in deposits of 250 L after each rainfall event, measured at a meteorological station. Larger rainfall intensities and erosivity were registered after summer and, thus, overland flow and sediment yield, but one order of magnitude higher in the burned plots than in the unburned ones. Especially, the difference in overland flow and soil loss between both set of plots were nearly three folds larger whether the rainfall intensity exceeded 30 mm h À1 during 15-min intensity. It is remarkable that the most erosive event generating the maximum values of overland flow and soil loss was registered 16 months after the experimental fire when a rainfall event of 99·2 mm h À1 occurred. This delay is considered as consequence of soil surface conditions and ash cover.
... The extreme character of the 2003 heat wave is in part related to the breaking of many temperature records across Europe [Barriopedro et al., 2011]. Stott et al. [2004] showed that the human influence has more than doubled the risk of such a severe heat wave, which has now, 10 years later, increased even more [Christidis et al., 2014]. Based on the long Central England Temperature (CET) time series, King et al. [2015a] have shown that anthropogenic forcings have induced a thirteenfold increase in the probabilities of occurrence of a warm record year such as 2014. ...
Observational analysis of Europe summer record-breaking temperatures suggests that their occurrence differs from that expected in a stationary climate since the late 1980s. The observed cold and warm record evolution is well simulated by the ensemble mean of 27 coupled models from the Coupled Model Intercomparison Project phase 5 (CMIP5). We find that this evolution is still today within the range of internal variability derived from CMIP5 preindustrial simulations. We then estimate a time of emergence of the summer record anthropogenic influence in a world under a business as usual greenhouse gas emission scenario. We suggest a time of emergence around 2020 for the cold records and 2030 for the warm ones with an uncertainty of ± 20 years. By 2100, the multimodel ensemble mean indicates a tenfold increase of the number of warm records compared to the first half of the twentieth century and the quasi-disappearance of cold records.
... To test the sensitivity of the results to these temperature and duration thresholds, we also examined heat waves of shorter durations (n = 1 to n = 3 days) and reduced the | observed temperature thresholds to 40°C for Adelaide and 39°C for Melbourne (see online supplemental material). To estimate the anthropogenic inf luence in a quantitative manner, we use the fraction of attributable risk (FAR; Allen 2003; Stott et al. 2004) in which the probability of heat waves occurring is compared in the " all forcing " and " natural " scenarios. Here, we aggregate the 10 " natural " ensembles in order to calculate a best estimate of FAR, while we consider each of the " natural " ensembles separately in order to better sample the possible range of FAR values and estimate uncertainties. ...
Anthropogenic climate change very likely increased the likelihood of prolonged heat waves like that experienced in Adelaide in January 2014 by at least 16%. The influence for Melbourne is less clear.
... Bootstrapping was performed 10 000 times per period , which respectively sampled (with replacement) 50% of all 19-day May anomalies from the control and forced simulations. Bootstrapping is commonplace in FAR assessments (e.g., Stott et al. 2004; Lewis and Karoly 2013), estimating the uncertainty surrounding the FAR value. Only 50% of anomalies in both experiments are resampled to account for uncertainty in sample size. ...
Anthropogenic activity has increased the risk of Australian heatwaves during late autumn similar to the 2014 event by up to 23-fold, compared to climate conditions under no anthropogenic influence.
... In some cases it is therefore not possible to make an attribution statement (e.g.). There is generally greater confidence in attribution of temperature extremes (e.g. Stott, Stone, & Allen, 2004) than precipitation extremes. Modelling the influence of climate change on tropical cyclones would require large ensembles of simulations at higher resolutions, increasing the computing power needed to obtain robust results by orders of magnitude. ...
In 2013 the Warsaw International Mechanism (WIM) for loss and damage (L&D) associated with climate change impacts was established under the United Nations Framework Convention on Climate Change (UNFCCC). For scientists, L&D raises questions around the extent that such impacts can be attributed to anthropogenic climate change, which may generate complex results and be controversial in the policy arena. This is particularly true in the case of probabilistic event attribution (PEA) science, a new and rapidly evolving field that assesses whether changes in the probabilities of extreme events are attributable to GHG emissions. If the potential applications of PEA are to be considered responsibly, dialogue between scientists and policy makers is fundamental.
Two key questions are considered here through a literature review and key stakeholder interviews with representatives from the science and policy sectors underpinning L&D. These provided the opportunity for in-depth insights into stakeholders’ views on firstly, how much is known and understood about PEA by those associated with the L&D debate? Secondly, how might PEA inform L&D and wider climate policy? Results show debate within the climate science community, and limited understanding among other stakeholders, around the sense in which extreme events can be attributed to climate change. However, stakeholders do identify and discuss potential uses for PEA in the WIM and wider policy, but it remains difficult to explore precise applications given the ambiguity surrounding L&D. This implies a need for stakeholders to develop greater understandings of alternative conceptions of L&D and the role of science, and also identify how PEA can best be used to support policy, and address associated challenges.
... For example, the exceptional European heat wave in the summer of 2003 resulted in an area-average temperature anomaly of 2.3 @BULLET C compared with the 1961–1990 average. By simulating probability distributions of summer temperatures with and without anthropogenic emissions it was shown that the risk of a 2003 heat wave has increased by a factor of at least two due to human influence (Stott et al., 2004). Attributing changes in flood risk to carbon dioxide emissions is not yet feasible using the same approach because of the very large uncertainty in modelled precipitation changes at the river catchment scale (Prudhomme et al., 2003). ...
In recent years, South Korea has experienced a notable escalation in the intensity and frequency of summer heat. To quantitatively gauge the impact of global warming on Korean summers, this study employs the Time of Emergence (TOE) method, assessing when the effects of global warming surpass natural climate variability. Determining a precise regional TOE is challenging due to disparities between modeled climates and observations. For peak summer seasons (July and August), TOE estimates range from the 2010s to the early 2030s in Shared Socioeconomic Pathways (SSP) 5-8.5, suggesting an imminent or already reached TOE. However, for the same scenario, different methodologies and datasets project the TOE to the late 21st century, suggesting the existence of uncertainty in the TOE. One reason for this uncertainty is the discrepancies identified between climate models and observations, which suggest that climate models could delay the TOE beyond the present time. Furthermore, from 1959 to 2014, global warming accounts for less than 10% of the observed temperature. Despite this, the strengthening of global warming signals is confirmed, leading to the expectation of more extreme events than those seen in the 2018 heat wave. This raises questions about current estimates of TOE and emphasizes the need for robust climate modeling to inform effective climate action.
Temperature is the most concerned factor in the human–environment interactions. Apparent temperature accounting for other meteorological variables, such as humidity, wind speed and solar radiation, is the equivalent temperature, and it is a more accurate indicator to reflect human's environmental temperature perception. High‐quality apparent temperature data are urgently needed for the further research on human–environment interactions. At the same time, as global heat waves continue to increase in frequency, duration and intensity, understanding the impact of heat waves on human health needs human perception‐based heat wave data. Using ERA5 hourly data on single levels of 2 m temperature, wind speed, dewpoint temperature and solar radiation, this study developed a global apparent temperature and heat wave (GATHW) toolbox based on the Climate Data Store (CDS) online platform. This toolbox allows using three methods to calculate daily apparent temperature and heat wave at three spatial resolutions of 0.25°, 0.5° and 1° respectively. It can realize online calculation, display and real‐time download and is updated in near real‐time. The global daily apparent temperature and annual heat wave dataset from 2006 to 2020 calculated by the toolbox can be obtained from https://www.doi.org/10.5281/zenodo.4764325. After evaluation, this dataset can well reflect the typical extreme temperatures and heat wave events, and is more accurate, with higher resolution and faster update frequency than similar data products, which can provide data support for the study of human–environmental interactions and extreme climate events. Technical route for data production.
Given the current confirmed permafrost degradation and its considerable impacts on ecosystems, water resources, infrastructure and climate, there is great interest in understanding the causes of
permafrost degradation. Using the surface frost index (SFI) model and multimodel data from the fifth phase of the Coupled Model intercomparison Project (CMIP5), this study, for the first time, investigates external anthropogenic and natural forcing impacts on historical (1921–2005) near-surface permafrost change in the Northern Hemisphere. The results show that anthropogenic greenhouse gas (GHG) forcing produces a significant decrease in the area of near-surface permafrost distribution at a rate of 0.46 × 106 km2 decade−1, similar to observations and the historical simulation (ALL). Anthropogenic aerosol (AA) forcing yields an increase in near-surface permafrost distribution area at a rate of 0.25 × 106 km2 decade−1. Under natural (NAT) forcing, there is a weak trend and distinct decadal variability in near-surface permafrost area. The effects of ALL and GHG forcings are detectable in the observed change in historical near-surface permafrost area, but the effects of NAT and AA forcings are not detected using the optimal fingerprint
methods. This indicates that the observed near-surface permafrost degradation can be largely attributed to GHG-induced warming, which has decreased the near-surface permafrost area in the Northern Hemisphere by approximately 0. 21 × 106 km2 decade−1 on average over the study period, according to the attribution analysis.
Understanding the unfolding challenges of climate change relies on climate models, many of which have large summer warm and dry biases over Northern Hemisphere continental mid-latitudes. This work, using the example of the model used in the updated version of the weather@home distributed climate model framework, shows the potential for improving climate model simulations through a multi-phased parameter refinement approach, particularly over northwestern United States(NWUS). Each phase consists of 1) creating a perturbed physics ensemble with the coupled global – regional atmospheric model, 2) building statistical emulators that estimate climate metrics as functions of parameter values, 3) and using the emulators to further refine the parameter space. The refinement process includes sensitivity analyses to identify the most influential parameters for various model output metrics; results are then used to cull parameters with little influence. Three phases of this iterative process are carried out before the results are considered to be satisfactory; that is, a handful of parameter sets are identified that meet acceptable bias reduction criteria. Results not only indicate that 74% of the NWUS regional warm biases can be reduced by refining global atmospheric parameters that control convection and hydrometeor transport, and land surface parameters that affect plant photosynthesis, transpiration and evaporation, but also suggest that this iterative approach to perturbed physics has an important role to play in the evolution of physical parameterizations.
In the summer of 2003 the temperatures reached were responsible for a large number of deaths in Europe. A year after this fact, many countries had implemented some sort of plan of prevention against excessive temperatures. Plans that had already shown its ability to prevent a large proportion of avoidable mortality in other latitudes. Since then, a lot of papers have been published providing new data on health effects of a heat wave, which can help increase the efficiency of these prevention plans. Knowing the weather conditions at risk, defining "heat wave" or to take into account the time that the plan should be active from the study of the relationship between temperature and their effects on health, to identify weather patterns that modulate the relationship between temperature and mortality, locate the profile of people at risk or to develop protocols for action as accurately as possible and based on scientific knowledge are elements drawn from studies carried on in recent years that should be taken into account.
In the midsummer of 2013, Central and Eastern China (CEC) was hit by an extraordinary heat event, with the region experiencing the warmest July-August on record. To explore how human-induced greenhouse gas emissions and natural internal variability contributed to this heat event, we compare observed July-August mean surface air temperature with that simulated by climate models. We find that both atmospheric natural variability and anthropogenic factors contributed to this heat event. This extreme warm midsummer was associated with a positive high-pressure anomaly that was closely related to the stochastic behavior of atmospheric circulation. Diagnosis of CMIP5 models and large ensembles of two atmospheric models indicates that human influence has substantially increased the chance of warm mid-summers such as 2013 in CEC, although the exact estimated increase depends on the selection of climate models.
The effect of weather on disease was investigated based on results reported in academic papers. Weather-sensitive disease was selected by analyzing the frequency distributions of diseases and correlations between diseases and meteorological factors (e.g., temperature, humidity, pressure, and wind speed). Correlations between disease and meteorological factors were most frequently reported for myocardial infarction (MI) (28%) followed by chronic ischemic heart disease (CHR) (12%), stroke (STR) (10%), and angina pectoris (ANG) (5%). These four diseases had significant correlations with temperature (meaningful correlation for MI and negative correlations for CHR, STR, and ANG). Selecting MI, as a representative weather-sensitive disease, and summarizing the quantitative correlations with meteorological factors revealed that, daily hospital admissions for MI increased approximately 1.7%-2.2% with each 1℃ decrease in physiologically equivalent temperature. On the days when MI occurred in three or more patients larger daily temperature ranges (2.3℃ increase) were reported compared with the days when MI occurred in fewer than three patients. In addition, variations in pressure (10 mbar, 1016 mbar standard) and relative humidity (10%) contributed to an 11%-12% increase in deaths from MI and an approximately 10% increase in the incidence of MI, respectively.
This chapter sketches the problems of climate change and allocation of the responsibility for tackling it. In view of the threats to key human rights posed by observed and projected climatic changes, climate change is conceptualized as a moral harm. We explore how the burdens involved in remedying the problem should be allocated, focusing on the principle of moral responsibility that plays a central role in common-sense morality. The responsibilities of individual emitters have been underestimated because important doubts exist about the agency of individuals in complex global dynamics such as climate change. We contrast this view with the observation that people can psychologically reconstruct their contribution to climate change, in order to evade moral responsibility for it.
Autor a quien debe ser dirigida la correspondencia Resumen Se modela la temperatura del aire a 850 milibares para determinar su potencial como indicador de las ondas cálidas en el noroeste de México. El análisis se realizó a nivel de meso-escala pero se enfocó para la ciudad de Mexicali, México. Se identificaron las principales variables que causan la formación de una onda cálida, y se propuso la temperatura del aire a 850 milibares como un indicador de su desarrollo. Esto se hace en función de ocho variables climáticas, utilizando seis algoritmos de inteligencia artificial. La técnica numérica de redes neuronales mostró un mejor desempeño, obteniéndose un coeficiente de regresión de 0.76 con un valor-p de 0.0019. Se concluye que este modelo no lineal es una herramienta prometedora que podría utilizarse en un sistema de alerta de este peligroso fenómeno atmosférico. Palabras clave: onda cálida; temperatura del aire; inteligencia artificial; redes neuronales. Abstract The temperatures of atmospheric air at 850 millibars are modeled to determine their potential as an indicator of heat waves in northwest Mexico. The analysis was performed at meso-scale level but focused on the city of Mexicali, Mexico. The main variables that cause the formation of a heat wave are identified, and the modeling of air temperature at 850 milibars as an indicator of its development. This is done considering eight climate variables, using six artificial intelligence algorithms. The numerical technique of artificial neural networks showed a better performance, obtaining a regression coefficient of 0.76 with a p-value of 0.0019. It is concluded that this non-linear model is a promising tool that could be used in a warning system of this dangerous atmospheric phenomenon.
Stomatal conductance links plant water use and carbon uptake, and is a critical process for the land surface component of climate models. However, stomatal conductance schemes commonly assume that all vegetation with the same photosynthetic pathway use identical plant water use strategies whereas observations indicate otherwise. Here, we implement a new stomatal scheme derived from optimal stomatal theory and constrained by a recent global synthesis of stomatal conductance measurements from 314 species, across 56 field sites. Using this new stomatal scheme, within a global climate model, subtantially increases the intensity of future heatwaves across Northern Eurasia. This indicates that our climate model has previously been under-predicting heatwave intensity. Our results have widespread implications for other climate models, many of which do not account for differences in stomatal water-use across different plant functional types, and hence, are also likely under projecting heatwave intensity in the future.
During June, July and August 2003, an exceptional heat wave affected western and central Europe. In Piedmont, a region located in northwestern Italy at the foot of the Alps, many stations recorded the highest mean summer temperatures since the beginning of their instrumental record. Some consequences of this extraordinary hot summer in Piedmont and in many European countries include severe drought conditions, with strong effects on agriculture and electric production, an acceleration of glacier ablation, and an increase in the frequency of forest fires. This heat wave has been analyzed by running a Soil-Vegetation Atmosphere Transfer scheme for 5 years (1999-2003): the LSPM (Land Surface Process Model). The attention was focused on energy and hydrologic budget components by performing two simulations in climatically different sub-areas of Piedmont. The increment in the observed solar radiation during summer 2003 produced an increment in the net radiation, which in turn generated an increase of sensible (more) and latent (less) heat flux, and soil-vegetation heat flux. The latter caused a consistent warming of soil and vegetation surfaces, which acted partially as a negative feedback increasing the longwave radiation emitted by the terrestrial surface. Latent heat flux showed a small increment in summer 2003, because the evapotranspiration was limited by the soil moisture unavailability, particularly during July and August, due to the scarcity of precipitations during the previous spring. The drought conditions, acting as a positive feedback, caused the effects of the heat wave to be more severe, favored its persistence and enhanced the further reduction of soil moisture. The comparison among the results of the two simulations allowed to highlight the role of two phenomena that concurred to exacerbate the heat wave: the enhancement of the drought conditions and the increment of the adiabatic compression connected with the anticyclonic conditions. A rough estimate allowed us to quantify in about 2 C the contribution of the former.
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 eff�ects of human influence on climate.
We present an overview of practices and challenges related to the detection and attribution of observed changes in climate extremes. Detection is the identification of a statistically significant change in the extreme values of a climate variable over some period of time. Issues in detection discussed include data quality, coverage, and completeness. Attribution takes that detection of a change and uses climate model simulations to evaluate whether a cause can be assigned to that change. Additionally, we discuss a newer field of attribution, event attribution, where individual extreme events are analyzed for the express purpose of assigning some measure of whether that event was directly influenced by anthropogenic forcing of the climate system.
Extreme weather and climate‐related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human‐induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers. WIREs Clim Change 2016, 7:23–41. doi: 10.1002/wcc.380
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We investigate the relative contribution from natural and human-caused forcings to the 2014 hot spring with high mean temperatures in northern China using a two-step attribution procedure. We show that the spring temperature increase during the period 1958-2014 could be explained by the combined effects of anthropogenic and natural forcings with human influence dominating. The 2014 spring temperature is 2.2 °C above the 1961–90 mean of which 0.2 °C of the increase may be due to the urbanization effect and 1.5° C of the increase may be due to external influence on climate. This translates to about an 11-fold increase in the probability of an event such as the extreme 2014 hot spring occurring. We also show that urbanization bias in the temperature data series plays an important role in the observed high temperature event. After removing the urbanization effects, the climate models still underestimate the observed increasing trend in the spring temperature, supporting the deduction by a few of our previous studies that the current estimates of urbanization effects on surface air temperature trends of the national meteorological stations in mainland China should be regarded as the lowest values due to the extreme difficulty to find real rural stations in the country.
Forests provide essential benefits and services as an important component of terrestrial ecosystems. Their functionality and health result from multiple and cumulative interactions of biotic and abiotic stress factors such as air pollution, climate change, changes in land use, and poor management practices. A forest monitoring system was established to identify, analyze and assess the degradation of European forests. Two levels of forest monitoring were developed: I) large-scale forest condition surveys, based on an European grid system starting in 1986 and II) an intensive non-systematic survey network placed in representative forest ecosystems starting in 1994. Romania implemented both level I (1990-1991) and level II (1991-1992) forest monitoring surveys with the results showing the effects of increased air temperatures and a drastic decrease of precipitation since the decade of 1971-1980. Thus, the highest values of damaged trees (crown defoliation >25%) percent were recorded in 1993, 1994, 2000 and 2003 both in the national and European networks. Also, in southern and South-Eastern Romania the forests are more frequently damaged as a response to worsening of climatic factors in this region in recent decades, with temperatures rising 0.7-0.8 °C. In general, in Romania, ozone concentrations remained below the critical threshold (40-50 ppb) for affecting growth or health of trees. The levels of S-SO4 and N-NO3 declined in the atmosphere but the accumulation continued to increase in the soil, leading to soil acidification, mainly at depths of 10-40cm). In general, during the last decade, Romanian forests were affected at low to medium intensities with damage rate up to 11% of the trees and the status of general forest health improved slightly.
The summer of 2003 was probably the hottest in Europe since at latest ad 1500, and unusually large numbers of heat-related deaths were reported in France, Germany and Italy. It is an ill-posed question whether the 2003 heatwave was caused, in a simple deterministic sense, by a modification of the external influences on climate--for example, increasing concentrations of greenhouse gases in the atmosphere--because almost any such weather event might have occurred by chance in an unmodified climate. However, it is possible to estimate by how much human activities may have increased the risk of the occurrence of such a heatwave. Here we use this conceptual framework to estimate the contribution of human-induced increases in atmospheric concentrations of greenhouse gases and other pollutants to the risk of the occurrence of unusually high mean summer temperatures throughout a large region of continental Europe. Using a threshold for mean summer temperature that was exceeded in 2003, but in no other year since the start of the instrumental record in 1851, we estimate it is very likely (confidence level >90%) that human influence has at least doubled the risk of a heatwave exceeding this threshold magnitude.