Editing IPCC:AR6/WGII/Chapter-4 (section)

=== 4.3.4 Observed Impacts on Urban and Peri-Urban Sectors ===

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All previous IPCC reports have focused on future water-related risks to urban areas due to climate change rather than documented observed impacts.

Climate extremes have profound implications for urban and peri-urban water management, particularly in an increasingly urbanised world ( ''high confidence'' ). Over half (54%) of the global population currently lives in cities ( [[#WWAP--2019|WWAP, 2019]] ), and global urbanisation rates continue to increase across all SSPs ( [[#Jiang--2017|Jiang and O’Neill, 2017]] ). Using observed station data for 217 urban areas worldwide, [[#Mishra--2015|Mishra et al. (2015)]] noted that 17% of cities experienced statistically significant increases ( ''p'' value &lt; 0.05) in the frequency of daily precipitation extremes from 1973 to 2012 and hypothesised that such observed climate changes in urban areas were largely due to large-scale changes rather than local land cover changes.

Since AR5, factors such as rapid population growth, urbanisation, ageing infrastructure and changes in water use have also magnified climate risks, such as drought and flooding, and contributed to urban and peri-urban water insecurity ( ''medium agreement, medium evidence'' ) ( [[#4.1.2|Section 4.1.2]] ). For example, despite an increase in flooding events from 1.1 flood events yr –1 (1986–2005) to five flood events yr –1 (2006–2016) in Ouagadougou (Burkina Faso), analyses of rainfall indices showed few have significant trends at a 5% level over the period 1961–2015 and that the generalised extreme value distribution fit the time series of annual maximum daily rainfall (Tazen et al., 2019). On the other hand, long-term annual variations of maximum hourly precipitation in Shanghai (China) increased significantly during 1916–2014, especially from 1981.

Advances in the attribution of extreme weather events have made it possible to determine the causal relationship between droughts, floods and climate change for some cities, particularly those with long hydro-meteorological records ( [[#Bader--2018|Bader et al., 2018]] ; [[#Otto--2020|Otto et al., 2020]] ). Attribution analysis shows that urbanisation contributed to the increase in both frequencies of local and abrupt heavy rainfall events in the city, at a rate of 1.5 and 1.8 10 yr –1 , respectively ( [[#Liang--2017|Liang and Ding, 2017]] ). A multi-method attribution showed that the likelihood of prolonged rainfall deficit in Cape Town (South Africa) during 2015–2017 was made more likely by a factor of 3.3 (1.4–6.4) due to anthropogenic climate change ( [[#Otto--2018|Otto et al., 2018]] ). These results show that climate change has impacted the return time of extreme droughts in the Western Cape, exceeding the capacity of the existing water supply system to cope ( [[#Otto--2018|Otto et al., 2018]] ) (Box 9.4; 9.8.2). In Baton Rouge (USA), a rapid attribution study showed that the probability of an event such as the intense precipitation and flash flooding of August 2016 has increased by at least a factor of 1.4 due to radiative forcing (USA) ( [[#van%20der%20Wiel--2017|van der Wiel et al., 2017]] ). In Houston (USA), a study found that the combination of urbanisation and climate change nearly doubled peak discharge (84%) during Hurricane Harvey (August 2017), suggesting that land use change magnified the effects of climate change on catchment response to extreme precipitation events ( [[#Sebastian--2019|Sebastian et al., 2019]] ) (14.4.3.1; Box 14.5 The Economic Consequences of Climate Change in North America, Cross-Chapter Box DISASTER in Chapter 4). According to a multi-method approach, the 2014/2015 drought event in Sao Paulo (Brazil) was more likely to have been driven by water use changes and population growth than climate change ( [[#Otto--2015|Otto et al., 2015]] ) (Cross-Chapter Box DISASTER in Chapter 4).

The science of weather event attribution requires high-quality observational data and climate models that are currently available only in highly developed countries ( [[#Otto--2020|Otto et al., 2020]] ). In addition, further research is necessary to determine the impacts of climate change on water-related extremes in the urban areas of developing countries ( [[#Bai--2018|Bai et al., 2018]] ). For example, a combination of observational analysis and global coupled climate models showed that the 2015 flooding event in Chennai (India) could not be attributed to anthropogenic climate change, with the effects of that being relatively small in the region due to the impact of GHG increases being largely counteracted by those of aerosols ( [[#van%20Oldenborgh--2017a|van Oldenborgh et al., 2017a]] ) ( [[#4.2.5|Section 4.2.5]] ). Further research is also required to determine the impacts of climate change on water-related extremes in informal settlements where vulnerability to water insecurity is high due to poverty, overcrowding, poor-quality housing and lack of basic infrastructure ( [[#Scovronick--2015|Scovronick et al., 2015]] ; [[#Grasham--2019|Grasham et al., 2019]] ; [[#Williams--2019|Williams et al., 2019]] ; [[#Satterthwaite--2020|Satterthwaite et al., 2020]] ).

In summary, water-related hazards such as drought and flooding have been exacerbated by climate change in some cities ( ''high confidence'' ). Further research is necessary to determine the extent and nature of water-related climate change impacts in the urban areas of developing countries ( ''high confidence'' ).

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'''Cross-Chapter Box DISASTER | Disasters as the Public Face of Climate Change'''
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Authors: Aditi Mukherji (India, Chapter 4), Guéladio Cissé (Mauritania/Switzerland/France, Chapter 7), Caroline Zickgraf (Contributing Author), Paulina Aldunce (Chile, Chapter 7), Liliana Raquel Miranda Sara (Peru, Chapter 12), William Solecki, (USA, Chapter 17), Friederike Otto (UK, WGI), François Gemenne (France, WGI), Martina Angela Caretta (Sweden, Chapter 4);, Richard Jones (UK, WGI); Richard Betts (UK, Chapter 4), Maarten van Aalst (the Netherlands, Chapter 16), Jakob Zscheischler (Switzerland), Kris Murray (UK), Mauro E. González (Chile).

Introduction

Some extreme weather events are increasing in frequency and (or) severity as a result of climate change ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ) ( ''high confidence'' ). These include extreme rainfall events ( [[#Roxy--2017|Roxy et al., 2017]] ; [[#Myhre--2019|Myhre et al., 2019]] ; [[#Tabari--2020|Tabari, 2020]] ); extreme and prolonged heat leading to catastrophic fires ( [[#Bowman--2017|Bowman et al., 2017]] ; [[#Krikken--2019|Krikken et al., 2019]] ; [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ); and more frequent and stronger cyclones/hurricanes and resulting extreme rainfall ( [[#Griego--2020|Griego et al., 2020]] ). These extreme events, coupled with high vulnerability and exposure in many parts of the world, turn into disasters and affect millions of people every year. New advances enable the detection and attribution of these extreme events to climate change ( [[#Otto--2016|Otto et al., 2016]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] ), with the most recent study saying that heavy rains leading to devastating floods in western Europe that captured the world’s attention in July 2021 were made more likely due to climate change ( [[#Kreienkamp--2021|Kreienkamp et al., 2021]] ). Most WGII chapters (this volume) report various extreme event-induced disasters and their societal impacts. This cross-chapter box brings together authors from WGI and WGII to emphasise that disasters following extreme events have become the most visible and public face of climate change ( [[#Solecki--2014|Solecki and Rosenzweig, 2014]] ). These disasters reflect immediate societal and political implications of rising risks ( ''high confidence'' ), but also provide windows of opportunity to raise awareness about climate change and to implement disaster-reduction policies and strategies ( ''high confidence'' ) ( [[#Albright--2020|Albright, 2020]] ; [[#Boudet--2020|Boudet et al., 2020]] ).

Here, we document eight catastrophic climate-related disasters that took place between 2017 and 2021. These disasters resulted in the loss of lives and livelihoods and had adverse impacts on biodiversity, health, infrastructure and the economy. These disasters provided important rallying points for discussions around climate change, equity and vulnerability in some cases. These disasters also offer valuable lessons about the role of effective climate change adaptation in managing disaster risks and the importance of Loss and Damage mechanisms in global negotiation processes ( [[#Jongman--2014|Jongman et al., 2014]] ; [[#Mechler--2014|Mechler et al., 2014]] ; [[#Cutter--2015|Cutter and Gall, 2015]] ).

Case 1. Compounded events and impacts on human systems: Cyclones Idai and Kenneth in Mozambique in 2019

While individual events alone can lead to major disasters, when several events occur in close spatial and temporal proximity, impacts get compounded, with catastrophic results ( [[#Zscheischler--2018|Zscheischler et al., 2018]] ; [[#Zscheischler--2020|Zscheischler et al., 2020]] ). In March 2019, Cyclone Idai (category 2) was the deadliest storm on record to strike the African continent, with the coastal city of Beira in Mozambique being particularly hard hit with at least 602 deaths ( [[#CRED--2019|CRED, 2019]] ; [[#Zehra--2019|Zehra et al., 2019]] ; [[#Phiri--2020|Phiri et al., 2020]] ). Nationally, Idai caused massive housing, water supply, drainage and sanitation destruction, but its impact extended to South Africa through disruption of the regional electricity grid ( [[#Yalew--2020|Yalew et al., 2020]] ). In April 2019, amidst heightened vulnerabilities in the aftermath of cyclone Idai, cyclone Kenneth (category 4) hit the country, affecting 254,750 people and destroying more than 45,000 homes ( [[#Kahn--2019|Kahn et al., 2019]] ). These circumstances caused the rapid spread of cholera, which triggered a massive vaccination programme to control the epidemic ( [[#Kahn--2019|Kahn et al., 2019]] ; [[#Lequechane--2020|Lequechane et al., 2020]] ). While there were no specific detection and attribution studies for Idai and Kenneth, overall, there is ''high confidence'' that the rainfall associated with tropical cyclones is more intense because of global warming. However, there remain significant uncertainties about the impact of climate change on the numbers and strength of tropical cyclones per se ( [[#Walsh--2019|Walsh et al., 2019]] ; Zhang G. et al., 2020).

Case 2. COVID-19 as the compounding risk factor: Cyclone Amphan in India and Bangladesh, 2020

Cyclone Amphan hit coastal West Bengal and Bangladesh on 20 May 2020. It was the first supercyclone to form in the Bay of Bengal since 1999 and one of the fiercest to hit West Bengal, India, in the last 100 years. The cyclone intensified from a cyclonic storm (category 1) to a supercyclone (category 5) in less than 36 hours ( [[#Balasubramanian--2020|Balasubramanian and Chalamalla, 2020]] ). Several hours before and on 20 May, extreme rain events resulted in heavy cumulative rainfall, flash flooding and landslides in several adjoining districts ( [[#Mishra--2020|Mishra and Vanganuru, 2020]] ). As per the initial estimates, about 1600 km 2 area in the mangrove forests of ''Sundarbans'' were damaged, and over 100 lives were lost. Earlier cyclones in the region have shown that impacts of these events are gendered ( [[#Roy--2019|Roy, 2019]] ). The cyclone damage was somewhat lessened due to the delta’s mangroves ( [[#Sen--2020|Sen, 2020]] ). The estimated damage was USD 13.5 billion. Cyclone Amphan was the largest source of displacement in 2020, with 2.4 million displacements in India alone, of which 800,000 were pre-emptive evacuations by authorities ( [[#IDMC--2020|IDMC, 2020]] ). Because it happened amidst the COVID-19 crisis, evacuation plans were constrained due to social distancing norms ( [[#Baidya--2020|Baidya et al., 2020]] ). Social media played an important role in disseminating pre-cyclone warnings and information on post-cyclone relief work ( [[#Crayton--2020|Crayton et al., 2020]] ; [[#Poddar--2020|Poddar et al., 2020]] ).

Case 3. Further exacerbating inequities in human systems: Hurricane Harvey, USA, 2017

Hurricane Harvey, a category 4 hurricane, made landfall on Texas and Louisiana in August 2017, causing catastrophic flooding and 80 deaths and inflicting $125 billion (2017 USD) in damage, of which $67 billion (2017 USD) was attributable to climate change ( [[#Frame--2020|Frame et al., 2020]] ). Several studies estimated the return period of the rainfall associated with this event and assessed that human-induced climate change increased the likelihood by a factor of approximately three using a combination of observations and climate models ( [[#Risser--2017|Risser and Wehner, 2017]] ; [[#van%20Oldenborgh--2017b|van Oldenborgh et al., 2017b]] ). The impacts of Hurricane Harvey were exacerbated by extensive residential development in flood-prone locations. A study showed that urbanisation increased the probability of such extreme flood events several folds (Zhang W. et al., 2018) through the alteration of ground cover and disruption and redirection of water flow. Water quality in cities also deteriorated ( [[#Horney--2018|Horney et al., 2018]] ; [[#Landsman--2019|Landsman et al., 2019]] ), and 85% of flooded land subsided at a rate of 5 mm yr –1 following the event ( [[#Miller--2019|Miller and Shirzaei, 2019]] ). Notably, the impacts of Harvey were unequally distributed along racial and social categories in the greater Houston area. Neighbourhoods with larger Black, Hispanic and disabled populations were the worst affected by the flooding following the storm and rainfall ( [[#Chakraborty--2018|Chakraborty et al., 2018]] ; [[#Chakraborty--2019|Chakraborty et al., 2019]] ; [[#Collins--2019b|Collins et al., 2019b]] ). In addition, racial and ethnic disparities were shown to impact post-disaster needs, ranging from household damage to mental health and recovery ( [[#Collins--2019b|Collins et al., 2019b]] ; [[#Flores--2020|Flores et al., 2020]] ; [[#Griego--2020|Griego et al., 2020]] ).

Case 4. Impacts worsened due to sociocultural and political conditions: The “Coastal Niño” in Peru, 2017

The Coastal Niño event of 2017 led to extreme rainfall in Peru, which was made more likely by at least 1.5 times as compared to pre-industrial times due to anthropogenic climate change and Coastal Niño ( [[#Christidis--2019|Christidis et al., 2019]] ) and comparable to the El Niño events of 1982–1983 and 1997–1998 ( [[#Poveda--2020|Poveda et al., 2020]] ). This event showed evidence of larger anomalies in flood exposure ( [[#Muis--2018|Muis et al., 2018]] ; [[#Christidis--2019|Christidis et al., 2019]] ; [[#Rodríguez-Morata--2019|Rodríguez-Morata et al., 2019]] ) and sediment transport ( [[#Morera--2017|Morera et al., 2017]] ). In Peru, this Niño event led to USD 6 to 9 billion of monetary losses, more than a million inhabitants were affected, 6614 km of roads were damaged, 326 bridges were destroyed, 41,632 homes were damaged or became uninhabitable and 2150 schools and 726 health posts were damaged ( [[#French--2017|French and Mechler, 2017]] ; [[#French--2020|French et al., 2020]] ), leaving half of the country in a state of emergency ( [[#Christidis--2019|Christidis et al., 2019]] ). Furthermore, institutional and systemic sociocultural and political conditions at multiple levels significantly worsened disaster risk management which hampered response and recovery ( [[#French--2020|French et al., 2020]] ). Citizens and zero-order responders proved to be more effective and quicker than national disaster risk management response ( [[#Briones--2019|Briones et al., 2019]] ).

Case 5. Triggering institutional response for future preparedness: Mega-fires of Chile, 2017

The mega-fire that occurred in Chile in January 2017 had the highest severity recorded on the planet ( [[#CONAF--2017|CONAF, 2017]] ), burning in three weeks an area close to 350,000 hectares in south-central Chile. These events have been associated with the prolonged ongoing drought that has persisted for more than one decade and with the increase in heat waves ( [[#González--2018|González et al., 2018]] ; [[#Miranda--2020|Miranda et al., 2020]] ). This extreme drought and the total burned area of the last decades have been attributed to anthropogenic climate change in at least 25% and 20% of their severity, respectively ( [[#Boisier--2016|Boisier et al., 2016]] ). The mega-fire of summer 2017 resulted in 11 deaths, more than 1500 houses burned and the destruction of the small town of Santa Olga. The smoke from these fires exposed 9.5 million people to air pollution, causing an estimated 76 premature deaths ( [[#Bowman--2017|Bowman et al., 2017]] ; [[#González--2020|González et al., 2020]] ). The direct costs incurred by the State exceeded USD 360 million ( [[#González--2020|González et al., 2020]] ). The 2017 mega-fires led to a series of institutional responses such as management plans that include preventive forestry techniques, regulatory plans containing rural–urban interface areas, an emergency forest fire plan, and promotion of native species ( [[#González--2020|González et al., 2020]] ).

Case 6. Loss of human lives and biodiversity: Bushfires in Australia, 2019/2020

In the summer of 2019/2020, bushfires in Australia killed 417 people due to smoke and killed between 0.5 and 1.5 billion wild animals and tens of thousands of livestock ( [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ). These fires also destroyed approximately 5900 buildings and burnt 97,000 km 2 of vegetation, which provided habitat for 832 species of native vertebrate fauna. Seventy taxa had more than 30% of their habitat impacted, including 21 already identified as threatened with extinction ( [[#Ward--2020|Ward et al., 2020]] ). In addition, millions of people experienced levels of smoke 20 times higher than the government-identified safe level. The year 2019 had been Australia’s warmest and driest year on record. In the summer of 2019/2020, the seasonal mean and mean maximum temperatures were the hottest by almost 1°C above the previous record. Eight of the 10 hottest days on record for national mean temperatures occurred in December 2019. While the prevailing weather conditions were strongly influenced by the Indian Ocean Dipole pressure pattern, with a contribution from weakly positive ENSO conditions in the Pacific, the fact that Australia is approximately 1°C warmer than the early 20th century demonstrates links to anthropogenic climate change. Eight climate models using event attribution methodologies (comparison of simulations with present-day and pre-industrial forcings) indicates that anthropogenic climate change made the heat conditions of December 2019 more than twice as likely ( [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ).

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Cross-Chapter Box DISASTER

Case 7. Improved preparedness reduced mortality: Heatwave in Europe, 2019

In 2019, Europe experienced several record-breaking heatwaves. In June, the first one featured record heat for that time in early summer, with temperatures of 6°C–10°C above normal over most of France and Germany, northern Spain, northern Italy, Switzerland, Austria and the Czech Republic (Climate, 2019). The second heatwave also resulted in all-time records for Belgium, Germany, Luxembourg, the Netherlands and the UK in July. Attribution studies ( [[#Vautard--2020|Vautard et al., 2020]] ) demonstrated that these would have had extremely small odds in the absence of human-induced climate change or would have been 1.5°C–3°C colder without human-induced climate change. This study concluded that state-of-the-art climate models underestimate the trends in local heat extremes compared to the observed trend. Since the 2003 heatwave, which resulted in tens of thousands of deaths across Europe, many European countries have adopted heatwave plans, including early warning systems. Therefore, mortality in 2019 was substantially lower than it might have been. Unfortunately, mortality is not registered systematically across Europe, and therefore, comprehensive analyses are missing. But even based on the countries that provide the numbers, more specifically France, Belgium and the Netherlands, the European heatwave of 2019 resulted in over 2500 deaths ( [[#CRED--2019|CRED, 2019]] ). Despite their deadliness and the fact that climate change increases the frequency, intensity and duration of heatwaves globally ( [[#Perkins-Kirkpatrick--2020|Perkins-Kirkpatrick and Lewis, 2020]] ), heatwaves are not consistently reported in many countries ( [[#Harrington--2020|Harrington and Otto, 2020]] ), rendering it currently impossible to estimate climate change impacts on lives and livelihoods comprehensively.

Case 8. Loss of human lives and property: Floods in Europe in 2021

From 12 to 15 July 2021, extreme rainfall in Germany, Belgium, Luxembourg and neighbouring countries led to severe flooding. The severe flooding was caused by very heavy rainfall over a period of 1–2 d, wet conditions prior to the event and local hydrological factors. The observed rainfall amounts in the Ahr/Erft region and the Belgian part of the Meuse catchment substantially exceeded previous records for observed rainfall. An attribution study ( [[#Kreienkamp--2021|Kreienkamp et al., 2021]] ) focused on the heavy rainfall rather than river discharge and water levels, because sufficient hydrological data was not available, partly because hydrological monitoring systems were destroyed by the event. Considering a larger region of western Europe between the northern side of the Alps and the Netherlands, in any given location, one such event can be expected every 400 years on average in the current climate. The floods resulted in least 222 fatalities and substantial damage to houses, roads, communication infrastructure, motorways, railway lines and bridges.

'''Table Cross-Chapter Box DISASTER.1 |''' Summarising impacts, losses and damages, displacement and climate change detection and attribution of these seven disaster case studies.

{| class="wikitable"
|-
! Name of the disaster event

! Impacts, losses and damages; and displacement

! Climate change detection and attribution

|-
| Cyclones Idai and Kenneth, March and April 2019, Mozambique, Africa

| 254,750 affected people, and more than 45,000 houses were destroyed. Sparked cholera outbreaks that resulted in 6600 cases and over 200 deaths. More than 500,000 people were displaced in 2019. As of 31 December 2019, more than 132,000 people were internally displaced in Mozambique ( [[#IDMC--2020|IDMC, 2020]] ).

| There are no detection and attribution studies on Idai and Kenneth, but it is known that rainfall associated with tropical cyclones are now more intense because of global warming, but there remain significant uncertainties concerning changes in the number and strength of the cyclones themselves ( [[#Walsh--2019|Walsh et al., 2019]] ; Zhang G. et al., 2020).

|-
| Cyclone Amphan, May 2020, West Bengal, India and Bangladesh

| About 1600 km 2 area in the mangrove forests of Sundarbans were damaged. The city of Kolkata lost a substantial portion of its green cover due to Amphan. The estimated damage was USD 13.5 billion. Cyclone Amphan was the largest source of displacement in 2020, with 2.4 million displacements in India and a similar number in Bangladesh. Out of these 2.4 million, roughly 800,000 were pre-emptive evacuations or organised by the authorities ( [[#IDMC--2020|IDMC, 2020]] ).

| The combined decline of both aerosols (due to COVID-19-related lockdowns) and clouds may have contributed to the increased sea surface temperature, further compounding the climate change-related warming of the oceans ( [[#Vinoj--2020|Vinoj and Swain, 2020]] ). However, there are no attribution studies on tropical cyclones in the Indian Ocean.

|-
| Hurricane Harvey, 2017, USA

| Catastrophic flooding and many deaths inflicted $125 billion (2017 USD). In addition, economic costs due to the rainfall are estimated at $90 billion, of which $67 billion are attributed to climate change ( [[#Frame--2020|Frame et al., 2020]] ).

| Several attribution studies found that the rainfall associated with Harvey has increased by a factor of three, while intensity in rainfall and wind speed also increased due to human-induced climate change ( [[#Emanuel--2017|Emanuel, 2017]] ; [[#Risser--2017|Risser and Wehner, 2017]] ; [[#Patricola--2018|Patricola and Wehner, 2018]] ; [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ).

|-
| Coastal Niño 2017, Peru

| USD 6–9 billion monetary losses with 114 deaths, 414 injuries and 1.08 million inhabitants affected. In addition, 6614 km of improved roads were damaged, 326 bridges destroyed, 41,632 homes destroyed or uninhabitable, and 242,433 homes, 2150 schools and 726 health centres damaged.

| Clear anthropogenic climate change fingerprint detected. For example, while the anomalously warm ocean favoured extreme rainfall of March 2017 in Peru, the human influence was estimated to make such events at least 1.5 times more likely ( [[#Christidis--2019|Christidis et al., 2019]] ).

|-
| Mega-fires in Chile, January 2017

| The mega-fire that occurred in Chile in January 2017 burned in three weeks an area close to 3500 km 2 in south-central Chile. As a result, thousands of people were displaced.

| There is no attribution study on the fires in Chile (yet). Still, there is an increasing number of attribution studies on wildfires worldwide, finding that because climate change has increased the likelihood of extreme heat, which is part of the fire weather, the likelihood of wildfire weather conditions has increased too ( [[#Krikken--2019|Krikken et al., 2019]] ; [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ).

|-
| Australian bushfires of 2019/2020

| Killed 417 people due to smoke, and between 0.5 and 1.5 billion wild animals and tens of thousands of livestock. Destroyed ~5900 buildings and burnt 97,000 km 2 of vegetation that provided habitat for 832 species of native vertebrate fauna.

| Anthropogenic climate change made the extreme heat condition of December 2019 more than twice as likely ( [[#van%20Oldenborgh--2020|van Oldenborgh et al., 2020]] ).

|-
| Heatwaves of Europe, 2019

| Record heat in several European countries, and deadliest global disaster of 2019, with over 2500 deaths ( [[#CRED--2019|CRED, 2019]] )

| There have been many attribution studies on heatwaves in Europe, finding that human-induced climate change is increasing the frequency and intensity of heatwaves. In the case of 2019, the observed heat would have been extremely unlikely without climate change. The studies also find that climate models underestimate the increase in heat waves in Europe compared to observed trends ( [[#Vautard--2020|Vautard et al., 2020]] ).

|-
| Floods in western Europe (Germany, Belgium), July 2021

| Severe flooding resulting in at least 222 fatalities and substantial damage to houses, roads, communication infrastructure, motorways, railway lines and bridges. Some communities were cut off for days due to road closures, inhibiting emergency responses, including evacuation.

| Climate change was found to have increased the intensity of the maximum 1-d rainfall event in the summer season in this large region by about 3–19% compared to a global climate 1.2°C cooler than at the present day. The increase was similar for the 2-d event. The likelihood of such an event today was found to have increased by a factor between 1.2 and 9 for both the 1-d and 2-d events in the large region ( [[#Kreienkamp--2021|Kreienkamp et al., 2021]] ).

|}

Disaster risk reduction needs to be a central component of adaptation and mitigation for meeting Sustainable Development Goals and for a climate-resilient future

Disasters resulting from extreme events are increasingly experienced by a large section of human population ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). Disasters expose inequalities in natural and managed systems and human systems as they disproportionately affect poor and marginalised communities like ethnic minorities, people of colour, Indigenous Peoples, women and children. Therefore, disaster risk reduction is fundamental for climate justice and climate resilient development ( [[#UNISDR--2015|UNISDR, 2015]] ). Far from being disconnected policy objectives, disaster risk reduction and climate change mitigation/adaptation are two sides of the same coin as recognised explicitly by the Paris Agreement and Sendai Framework of 2015. There can be no sustainable development without disaster risk reduction, as explicitly recognised by the SDGs of 2015. Furthermore, disaster events can increase awareness among citizens and provide a platform for all important stakeholders, including climate activists, to come together, and give a clarion call for the urgency of climate action.

In summary, disasters are a stark illustration of the potential for extreme weather events to impact people and other species. With the frequency, severity and (or) likelihood of several types of extreme weather increasing, disasters can increasingly be regarded as ‘the public face of climate change’ ( ''high confidence'' ). Detection and attribution studies make the climate change fingerprint of several types of disasters increasingly clear ( ''high confidence'' ). Moreover, existing vulnerabilities and exposures play an important role in turning extreme events into disasters, further exacerbating existing racial, gender and social inequalities ( ''high confidence'' ). Therefore, disaster risk reduction needs to be central to adaptation and mitigation efforts to meet the SDGs and the Paris Agreement for a climate-resilient future.

Cross-Chapter Box DISASTER

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