Editing IPCC:AR6/WGI/Chapter-12 (section)

== 12.3 Climatic Impact-drivers for Sectors ==

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Climate change becomes relevant for regional impact management and for risk assessment when changes in mean conditions or episodic events affect natural and societal assets (system components with socio-economic, cultural or intrinsic value) positively or negatively (Table 12.2). Decision makers, policymakers, risk managers and engineers therefore benefit from climate information that tracks key trends and exceedance of thresholds that represent crucial challenges for natural and human systems. While useful indices can vary widely for a given sector and precise tolerance threshold values are often unknown, common metrics, categories and progressions of threshold levels allow experts to recognize coherent messages concerning altered regional impacts and risk profiles under climate change.

This section surveys the links between CIDs and affected sectors; not to perform specific climate change impact or risk assessments (see AR6 WGII), but to describe key indices (among many) that quantify these links as guidance for stakeholders seeking applicable climate information. This survey builds on the work of the World Meteorological Organization Expert Team on Sector-Specific Climate Indices (ET-SCI) and previous IPCC assessments, notably AR5 WGII ( [[#Birkmann--2014|Birkmann et al., 2014]] ; [[#IPCC--2014a|IPCC, 2014a]] ) and IPCC Special Reports ( [[#IPCC--2018|IPCC, 2018]] , 2019b, c) that have assessed climate hazards affecting sectors but is organized from a CID perspective drawing also upon recent summaries of sectoral hazards ( [[#Mora--2018|Mora et al., 2018]] ; [[#ICOMOS--2019|ICOMOS, 2019]] ; [[#Yokohata--2019|Yokohata et al., 2019]] ). Impacts, risks and opportunities are rarely attributable to a single CID index or threshold, but climate shifts that push conditions outside of expected conditions and beyond tolerance levels are indicative of impact, risk or benefit given vulnerability and exposure. Focus is on direct sectoral connections of a CID ( [[#Hallegatte--2010|Hallegatte and Przyluski, 2010]] ) rather than cascading or secondary effects (e.g., water-borne diseases following a flood, mental health challenges following a severe storm, or the effects of drought on poverty), as these are strongly affected by exposure, vulnerability and response, as discussed in the WGII Report.

Table 12.2 presents a summary of ( [[#12.3|Section 12.3]] connections between CIDs as defined in Table 12.1 and key sectoral assets, utilizing the WGII organization of sectors (corresponding to WGII Chapters 2–8). Colours are shown for connections with at least ''medium confidence'' as assessed from sectoral impacts and risk literature, with relevance assessed according to the prominence of that specific CID/asset connection in analyses of current and future impacts and risk. Within each sector there is a multitude of specific sectoral systems that may be affected by CID increases and decreases, with consequences further distinguished by region, background climate and socio-economic or ecological context of the affected asset. Our aim is therefore to recognize important drivers and the common attributes of change within each CID that scientists and practitioners monitor to understand current and future challenges for important asset groups, thereby pointing to the climate information that needs to be tailored and analysed for impacts and for risk assessment ( [[#12.6|Section 12.6]] ). Additional effects whereby CIDs affect each other (across Table 12.2 columns) are discussed as climatic phenomena within WGI. The ways sectoral assets affect each other (across Table 12.2 rows) are described throughout WGII, for example with information about the suitability of future climate zones and climate velocity challenges for a given asset potentially drawing from multiple CIDs and associated system tolerance thresholds ( [[#Hamann--2015|Hamann et al., 2015]] ). Some broad connections indicated as ''low confidence'' may be under-represented in the literature or could be acute under specific circumstances.

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'''Table 12.2''' '''|''' '''Relevance of key climatic impact-drivers (and their respective changes in intensity, frequency, duration, timing and spatial extent) for major categories of sectoral assets, as assessed with at least''' ''medium confidence'' '''in [[#12.3|Section 12.3]] across many studies and applications.''' ‘High relevance’ indicates climatic impact-drivers that are most prominent and widely studied for their direct connection to assets, while lower relevance indicates weaker linkages and less commonly-studied driving behaviours. Specific levels of risk and opportunity depend on the changing character of regional hazards, vulnerability and exposure as assessed in WGII.

[[File:026a146d497cb21f8daeb5d94d20d213 IPCC_AR6_WGI_Chapter12_Table_12_2a.jpg]] [[File:5af8b391dfb0485caf32b5b47bfd920d IPCC_AR6_WGI_Chapter12_Table_12_2b.jpg]]

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=== 12.3.1 Heat and Cold ===

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==== 12.3.1.1 Mean Air Temperature ====

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Information about increasing mean annual and seasonal air temperature is relevant in the determination of suitable species range for terrestrial, freshwater and intertidal species ( [[#Thomas--2004|Thomas et al., 2004]] ; [[#Elith--2010|Elith et al., 2010]] ; [[#Hincapie--2013|Hincapie and Caicedo, 2013]] ; [[#Cooper--2014|Cooper, 2014]] ; [[#Krist--2014|Krist et al., 2014]] ; [[#Lindner--2014|Lindner et al., 2014]] ; [[#Saintilan--2014|Saintilan et al., 2014]] ; [[#Lenoir--2015|Lenoir and Svenning, 2015]] ; [[#Myers-Smith--2015|Myers-Smith et al., 2015]] ; [[#Urban--2015|Urban, 2015]] ; [[#Thorne--2017|Thorne et al., 2017]] ). Ocean ecosystems are affected by the ocean temperature CID (described in [[#12.3.6.1|Section 12.3.6.1]] ). Species redistribution and extinction studies also need information about climate velocity, a comparison of the pace of warming to geographical temperature gradients that indicates the rate at which a species would have to move to maintain its climatological temperature ( [[#Thomas--2004|Thomas et al., 2004]] ; [[#Loarie--2009|Loarie et al., 2009]] ; [[#Dobrowski--2013|Dobrowski et al., 2013]] ; [[#Burrows--2014|Burrows et al., 2014]] ; [[#Dobrowski--2016|Dobrowski and Parks, 2016]] ; [[#Sittaro--2017|Sittaro et al., 2017]] ) with some studies incorporating additional variables beyond temperature ( [[#Hamann--2015|Hamann et al., 2015]] ). Many freshwater ecosystems are strongly constrained by stream and lake temperatures ( [[#Scheurer--2009|Scheurer et al., 2009]] ; [[#Comte--2013|Comte and Grenouillet, 2013]] ; [[#Contador--2014|Contador et al., 2014]] ; [[#Knouft--2017|Knouft and Ficklin, 2017]] ). Warmer and more stratified lake temperatures are more conducive to cyanobacteria blooms with implications for ecosystem health and water resource quality ( [[#Whitehead--2009|Whitehead et al., 2009]] ; [[#Moss--2011|Moss et al., 2011]] ; [[#Jones--2014|Jones and Brett, 2014]] ; [[#Chapra--2017|Chapra et al., 2017]] ; [[#Shatwell--2019|Shatwell et al., 2019]] ). Consideration of nighttime and daytime temperature trends also elucidates different biophysical effects on vegetation ( [[#Peng--2013|Peng et al., 2013]] ). Changes in the seasonal timing caused by warming trends are critical to species ranges and ecosystem function ( [[#Pearce-Higgins--2015|Pearce-Higgins et al., 2015]] ; [[#Hughes--2017b|Hughes et al., 2017b]] ), and indices that characterize the onset of spring shed light on plant emergence and development ( [[#Ault--2015|Ault et al., 2015]] ).

Mean air temperature dictates many aspects of crop cultivation, livestock production, agroforestry and output from freshwater aquaculture and fisheries, as well as the potential for food contamination. Mean warming alters suitable cultivation zones for crop species ( [[#Bragança--2016|Bragança et al., 2016]] ; [[#Gendron%20St-Marseille--2019|Gendron St-Marseille et al., 2019]] ; [[#IPCC--2019c|IPCC, 2019c]] ) and tree species ( [[#Hanewinkel--2013|Hanewinkel et al., 2013]] ; [[#Fei--2017|Fei et al., 2017]] ). Crop and ecosystem service productivity often responds directly to mean temperatures, although this is dependent on farming systems ( [[#Bassu--2014|Bassu et al., 2014]] ; [[#Challinor--2014|Challinor et al., 2014]] ; [[#Lobell--2014|Lobell and Tebaldi, 2014]] ; [[#Rosenzweig--2014|Rosenzweig et al., 2014]] ; [[#Asseng--2015|Asseng et al., 2015]] ; [[#Li--2015|Li et al., 2015]] ; [[#Fleisher--2017|Fleisher et al., 2017]] ; [[#Zhao--2017|Zhao et al., 2017]] ; [[#Smith--2019|Smith and Fazil, 2019]] ). Many studies relate plant development (phenology), insect generation cycles and pest outbreaks to growing degree days, an aggregation of daily thermal units above a threshold (e.g., T <sub>mean</sub> &gt;5°C) that accelerates with warmer conditions ( [[#Hof--2016|Hof and Svahlin, 2016]] ; [[#Ruosteenoja--2016|Ruosteenoja et al., 2016]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ). Many plants respond to changes in nighttime temperatures that affect respiration and transpiration rates (Narayanan et al., 2015; X. [[#Chen--2019|]] [[#Chen--2019|Chen et al., 2019]] ), and warming of the soil column is also relevant to determine plant sprouting ( [[#Grotjahn--2021|Grotjahn, 2021]] ). A number of indices have been developed to represent the length of the viable local growing season, including a count of days where T <sub>max</sub> &gt;5°C ( [[#Mueller--2015|Mueller et al., 2015]] ) or the period between a year’s first and last set of five consecutive days with a weighted T <sub>mean</sub> ≥10°C (G. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). Warmer conditions and altered seasonality modify the range and metabolism of some pollinators, pests, diseases and weeds ( [[#Wolfe--2008|Wolfe et al., 2008]] ; [[#Bebber--2015|Bebber, 2015]] ; [[#Aljaryian--2016|Aljaryian and Kumar, 2016]] ; IPBES, 2016; [[#Ramesh--2017|Ramesh et al., 2017]] ; [[#Deutsch--2018|Deutsch et al., 2018]] ; [[#Nyangiwe--2018|Nyangiwe et al., 2018]] ) and may reduce the effectiveness of winter storage for farmers and caching species ( [[#Sutton--2016|Sutton et al., 2016]] ).

Warming raises accumulated seasonal heat indices used in livestock production, especially when humidity is high ( [[#Key--2014|Key et al., 2014]] ; [[#Lallo--2018|Lallo et al., 2018]] ), determines aquaculture suitability and is important for wild fish species migration ( [[#Tripathi--2016|Tripathi et al., 2016]] ; [[#Brander--2017|Brander et al., 2017]] ). Agricultural planners may also calculate how overall warming trends alter the accumulation of vernalization units or chilling hours for agricultural or horticultural crops (often accumulated temperature deficit below a given daily or hourly threshold; [[#Dennis--2009|Dennis and Peacock, 2009]] ; [[#Luedeling--2012|Luedeling, 2012]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). Warming in the post-harvest is also important for the determination of spoilage and waste ( [[#Stathers--2013|Stathers et al., 2013]] ) as well as food-borne diseases ( [[#Kovats--2004|Kovats et al., 2004]] ; [[#Mbow--2019|Mbow et al., 2019]] ).

Warming affects road degradation rates ( [[#Chinowsky--2012|Chinowsky and Arndt, 2012]] ; [[#Espinet--2016|Espinet et al., 2016]] ) and warming rates inform designs for long-term energy efficiency of buildings ( [[#Kalvelage--2014|Kalvelage et al., 2014]] ). Mean temperature drives seasonal energy demand, often expressed using winter heating degree days (the accumulated deficit of daily temperatures below a ‘comfortable’ indoor temperature, e.g., 15.5°C) and summer cooling degree days (the accumulated excess of temperature above a ‘comfortable’ level, e.g., 18°C; [[#Spinoni--2015|Spinoni et al., 2015]] ; [[#Arnell--2019|Arnell et al., 2019]] ). Energy resources may also need information on warming trends to determine suitable zones and overall productivity for biofuels and solar panels, the efficiency of which decreases with higher temperatures ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Wild--2015|Wild et al., 2015]] ; [[#Solaun--2019|Solaun and Cerdá, 2019]] ).

Health impacts and risk studies compare seasonal temperature conditions to limiting thresholds to understand range shifts and incubation rates for pathogens, disease vectors and zoonotic hosts (e.g., mosquitoes, ticks; [[#Caminade--2012|Caminade et al., 2012]] , 2014; [[#Eisen--2013|Eisen and Moore, 2013]] ; [[#Lima--2016|Lima et al., 2016]] ; [[#Ogden--2017|Ogden, 2017]] ; [[#Monaghan--2018|Monaghan et al., 2018]] ) and warming of surface ocean and lake waters conducive to bacterial outbreaks ( [[#Baker-Austin--2013|Baker-Austin et al., 2013]] ; [[#Jacobs--2015|Jacobs et al., 2015]] ; [[#Vezzulli--2015|Vezzulli et al., 2015]] ). Warmer conditions can also affect tourism ( [[#Kovács--2017|Kovács et al., 2017]] ) and impact human health by lengthening the allergy season and increasing pollen concentration ( [[#Hamaoui-Laguel--2015|Hamaoui-Laguel et al., 2015]] ; [[#Kinney--2015a|Kinney et al., 2015a]] ; [[#Lake--2017|Lake et al., 2017]] ; [[#Upperman--2017|Upperman et al., 2017]] ; [[#Sapkota--2019|Sapkota et al., 2019]] ; [[#Ziska--2019|Ziska et al., 2019]] ).

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==== 12.3.1.2 Extreme Heat ====

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Impacts and risk assessments utilize a large variety of indices and approaches tailored to evaluate heat impacts on human health ( [[#Sanderson--2017|Sanderson et al., 2017]] ; [[#Gao--2018|]] [[#Gao--2018|C. Gao et al., 2018]] ; [[#McGregor--2018|McGregor and Vanos, 2018]] ; [[#Staiger--2019|Staiger et al., 2019]] ; J. [[#Zhu--2019|]] [[#Zhu--2019|Zhu et al., 2019]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ). A mixture of simple and complex heat stress indices often combine extreme temperatures and high humidity to capture human health challenges ( [[#Aström--2013|Aström et al., 2013]] ; [[#Chow--2016|Chow et al., 2016]] ; [[#Dahl--2017a|Dahl et al., 2017a]] ; [[#Im--2017|Im et al., 2017]] ; [[#Coffel--2018|Coffel et al., 2018]] ; J. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Vanos--2020|Vanos et al., 2020]] ). Different optimum temperatures and extreme heat thresholds based on local distributions are needed to reflect acclimation of different locations and populations ( [[#Hajat--2014|Hajat et al., 2014]] ; [[#WHO--2014|WHO, 2014]] ; [[#Kinney--2015b|Kinney et al., 2015b]] ; [[#Russo--2015|Russo et al., 2015]] ; [[#Petitti--2016|Petitti et al., 2016]] ; [[#Dosio--2017|Dosio, 2017]] ; [[#Cheng--2018|Cheng et al., 2018]] ; [[#Lay--2018|Lay et al., 2018]] ; [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ). Hot and humid heat episodes can be deadly ( [[#Mora--2017|Mora et al., 2017]] ), are associated with elevated hospital intake ( [[#Goldie--2017|Goldie et al., 2017]] ) and lower safety and productivity of outdoor labourers ( [[#Dunne--2013|Dunne et al., 2013]] ; [[#Graff%20Zivin--2014|Graff Zivin and Neidell, 2014]] ; [[#Kjellstrom--2016|Kjellstrom et al., 2016]] ; [[#Pal--2016|Pal and Eltahir, 2016]] ; Y. [[#Zhao--2016|]] [[#Zhao--2016|Zhao et al., 2016]] ; [[#Mora--2017|Mora et al., 2017]] ; [[#Watts--2018|Watts et al., 2018]] ; [[#Orlov--2019|Orlov et al., 2019]] ). Elevated nighttime temperatures prevent the human body from experiencing relief from heat stress ( [[#Zhang--2012|Zhang et al., 2012]] ) and can be tracked over extended periods of sequential day and night heat extremes ( [[#Murage--2017|Murage et al., 2017]] ; [[#Mukherjee--2018|Mukherjee and Mishra, 2018]] ). Extreme heat also exacerbates asthma, respiratory difficulties and response to airborne allergens such as hay fever ( [[#Upperman--2017|Upperman et al., 2017]] ). Extreme heat affects outdoor exercise such as the use of bike-share facilities ( [[#Heaney--2019|Heaney et al., 2019]] ; [[#Vanos--2020|Vanos et al., 2020]] ). Large-scale recreational and sporting events such as marathons and tennis tournaments monitor heat extremes when determining the viability of host cities ( [[#Smith--2016|Smith et al., 2016]] , 2018).

Short-term exposure of crops to temperatures beyond a critical temperature threshold can lead to lower yields and above a limiting temperature threshold, crops may fail altogether ( [[#Schlenker--2009|Schlenker and Roberts, 2009]] ; [[#Lobell--2012|Lobell et al., 2012]] , 2013; [[#Gourdji--2013|Gourdji et al., 2013]] ; [[#Deryng--2014|Deryng et al., 2014]] ; [[#Schauberger--2017|Schauberger et al., 2017]] ; [[#Tesfaye--2017|Tesfaye et al., 2017]] ; [[#Vogel--2019|Vogel et al., 2019]] ). The exact level of these thresholds depends on species, cultivar and farm management ( [[#Hatfield--2015|Hatfield and Prueger, 2015]] ; [[#Hatfield--2015|Hatfield et al., 2015]] ; [[#Bisbis--2018|Bisbis et al., 2018]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). The timing of heatwaves is particularly important, as extreme heat is more damaging during critical phenological stages ( [[#Teixeira--2013|Teixeira et al., 2013]] ; [[#Eyshi%20Rezaei--2015|Eyshi Rezaei et al., 2015]] ; [[#Fontana--2015|Fontana et al., 2015]] ; [[#Wang--2017|]] [[#Wang--2017|]] [[#Wang--2017|B. Wang et al., 2017]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ). Extreme canopy temperatures, rather than 2 m air temperatures, may be a more robust biophysical indicator of heat impacts on crop production ( [[#Siebert--2017|Siebert et al., 2017]] ). Heat stress indices based upon temperature and humidity determine livestock productivity as well as conception and mortality rates ( [[#Key--2014|Key et al., 2014]] ; [[#Dash--2016|Dash et al., 2016]] ; [[#Pragna--2016|Pragna et al., 2016]] ; [[#Rojas-Downing--2017|Rojas-Downing et al., 2017]] ).

Heat extremes factor in mortality, morbidity and the range of some thermally sensitive ecosystem species ( [[#Smith--2015|Smith and Nagy, 2015]] ; [[#Ratnayake--2019|Ratnayake et al., 2019]] ; [[#Thomsen--2019|Thomsen et al., 2019]] ). Combined heat and drought stress can reduce forest and grassland primary productivity ( [[#Ciais--2005|Ciais et al., 2005]] ; [[#De%20Boeck--2018|De Boeck et al., 2018]] ) and even cause tree mortality at higher extremes ( [[#Teskey--2015|Teskey et al., 2015]] ).

Extreme heat events raise temperatures in buildings and cities already warmed by the urban heat island effect ( [[#Gaffin--2012|Gaffin et al., 2012]] ; [[#Oleson--2018|Oleson et al., 2018]] ; [[#Zhao--2018|Zhao et al., 2018]] ; [[#Mauree--2019|Mauree et al., 2019]] ; Box 10.3) and can induce disruptions in critical infrastructure networks ( [[#Chapman--2013|Chapman et al., 2013]] ). Heat affects transportation infrastructure by warping roads and airport runways ( [[#Chinowsky--2012|Chinowsky and Arndt, 2012]] ) or buckling railways ( [[#Dobney--2010|Dobney et al., 2010]] ; [[#Dépoues--2017|Dépoues, 2017]] ; [[#Chinowsky--2019|Chinowsky et al., 2019]] ), and high temperatures reduce air density leading to aircraft take-off weight restrictions ( [[#Coffel--2017|Coffel et al., 2017]] ; [[#Palko--2017|Palko, 2017]] ; T. [[#Zhou--2018|]] [[#Zhou--2018|Zhou et al., 2018]] ). Heat extremes increase peak cooling demand and challenge transmission and transformer capacity ( [[#Sathaye--2013|Sathaye et al., 2013]] ; [[#Russo--2016|Russo et al., 2016]] ; [[#Craig--2018|Craig et al., 2018]] ; X. [[#Gao--2018|]] [[#Gao--2018|Gao et al., 2018]] ) and may cause transmission lines to sag or fail ( [[#Gupta--2012|Gupta et al., 2012]] ). Thermal and nuclear electricity plants may be challenged when using warmer river waters for cooling or when mixing waste waters back into waterways without causing ecosystem impacts ( [[#Kopytko--2011|Kopytko and Perkins, 2011]] ; [[#van%20Vliet--2016|van Vliet et al., 2016]] ; [[#Tobin--2018|Tobin et al., 2018]] ). Extreme temperature can also reduce photovoltaic panel efficiency ( [[#Jerez--2015|Jerez et al., 2015]] ).

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==== 12.3.1.3 Cold Spells ====

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The magnitude and timing (relative to developmental stages) of cold extremes (such as the typical coldest day of the year) set limits in the range of species habitat for ecosystems as well as for agricultural and forest pests ( [[#Osland--2013|Osland et al., 2013]] ; [[#Cavanaugh--2014|Cavanaugh et al., 2014]] ; [[#Parker--2016|Parker and Abatzoglou, 2016]] ; [[#Brunner--2018|Brunner et al., 2018]] ; [[#Unterberger--2018|Unterberger et al., 2018]] ). Cold air outbreaks can lead to chilling injuries for crops (even above 0°C) and may kill outdoor livestock (particularly young animals; [[#Mader--2010|Mader et al., 2010]] ; [[#Liu--2013|Liu et al., 2013]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ), but are often necessary for crop chill requirements ( [[#Dennis--2009|Dennis and Peacock, 2009]] ).

Increases in human mortality can occur on exceptionally cold days (e.g., &lt;1st percentile of temperatures in winter) although thresholds and human-perceived temperatures linked to wind speed (i.e., ‘wind chill’) vary geographically due to acclimatization ( [[#Li--2013|Li et al., 2013]] ; [[#Gao--2015|Gao et al., 2015]] ; J. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; J. [[#Zhu--2019|]] [[#Zhu--2019|Zhu et al., 2019]] ). The timing of ‘unseasonal’ cold spells also affect human health ( [[#Kinney--2015b|Kinney et al., 2015b]] ). Extreme cold can increase heat and electricity demand ( [[#Stuivenvolt-Allen--2019|Stuivenvolt-Allen and Wang, 2019]] ), cause water pipes to burst, and mechanically alter roads, railroads and buildings ( [[#Underwood--2017|Underwood et al., 2017]] ).

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==== 12.3.1.4 Frost ====

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Frost (T <sub>min</sub> &lt;0°C) is a natural and fundamental aspect of many ecosystems, with more extreme conditions defined as ice (or icing) days (T <sub>max</sub> &lt;0°C) ( [[#Vincent--2018|L.A. Vincent et al., 2018]] ). Agricultural systems planning (e.g., planting calendars, seed selection or the opportunity to double-crop) requires information about the start and end of the frost-free season ( [[#Wypych--2017|Wypych et al., 2017]] ; [[#Wolfe--2018|Wolfe et al., 2018]] ). Crops and wild plants can be directly damaged by frost, but hard or killing frosts (at a threshold several degrees below freezing) can kill crops or lower harvest quality depending on duration (which relates to soil temperature penetration) and plant developmental stage ( [[#Crimp--2016a|Crimp et al., 2016a]] ; [[#Cradock-Henry--2017|Cradock-Henry, 2017]] ; G. [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). Earlier disappearance of snow cover reduces natural insulation that protects plants and burrowing animals from hard frost damages ( [[#Trnka--2014|Trnka et al., 2014]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ). In some cases an early season warm spell may reduce plant hardiness or induce fruit tree flowering that exposes plants to devastating subsequent frost impacts ( [[#Hufkens--2012|Hufkens et al., 2012]] ; [[#Hatfield--2014|Hatfield et al., 2014]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ; [[#Brunner--2018|Brunner et al., 2018]] ; [[#DeGaetano--2018|DeGaetano, 2018]] ; [[#Unterberger--2018|Unterberger et al., 2018]] ; [[#Wolfe--2018|Wolfe et al., 2018]] ). Shifts in the seasonality of frozen soils also affect groundwater recharge and surface streamflow for water resource applications, particularly when peak precipitation is shifted to a season that no longer has frozen soils ( [[#Jyrkama--2007|Jyrkama and Sykes, 2007]] ).

Regional information about the spring and autumn seasonal periods in which freeze-thaw cycles are common (such as the dates of first spring thaw and last spring frost, or the number of days where T <sub>max</sub> &gt;0°C and T <sub>min</sub> &lt;0°C) are particularly useful in estimating the rate of potential road and building damage or determining seasonal truck weight restrictions ( [[#Kvande--2009|Kvande and Lisø, 2009]] ; [[#Chinowsky--2012|Chinowsky and Arndt, 2012]] ; [[#Palko--2017|Palko, 2017]] ; [[#Daniel--2018|Daniel et al., 2018]] ). The altitude of the freezing level also identifies portions of mountain slopes where freeze/thaw transitions or changes in snowpack condition can influence landslide and snow avalanche hazards ( [[#Coe--2018|Coe et al., 2018]] ). The geographical distribution of frost is also a determining factor in the range of vectors for human diseases such as malaria (X. [[#Zhao--2016|]] [[#Zhao--2016|Zhao et al., 2016]] ; [[#Smith--2020|Smith et al., 2020]] ).

Figure 12.3 illustrates how successive heat and cold hazards can potentially affect important natural and human systems, with climatic pressures reaching new sectoral assets or becoming increasingly severe as conditions become more extreme. While the precise value of any CID threshold may depend strongly on local environmental and system characteristics, there are common patterns and interdependencies in the types of thresholds encountered. Changes in the regional profile of CIDs can thus substantially alter threshold exceedance likelihoods.

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[[File:1fbbe02ba84f8df0d3124b2557ff7990 IPCC_AR6_WGI_Figure_12_3.png]]

'''Figure 12.3''' '''|''' '''Conceptual illustration of representative climatic impact-driver thresholds showing how graduating thresholds affect successive sectoral assets and lead to potentially more acute hazards as conditions become more extreme (exact values are not shown as these must be tailored to reflect diverse vulnerabilities of regional assets).''' Representative threshold definitions (T = instantaneous temperature; T '''''' = mean temperature): '''Cities and Infrastructures:''' T <sub>trans</sub> = temperature at which energy transmission lines efficiency reduced; T <sub>aircraft</sub> = temperature at which aircraft become weight-restricted for takeoff; T <sub>hotroads</sub> = temperature above which roads begin to warp; T <sub>stream</sub> = temperature at which streams are not capable of adequately cooling thermal plants; CDD <sub>min</sub> = minimum temperature for calculating cooling degree days; HDD <sub>max</sub> = maximum temperature for calculating heating degree days; T <sub>ice</sub> = temperature at which ice threatens transportation; T '''''' <sub>permafrost</sub> = mean seasonal temperature above which permafrost thaws at critical depths; T <sub>coldroads</sub> = temperature below which road asphalt performance suffers. '''Health:''' T <sub>deadly</sub> = temperature above which prolonged exposure may be deadly (often combined with humidity for heat indices); T <sub>severe</sub> = temperature above which prolonged exposure may cause elevated morbidity; T '''''' <sub>blooms</sub> = mean temperature for harmful algal or cyanobacteria blooms; T <sub>danger</sub> = level of dangerous cold temperatures (often combined with wind for chill indices); T <sub>overwinter</sub> = temperature below which disease vector species cannot survive winter. '''Ecosystems''' (CID indices for air and ocean temperature): T <sub>hotlim</sub> and T <sub>coldlim</sub> = limiting hot and cold temperatures for a given species range; T <sub>frost</sub> = frost threshold; T '''''' <sub>max</sub> and T '''''' <sub>min</sub> = maximum and minimum suitable annual mean temperatures for a given species; T <sub>crit</sub> = critical temperature above which a given species is stressed. '''Agriculture:''' T <sub>hotlim</sub> = temperature above which a crop or livestock species dies; T <sub>hotpest</sub> = maximum (or ‘lethal’) temperature above which an agricultural pest/disease/weed cannot survive; T <sub>crit</sub> = temperature at which productivity for a given crop is depressed; T '''''' <sub>opt</sub> = optimal mean temperature for a given plant’s productivity; GDD <sub>min</sub> = threshold temperature for growing degree days determining plant development; T <sub>chill</sub> = temperature below which chilling units are accumulated; T <sub>frost</sub> = temperature below which frost occurs; T <sub>hfrost</sub> = temperature below which a hard frost threatens crops or livestock; T <sub>coldpest</sub> = minimum winter temperature below which a given agricultural pest cannot survive; T <sub>coldlim</sub> = minimum temperature below which a given crop cannot survive.

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<span id="wet-and-dry"></span>
=== 12.3.2 Wet and Dry ===

<div id="h2-2-siblings" class="h2-siblings"></div>

<div id="12.3.2.1" class="h3-container"></div>

<span id="mean-precipitation"></span>
==== 12.3.2.1 Mean Precipitation ====

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Changes in mean precipitation alter total water resources and long-term surface, snowpack and groundwater reservoirs ( [[#Schewe--2014|Schewe et al., 2014]] ). Annual and seasonal wet trends can alter the suitable geographic range of species, with implications for biodiversity and vector-borne diseases ( [[#Knouft--2017|Knouft and Ficklin, 2017]] ; [[#Smith--2020|Smith et al., 2020]] ). The rate at which higher total streamflow increases river erosion and changes sediment loading is relevant for fish breeding ( [[#Scheurer--2009|Scheurer et al., 2009]] ), the location of riverine salt fronts that affect coastal agriculture and ecosystems ( [[#Chun--2018|Chun et al., 2018]] ; [[#Vu--2018|Vu et al., 2018]] ), coastal freshwater stratification ( [[#Baker-Austin--2013|Baker-Austin et al., 2013]] ; [[#Bell--2013|Bell et al., 2013]] ), and the accretion of sediment in estuaries and beaches ( [[#Syvitski--2007|Syvitski and Milliman, 2007]] ). Wetter conditions may shift tourist appeal ( [[#Kovács--2017|Kovács et al., 2017]] ) and alter the pace of degradation for paved and especially unpaved roads ( [[#Chinowsky--2012|Chinowsky and Arndt, 2012]] ).

Many agricultural systems require minimum rainfall totals or rely upon irrigation ( [[#Mbow--2019|Mbow et al., 2019]] ). The length of the wet season helps determine the potential for multiple cropping seasons, but inconsistency of wet season arrival times poses challenges for farm management ( [[#Waha--2020|Waha et al., 2020]] ). Wetter growing season conditions increase the chance of waterlogging, which can delay planting or damage planted seeds ( [[#Rosenzweig--2002|Rosenzweig et al., 2002]] ; [[#Ben-Ari--2018|Ben-Ari et al., 2018]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ; [[#Wolfe--2018|Wolfe et al., 2018]] ; [[#Kolberg--2019|Kolberg et al., 2019]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). [[#Tomasek--2017|Tomasek et al. (2017)]] calculated ‘workable days’ for agricultural machinery around planting and harvest time set in part by limits in soil moisture saturation below which farmers can utilize critical machinery with less rutting or soil compaction. Wetter conditions may also increase canopy moisture that is conducive to crop pathogens ( [[#Garrett--2006|Garrett et al., 2006]] ; [[#Kilroy--2015|Kilroy, 2015]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ).

<div id="12.3.2.2" class="h3-container"></div>

<span id="river-flood"></span>
==== 12.3.2.2 River Flood ====

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A large variety of climate indices and models are utilized to understand how river flooding affects both natural or built environments with highly variable hazard thresholds, given unique local topography and engineered defences such as dams and polders ( [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Ekström--2018|Ekström et al., 2018]] ). Key transportation routes, built infrastructure and agricultural lands are threatened when floods exceed design standards commonly based around flood magnitudes of a given historic return period (e.g., 1-in-100-year flood event), an annual exceedance probability or precipitation intensity-duration-frequency relationships with key indices (e.g., 10-day cumulative precipitation) related to catchment size and properties ( [[#Hirabayashi--2013|Hirabayashi et al., 2013]] ; [[#Arnell--2014|Arnell and Lloyd-Hughes, 2014]] ; [[#Kundzewicz--2014|Kundzewicz et al., 2014]] ; [[#Arnell--2016|Arnell and Gosling, 2016]] ; [[#Dikanski--2016|Dikanski et al., 2016]] ; [[#Gosling--2016|Gosling and Arnell, 2016]] ; [[#Forzieri--2017|Forzieri et al., 2017]] ; [[#Fluixá-Sanmartín--2018|Fluixá-Sanmartín et al., 2018]] ; [[#Koks--2019|Koks et al., 2019]] ). Floods and high-flow events can scour river beds and elevate silt loads, reducing water quality and accelerating deposition in estuaries and reservoirs ( [[#Khan--2018|Khan et al., 2018]] ; [[#Parasiewicz--2019|Parasiewicz et al., 2019]] ). Floods can knock down, drown or wash away crops and livestock, and partially submerged plants can have yield reduction depending on water turbidity and their development stage ( [[#Ruane--2013|Ruane et al., 2013]] ; [[#Shrestha--2019|Shrestha et al., 2019]] ). Basin snowpack properties may also be important during heavy rain events, as rain-on-snow events can lead to rapid acceleration of flood stages that threaten wildlife and society ( [[#Hansen--2014|Hansen et al., 2014]] ).

<div id="12.3.2.3" class="h3-container"></div>

<span id="heavy-precipitation-and-pluvial-flood"></span>
==== 12.3.2.3 Heavy Precipitation and Pluvial Flood ====

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Heavy downpours can lead to pluvial flooding in cities, roadways, farmland, subway tunnels and buildings (particularly those with basements; [[#Grahn--2017|Grahn and Nyberg, 2017]] ; [[#Palko--2017|Palko, 2017]] ; [[#Pregnolato--2017|Pregnolato et al., 2017]] ; [[#Orr--2018|Orr et al., 2018]] ). Heavy precipitation may overwhelm city transportation and storm water drainage systems, which are typically designed using intensity-duration-frequency information such as the return periods for 1-, 6- or 24-hour rainfall totals ( [[#Kermanshah--2017|Kermanshah et al., 2017]] ; [[#Depietri--2018|Depietri and McPhearson, 2018]] ; [[#Rosenzweig--2018|Rosenzweig et al., 2018]] ; [[#Courty--2019|Courty et al., 2019]] ). Heavy rain events can directly cause leaf loss and damage, or knock over crops, also driving pollutant entrainment and erosion hazards in terrestrial ecosystems and farmland, with downstream ramifications for water quality ( [[#Hatfield--2014|Hatfield et al., 2014]] ; [[#Segura--2014|Segura et al., 2014]] ; [[#Li--2016|Li and Fang, 2016]] ; [[#Chhetri--2019|Chhetri et al., 2019]] ). The proportion of total precipitation that falls in heavy events also affects the percentage that is retained in the soil column, altering groundwater recharge and deep soil moisture content for agricultural use ( [[#Fishman--2016|Fishman, 2016]] ; [[#Lesk--2020|Lesk et al., 2020]] ).

<div id="12.3.2.4" class="h3-container"></div>

<span id="landslide"></span>
==== 12.3.2.4 Landslide ====

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Landslides, mudslides, rockfalls and other mass movements can lead to fatalities, destroy infrastructure and housing stock, and block critical transportation routes. Climate models cannot resolve these complex slope failure processes (nor triggering mechanisms such as earthquakes), so most studies rely on proxies or conditions conducive to slope failure ( [[#Gariano--2016|Gariano and Guzzetti, 2016]] ; [[#Ho--2017|Ho et al., 2017]] ). Common indices include precipitation intensity-duration thresholds ( [[#Brunetti--2010|Brunetti et al., 2010]] ; [[#Khan--2012|Khan et al., 2012]] ; [[#Melchiorre--2012|Melchiorre and Frattini, 2012]] ) and thresholds related to antecedent wet periods and extreme rainfall intensities ( [[#Alvioli--2018|Alvioli et al., 2018]] ; [[#Monsieurs--2019|Monsieurs et al., 2019]] ). Landslides and rockfalls may also be exacerbated by permafrost thaw and receding glaciers in polar and mountain areas ( [[#Cook--2016|Cook et al., 2016]] ; [[#Haeberli--2017|Haeberli et al., 2017]] ; [[#Patton--2019|Patton et al., 2019]] ).

<div id="12.3.2.5" class="h3-container"></div>

<span id="aridity"></span>
==== 12.3.2.5 Aridity ====

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Aridity indices may track long-term changes in precipitation, evapotranspiration demand, surface water, groundwater or soil moisture ( [[#Sherwood--2014|Sherwood and Fu, 2014]] ; [[#Herrera-Pantoja--2015|Herrera-Pantoja and Hiscock, 2015]] ; B.I. [[#Cook--2020|]] [[#Cook--2020|Cook et al., 2020]] ). Changes in soil moisture and surface water can shift the rate of carbon uptake by ecosystems ( [[#Humphrey--2018|Humphrey et al., 2018]] ) and alter suitable climate zones for wild species and agricultural cultivation ( [[#Feng--2013|Feng and Fu, 2013]] ; [[#Garcia--2014|Garcia et al., 2014]] ; [[#Huang--2016a|Huang et al., 2016a]] ; [[#Schlaepfer--2017|Schlaepfer et al., 2017]] ; [[#Fatemi--2018|Fatemi et al., 2018]] ; [[#IPCC--2019c|IPCC, 2019c]] ) as well as the prevalence of related pests and pathogen-carrying vectors ( [[#Paritsis--2011|Paritsis and Veblen, 2011]] ; [[#Smith--2020|Smith et al., 2020]] ). Water table depth, in relation to rooting depth, is also important for farms and forests under dry conditions ( [[#Feng--2006|Feng et al., 2006]] ). A reduction in water availability (via aridity or hydrological drought) challenges water supplies needed for for municipal, industrial, agriculture and hydropower use ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Arnell--2014|Arnell and Lloyd-Hughes, 2014]] ; [[#Schewe--2014|Schewe et al., 2014]] ; [[#Gosling--2016|Gosling and Arnell, 2016]] ; [[#van%20Vliet--2016|van Vliet et al., 2016]] ).

<div id="12.3.2.6" class="h3-container"></div>

<span id="hydrological-drought"></span>
==== 12.3.2.6 Hydrological Drought ====

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Water managers often utilize a variety of hydrological drought indices and hydrological models to characterize water resources, low flow conditions and the potential for irrigation ( [[#Wanders--2015|Wanders and Wada, 2015]] ; [[#Mukherjee--2018|Mukherjee et al., 2018]] ). Low flow volume and intermittency thresholds can indicate reductions in dissolved oxygen, more concentrated pollutants, and higher stream temperatures relevant for ecosystems, water resource quality and thermal power plant cooling ( [[#Feeley--2008|Feeley et al., 2008]] ; [[#Döll--2012|Döll and Schmied, 2012]] ; [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Prudhomme--2014|Prudhomme et al., 2014]] ; [[#van%20Vliet--2016|van Vliet et al., 2016]] ). Low water levels may also restrict waterway navigation for commerce and recreation ( [[#Forzieri--2018|Forzieri et al., 2018]] ).

<div id="12.3.2.7" class="h3-container"></div>

<span id="agricultural-and-ecological-drought"></span>
==== 12.3.2.7 Agricultural and Ecological Drought ====

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Agricultural and ecological drought indices relate to the ability of plants to meet growth and transpiration needs (Table 11.3; [[#Zargar--2011|Zargar et al., 2011]] ; [[#Lobell--2015|Lobell et al., 2015]] ; [[#Pedro-Monzonís--2015|Pedro-Monzonís et al., 2015]] ; [[#Bachmair--2016|Bachmair et al., 2016]] ; [[#Wehner--2017|Wehner et al., 2017]] ; [[#Naumann--2018|Naumann et al., 2018]] ) and the timing and duration of droughts can lead to substantially different impacts ( [[#Peña-Gallardo--2019|Peña-Gallardo et al., 2019]] ). Drought stress for agriculture and ecosystems is difficult to directly observe, and therefore scientists use a variety of drought indices (Table 11.3), proxy information about changes in precipitation supply and reference evapotranspiration demand, the ratio of actual/potential evapotranspiration or a deficit in available soil water content, particularly at rooting level ( [[#Park%20Williams--2013|Park Williams et al., 2013]] ; [[#Trnka--2014|Trnka et al., 2014]] ; C.D. [[#Allen--2015|]] [[#Allen--2015|Allen et al., 2015]] ; [[#Svoboda--2017|Svoboda and Fuchs, 2017]] ; [[#Mäkinen--2018|Mäkinen et al., 2018]] ; [[#Otkin--2018|Otkin et al., 2018]] ). Severe water stress can lead to crop failure, in particular when droughts persist for an extended period or occur during key plant developmental stages ( [[#Hatfield--2014|Hatfield et al., 2014]] ; [[#Jolly--2015|Jolly et al., 2015]] ; [[#Leng--2019|Leng and Hall, 2019]] ). Projections of high wind speed and low humidity (even for just a portion of the day) can also inform studies examining fruit desiccation and rice cracking ( [[#Grotjahn--2021|Grotjahn, 2021]] ). Drought also raises disease infection rates for West Nile virus ( [[#Paull--2017|Paull et al., 2017]] ), and the alternation of dry and wet spells induces swelling and shrinkage of clay soils that can lead to sinkholes and destabilize buildings ( [[#Hadji--2014|Hadji et al., 2014]] ).

<div id="12.3.2.8" class="h3-container"></div>

<span id="fire-weather"></span>
==== 12.3.2.8 Fire Weather ====

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Complex fire weather indices shed light on conditions that increase the likelihood of wildfire and shifts in the fire season ( [[#Flannigan--2013|Flannigan et al., 2013]] ; [[#Bedia--2015|Bedia et al., 2015]] ; [[#Jolly--2015|Jolly et al., 2015]] ; [[#Harvey--2016|Harvey, 2016]] ; [[#Littell--2016|Littell et al., 2016]] ; [[#Westerling--2016|Westerling, 2016]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ), which pose particularly acute challenges for indigenous communities ( [[#Christianson--2019|Christianson and McGee, 2019]] ). Projection of future lightning frequency provides information on an important natural triggering mechanism, particularly when coupled with long-term warming and drying trends ( [[#Romps--2014|Romps et al., 2014]] ; [[#Jin--2015|Jin et al., 2015]] ; [[#Veraverbeke--2017|Veraverbeke et al., 2017]] ). Fuel aridity metrics also help determine vegetative fuel desiccation and therefore the ignitability, flammability and spread of fires when they occur ( [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ). The presence of snow cover can influence the length of the fire season and the penetration of fire danger into new portions of the Arctic tundra ( [[#Young--2017|Young et al., 2017]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ). Data on the changing characteristics of local wind circulations like the Santa Ana in California shed light on future intensity and spread patterns for fires ( [[#Jin--2015|Jin et al., 2015]] ). Fires also produce smoke plumes that reduce air and water quality (via deposition), adversely affecting health, visibility and water resources both near and far downwind ( [[#Dennekamp--2011|Dennekamp and Abramson, 2011]] ; [[#McKenzie--2014|McKenzie et al., 2014]] ; [[#Dreessen--2016|Dreessen et al., 2016]] ; [[#Liu--2016|Liu et al., 2016]] ; [[#Martin--2016|Martin, 2016]] ).

<div id="12.3.3" class="h2-container"></div>

<span id="wind"></span>
=== 12.3.3 Wind ===

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<div id="12.3.3.1" class="h3-container"></div>

<span id="mean-wind-speed"></span>
==== 12.3.3.1 Mean Wind Speed ====

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Changes in the speed and direction of prevailing winds can alter the profile of seed dispersal, windblown pest and disease vectors, animal activities, and dust or pollen dispersal affecting ecosystems, agriculture and human health ( [[#Reid--2009|Reid and Gamble, 2009]] ; [[#Bullock--2012|Bullock et al., 2012]] ; [[#Hellberg--2016|Hellberg and Chu, 2016]] ; [[#Nourani--2017|Nourani et al., 2017]] ). Seasonal winds influence algal blooms, ecosystems and fisheries via lake mixing, ocean currents and coastal upwelling ( [[#Bakun--2015|Bakun et al., 2015]] ; [[#Townhill--2018|Townhill et al., 2018]] ; [[#Woolway--2020|Woolway et al., 2020]] ). Changes to wind density also modify a region’s wind and wave renewable energy endowment ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Sierra--2017|Sierra et al., 2017]] ; [[#Craig--2018|Craig et al., 2018]] ; [[#Devis--2018|Devis et al., 2018]] ; [[#Tobin--2018|Tobin et al., 2018]] ; [[#Yalew--2020|Yalew et al., 2020]] ). D. [[#Li--2020|]] [[#Li--2020|]] [[#Li--2020|Li et al. (2020)]] and [[#Karnauskas--2018a|Karnauskas et al. (2018a)]] evaluated wind thresholds at turbine height (about 80–100 m above ground) including periods outside of cut-in (2.5–3 m s <sup>–1</sup> ) and cut-out (about 25 m s <sup>–1</sup> ) levels beyond which given turbines could not operate.

<div id="12.3.3.2" class="h3-container"></div>

<span id="severe-wind-storm"></span>
==== 12.3.3.2 Severe Wind Storm ====

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High winds associated with severe storms can destroy trees and houses, break plant stems and knock fruits, nuts and grains to the ground, with tolerance thresholds depending on crop species and developmental stage ( [[#Seidl--2017|Seidl et al., 2017]] ; [[#Lai--2018|Lai, 2018]] ; [[#Elsner--2019|Elsner et al., 2019]] ; [[#Grotjahn--2021|Grotjahn, 2021]] ). Severe storms particularly threaten energy infrastructure, with maximum wind speed associated with treefall and breaking of above-ground electrical transmission lines ( [[#Ward--2013|Ward, 2013]] ; [[#Nik--2020|Nik et al., 2020]] ). The profile of heavy wind gusts is also required in the design of skyscrapers (C.-H. [[#Wang--2013|]] [[#Wang--2013|Wang et al., 2013]] ) and bridges ( [[#Mondoro--2018|Mondoro et al., 2018]] ). Severe storms are difficult to simulate at the relatively coarse spatial scales of Earth system models, thus scientists often project changes by noting areas with increased convective available potential energy (CAPE) and strong low-level wind shear as these are conducive to tornado formation ( [[#Diffenbaugh--2013|Diffenbaugh et al., 2013]] ; [[#Tippett--2016|Tippett et al., 2016]] ; [[#Glazer--2021|Glazer et al., 2021]] ).

<div id="12.3.3.3" class="h3-container"></div>

<span id="tropical-cyclone"></span>
==== 12.3.3.3 Tropical Cyclone ====

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Tropical cyclones and severe coastal storms can deliver wind, water and coastal hazards with the potential for widespread mortality and damages to cities, housing, transportation and energy infrastructure, ecosystems and agricultural lands ( [[#Burkett--2011|Burkett, 2011]] ; [[#NASEM--2012|NASEM, 2012]] ; [[#Bell--2013|Bell et al., 2013]] ; [[#Wehof--2014|Wehof et al., 2014]] ; [[#Ward--2016|Ward et al., 2016]] ; [[#Cheal--2017|Cheal et al., 2017]] ; [[#Godoi--2018|Godoi et al., 2018]] ; [[#Koks--2019|Koks et al., 2019]] ; [[#Pinnegar--2019|Pinnegar et al., 2019]] ). Storm planning is often tied to the Saffir –Simpson scale related to peak sustained wind speed ( [[#Izaguirre--2021|Izaguirre et al., 2021]] ), with several indices focusing on storms’ overall power and energy, size and translation speed to anticipate destructive potential ( [[#Knutson--2015|Knutson et al., 2015]] ; [[#Wang--2016|Wang and Toumi, 2016]] ; [[#Parker--2018|Parker et al., 2018]] ; [[#Hassanzadeh--2020|Hassanzadeh et al., 2020]] ).

<div id="12.3.3.4" class="h3-container"></div>

<span id="sand-and-dust-storm"></span>
==== 12.3.3.4 Sand and Dust Storm ====

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Sand and dust storms erode soils, damage crops and induce problems for health, transportation, mechanical equipment and built infrastructure corresponding to the magnitude and duration of high winds and particulate matter concentrations ( [[#Goudie--2014|Goudie, 2014]] ; [[#O’Loingsigh--2014|O’Loingsigh et al., 2014]] ; [[#Crooks--2016|Crooks et al., 2016]] ; [[#Barreau--2017|Barreau et al., 2017]] ; [[#Bhattachan--2018|Bhattachan et al., 2018]] ; [[#Al%20Ameri--2019|Al Ameri et al., 2019]] ; [[#Middleton--2019|Middleton et al., 2019]] ). Dust events may be represented as the number of dust hours per year and by particulate matter (PM) concentrations ( [[#Leys--2011|Leys et al., 2011]] ; [[#Spickett--2011|Spickett et al., 2011]] ; [[#Hand--2016|Hand et al., 2016]] ). Photovoltaic panels can lose energy production efficiency with dust accumulation ( [[#Patt--2013|Patt et al., 2013]] ; [[#Javed--2017|Javed et al., 2017]] ). It is also useful to track dust storm deposition of nutrients necessary for coral and tropical forest systems, but they may also feed algal blooms harming lake and coastal ecosystems, health and recreation ( [[#Jickells--2005|Jickells et al., 2005]] ; [[#Hallegraeff--2014|Hallegraeff et al., 2014]] ; [[#Gabric--2016|Gabric et al., 2016]] ). Dust storms also cause air pollution and redistribute the soil-based fungus associated with Valley fever ( [[#Barreau--2017|Barreau et al., 2017]] ; [[#Coopersmith--2017|Coopersmith et al., 2017]] ; [[#Tong--2017|Tong et al., 2017]] ; [[#Gorris--2018|Gorris et al., 2018]] ).

<div id="12.3.4" class="h2-container"></div>

<span id="snow-and-ice"></span>
=== 12.3.4 Snow and Ice ===

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Cryospheric changes are a focus of ( [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] and were central to the recent IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; [[#IPCC--2019b|IPCC, 2019b]] ). Here we focus on the ways that scientists use snow and ice CIDs to understand current and future societal impacts and risks.

<div id="12.3.4.1" class="h3-container"></div>

<span id="snow-glacier-and-ice-sheet"></span>
==== 12.3.4.1 Snow, Glacier and Ice Sheet ====

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A large number of indices have been used in water resource and ecosystem studies to track changes in snow under current and future climate conditions, including measurements of the snow water equivalent at key seasonal dates, the fraction of precipitation falling as snow, the first and last days of snow cover, and cold season temperatures ( [[#Mills--2013|Mills et al., 2013]] ; [[#Pierce--2013|Pierce and Cayan, 2013]] ; [[#Berghuijs--2014|Berghuijs et al., 2014]] ; [[#Klos--2014|Klos et al., 2014]] ; [[#Musselman--2017|Musselman et al., 2017]] ; [[#Rhoades--2018|Rhoades et al., 2018]] ). Impact studies also examine shifts in seasonal streamflow for snow-fed river basins ( [[#Mote--2005|Mote et al., 2005]] ; [[#Pederson--2011|Pederson et al., 2011]] ; [[#Beniston--2014|Beniston and Stoffel, 2014]] ; [[#Coppola--2014b|Coppola et al., 2014b]] , 2018; [[#Fyfe--2017|Fyfe et al., 2017]] ; [[#Islam--2017|Islam et al., 2017]] ; [[#Knouft--2017|Knouft and Ficklin, 2017]] ) as well as the geographic extent of snow cover and the depth of frosts when snow cover’s natural insulation is absent ( [[#Scheurer--2009|Scheurer et al., 2009]] ; [[#Millar--2015|Millar and Stephenson, 2015]] ). Studies examining the impact of snow changes on winter recreation and transportation have used thresholds of about 30 cm snow depth or snow water equivalent &gt;10 cm to determine the length of the season for alpine and cross-country skiing and snowmobiling ( [[#Damm--2017|Damm et al., 2017]] ; [[#Wobus--2017b|Wobus et al., 2017b]] ; [[#Spandre--2019|Spandre et al., 2019]] ; [[#Steiger--2019|Steiger et al., 2019]] ; [[#Abegg--2021|Abegg et al., 2021]] ). Changes in snow quality also affect recreational activities ( [[#Rutty--2017|Rutty et al., 2017]] ), and artificial snowmaking can augment recreational snowpack depending on the number of suitable snowmaking hours (e.g., where wet bulb globe temperature (WBGT) &lt;–2.2°C; [[#Wobus--2017b|Wobus et al., 2017b]] ). Local detail may also be provided by tracking the seasonal rain–snow transition line across space and elevation ( [[#Berghuijs--2014|Berghuijs et al., 2014]] ) ( [[#Pierce--2013|Pierce and Cayan, 2013]] ; [[#Berghuijs--2014|Berghuijs et al., 2014]] ; [[#Klos--2014|Klos et al., 2014]] ; [[#Musselman--2017|Musselman et al., 2017]] ).

Change in ice sheet and glacier spatial extent and surface mass balance is relevant for polar and high mountain ecosystems and downstream assets that rely on glacial water resources (J.R. [[#Lee--2017|]] [[#Lee--2017|Lee et al., 2017]] ; [[#Milner--2017|Milner et al., 2017]] ; [[#Huss--2018|Huss and Hock, 2018]] ; [[#Schaefli--2019|Schaefli et al., 2019]] ). The loss of glaciers reduces the thermal consistency of cold streams suitable for some freshwater species ( [[#Giersch--2017|Giersch et al., 2017]] ), and parks and recreation areas may lose appeal as glaciers and seasonal snow cover retreat ( [[#Gonzalez--2018|Gonzalez et al., 2018]] ; [[#Wang--2019|Wang and Zhou, 2019]] ). Rapid glacial retreat can lead to glacial lakes and outburst floods that endanger downstream communities ( [[#Carrivick--2016|Carrivick and Tweed, 2016]] ; [[#Cook--2016|Cook et al., 2016]] ; [[#Harrison--2018|Harrison et al., 2018]] ).

<div id="12.3.4.2" class="h3-container"></div>

<span id="permafrost"></span>
==== 12.3.4.2 Permafrost ====

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Changes in permafrost temperature, extent and active layer thickness are metrics that track how permafrost thaw below, for example, roads, airstrips, rails and building foundations in high-latitude and mountain regions may destabilize settlements and critical infrastructure ( [[#Pendakur--2016|Pendakur, 2016]] ; [[#Derksen--2018|Derksen et al., 2018]] ; [[#Duvillard--2019|Duvillard et al., 2019]] ; [[#Olsson--2019|Olsson et al., 2019]] ; [[#Streletskiy--2019|Streletskiy et al., 2019]] ). Warmer conditions can also affect ecosystems, built infrastructure and water resources through thawing of especially ice-rich permafrost (≥20% ice content) and by thawing of ice wedges ( [[#Shiklomanov--2017|Shiklomanov et al., 2017]] ; [[#Hjort--2018|Hjort et al., 2018]] ), creation of thermokarst ponds and increased subsurface drainage for polar and high-mountain wetlands ( [[#Walvoord--2016|Walvoord and Kurylyk, 2016]] ; [[#Farquharson--2019|Farquharson et al., 2019]] ) and the release of water pollutants such as mercury ( [[#Burkett--2011|Burkett, 2011]] ; [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Schuster--2018|Schuster et al., 2018]] ).

<div id="12.3.4.3" class="h3-container"></div>

<span id="lake-river-and-sea-ice"></span>
==== 12.3.4.3 Lake, River and Sea Ice ====

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Reductions in the duration of thick sea, lake and river ice influence ecosystems as well as ice fishing, hunting, dog sledding and snowmobiling, which are recreation activities for some but vital aspects of many traditional indigenous communities ( [[#Durkalec--2015|Durkalec et al., 2015]] ; [[#AMAP--2017|AMAP, 2017]] ; [[#Baztan--2017|Baztan et al., 2017]] ; [[#Arp--2018|Arp et al., 2018]] ; [[#Rokaya--2018|Rokaya et al., 2018]] ; [[#Knoll--2019|Knoll et al., 2019]] ; [[#Meredith--2019|Meredith et al., 2019]] ; [[#Sharma--2019|Sharma et al., 2019]] ). The seasonal extent of thin ice and iceberg density also determines the viability of shipping lanes and seasonal roads ( [[#Valsson--2011|Valsson and Ulfarsson, 2011]] ; [[#Pizzolato--2016|Pizzolato et al., 2016]] ; [[#AMAP--2017|AMAP, 2017]] ; [[#Mullan--2017|Mullan et al., 2017]] ; [[#Sturm--2017|Sturm et al., 2017]] ), oil and gas exploration timing ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ) and the seasonality of phytoplankton blooms ( [[#Oziel--2017|Oziel et al., 2017]] ). Sea ice is a critical aspect of some ecosystems and fisheries ( [[#Massom--2010|Massom and Stammerjohn, 2010]] ; [[#Jenouvrier--2014|Jenouvrier et al., 2014]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ; [[#Meredith--2019|Meredith et al., 2019]] ). Various definitions of ‘ice free’ Arctic Ocean conditions can be tailored to represent transportation needs, including thresholds of ice coverage (&lt;5% or &lt;30% or &lt;1 million km <sup>2</sup> ) in September or over a four-month period ( [[#Laliberté--2016|Laliberté et al., 2016]] ; [[#Jahn--2018|Jahn, 2018]] ).

<div id="12.3.4.4" class="h3-container"></div>

<span id="heavy-snowfall-and-ice-storm"></span>
==== 12.3.4.4 Heavy Snowfall and Ice Storm ====

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Heavy snowfall is a substantial concern for cities, settlements and key transportation and energy infrastructure ( [[#Ward--2013|Ward, 2013]] ; [[#Palko--2017|Palko, 2017]] ; [[#Janoski--2018|Janoski et al., 2018]] ; [[#Collins--2019|Collins et al., 2019]] ). Heavy snowfall can interfere with transportation ( [[#Herring--2018|Herring et al., 2018]] ) and cause a loss of both work and school days depending on local snow removal infrastructure. Freezing rain and ice storms can be treacherous for road and air travel ( [[#Tamerius--2016|Tamerius et al., 2016]] ), and can knock down power and telecommunication lines if ice accumulation is high ( [[#Degelia--2016|Degelia et al., 2016]] ). Rain-on-snow events can create a solid barrier that hinders wildlife and livestock grazing that is important to indigenous communities ( [[#Forbes--2016|Forbes et al., 2016]] ). Shifts in the frequency, seasonal timing and regions susceptible to ice storms alter risks for agriculture and infrastructure ( [[#Lambert--2011|Lambert and Hansen, 2011]] ; [[#Klima--2015|Klima and Morgan, 2015]] ; [[#Ning--2015|Ning and Bradley, 2015]] ; [[#Groisman--2016|Groisman et al., 2016]] ).

<div id="12.3.4.5" class="h3-container"></div>

<span id="hail"></span>
==== 12.3.4.5 Hail ====

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Information on the changing frequency and size distribution of hail can help stakeholders build resilience for agriculture, vehicles, transportation infrastructure and buildings, solar panels and wild species that see critical damage at particular hail size thresholds ( [[#Dessens--2007|Dessens et al., 2007]] ; [[#Webb--2009|Webb et al., 2009]] ; [[#Patt--2013|Patt et al., 2013]] ; [[#Fiss--2019|Fiss et al., 2019]] ). Most climate models do not directly resolve hail and therefore studies often examine proxies associated with severe mesoscale storms ( [[#Tippett--2015|Tippett et al., 2015]] ; [[#Prein--2018|Prein and Holland, 2018]] ), although some regional studies now utilize hail-resolving models ( [[#Mahoney--2012|Mahoney et al., 2012]] ; [[#Brimelow--2017|Brimelow et al., 2017]] ).

<div id="12.3.4.6" class="h3-container"></div>

<span id="snow-avalanche"></span>
==== 12.3.4.6 Snow Avalanche ====

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Information about the changing frequency and seasonal timing of snow avalanches is important to assess threats to transportation routes, infrastructure, recreational skiing and people living in alpine communities ( [[#Lazar--2008|Lazar and Williams, 2008]] ; [[#Mock--2017|Mock et al., 2017]] ; [[#Ballesteros-Cánovas--2018|Ballesteros-Cánovas et al., 2018]] ; [[#Hock--2019|Hock et al., 2019]] ). Like landslides and other mass movements, snow avalanches are not directly resolved by climate models and are thus tracked using proxy climate information describing snow avalanche susceptibility, particularly the snow water equivalent, and triggering mechanisms such as warm spells, high winds, rain-on-snow and heavy precipitation ( [[#Hock--2019|Hock et al., 2019]] ). The quality of snow also provides insight into avalanche hazards ( [[#Mock--2017|Mock et al., 2017]] ), with the seasonal altitude of wet snowpack (&gt;0.5% liquid water by volume) particularly important in determining characteristics of potential avalanches ( [[#Castebrunet--2014|Castebrunet et al., 2014]] ).

<div id="12.3.5" class="h2-container"></div>

<span id="coastal"></span>
=== 12.3.5 Coastal ===

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The SROCC included in-depth discussions of threats facing the world’s coastlines ( [[#IPCC--2019b|IPCC, 2019b]] ) and [[IPCC:Wg1:Chapter:Chapter-9#9.6|Section 9.6]] provides further discussion on coastal processes. Here we note major connections between coastal CIDs and ecosystem and societal assets near coastlines.

<div id="12.3.5.1" class="h3-container"></div>

<span id="relative-sea-level"></span>
==== 12.3.5.1 Relative Sea Level ====

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Sea level rise hazards for coastal ecosystems, infrastructure, farmland, cities and settlements in a particular region are often driven by regional changes in relative sea level (RSL) that account for land uplift or subsidence and thus represent local asset vulnerability better than global mean sea level (Box 9.1; [[#Hallegatte--2013|Hallegatte et al., 2013]] ; [[#Hinkel--2013|Hinkel et al., 2013]] ; [[#McInnes--2016|McInnes et al., 2016]] ; [[#Weatherdon--2016|Weatherdon et al., 2016]] ; [[#Brown--2018|Brown et al., 2018]] ; [[#IPCC--2019b|IPCC, 2019b]] ; [[#Rasoulkhani--2020|Rasoulkhani et al., 2020]] ). Vertical land motion (i.e., land subsidence) caused by local fluid (gas or groundwater) extraction can also have a large influence on relative sea levels ( [[#Minderhoud--2020|Minderhoud et al., 2020]] ). Several indices have been suggested to signify coastal inundation, including a threshold when the local land elevation falls below the local mean higher high water (MHHW) that is close to the ‘high tide’ level ( [[#Kulp--2019|Kulp and Strauss, 2019]] ) or a threshold when flooding occurs about once every two weeks ( [[#Sweet--2014|Sweet and Park, 2014]] ; [[#Dahl--2017b|Dahl et al., 2017b]] ). RSL rise (or RSLR) can drive increased inland penetration of above-ground and subterranean salt water fronts (i.e., salinity intrusion) affecting coastal ecosystems, agriculture and water resources ( [[#Ferguson--2012|Ferguson and Gleeson, 2012]] ; [[#Kirwan--2013|Kirwan and Megonigal, 2013]] ; [[#Rotzoll--2013|Rotzoll and Fletcher, 2013]] ; [[#Chen--2016|Chen et al., 2016]] ; [[#Colombani--2016|Colombani et al., 2016]] ; [[#Holding--2016|Holding et al., 2016]] ; [[#Sawyer--2016|Sawyer et al., 2016]] ; [[#Mohammed--2018|Mohammed and Scholz, 2018]] ). The rate of RSLR can determine the survival and net pressure on niche coastal ecosystems such as mangroves, tidal flats, sea grasses and coral reefs ( [[#Hubbard--2008|Hubbard et al., 2008]] ; [[#Craft--2009|Craft et al., 2009]] ; [[#Bell--2013|Bell et al., 2013]] ; [[#Kirwan--2013|Kirwan and Megonigal, 2013]] ; [[#Alongi--2015|Alongi, 2015]] ; [[#Ellison--2015|Ellison, 2015]] ; [[#Lovelock--2015|Lovelock et al., 2015]] ; [[#Ward--2016|Ward et al., 2016]] ; [[#Lee--2018|Lee et al., 2018]] ).

<div id="12.3.5.2" class="h3-container"></div>

<span id="coastal-flood"></span>
==== 12.3.5.2 Coastal Flood ====

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Episodic coastal flooding of coastal communities, farmland, buildings, transportation routes, industry and other infrastructure is caused by extreme total water levels (ETWL), which is the combination of RSL, tides, storm surge and high wave setup at the shoreline ( [[#Vitousek--2017|Vitousek et al., 2017]] ; [[#Melet--2018|Melet et al., 2018]] ; [[#Vousdoukas--2018|Vousdoukas et al., 2018]] , 2020a; [[#Koks--2019|Koks et al., 2019]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ). Coastal settlement and infrastructure design often uses coastal flooding metrics such as the ETWL frequency distribution or the 100-year average return interval storm tide (storm surge + high tide) level ( [[#McInnes--2016|McInnes et al., 2016]] ; [[#Mills--2016|Mills et al., 2016]] ; [[#Walsh--2016b|Walsh et al., 2016b]] ; [[#Zheng--2017|Zheng et al., 2017]] ). The duration of floods that overtop coastal protection, due to extreme coastal water levels (ECWL), is important for port and harbour operations and coastal energy infrastructure thresholds ( [[#Bilskie--2016|Bilskie et al., 2016]] ; [[#Camus--2017|Camus et al., 2017]] ). Frequent inundation by salt water can also have significant impacts on water resources, crops, aquaculture and transportation systems due to corrosion and undercutting of coastal roads, bridges and rails ( [[#Zimmerman--2010|Zimmerman and Faris, 2010]] ; N. [[#Ahmed--2019|]] [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Gopalakrishnan--2019|Gopalakrishnan et al., 2019]] ).

<div id="12.3.5.3" class="h3-container"></div>

<span id="coastal-erosion"></span>
==== 12.3.5.3 Coastal Erosion ====

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Effective management of coastal ecosystems, cities, settlements, beaches and infrastructure requires information about coastal erosion driven by storm surge, waves and sea level rise ( [[#Dawson--2009|Dawson et al., 2009]] ; [[#Hinkel--2013|Hinkel et al., 2013]] ; [[#Harley--2017|Harley et al., 2017]] ; [[#Mentaschi--2017|Mentaschi et al., 2017]] ). Coastal erosion is generally accompanied by shoreline retreat, which can occur as a gradual process (e.g., due to sea level rise) or as an episodic event due to storm surge and/or extreme waves, especially when combined with high tide ( [[#Ranasinghe--2016|Ranasinghe, 2016]] ). The most commonly used shoreline retreat index is the magnitude of shoreline retreat by a pre-determined planning horizon such as 50 or 100 years into the future. Commonly used metrics for episodic coastal erosion include the beach erosion volume due to the 100-year recurrence storm wave height, the full exceedance probability distribution of coastal erosion volume ( [[#Li--2014a|Li et al., 2014a]] ; [[#Pender--2015|Pender et al., 2015]] ; [[#Ranasinghe--2017|Ranasinghe and Callaghan, 2017]] ) and the cumulative storm energy and storm power index ( [[#Godoi--2018|Godoi et al., 2018]] ). The destruction or overtopping of barrier islands may lead to irreversible changes in the physical system as well as in coastal ecosystems ( [[#Carrasco--2016|Carrasco et al., 2016]] ; [[#Zinnert--2019|Zinnert et al., 2019]] ). Shoreline position change rates along inlet-interrupted coasts may also be affected by changes in river flows and fluvial sediment supply ( [[#Hinkel--2013|Hinkel et al., 2013]] ; [[#Bamunawala--2018|Bamunawala et al., 2018]] ; [[#Ranasinghe--2019|Ranasinghe et al., 2019]] ). Permafrost thaw and Arctic sea ice decline also reduce natural coastal protection from wave erosion for communities and industry ( [[#Forbes--2011|Forbes, 2011]] ; [[#Melvin--2017|Melvin et al., 2017]] ).

<div id="12.3.6" class="h2-container"></div>

<span id="oceanic"></span>
=== 12.3.6 Oceanic ===

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Oceanic changes and impacts were a substantial focus of SROCC ( [[#IPCC--2019b|IPCC, 2019b]] ). [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] of this Report assesses changes in ocean processes, and here we note major connections used by scientists to understand how oceanic CIDs affect ecosystems and society.

<div id="12.3.6.1" class="h3-container"></div>

<span id="mean-ocean-temperature"></span>
==== 12.3.6.1 Mean Ocean Temperature ====

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Shifts in thermal zones affect the suitability of fisheries and marine and coastal species habitat and migration routes ( [[#Hoegh-Guldberg--2010|Hoegh-Guldberg and Bruno, 2010]] ; [[#Doney--2012|Doney et al., 2012]] ; [[#Burrows--2014|Burrows et al., 2014]] ; [[#Urban--2015|Urban, 2015]] ; [[#Hixson--2016|Hixson and Arts, 2016]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ; N. [[#Ahmed--2019|]] [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ). Intertidal species are particularly dependent on suitable conditions for both air and sea surface temperatures ( [[#Monaco--2019|Monaco and McQuaid, 2019]] ). The structure of ocean warming also affects the intensity of upper-ocean stratification and the timing and strength of coastal upwelling (driven also by mean wind changes), which alters the vertical transport of oxygen- and nutrient-rich waters affecting fishery and marine ecosystem productivity (D. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ).

<div id="12.3.6.2" class="h3-container"></div>

<span id="marine-heatwave"></span>
==== 12.3.6.2 Marine Heatwave ====

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Marine heatwaves (MHW) push water temperatures above key thresholds and have been associated with coral bleaching episodes, species shifts and harmful algal blooms that can disrupt ecosystems, tourism and human health (Box 9.2; [[#Wernberg--2016|Wernberg et al., 2016]] ; [[#Arias-Ortiz--2018|Arias-Ortiz et al., 2018]] ; [[#Oliver--2018|Oliver et al., 2018]] ; [[#Frölicher--2019|Frölicher, 2019]] ; [[#Smale--2019|Smale et al., 2019]] ; [[#Sully--2019|Sully et al., 2019]] ). The duration and return period of marine heatwaves provide insight into aggregate stresses on marine species, fisheries and ecosystems, with various indices gauging cumulative intensity or the number of days, weeks or months exceeding critical thresholds ( [[#Frieler--2013|Frieler et al., 2013]] ; [[#Frölicher--2018|Frölicher et al., 2018]] ; [[#Hughes--2018b|Hughes et al., 2018b]] ; [[#Cheung--2020|Cheung and Frölicher, 2020]] ). [[#Hobday--2016|Hobday et al. (2016)]] defined marine heatwaves as the exceedance of the 90th percentile of the sea surface temperature (SST) distribution on a given Julian day during five or more consecutive days, while Box 9.2, Figure 1 shows MHW as an exceedance of 99th-percentile 11-day de-seasonalized SSTs. The return period of marine heatwaves is also critical in determining a coral system’s ability to recover before the next event ( [[#Hughes--2018a|Hughes et al., 2018a]] ).

<div id="12.3.6.3" class="h3-container"></div>

<span id="ocean-acidity"></span>
==== 12.3.6.3 Ocean Acidity ====

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Uptake of atmospheric CO <sub>2</sub> and subsequent increases in dissolved CO <sub>2</sub> lowers ocean pH and can reduce carbonate ion concentrations below critical calcium carbonate saturation thresholds for marine and aquatic organisms growth, reproduction and/or survival, with extended implications for marine ecosystems including fisheries ( [[#Bell--2013|Bell et al., 2013]] ; [[#Kroeker--2013|Kroeker et al., 2013]] ; [[#Barange--2014|Barange et al., 2014]] ; [[#Dutkiewicz--2015|Dutkiewicz et al., 2015]] ; [[#Ekstrom--2015|Ekstrom et al., 2015]] ; [[#Gattuso--2015|Gattuso et al., 2015]] ; [[#Mathis--2015a|Mathis et al., 2015a]] ; [[#Nagelkerken--2015|Nagelkerken and Connell, 2015]] ; [[#Behrenfeld--2016|Behrenfeld et al., 2016]] ; [[#Nagelkerken--2016|Nagelkerken and Munday, 2016]] ; [[#Tripathi--2016|Tripathi et al., 2016]] ; [[#Jiang--2018|Jiang et al., 2018]] ; [[#Weiss--2018|Weiss et al., 2018]] ; N. [[#Ahmed--2019|]] [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ). Lower pH may provide more favourable conditions for toxic algal blooms ( [[#Riebesell--2018|Riebesell et al., 2018]] ) and can interact with hypoxic zones to impact ecosystems ( [[#Gobler--2016|Gobler and Baumann, 2016]] ; [[#Cai--2017|Cai et al., 2017]] ).

<div id="12.3.6.4" class="h3-container"></div>

<span id="ocean-salinity"></span>
==== 12.3.6.4 Ocean Salinity ====

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Changes in currents, sea ice brine rejection and net freshwater flux in the ocean can alter salinity with effects on mixed layer structure, density stratification and the vertical movement of nutrients and marine organisms ( [[#Freeland--2013|Freeland, 2013]] ; [[#Haumann--2016|Haumann et al., 2016]] ).

<div id="12.3.6.5" class="h3-container"></div>

<span id="dissolved-oxygen"></span>
==== 12.3.6.5 Dissolved Oxygen ====

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Ocean warming and increased stratification decrease the oxygen content of the ocean ( [[#Griffiths--2017|Griffiths et al., 2017]] ; [[#Schmidtko--2017|Schmidtko et al., 2017]] ; [[#Bindoff--2019|Bindoff et al., 2019]] ), lead to an expansion of oxygen minimum zones in the open ocean ( [[#Stramma--2012|Stramma et al., 2012]] ; [[#Zhang--2013|Zhang et al., 2013]] ) and exacerbate the creation of anoxic ‘dead zones’ in the coastal oceans ( [[#Breitburg--2018|Breitburg et al., 2018]] ). Such a decline (characterized by successive dissolved oxygen concentration thresholds) could affect a wide range of marine organisms and reduce marine habitats ( [[#Chan--2008|Chan et al., 2008]] ; [[#Vaquer-Sunyer--2008|Vaquer-Sunyer and Duarte, 2008]] ; [[#Hoegh-Guldberg--2010|Hoegh-Guldberg and Bruno, 2010]] ; [[#Altieri--2015|Altieri and Gedan, 2015]] ; [[#Breitburg--2018|Breitburg et al., 2018]] ) and can also lead to further local acidification ( [[#Zhang--2016|Zhang and Gao, 2016]] ; [[#Laurent--2017|Laurent et al., 2017]] ).

<div id="12.3.7" class="h2-container"></div>

<span id="other-climatic-impact-drivers"></span>
=== 12.3.7 Other Climatic Impact-drivers ===

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<div id="12.3.7.1" class="h3-container"></div>

<span id="air-pollution-weather"></span>
==== 12.3.7.1 Air Pollution Weather ====

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Although future air pollution will be strongly driven by air quality policies, anthropogenically-driven changes to temperature, humidity, precipitation and synoptic patterns have the potential to affect the emissions, production, concentration and transport of particulate matter (e.g., from dust, fires, pollen) and gaseous pollutants such as sulphur dioxide, tropospheric ozone and nitrogen dioxide (Section 6.5) with resulting impacts on human health, agriculture and ecosystems ( [[#Ren--2011|Ren et al., 2011]] ; [[#Fiore--2015|Fiore et al., 2015]] ; [[#Kinney--2015a|Kinney et al., 2015a]] ; [[#Tian--2016|Tian et al., 2016]] ; [[#Orru--2017|Orru et al., 2017]] ; [[#Emberson--2018|Emberson et al., 2018]] ; [[#Hayes--2020|Hayes et al., 2020]] ). Information about conditions leading to poor air quality is also important for visibility in natural parks and tourist locations ( [[#Yue--2013|Yue et al., 2013]] ; [[#Val%20Martin--2015|Val Martin et al., 2015]] ), as well as the efficiency of solar photovoltaic panels ( [[#Sweerts--2019|Sweerts et al., 2019]] ). Relevant information about conditions favouring air pollution includes tracking warmer conditions that accelerate ozone formation ( [[#Peel--2013|Peel et al., 2013]] ; [[#Schnell--2016|Schnell et al., 2016]] ) and the frequency and duration of stagnant air events ( [[#Horton--2014|Horton et al., 2014]] ; [[#Fann--2015|Fann et al., 2015]] ; [[#Lelieveld--2015|Lelieveld et al., 2015]] ; [[#Vautard--2018|Vautard et al., 2018]] ), although no regional index has proven sufficient to capture regional changes or acute events ( [[#Kerr--2018|Kerr and Waugh, 2018]] ; [[#Schnell--2018|Schnell et al., 2018]] ). By contrast, precipitation and moister air tend to reduce pollution (Section 6.5).

<div id="12.3.7.2" class="h3-container"></div>

<span id="atmospheric-carbon-dioxide-at-surface"></span>
==== 12.3.7.2 Atmospheric Carbon Dioxide at Surface ====

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Carbon dioxide (CO <sub>2</sub> ) is a well-mixed greenhouse gas with global repercussions on Earth’s energy balance; however, atmospheric CO <sub>2</sub> concentration changes at the land surface also affect plant functions within ecosystems and agriculture (see also Chapter 5). High CO <sub>2</sub> concentration can increase photosynthesis rates and primary production within natural ecosystems ( [[#Norby--2010|Norby et al., 2010]] ; [[#Ratliff--2015|Ratliff et al., 2015]] ; [[#Zhu--2016|Zhu et al., 2016]] ) and agricultural crops ( [[#Hatfield--2011|Hatfield et al., 2011]] ; [[#Leakey--2012|Leakey et al., 2012]] ; [[#Bell--2013|Bell et al., 2013]] ; [[#Glenn--2014|Glenn et al., 2014]] ; [[#Nagelkerken--2015|Nagelkerken and Connell, 2015]] ; [[#Behrenfeld--2016|Behrenfeld et al., 2016]] ; [[#Deryng--2016|Deryng et al., 2016]] ; [[#Kimball--2016|Kimball, 2016]] ). High CO <sub>2</sub> concentration affects total biomass and plant sugar content important to bioenergy production ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ), but also helps some pests and weeds flourish ( [[#Hamilton--2005|Hamilton et al., 2005]] ; [[#Wolfe--2008|Wolfe et al., 2008]] ; [[#Valerio--2013|Valerio et al., 2013]] ; [[#Korres--2016|Korres et al., 2016]] ; [[#Stinson--2016|Stinson et al., 2016]] ; [[#Ramesh--2017|Ramesh et al., 2017]] ), while potentially shifting the effectiveness of herbicides ( [[#Varanasi--2016|Varanasi et al., 2016]] ; [[#Refatti--2019|Refatti et al., 2019]] ). Higher CO <sub>2</sub> concentration reduces transpiration losses during drought conditions ( [[#Cammarano--2016|Cammarano et al., 2016]] ; [[#Deryng--2016|Deryng et al., 2016]] ; [[#Swann--2016|Swann et al., 2016]] ; [[#Durand--2018|Durand et al., 2018]] ), which also changes the energy balance within the plant canopy ( [[#Webber--2017|Webber et al., 2017]] ). Higher CO <sub>2</sub> reduces the nutritional density of crops and forage lands ( [[#Loladze--2014|Loladze, 2014]] ; [[#Müller--2014|Müller et al., 2014]] ; [[#Myers--2014|Myers et al., 2014]] , 2017; X. [[#Li--2016|]] [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Lee--2017|]] [[#Lee--2017|M.A. Lee et al., 2017]] ; [[#Smith--2018|Smith and Myers, 2018]] ; [[#Zhu--2018|Zhu et al., 2018]] ; [[#Beach--2019|Beach et al., 2019]] ) and can increase the production of toxins ( [[#Ziska--2007|Ziska et al., 2007]] ) and allergenic pollen ( [[#Schmidt--2016|Schmidt, 2016]] ).

<div id="12.3.7.3" class="h3-container"></div>

<span id="radiation-at-surface"></span>
==== 12.3.7.3 Radiation at Surface ====

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Changes in surface solar and longwave radiation fluxes alter photosynthesis rates and potential evapotranspiration for natural ecosystems and food, fibre and energy crops ( [[#Mäkinen--2018|Mäkinen et al., 2018]] ); changes in radiation fluxes can also shift solar energy resources ( [[#Schaeffer--2012|Schaeffer et al., 2012]] ; [[#Jerez--2015|Jerez et al., 2015]] ; [[#Wild--2015|Wild et al., 2015]] ; [[#Fant--2016|Fant et al., 2016]] ; [[#Craig--2018|Craig et al., 2018]] ). Plants and aquatic systems particularly respond to changes in photosynthetically active radiation (PAR) and the fraction of diffuse radiation ( [[#Proctor--2018|Proctor et al., 2018]] ; [[#Ren--2018|Ren et al., 2018]] ; [[#Ryu--2018|Ryu et al., 2018]] ). Increases in ultraviolet radiation can also detrimentally affect ecosystems and human health ( [[#Barnes--2019|Barnes et al., 2019]] ).

<div id="12.3.7.4" class="h3-container"></div>

<span id="additional-relevant-climatic-impact-drivers"></span>
==== 12.3.7.4 Additional Relevant Climatic Impact-drivers ====

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Additional CIDs may be relevant for regional studies but are not the focus of assessment in this Report. For example, information about changes in the frequency and seasonal timing of fog helps anticipate airport delays and cool beach days, and is also important for water delivery and retention in coastal ecological and agricultural systems ( [[#Torregrosa--2014|Torregrosa et al., 2014]] ).

Threats to many sectoral assets and associated systems may also be compounded when multiple hazards occur simultaneously in the same place, affect multiple regions at the same time, or occur in a sequence that may amplify overall impact ( [[IPCC:Wg1:Chapter:Chapter-11#11.8|Section 11.8]] ; [[#IPCC--2012|IPCC, 2012]] ; [[#Clarke--2018|Clarke et al., 2018]] ; [[#Zscheischler--2018|Zscheischler et al., 2018]] ; [[#Raymond--2020|Raymond et al., 2020]] ). There is emerging literature on many connected extremes and their associated hazards (e.g., climatic conditions that could drive multi-breadbasket failures; [[#Trnka--2019|Trnka et al., 2019]] ; [[#Kornhuber--2020|Kornhuber et al., 2020]] ), but a full accounting is not practical here especially considering the many possible CID combinations and the need to assess how exposed systems would be vulnerable to compound CIDs (assessed in WGII). Table 12.2 is once again instructive here in considering hazard-related storylines, as the multiple CIDs affecting a given sectoral asset (assessing across a row of Table 12.2) point to potentially dangerous hazard combinations. Similarly, change in a single CID has the potential to affect multiple sectoral assets (assessing down a column of Table 12.2) in a manner with broader systemic implications (AR6 WGII).

'''Recent literature defines CID indices to represent trends and thresholds that influence sectoral assets, albeit with considerable variation owing to the unique characteristics of regional and sectoral assets. Indices include direct information about the CID’s profile (magnitude, frequency, duration, timing, spatial extent) or utilize atmospheric conditions as a proxy for CIDs that are more difficult to directly observe or simulate. Each sector is affected by multiple CIDs, and each CID affects multiple sectors. Assets within the same sector may require different or tailored indices even for the same CID. These indices may be defined to capture graduated thresholds associated with tipping points or inflection points in a particular sectoral vulnerability, with commonalities in the types of processes these thresholds represent even as their precise magnitude may vary by specific sectoral system and asset.'''

<div id="12.4" class="h1-container"></div>

<span id="regional-information-on-changing-climate"></span>