IPCC:AR6/SRCCL/SPM - Revision history

Laura: /* C Enabling response options */

2026-05-30T08:44:24Z

C Enabling response options

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	[[File:9db64fdb8276bc0a2fbcf5a06099f7e5 SPM4B-approval-v4-USletter-791x1024.jpg]]		[[File:9db64fdb8276bc0a2fbcf5a06099f7e5 SPM4B-approval-v4-USletter-791x1024.jpg]]

	Future scenarios provide a framework for understanding the implications of mitigation and socioeconomics on land. The Shared Socioeconomic Pathways (SSPs) span a range of different socioeconomic assumptions (Box SPM.1). They are combined with Representative Concentration Pathways (RCPs)<sup>[[#fn:36\|36]]</sup> which imply different levels of mitigation. The changes in cropland, pasture, bioenergy cropland, forest, and natural land from 2010 are shown. For this Figure, Cropland includes all land in food, feed, and fodder crops, as well as other arable land (cultivated area). This category includes first generation non-forest bioenergy crops (e.g., corn for ethanol, sugar cane for ethanol, soybeans for biodiesel), but excludes second generation bioenergy crops. Pasture includes categories of pasture land, not only high-quality rangeland, and is based on FAO definition of ‘permanent meadows and pastures’. Bioenergy cropland includes land dedicated to second generation energy crops (e.g., switchgrass, miscanthus, fast-growing wood species). Forest includes managed and unmanaged forest. Natural land includes other grassland, savannah, and shrubland. '''Panel A''' : This panel shows integrated assessment model (IAM)<sup>[[#fn:37\|37]]</sup> results for SSP1, SSP2 and SSP5 at RCP1.9.<sup>[[#fn:38\|38]]</sup> For each pathway, the shaded areas show the range across all IAMs; the line indicates the median across models. For RCP1.9, SSP1, SSP2 and SSP5 results are from five, four and two IAMs respectively. '''Panel B''' : Land use and land cover change are indicated for various SSP-RCP combinations, showing multi-model median and range (min, max). (Box SPM.1) {1.3.2, 2.7.2, 6.1, 6.4.4, 7.4.2, 7.4.4, 7.4.5, 7.4.6, 7.4.7, 7.4.8, 7.5.3, 7.5.6, Cross-Chapter Box 1 in Chapter 1, Cross-Chapter Box 9 in Chapter 6}		Future scenarios provide a framework for understanding the implications of mitigation and socioeconomics on land. The Shared Socioeconomic Pathways (SSPs) span a range of different socioeconomic assumptions (Box SPM.1). They are combined with Representative Concentration Pathways (RCPs)<sup>[[#fn:36\|36]]</sup> which imply different levels of mitigation. The changes in cropland, pasture, bioenergy cropland, forest, and natural land from 2010 are shown. For this Figure, Cropland includes all land in food, feed, and fodder crops, as well as other arable land (cultivated area). This category includes first generation non-forest bioenergy crops (e.g., corn for ethanol, sugar cane for ethanol, soybeans for biodiesel), but excludes second generation bioenergy crops. Pasture includes categories of pasture land, not only high-quality rangeland, and is based on FAO definition of ‘permanent meadows and pastures’. Bioenergy cropland includes land dedicated to second generation energy crops (e.g., switchgrass, miscanthus, fast-growing wood species). Forest includes managed and unmanaged forest. Natural land includes other grassland, savannah, and shrubland. '''Panel A''': This panel shows integrated assessment model (IAM)<sup>[[#fn:37\|37]]</sup> results for SSP1, SSP2 and SSP5 at RCP1.9.<sup>[[#fn:38\|38]]</sup> For each pathway, the shaded areas show the range across all IAMs; the line indicates the median across models. For RCP1.9, SSP1, SSP2 and SSP5 results are from five, four and two IAMs respectively. '''Panel B''': Land use and land cover change are indicated for various SSP-RCP combinations, showing multi-model median and range (min, max). (Box SPM.1) {1.3.2, 2.7.2, 6.1, 6.4.4, 7.4.2, 7.4.4, 7.4.5, 7.4.6, 7.4.7, 7.4.8, 7.5.3, 7.5.6, Cross-Chapter Box 1 in Chapter 1, Cross-Chapter Box 9 in Chapter 6}

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	<span id="d-action-in-the-near-term"></span>		<span id="d-action-in-the-near-term"></span>

	== D Action in the near-term ==		== D Action in the near-term ==

Laura: /* Box SPM.1 Shared Socio-economic Pathways (SSPs) */

2026-05-30T08:43:19Z

Box SPM.1 Shared Socio-economic Pathways (SSPs)

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	* SSP5 includes a peak and decline in population (~7 billion in 2100), high income, reduced inequalities, and free trade. This pathway includes resource-intensive production, consumption and lifestyles. Relative to other pathways, SSP5 has high challenges to mitigation, but low challenges to adaptation (i.e., high adaptive capacity).		* SSP5 includes a peak and decline in population (~7 billion in 2100), high income, reduced inequalities, and free trade. This pathway includes resource-intensive production, consumption and lifestyles. Relative to other pathways, SSP5 has high challenges to mitigation, but low challenges to adaptation (i.e., high adaptive capacity).
	* The SSPs can be combined with Representative Concentration Pathways (RCPs) which imply different levels of mitigation, with implications for adaptation. Therefore, SSPs can be consistent with different levels of global mean surface temperature rise as projected by different SSP-RCP combinations. However, some SSP-RCP combinations are not possible; for instance RCP2.6 and lower levels of future global mean surface temperature rise (e.g., 1.5ºC) are not possible in SSP3 in modelled pathways. {1.2.2, 6.1.4, Cross-Chapter Box 1 in Chapter 1, Cross-Chapter Box 9 in Chapter 6}		* The SSPs can be combined with Representative Concentration Pathways (RCPs) which imply different levels of mitigation, with implications for adaptation. Therefore, SSPs can be consistent with different levels of global mean surface temperature rise as projected by different SSP-RCP combinations. However, some SSP-RCP combinations are not possible; for instance RCP2.6 and lower levels of future global mean surface temperature rise (e.g., 1.5ºC) are not possible in SSP3 in modelled pathways. {1.2.2, 6.1.4, Cross-Chapter Box 1 in Chapter 1, Cross-Chapter Box 9 in Chapter 6}
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	<div id="article-800-2-block-3"></div>		<div id="article-800-2-block-3"></div>

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	<span id="b-adaptation-and-mitigation-response-options"></span>		<span id="b-adaptation-and-mitigation-response-options"></span>

	== B Adaptation and mitigation response options ==		== B Adaptation and mitigation response options ==

Laura: /* A People, land and climate in a warming world */

2026-05-30T08:42:48Z

A People, land and climate in a warming world

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	Total net GHG emissions from AFOLU emissions represent 12.0 ± 2.9 GtCO <sub>2</sub> eq yr <sup>-1</sup> during 2007–2016. This represents 23% of total net anthropogenic emissions {Table SPM.1}. <sup>[[#fn:24\|24]]</sup> Other approaches, such as global food system, include agricultural emissions and land use change (i.e., deforestation and peatland degradation), as well as outside farm gate emissions from energy, transport and industry sectors for food production. Emissions within farm gate and from agricultural land expansion contributing to the global food system represent 16–27% of total anthropogenic emissions (''medium confidence''). Emissions outside the farm gate represent 5–10% of total anthropogenic emissions (''medium confidence''). Given the diversity of food systems, there are large regional differences in the contributions from different components of the food system (''very high confidence''). Emissions from agricultural production are projected to increase (''high confidence''), driven by population and income growth and changes in consumption patterns (''medium confidence''). {5.5, Table 5.4}		Total net GHG emissions from AFOLU emissions represent 12.0 ± 2.9 GtCO <sub>2</sub> eq yr <sup>-1</sup> during 2007–2016. This represents 23% of total net anthropogenic emissions {Table SPM.1}. <sup>[[#fn:24\|24]]</sup> Other approaches, such as global food system, include agricultural emissions and land use change (i.e., deforestation and peatland degradation), as well as outside farm gate emissions from energy, transport and industry sectors for food production. Emissions within farm gate and from agricultural land expansion contributing to the global food system represent 16–27% of total anthropogenic emissions (''medium confidence''). Emissions outside the farm gate represent 5–10% of total anthropogenic emissions (''medium confidence''). Given the diversity of food systems, there are large regional differences in the contributions from different components of the food system (''very high confidence''). Emissions from agricultural production are projected to increase (''high confidence''), driven by population and income growth and changes in consumption patterns (''medium confidence''). {5.5, Table 5.4}

	'''A.4 '''<br />		'''A.4'''<br />
	'''Changes in land conditions,<sup>[[#fn:25\|25]]</sup> either from land-use or climate change, affect global and regional climate (''high confidence''). At the regional scale, changing land conditions can reduce or accentuate warming and affect the intensity, frequency and duration of extreme events. The magnitude and direction of these changes vary with location and season (''high confidence''). {Executive Summary Chapter 2, 2.3, 2.4, 2.5, 3.3}'''		'''Changes in land conditions,<sup>[[#fn:25\|25]]</sup> either from land-use or climate change, affect global and regional climate (''high confidence''). At the regional scale, changing land conditions can reduce or accentuate warming and affect the intensity, frequency and duration of extreme events. The magnitude and direction of these changes vary with location and season (''high confidence''). {Executive Summary Chapter 2, 2.3, 2.4, 2.5, 3.3}'''

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	Changes in forest cover, for example from afforestation, reforestation and deforestation, directly affect regional surface temperature through exchanges of water and energy (''high confidence''). <sup>[[#fn:27\|27]]</sup> Where forest cover increases in tropical regions cooling results from enhanced evapotranspiration (''high confidence''). Increased evapotranspiration can result in cooler days during the growing season (''high confidence'') and can reduce the amplitude of heat related events (''medium confidence''). In regions with seasonal snow cover, such as boreal and some temperate regions, increased tree and shrub cover also has a wintertime warming influence due to reduced surface albedo (''high confidence''). <sup>[[#fn:28\|28]]</sup> {2.3, 2.4.3, 2.5.1, 2.5.2, 2.5.4}		Changes in forest cover, for example from afforestation, reforestation and deforestation, directly affect regional surface temperature through exchanges of water and energy (''high confidence''). <sup>[[#fn:27\|27]]</sup> Where forest cover increases in tropical regions cooling results from enhanced evapotranspiration (''high confidence''). Increased evapotranspiration can result in cooler days during the growing season (''high confidence'') and can reduce the amplitude of heat related events (''medium confidence''). In regions with seasonal snow cover, such as boreal and some temperate regions, increased tree and shrub cover also has a wintertime warming influence due to reduced surface albedo (''high confidence''). <sup>[[#fn:28\|28]]</sup> {2.3, 2.4.3, 2.5.1, 2.5.2, 2.5.4}

	A.4.6		A.4.6<br />

	Both global warming and urbanisation can enhance warming in cities and their surroundings (heat island effect), especially during heat related events, including heat waves (''high confidence''). Night-time temperatures are more affected by this effect than daytime temperatures (''high confidence''). Increased urbanisation can also intensify extreme rainfall events over the city or downwind of urban areas (''medium confidence''). {2.5.1, 2.5.2, 2.5.3, 4.9.1, Cross-Chapter Box 4 in Chapter 2}		Both global warming and urbanisation can enhance warming in cities and their surroundings (heat island effect), especially during heat related events, including heat waves (''high confidence''). Night-time temperatures are more affected by this effect than daytime temperatures (''high confidence''). Increased urbanisation can also intensify extreme rainfall events over the city or downwind of urban areas (''medium confidence''). {2.5.1, 2.5.2, 2.5.3, 4.9.1, Cross-Chapter Box 4 in Chapter 2}

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	<span id="box-spm.1-shared-socio-economic-pathways-ssps"></span>		<span id="box-spm.1-shared-socio-economic-pathways-ssps"></span>
			<div class="container-box col-regular">

	== Box SPM.1 Shared Socio-economic Pathways (SSPs) ==		== Box SPM.1 Shared Socio-economic Pathways (SSPs) ==

Laura: /* A People, land and climate in a warming world */

2026-05-30T08:40:55Z

A People, land and climate in a warming world

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	[[File:720509405e4f4330a7f9b5e0bdf605a3 SPM1-approval-v7-USletter-791x1024.png]]		[[File:720509405e4f4330a7f9b5e0bdf605a3 SPM1-approval-v7-USletter-791x1024.png]]

	A representation of the land use and observed climate change covered in this assessment report. Panels A-F show the status and trends in selected land use and climate variables that represent many of the core topics covered in this report. The annual time series in B and D-F are based on the most comprehensive, available data from national statistics, in most cases from FAOSTAT which starts in 1961. Y-axes in panels D-F are expressed relative to the starting year of the time series (rebased to zero). Data sources and notes: '''A''': The warming curves are averages of four datasets {2.1, Figure 2.2, Table 2.1} '''B''': N <sub>2</sub> O and CH <sub>4</sub> from agriculture are from FAOSTAT; Net CO <sub>2</sub> emissions from FOLU using the mean of two bookkeeping models (including emissions from peatland fires since 1997). All values expressed in units of CO <sub>2</sub> -eq are based on AR5 100-year Global Warming Potential values without climate-carbon feedbacks (N <sub>2</sub> O=265; CH <sub>4</sub> =28). (Table SPM.1) {1.1, 2.3} '''C''': Depicts shares of different uses of the global, ice-free land area for approximately the year 2015, ordered along a gradient of decreasing land-use intensity from left to right. Each bar represents a broad land cover category; the numbers on top are the total percentage of the ice-free area covered, with uncertainty ranges in brackets. Intensive pasture is defined as having a livestock density greater than 100 animals/km². The area of ‘forest managed for timber and other uses’ was calculated as total forest area minus ‘primary/intact’ forest area. {1.2, Table 1.1, Figure 1.3} '''D''': Note that fertiliser use is shown on a split axis. The large percentage change in fertiliser use reflects the low level of use in 1961 and relates to both increasing fertiliser input per area as well as the expansion of fertilised cropland and grassland to increase food production. {1.1, Figure 1.3} '''E''': Overweight population is defined as having a body mass index (BMI) > 25 kg m <sup>-2</sup>; underweight is defined as BMI < 18.5 kg m <sup>-2</sup>. {5.1, 5.2} '''F''' : Dryland areas were estimated using TerraClimate precipitation and potential evapotranspiration (1980-2015) to identify areas where the Aridity Index is below 0.65. Population data are from the HYDE3.2 database. Areas in drought are based on the 12-month accumulation Global Precipitation Climatology Centre Drought Index. The inland wetland extent (including peatlands) is based on aggregated data from more than 2000 time series that report changes in local wetland area over time. {3.1, 4.2, 4.6}		A representation of the land use and observed climate change covered in this assessment report. Panels A-F show the status and trends in selected land use and climate variables that represent many of the core topics covered in this report. The annual time series in B and D-F are based on the most comprehensive, available data from national statistics, in most cases from FAOSTAT which starts in 1961. Y-axes in panels D-F are expressed relative to the starting year of the time series (rebased to zero). Data sources and notes: '''A''': The warming curves are averages of four datasets {2.1, Figure 2.2, Table 2.1} '''B''': N <sub>2</sub> O and CH <sub>4</sub> from agriculture are from FAOSTAT; Net CO <sub>2</sub> emissions from FOLU using the mean of two bookkeeping models (including emissions from peatland fires since 1997). All values expressed in units of CO <sub>2</sub> -eq are based on AR5 100-year Global Warming Potential values without climate-carbon feedbacks (N <sub>2</sub> O=265; CH <sub>4</sub> =28). (Table SPM.1) {1.1, 2.3} '''C''': Depicts shares of different uses of the global, ice-free land area for approximately the year 2015, ordered along a gradient of decreasing land-use intensity from left to right. Each bar represents a broad land cover category; the numbers on top are the total percentage of the ice-free area covered, with uncertainty ranges in brackets. Intensive pasture is defined as having a livestock density greater than 100 animals/km². The area of ‘forest managed for timber and other uses’ was calculated as total forest area minus ‘primary/intact’ forest area. {1.2, Table 1.1, Figure 1.3} '''D''': Note that fertiliser use is shown on a split axis. The large percentage change in fertiliser use reflects the low level of use in 1961 and relates to both increasing fertiliser input per area as well as the expansion of fertilised cropland and grassland to increase food production. {1.1, Figure 1.3} '''E''': Overweight population is defined as having a body mass index (BMI) > 25 kg m <sup>-2</sup>; underweight is defined as BMI < 18.5 kg m <sup>-2</sup>. {5.1, 5.2} '''F''': Dryland areas were estimated using TerraClimate precipitation and potential evapotranspiration (1980-2015) to identify areas where the Aridity Index is below 0.65. Population data are from the HYDE3.2 database. Areas in drought are based on the 12-month accumulation Global Precipitation Climatology Centre Drought Index. The inland wetland extent (including peatlands) is based on aggregated data from more than 2000 time series that report changes in local wetland area over time. {3.1, 4.2, 4.6}

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Laura: /* A People, land and climate in a warming world */

2026-05-30T08:40:35Z

A People, land and climate in a warming world

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	[[File:720509405e4f4330a7f9b5e0bdf605a3 SPM1-approval-v7-USletter-791x1024.png]]		[[File:720509405e4f4330a7f9b5e0bdf605a3 SPM1-approval-v7-USletter-791x1024.png]]

	A representation of the land use and observed climate change covered in this assessment report. Panels A-F show the status and trends in selected land use and climate variables that represent many of the core topics covered in this report. The annual time series in B and D-F are based on the most comprehensive, available data from national statistics, in most cases from FAOSTAT which starts in 1961. Y-axes in panels D-F are expressed relative to the starting year of the time series (rebased to zero). Data sources and notes: '''A''' : The warming curves are averages of four datasets {2.1, Figure 2.2, Table 2.1} '''B''' : N <sub>2</sub> O and CH <sub>4</sub> from agriculture are from FAOSTAT; Net CO <sub>2</sub> emissions from FOLU using the mean of two bookkeeping models (including emissions from peatland fires since 1997). All values expressed in units of CO <sub>2</sub> -eq are based on AR5 100-year Global Warming Potential values without climate-carbon feedbacks (N <sub>2</sub> O=265; CH <sub>4</sub> =28). (Table SPM.1) {1.1, 2.3} '''C''' : Depicts shares of different uses of the global, ice-free land area for approximately the year 2015, ordered along a gradient of decreasing land-use intensity from left to right. Each bar represents a broad land cover category; the numbers on top are the total percentage of the ice-free area covered, with uncertainty ranges in brackets. Intensive pasture is defined as having a livestock density greater than 100 animals/km². The area of ‘forest managed for timber and other uses’ was calculated as total forest area minus ‘primary/intact’ forest area. {1.2, Table 1.1, Figure 1.3} '''D''' : Note that fertiliser use is shown on a split axis. The large percentage change in fertiliser use reflects the low level of use in 1961 and relates to both increasing fertiliser input per area as well as the expansion of fertilised cropland and grassland to increase food production. {1.1, Figure 1.3} '''E''' : Overweight population is defined as having a body mass index (BMI) > 25 kg m <sup>-2</sup>; underweight is defined as BMI < 18.5 kg m <sup>-2</sup>. {5.1, 5.2} '''F''' : Dryland areas were estimated using TerraClimate precipitation and potential evapotranspiration (1980-2015) to identify areas where the Aridity Index is below 0.65. Population data are from the HYDE3.2 database. Areas in drought are based on the 12-month accumulation Global Precipitation Climatology Centre Drought Index. The inland wetland extent (including peatlands) is based on aggregated data from more than 2000 time series that report changes in local wetland area over time. {3.1, 4.2, 4.6}		A representation of the land use and observed climate change covered in this assessment report. Panels A-F show the status and trends in selected land use and climate variables that represent many of the core topics covered in this report. The annual time series in B and D-F are based on the most comprehensive, available data from national statistics, in most cases from FAOSTAT which starts in 1961. Y-axes in panels D-F are expressed relative to the starting year of the time series (rebased to zero). Data sources and notes: '''A''': The warming curves are averages of four datasets {2.1, Figure 2.2, Table 2.1} '''B''': N <sub>2</sub> O and CH <sub>4</sub> from agriculture are from FAOSTAT; Net CO <sub>2</sub> emissions from FOLU using the mean of two bookkeeping models (including emissions from peatland fires since 1997). All values expressed in units of CO <sub>2</sub> -eq are based on AR5 100-year Global Warming Potential values without climate-carbon feedbacks (N <sub>2</sub> O=265; CH <sub>4</sub> =28). (Table SPM.1) {1.1, 2.3} '''C''': Depicts shares of different uses of the global, ice-free land area for approximately the year 2015, ordered along a gradient of decreasing land-use intensity from left to right. Each bar represents a broad land cover category; the numbers on top are the total percentage of the ice-free area covered, with uncertainty ranges in brackets. Intensive pasture is defined as having a livestock density greater than 100 animals/km². The area of ‘forest managed for timber and other uses’ was calculated as total forest area minus ‘primary/intact’ forest area. {1.2, Table 1.1, Figure 1.3} '''D''': Note that fertiliser use is shown on a split axis. The large percentage change in fertiliser use reflects the low level of use in 1961 and relates to both increasing fertiliser input per area as well as the expansion of fertilised cropland and grassland to increase food production. {1.1, Figure 1.3} '''E''': Overweight population is defined as having a body mass index (BMI) > 25 kg m <sup>-2</sup>; underweight is defined as BMI < 18.5 kg m <sup>-2</sup>. {5.1, 5.2} '''F''' : Dryland areas were estimated using TerraClimate precipitation and potential evapotranspiration (1980-2015) to identify areas where the Aridity Index is below 0.65. Population data are from the HYDE3.2 database. Areas in drought are based on the 12-month accumulation Global Precipitation Climatology Centre Drought Index. The inland wetland extent (including peatlands) is based on aggregated data from more than 2000 time series that report changes in local wetland area over time. {3.1, 4.2, 4.6}

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Laura: /* A People, land and climate in a warming world */

2026-05-30T08:40:06Z

A People, land and climate in a warming world

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	A.2.8<br />		A.2.8<br />
	Climate change has already affected food security due to warming, changing precipitation patterns, and greater frequency of some extreme events (''high confidence''). Studies that separate out climate change from other factors affecting crop yields have shown that yields of some crops (e.g., maize and wheat) in many lower-latitude regions have been affected negatively by observed climate changes, while in many higher-latitude regions, yields of some crops (e.g., maize, wheat, and sugar beets) have been affected positively over recent decades (''high confidence''). Climate change has resulted in lower animal growth rates and productivity in pastoral systems in Africa (''high confidence''). There is robust evidence that agricultural pests and diseases have already responded to climate change resulting in both increases and decreases of infestations (''high confidence''). Based on indigenous and local knowledge, climate change is affecting food security in drylands, particularly those in Africa, and high mountain regions of Asia and South America. <sup>[[#fn:20\|20]]</sup> {5.2.1, 5.2.2, 7.2.2}		Climate change has already affected food security due to warming, changing precipitation patterns, and greater frequency of some extreme events (''high confidence''). Studies that separate out climate change from other factors affecting crop yields have shown that yields of some crops (e.g., maize and wheat) in many lower-latitude regions have been affected negatively by observed climate changes, while in many higher-latitude regions, yields of some crops (e.g., maize, wheat, and sugar beets) have been affected positively over recent decades (''high confidence''). Climate change has resulted in lower animal growth rates and productivity in pastoral systems in Africa (''high confidence''). There is robust evidence that agricultural pests and diseases have already responded to climate change resulting in both increases and decreases of infestations (''high confidence''). Based on indigenous and local knowledge, climate change is affecting food security in drylands, particularly those in Africa, and high mountain regions of Asia and South America. <sup>[[#fn:20\|20]]</sup> {5.2.1, 5.2.2, 7.2.2}


	'''A.3'''<br />		'''A.3'''<br />

Laura: /* A People, land and climate in a warming world */

2026-05-30T08:39:48Z

A People, land and climate in a warming world

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	<div id="article-800-2-block-3"></div>		<div id="article-800-2-block-3"></div>

	'''A.2'''		'''A.2'''<br />

	'''Since the pre-industrial period, the land surface air temperature has risen nearly twice as much as the global average temperature (''high confidence''). Climate change, including increases in frequency and intensity of extremes, has adversely impacted food security and terrestrial ecosystems as well as contributed to desertification and land degradation in many regions (''high confidence''). {2.2, 3.2, 4.2, 4.3, 4.4, 5.1, 5.2, Executive Summary Chapter 7, 7.2}'''		'''Since the pre-industrial period, the land surface air temperature has risen nearly twice as much as the global average temperature (''high confidence''). Climate change, including increases in frequency and intensity of extremes, has adversely impacted food security and terrestrial ecosystems as well as contributed to desertification and land degradation in many regions (''high confidence''). {2.2, 3.2, 4.2, 4.3, 4.4, 5.1, 5.2, Executive Summary Chapter 7, 7.2}'''

Laura at 08:39, 30 May 2026

2026-05-30T08:39:26Z

Show changes

Laura: /* A People, land and climate in a warming world */

2026-05-30T08:38:44Z

A People, land and climate in a warming world

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	<div id="article-800-2-block-3"></div>		<div id="article-800-2-block-3"></div>

	'''A.2'''		'''A.2'''<br />
	'''Since the pre-industrial period, the land surface air temperature has risen nearly twice as much as the global average temperature (''high confidence''). Climate change, including increases in frequency and intensity of extremes, has adversely impacted food security and terrestrial ecosystems as well as contributed to desertification and land degradation in many regions (''high confidence''). {2.2, 3.2, 4.2, 4.3, 4.4, 5.1, 5.2, Executive Summary Chapter 7, 7.2}'''		'''Since the pre-industrial period, the land surface air temperature has risen nearly twice as much as the global average temperature (''high confidence''). Climate change, including increases in frequency and intensity of extremes, has adversely impacted food security and terrestrial ecosystems as well as contributed to desertification and land degradation in many regions (''high confidence''). {2.2, 3.2, 4.2, 4.3, 4.4, 5.1, 5.2, Executive Summary Chapter 7, 7.2}'''

Laura: /* A People, land and climate in a warming world */

2026-05-30T08:38:05Z

A People, land and climate in a warming world

← Older revision		Revision as of 08:38, 30 May 2026
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	'''A.2'''		'''A.2'''

	'''Since the pre-industrial period, the land surface air temperature has risen nearly twice as much as the global average temperature (''high confidence''). Climate change, including increases in frequency and intensity of extremes, has adversely impacted food security and terrestrial ecosystems as well as contributed to desertification and land degradation in many regions (''high confidence''). {2.2, 3.2, 4.2, 4.3, 4.4, 5.1, 5.2, Executive Summary Chapter 7, 7.2}'''		'''Since the pre-industrial period, the land surface air temperature has risen nearly twice as much as the global average temperature (''high confidence''). Climate change, including increases in frequency and intensity of extremes, has adversely impacted food security and terrestrial ecosystems as well as contributed to desertification and land degradation in many regions (''high confidence''). {2.2, 3.2, 4.2, 4.3, 4.4, 5.1, 5.2, Executive Summary Chapter 7, 7.2}'''