Land–Climate interactions

Energy is redistributed from the warm equator to the colder poles through large-scale atmospheric and oceanic processes driving the Earth’s weather and climate (Oort and Peixóto 1983²⁶; Carissimo et al. 1985²⁷; Yang et al. 2015a²⁸). Subsequently, a number of global climate zones have been classified ranging from large-scale primary climate zones (tropical, sub-tropical, temperate, sub-polar, polar) to much higher-resolution, regional climate zones (e.g., the Köppen-Geiger classification, Kottek et al. 2006²⁹). Biomes are adapted to regional climates (Figure 2.1) and may shift as climate, land surface characteristics (e.g., geomorphology, hydrology), CO₂ fertilisation and fire interact. These biomes and the processes therein are subject to modes of natural variability in the ocean-atmosphere system that result in regionally wetter/dryer or hotter/cooler periods having temporal scales from weeks to months (e.g., Southern Annular Mode), months to seasons (e.g., Madden-Julian Oscillation), years (e.g., El Niño Southern Oscillation) and decades (e.g., Pacific Decadal Oscillation). Furthermore, climate and weather extremes (such as drought, heatwaves, very heavy rainfall, strong winds), whose frequency, intensity and duration are often a function of large-scale modes of variability, impact ecosystems at various space and timescales.

It is very likely that changes to natural climate variability as a result of global warming has and will continue to impact terrestrial ecosystems with subsequent impacts on land processes (Hulme et al. 1999³⁰; Parmesan and Yohe 2003³¹; Di Lorenzo et al. 2008³²; Kløve et al. 2014³³; Berg et al. 2015³⁴; Lemordant et al. 2016³⁵; Pecl et al. 2017³⁶). This chapter assesses climate variability and change, particularly extreme weather and climate, in the context of desertification, land degradation, food security and terrestrial ecosystems more generally. This section does specifically assess the impacts of climate variability and climate change on desertification, land degradation and food security as these impacts are assessed respectively in Chapters 3, 4 and 5. This chapter begins with an assessment of observed warming on land.

Based on analysis of several global and regional land surface air temperature (LSAT) datasets, AR5 concluded that the global LSAT had increased over the instrumental period of record, with the warming rate approximately double that reported over the oceans since 1979 and that ‘it is certain that globally averaged LSAT has risen since the late 19th century and that this warming has been particularly marked since the 1970s’. Warming found in the global land datasets is also in a broad agreement with station observations (Hartmann et al. 2013a³⁷).

Since AR5, LSAT datasets have been improved and extended. The National Center for Environmental Information, which is a part of the US National Oceanic and Atmospheric Administration (NOAA), developed a new, fourth version of the Global Historical Climatology Network monthly dataset (GHCNm, v4). The dataset provides an expanded set of station temperature records with more than 25,000 total monthly temperature stations compared to 7200 in versions v2 and v3 (Menne et al. 2018³⁸). Goddard Institute for Space Studies, which is a part of the US National Aeronautics and Space Administration, (NASA/ GISS), provides estimate of land and ocean temperature anomalies (GISTEMP). The GISTEMP land temperature anomalies are based upon primarily NOAA/GHCN version 3 dataset (Lawrimore et al. 2011³⁹) and account for urban effects through nightlight adjustments (Hansen et al. 2010⁴⁰). The Climatic Research Unit (CRU) of the University of East Anglia, UK (CRUTEM) dataset, now version CRUTEM4.6,

According to the available observations in the four datasets, the globally averaged LSAT increased by 1.44°C from the preindustrial period (1850–1900) to the present day (1999–2018).The warming from the late 19th century (1881–1900) to the present day (1999–2018) was 1.41°C (1.31°C–1.51°C) (Table 2.1). The 1.31°C–1.51°C range represents the spread in median estimates from the four available land datasets and does not reflect uncertainty in data coverage or methods used. Based on the BEST dataset (the longest dataset with the most extensive land coverage) the total observed increase in LSAT between the average of the 1850–1900 period and the 2006– 2015 period was 1.53°C, (1.38–1.68°C; 95% confidence), while the GMST increase for the same period was 0.87°C (0.75–0.99°C; 90% confidence) (IPCC, 2018: Summary for policymakers, Allen et al. 2018⁴³).

The extended and improved land datasets reaffirmed the AR5 conclusion that it is certain that globally averaged LSAT has risen since the preindustrial period and that this warming has been particularly marked since the 1970s (Figure 2.2).

There is high confidence that heat extremes such as unusually hot nights, extremely high daytime temperatures, heatwaves and drought are damaging to crop production (Chapter 5). Extreme heat events impact a wide variety of tree functions including reduced photosynthesis, increased photooxidative stress, leaves abscise, a decreased growth rate of remaining leaves and decreased growth of the whole tree (Teskey et al. 2015²⁸¹). Although trees are more resilient to heat stress than grasslands (Teuling et al. 2010²⁸²), it has been observed that different types of forest (e.g., needleleaf vs broadleaf) respond differently to drought and heatwaves (Babst et al. 2012²⁸³). For example, in the Turkish Anatolian forests net primary productivity (NPP) generally decreased during drought and heatwave events between 2000 and 2010 but in a few other regions, NPP of needle leaf forests increased (Erşahin et al. 2016²⁸⁴). However, forests may become less resilient to heat stress in future due to the long recovery period required to replace lost biomass and the projected increased frequency of heat and drought events (Frank et al. 2015a²⁸⁵; McDowell and Allen 2015²⁸⁶; Johnstone et al. 2016²⁸⁷; Stevens-Rumann et al. 2018²⁸⁸). Additionally, widespread regional tree mortality may be triggered directly by drought and heat stress (including warm winters) and exacerbated by insect outbreak and fire (Neuvonen et al. 1999²⁸⁹; Breshears et al. 2005²⁹⁰; Berg et al. 2006²⁹¹; Soja et al. 2007²⁹²; Kurz et al. 2008²⁹³; Allen et al. 2010²⁹⁴).

A large number of extreme rainfall events have been documented over the past decades (Coumou and Rahmstorf 2012³²⁹; Seneviratne et al. 2012³³⁰; Trenberth 2012³³¹; Westra et al. 2013³³²; Espinoza et al. 2014³³³; Guhathakurta et al. 2017³³⁴; Taylor et al. 2017³³⁵; Thompson et al. 2017³³⁶; Zilli et al. 2017³³⁷). The observed shift in the trend distribution of precipitation extremes is more distinct than for annual mean precipitation and the global land fraction experiencing more intense precipitation events is larger than expected from internal variability (Fischer and Knutti 2014³³⁸; Espinoza et al. 2018³³⁹; Fischer et al. 2013³⁴⁰). As a result of global warming, the number of record-breaking rainfall events globally has increased significantly by 12% during the period 1981–2010 compared to those expected due to natural multi-decadal climate variability (Lehmann et al. 2015³⁴¹). The IPCC SR15 reports robust increases in observed precipitation extremes for annual maximum 1-day precipitation (RX1day) and consecutive 5-day precipitation (RX5day) (Hoegh-Guldberg et al. 2018³⁴²; Schleussner et al. 2017³⁴³). A number of extreme rainfall events have been attributed to human influence (Min et al. 2011³⁴⁴; Pall et al. 2011³⁴⁵; Sippel and Otto 2014³⁴⁶; Trenberth et al. 2015³⁴⁷; Krishnan et al. 2016³⁴⁸) and the largest fraction of anthropogenic influence is evident in the most rare and extreme events (Fischer and Knutti 2014³⁴⁹).

More intense rainfall leads to water redistribution between surface and ground water in catchments as water storage in the soil decreases (green water) and runoff and reservoir inflow increases (blue water) (Liu and Yang 2010³⁸⁴; Eekhout et al. 2018³⁸⁵). This results in increased surface flooding and soil erosion, increased plant water stress and reduced water security, which in terms of agriculture means an increased dependency on irrigation and reservoir storage (Nainggolan et al. 2012³⁸⁶; Favis-Mortlock and Mullen 2011³⁸⁷; García- Ruiz et al. 2011³⁸⁸; Li and Fang 2016³⁸⁹; Chagas and Chaffe 2018³⁹⁰). As there is high confidence of a positive correlation between global warming and future flood risk, land cover and processes are likely to be negatively impacted, particularly near rivers and in floodplains (Kundzewicz et al. 2014³⁹¹; Alfieri et al. 2016³⁹²; Winsemius et al. 2016³⁹³; Arnell and Gosling 2016³⁹⁴; Alfieri et al. 2017³⁹⁵; Wobus et al. 2017³⁹⁶).

In agricultural systems, heavy precipitation and inundation can delay planting, increase soil compaction and cause crop losses through anoxia and root diseases (Posthumus et al. 2009³⁹⁷). In tropical regions, flooding associated with tropical cyclones can lead to crop failure from both rainfall and storm surge. In some cases, flooding can affect yield more than drought, particularly in tropical regions (e.g., India) and in some mid/high latitude regions such as China and central and northern Europe (Zampieri et al. 2017³⁹⁸). Waterlogging of croplands and soil erosion also negatively affect farm operations and block important transport routes (Vogel and Meyer 2018³⁹⁹; Kundzewicz and Germany 2012⁴⁰⁰). Flooding can be beneficial in drylands if the floodwaters infiltrate and recharge alluvial aquifers along ephemeral river pathways, extending water availability into dry seasons and drought years, and supporting riparian systems and human communities (Kundzewicz and Germany 2012; Guan et al. 2015⁴⁰¹). Globally, the impact of rainfall extremes on agriculture is less than that of temperature extremes and drought, although in some regions and for some crops, extreme precipitation explains a greater component of yield variability, for example, of maize in the Midwestern USA and southern Africa (Ray et al. 2015⁴⁰²; Lesk et al. 2016⁴⁰³; Vogel et al. 2019⁴⁰⁴).

The modelled AFOLU flux of 5.5 ± 3.7 GtCO₂ yr^–1 over the period 2008–2017 represents a net value. It consists of both gross emissions of CO₂ from deforestation, forest degradation and the oxidation of wood products, as well as gross removals of CO₂ in forests and soils recovering from harvests and agricultural abandonment (Figure 2.4). The uncertainty of these gross fluxes is high because few studies report gross fluxes from AFOLU. Houghton and Nassikas (2017⁵⁸³) estimated gross emissions to be as high as 20.2 GtCO₂ yr^–1 (limited evidence, low agreement) (Figure 2.4), and even this may be an underestimate because the land-use change data used from FAOSTAT (Tubiello et al. 2013⁵⁸⁴) is itself a net of all changes within a country.

Gross emissions and removals of CO₂ result from rotational uses of land, such as wood harvest and shifting cultivation, including regrowth. These gross fluxes are more informative for assessing the timing and potential for mitigation than estimates of net fluxes, because the gross fluxes include a more complete accounting of individual activities. Gross emissions from rotational land use in the tropics are approximately 37% of total CO₂ emissions, rather than 14%, as suggested by net AFOLU emissions (Houghton and Nassikas 2018⁵⁸⁵). Further, if the forest is replanted or allowed to regrow, gross removals of nearly the same magnitude would be expected to continue for decades.

The net land sink averaged 11.2 ± 2.6 GtCO₂ yr^–1 (likely range) over 2007–2016 (Table 2.3.2), but its gross components have not been estimated at the global level. There are many studies that suggest increasing emissions of carbon are due to indirect environmental effects and natural disturbance, for example, temperature-induced increases in respiration rates (Bond-Lamberty et al. 2018⁵⁸⁶), increased tree mortality (Brienen et al. 2015⁵⁸⁷; Berdanier and Clark 2016⁵⁸⁸; McDowell et al. 2018⁵⁸⁹) and thawing permafrost (Schuur et al. 2015⁵⁹⁰). The global carbon budget indicates that land and ocean sinks have increased over the last six decades in proportion to total CO₂ emissions (Le Quéré et al. 2018⁵⁹¹) (robust evidence, high agreement). That means that any emissions must have been balanced by even larger removals (likely driven by CO₂ fertilisation, climate change, nitrogen deposition, erosion and redeposition of soil carbon, a reduction in areas burned, aerosol-induced cooling and changes in natural disturbances) (Box 2.3).

Climate change is expected to impact terrestrial biogeochemical cycles via an array of complex feedback mechanisms that will act to either enhance or decrease future CO₂ emissions from land. Because the gross emissions and removals from environmental changes are not constrained at present, the balance of future positive and negative feedbacks remains uncertain. Estimates from climate models in Coupled Model Intercomparison Project 5 (CMIP5) exhibit large differences for different carbon and nitrogen cycle feedbacks and how they change in a warming climate (Anav et al. 2013⁵⁹²; Friedlingstein et al. 2006⁵⁹³; Friedlingstein, et al. 2014⁵⁹⁴). The differences are in large part due to the uncertainty regarding how primary productivity and soil respiration will respond to environmental changes, with many of the models not even agreeing on the sign of change. Furthermore, many models do not include a nitrogen cycle, which may limit the CO₂ fertilisation effect in the future (Box 2.3). There is an increasing amount of observational data available and methods to constrain models (e.g., Cox et al. 2013⁵⁹⁵; Prentice, et al., 2015⁵⁹⁶) which can reduce uncertainty.

If CO₂ concentrations decline in the future as a result of low 2 emissions and large negative emissions, the global land and
ocean sinks are expected to weaken (or even reverse). The oceans
are expected to release CO₂ back to the atmosphere when the concentration declines (Ciais et al. 2013a⁵⁹⁷; Jones et al. 2016⁵⁹⁸).

This means that to maintain atmospheric CO₂ and temperature at low levels, both the excess CO₂ from the atmosphere and the CO₂ progressively outgassed from the ocean and land sinks will need to be removed. This outgassing from the land and ocean sinks is called the ‘rebound effect’ of the global carbon cycle (Ciais et al. 2013a⁵⁹⁹). It will reduce the effectiveness of negative emissions and increase the deployment level needed to achieve a climate stabilisation target (Jackson et al. 2017⁶⁰⁰; Jones et al. 2016⁶⁰¹) (limited evidence, high agreement).

In 2017, the globally averaged atmospheric concentration of CH₄ was 1850 ± 1 ppbv (Figure 2.8A). Systematic measurements of atmospheric CH₄ concentrations began in the mid-1980s and trends show a steady increase between the mid-1980s and early- 1990s, slower growth thereafter until 1999, a period of no growth between 1999 and 2006, followed by a resumption of growth in 2007. The growth rates show very high inter-annual variability with a negative trend from the beginning of the measurement period until about 2006, followed by a rapid recovery and continued high inter- annual variability through 2017 (Figure 2.8B). The growth rate has been higher over the past 4 years (high confidence) (Nisbet et al. 2019⁶⁰²). The trend in δ¹³C-CH₄ prior to 2000 with less depleted ratios indicated that the increase in atmospheric concentrations was due to thermogenic (fossil) CH₄ emissions; the reversal of this trend after 2007 indicates a shift to biogenic sources (Figure 2.8C).

Understanding the underlying causes of temporal variation in atmospheric CH₄ concentrations is an active area of research. Several studies concluded that inter-annual variability of CH₄ growth was driven by variations in natural emissions from wetlands (Rice et al. 2016⁶⁰³; Bousquet et al. 2006⁶⁰⁴; Bousquet et al. 2011⁶⁰⁵). These modelling efforts concluded that tropical wetlands were responsible for between 50 and 100% of the inter-annual fluctuations and the renewed growth in atmospheric concentrations after 2007. However, results were inconsistent for the magnitude and geographic distribution of the wetland sources between the models. Pison et al. (2013)⁶⁰⁶ used two atmospheric inversion models and the ORCHIDEE model and found greater uncertainty in the role of wetlands in inter-annual variability between 1990 and 2009 and during the 1999–2006 pause. Poulter et al. (2017)⁶⁰⁷ used several biogeochemical models and inventory-based wetland area data to show that wetland CH₄ emissions increases in the boreal zone have been offset by decreases in the tropics, and concluded that wetlands have not contributed significantly to renewed atmospheric CH₄ growth.

Agricultural emissions are predominantly from enteric fermentation and rice, with manure management and waste burning contributing
small amounts (Figure 2.9). Since 2000, livestock production has been responsible for 33% of total global emissions and 66% of agricultural emissions (EDGAR 4.3.2 database, May 2018; USEPA 2012⁶²⁸; Tubiello et al. 2014⁶²⁹; Janssens-Maenhout et al. 2017b⁶³⁰). Asia has the largest livestock emissions (37%) and emissions in the region have been growing by around 2% per year over the same period.

North America is responsible for 26% and emissions are stable; Europe is responsible for around 8% of emissions, and these are decreasing slightly (<1% per year). Africa is responsible for 14%, but emissions are growing fastest in this region at around 2.5% y^–1. In Latin America and the Caribbean, livestock emissions are decreasing at around 1.6% per year and the region makes up 16% of emissions.

Agriculture is responsible for approximately two-thirds of N₂O emissions (robust evidence, high agreement) (Janssens-Maenhout et al. 2017b⁶⁷⁸). Total emissions from this sector are the sum of direct and indirect emissions. Direct emissions from soils are the result of mineral fertiliser and manure application, manure management, deposition of crop residues, cultivation of organic soils and inorganic nitrogen inputs through biological nitrogen fixation. Indirect emissions come from increased warming, enrichment of downstream water bodies from runoff, and downwind nitrogen deposition on soils. The main driver of N₂O emissions in croplands is a lack of synchronisation between crop nitrogen demand and soil nitrogen supply, with approximately 50% of nitrogen applied to agricultural land not taken up by the crop (Zhang et al. 2017⁶⁷⁹). Cropland soils emit over 3 TgN₂O-N yr^–1 (medium evidence, high agreement) (Janssens-Maenhout et al. 2017b⁶⁸⁰; Saikawa et al. 2014⁶⁸¹). Regional inverse modelling studies show larger tropical emissions than the inventory approaches and they show increases in N₂O emissions from the agricultural sector in South Asia, Central America, and South America (Saikawa et al. 2014⁶⁸²; Wells et al. 2018⁶⁸³).

Emissions of N₂O from pasturelands and rangelands have increased by as much as 80% since 1960 due to increased manure production and deposition (robust evidence, high agreement) (de Klein et al. 2014⁶⁸⁴; Tian et al. 2018⁶⁸⁵; Chadwick et al. 2018⁶⁸⁶; Dangal et al. 2019⁶⁸⁷; Cardenas et al. 2019⁶⁸⁹). Studies consistently report that pasturelands and rangelands are responsible for around half of the total agricultural N₂O emissions (Davidson 2009⁶⁹⁰; Oenema et al. 2014⁶⁹¹; Dangal et al. 2019⁶⁹²). An analysis by Dangal et al. (2019) shows that, while managed pastures make up around one-quarter of the global grazing lands, they contribute 86% of the net global N₂O emissions from grasslands and that more than half of these emissions are related to direct deposition of livestock excreta on soils.

Depending on the dust mineralogy, mixing state and size, dust particles can absorb or scatter shortwave and longwave radiation. Dust particles serve as cloud condensation nuclei and ice nuclei. They can influence the microphysical properties of clouds, their lifetime and precipitation rate (Kok et al. 2018⁸⁰⁹). New and improved understanding of processes controlling emissions and transport of dust, its regional patterns and variability, as well as its chemical composition, has been developed since AR5.

While satellites remain the primary source of information to locate dust sources and atmospheric burden, in-situ data remains critical to constrain optical and mineralogical properties of the dust (DiBiagio et al. 2017⁸¹⁰; Rocha-Lima et al. 2018⁸¹¹). Dust particles are composed of minerals, including iron oxides which strongly absorb shortwave radiation and provide nutrients for marine ecosystems. Another mineral such as feldspar is an efficient ice nuclei (Harrison et al. 2016⁸¹²). Dust mineralogy varies depending on the native soils, so global databases were developed to characterise the mineralogical composition of soils for use in weather and climate models (Journet et al. 2014⁸¹³; Perlwitz et al. 2015⁸¹⁴). New field campaigns, as well as new analyses of observations from prior campaigns, have produced insights into the role of dust in western Africa in climate system, such as long-ranged transport of dust across the Atlantic (Groß et al. 2015⁸¹⁵) and the characterisation of aerosol particles and their ability to act as ice and cloud condensation nuclei (Price et al. 2018⁸¹⁶). Size distribution at emission is another key parameter controlling dust interactions with radiation. Most models now use the parametrisation of Kok (2011)⁸¹⁷ based on the theory of brittle material. It was shown that most models underestimate the size of the global dust cycle (Kok 2011⁸¹⁸).

A limited number of model-based studies found that dust emissions have increased significantly since the late 19th century: by 25% from the preindustrial period to the present day (e.g., from 729 Tg yr^–1 to 912 Tg yr^–1) with about 50% of the increase driven by climate change and about 40% driven by land use cover change, such as conversion of natural land to agriculture (low confidence) (Stanelle et al. 2014⁸²¹). These changes resulted in a clear sky radiative forcing at the top of the atmosphere of –0.14 W m^–2 (Stanelle et al. 2014⁸²²). The authors found that, in North Africa, most dust is of natural origin, with a recent 15% increase in dust emissions attributed to climate change. In North America two-thirds of dust emissions take place on agricultural lands and both climate change and land-use change jointly drive the increase; between the pre-industrial period and the present day, the overall effect of changes in dust was –0.14 W m^–2 cooling of clear sky net radiative forcing on top of the atmosphere, with –0.05 W m^–2 from land use and –0.083 W m^–2 from changes in climate.

The comparison of observations for vertically integrated mass of atmospheric dust per unit area (i.e., dust mass path (DMP)) obtained from the remotely sensed data and the DMP from CMIP5 models reveal that the model-simulate range of DMP was much lower than the estimates (Evan et al. 2014⁸²³). ESMs typically do not reproduce inter- annual and longer timescales variability seen in observations (Evan et al. 2016⁸²⁴). Analyses of the CMIP5 models (Evan 2018⁸²⁵; Evan et al. 2014⁸²⁶) reveal that all climate models systematically underestimate dust emissions, the amount of dust in the atmosphere and its inter- annual variability (medium confidence).

One commonly suggested reason for the lack of dust variability in climate models is the models’ inability to simulate the effects of land surface changes on dust emission (Stanelle et al. 2014⁸²⁷). Models that account for changes in land surface show more agreement with the satellite observations both in terms of aerosol optical depth and DMP (Kok et al. 2014⁸²⁸). New prognostic dust emissions models are now able to account for both changes in surface winds and vegetation characteristics (e.g., leaf area index and stem area index) and soil water, ice and snow cover (Evans et al. 2016⁸²⁹). As a result, new modelling studies (e.g., Evans et al. 2016⁸³⁰) indicate that, in regions where soil and vegetation respond strongly to ENSO events, such as in Australia, inclusion of dynamic vegetation characteristics into dust emission parameterisations improves comparisons between the modelled and observed relationship with long-term climate variability (e.g., ENSO) and dust levels (Evans et al. 2016⁸³¹). Thus, there has been progress in incorporating the effects of vegetation, soil moisture, surface wind and vegetation on dust emission source functions, but the number of studies demonstrating such improvement remains small (limited evidence, medium agreement).

There is no agreement about the direction of future changes in dust emissions. Atmospheric dust loading is projected to increase over the southern edge of the Sahara in association with surface wind and precipitation changes (Pu and Ginoux, 2018⁸³²), while Evan et al. (2016)⁸³³ project a decline in African dust emissions. Dust optical depth (DOD) is also projected to increase over the central Arabian peninsula in all seasons, and to decrease over northern China from March-April-May to September-October-November (Pu and Ginoux 2018⁸³⁴). Climate models project rising drought risks over the south-western and central US in the 21st century. The projected drier regions largely overlay the major dust sources in the US. However, whether dust activity in the US will increase in the future is not clear, due to the large uncertainty in dust modelling (Pu and Ginoux 2017⁸³⁵). Future trends of dust emissions will depend on changes in precipitation patterns and atmospheric circulation (limited evidence, high agreement). However, implication of changes in human activities, including mitigation (e.g., bioenergy production) and adaption (e.g., irrigation) are not characterised in the current literature.

OC is a major component of aerosol mass concentration, and it originates from different anthropogenic (combustion processes) and natural (natural biogenic emissions) sources (Robinson et al. 2007⁸⁴¹). A large fraction of OC in the atmosphere has a secondary origin, as it can be formed in the atmosphere through condensation to the aerosol phase of low vapour pressure gaseous compounds emitted as primary pollutants or formed in the atmosphere. This component is SOA (Hodzic et al. 2016⁸⁴²). A third component of the optically active aerosols is the so-called brown carbon (BrC), an organic material that shows enhanced solar radiation absorption at short wavelengths (Wang et al. 2016b⁸⁴³; Laskin et al. 2015⁸⁴⁴; Liu et al. 2016a⁸⁴⁵; Bond et al. 2013⁸⁴⁶; Saturno et al. 2018⁸⁴⁷).

OC and EC have distinctly different optical properties, with OC being important for the scattering properties of aerosols and EC central for the absorption component (Rizzo et al. 2013⁸⁴⁸; Tsigaridis et al. 2014⁸⁴⁹; Fuzzi et al. 2015⁸⁵⁰). While OC is reflective and scatters solar radiation, it has a cooling effect on climate. On the other side, BC and BrC absorb solar radiation and they have a warming effect in the climate system (Bond et al. 2013⁸⁵¹).

Annual global emission estimates of BC range from 7.2–7.5 Tg yr^–1 (using bottom-up inventories) (Bond et al. 2013⁸⁷⁸; Klimont et al. 2017⁸⁷⁹) up to 17.8 ± 5.6 Tg yr^–1 (using a fully coupled climate-aerosol-urban model constrained by aerosol measurements) (Cohen and Wang 2014⁸⁸⁰), with considerably higher BC emissions for Eastern Europe, southern East Asia, and Southeast Asia, mostly due to higher anthropogenic BC emissions estimates. A significant source of BC, the net trend in global burned area from 2000–2012 was a modest decrease of 4.3 Mha yr^–1 (–1.2% yr^–1).

Carbonaceous aerosols are important in urban areas as well as pristine continental regions, since they can be responsible for 50–85% of PM2.5 (Contini et al. 2018⁸⁸¹; Klimont et al. 2017⁸⁸²). In boreal and tropical forests, carbonaceous aerosols originate from BVOC oxidation (Section 2.4.3). The largest global source of BC aerosols is open burning of forests, savannah and agricultural lands with emissions of about 2700 Gg yr^–1 in the year 2000 (Bond et al. 2013⁸⁸³).

ESMs most likely underestimate globally averaged EC emissions (Bond et al. 2013⁸⁸⁴; Cohen and Wang 2014⁸⁸⁵), although recent emission inventories have included an upwards adjustment in these numbers (Hoesly et al. 2018⁸⁸⁶). Vertical EC profiles have also been shown to be poorly constrained (Samset et al. 2014⁸⁸⁷), with a general tendency of too much EC at high altitudes. Models differ strongly in the magnitude and importance of the coating-enhancement of ambient EC absorption (Boucher et al. 2016⁸⁸⁸; Gustafsson and Ramanathan 2016⁸⁸⁹) in their estimated lifetime of these particles, as well as in dry and wet removal efficiency (limited evidence, medium agreement) (Mahmood et al. 2016⁸⁹⁰).

The equilibrium in emissions and concentrations between the scattering properties of organic aerosol versus the absorption component of BC is a key ingredient in the future climatic projections of aerosol effects (limited evidence, high agreement). The uncertainties in net climate forcing from BC-rich sources are substantial, largely due to lack of knowledge about cloud interactions with both BC and co-emitted OC. A strong positive forcing of about 1.1 wm^–2 was calculated by Bond et al. (2013), but this forcing is balanced by a negative forcing of –1.45 wm^–2, and shows clearly a need to work on the co-emission issue for carbonaceous aerosols. The forcing will also depend on the aerosol-cloud interactions, where carbonaceous aerosol can be coated and change their CCN capability. It is difficult to estimate the changes in any of these components in a future climate, but this will strongly influence the radiative forcing (high confidence) (Contini et al. 2018⁸⁹¹; Boucher et al. 2013⁸⁹²; Bond et al. 2013⁸⁹³).

De Coninck et al. (2018)⁸⁹⁴ reported studies estimating a lower global temperature effect from BC mitigation (e.g., Samset et al. 2014⁸⁹⁵; Boucher et al. 2016⁸⁹⁶), although commonly used models do not capture properly observed effects of BC and co-emissions on climate (e.g., Bond et al. 2013⁸⁹⁷). Regionally, the warming effects can be substantially larger, for example, in the Arctic (Sand et al. 2015⁸⁹⁸) and high mountain regions near industrialised areas or areas with heavy biomass-burning impacts (high confidence) (Ming et al. 2013⁸⁹⁹).

Due to the short atmospheric lifetime of carbonaceous aerosols in the atmosphere, of the order of a few days, most studies dealing with the future concentration levels have a regional character (Cholakian et al. 2018⁹⁰⁰; Fiore et al. 2012⁹⁰¹). The studies agree that the uncertainties in changes in emissions of aerosols and their precursors are generally higher than those connected to climate change itself. Confidence in future changes in carbonaceous aerosol concentration projections is limited by the reliability of natural and anthropogenic emissions (including wildfires, largely caused by human activity) of primary aerosol as well as that of the precursors. The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is endorsed by the Coupled-Model Intercomparison Project 6 (CMIP6) and is designed to quantify the climate impacts of aerosols and chemically reactive gases (Lamarque et al. 2013⁹⁰²). These simulations calculated future responses to SLCF emissions for the RCP scenarios in terms of concentration changes and radiative forcing. Carbonaceous aerosol emissions are expected to increase in the near future due to possible increases in open biomass-burning emissions (from forest, savannah and agricultural fires), and increase in SOA from oxidation of BVOCs (medium confidence) (Tsigaridis et al. 2014⁹⁰³; van Marle et al. 2017b⁹⁰⁴; Giglio et al. 2013⁹⁰⁵).

BVOCs are rapidly oxidised in the atmosphere to form less volatile compounds that can condense and form SOA. In boreal and tropical forests, carbonaceous aerosols originate from BVOC oxidation, of which isoprene and terpenes are the most important precursors (Claeys et al. 2004⁹¹⁶; Hu et al. 2015⁹¹⁷; De Sá et al. 2017⁹¹⁸; de Sá et al. 2018⁹¹⁹; Liu et al. 2016b⁹²⁰). See the following sub-section for more detail.

BVOCs are the most important precursors of SOA. The transformation process of BVOCs affects the aerosol size distribution both by contributing to new particle formation and to the growth of larger pre-existing particles. SOA affects the scattering of radiation by the particles themselves (direct aerosol effect), but also changes the amount of CCN and the lifetime and optical properties of clouds (indirect aerosol effect).

Climate warming over the past 30 years, together with the longer growing season experienced in boreal and temperate environments, have increased BVOC global emissions since the preindustrial times (limited evidence, medium agreement) (Peñuelas 2009⁹⁵¹; Sanderson et al. 2003⁹⁵²; Pacifico et al. 2012⁹⁵³). This was opposed by lower BVOC emissions caused by the historical conversion of natural vegetation and forests to cropland (limited evidence, medium agreement) (Unger 2013⁹⁵⁴, 2014a⁹⁵⁵; Fu and Liao 2014⁹⁵⁶). The consequences of historical anthropogenic land cover change were a decrease in the global formation of SOA (–13%) (Scott et al. 2017⁹⁵⁷) and tropospheric burden (–13%) (Heald and Geddes 2016⁹⁵⁸). This has resulted in a positive radiative forcing (and thus warming) from 1850–2000 of 0.017 W m^–2 (Heald and Geddes 2016⁹⁵⁹), 0.025 W m^–2 (Scott et al. 2017⁹⁶⁰) and 0.09 W m^–2 (Unger 2014b⁹⁶¹) through the direct aerosol effect. In present-day conditions, global SOA production from all sources spans between 13 and 121 Tg yr^–1 (Tsigaridis et al. 2014⁹⁶²). The indirect aerosol effect (change in cloud condensation nuclei), resulting from land use induced changes in BVOC emissions, adds an additional positive radiative forcing of 0.008 W m^–2 (Scott et al. 2017⁹⁶³). More studies with different model setups are needed to fully assess this indirect aerosol effect associated with land use change from the preindustrial to present. CMIP6 global emissions pathways (Hoesly et al. 2018⁹⁶⁴; Gidden et al. 2018⁹⁶⁵) estimates global VOCs emissions in 2015 at 230 MtVOC yr^–1. They also estimated that, from 2000–2015, emissions were up from 200–230 MtVOC yr^–1.

Studies suggest that increasing temperature will change BVOC emissions through change in species composition and rate of BVOC production. A further 2°C–3°C rise in the mean global temperature could increase BVOC global emissions by an additional 30–45% (Peñuelas and Llusià 2003⁹⁷⁵). In two modelling studies, the impact on climate from rising BVOC emissions was found to become even larger with decreasing anthropogenic aerosol emissions (Kulmala et al. 2013⁹⁷⁶; Sporre et al. 2019⁹⁷⁷). A negative feedback on temperature, arising from the BVOC-induced increase in the first indirect aerosol effect, has been estimated by two studies to be in the order of –0.01 W m^–2 K (Scott et al. 2018b⁹⁷⁸; Paasonen et al. 2013⁹⁷⁹). Enhanced aerosol scattering from increasing BVOC emissions has been estimated to contribute to a global gain in BVOC emissions of 7% (Rap et al. 2018⁹⁸⁰). In a warming planet, BVOC emissions are expected to increase but magnitude of this increase is unknown and will depend on future land use change, in addition to climate (limited evidence, medium agreement).

There is also high confidence from observation-based estimates that mean annual daytime temperatures are warmer following deforestation, while night-time temperatures are cooler (Schultz et al. 2017¹¹⁵⁰; Wickham et al. 2014¹¹⁵¹; Juang et al. 2007¹¹⁵²; Tang et al. 2018¹¹⁵³; Prevedello et al. 2019¹¹⁵⁴; Peng et al. 2014¹¹⁵⁵; Zhang et al. 2014b¹¹⁵⁶; Li et al. 2015b¹¹⁵⁷; Alkama and Cescatti 2016¹¹⁵⁸). Deforestation then increases the amplitude of diurnal temperature variations while forestation reduces it (high confidence). Two main reasons have been put forward to explain why nights are warmer in forested areas: their larger capacity to store heat and the existence of a nocturnal temperature inversion bringing warmer air from the higher atmospheric levels down to the surface.

In addition to those seasonal and diurnal fluctuations, Lejeune et al. (2018)¹¹⁵⁹ found systematic warming of the hottest summer days following historical deforestation in the northern mid-latitudes, and this echoes Strandberg and Kjellström (2018)¹¹⁶⁰ who argue that the August 2003 and July 2010 heatwaves could have been largely mitigated if Europe had been largely forested.

In a combined modelling of large-scale forestation of western Europe and climate change scenario (SRES A2), Gálos et al. (2013)¹¹⁶¹ found relatively small dampening potential of additional forest on ambient air temperature at the end of the 21st century when compared to the beginning (the cooling resulting from land cover changes is –0.5°C whereas the GHG-induced warming exceeds 2.5°C). Influence on rainfall was, however, much larger and significant. Projected annual rainfall decreases following warming were cancelled in Germany and significantly reduced in both France and Ukraine through forestation. In addition, forestation decreased the number of warming-induced dry days but increased the number of extreme precipitation events.

The net impact of forestation, combining both biophysical and biogeochemical effects, has been tested in the warmer world predicted by RCP 8.5 scenario (Sonntag et al. 2016¹¹⁶², 2018¹¹⁶³). The cooling effect from the addition of 8 Mkm2 of forests following the land use RCP 4.5 scenario was too small (–0.27°C annually) to dampen the RCP 8.5 warming. However, it reached about –1°C in some temperate regions and –2.5°C in boreal ones. This is accompanied by a reduction in the number of extremely warm days.

Global and regional impacts of deforestation/forestation in boreal regions

Consistent with what we have previously discussed for temperate and tropical regions, large-scale boreal deforestation induces a biogeochemical warming of +0.11 ± 0.09°C (Figure 2.17). But contrary to those other latitudinal bands, the biophysical effect is a consistent cooling across all models (–0.55 ± 0.29°C when averaged globally). It is also significantly larger than the biogeochemical warming (e.g., Dass et al. (2013)¹¹⁶⁴, Longobardi et al. (2016a)¹¹⁶⁵, Devaraju et al. (2015a)¹¹⁶⁶, Bathiany et al. (2010)¹¹⁶⁷, Devaraju et al. (2018)¹¹⁶⁸) and is driven by the increased albedo, enhanced by the snow-albedo feedback as well as by an increase in sea-ice extent in the Arctic. Over boreal lands, the cooling is as large as –1.8 ± 1.2°C. However, this means that annual cooling masks a seasonal contrast, as discussed in Strandberg and Kjellström (2018)¹¹⁶⁹ and Gao et al. (2014)¹¹⁷⁰: during summer time, following the removal of forest, the decreased evapotranspiration results in a significant summer warming that outweighs the effect of an increased albedo effect.

The same observation-based estimates (as discussed in the previous subsection) show similar patterns for the temperate latitudes: seasonal and daily contrasts. Schultz et al. (2017)¹¹⁷¹, however, found that mean annual night-time changes are as large as daytime ones in those regions (mean annual nocturnal cooling of –1.4 ± 0.10°C, balanced by mean annual daytime warming of 1.4 ± 0.04°C). This contrasts with both temperate and tropical regions where daytime changes are always larger than the night-time ones.

Arora and Montenegro (2011)¹¹⁷² combined large-scale forestation and climate change scenario (SRES A2): forestation of either 50% or 100% of the total agricultural area was gradually prescribed between years 2011 and 2060 everywhere. In addition, boreal, temperate and tropical forestation have been tested separately. Both biophysical and biogeochemical effects were accounted for. The net simulated impact of forestation was a cooling varying from –0.04°C to –0.45°C, depending on the location and magnitude of the additional forest cover. It was, however, quite marginal compared to the large global warming resulting from anthropogenic GHG emissions (+3°C at the end of the 21st century). In their experiment, forestation in boreal regions led to biophysical warming and biogeochemical cooling that compensated each other, whereas forestation in the tropics led to both biophysical and biogeochemical cooling. The authors concluded that tropical forestation is three times more effective at cooling down climate than boreal or temperate forestation.

There have been little changes in net cropland area over the past 50 years (at the global scale) compared to continuous changes in land management (Erb et al. 2017¹¹⁸³). Similarly, in Europe, change in forest management has resulted in a very significant anthropogenic land change. Management affects water, energy and GHG fluxes exchanged between the land and the atmosphere, and thus affects temperature and rainfall, sometimes to the same extent as changes in land cover do (as discussed in Luyssaert et al. (2014)¹¹⁸⁴).

The effects of irrigation, which is a practice that has been substantially studied, including one attempt to manage solar radiation via increases in cropland albedo (geoengineering the land) are assessed, along with a discussion of recent findings on the effects of forest management on local climate, although there is not enough literature yet on this topic to carry out a thorough assessment. The effects of urbanisation on climate are assessed in a specific cross-chapter box within this chapter (Cross-Chapter Box 4 in this chapter).

There is medium evidence but high agreement that soil moisture conditions influence the frequency and magnitude of extremes such as drought and heatwaves. Observational evidence indicates that dry soil moisture conditions favour heatwaves, in particular in regions where evapotranspiration is limited by moisture availability (Mueller and Seneviratne 2012¹²⁹⁰; Quesada et al. 2012¹²⁹¹; Miralles et al. 2018¹²⁹²; Geirinhas et al. 2018¹²⁹³; Miralles et al. 2014¹²⁹⁴; Chiang et al. 2018¹²⁹⁵; Dong and Crow 2019¹²⁹⁶; Hirschi et al. 2014¹²⁹⁷).

In future climate projections, soil moisture plays an important role in the projected amplification of extreme heatwaves and drought in many regions of the world (medium confidence) (Seneviratne et al. 2013¹²⁹⁸; Vogel et al. 2017¹²⁹⁹; Donat et al. 2018¹³⁰⁰; Miralles et al. 2018¹³⁰¹). In addition, the areas where soil moisture affects heat extremes will not be located exactly where they are today. Changes in rainfall, temperature, and thus in evapotranspiration, will induce changes in soil moisture and therefore where temperature and latent heat flux will be negatively coupled (Seneviratne et al. 2006¹³⁰²; Fischer et al. 2012¹³⁰³). Quantitative estimates of the actual role of soil moisture feedbacks are, however, very uncertain due to the low confidence in projected soil moisture changes (IPCC 2013a¹³⁰⁴), to weaknesses in the representation of soil moisture–atmosphere interactions in climate models (Sippel et al. 2017¹³⁰⁵; Ukkola et al. 2018¹³⁰⁶; Donat et al. 2018¹³⁰⁷; Miralles et al. 2018¹³⁰⁸) and to methodological uncertainties associated with the soil moisture prescription framework commonly used to disentangle the effect of soil moisture on changes in temperature extremes (Hauser et al. 2017¹³⁰⁹).

Where soil moisture is predicted to decrease in response to climate change in the subtropics and temperate latitudes, this drying could be enhanced by the existence of soil moisture feedbacks (low confidence) (Berg et al. 2016¹³¹⁰). The initial decrease in precipitation and increase in potential evapotranspiration and latent heat flux, in response to global climate change, leads to decreased soil moisture at those latitudes and can potentially amplify both. Such a feature is consistent with evidence that, in a warmer climate, land and atmosphere will be more strongly coupled via both the water and energy cycles (Dirmeyer et al. 2014¹³¹¹; Guo et al. 2006¹³¹²). This increased sensitivity of atmospheric response to land perturbations implies that changes in land uses and cover are expected to have more impact on climate in the future than they do today.

Beyond temperature, it has been suggested that soil moisture feedbacks influence precipitation occurrence and intensity. But the importance, and even the sign of this feedback, is still largely uncertain and debated (Tuttle and Salvucci 2016¹³¹³; Yang et al. 2018¹³¹⁴; Froidevaux et al. 2014¹³¹⁵; Guillod et al. 2015¹³¹⁶).

Reducing non-CO₂ emissions from agriculture through cropland nutrient management, enteric fermentation, manure management, rice cultivation and fertiliser production has a total mitigation potential of 0.30–3.38 GtCO₂-eq yr^–1 (medium confidence) (combined sub-category measures in Figure 2.24, details below) with a further 0.25–6.78 GtCO₂-eq yr^–1 from soil carbon management (Section 2.6.1.3). Other literature that looks at broader categories finds mitigation potential of 1.4–2.3 GtCO₂-eq yr^–1 from improved cropland management (Smith et al. 2008¹⁴⁹⁶, 2014¹⁴⁹⁷; Pradhan et al., 2013¹⁴⁹⁸); 1.4–1.8 GtCO₂-eq yr^–1 from improved grazing land management (Conant et al. 2017¹⁴⁹⁹; Herrero et al. 2016¹⁵⁰⁰; Smith et al. 2008¹⁵⁰¹, 2014¹⁵⁰²) and 0.2–2.4 GtCO₂-eq yr^–1 from improved livestock management (Smith et al. 2008¹⁵⁰³, 2014¹⁵⁰⁴; Herrero et al. 2016¹⁵⁰⁵, FAO 2007¹⁵⁰⁶). Detailed discussions of the mitigation potential of agricultural response options and their co-benefits are provided in Chapter 5 and Sections 5.5 and 5.6.

The mitigation potential for reducing and/or halting deforestation and degradation ranges from 0.4–5.8 GtCO₂ yr^–1 (high confidence) (Griscom et al. 2017¹⁵³³; Hawken 2017¹⁵³⁴; Busch and Engelmann 2017¹⁵³⁵; Baccini et al. 2017¹⁵³⁶; Zarin et al. 2016¹⁵³⁷; Federici et al. 2015¹⁵³⁸; Carter et al. 2015¹⁵³⁹; Houghton et al. 2015¹⁵⁴⁰; Smith et al. 2013a¹⁵⁴¹; Houghton and Nassikas 2018¹⁵⁴²). The higher figure represents a complete halting of land use conversion in forests and peatlands (i.e., assuming recent rates of carbon loss are saved each year). Separate estimates of degradation only range from 1.0–2.18 GtCO₂ yr^–1. Reduced deforestation and forest degradation include conservation of existing carbon pools in vegetation and soil through protection in reserves, controlling disturbances such as fire and pest outbreaks, and changing management practices. Differences in estimates stem from varying land cover definitions, the time periods assessed and the carbon pools included (most higher estimates include belowground, dead wood, litter, soil and peat carbon). When deforestation and degradation are halted, it may take many decades to fully recover the biomass initially present in native ecosystems (Meli et al. 2017¹⁵⁴³) (Section 4.8.3).

The global mitigation potential for increasing soil organic matter stocks in mineral soils is estimated to be in the range of 0.4–8.64 GtCO₂ yr^–1 (high confidence), though the full literature range is wider with high uncertainty related to some practices (Fuss et al. 2018¹⁶³⁴; Sommer and Bossio 2014¹⁶³⁵; Lal 2010¹⁶³⁶; Lal et al. 2004¹⁶³⁷; Conant et al. 2017¹⁶³⁸; Dickie et al. 2014¹⁶³⁹; Frank et al. 2017a¹⁶⁴⁰; Griscom et al. 2017¹⁶⁴¹; Herrero et al. 2015¹⁶⁴², 2016¹⁶⁴³; McLaren 2012¹⁶⁴⁴; Paustian et al. 2016¹⁶⁴⁵; Poeplau and Don 2015¹⁶⁴⁶; Powlson et al. 2014¹⁶⁴⁷; Smith et al. 2016c¹⁶⁴⁸; Zomer et al. 2017¹⁶⁴⁹). Some studies have separate potentials for soil carbon sequestration in croplands (0.25–6.78 GtCO₂ yr^–1) (Griscom et al. 2017¹⁶⁵⁰; Hawken 2017¹⁶⁵¹; Frank et al. 2017a¹⁶⁵²; Paustian et al. 2016¹⁶⁵³; Herrero et al. 2016¹⁶⁵⁴; Henderson et al. 2015¹⁶⁵⁵; Dickie et al. 2014¹⁶⁵⁶; Conant et al. 2017¹⁶⁵⁷; Lal 2010¹⁶⁵⁸) and soil carbon sequestration in grazing lands (0.13–2.56 GtCO₂ yr^–1) (Griscom et al. 2017¹⁶⁵⁹; Hawken 2017¹⁶⁶⁰; Frank et al. 2017a¹⁶⁶¹; Paustian et al. 2016¹⁶⁶²; Powlson et al. 2014¹⁶⁶³; McLaren 2012¹⁶⁶⁴; Zomer et al. 2017¹⁶⁶⁵; Smith et al. 2015¹⁶⁶⁶; Sommer and Bossio 2014¹⁶⁶⁷; Lal 2010¹⁶⁶⁸). The potential for soil carbon sequestration and storage varies considerably depending on prior and current land management approaches, soil type, resource availability, environmental conditions, microbial composition and nutrient availability among other factors (Hassink and Whitmore 1997¹⁶⁶⁹; Smith and Dukes 2013¹⁶⁷⁰; Palm et al. 2014¹⁶⁷¹; Lal 2013¹⁶⁷²; Six et al. 2002¹⁶⁷³; Feng et al. 2013¹⁶⁷⁴). Soils are a finite carbon sink and sequestration rates may decline to negligible levels over as little as a couple of decades as soils reach carbon saturation (West et al. 2004¹⁶⁷⁵; Smith and Dukes 2013¹⁶⁷⁶). The sink is at risk of reversibility, in particular due to increased soil respiration under higher temperatures (Section 2.3).

Protection and restoration of wetlands, peatlands and coastal habitats reduces net carbon loss (primarily from sediment/soils) and provides continued or enhanced natural CO2 removal (Section 4.9.4). Reducing annual emissions from peatland conversion, draining and burning could mitigate 0.45–1.22 GtCO2-eq yr–1 up to 2050 (medium confidence) (Hooijer et al. 2010¹⁷³²; Griscom et al. 2017¹⁷³³; Hawken 2017¹⁷³⁴) and peatland restoration 0.15–0.81 (low confidence) (Couwenberg et al. 2010¹⁷³⁵; Griscom et al. 2017¹⁷³⁶). The upper end from Griscom et al. (2017)¹⁷³⁷ represents a maximum sustainable potential (accounting for biodiversity and food security safeguards) for rewetting and biomass enhancement. Wetland drainage and rewetting was included as a flux category under the second commitment period of the Kyoto Protocol, with significant management knowledge gained over the last decade (IPCC 2013b). However, there are high uncertainties as to carbon storage and flux rates, in particular the balance between CH4 sources and CO2 sinks (Spencer et al. 2016¹⁷³⁸). Peatlands are sensitive to climate change which may increase carbon uptake by vegetation and carbon emissions due to respiration, with the balance being regionally dependent (high confidence). There is low confidence about the future peatland sink globally. Some peatlands have been found to be resilient to climate change (Minayeva and Sirin 2012¹⁷³⁹), but the combination of land use change and climate change may make them vulnerable to fire (Sirin et al. 2011¹⁷⁴⁰). While models show mixed results for the future sink (Spahni et al. 2013¹⁷⁴¹; Chaudhary et al. 2017¹⁷⁴²; Ise et al. 2008¹⁷⁴³), a study that used extensive historical data sets to project change under future warming scenarios found that the current global peatland sink could increase slightly until 2100 and decline thereafter (Gallego-Sala et al. 2018¹⁷⁴⁴).

An introduction and overview of bioenergy and bioenergy with carbon capture and storage (BECCS) can be found in Cross-Chapter Boxes 7 and 12, and Chapters 6 and 7. CCS technologies are discussed in SR15. The discussion below refers to modern bioenergy only (e.g., liquid biofuels for transport and the use of solid biofuels in combined heat and power plants).

The mitigation potential of bioenergy coupled with CCS (i.e., BECCS), is estimated to be between 0.4 and 11.3 GtCO₂ yr^–1 (medium confidence) based on studies that directly estimate mitigation for BECCS (not bioenergy) in units of CO₂ (not EJ) (McLaren 2012¹⁷⁵⁸; Lenton 2014¹⁷⁵⁹; Fuss et al. 2018¹⁷⁶⁰; Turner et al. 2018b¹⁷⁶¹; Lenton 2010¹⁷⁶²; Koornneef et al. 2012¹⁷⁶³; Powell and Lenton 2012¹⁷⁶⁴). SR15 reported a potential of 1–85 GtCO₂ yr^–1 which they noted could be narrowed to a range of 0.5–5 GtCO₂ yr^–1 when taking account of sustainability aims (Fuss et al. 2018¹⁷⁶⁵). The upper end of the SR15 range is considered as a theoretical potential. Previously, the IPCC Special Report on Renewable Energy Sources concluded the technical potential of biomass supply for energy (without BECCS) could reach 100–300 EJ yr–1 by 2050, which would be 2–15 GtCO₂ yr^–1 (using conversion factors 1 EJ = 0.02–0.05 GtCO₂ yr^–1 emission reduction, SR15). A range of recent studies including sustainability or economic constraints estimate that 50–244 EJ (1–12 GtCO₂ yr^–1 using the conversion factors above) of bioenergy could be produced on 0.1–13 Mkm² of land (Fuss et al. 2018¹⁷⁶⁶; Chan and Wu 2015¹⁷⁶⁷; Schueler et al. 2016¹⁷⁶⁷; Wu et al. 2013¹⁷⁶⁸; Searle and Malins 2015¹⁷⁶⁹; Wu et al. 2019¹⁷⁷⁰; Heck et al. 2018¹⁷⁷¹; Fritz et al. 2013¹⁷⁷²).

There is high confidence that the most important factors determining future biomass supply for energy are land availability and land productivity (Berndes et al. 2013¹⁷⁷³; Creutzig et al. 2015a¹⁷⁷⁴; Woods et al. 2015¹⁷⁷⁵; Daioglou et al. 2019¹⁷⁷⁶). Estimates of marginal/degraded lands currently considered available for bioenergy range from 3.2–14.0 Mkm2, depending on the adopted sustainability criteria, land class definitions, soil conditions, land mapping method and environmental and economic considerations (Campbell et al. 2008¹⁷⁷⁸; Cai et al. 2011¹⁷⁷⁹; Lewis and Kelly 2014¹⁷⁸⁰).

Weathering is the natural process of rock decomposition via chemical and physical processes in which CO₂ is removed from the atmosphere and converted to bicarbonates and/or carbonates (IPCC 2005¹⁸⁴⁶). Formation of calcium carbonates in the soil provides a permanent sink for mineralised organic carbon (Manning 2008¹⁸⁴⁷; Beerling et al. 2018¹⁸⁴⁸). Mineral weathering can be enhanced through grinding up rock material to increase the surface area, and distributing it over land to provide carbon removals of 0.5–4.0 GtCO₂ yr^–1 (medium confidence) (Beerling et al. 2018¹⁸⁴⁹; Lenton 2010¹⁸⁵⁰; Smith et al. 2016a¹⁸⁵¹; Taylor et al. 2016¹⁸⁵²). While the geochemical potential is quite large, agreement on the technical potential is low due to a variety of unknown parameters and limits, such as rates of mineral extraction, grinding, delivery and challenges with scaling and deployment.

Demand-side management has the potential for climate change mitigation via reducing emissions from production, switching to consumption of less emission intensive commodities and making land available for CO₂ removal (Section 5.5.2). Reducing food losses and waste increases the overall efficiency of food value chains (with less land and inputs needed) along the entire supply chain and has the potential to mitigate 0.8–4.5 GtCO₂-eq yr^–1 (high confidence) (Bajželj et al. 2014¹⁸⁵³; Dickie et al 2014¹⁸⁵⁴; Hawken 2017¹⁸⁵⁵; Hiç et al. 2016¹⁸⁵⁶) (Section 5.5.2.5).

Shifting to diets that are lower in emissions-intensive foods like beef delivers a mitigation potential of 0.7–8.0 GtCO₂-eq yr^–1 (high confidence) (Bajželj et al. 2014¹⁸⁵⁷; Dickie et al. 2014¹⁸⁵⁸; Herrero et al. 2016¹⁸⁵⁹; Hawken 2017¹⁸⁶⁰; Springmann et al. 2016¹⁸⁶¹; Tilman and Clark 2014¹⁸⁶²; Hedenus et al. 2014¹⁸⁶³; Stehfest et al. 2009¹⁸⁶⁴) with most of the higher end estimates (>6 GtCO₂-eq yr^–1) based on veganism, vegetarianism or very low ruminant meat consumption (Section 5.5.2). In addition to direct mitigation gains, decreasing meat consumption, primarily of ruminants, and reducing wastes further reduces water use, soil degradation, pressure on forests and land used for feed potentially freeing up land for mitigation (Tilman and Clark 2014¹⁸⁶⁵) (Chapters 5 and 6). Additionally, consumption of locally produced food, shortening the supply chain, can in some cases minimise food loss, contribute to food security and reduce GHG emissions associated with energy consumption and food loss (Section 5.5.2.6).