Editing IPCC:AR6/WGI/Chapter-2 (section)

=== 2.3.4 Biosphere ===

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This section is limited to a few biological indicators to demonstrate the close links between the biosphere and physical forcing. In selecting the indicators, we focussed on (i) those that are observable at large (global) spatial scales and over long (two decades or more) temporal scales, using standardized and consistent procedures; and (ii) those that are illustrative of the influence of the physical system on the biological realm. Chapters 2 and 3 of AR6 WGII undertake a more holistic assessment of biospheric impacts.

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==== 2.3.4.1 Seasonal Cycle of Atmospheric CO <sub>2</sub> ====

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The AR5 noted that because CO <sub>2</sub> uptake by photosynthesis occurs only during the growing season, the greater land mass in the NH imparts a characteristic ‘sawtooth’ seasonal cycle in atmospheric CO <sub>2</sub> . The SRCCL similarly stated that due to strong seasonal patterns of growth, NH terrestrial ecosystems are largely responsible for the seasonal variations in global atmospheric CO <sub>2</sub> concentrations. Neither AR5 nor SRCCL made a confidence statement about observed changes in the amplitude of the seasonal cycle of CO <sub>2</sub> .

In situ observations of CO <sub>2</sub> generally depict a rising amplitude of the seasonal cycle over the past half century, especially north of about 45°N (Figure 2.30). For example, an amplitude increase of 6 ± 2.6% per decade has been observed at the Barrow surface observatory in Alaska over 1961–2011 ( [[#Graven--2013|Graven et al., 2013]] ), with slightly slower increases thereafter. Aircraft data north of 45°N exhibit an amplitude increase of 57 ± 7% at 500 mb versus an increase of 26 ± 18% for 35°N–45°N between field campaigns in 1958–1961 and 2009–2011 ( [[#Graven--2013|Graven et al., 2013]] ). Increases in amplitude for the period 1980–2012 are apparent at eight surface observatories north of 50°N ( [[#Piao--2018|Piao et al., 2018]] ), related primarily to a larger drawdown in June and July. Trends in seasonal cycle amplitude at lower latitudes are smaller (if present at all); for instance, the increase at the Mauna Loa observatory in Hawaii since the early 1960s is only about half as large as at Barrow ( [[#Graven--2013|Graven et al., 2013]] ), and only one other low-latitude observatory has a significant increase from 1980–2012 ( [[#Piao--2018|Piao et al., 2018]] ). There is a weak signal of an increase in amplitude at the Sinhagad observatory in western India in recent years ( [[#Chakraborty--2020|Chakraborty et al., 2020]] ). Generally speaking, larger increases in the Arctic and boreal regions are indicative of changes in vegetation and carbon cycle dynamics in northern ecosystems ( [[#Forkel--2016|Forkel et al., 2016]] ), though increased carbon uptake can also result from other factors such as warmer- and wetter-than-normal conditions.

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[[File:be5dbe4f2d92ae62b5a85f9cd87ba02b IPCC_AR6_WGI_Figure_2_30.png]]

'''Figure 2.3''' '''0 |''' '''Changes in the amplitude of the seasonal cycle of CO''' <sub>2</sub> '''. (a)''' Observed peak-to-trough seasonal amplitude given by the day of year of downward zero crossing, of CO <sub>2</sub> concentration at Barrow (71°N, blue) and Mauna Loa (20°N, black). Seasonal CO <sub>2</sub> cycles observed at '''(b)''' Barrow and '''(c)''' Mauna Loa for the 1961–1963 or 1958–1963 and 2017–2019 time periods. The first six months of the year are repeated. Reprinted with permission from AAAS. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

Recent satellite-based, global-scale estimates of seasonal variations in atmospheric CO <sub>2</sub> for the period 2003–2018 show that the seasonal variations in the SH are out of phase with those in the NH ( [[#Reuter--2020|Reuter et al., 2020]] ), which is consistent with the phenological shifts in primary productivity between hemispheres. The net effect of the phase shift between the two hemispheres is to dampen the amplitude of the global average seasonal cycle. These integrated results also show that the amplitude of the oscillations has been increasing in the SH, from about 2009, but comparison with data from Baring head suggests that periods of high seasonal oscillation had occurred at that location in the SH prior to 1995.

In summary, there is ''very'' ''high confidence'' that the amplitude of the seasonal cycle of atmospheric CO <sub>2</sub> has increased at mid-to-high NH latitudes since the early 1960s. The observed increase is generally consistent with greater greening during the growing season and an increase in the length of the growing season over the high northern latitudes. Similarly, globally-integrated results from the SH also show an increase in seasonal amplitude of atmospheric CO <sub>2</sub> signal, from around 2009 to 2018 ( ''low confidence'' ).

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==== 2.3.4.2 Marine Biosphere ====

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===== 2.3.4.2.1 Large-scale distribution of marine biota =====

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SROCC pointed out that long-term global observations of many key ocean variables, including phytoplankton, have not reached the density and accuracy necessary for detecting change. But SROCC noted the good comparability between short time-scale single-sensor ocean-colour products and a longer time-scale, climate-quality time series of multi-sensor, inter-sensor-bias-corrected, and error-characterized, global data on chlorophyll-a concentration in the surface layers of the ocean. With respect to oligotrophic gyres, AR5 WGII concluded that the oligotrophic subtropical gyres of the Atlantic and Pacific Oceans are expanding and that they indicate declining phytoplankton stocks in these waters ( ''limited evidence, low agreement'' ). With respect to distributions of marine organisms, AR5 WGII reported range shifts of benthic, pelagic, and demersal species and communities ( ''high confidence'' ), though the shifts were not uniform.

Phytoplankton are responsible for marine primary production through photosynthesis; they are a major player in the ocean carbon cycle. They have a high metabolic rate and respond fast to changes in environmental conditions (light, temperature, nutrients, mixing), and as such, serve as a key indicator for change in marine ecosystems. Concentration of chlorophyll-a, the major photosynthetic pigment in all phytoplankton, is often used as a measure of phytoplankton biomass. As primary producers, they are also food for organisms at higher trophic levels. The multi-sensor time series of chlorophyll-a concentration has now been updated ( [[#Sathyendranath--2019|Sathyendranath et al., 2019]] ) to cover 1998–2018. Figure 2.31 shows that global trends in chlorophyll-a for the last two decades are insignificant over large areas of the global ocean ( [[#von%20Schuckmann--2019|von Schuckmann et al., 2019]] ), but some regions exhibit significant trends, with positive trends in parts of the Arctic and the Antarctic waters (&gt;3% yr <sup>–1</sup> ), and both negative and positive trends (within ± 3% yr <sup>–1</sup> ) in parts of the tropics, subtropics and temperate waters. The interannual variability in chlorophyll-a data in many regions is strongly tied to indices of climate variability ( [[#2.4|Section 2.4]] and Annex IV) and changes in total concentration are typically associated with changes in phytoplankton community structure (e.g., [[#Brewin--2012|Brewin et al., 2012]] ; [[#Racault--2017b|Racault et al., 2017b]] ). Variability in community structure related to El Niño has, in turn, been linked to variability in fisheries, for example in the catch of anchovy ( ''Engraulis ringens'' ) in the Humboldt current ecosystem ( [[#Jackson--2011|Jackson et al., 2011]] ).

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[[File:8e2ab16ad7dde7fa160828a9cd447055 IPCC_AR6_WGI_Figure_2_31.png]]

'''Figure 2.''' '''31 |''' '''Phytoplankton dynamics in the ocean. (a)''' Climatology of chlorophyll-a concentration derived from ocean-colour data (1998–2018); '''(b)''' Linear trends in chlorophyll concentration. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after [[#Santer--2008|Santer et al. (2008)]] (‘×’ marks denote non-significant changes). '''(c)''' Histogram of linear trends in chlorophyll concentration, after area weighting and with per-pixel uncertainty estimates based on comparison with in situ data. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

Since AR5 WGII, analysis of a longer time series of ocean-colour data (1998–2012) has shown ( [[#Aiken--2017|Aiken et al., 2017]] ) that the expansion of the low nutrient part of the North Atlantic oligotrophic gyre was significant, at 0.27 × 10 <sup>6</sup> km <sup>2</sup> per decade, but that the rate was much lower than that reported earlier by [[#Polovina--2008|Polovina et al. (2008)]] . Furthermore, [[#Aiken--2017|Aiken et al. (2017)]] reported no significant trend in the oligotrophic area of the South Atlantic Gyre. With the time series extended to 2016, [[#von%20Schuckmann--2018|von Schuckmann et al. (2018)]] reported that since 2007, there was a general decreasing trend in the areas of the North and South Pacific oligotrophic Gyres, while the North and South Atlantic oligotrophic Gyres remained stable, with little change in area, consistent with Aiken et al ''.'' (2017). The changing sign of trends in the areal extent of the oligotrophic gyres with increase in the length of the time series raises the possibility that these changes arise from interannual to multi-decadal variability. The time series of ocean-colour data is too short to discern any trend that might be superimposed on such variability.

Similarly, there is limited consistent and long-term information on large-scale distributions of marine organisms at higher trophic levels. But there are increased indications since AR5 and SROCC that the distributions of various higher trophic-level organisms are shifting both polewards and to deeper levels ( [[#Edwards--2016|Edwards et al., 2016]] ; [[#Haug--2017|Haug et al., 2017]] ; [[#Atkinson--2019|Atkinson et al., 2019]] ; [[#Lenoir--2020|Lenoir et al., 2020]] ; [[#Pinsky--2020|Pinsky et al., 2020]] ), mostly consistent with changes in temperature. However observations also show a smaller set of counter-intuitive migrations towards warmer and shallower waters, which could be related to changes in phenology and in larval transport by currents ( [[#Fuchs--2020|Fuchs et al., 2020]] ). There are also strengthening indications of greater representation by species with warm-water affinity in marine communities, consistent with expectations under observed warming ( [[#Burrows--2019|Burrows et al., 2019]] ). There are indications that pre-1850 CE plankton communities are different from their modern counterparts globally ( [[#Jonkers--2019|Jonkers et al., 2019]] ). Indicators of geographical distributions of species (mostly from coastal waters) suggest that the rates at which some species are leaving or arriving at an ecosystem are variable, leading to changes in community composition ( [[#Blowes--2019|Blowes et al., 2019]] ), with ''likely'' greater representation of warm-water species in some locations ( [[#Burrows--2019|Burrows et al., 2019]] ).

In summary, there is ''high confidence'' that the latitudinal and depth limits of the distribution of various organisms in the marine biome are changing. There is ''medium confidence'' that there are differences in the responses of individual species relative to each other, such that the species compositions of ecosystems are changing. There is ''medium confidence'' that chlorophyll concentration in the surface shows weak negative and positive trends in parts of low and mid latitudes, and weak positive trends in some high-latitude areas. There is ''medium confidence'' that the large-scale distribution of the oligotrophic gyre provinces is subject to significant inter-annual variations, but ''low confidence'' in the long-term trends in the areal extent of these provinces because of insufficient length of direct observations.

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===== 2.3.4.2.2 Marine primary production =====

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SROCC expressed ''low confidence'' in satellite-based estimates of trends in marine primary production, citing insufficient length of the time series and lack of corroborating in situ measurements and independent validation time series. The report also cites significant mismatches in absolute values and decadal trends in primary production when different satellite-based products are compared.

Recent model-based results with assimilation of satellite data ( [[#Gregg--2019|Gregg and Rousseaux, 2019]] ), show global annual mean marine primary production of around 38 (±1.13) PgC yr <sup>–1</sup> over 1998–2015. This new result lies towards the low end of values reported in earlier, satellite-based, studies (range 36.5–67 PgC yr <sup>–1</sup> , reported in [[#Sathyendranath--2020|Sathyendranath et al. (2020)]] ). Reconciling the results of [[#Gregg--2019|Gregg and Rousseaux (2019)]] with earlier satellite-based studies leads to a mean of 47 (±7.8) PgC yr <sup>–1</sup> . There is a strong correlation between interannual regional variability in marine primary production and climate variability ( [[#Racault--2017b|Racault et al., 2017b]] ; [[#Gregg--2019|Gregg and Rousseaux, 2019]] ). The increase in primary production in the Arctic has been associated with retreating sea ice and with increases in nutrient supply and chlorophyll concentration ( [[#Lewis--2020|Lewis et al., 2020]] ). [[#Gregg--2019|Gregg and Rousseaux (2019)]] reported a decreasing trend in marine primary production, of –0.8 PgC (–2.1%) per decade globally. There is ''low confidence'' in this trend because of the small number of studies and the short length of the time series (&lt;20 years).

In conclusion, there is ''low confidence'' because of the small number of recent studies and the insufficient length of the time series analysed that marine primary production is 47 (± 7.8) PgC yr <sup>–1</sup> . A small decrease in productivity is evident globally for the period 1998–2015, but regional changes are larger and of opposing signs ( ''low confidence'' ).

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===== 2.3.4.2.3 Marine phenology =====

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Phenology is the study of the timing of important events in the annual life cycle of organisms (plants or animals; see also Annex VII: Glossary). The AR5 WGII noted that the timing of various seasonal biological events in the ocean had advanced by more than four days per decade over the previous 50-year period and concluded that there was ''high confidence'' in observed changes in the phenological metrics of marine organisms. The AR5 WGII further reported that, of those observations that showed a response, 81% of changes in phenology, distribution and abundance were consistent with anticipated responses to climate warming according to theoretical expectations, corroborated by updates in SROCC. The consequent current and future impacts on interactions between species, including competition and predator-prey dynamics, were noted with ''high confidence'' .

There are additional indications that phenological metrics related to different species are changing, but not always in a similar manner. For example, many seabirds are breeding earlier, while others are breeding later ( [[#Sydeman--2015|Sydeman et al., 2015]] ). Planktonic organisms in the North Atlantic are also responding differently to each other when subjected to the same environmental changes ( [[#Edwards--2004|Edwards and Richardson, 2004]] ). Furthermore, different factors could be responsible for triggering phenological responses in different stages in the life cycle of a single organism ( [[#Koeller--2009|Koeller et al., 2009]] ). The shift in the distribution of many benthic invertebrates on the North-west Atlantic shelf, including some commercially important shellfish, could be explained by phenology and larval transport, and the shift and contraction of species range have been associated with higher mortality ( [[#Fuchs--2020|Fuchs et al., 2020]] ). Changes in phytoplankton phenological indicators globally ( [[#Racault--2012|Racault et al., 2012]] ; [[#Sapiano--2012|Sapiano et al., 2012]] ) have been linked to indicators of climate variability, such as the multivariate ENSO Index ( [[#Racault--2017a|Racault et al., 2017a]] ), with responses varying across ecological provinces of the ocean ( [[#Longhurst--2007|Longhurst, 2007]] ).

Phenological links between multiple components of an ecosystem have to be maintained intact, to retain system integrity. Since all higher pelagic organisms depend on phytoplankton for their food, either directly or indirectly, a match favours survival, and a mismatch is antagonistic to survival. Match represents synchronicity in the phenological events of both prey and predator. There are indications from ocean-colour data used in conjunction with fisheries data that the survival rate of various larger marine organisms depends on phenological metrics related to the seasonality of phytoplankton growth. Such links have been demonstrated, for example, for haddock ( ''Melanogrammus aeglefinus'' ) in the North-west Atlantic ( [[#Platt--2003|Platt et al., 2003]] ); northern shrimp in the North Atlantic ( [[#Koeller--2009|Koeller et al., 2009]] ; [[#Ouellet--2011|Ouellet et al., 2011]] ); sardine ( ''Sardinella aurita'' ) off the Ivory coast ( [[#Kassi--2018|Kassi et al., 2018]] ); cod ( ''Gadus morhua'' ) and haddock ( ''Melanogrammus aeglefinus'' ) larvae in the North-West Atlantic ( [[#Trzcinski--2013|Trzcinski et al., 2013]] ); and oil sardine ( ''Sardinella longiceps'' ) off the south-west coast of India. [[#Borstad--2011|Borstad et al. (2011)]] showed that fledgling production rate of rhinoceros auklets ( ''Cerorhinca monocerata'' ) on a remote island in coastal north-eastern Pacific was related to seasonal values of chlorophyll-a biomass in the vicinity of the island.

In summary, new in situ data as well as satellite observations strengthen AR5 and SROCC findings that various phenological metrics for many species of marine organisms have changed in the last half century ( ''high confidence'' ), though many regions and many species of marine organisms remain under-sampled or even unsampled. The changes vary with location and with species ( ''high confidence'' ). There is a strong dependence of survival in higher trophic-level organisms (fish, exploited invertebrates, birds) on the availability of food at various stages in their life cycle, which in turn depends on phenologies of both ( ''high confidence'' ). There is a gap in our understanding of how the varied responses of marine organisms to climate change, from a phenological perspective, might threaten the stability and integrity of entire ecosystems.

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==== 2.3.4.3 Terrestrial Biosphere ====

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===== 2.3.4.3.1 Growing season and phenology changes =====

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The AR5 WGII briefly discussed large-scale changes in the length of the growing season but made no confidence statement about observed trends. However, AR5 did conclude with ''high confidence'' that warming contributed to an overall spring advancement in the NH.

Recent in situ analyses document increases in the length of the thermal growing season (i.e., the period of the year when temperatures are warm enough to support growth) over much of the extratropical land surface since at least the mid-20th century. Over the NH as a whole, an increase of about 2.0 days per decade is evident for 1951–2018 ( [[#Dunn--2020|Dunn et al., 2020]] ), with slightly larger increases north of 45°N ( [[#Barichivich--2013|Barichivich et al., 2013]] ). Over North America, a rise of about 1.3 days per decade is apparent in the United States for 1900–2014 ( [[#Kukal--2018|Kukal and Irmak, 2018]] ), with larger increases after 1980 ( [[#McCabe--2015|McCabe et al., 2015]] ); likewise, all ecozones in Canada experienced increases from 1950–2010 ( [[#Pedlar--2015|Pedlar et al., 2015]] ). Growing season length in China increased by at least 1.0 days per decade since 1960 ( [[#Xia--2018|Xia et al., 2018]] ) and by several days per decade in South Korea since 1970 ( [[#Jung--2015|Jung et al., 2015]] ). In general, changes in phenological indicators are consistent with the increase in growing season length documented by instrumental data ( [[#Parmesan--2015|Parmesan and Hanley, 2015]] ). Several long-term, site-specific records illustrate the unusualness of recent phenological changes relative to interannual variability; for example, peak bloom dates for cherry blossoms in Kyoto, Japan have occurred progressively earlier in the growing season in recent decades, as have grape harvest dates in Beaune, France (Figure 2.32).

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[[File:d764bd8f474028bd82eba6568ef91e06 IPCC_AR6_WGI_Figure_2_32.png]]

'''Figure 2.32 |''' '''Phenological indicators of changes in growing season. (a)''' Cherry blossom peak bloom in Kyoto, Japan; '''(b)''' grape harvest in Beaune, France; '''(c)''' spring phenology index in eastern China; '''(d)''' full flower of Piedmont species in Philadelphia, USA; '''(e)''' grape harvest in Central Victoria, Australia; '''(f)''' start of growing season in Tibetan Plateau, China. Red lines depict the 25-year moving average (top row) or the nine-year moving average (middle and bottom rows) with the minimum roughness boundary constraint of Mann (2004). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

Changes in the length of the photosynthetically active growing season (derived from the Normalized Difference Vegetation Index (NDVI)) are also evident over many land areas since the early 1980s. Increases of about 2.0 days per decade are apparent north of 45°N since the early 1980s (centred over mid-latitude Eurasia and north-eastern North America), with indications of a reversal to a decline in season length starting in the early 2000s ( [[#Barichivich--2013|Barichivich et al., 2013]] ; [[#Zhao--2015|Zhao et al., 2015]] ; [[#Garonna--2016|Garonna et al., 2016]] ; Q. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ; [[#Park--2016|Park et al., 2016]] ). Satellite-based records suggest that most NH regions have experienced both an earlier start and a later end to the growing season, a finding supported by ground-based data ( [[#Piao--2020|Piao et al., 2020]] ). A number of studies also capture increases in growing season length over the Canadian Arctic (W. [[#Chen--2016|]] [[#Chen--2016|Chen et al., 2016]] ), Fennoscandia ( [[#Høgda--2013|Høgda et al., 2013]] ), most of Europe ( [[#Garonna--2014|Garonna et al., 2014]] ), and parts of sub-Saharan Africa ( [[#Vrieling--2013|Vrieling et al., 2013]] ).

The general consistency between in situ and satellite estimates over the NH is noteworthy given that many factors independently contribute to uncertainty in observed changes. For example, there is no universally accepted definition of growing season length across in situ analyses; some define the growing season as the period based on a temperature threshold (e.g., 5°C) whereas others use the frost-free period. Spatial and temporal coverage can also affect conclusions based upon in situ studies ( [[#Donat--2013b|Donat et al., 2013b]] ). For satellite analyses, uncertainties can be related to the satellite datasets themselves (e.g., satellite drift, sensor differences, calibration uncertainties, atmospheric effects); and to the methods for determining phenological metrics (e.g., start, end, and length of season; S. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ).

In summary, based on multiple independent analyses of in situ, satellite, and phenological data, there is ''high confidence'' that the length of the growing season has increased over much of the extratropical NH since at least the mid-20th century.

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===== 2.3.4.3.2 Terrestrial ecosystems =====

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The AR5 WGII concluded that many terrestrial species have shifted their geographic ranges in recent decades ( ''high confidence'' ). Similarly, SRCCL assessed that many land species have experienced range size and location changes as well as altered abundances over recent decades ( ''high confidence'' ). SROCC noted that species composition and abundance have markedly changed in high mountain ecosystems in recent decades ( ''very high confidence'' ).

Paleoclimate reconstructions document large-scale biome shifts from the deep past through the Holocene (e.g., [[#Hoogakker--2016|Hoogakker et al., 2016]] ). The northernmost location of the treeline is a representative indicator in this regard (Figure 2.34). During the MPWP, boreal forest extended to the Arctic coast, with the northernmost treeline being about 4° to 10° latitude further north than at present; temperate forests and grasslands were also shifted poleward (with reduced tundra extent), while savannahs and woodlands were more expansive in Africa and Australia at the expense of deserts (Cross-Chapter Box 2.4, Figure 1b; [[#Salzmann--2008|Salzmann et al., 2008]] , 2013; [[#Sniderman--2016|Sniderman et al., 2016]] ; [[#Andrae--2018|Andrae et al., 2018]] ). During the LGM, tundra and steppe expanded whereas forests were globally reduced in extent ( [[#Prentice--2000|Prentice et al., 2000]] ; [[#Binney--2017|Binney et al., 2017]] ), the northern treeline being about 17° to 23° latitude south of its present-day location in most areas. During the LDT, pervasive ecosystem transformations occurred in response to warming and other climatic changes ( [[#Nolan--2018|Nolan et al., 2018]] ; [[#Fordham--2020|Fordham et al., 2020]] ). By the MH, North Africa had experienced a widespread conversion from grasslands to desert ( [[#Hoelzmann--1998|Hoelzmann et al., 1998]] ; [[#Prentice--2000|Prentice et al., 2000]] ; [[#Sha--2019|Sha et al., 2019]] ), and the northernmost treeline had shifted poleward again to about 1° to 3° latitude north of its current location ( [[#MacDonald--2000|MacDonald et al., 2000]] ; [[#Binney--2009|Binney et al., 2009]] ; [[#Williams--2011|Williams et al., 2011]] ). Over the past half century, there has been an increase in the spatial synchrony of annual tree growth across all continents that is unprecedented during the past millennium ( [[#Manzanedo--2020|Manzanedo et al., 2020]] ). Elevated rates of vegetation change in the Holocene are consistent with climate variability ( [[#Shuman--2019|Shuman et al., 2019]] ), intensified human land use ( [[#Fyfe--2015|Fyfe et al., 2015]] ; [[#Marquer--2017|Marquer et al., 2017]] ), and resulting increased ecosystem novelty ( [[#Finsinger--2017|Finsinger et al., 2017]] ; K.D. [[#Burke--2019|]] [[#Burke--2019|Burke et al., 2019]] ).

Long-term ecological records capture extensive range shifts during the 20th and early 21st centuries ( [[#Lenoir--2015|Lenoir and Svenning, 2015]] ; [[#Pecl--2017|Pecl et al., 2017]] ). Research has been most extensive for North America and western Eurasia, with fewer studies for central Africa, eastern Asia, South America, Greenland, and Antarctica ( [[#Lenoir--2015|Lenoir and Svenning, 2015]] ). Most documented changes are toward cooler conditions – that is, poleward and upslope ( [[#Lenoir--2008|Lenoir et al., 2008]] ; [[#Harsch--2009|Harsch et al., 2009]] ; [[#Elmendorf--2015|Elmendorf et al., 2015]] ; [[#Parmesan--2015|Parmesan and Hanley, 2015]] ; [[#Evans--2017|Evans and Brown, 2017]] ). Notably, a large, quasi-global analysis ( [[#Chen--2011|Chen et al., 2011]] ) estimated that many insect, bird, and plant species had shifted by 17 (±3) km per decade toward higher latitudes and 11 (±2) m per decade toward higher elevations since the mid-20th century, with changes in both the leading and trailing edges of species ranges ( [[#Rumpf--2018|Rumpf et al., 2018]] ). Over the past century, long-term ecological surveys also show that species turnover (i.e., the total number of gains and losses of species within an area) has significantly increased across a broad array of ecosystems ( [[#Dornelas--2014|Dornelas et al., 2014]] , 2019), including undisturbed montane areas worldwide ( [[#Gibson-Reinemer--2015|Gibson-Reinemer et al., 2015]] ). Despite global losses to biodiversity, however, most local assemblages have experienced a change in biodiversity rather than a systematic loss ( [[#Pimm--2014|Pimm et al., 2014]] ). With increased species turnover, the novelty of contemporary communities relative to historical baselines has risen ( [[#Hobbs--2009|Hobbs et al., 2009]] ; [[#Radeloff--2015|Radeloff et al., 2015]] ) due to greater spatial homogenization, mixtures of exotic and native species, altered disturbance regimes, and legacies of current or historic land use ( [[#Olden--2006|Olden and Rooney, 2006]] ; [[#Schulte--2007|Schulte et al., 2007]] ; [[#Thompson--2013|Thompson et al., 2013]] ; [[#Goring--2016|Goring et al., 2016]] ). In general, terrestrial species have had lower rates of turnover than marine species ( [[#Dornelas--2018|Dornelas et al., 2018]] ; [[#Blowes--2019|Blowes et al., 2019]] ).

There are exceptions to the general pattern of poleward/upslope migration. For some species, various biotic and abiotic factors (such as precipitation and land use) supersede the physiological effects of temperature ( [[#Vanderwal--2013|Vanderwal et al., 2013]] ; [[#Gibson-Reinemer--2015|Gibson-Reinemer and Rahel, 2015]] ; [[#Ordonez--2016|Ordonez et al., 2016]] ; [[#Scheffers--2016|Scheffers et al., 2016]] ; [[#Lenoir--2020|Lenoir et al., 2020]] ). For other species, poleward migration is slower than expectations from the observed temperature increases. Trees are one such example because of their long lifespan and gradual maturity ( [[#Renwick--2015|Renwick and Rocca, 2015]] ); in fact, poleward advance is only evident at about half of the sites in a large global dataset of treeline dynamics for 1900-present ( [[#Harsch--2009|Harsch et al., 2009]] ). Furthermore, the northernmost extent of treeline at present (roughly 73°N) is actually somewhat south of its location in the MH ( [[#MacDonald--2008|MacDonald et al., 2008]] ) despite an expanding growing season in the extratropical NH since the mid-20th century ( [[#2.3.4.3.1|Section 2.3.4.3.1]] ).

Consistent with species range shifts, SRCCL noted that there have been changes in the geographical distribution of climate zones. Poleward shifts in temperate and continental climates are evident across the globe over 1950–2010, with decreases in the area (and increases in the average elevation) of polar climates ( [[#Chan--2015|Chan and Wu, 2015]] ). Zonal changes towards higher latitudes in winter plant hardiness regions are apparent since the 1970s over the central and eastern USA, with elevational changes also being important in the western USA ( [[#Daly--2012|Daly et al., 2012]] ). A clear northward shift in winter plant hardiness zones is detectable across western Canada since 1930, with somewhat lesser changes in the south-eastern part of the country ( [[#McKenney--2014|McKenney et al., 2014]] ). A northward migration of agro-climate zones is also evident over Europe since the mid-1970s ( [[#Ceglar--2019|Ceglar et al., 2019]] ). In addition, a shift toward more arid climate zones is apparent in some areas, such as the Asian monsoon region ( [[#Son--2015|Son and Bae, 2015]] ) as well as parts of South America and Africa ( [[#Spinoni--2015|Spinoni et al., 2015]] ).

In summary, there is ''very high confidence'' that many terrestrial species have shifted their geographic ranges poleward and/or upslope over the past century, with increased rates of species turnover. There is ''high confidence'' that the geographical distribution of climate zones has shifted in many parts of the world.

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===== 2.3.4.3.3 Global greening and browning =====

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The AR5 WGII briefly discussed changes in global vegetation greenness derived from satellite proxies for photosynthetic activity. Observed trends varied in their strength and consistency, and AR5 thus made no confidence statement on observed changes. The SRCCL subsequently concluded that greening had increased globally over the past 2–3 decades ( ''high confidence'' ).

Vegetation index data derived from AVHRR and MODIS depicts increases in aspects of vegetation greenness (i.e., green leaf area and/or mass) over the past four decades ( [[#Piao--2020|Piao et al., 2020]] ). NDVI increased globally from the early 1980s through the early 2010s (Y. [[#Liu--2015|]] [[#Liu--2015|Liu et al., 2015]] a). N. [[#Pan--2018|]] [[#Pan--2018|Pan et al. (2018)]] found NDVI increases over about 70% of the Earth’s vegetated surface through 2013, and [[#Osborne--2018|Osborne et al. (2018)]] noted strong upward changes in NDVI in the circumpolar Arctic through 2016. Globally integrated Leaf Area Index (LAI) also rose from the early 1980s through at least the early 2010s ( [[#Zhu--2016|Zhu et al., 2016]] ; [[#Forzieri--2017|Forzieri et al., 2017]] ; [[#Jiang--2017|Jiang et al., 2017]] ; [[#Xiao--2017|Xiao et al., 2017]] ) and probably through near-present; for example, [[#Chen--2019|]] [[#Chen--2019|C. Chen et al. (2019)]] documented an LAI increase over one-third of the global vegetated area from 2000–2017. Although less frequently analysed for temporal trends, Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) likewise increased over many global land areas (particularly China, India, and eastern Europe) in the past two decades (Figure 2.33; [[#Forkel--2014|Forkel et al., 2014]] ; [[#Gobron--2018|Gobron, 2018]] ; [[#Keenan--2018|Keenan and Riley, 2018]] ). There are also documented changes in specific vegetation types, such as a 7% rise in global tree cover for 1982–2016 ( [[#Song--2018|Song et al., 2018]] ) and an expansion of shrub extent in the Arctic tundra over 1982–2017 ( [[#Myers-Smith--2020|Myers-]] [[#Smith--2020|]] [[#Smith--2020|Smith et al., 2020]] ). The increased greening is largely consistent with CO <sub>2</sub> fertilization at the global scale, with other changes being noteworthy at the regional level ( [[#Piao--2020|Piao et al., 2020]] ); examples include agricultural intensification in China and India (X. [[#Chen--2019|]] [[#Chen--2019|Chen et al., 2019]] ; [[#Gao--2019|Gao et al., 2019]] ) and temperature increases in the northern high latitudes ( [[#Kong--2017|Kong et al., 2017]] ; [[#Keenan--2018|Keenan and Riley, 2018]] ) and in other areas such as the Loess Plateau in central China (Y. [[#Wang--2018|]] [[#Wang--2018|Wang et al., 2018]] ). Notably, some areas (such as parts of Amazonia, central Asia, and the Congo basin) have experienced browning (i.e., decreases in green leaf area and/or mass) ( [[#Hoogakker--2015|Hoogakker et al., 2015]] ; [[#Gottschalk--2016|Gottschalk et al., 2016]] ; [[#Anderson--2019|Anderson et al., 2019]] ). Because rates of browning have exceeded rates of greening in some regions since the late 1990s, the increase in global greening has been somewhat slower in the last two decades (T.-Y. [[#Pan--2018|]] [[#Pan--2018|Pan et al., 2018]] ).

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[[File:6ff276a39d9fc3ae50a6b58d2122d61b IPCC_AR6_WGI_Figure_2_33.png]]

'''Figure 2.''' '''33 |''' '''Satellite-based trends in fraction of absorbed photosynthetically active radiation (per decade) for 1998–2019.''' Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after [[#Santer--2008|Santer et al. (2008)]] ; ‘×’ marks denote non-significant trend). Unvegetated areas such as barren deserts (grey) and ice sheets (white) have no trend in FAPAR. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

Global-scale linear trends differ substantially across products for the same periods and trend metrics used ( [[#Jiang--2017|Jiang et al., 2017]] ). Several factors contribute to this large span in estimated changes. Remotely sensed vegetation products vary in their spatial and temporal completeness as well as resolution and are sensitive to contamination from atmospheric composition, clouds, snow cover, and anisotropy, as well as orbital changes and sensor degradations ( [[#de%20Jong--2012|de Jong et al., 2012]] ; [[#Zhu--2016|Zhu et al., 2016]] ; [[#Jiang--2017|Jiang et al., 2017]] ; [[#Xiao--2017|Xiao et al., 2017]] ; N. [[#Pan--2018|]] [[#Pan--2018|Pan et al., 2018]] ). Ground-based measurements suitable for calibration and validation are scarce before 2000 ( [[#Xiao--2017|Xiao et al., 2017]] ), and the recalibration of satellite records (e.g., as in from MODIS Collection 5 to 6) can affect trends ( [[#Piao--2020|Piao et al., 2020]] ). It is possible that the increase in greenness over 2000–2015 is larger than the increase in gross primary production (based on flux tower measurements and MODIS Collection 6 data) (L. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ). Land use changes and altered disturbance regimes (e.g., floods, fires, diseases) may mask large-scale signals ( [[#Franklin--2016|Franklin et al., 2016]] ). In addition, there is a plethora of models for the identification of phenological metrics from satellite data as well as a variety of statistical techniques for analysing historical changes (S. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ).

In summary, there is ''high confidence'' that vegetation greenness (i.e., green leaf area and/or mass) has increased globally since the early 1980s. However, there is ''low confidence'' in the magnitude of this increase owing to the large range in available estimates.

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