Editing IPCC:AR6/WGII/Chapter-3 (section)

==== 3.4.3.5 Changes in Primary Production and Biological Carbon Export Flux ====

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===== 3.4.3.5.1 Observed changes in primary production =====

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Analyses of satellite-derived primary production over the past two decades (1998–2018) showed generally weak and negative trends (up to −3.0%) at low and mid latitudes ( [[#Kulk--2020|Kulk et al., 2020]] ). In contrast, positive trends occurred in large areas of the South Atlantic and South Pacific Oceans, as well as in polar and coastal (upwelling) regions (up to +4.5%; Cross-Chapter Paper 6; [[#Kulk--2020|Kulk et al., 2020]] ). Data-assimilating ocean biogeochemical models estimate a global decline in primary production of 2.1% per decade in the period 1998–2015, driven by the shoaling mixed layer and decreasing nitrate concentrations ( [[#Gregg--2019|Gregg and Rousseaux, 2019]] ). This is consistent with previous assessments that identified ocean warming and increased stratification as the main drivers ( ''high confidence'' ) affecting the regional variability in primary production Bindoff et al. (2019). However, as noted in SROCC and WGI AR6 [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] (Table 3.24; [[#Gulev--2021|Gulev et al., 2021]] ), observed interannual changes in primary production on global and regional scales are nonlinear and largely influenced by natural temporal variability, providing ''low confidence'' in the trends.

'''Table 3.24 |''' Summary of previous IPCC assessments of ocean primary production and carbon export flux

{| class="wikitable"
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! Process

! Observed impacts

! Projected impacts

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| ''SROCC ( [[#Bindoff--2019a|Bindoff et al., 2019a]] )''

|
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| Open-ocean

primary production

| ‘Past open-ocean productivity trends, including those determined by satellites, [are appraised with ''low confidence'' ] due to newly identified region-specific drivers of microbial growth and the lack of corroborating ''in situ'' time series datasets.’

| ‘Net primary productivity (NPP) is ''very likely'' to decline by 4–11% by 2081–2100, relative to 2006–2015, across CMIP5 models for RCP8.5, but there is ''low confidence'' for this estimate due to the ''medium agreement'' among models and the ''limited evidence'' from observations. The tropical ocean NPP will ''very likely'' to decline by 7–16% for RCP8.5, with ''medium confidence'' as there are improved constraints from historical variability in this region.’

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| Open-ocean carbon export

| ‘Analyses of long-term trends in primary production and particle export production, as well as model simulations, reveal that increasing temperatures, leading to enhanced stratification and nutrient limitation, will have the greatest influence on decreasing the flux of particulate organic carbon (POC) to the deep ocean. However, different lines of evidence (including observation, modelling and experimental studies) provide ''low confidence'' on the mechanistic understanding of how climate drivers affect different components of the biological pump in the epipelagic ocean, as well as changes in the efficiency and magnitude of carbon export in the deep ocean.’

| ‘The projected changes in export production can be larger than global primary production because they are affected by both, the NPP changes, but also how shifts in food-web structure modulates the ‘transfer efficiency’ of particulate organic material, which then affects the sinking speed and lability of exported particles through the ocean interior to the sea floor.’

‘As export production is a much better understood net integral of changing net nutrient supply and can be constrained by interior ocean nutrient and oxygen levels, there is ''medium confidence'' in projections for global [export production] changes [based on CMIP5 model runs].’

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| ''WGI AR6 Chapters 2, 5 ( [[#Canadell--2021|Canadell et al., 2021]] ; [[#Gulev--2021|Gulev et al., 2021]] )''

|
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| Open-ocean

primary production

| Global ocean marine primary production is estimated to be 47 ± 7.8 PgC yr –1 with ''low confidence'' because of the small number of recent studies and the insufficient length of the time series analysed. A small decrease in productivity is evident globally for the period 1998–2015, but regional changes are larger and of opposing signs ( ''low confidence'' ) (WGI AR6 [[IPCC:Wg2:Chapter:Chapter-2#2.3|Section 2.3.4.2.2]] ; [[#Gulev--2021|Gulev et al., 2021]] ).

| ‘In CMIP5 models run under RCP8.5, [POC] export flux is projected to decline by 1–12% by 2100 ( [[#Taucher--2011|Taucher and Oschlies, 2011]] ; [[#Laufkötter--2015|Laufkötter et al., 2015]] ). Similar values are predicted in 18 CMIP6 models, with declines of 2.5–21.5% (median: −14%) [...] between 1900 and 2100 under the SSP5-8.5 scenario. The mechanisms driving these changes vary widely between models due to differences in parameterisation of particle formation, remineralisation and plankton community structure’ (WGI AR6 [[IPCC:Wg2:Chapter:Chapter-5#5.4.4.2|Section 5.4.4.2]] ; [[#Canadell--2021|Canadell et al., 2021]] ).

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===== 3.4.3.5.2 Projected changes in primary production =====

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Across 10 CMIP5 and 13 CMIP6 ESM ensembles, global mean NPP is projected to decline by 2080–2099 relative to 2006–2015, under all RCPs and SSPs ( [[#Kwiatkowski--2020|Kwiatkowski et al., 2020]] ). However, under comparable radiative forcing, the CMIP6 multi-model mean projections of primary production declines (mean ± SD: −0.56 ± 4.12% under SSP1-2.6, and −3.00 ± 9.10% under SSP5-8.5) are less than those of previous CMIP5 models (3.42 ± 2.47% under RCP2.6, and 8.54 ± 5.88% under RCP8.5) (WGI AR6 [[IPCC:Wg2:Chapter:Chapter-5#5.4.4.2|Section 5.4.4.2]] ; [[#Kwiatkowski--2020|Kwiatkowski et al., 2020]] ; [[#Canadell--2021|Canadell et al., 2021]] ). The inter-model uncertainty associated with CMIP6 NPP projections is larger than in CMIP5, and it is consistently larger than the scenario uncertainty. For each SSP across the CMIP6 ensemble, individual models project both increases and decreases in global primary production, reflecting a diverse suite of bottom-up and top-down ecological processes, which are variously parameterised across models ( [[#Laufkötter--2015|Laufkötter et al., 2015]] ; [[#Bindoff--2019a|Bindoff et al., 2019a]] ). Furthermore, accurate simulation of many of the biogeochemical tracers upon which NPP depends (e.g., the distribution of iron; [[#Tagliabue--2016|Tagliabue et al., 2016]] ; [[#Bindoff--2019a|Bindoff et al., 2019a]] ) remains a significant and ongoing challenge to ESMs ( ''high confidence'' ) ( [[#Séférian--2020|Séférian et al., 2020]] ).

Regionally, multi-model mean changes in primary production show generally similar patterns of large declines in the North Atlantic and the western equatorial Pacific, while in the high latitudes, primary production consistently increases in CMIP5 and CMIP6 by 2100 (Cross-Chapter Paper 6; [[#Kwiatkowski--2020|Kwiatkowski et al., 2020]] ). In the Indian Ocean and subtropical North Pacific, which were regions of consistent NPP decline in CMIP5 projections ( [[#Bopp--2013|Bopp et al., 2013]] ), the regional declines are reduced in magnitude, less spatially extensive and are typically less robust in CMIP6. Further assessment of simultaneous changes in processes such as nutrient advection, nitrogen fixation, the microbial loop and top-down grazing pressure (WGI AR6 [[IPCC:Wg2:Chapter:Chapter-5#5.4.4.2|Section 5.4.4.2]] ; [[#Laufkötter--2015|Laufkötter et al., 2015]] ; [[#Bindoff--2019a|Bindoff et al., 2019a]] ; [[#Canadell--2021|Canadell et al., 2021]] ) are required to fully understand the regional primary production response in CMIP6 ( [[#Kwiatkowski--2020|Kwiatkowski et al., 2020]] ). Given the regional variations in the estimates of primary production changes and the uncertainty in the representation of the dominant drivers, there remains ''low confidence'' in the projected global decline in NPP.

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===== 3.4.3.5.3 Observed processes driving changes in global export flux =====

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The SROCC ''medium confidence'' assessment that warming, stratification, declines in productivity and changes in plankton community in the epipelagic zone result in reduced export of primary production to deeper layers (Table 3.24) is supported by subsequent literature ( [[#Bach--2019|Bach et al., 2019]] ; [[#Leung--2021|Leung et al., 2021]] ). Particulate organic carbon export efficiency is constrained by altered mixing and nutrient availability ( [[#Boyd--2019|Boyd et al., 2019]] ; [[#Lundgreen--2019|Lundgreen et al., 2019]] ), particle fragmentation ( [[#Briggs--2020|Briggs et al., 2020]] ) as well as viral, microbial and planktonic community structure ( [[#Fu--2016|Fu et al., 2016]] ; [[#Guidi--2016|Guidi et al., 2016]] ; [[#Flombaum--2020|Flombaum et al., 2020]] ; [[#Kaneko--2021|Kaneko et al., 2021]] ) and metabolic rates (Cavan et al., 2019). These processes are strongly interlinked, and their net effect on primary production export from the upper ocean remains difficult to quantify observationally ( [[#Boyd--2019|Boyd et al., 2019]] ). Since SROCC, there is increasing evidence that ocean deoxygenation can alter zooplankton community structure ( [[#Wishner--2018|Wishner et al., 2018]] ), zooplankton respiration rates ( [[#Cass--2014|Cass and Daly, 2014]] ; [[#Cavan--2017|Cavan et al., 2017]] ) and patterns of diel vertical migration ( [[#Aumont--2018|Aumont et al., 2018]] ), which may focus remineralisation of organic carbon at the upper margins of OMZs ( [[#3.4.3.4|Section 3.4.3.4]] on depth shifts due to OMZ; [[#Bianchi--2013|Bianchi et al., 2013]] ; [[#Archibald--2019|Archibald et al., 2019]] ).

Data on export flux from the upper ocean are limited either in coverage and consistency (ship-board sampling) or duration (sediment traps), and are subject to considerable spatial variability (as shown in satellite observations ( [[#Boyd--2019|Boyd et al., 2019]] ). As a result, trends are weak, inconsistent and often not statistically significant ( [[#Lomas--2010|Lomas et al., 2010]] ; [[#Cael--2017|Cael et al., 2017]] ; [[#Muller-Karger--2019|Muller-Karger et al., 2019]] ; [[#Xie--2019|Xie et al., 2019]] ). Deep-ocean fluxes are similarly equivocal ( [[#Smith--2018|Smith et al., 2018]] ; [[#Fischer--2019|Fischer et al., 2019]] ; [[#Fischer--2020|Fischer et al., 2020]] ). In coming years, an increasing number of Argo floats equipped with bio-optical sensors should help improve estimates of particle flux spatio-temporal variability (e.g., [[#Dall’Olmo--2016|Dall’Olmo et al., 2016]] ).

Projected changes

SROCC and WGI AR6 reported global declines in POC export flux, from −8.9 to −15.8% by 2100 relative to 2000 under RCP8.5 in CMIP5 models, and −2.5 to −21.5% (median value: −14%) between 1900 and 2100 under SSP5-8.5 in CMIP6 models (Table 3.24; WGI AR6 5.4.4.2; [[#Bindoff--2019a|Bindoff et al., 2019a]] ; [[#Canadell--2021|Canadell et al., 2021]] ). In CMIP5 model runs, the decrease in the sinking flux of organic matter from the upper ocean into the ocean interior was strongly related to the changes in stratification that reduce net nutrient supply ( [[#Fu--2016|Fu et al., 2016]] ; [[#Bindoff--2019a|Bindoff et al., 2019a]] ), especially in tropical regions, and the projections for global export production changes are reported with ''medium confidence'' . Increasing model complexity with more widespread representation of ocean biogeochemical processes between CMIP5 and CMIP6, and inclusion of more than one or two classes of phyto- and zooplankton, will provide opportunities to improve assessments of how climate-induced drivers affect different components of biological carbon pump in the epipelagic ocean, as well as changes in the efficiency and magnitude of carbon export in the deep ocean ( ''high confidence'' ) (see Box 3.3; [[#Le%20Quéré--2016|Le Quéré et al., 2016]] ; [[#Séférian--2020|Séférian et al., 2020]] ; [[#Wright--2021|Wright et al., 2021]] ).

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'''Box 3.2 | Marine Birds and Mammals'''
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Marine birds (seabirds and shorebirds) and mammals include charismatic species and species that are economically, culturally and ecologically important ( [[#Sydeman--2015|Sydeman et al., 2015]] ; [[#Albouy--2020|Albouy et al., 2020]] ; [[#Pimiento--2020|Pimiento et al., 2020]] ). Their long generation times and slow population growth suggests limited evolutionary resilience to rapid climate change ( [[#3.3.4|Section 3.3.4]] ; [[#Sydeman--2015|Sydeman et al., 2015]] ; [[#Miller--2018|Miller et al., 2018]] ). According to the Red List Species Assessments of the International Union for Conservation of Nature ( [[#IUCN--2020|IUCN, 2020]] ), the greatest current hazards to these groups include human use of biological resources and areas, invasive species and pollution (see Figure Box 3.2.1; [[#Dias--2019|Dias et al., 2019]] ; [[#Lusseau--2021|Lusseau et al., 2021]] ). Impacts of climate change and severe weather are ranked among the five most-important hazards influencing 131 and 45 bird and mammal species, respectively (see Figure Box 3.2.1 for selection of species), including 24 bird and 7 mammal species that are currently listed as endangered, critically endangered or threatened. Furthermore, according to these IUCN assessments, climate change and severe weather are expected to impact an additional 122 and 18 marine bird and mammal species over the next 50–100 years, respectively (see Figure Box 3.2.1; [[#Dias--2019|Dias et al., 2019]] ).

[[File:abd6e7cedbae3d299905be104b2f8363 IPCC_AR6_WGII_Figure_3_Box_3_2_1.png]]

'''Figure Box 3.2.1 |''' '''Hazard assessment for marine birds and mammals.''' Number of (a) marine birds and (b) mammals currently impacted by different hazards (blue), and numbers of additional species expected to be exposed to these threats over the next 50–100 years (red), as assessed in the International Union for Conservation of Nature Red List ( [[#IUCN--2020|IUCN, 2020]] ). Seabird species include species in the key orders ''Sphenisciformes'' , ''Pelecaniformes, Suliformes, Anseriformes, Procellariiformes'' and ''Charadriiformes'' categorised as inhabitants of marine ecosystems ( ''n'' = 483 species, assessed in the period 2016–2019). Marine mammal species include the species reviewed by [[#Lusseau--2021|Lusseau et al. (2021)]] ( ''n'' = 136 species, assessed in the period 2008–2019).

Marine birds and mammals are vulnerable to climate-induced loss of breeding and foraging habitats such as sea ice ( [[#3.4.2.1|Section 3.4.2.1]] 2), sandy beaches ( [[#3.4.2.6|Section 3.4.2.6]] ), salt marshes ( [[#3.4.2.5|Section 3.4.2.5]] ) and seagrass beds ( ''high confidence'' ) ( [[#3.4.2.5|Section 3.4.2.5]] ; [[#Sydeman--2015|Sydeman et al., 2015]] ; [[#Bindoff--2019a|Bindoff et al., 2019a]] ; [[#Ropert-Coudert--2019|Ropert-Coudert et al., 2019]] ; [[#Von%20Holle--2019|Von Holle et al., 2019]] ; [[#Albouy--2020|Albouy et al., 2020]] ; [[#Amano--2020|Amano et al., 2020]] ; [[#Bestley--2020|Bestley et al., 2020]] ; [[#Grose--2020|Grose et al., 2020]] ). With warming, shorebird population abundances decline in the tropics, ''likely'' due to heat stress and habitat loss, and increase at higher latitudes ( [[#Amano--2020|Amano et al., 2020]] ). Marine mammals dependent on sea ice habitat are particularly vulnerable to warming ( ''medium confidence'' ) ( [[#Albouy--2020|Albouy et al., 2020]] ; [[#Bestley--2020|Bestley et al., 2020]] ; [[#Lefort--2020|Lefort et al., 2020]] ), yet vulnerability can differ between populations. Ongoing sea ice loss is decreasing some polar bear populations while others remain stable, ''likely'' related to past harvesting history, regional differences in sea ice phenology and ecosystem productivity ( [[#Hamilton--2019|Hamilton and Derocher, 2019]] ; [[#Molnár--2020|Molnár et al., 2020]] ). Nevertheless, even under an intermediate emission scenario RCP4.5, increasing ice-free periods will ''likely'' reduce both recruitment and adult survival across most polar bear populations over the next four decades, threatening their existence ( ''medium confidence'' ) (see Figure Box 3.2.2; [[#Molnár--2020|Molnár et al., 2020]] ).

Climate change is affecting marine food-web dynamics ( ''high confidence'' ) (Sections 3.4.2, 3.4.3), and the vulnerability and adaptive capacity of marine birds and mammals to such changes is linked to the species’ breeding and feeding ecology. Higher-vulnerability species include central-place foragers (confined to, for example, breeding colonies fixed in space), diet and habitat specialists, and species with restricted distributions such as marine mammal populations in SES ( ''medium confidence'' ) ( [[#McMahon--2019|McMahon et al., 2019]] ; [[#Ropert-Coudert--2019|Ropert-Coudert et al., 2019]] ; [[#Albouy--2020|Albouy et al., 2020]] ; [[#Grose--2020|Grose et al., 2020]] ; [[#Sydeman--2021|Sydeman et al., 2021]] ). Surface-feeding and piscivorous marine birds appear to be more vulnerable to food-web changes than diving seabirds and planktivorous seabirds ( ''medium confidence'' ) ( [[#Sydeman--2021|Sydeman et al., 2021]] ). During the 2014–2015 Pacific heatwave, around 1 million piscivorous common murres died along a 1500 km coastal stretch in the Pacific USA due to reduced prey availability ( [[#Jones--2018b|Jones et al., 2018b]] ; [[#Piatt--2020|Piatt et al., 2020]] ). Marine birds are vulnerable to phenological shifts in food-web dynamics, as they have limited phenotypic plasticity of reproductive timing, with potentially little scope for evolutionary adaptation ( ''medium confidence'' ) ( [[#Keogan--2018|Keogan et al., 2018]] ), although changes in reproduction timing are observed in several species ( [[#3.4.4|Section 3.4.4.1]] ; [[#Sydeman--2015|Sydeman et al., 2015]] ; [[#Descamps--2019|Descamps et al., 2019]] ; [[#Sauve--2019|Sauve et al., 2019]] ). There is ''limited evidence'' of marine mammals’ capacity to adapt to shifting phenologies, but observed responses include changes in the onset of migrations, moulting and breeding ( [[#3.4.4|Section 3.4.4.1]] ; [[#Ramp--2015|Ramp et al., 2015]] ; [[#Hauser--2017|Hauser et al., 2017]] ; [[#Beltran--2019|Beltran et al., 2019]] ; [[#Bowen--2020|Bowen et al., 2020]] ; [[#Szesciorka--2020|Szesciorka et al., 2020]] ).

[[File:b63992502eb0157e86141d72a4d2831a IPCC_AR6_WGII_Figure_3_Box_3_2_2.png]]

'''Figure Box 3.2.2 |''' '''Modelled risk timelines for demographic impacts on circumpolar polar bear subpopulations, and associated confidence assessments, due to extended fasting periods with loss of sea ice.''' Years of first impact on cub recruitment (yellow), adult male survival (blue) and adult female survival (red) are shown for the (left) RCP4.5 and (right) RCP8.5. (Data from [[#Molnár--2020|Molnár et al., 2020]] ).

Increased emergence of infectious disease in mammals and birds is expected with ocean warming, due to new transmission pathways from changing species distributions, higher species densities caused by habitat loss and increased vulnerability due to environmental stress on individuals ( ''limited evidence'' ) ( [[#Sydeman--2015|Sydeman et al., 2015]] ; [[#VanWormer--2019|VanWormer et al., 2019]] ; [[#Sanderson--2020|Sanderson and Alexander, 2020]] ). Marine birds and mammals are ''likely'' to suffer from increased mortalities due to increasing frequencies of HABs, and of extreme weather, at sea, on sea ice, and in terrestrial breeding habitats ( [[#Broadwater--2018|Broadwater et al., 2018]] ; [[#Gibble--2018|Gibble and Hoover, 2018]] ; [[#Ropert-Coudert--2019|Ropert-Coudert et al., 2019]] ; [[#Grose--2020|Grose et al., 2020]] ). Also, climate-change driven distributional shifts have strengthened interactions with other anthropogenic impacts, through, for example, increasing risks of ship strikes and bycatch ( ''medium confidence'' ) (e.g., [[#Hauser--2018|Hauser et al., 2018]] ; [[#Krüger--2018|Krüger et al., 2018]] ; [[#Record--2019|Record et al., 2019]] ; [[#Santora--2020|Santora et al., 2020]] ).

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