Editing IPCC:AR6/WGI/Chapter-5 (section)

===== 5.2.1.4.1 Trend in land–atmosphere CO <sub>2</sub> exchange =====

<div id="h4-3-siblings" class="h4-siblings"></div>

The global net land CO <sub>2</sub> sink is assessed to have grown over the past six decades ( [[#Sarmiento--2010|Sarmiento et al., 2010]] ; [[#Ballantyne--2017|Ballantyne et al., 2017]] ; [[#Le%20Quéré--2018b|Le Quéré et al., 2018b]] ; [[#Ciais--2019|Ciais et al., 2019]] ; [[#Friedlingstein--2020|Friedlingstein et al., 2020]] ) ( ''high confidence'' ). Estimated as residual from the mass balance budget of fossil fuel CO <sub>2</sub> emissions minus atmospheric CO <sub>2</sub> growth and the ocean CO <sub>2</sub> sink, the global net land CO <sub>2</sub> sink (including both land CO <sub>2</sub> sink and net land-use change emissions) increased from 0.3 ± 0.6 PgC yr <sup>–1</sup> during the 1960s to 1.8 ± 0.8 PgC yr <sup>–1</sup> during the 2010s (Friedlingstein et al., 2020). An increasing global net land CO <sub>2</sub> sink since the 1980s (Figure 5.10) was consistently suggested both by atmospheric inversions (e.g., [[#Peylin--2013|Peylin et al., 2013]] ) and by DGVMs (e.g., [[#Sitch--2015|Sitch et al., 2015]] ; [[#Friedlingstein--2019|Friedlingstein et al., 2019]] ). The Northern Hemisphere contributes more to the net increase in the land CO <sub>2</sub> sink compared to the Southern Hemisphere ( [[#Ciais--2019|Ciais et al., 2019]] ), and boreal and temperate forests probably contribute the most ( [[#Tagesson--2020|Tagesson et al., 2020]] ). Attributing an increased net land CO <sub>2</sub> sink to finer regional scales remains challenging, but inversions of satellite-based column CO <sub>2</sub> products that have emerged since AR5 are a promising tool to further constrain regional land-atmosphere CO <sub>2</sub> exchange ( [[#Ciais--2013|Ciais et al., 2013]] ; [[#Houweling--2015|Houweling et al., 2015]] ; [[#Reuter--2017|Reuter et al., 2017]] ; [[#O’Dell--2018|O’Dell et al., 2018]] ; [[#Palmer--2019|Palmer et al., 2019]] ).

<div id="_idContainer026" class="Basic-Text-Frame"></div>

[[File:24ef3e804f36e2594577172c9a2db0f5 IPCC_AR6_WGI_Figure_5_10.png]]

'''Figure 5.10 |''' '''Trends of the net land CO''' <sub>2</sub> '''sink and related vegetation observations during 1980–2019''' . '''(a)''' Net land CO <sub>2</sub> sink. The residual net land CO <sub>2</sub> sink is estimated from the global CO <sub>2</sub> mass balance (fossil fuel emissions minus atmospheric CO <sub>2</sub> growth rate and ocean CO <sub>2</sub> sink). Inversions indicate the net land CO <sub>2</sub> sink estimated by an ensemble of four atmospheric inversions. Dynamic Global Vegetation Models (DGVMs) indicate the mean net land CO <sub>2</sub> sink estimated by 17 dynamic global vegetation models driven by climate change, rising atmospheric CO <sub>2</sub> , land-use change and nitrogen deposition change (for carbon-nitrogen models). The positive values indicate net CO <sub>2</sub> uptake from the atmosphere. '''(b)''' Normalized difference vegetation index (NDVI). The anomaly of global area-weighted NDVI observed by Advanced Very High Resolution Radiometer (AVHRR) and MODIS satellite sensors. AVHRR data are accessible during 1982–2016 and MODIS data are accessible during 2000–2018. '''(c)''' Near-infrared reflectance of vegetation (NIRv) and contiguous solar-induced chlorophyll fluorescence (CSIF). The standardized anomaly of area-weighted NIRv during 2001–2018 ( [[#Badgley--2017|Badgley et al., 2017]] ) and CSIF during 2000–2018 ( [[#Zhang--2018|Zhang et al., 2018]] ). '''(d)''' Gross primary production (GPP). The GPP from [[#Cheng--2017|Cheng et al. (2017)]] , DGVMs and MODIS GPP product (MOD17A3). GPP from [[#Cheng--2017|Cheng et al. (2017)]] is based on an analytical model driven by climate change, rising atmospheric CO <sub>2</sub> , AVHRR leaf area index datasets and evapotranspiration datasets. GPP from DGVMs is the ensemble mean global GPP estimated by the same 17 DGVMs that provide the net land CO <sub>2</sub> sink estimates. Shaded area indicates 1– σ inter-model spread except for atmospheric inversions, whose ranges were used due to limited number of models. Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).

Carbon uptake by vegetation photosynthesis exerts a first-order control over the net land CO <sub>2</sub> sink. Several lines of evidence show enhanced vegetation photosynthesis over the past decades ( ''medium to high confidence'' ) (Figure 5.10), including increasing satellite-derived vegetation greenness (e.g., see Chapter 2; [[#Mao--2016|Mao et al., 2016]] ; [[#Zhu--2016|Zhu et al., 2016]] ; [[#Jia--2019|Jia et al., 2019]] ) and satellite-derived photosynthesis indicators (e.g., [[#Badgley--2017|Badgley et al., 2017]] ; [[#Zhang--2018|Zhang et al., 2018]] ), change in atmospheric concentration of carbonyl sulphide ( [[#Campbell--2017|Campbell et al., 2017]] ), enhanced seasonal CO <sub>2</sub> amplitude ( [[#Graven--2013|Graven et al., 2013]] ; [[#Forkel--2016|Forkel et al., 2016]] ), observation-driven inference of increasing photosynthesis CO <sub>2</sub> uptake based mostly on enhanced water use efficiency ( [[#Cheng--2017|Cheng et al., 2017]] ), and DGVM simulated increase of photosynthesis CO <sub>2</sub> uptake ( [[#Anav--2015|Anav et al., 2015]] ).

Substantial progress has been made since AR5 on attributing change of the global net land CO <sub>2</sub> sink. Increasing global net land CO <sub>2</sub> sink since the 1980s is mainly driven by the fertilization effect from rising atmospheric CO <sub>2</sub> concentrations ( [[#Schimel--2015|Schimel et al., 2015]] ; [[#Sitch--2015|Sitch et al., 2015]] ; [[#Fernández-Martínez--2019|Fernández-Martínez et al., 2019]] ; [[#O’Sullivan--2019|O’Sullivan et al., 2019]] ; [[#Tagesson--2020|Tagesson et al., 2020]] ; [[#Walker--2021|Walker et al., 2021]] ) ( ''medium confidence'' ). Increasing nitrogen deposition ( [[#de%20Vries--2009|de Vries et al., 2009]] ; [[#Devaraju--2016|Devaraju et al., 2016]] ; [[#Huntzinger--2017|Huntzinger et al., 2017]] ) or the synergy between increasing nitrogen deposition and atmospheric CO <sub>2</sub> concentration ( [[#O’Sullivan--2019|O’Sullivan et al., 2019]] ) could have also contributed to the increasing global net land CO <sub>2</sub> sink. The effects of climate change alone on the global net land CO <sub>2</sub> sink is so divergent that even the signs (directions) of the effects are not the same across DGVMs (e.g., [[#Huntzinger--2017|Huntzinger et al., 2017]] ).

Lower fire emissions of CO <sub>2</sub> and enhanced vegetation carbon uptake due to reduced global burned area have contributed to the increasing global net land CO <sub>2</sub> sink in the recent decade ( [[#Arora--2018|Arora and Melton, 2018]] ; [[#Yin--2020|Yin et al., 2020]] ) ( ''low to medium confidence'' ). Satellite observations reveal a declining trend in global burned area by about 20% over past two decades ( [[#Andela--2017|Andela et al., 2017]] ; [[#Earl--2018|Earl and Simmonds, 2018]] ; [[#Forkel--2019|Forkel et al., 2019]] ), a trend most pronounced in regions like northern Africa ( [[#Forkel--2019|Forkel et al., 2019]] ; [[#Zubkova--2019|Zubkova et al., 2019]] ; [[#Bowman--2020|Bowman et al., 2020]] ) and Mediterranean Europe ( [[#Turco--2016|Turco et al., 2016]] ). However, burned area trends are highly heterogeneous regionally with increasing trends reported in regions like western United States ( [[#Holden--2018|Holden et al., 2018]] ; [[#Abatzoglou--2019|Abatzoglou et al., 2019]] ). Some regions (e.g., Amazon basin and Australia) experienced record-breaking fire events in 2019 and 2020 (e.g., [[#Boer--2020|Boer et al., 2020]] ), whose effects on burned area trends remain to be explored. The burned area trends were primarily attributed to both human-induced climate change and human activities ( [[#Jolly--2015|Jolly et al., 2015]] ; [[#Andela--2017|Andela et al., 2017]] ; [[#Holden--2018|Holden et al., 2018]] ; [[#Turco--2018|Turco et al., 2018]] ; [[#Teckentrup--2019|Teckentrup et al., 2019]] ; [[#Bowman--2020|Bowman et al., 2020]] ), as well as changing frequency of lightning in the boreal region (Veraverbeke et al., 2017). In addition to changes in the burned area, fire dynamics could affect the trend in land-atmosphere CO <sub>2</sub> exchange indirectly through increasing concentration of air pollutants (see Section 6.3.4 for impacts of ozone and aerosol on the carbon cycle; [[#Yue--2018|Yue and Unger, 2018]] ; [[#Lasslop--2019|Lasslop et al., 2019]] ).

Significant uncertainties remain for the land CO <sub>2</sub> sink partition of processes due to challenges in reconciling multiple-scale evidence from experiments to the globe ( [[#Fatichi--2019|Fatichi et al., 2019]] ; [[#Walker--2021|Walker et al., 2021]] ), due to large spatial and inter-model differences in diagnosing dominant driving factors affecting the net land CO <sub>2</sub> sink ( [[#Huntzinger--2017|Huntzinger et al., 2017]] ; [[#Fernández-Martínez--2019|Fernández-Martínez et al., 2019]] ), and due to model deficiency in process representations ( [[#He--2016|He et al., 2016]] ). Nitrogen dynamics, a major gap in DGVMs identified in AR5, have now been incorporated in about half of the DGVMs contributing to the carbon budget of the Global Carbon Project (GCP) (see [[#Le%20Quéré--2018a|Le Quéré et al. (2018a)]] for model characteristics) and a growing number of ESMs ( [[#Arora--2020|Arora et al., 2020]] ). However, as the representations of carbon–nitrogen interactions vary greatly among models, large uncertainties remain on how nitrogen cycling regulates the response of ecosystem carbon uptake to higher atmospheric CO <sub>2</sub> ( [[#Walker--2015|Walker et al., 2015]] ; [[#Wieder--2019|Wieder et al., 2019]] ; [[#Davies-Barnard--2020|Davies-Barnard et al., 2020]] ; [[#Meyerholt--2020|Meyerholt et al., 2020]] ; see [[#5.4.1|Section 5.4.1]] ). Fire modules have been incorporated into 10 of 16 DGVMs contributing to the global carbon budget ( [[#Le%20Quéré--2018a|Le Quéré et al., 2018a]] ), and a growing number of models have representations of human ignitions and fire suppression processes ( [[#Rabin--2017|Rabin et al., 2017]] ; [[#Teckentrup--2019|Teckentrup et al., 2019]] ). There are also growing DGVM developments to include management practices ( [[#Pongratz--2018|Pongratz et al., 2018]] ) and the effects of secondary forest regrowth ( [[#Pugh--2019|Pugh et al., 2019]] ), though models still under-represent intensively managed ecosystems, such as croplands and managed forests ( [[#Guanter--2014|Guanter et al., 2014]] ; [[#Thurner--2017|Thurner et al., 2017]] ). Processes that have not yet played a significant role in the land CO <sub>2</sub> sink of the past decades but can grow in importance, include permafrost (Box 5.1) and peatlands dynamics ( [[#Dargie--2017|Dargie et al., 2017]] ; [[#Gibson--2019|Gibson et al., 2019]] ), have also been incorporated in some DGVMs ( [[#Koven--2015b|Koven et al., 2015b]] ; [[#Burke--2017a|Burke et al., 2017a]] ; [[#Guimberteau--2018|Guimberteau et al., 2018]] ). Growing numbers and varieties of Earth observations are being jointly used to drive and benchmark models, helping to further identify missing key processes or mechanisms that are poorly represented in the current generation of DGVMs (e.g., [[#Collier--2018|Collier et al., 2018]] ).

<div id="5.2.1.4.2" class="h4-container"></div>

<span id="interannual-variability-in-landatmosphere-co-2-exchange"></span>