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Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson’s r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson’s r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. We assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO 2 enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle. 

Abstract Despite their sparse vegetation, dryland regions exert a huge influence over global biogeochemical cycles because they cover more than 40% of the world surface (Schimel 2010 Science 327 418–9). It is thought that drylands dominate the inter-annual variability (IAV) and long-term trend in the global carbon (C) cycle (Poulter et al 2014 Nature 509 600–3, Ahlstrom et al 2015 Science 348 895–9, Zhang et al 2018 Glob. Change Biol . 24 3954–68). Projections of the global land C sink therefore rely on accurate representation of dryland C cycle processes; however, the dynamic global vegetation models (DGVMs) used in future projections have rarely been evaluated against dryland C flux data. Here, we carried out an evaluation of 14 DGVMs (TRENDY v7) against net ecosystem exchange (NEE) data from 12 dryland flux sites in the southwestern US encompassing a range of ecosystem types (forests, shrub- and grasslands). We find that all the models underestimate both mean annual C uptake/release as well as the magnitude of NEE IAV, suggesting that improvements in representing dryland regions may improve global C cycle projections. Across all models, the sensitivity and timing of ecosystem C uptake to plant available moisture was at fault. Spring biases in gross primary production (GPP) dominate the underestimate of mean annual NEE, whereas models’ lack of GPP response to water availability in both spring and summer monsoon are responsible for inability to capture NEE IAV. Errors in GPP moisture sensitivity at high elevation forested sites were more prominent during the spring, while errors at the low elevation shrub and grass-dominated sites were more important during the monsoon. We propose a range of hypotheses for why model GPP does not respond sufficiently to changing water availability that can serve as a guide for future dryland DGVM developments. Our analysis suggests that improvements in modeling C cycle processes across more than a quarter of the Earth’s land surface could be achieved by addressing the moisture sensitivity of dryland C uptake. 

Abstract. Evapotranspiration (ET) is critical in linking global water, carbon andenergy cycles. However, direct measurement of global terrestrial ET is notfeasible. Here, we first reviewed the basic theory and state-of-the-artapproaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surfacemodels (LSMs). We then utilized 4 remote-sensing-based physical models,2 machine-learning algorithms and 14 LSMs to analyze the spatial andtemporal variations in global terrestrial ET. The results showed that theensemble means of annual global terrestrial ET estimated by these threecategories of approaches agreed well, with values ranging from 589.6 mm yr−1(6.56×104 km3 yr−1) to 617.1 mm yr−1(6.87×104 km3 yr−1). For the period from 1982 to 2011, boththe ensembles of remote-sensing-based physical models and machine-learningalgorithms suggested increasing trends in global terrestrial ET (0.62 mm yr−2 with a significance level of p<0.05 and 0.38 mm yr−2 with a significance level of p<0.05,respectively). In contrast, the ensemble mean of the LSMs showed nostatistically significant change (0.23 mm yr−2, p>0.05),although many of the individual LSMs reproduced an increasing trend.Nevertheless, all 20 models used in this study showed that anthropogenicEarth greening had a positive role in increasing terrestrial ET. Theconcurrent small interannual variability, i.e., relative stability, found inall estimates of global terrestrial ET, suggests that a potentialplanetary boundary exists in regulating global terrestrial ET, with the value of this boundary beingaround 600 mm yr−1. Uncertainties among approaches were identified inspecific regions, particularly in the Amazon Basin and arid/semiaridregions. Improvements in parameterizing water stress and canopy dynamics,the utilization of new available satellite retrievals and deep-learning methods,and model–data fusion will advance our predictive understanding of globalterrestrial ET. 

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions andtheir redistribution among the atmosphere, ocean, and terrestrial biospherein a changing climate is critical to better understand the global carboncycle, support the development of climate policies, and project futureclimate change. Here we describe and synthesize data sets and methodologies toquantify the five major components of the global carbon budget and theiruncertainties. Fossil CO2 emissions (EFOS) are based on energystatistics and cement production data, while emissions from land-use change(ELUC), mainly deforestation, are based on land use and land-use changedata and bookkeeping models. Atmospheric CO2 concentration is measureddirectly, and its growth rate (GATM) is computed from the annualchanges in concentration. The ocean CO2 sink (SOCEAN) is estimatedwith global ocean biogeochemistry models and observation-baseddata products. The terrestrial CO2 sink (SLAND) is estimated withdynamic global vegetation models. The resulting carbon budget imbalance(BIM), the difference between the estimated total emissions and theestimated changes in the atmosphere, ocean, and terrestrial biosphere, is ameasure of imperfect data and understanding of the contemporary carboncycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, withfossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission(including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1(40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9  ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with aBIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low orsinks were too high). The global atmospheric CO2 concentration averaged over2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest anincrease in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %)globally and atmospheric CO2 concentration reaching 417.2 ppm, morethan 50 % above pre-industrial levels (around 278 ppm). Overall, the meanand trend in the components of the global carbon budget are consistentlyestimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadalvariability in CO2 fluxes. Comparison of estimates from multipleapproaches and observations shows (1) a persistent large uncertainty in theestimate of land-use change emissions, (2) a low agreement between thedifferent methods on the magnitude of the land CO2 flux in the northernextratropics, and (3) a discrepancy between the different methods on thestrength of the ocean sink over the last decade. This living data updatedocuments changes in the methods and data sets used in this new globalcarbon budget and the progress in understanding of the global carbon cyclecompared with previous publications of this data set. The data presented inthis work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b). 

Abstract. Understanding and quantifying the global methane (CH4) budgetis important for assessing realistic pathways to mitigate climate change.Atmospheric emissions and concentrations of CH4 continue to increase,making CH4 the second most important human-influenced greenhouse gas interms of climate forcing, after carbon dioxide (CO2). The relativeimportance of CH4 compared to CO2 depends on its shorteratmospheric lifetime, stronger warming potential, and variations inatmospheric growth rate over the past decade, the causes of which are stilldebated. Two major challenges in reducing uncertainties in the atmosphericgrowth rate arise from the variety of geographically overlapping CH4sources and from the destruction of CH4 by short-lived hydroxylradicals (OH). To address these challenges, we have established aconsortium of multidisciplinary scientists under the umbrella of the GlobalCarbon Project to synthesize and stimulate new research aimed at improvingand regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paperdedicated to the decadal methane budget, integrating results of top-downstudies (atmospheric observations within an atmospheric inverse-modellingframework) and bottom-up estimates (including process-based models forestimating land surface emissions and atmospheric chemistry, inventories ofanthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated byatmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximumestimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or∼ 60 % is attributed to anthropogenic sources, that isemissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009),and 24 Tg CH4 yr−1 larger than the one reported in the previousbudget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4emissions have been tracking the warmest scenarios assessed by theIntergovernmental Panel on Climate Change. Bottom-up methods suggest almost30 % larger global emissions (737 Tg CH4 yr−1, range 594–881)than top-down inversion methods. Indeed, bottom-up estimates for naturalsources such as natural wetlands, other inland water systems, and geologicalsources are higher than top-down estimates. The atmospheric constraints onthe top-down budget suggest that at least some of these bottom-up emissionsare overestimated. The latitudinal distribution of atmosphericobservation-based emissions indicates a predominance of tropical emissions(∼ 65 % of the global budget, < 30∘ N)compared to mid-latitudes (∼ 30 %, 30–60∘ N)and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methanebudget is attributable to natural emissions, especially those from wetlandsand other inland waters. Some of our global source estimates are smaller than those in previouslypublished budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due toimproved partition wetlands and other inland waters. Emissions fromgeological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overalldiscrepancy between bottom-up and top-down estimates has been reduced byonly 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methanebudget include (i) a global, high-resolution map of water-saturated soilsand inundated areas emitting methane based on a robust classification ofdifferent types of emitting habitats; (ii) further development ofprocess-based models for inland-water emissions; (iii) intensification ofmethane observations at local scales (e.g., FLUXNET-CH4 measurements)and urban-scale monitoring to constrain bottom-up land surface models, andat regional scales (surface networks and satellites) to constrainatmospheric inversions; (iv) improvements of transport models and therepresentation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/orco-emitted species such as ethane to improve source partitioning. The data presented here can be downloaded fromhttps://doi.org/10.18160/GCP-CH4-2019 (Saunois et al., 2020) and from theGlobal Carbon Project. 

Abstract. Accurate assessment of anthropogenic carbon dioxide(CO2) emissions and their redistribution among the atmosphere,ocean, and terrestrial biosphere – the “global carbon budget” – isimportant to better understand the global carbon cycle, support thedevelopment of climate policies, and project future climate change. Here wedescribe data sets and methodology to quantify the five major components ofthe global carbon budget and their uncertainties. Fossil CO2emissions (EFF) are based on energy statistics and cementproduction data, while emissions from land use and land-use change (ELUC),mainly deforestation, are based on land use and land-use change data andbookkeeping models. Atmospheric CO2 concentration is measureddirectly and its growth rate (GATM) is computed from the annualchanges in concentration. The ocean CO2 sink (SOCEAN)and terrestrial CO2 sink (SLAND) are estimated withglobal process models constrained by observations. The resulting carbonbudget imbalance (BIM), the difference between the estimatedtotal emissions and the estimated changes in the atmosphere, ocean, andterrestrial biosphere, is a measure of imperfect data and understanding ofthe contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2008–2017), EFF was9.4±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.7±0.02 GtC yr−1,SOCEAN 2.4±0.5 GtC yr−1, and SLAND 3.2±0.8 GtC yr−1, with a budget imbalance BIM of0.5 GtC yr−1 indicating overestimated emissions and/or underestimatedsinks. For the year 2017 alone, the growth in EFF was about 1.6 %and emissions increased to 9.9±0.5 GtC yr−1. Also for 2017,ELUC was 1.4±0.7 GtC yr−1, GATM was 4.6±0.2 GtC yr−1, SOCEAN was 2.5±0.5 GtC yr−1, and SLAND was 3.8±0.8 GtC yr−1,with a BIM of 0.3 GtC. The global atmosphericCO2 concentration reached 405.0±0.1 ppm averaged over 2017.For 2018, preliminary data for the first 6–9 months indicate a renewedgrowth in EFF of +2.7 % (range of 1.8 % to 3.7 %) basedon national emission projections for China, the US, the EU, and India andprojections of gross domestic product corrected for recent changes in thecarbon intensity of the economy for the rest of the world. The analysispresented here shows that the mean and trend in the five components of theglobal carbon budget are consistently estimated over the period of 1959–2017,but discrepancies of up to 1 GtC yr−1 persist for the representationof semi-decadal variability in CO2 fluxes. A detailed comparisonamong individual estimates and the introduction of a broad range ofobservations show (1) no consensus in the mean and trend in land-use changeemissions, (2) a persistent low agreement among the different methods onthe magnitude of the land CO2 flux in the northern extra-tropics,and (3) an apparent underestimation of the CO2 variability by oceanmodels, originating outside the tropics. This living data update documentschanges in the methods and data sets used in this new global carbon budgetand the progress in understanding the global carbon cycle compared withprevious publications of this data set (Le Quéré et al., 2018, 2016,2015a, b, 2014, 2013). All results presented here can be downloaded fromhttps://doi.org/10.18160/GCP-2018. 

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the global carbon budget – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2007–2016), EFF was 9.4 ± 0.5 GtC yr−1, ELUC 1.3 ± 0.7 GtC yr−1, GATM 4.7 ± 0.1 GtC yr−1, SOCEAN 2.4 ± 0.5 GtC yr−1, and SLAND 3.0 ± 0.8 GtC yr−1, with a budget imbalance BIM of 0.6 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1. Also for 2016, ELUC was 1.3 ± 0.7 GtC yr−1, GATM was 6.1 ± 0.2 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1, and SLAND was 2.7 ± 1.0 GtC yr−1, with a small BIM of −0.3 GtC. GATM continued to be higher in 2016 compared to the past decade (2007–2016), reflecting in part the high fossil emissions and the small SLAND consistent with El Niño conditions. The global atmospheric CO2 concentration reached 402.8 ± 0.1 ppm averaged over 2016. For 2017, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.0 % (range of 0.8 to 3.0 %) based on national emissions projections for China, USA, and India, and projections of gross domestic product (GDP) corrected for recent changes in the carbon intensity of the economy for the rest of the world. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quéré et al., 2016, 2015b, a, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017 (GCP, 2017).