Global Carbon Budget 2021

Abstract

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data-products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. 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 first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gases inventories, supporting the assessment of collective countries’ climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1 and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021, suggest a rebound in EFOS relative to 2020 of +4.9 % (4.1 % to 5.7 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra- tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2020; Friedlingstein et al., 2019; Le Quéré et al., 2018b, 2018a, 2016, 2015b, 2015a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Land Use Effects on Climate: Current State, Recent Progress, and Emerging Topics

“…Accurate estimates of CO 2 fluxes are essential for assessing changes in the global anthropogenic carbon cycle as provided, for instance, by the annually updated global carbon budget (GCB) of the Global Carbon Project [10]. The highest relative uncertainties within the most recent estimates are related to carbon fluxes from LULCC [10,79]. These originate from uncertainties in (1) the underlying LULCC maps, (2) different comprehensiveness and levels of complexity of the LULCC practices implemented in models, (3) lack of observational constraints for model parameters and methodologies for the processing of satellite data, (4) different model assumptions and setups, and (5) inconsistencies in common terminology and definitions.…”

“…A recent revision of the underlying land use areas reported by countries to the Food and Agricultural Organization's FAOSTAT [85] combined with a change in how national estimates of agricultural areas are distributed in space is the main reason for a change in trend from increasing to decreasing LULCC emissions in recent years (from GCB2020 to the data release of GCB2021). Yet, the uncertainties are very large, and the change in trend is thus not statistically significant [79]. For satellite-based LULCC data, further uncertainties arise due to the difficulty of distinguishing anthropogenic from natural and climate-driven LULCC dynamics on a global scale (see Spotlight: How Can We Derive CO 2 Fluxes Related to LULCC from Satellites?).…”

“…Adjustments have been developed that allow for a translation and, thus, direct comparison of modeled and inventory-based GHG fluxes [102,103]. An application to the annual updates of the global carbon budget shows the global carbon cycle models are consistent with the national GHG inventories when parts of the natural sink on managed forests-making up almost half of the total natural land sink-is redistributed to the land use sector [79]. When considering natural fluxes on managed land, the net Additional historical records (e.g., [30]), earth observations (e.g., [50,82]), crowd-sourcing (e.g., [104]), scenario ranges (e.g., [81,105]) land use flux globally turns from a substantial source of CO 2 emissions in the global models' assessment (see Biogeochemical Effects) to a CO 2 sink reported by countries [79].…”

The sensitivity of pCO<sub>2</sub> reconstructions in the Southern Ocean to sampling scales: a semi-idealized model sampling and reconstruction approach

“…One of the major contributions from machine learning methods has been the increased confidence in the constraints for the climatology, interannual variability and trends of 𝑝CO ! in the global ocean and particularly in the Southern Ocean (Hauck et al, 2020;Canadell et al, 2021;Friedlingstein et al, 2021). These semi-independent empirical models converge on the canonical representation of the seasonal cycle of 𝑝CO !…”

“…This started with largely observation-based approaches which constrained the seasonal cycle climatology (Takahashi et al, 2009(Takahashi et al, , 2012 and set requirements to resolve the variability (Lenton et al, 2006;Monteiro et al, 2015). The advent of a globally coordinated surface ocean CO2 data, SOCAT (Bakker et al, 2016), together with machine learning methods (Landschützer et al, 2014(Landschützer et al, , 2016Rödenbeck et al, 2015) provided a basis for spatial and temporal gap filling that has resulted in an internally consistent set of reconstructions for the ocean and Southern Ocean CO2 fluxes that contribute to the global carbon budget (Canadell et al, 2021;Fay et al, 2021;Friedlingstein et al, 2021).…”