National CO₂ budgets (2015–2020) inferred from atmospheric CO₂ observations in support of the Global Stocktake

Abstract

Abstract. Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries’ carbon budgets. These estimates are based on "top-down" NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements, or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with "bottom-up" estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1° × 1° gridded dataset and as a country-level dataset. Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 PgCO2 yr-1 (0.90–1.25 PgC yr-1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

Predictability of fossil fuel CO2 from air quality emissions

“…While our current results indicate that we can narrow discrepancies, structural errors can not be fully mitigated. On the other hand, top-down approaches, which use atmospheric CO 2 , can provide low-latency information on national scale carbon balance 36,50 . Near-term coevolution of AQ and carbon with these data could be used to partition natural and anthropogenic carbon drivers 16,51 and compliment local-scale atmospheric approaches 22 .…”

Carbon uptake in Eurasian boreal forests dominates the high‐latitude net ecosystem carbon budget

“…In contrast to the boreal zone, a majority of the models evaluated here (including TCFM-Arctic) indicated the tundra domain as being, on average, neutral or a small source for NEE. However, adjusting the TCFM-Arctic tundra NEE budget to account for a potentially large underestimation of episodic CO 2 emissions during spring and autumn shoulder seasons (Arndt et al, 2020;Byrne, Baker, et al, 2022;Byrne, Liu, et al, 2022;Commane et al, 2017;Liu et al, 2022;Schiferl et al, 2022) and source (when soils are warm and less wet) (e.g., Euskirchen et al, 2014;Laine et al, 2019;Olefeldt et al, 2017;Rinne et al, 2020;Schulze et al, 1999) depending on water table depth and soil wetness.…”

“…We compared the resulting TCFM-Arctic budgets with regional flux estimates (SI Section 8) from an Arctic-boreal version of the Community Land Model Version 5 (CLM 5; Birch et al, 2021); satelliteinformed SMAP L4_C and MODIS (MOD17A2H) CO 2 flux products (Kimball et al, 2012;Running et al, 2004); statistically upscaled CO 2 estimates from Virkkala et al (2021) and FluxCom (Jung et al, 2020); statistically upscaled CH 4 estimates from Peltola et al (2019), and results from atmospheric inversions-Atmospherically-enhanced Inversion (ACI) models (Liu et al, 2020(Liu et al, , 2022 and the v10 Orbiting Carbon Observatory-2 inversion modeling intercomparison project (v10 OCO-2 MIP; Byrne, Baker, et al, 2022;Byrne, Liu, et al, 2022) experiments. Additionally, we evaluated our terrestrial CH 4 emission estimates against assimilation records from CarbonTracker-CH 4 (Bruhwiler et al, 2014).…”

“…SMAP L4_C and CLM 5.0 indicated a small NEE source (8 and 21 Tg CO 2 -C year −1 ), relative to a much larger source (245 Tg CO 2 -C year −1 ) estimated by FluxCom RS. Whereas TCFM-Arctic, the ACI ensemble(Liu et al, 2020), the OCO-2 MIP experiments(Byrne, Baker, et al, 2022;Byrne, Liu, et al, 2022), FluxCom RS + METEO and Virkkala et al (2021) showed a small to moderate NEE sink (−16, −21, −49 to −32, −80, −97 Tg CO 2 -C year −1 , respectively). Variability in NEE estimates was also observed within the boreal zone.…”

Enhanced India‐Africa Carbon Uptake and Asia‐Pacific Carbon Release Associated With the 2019 Extreme Positive Indian Ocean Dipole

“…The prior surface carbon fluxes include (a) fossil fuel carbon emissions, which were calculated as the average of the Carbon Dioxide Information Analysis Center product (Andres et al, 2011) and the Open-source Data Inventory of Anthropogenic CO 2 emission product (Oda et al, 2018); (b) ocean carbon fluxes, which were from the pCO 2 -Clim prior of CT2019B derived from the climatology of seawater pCO2 (Takahashi et al, 2009); (c) land carbon fluxes (net ecosystem exchange [NEE]), which were simulated by using the Boreal Ecosystem Productivity Simulator (Chen et al, 1999), a process-based and remote sensing data-driven ecosystem model; and (d) wildfire carbon emissions, which were from the Global Fire Emissions Database version 4.1 (GFEDv4) (van der Werf et al, 2017). For this study, we performed additional validations at several surface flask sites across the study regions, showing the good performance of GCASv2 (Table S1 and Figure S2 in Supporting Information S1), and made a comparison with the ensemble NBP anomalies from OCO-2 v10 MIP LNLG experiment (Byrne et al, 2022) in September-December (SOND) 2019, showing that except over the Indo-China peninsula, they have basically consistent anomalous NBP patterns and comparable magnitudes over other regions of the IORC (Text S1 and Figure S3 in Supporting Information S1).…”