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 methodologies 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 year 2021, EFOS increased by 5.1 % relative to 2020, with fossil 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 a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, 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 change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, 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. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).
Abstract. Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model-data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO 2 ) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6 × 10 5 km 2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO 2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO 2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO 2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO 2 , decreased precipPublished by Copernicus Publications on behalf of the European Geosciences Union. itation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.
Ecological forecasting of forest productivity involves integrating observations into a process‐based model and propagating the dominant components of uncertainty to generate probability distributions for future states and fluxes. Here, we develop a forecast for the biomass change in loblolly pine (Pinus taeda) forests of the southeastern United States and evaluate the relative contribution of different forms of uncertainty to the total forecast uncertainty. Specifically, we assimilated observations of carbon and flux stocks and fluxes from sites across the region, including global change experiments, into a forest ecosystem model to calibrate the parameter distributions and estimate the process uncertainty (i.e., model structure uncertainty revealed in the residuals of the calibration). Using this calibration, we forecasted the change in biomass within each 12‐digit Hydrologic (H12) unit across the native range of loblolly pine between 2010 and 2055 under the Representative Concentration Pathway 8.5 scenario. Averaged across the region, productivity is predicted to increase by a mean of 31% between 2010 and 2055 with an average forecast 95% quantile interval of ±15 percentage units. The largest increases were predicted in cooler locations, corresponding to the largest projected changes in temperature. The forecasted mean change varied considerably among the H12 units (3–80% productivity increase), but only units in the warmest and driest extents of the loblolly pine range had forecast distributions with probabilities of a decline in productivity that exceeded 5%. By isolating the individual components of the forecast uncertainty, we found that ecosystem model process uncertainty made the largest individual contribution. Ecosystem model parameter and climate model uncertainty had similar contributions to the overall forecast uncertainty, but with differing spatial patterns across the study region. The probabilistic framework developed here could be modified to include additional sources of uncertainty, including changes due to fire, insects, and pests: processes that would result in lower productivity changes than forecasted here. Overall, this study presents an ecological forecast at the ecosystem management scale so that land managers can explicitly account for uncertainty in decision analysis. Furthermore, it highlights that future work should focus on quantifying, propagating, and reducing ecosystem model process uncertainty.
The Southern Ocean plays a critical role in the global carbon cycle. The dominating current in the region, the Antarctic Circumpolar Current (ACC), circumnavigates the globe and connects all of the major ocean basins. Its overturning circulation brings up the deepwater and subducts thermocline and intermediate waters that are major conduits for the oceanic uptake of heat and anthropogenic carbon (Armour et al., 2016; Ben Bronselaer & Zanna, 2020;Toggweiler & Russell, 2008), contributing over 40% of the ocean carbon uptake (Khatiwala et al., 2009). Despite its importance, there are large uncertainties surrounding components controlling the regional carbon flux. While the state-of-the-art Earth System Models (ESMs) generally reproduce the annual mean carbon flux in the region, its seasonal cycle is often poorly correlated with observations (Jiang et al., 2014;Mongwe et al., 2016).The ACC is filled with mesoscale (approx. 10-100 km) features with meandering jets and vortices (eddies) that play central roles to maintain the stratification and the overturning circulation with global implications (Gnanadesikan, 1999;Johnson & Bryden, 1989;Marshall & Radko, 2003;Marshall & Speer, 2012). This rich mesoscale eddy field is critical for the transport of carbon and nutrients in the region, but the current generation of ESMs cannot resolve these features due to their coarse resolution and the challenge of parameterizing eddies realistically.To study the influence of mesoscale eddies on the carbon cycle in this region, we perform and compare three computational simulations for a sensitivity study using a regional physical and biological model with a 10km horizontal resolution. This modeling study is focused on the Drake Passage region, which is the only section of the ACC bounded by land topography to the north and south and relatively well sampled by ship-based
<p>The ocean is a critical component of the global carbon budget. With a carbon reservoir substantially larger than the atmosphere's and an air-sea carbon flux absorbing approximately 25% of anthropogenic carbon annually, understanding and quantifying the air-sea carbon dioxide (CO<sub>2</sub>) flux and ocean carbon storage is essential for climate research. With this in mind, we developed a two-step neural network approach (SOM-FFN) to reconstruct the partial pressure of carbon dioxide (pCO<sub>2</sub>) at a 1&#176;x1&#176;&#160;resolution, providing an important global observational resource. Uncertainties in neural network and other interpolation techniques are, however, still substantial and remain poorly quantified, especially for remote or infrequently sampled regions. These uncertainties, which include mapping or extrapolation uncertainties as well as uncertainties in wind and gas transfer formulations, have a significant effect on our ability to balance regional and global carbon budgets. Therefore, we are reporting on the development of a two dimensional (longitude and latitude) gridded uncertainty product, available publicly alongside our standard neural network air-sea CO<sub>2</sub> flux output from the SOM-FFN method. This dataset will pave the way for a better guided use of the computed air-sea CO<sub>2</sub> fluxes and their regional uncertainties, taking into account major sources of air-sea CO<sub>2</sub> flux uncertainty. Early analysis presented here allows for identification of regions of higher uncertainty, such as high latitude open ocean, and points to areas within the flux calculation where uncertainty must be further constrained in order to contribute to improving balance of regional carbon budgets in support of the UN stocktake.</p>
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