ALBERT

Description: 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 (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6  ×  105 km2 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 CO2 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 CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 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 CO2, decreased precipitation, 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.

Description: 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. However, the influence of different experimental treatments on those predictions depends on the exact methods and techniques used for data assimilation. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation of Pine Plantation Ecosystem Research, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the Southeastern U.S. to constrain parameters in a modified version of the 3-PG forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with non-experimental studies that spanned environmental gradients across an 8.6 × 105 km2 region. We optimized regionally representative posterior distributions for the most sensitive model parameters, which dependably predicted data from plots withheld from the data assimilation. The posterior distributions of parameters associated with ecosystem responses to CO2, precipitation, and nutrient addition, along with the corresponding regional changes in production associated with nutrient fertilization and drought, depended on how the experimental data were assimilated. In particular, assimilating nutrient addition experiments reduced the predicted sensitivity to nutrient fertilization while assimilated water manipulation experiments increased the sensitivity to drought. Further, it was necessary to assimilate data from the CO2 experimental enrichment site before other studies to constrain the parameters associated with the influence of CO2 on canopy photosynthesis. The ambient CO2 plots were numerous and had a large contribution to the cost function compared to the low number of elevated CO2 plots (289 ambient vs. 5 elevated plots). Overall, we demonstrated how three decades of research in southeastern U.S. planted pine forests can be used to develop data assimilation techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters. This approach allows for future predictions to be consistent with a rich history of ecosystem research across a region.

Description: 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 (E-FOS) are based on energy statistics and cement production data, while emissions from land-use change (E-LUC), 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 (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S-OCEAN) is estimated with global ocean biogeochemistry models and observation-based fCO(2) products. The terrestrial CO2 sink (S-LAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (B-IM), 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 incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as +/- 1 sigma. For the year 2022, E-FOS increased by 0.9% relative to 2021, with fossil emissions at 9.9 +/- 0.5 GtC yr(-1) (10.2 +/- 0.5 GtC yr(-1) when the cement carbonation sink is not included), and E-LUC was 1.2 +/- 0.7 GtC yr(-1), for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1 +/- 0.8 GtC yr(-1) (40.7 +/- 3.2 GtCO(2) yr(-1)). Also, for 2022, G(ATM) was 4.6 +/- 0.2 GtC yr(-1) (2.18 +/- 0.1 ppm yr(-1); ppm denotes parts per million), S-OCEAN was 2.8 +/- 0.4 GtC yr(-1), and S-LAND was 3.8 +/- 0.8 GtC yr(-1), with a B-IM of 0.1 GtC yr(-1) (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1 +/- 0.1 ppm. Preliminary data for 2023 suggest an increase in E-FOS relative to 2022 of +/- 1:1% (0.0% to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51% above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959-2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt Cyr(-1) persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (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 methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle.

Description: 〈jats:p〉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 fCO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system 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 incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9±0.5 Gt C yr−1 (10.2±0.5 Gt C yr−1 when the cement carbonation sink is not included), and ELUC was 1.2±0.7 Gt C yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1±0.8 Gt C yr−1 (40.7±3.2 Gt CO2 yr−1). Also, for 2022, GATM was 4.6±0.2 Gt C yr−1 (2.18±0.1 ppm yr−1; ppm denotes parts per million), SOCEAN was 2.8±0.4 Gt C yr−1, and SLAND was 3.8±0.8 Gt C yr−1, with a BIM of −0.1 Gt C yr−1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1±0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (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 methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023). 〈/jats:p〉

Description: 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±0.5GtCyr-1 (9.9±0.5GtCyr-1 when the cement carbonation sink is included), and ELUC was 1.1±0.7GtCyr-1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9±0.8GtCyr-1 (40.0±2.9GtCO2). Also, for 2021, GATM was 5.2±0.2GtCyr-1 (2.5±0.1ppmyr-1), SOCEAN was 2.9 ±0.4GtCyr-1, and SLAND was 3.5±0.9GtCyr-1, with a BIM of -0.6GtCyr-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±0.1ppm. 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 417.2ppm, more than 50% above pre-industrial levels (around 278ppm). 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 1GtCyr-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 10.18160/GCP-2022 (Friedlingstein et al., 2022b).