Global evaluation of particulate organic carbon flux parameterizations and implications for atmospheric pCO\u3csub\u3e2\u3c/sub\u3e

The shunt of photosynthetically derived particulate organic carbon (POC) from the euphotic zone and deep remineralization comprises the basic mechanism of the “biological carbon pump.” POC raining through the “twilight zone” (euphotic depth to 1 km) and “midnight zone” (1 km to 4 km) is remineralized back to inorganic form through respiration. Accurately modeling POC flux is critical for understanding the “biological pump” and its impacts on air‐sea CO2 exchange and, ultimately, long‐term ocean carbon sequestration. Yet commonly used parameterizations have not been tested quantitatively against global data sets using identical modeling frameworks. Here we use a single one‐dimensional physical‐biogeochemical modeling framework to assess three common POC flux parameterizations in capturing POC flux observations from moored sediment traps and thorium‐234 depletion. The exponential decay, Martin curve, and ballast model are compared to data from 11 biogeochemical provinces distributed across the globe. In each province, the model captures satellite‐based estimates of surface primary production within uncertainties. Goodness of fit is measured by how well the simulation captures the observations, quantified by bias and the root‐mean‐square error and displayed using “target diagrams.” Comparisons are presented separately for the twilight zone and midnight zone. We find that the ballast hypothesis shows no improvement over a globally or regionally parameterized Martin curve. For all provinces taken together, Martin\u27s b that best fits the data is [0.70, 0.98]; this finding reduces by at least a factor of 3 previous estimates of potential impacts on atmospheric pCO2 of uncertainty in POC export to a more modest range [−16 ppm, +12 ppm]

Global ocean particulate organic carbon flux merged with satellite parameters

Particulate organic carbon (POC) flux estimated from POC concentration observations from sediment traps and 234 Th are compiled across the global ocean. The compilation includes six time series locations: CARIACO, K2, OSP, BATS, OFP, and HOT. Efficiency of the biological pump of carbon to the deep ocean depends largely on biologically mediated export of carbon from the surface ocean and its remineralization with depth; thus biologically related parameters able to be estimated from satellite observations were merged at the POC observation sites. Satellite parameters include net primary production, percent microplankton, sea surface temperature, photosynthetically active radiation, diffuse attenuation coefficient at 490 nm, euphotic zone depth, and climatological mixed layer depth. Of the observations across the globe, 85% are concentrated in the Northern Hemisphere with 44% of the data record overlapping the satellite record. Time series sites accounted for 36% of the data, while 71% of the data are measured at≥ 500m with the most common deployment depths between 1000 and 1500m. This data set is valuable for investigations of CO2 drawdown, carbon export, remineralization, and sequestration. The compiled data can be freely accessed at doi.org/10.1594/PANGAEA.855600

Global carbon budget 2022

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)

Global Carbon Budget 2022

Accurate assessment of anthropogenic carbon dioxide (CO$_2$) 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 CO$_2$ 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 CO$_2$ concentration is measured directly, and its growth rate (G$_{ATM}$) is computed from the annual changes in concentration. The ocean CO$_2$ sink (S$_{OCEAN}$) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO$_2$ sink (S$_{LAND}$) is estimated with dynamic global vegetation 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 understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, E$_{FOS}$ 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 E$_{LUC}$ was 1.1 ± 0.7 GtC yr$^{−1}$, for a total anthropogenic CO$_2$ emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr$^{−1}$ (40.0 ± 2.9 GtCO$_2$). Also, for 2021, G$_{ATM}$ was 5.2 ± 0.2 GtC yr$^{−1}$ (2.5 ± 0.1 ppm yr$^{−1}$), S$_{OCEAN}$ was 2.9  ± 0.4 GtC yr$^{−1}$, and S$_{LAND}$ was 3.5 ± 0.9 GtC yr$^{−1}$, with a B$_{IM}$ of −0.6 GtC yr$^{−1}$ (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO$_2$ concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in E$_{FOS}$ relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO$_2$ 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 CO$_2$ 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 CO$_2$ 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)

Global Ocean Carbon Dioxide Flux Mapping Techniques: Evaluation, Development, and Discrepancies

Atmospheric CO2 is projected to increase for the foreseeable future. The amount of CO2 that remains in the atmosphere is regulated, in large part, by the ocean. As the long-term response to the changing atmospheric pCO2 unfolds, the ocean sink will continue to be modified on seasonal to decadal timescales by climate variability and change. The magnitude of this variability is an active area of research. Accurately quantifying this variability is a challenge given the paucity of direct in-situ observations. In order calculate the global air-sea CO2 sink, ocean pCO2 needs to be known, or at least accurately estimated, at all locations at regular intervals. Two approaches to estimate air-sea CO2 flux are, 1) from simulations of the Earth system and 2) data gap-filling mapping techniques. The goals of this thesis are to 1) rigorously quantify errors in a leading pCO2 and ocean CO2 sink mapping technique and 2) to evaluate the efficacy of adding Earth system model based estimates of ocean pCO2 as a first guess into machine learning based mapping techniques. To meet the first goal, we use a suite of Large Ensemble model members as a testbed to evaluate a leading pCO2 gap-filling approach (SOM-FFN). We find that the SOM-FFN performs well when sufficient data is available, but overestimates Southern Ocean decadal variability by about 39%. To meet our second goal, we incorporate Earth system model pCO2 output into machine learning techniques either by adding the output as an additional feature or by post-processing the model output by learning the misfit (misfit=observation-model) and correcting for it. We find that blending model output and observations using machine learning marginally improves prediction accuracy. In addition, we discuss the potential of the learned misfits as a new model diagnostic tool, which can be used to visualize spatiotemporal pCO2 estimates. Taken together, this study has significant implications in the development of carbon monitoring systems, in turn aiding policy making and improving our understanding of the evolution of the air-sea CO2 sink

Variability in the Global Ocean Carbon Sink From 1959 to 2020 by Correcting Models With Observations

The ocean reduces human impact on the climate by absorbing and sequestering CO2. From 1950s to the 1980s, observations of pCO(2) and related ocean carbon variables were sparse and uncertain. Thus, global ocean biogeochemical models (GOBMs) have been the basis for quantifying the ocean carbon sink. The LDEO-Hybrid Physics Data product (LDEO-HPD) interpolates sparse surface ocean pCO(2) data to global coverage by using GOBMs as priors, and applying machine learning to estimate full-coverage corrections. The largest component of the GOBM corrections are climatological. This is consistent with recent findings of large seasonal discrepancies in GOBMs, but contrasts the long-held view that interannual variability is a major source of GOBM error. This supports extension of the LDEO-HPD pCO(2) product back to 1959, using a climatology of model-observation misfits prior to 1982. Consistent with previous studies for 1980 onward, air-sea CO2 fluxes for 1959-2020 demonstrate response to atmospheric pCO(2) growth and volcanic eruptions

Global evaluation of particulate organic carbon flux parameterizations and implications for atmospheric pCO \u3c inf\u3e 2

The shunt of photosynthetically derived particulate organic carbon (POC) from the euphotic zone and deep remineralization comprises the basic mechanism of the “biological carbon pump.” POC raining through the “twilight zone” (euphotic depth to 1 km) and “midnight zone” (1 km to 4 km) is remineralized back to inorganic form through respiration. Accurately modeling POC flux is critical for understanding the “biological pump” and its impacts on air-sea CO2 exchange and, ultimately, long-term ocean carbon sequestration. Yet commonly used parameterizations have not been tested quantitatively against global data sets using identical modeling frameworks. Here we use a single one-dimensional physical-biogeochemical modeling framework to assess three common POC flux parameterizations in capturing POC flux observations from moored sediment traps and thorium-234 depletion. The exponential decay, Martin curve, and ballast model are compared to data from 11 biogeochemical provinces distributed across the globe. In each province, the model captures satellite-based estimates of surface primary production within uncertainties. Goodness of fit is measured by how well the simulation captures the observations, quantified by bias and the root-mean-square error and displayed using “target diagrams.” Comparisons are presented separately for the twilight zone and midnight zone. We find that the ballast hypothesis shows no improvement over a globally or regionally parameterized Martin curve. For all provinces taken together, Martin\u27s b that best fits the data is [0.70, 0.98]; this finding reduces by at least a factor of 3 previous estimates of potential impacts on atmospheric pCO2 of uncertainty in POC export to a more modest range [−16 ppm, +12 ppm]