We present an entirely new physics founded approach to estimating the social cost of carbon (SCC). For this, we developed our Ocean-Heat-Content Physics and Time Macro Economic Model (OPTiMEM) to estimate future heat content. The heat conjecture assumes that weather damages curves are stochastically proportional to ocean heat increase. We model carbon combustion, validate to datasets for greenhouse gas (GHG), temperature, and ocean heat content (OHC). We show that the social cost of 4 GHGs: CO2, CH4, N2O and halogenated hydrocarbons, cannot be single values, but must be represented by a kind of economic phase space. We propose very long-term carbon bonds to implement real discounting. This obviates the Gordian knot of the descriptivist versus prescriptivist discount disagreement that is unsolvable. Implementing these bonds leads to a new monitoring metric: real-dollar spending and bond discount rates compared to SC-GHG cost with variation on the discount scale, where the discount has no relationship to the pure rate of time preference (PRTP). This heat conjecture is based on OPTiMEM. OPTiMEM initiates from a fossil fuel consumption function to produce CO2, with 18 scenarios implemented to provide the uncertainty range. We provide 1:N year loss risk models (1:10, 1:100, 1:1000) that government, engineers, and actuaries should find useful. A scenario implementing DICE family of models carbon and growth assumptions shows +18° C is breached by 2210 CE, and +110° C by 2300 CE – both of which outcomes are obviously not compatible with the fairly rosy conclusions of DICE models. Concerns are raised about having enough low-cost fossil fuel for conversion to minimal CO_2 maximal energy return on energy invested (EROEI) power if nations wait too long, and low EROEI power is questioned because monetary value is dependent on energy.

The 14 C:C ratio in atmospheric CO 2 (expressed as Δ 14 C) is a powerful tracer of Earth system carbon cycle processes. In the 21st century, spatio‐temporal variations of atmospheric Δ 14 C are mainly the result of anthropogenic fossil CO 2 emissions, but the oceans and terrestrial biosphere also exert significant influence on its variations. Here we present a complete three‐dimensional representation of the impact of 14 CO 2 and CO 2 fluxes on atmospheric CO 2 and Δ 14 C for 2000 through 2012. We compare simulated atmospheric Δ 14 C with approximately 5,000 measurements from both the remote atmosphere and continental areas strongly influenced by fossil CO 2 emissions. These comparisons demonstrate that the spatio‐temporal characteristics of input surface fluxes developed in Part 1 of this study have high fidelity. Based on good model‐observation agreement, we used the model's ability to determine the relative contributions of fossil, oceanic, and terrestrial fluxes to simulated Δ 14 C to help explain the origin of the observed variations. During our study period, the pole‐to‐pole difference in atmospheric Δ 14 C increased, which our analysis indicates results from changes in both fossil and oceanic fluxes. Over the continents, we show that most short‐term variation of Δ 14 C in the PBL results from atmospheric mixing acting on fossil CO 2 fluxes. Overall, the validation of our simulations by comparison with observations demonstrates that we understand the processes affecting atmospheric Δ 14 C at a variety of spatial and temporal scales. This suggests that, especially with an expanded set of measurements, we can use Δ 14 C to better quantify and understand key carbon cycle processes, especially fossil CO 2 emissions.

Monitoring the long-term changes in the stable carbon isotope ratio of carbon dioxide (expressed as δ13C-CO2) is a useful tool to track the fate of atmospheric CO2 and variations in the global carbon cycle. Due to the small but significant change in global mean atmospheric δ13C-CO2 of approximately -0.75‰ over the last three decades, a robust and traceable method is required to track the long-term change in δ13C-CO2 globally. The Stable Isotope Lab at the INSTAAR of the University of Colorado Boulder has been partnering with NOAA’s Global Monitoring Laboratory since 1990 to measure CO2 stable isotopes within the Global Greenhouse Gas Reference Network. Here, we present our latest data product, globally distributed observations of δ13C-CO2 in the past 32 years. We have improved our traceability by moving our data onto the CO2 -in-air JRAS-06 isotopic scale (a representation of V-PDB). We also have established robust quality management systems and have improved our methods of quantifying uncertainty. Our data demonstrate how the interactions among the atmosphere, the biosphere and anthropogenic activities had been recorded in δ13C-CO2 from more than 70 sites worldwide. The data reveal distinct seasonal variations of δ13C-CO2 that change with latitude, highlighting spatial differences in the influence of anthropogenic activities, net photosynthesis, and ocean-atmosphere CO2 exchange. Long-term observations also show that the spatiotemporal patterns of δ13C-CO2 vary interannually, which is mainly related to the impact of climate variability on the terrestrial biosphere. We are actively engaged in using these data in complex modeling frameworks to better understand the inter-relationships between climate and the global carbon cycle.

The 14C:C ratio in atmospheric CO2 (expressed as Delta 14C) is a powerful tracer of Earth system carbon cycle processes. In the 21st century, spatio-temporal variations of atmospheric Delta 14C are mainly the result of anthropogenic fossil CO2 emissions, but the oceans and terrestrial biosphere also exert significant influence on its variations. Here we present a complete three-dimensional representation of the impact of 14CO2 and CO2 fluxes on atmospheric CO2 and Delta 14C for 2000 through 2012. We compare simulated atmospheric Delta 14C with approximately 5,000 measurements from both the remote atmosphere and continental areas strongly influenced by fossil CO2 emissions. These comparisons demonstrate that the spatio-temporal characteristics of input surface fluxes developed in Part 1 of this study have high fidelity. Based on good model-observation agreement, we used the model's ability to determine the relative contributions of fossil, oceanic, and terrestrial fluxes to simulated Delta 14C to help explain the origin of the observed variations. During our study period, the pole-to-pole difference in atmospheric Delta 14C increased, which our analysis indicates results from changes in both fossil and oceanic fluxes. Over the continents, we show that most short-term variation of Delta 14C in the PBL results from atmospheric mixing acting on fossil CO2 fluxes. Overall, the validation of our simulations by comparison with observations demonstrates that we understand the processes affecting atmospheric Delta 14C at a variety of spatial and temporal scales. This suggests that, especially with an expanded set of measurements, we can use Delta 14C to better quantify and understand key carbon cycle processes, especially fossil CO2 emissions.

Despite well-meaning scenarios that propose global CO2 emissions will decline presented in every IPCC report since 1988, the trend of global CO2 increase continues without significant change. Even if any individual nation manages to flatten its emissions, what matters is the trajectory of the globe. Together the gulf between climate science and climate economics, plus the urgent need for alternative methods of estimation, provided the incentives for development of our Ocean-Heat-Content (OHC) Physics and Time Macro Economic Model (OPTiMEM) system. To link NOAA damages to climate required creating a carbon consumption model to drive a physics model of climate. How fast could carbon be burned and how much coal, oil and natural gas was reasonably available? A carbon model driving climate meant burning the carbon, and modelling how the earth heated up. We developed this using the most recent best greenhouse gas equations and production models for CO2, CH4, N2O, and halogenated gases. This developed an ocean heat content model for the globe. Each step is validated against Known carbon consumption, CO2, temperature, and ocean heat content. This allows a physics founded model of climate costs to be projected.

Methane (CH 4 ) is the second most important greenhouse gas and has been rising following a brief period of stabilization from 1999 to 2006. Determining the cause of this rise is critical for reducing emissions and predicting future climate sensitivity. The carbon and hydrogen stable isotopic composition of atmospheric CH 4 is controlled by variability in isotopically distinguishable emission categories and fractionating sink processes. While most studies using atmospheric δ 13 C–CH 4 data suggest a dominantly microbial source for recent CH 4 growth, this understanding is not uniform, and uncertainties remain [S. Schwietzke et al. , Nature 538 , 88–91 (2016), S. Basu et al. , Atmos. Chem. Phys. 22 , 15351–15377 (2022), J. Thanwerdas, M. Saunois, A. Berchet, I. Pison, P. Bousquet, Atmos. Chem. Phys. 24 , 2129–2167 (2024)]. Here, we present a harmonized global measurement record of atmospheric δD–CH 4 and estimate emissions from 1999 to 2022 with global isotope mass balance calculations using both carbon and hydrogen isotopic ratios. We conduct thorough uncertainty analyses to separate absolute magnitude and emission trend uncertainties and find with high confidence that trends in δ 13 C–CH 4 and δD–CH 4 observations are both consistent with an entirely microbial emission driver of the post-2006 CH 4 rise, while fossil fuel emissions have remained relatively stable.

Methane (CH4) is the second most important greenhouse gas and has been rising following a brief period of stabilization from 1999 to 2006. Determining the cause of this rise is critical for reducing emissions and predicting future climate sensitivity. The carbon and hydrogen stable isotopic composition of atmospheric CH4 is controlled by variability in isotopically distinguishable emission categories and fractionating sink processes. While most studies using atmospheric δ13C-CH4 data suggest a dominantly microbial source for recent CH4 growth, this understanding is not uniform, and uncertainties remain [S. Schwietzke et al., Nature 538, 88-91 (2016), S. Basu et al., Atmos. Chem. Phys. 22, 15351-15377 (2022), J. Thanwerdas, M. Saunois, A. Berchet, I. Pison, P. Bousquet, Atmos. Chem. Phys. 24, 2129-2167 (2024)]. Here, we present a harmonized global measurement record of atmospheric δD-CH4 and estimate emissions from 1999 to 2022 with global isotope mass balance calculations using both carbon and hydrogen isotopic ratios. We conduct thorough uncertainty analyses to separate absolute magnitude and emission trend uncertainties and find with high confidence that trends in δ13C-CH4 and δD-CH4 observations are both consistent with an entirely microbial emission driver of the post-2006 CH4 rise, while fossil fuel emissions have remained relatively stable.

Despite inventory-based (“bottom-up”) estimates of fossil-fuel emissions likely being accurate to within 10% for most developed countries, estimates of emissions based on atmospheric measurements are needed to bolster confidence in bottom-up estimates as well as possibly reduce uncertainty and provide emissions estimates closer to real time. In the case of CO2, however, measurements of CO2 concentrations alone, whether from in situ or remote sensing approaches, are not sufficient to determine fossil emissions because of the confounding influence of terrestrial ecosystem CO2 exchange which influences, and even dominate, spatio-temporal gradients of CO2. The rarest isotope of C, 14C (radiocarbon), is completely absent from fossil CO2 because of radioactive decay allowing us to use precise measurements of atmospheric CO2 and its 14C:C ratio to isolate the fossil contribution to observed gradients. Within the context of an atmospheric inverse model, CO2 and 14C:C measurements provide constraints that yield optimized fluxes of both fossil and ecosystem CO2. Using a global, but US-focused, radiocarbon measurement and modeling system developed over the past 20 years, we are now able to estimate seasonal fossil CO2 emissions for different regions the U.S. between 2010 and 2019 with an estimated national, annual random error of ~ 2%. Our top-down estimates for 2010 and 2015 show a significant decrease in US fossil CO2 emissions in line with, yet independent from, the inventory-based estimates from the US EPA. Despite some limitations of top-down emissions related to systematic errors in atmospheric transport models, we expect that top-down year to year flux changes should be robust with respect to transport error, as has been shown previously in model intercomparison studies. Indeed, while our 2015 – 2010 decrease matches the EPA well, mean emissions are offset, although within 95% confidence intervals. Spatially, our results show that the eastern third of the US dominates national emissions and also dominates the observed decrease, consistent with the understanding that US emissions have decreased since their peak mainly as a result of a transition from coal- to gas-fired power plants, which are dominantly in this area. Here, we will compare our 2010-2019 emissions estimates with both the EPA’s official US inventory and other bottom-up estimates such as the Vulcan data product that were not used as a priori information in our inverse modeling system. We will also discuss the steps required to enhance our US-focused system and the extent to which our system might be applied to regions in other parts of the world.

The growth rate of the atmospheric abundance of methane (CH4) reached a record high of 15.4 ppb yr-1 between 2020 and 2022, but the mechanisms driving the accelerated CH4 growth have so far been unclear. In this work, we use measurements of the 13C:12C ratio of CH4 (expressed as δ13CCH4) from NOAA's Global Greenhouse Gas Reference Network and a box model to investigate potential drivers for the rapid CH4 growth. These measurements show that the record-high CH4 growth in 2020-2022 was accompanied by a sharp decline in δ13CCH4, indicating that the increase in CH4 abundance was mainly driven by increased emissions from microbial sources such as wetlands, waste, and agriculture. We use our box model to reject increasing fossil fuel emissions or decreasing hydroxyl radical sink as the dominant driver for increasing global methane abundance.

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. The data presented in this work are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023).

High-quality, long-time-series measurements of atmospheric greenhouse gases show interannual variability in the measured seasonal cycles. These changes can be analyzed to better understand the carbon cycle and the impact of climate drivers. However, nearly all discrete measurement records contain gaps and have noise due to the influence of local fluxes or synoptic variability. To facilitate analysis, filtering and curve-fitting techniques are often applied to these time series. Previous studies have recognized that there is an inherent uncertainty associated with this curve fitting, and the choice of a given mathematical method might introduce biases. Since uncertainties are seldom propagated to the metrics under study, this can lead to misinterpretation of the signal. In this study, we use an ensemble-based approach to quantify the uncertainty of the derived seasonal cycle metrics. We apply it to CO2 dry-air mole fraction time series from flask measurements in the Northern Hemisphere. We use this ensemble-based approach to analyze the carbon uptake period (CUP: the time of the year when the CO2 uptake is greater than the CO2 release): its onset, termination and duration. Previous studies have diagnosed CUP based on the dates on which the detrended, zero-centered seasonal cycle curve switches from positive to negative (the downward zero-crossing date, DZCD) and vice versa (upward zero-crossing date, UZCD). However, the UZCD is sensitive to the skewness of the CO2 seasonal cycle during the net carbon release period. Hence, we develop an alternative method proposed by Barlow et al. (2015) to estimate the onset and termination of the CUP based on a threshold defined in terms of the first derivative of the CO2 seasonal cycle. Using the ensemble approach, we arrive at a tighter constraint to the threshold by considering the annual uncertainty; we call this the ensemble of first derivative (EFD) method. Further, using the EFD approach and an additional curve-fitting algorithm, we show that (a) the uncertainty of the studied metrics is smaller using the EFD method than when approximated using the timing of the zero-crossing date (ZCD), and (b) the onset and termination dates derived with the EFD method provide more robust results, irrespective of the curve-fitting method applied to the data.

Atmospheric concentrations of methane, a powerful greenhouse gas, have strongly increased since 2007. Measurements of stable carbon isotopes of methane can constrain emissions if the isotopic compositions are known; however, isotopic compositions of methane emissions from wetlands are poorly constrained despite their importance. Here, we use a process-based biogeochemistry model to calculate the stable carbon isotopic composition of global wetland methane emissions. We estimate a mean global signature of −61.3 ± 0.7‰ and find that tropical wetland emissions are enriched by ~11‰ relative to boreal wetlands. Our model shows improved resolution of regional, latitudinal and global variations in isotopic composition of wetland emissions. Atmospheric simulation scenarios with the improved wetland isotopic composition suggest that increases in atmospheric methane since 2007 are attributable to rising microbial emissions. Our findings substantially reduce uncertainty in the stable carbon isotopic composition of methane emissions from wetlands and improve understanding of the global methane budget.

ABSTRACT We are observing a dramatic increase of CO2 in the atmosphere, unprecedented in the last several million years. Carbon isotopic ratios have been very useful in helping to untangle the respective roles of anthropogenic emissions and sources/sinks of CO2 in the oceans or terrestrial biosphere. However, this untangling has not been as simple as was often hoped. The isotope ratio signatures produced by emissions and removals that are present in atmospheric CO2 are always vigorously being erased by isotopic exchange with the oceans and terrestrial ecosystems, without there necessarily being any effect on total CO2. Especially in the last decades this pure isotopic exchange effect has led to gross errors that have clouded the public debate on climate change, obscuring mankind’s role. This paper traces my own struggle with the scientific and public sides of this issue, which I ran into from the start of my career in Groningen and throughout my years at NOAA. It is still relevant today.

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).

We have constructed an atmospheric inversion framework based on TM5-4DVAR to jointly assimilate measurements of methane and δ13C of methane in order to estimate source-specific methane emissions. Here we present global emission estimates from this framework for the period 1999–2016. We assimilate a newly constructed, multi-agency database of CH4 and δ13C measurements. We find that traditional CH4-only atmospheric inversions are unlikely to estimate emissions consistent with atmospheric δ13C data, and assimilating δ13C data is necessary to derive emissions consistent with both measurements. Our framework attributes ca. 85 % of the post-2007 growth in atmospheric methane to microbial sources, with about half of that coming from the tropics between 23.5∘ N and 23.5∘ S. This contradicts the attribution of the recent growth in the methane budget of the Global Carbon Project (GCP). We find that the GCP attribution is only consistent with our top-down estimate in the absence of δ13C data. We find that at global and continental scales, δ13C data can separate microbial from fossil methane emissions much better than CH4 data alone, and at smaller scales this ability is limited by the current δ13C measurement coverage. Finally, we find that the largest uncertainty in using δ13C data to separate different methane source types comes from our knowledge of atmospheric chemistry, specifically the distribution of tropospheric chlorine and the isotopic discrimination of the methane sink.

We use global airborne observations of propane (C3H8) and ethane (C2H6) from the Atmospheric Tomography (ATom) and HIAPER Pole-to-Pole Observations (HIPPO), as well as U.S.-based aircraft and tower observations by NOAA and from the NCAR FRAPPE campaign as tracers for emissions from oil and gas operations. To simulate global mole fraction fields for these gases, we update the default emissions' configuration of C3H8 used by the global chemical transport model, GEOS-Chem v13.0.0, using a scaled C2H6 spatial proxy. With the updated emissions, simulations of both C3H8 and C2H6 using GEOS-Chem are in reasonable agreement with ATom and HIPPO observations, though the updated emission fields underestimate C3H8 accumulation in the arctic wintertime, pointing to additional sources of this gas in the high latitudes (e.g., Europe). Using a Bayesian hierarchical model, we estimate global emissions of C2H6 and C3H8 from fossil fuel production in 2016-2018 to be 13.3 ± 0.7 (95% CI) and 14.7 ± 0.8 (95% CI) Tg/year, respectively. We calculate bottom-up hydrocarbon emission ratios using basin composition measurements weighted by gas production and find their magnitude is higher than expected and is similar to ratios informed by our revised alkane emissions. This suggests that emissions are dominated by pre-processing activities in oil-producing basins.