Reduced-complexity model for the impact of anthropogenic CO₂ emissions on future glacial cycles

We propose a reduced-complexity process-based model for the long-term evolution of the global ice volume, atmospheric CO2 concentration, and global mean temperature. The model's only external forcings are the orbital forcing and anthropogenic CO2 cumulative emissions. The model consists of a system of three coupled non-linear differential equations representing physical mechanisms relevant for the evolution of the climate–ice sheet–carbon cycle system on timescales longer than thousands of years. Model parameters are calibrated using paleoclimate reconstructions and the results of two Earth system models of intermediate complexity. For a range of parameters values, the model is successful in reproducing the glacial–interglacial cycles of the last 800 kyr, with the best correlation between modelled and global paleo-ice volume of 0.86. Using different model realisations, we produce an assessment of possible trajectories for the next 1 million years under natural and several fossil-fuel CO2 release scenarios. In the natural scenario, the model assigns high probability of occurrence of long interglacials in the periods between the present and 120 kyr after present and between 400 and 500 kyr after present. The next glacial inception is most likely to occur ∼50 kyr after present with full glacial conditions developing ∼90 kyr after present. The model shows that even already achieved cumulative CO2 anthropogenic emissions (500 Pg C) are capable of affecting the climate evolution for up to half a million years, indicating that the beginning of the next glaciation is highly unlikely in the next 120 kyr. High cumulative anthropogenic CO2 emissions (3000 Pg C or higher), which could potentially be achieved in the next 2 to 3 centuries if humanity does not curb the usage of fossil fuels, will most likely provoke Northern Hemisphere landmass ice-free conditions throughout the next half a million years, postponing the natural occurrence of the next glacial inception to 600 kyr after present or later.

Neural-network-based estimation of regional-scale anthropogenic CO₂ emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2, i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2, MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets.

Detecting the Responses of CO2 Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2

The continuing increase in atmospheric CO2 concentration caused by anthropogenic CO2 emissions significantly contributes to climate change driven by global warming. Satellite measurements of long-term CO2 data with global coverage improve our understanding of global carbon cycles. However, the sensitivity of the space-borne measurements to anthropogenic emissions on a regional scale is less explored because of data sparsity in space and time caused by impacts from geophysical factors such as aerosols and clouds. Here, we used global land mapping column averaged dry-air mole fractions of CO2 (XCO2) data (Mapping-XCO2), generated from a spatio-temporal geostatistical method using GOSAT and OCO-2 observations from April 2009 to December 2020, to investigate the responses of XCO2 to anthropogenic emissions at both global and regional scales. Our results show that the long-term trend of global XCO2 growth rate from Mapping-XCO2, which is consistent with that from ground observations, shows interannual variations caused by the El Niño Southern Oscillation (ENSO). The spatial distributions of XCO2 anomalies, derived from removing background from the Mapping-XCO2 data, reveal XCO2 enhancements of about 1.5–3.5 ppm due to anthropogenic emissions and seasonal biomass burning in the wintertime. Furthermore, a clustering analysis applied to seasonal XCO2 clearly reveals the spatial patterns of atmospheric transport and terrestrial biosphere CO2 fluxes, which help better understand and analyze regional XCO2 changes that are associated with atmospheric transport. To quantify regional anomalies of CO2 emissions, we selected three representative urban agglomerations as our study areas, including the Beijing-Tian-Hebei region (BTH), the Yangtze River Delta urban agglomerations (YRD), and the high-density urban areas in the eastern USA (EUSA). The results show that the XCO2 anomalies in winter well capture the several-ppm enhancement due to anthropogenic CO2 emissions. For BTH, YRD, and EUSA, regional positive anomalies of 2.47 ± 0.37 ppm, 2.20 ± 0.36 ppm, and 1.38 ± 0.33 ppm, respectively, can be detected during winter months from 2009 to 2020. These anomalies are slightly higher than model simulations from CarbonTracker-CO2. In addition, we compared the variations in regional XCO2 anomalies and NO2 columns during the lockdown of the COVID-19 pandemic from January to March 2020. Interestingly, the results demonstrate that the variations of XCO2 anomalies have a positive correlation with the decline of NO2 columns during this period. These correlations, moreover, are associated with the features of emitting sources. These results suggest that we can use simultaneously observed NO2, because of its high detectivity and co-emission with CO2, to assist the analysis and verification of CO2 emissions in future studies.

Neural Network Based Estimation of Regional Scale Anthropogenic CO₂ Emissions Using OCO-2 Dataset Over East and West Asia

Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas and its concentration is continuously increasing mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and designing mitigation strategies aimed at stabilizing the CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of column-averaged dry-air mole fraction of CO2 i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory 2 (OCO-2) to estimate the anthropogenic CO2 emissions using Generalized Regression Neural Network over East and West Asia. OCO-2 XCO2 and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variabilities. Then the XCO2 anomaly and ODIAC emission datasets from 2015 to 2018 were used to train the GRNN model, and finally, the anthropogenic CO2 emissions were estimated for 2019 based on the XCO2 anomalies derived for the same year. The XCO2-based estimated and the ODIAC actual CO2 emissions were compared and the results showed a good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional scale anthropogenic CO2 emissions and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets.

Incorporating the stable carbon isotope ¹³C in the ocean biogeochemical component of the Max Planck Institute Earth System Model

The stable carbon isotopic composition (δ13C) is an important variable to study the ocean carbon cycle across different timescales. We include a new representation of the stable carbon isotope 13C into the HAMburg Ocean Carbon Cycle model (HAMOCC), the ocean biogeochemical component of the Max Planck Institute Earth System Model (MPI-ESM). 13C is explicitly resolved for all oceanic carbon pools considered. We account for fractionation during air–sea gas exchange and for biological fractionation ϵp associated with photosynthetic carbon fixation during phytoplankton growth. We examine two ϵp parameterisations of different complexity: ϵpPopp varies with surface dissolved CO2 concentration (Popp et al., 1989), while ϵpLaws additionally depends on local phytoplankton growth rates (Laws et al., 1995). When compared to observations of δ13C of dissolved inorganic carbon (DIC), both parameterisations yield similar performance. However, with regard to δ13C in particulate organic carbon (POC) ϵpPopp shows a considerably improved performance compared to ϵpLaws. This is because ϵpLaws produces too strong a preference for 12C, resulting in δ13CPOC that is too low in our model. The model also well reproduces the global oceanic anthropogenic CO2 sink and the oceanic 13C Suess effect, i.e. the intrusion and distribution of the isotopically light anthropogenic CO2 in the ocean. The satisfactory model performance of the present-day oceanic δ13C distribution using ϵpPopp and of the anthropogenic CO2 uptake allows us to further investigate the potential sources of uncertainty of the Eide et al. (2017a) approach for estimating the oceanic 13C Suess effect. Eide et al. (2017a) derived the first global oceanic 13C Suess effect estimate based on observations. They have noted a potential underestimation, but their approach does not provide any insight about the cause. By applying the Eide et al. (2017a) approach to the model data we are able to investigate in detail potential sources of underestimation of the 13C Suess effect. Based on our model we find underestimations of the 13C Suess effect at 200 m by 0.24 ‰ in the Indian Ocean, 0.21 ‰ in the North Pacific, 0.26 ‰ in the South Pacific, 0.1 ‰ in the North Atlantic and 0.14 ‰ in the South Atlantic. We attribute the major sources of underestimation to two assumptions in the Eide et al. (2017a) approach: the spatially uniform preformed component of δ13CDIC in year 1940 and the neglect of processes that are not directly linked to the oceanic uptake and transport of chlorofluorocarbon-12 (CFC-12) such as the decrease in δ13CPOC over the industrial period. The new 13C module in the ocean biogeochemical component of MPI-ESM shows satisfying performance. It is a useful tool to study the ocean carbon sink under the anthropogenic influences, and it will be applied to investigating variations of ocean carbon cycle in the past.

Anthropogenic and natural controls on atmospheric <i>δ</i>¹³C-CO₂ variations in the Yangtze River delta: insights from a carbon isotope modeling framework

The atmospheric carbon dioxide (CO2) mixing ratio and its carbon isotope (δ13C-CO2) composition contain important CO2 sink and source information spanning from ecosystem to global scales. The observation and simulation for both CO2 and δ13C-CO2 can be used to constrain regional emissions and better understand the anthropogenic and natural mechanisms that control δ13C-CO2 variations. Such work remains rare for urban environments, especially megacities. Here, we used near-continuous CO2 and δ13C-CO2 measurements, from September 2013 to August 2015, and inverse modeling to constrain the CO2 budget and investigate the main factors that dominated δ13C-CO2 variations for the Yangtze River delta (YRD) region, one of the largest anthropogenic CO2 hotspots and densely populated regions in China. We used the WRF-STILT model framework with category-specified EDGAR v4.3.2 CO2 inventories to simulate hourly CO2 mixing ratios and δ13C-CO2, evaluated these simulations with observations, and constrained the total anthropogenic CO2 emission. We show that (1) top-down and bottom-up estimates of anthropogenic CO2 emissions agreed well (bias < 6 %) on an annual basis, (2) the WRF-STILT model can generally reproduce the observed diel and seasonal atmospheric δ13C-CO2 variations, and (3) anthropogenic CO2 emissions played a much larger role than ecosystems in controlling the δ13C-CO2 seasonality. When excluding ecosystem respiration and photosynthetic discrimination in the YRD area, δ13C-CO2 seasonality increased from 1.53 ‰ to 1.66 ‰. (4) Atmospheric transport processes in summer amplified the cement CO2 enhancement proportions in the YRD area, which dominated monthly δs (the mixture of δ13C-CO2 from all regional end-members) variations. These findings show that the combination of long-term atmospheric carbon isotope observations and inverse modeling can provide a powerful constraint on the carbon cycle of these complex megacities.

Decomposing oceanic temperature and salinity change using ocean carbon change

As the planet warms due to the accumulation of anthropogenic CO2 in the atmosphere, the global ocean uptake of heat can largely be described as a linear function of anthropogenic CO2 uptake. This relates the oceans mitigation of atmospheric warming and carbon sequestration, as well as its increasing heat content. Patterns of ocean salinity also change as the earth system warms due to hydrological cycle intensification and perturbations to air-sea freshwater fluxes. Local temperature and salinity change in the ocean may result from perturbed air-sea fluxes of heat and freshwater (excess temperature, salinity), or from variability resulting from reorganisation of the preindustrial temperature and salinity fields (redistributed temperature, salinity), which are largely due to circulation changes. Here, we present a novel method in which, by tracking the redistribution of preindustrial carbon, we may estimate the redistribution of temperature and salinity using only local spatial information. We demonstrate this technique by estimating the redistribution of heat and salinity in the NEMO OGCM coupled to the MEDUSA-2 Biogeochemistry model under a RCP8.5 scenario over 1860–2099. We find on the longest timescales, the patterns of excess heat and salinity storage are dominated by increases in excess heat and salinity in the Atlantic, and that excess salinity is generally negative in other basins, compensating for strong atmospheric transport of excess salinity to the Atlantic. We also find significant redistribution of heat away from the North Atlantic, and of salinity to the South Atlantic, consistent with AMOC slowdown. Temperature change at depth is accounted for predominately by redistributed, rather than excess heat, but the opposite is true for salinity, where the excess component accounts for the majority of changes at depth. Though by the end of the simulation excess heat is the largest contribution to density change and steric sea level rise, the storage of excess salinity greatly reduces variability in excess density, particularly in the Atlantic. Here, redistribution of the preindustrial heat and salinity fields also produce generally opposing changes in sea level, though patterns are less clear elsewhere. As expected, the regional strength of excess heat and salinity signal grows through the model run. In addition, the regional strength of the redistributed temperature and salinity signals also grow, indicating increasing circulation variability or systematic circulation change on at least the time scale of the model run.

Sub-Daily Natural CO2 Flux Simulation Based on Satellite Data: Diurnal and Seasonal Pattern Comparisons to Anthropogenic CO2 Emissions in the Greater Tokyo Area

During the last decade, advances in the remote sensing of greenhouse gas (GHG) concentrations by the Greenhouse Gases Observing SATellite-1 (GOSAT-1), GOSAT-2, and Orbiting Carbon Observatory-2 (OCO-2) have produced finer-resolution atmospheric carbon dioxide (CO2) datasets. These data are applicable for a top-down approach towards the verification of anthropogenic CO2 emissions from megacities and updating of the inventory. However, great uncertainties regarding natural CO2 flux estimates remain when back-casting CO2 emissions from concentration data, making accurate disaggregation of urban CO2 sources difficult. For this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) land products, meso-scale meteorological data, SoilGrids250 m soil profile data, and sub-daily soil moisture datasets to calculate hourly photosynthetic CO2 uptake and biogenic CO2 emissions with 500 m resolution for the Kantō Plain, Japan, at the center of which is the Tokyo metropolis. Our hourly integrated modeling results obtained for the period 2010–2018 suggest that, collectively, the vegetated land within the Greater Tokyo Area served as a daytime carbon sink year-round, where the hourly integrated net atmospheric CO2 removal was up to 14.15 ± 4.24% of hourly integrated anthropogenic emissions in winter and up to 55.42 ± 10.39% in summer. At night, plants and soil in the Greater Tokyo Area were natural carbon sources, with hourly integrated biogenic CO2 emissions equivalent to 2.27 ± 0.11%–4.97 ± 1.17% of the anthropogenic emissions in winter and 13.71 ± 2.44%–23.62 ± 3.13% in summer. Between January and July, the hourly integrated biogenic CO2 emissions of the Greater Tokyo Area increased sixfold, whereas the amplitude of the midday hourly integrated photosynthetic CO2 uptake was enhanced by nearly five times and could offset up to 79.04 ± 12.31% of the hourly integrated anthropogenic CO2 emissions in summer. The gridded hourly photosynthetic CO2 uptake and biogenic respiration estimates not only provide reference data for the estimation of total natural CO2 removal in our study area, but also supply prior input values for the disaggregation of anthropogenic CO2 emissions and biogenic CO2 fluxes when applying top-down approaches to update the megacity’s CO2 emissions inventory. The latter contribution allows unprecedented amounts of GOSAT and ground measurement data regarding CO2 concentration to be analyzed in inverse modeling of anthropogenic CO2 emissions from Tokyo and the Kantō Plain.