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Atmospheric carbon dioxide (CO2) accounts for the largest radiative forcing among anthropogenic greenhouse gases. There is, therefore, a pressing need to understand the rate at which CO2 accumulates in the atmosphere, including the interannual variations (IAVs) in this rate. IAV in the CO2 growth rate is a small signal relative to the long-term trend and the mean annual cycle of atmospheric CO2, and IAV is tied to climatic variations that may provide insights into long-term carbon–climate feedbacks. Observations from the Orbiting Carbon Observatory-2 (OCO-2) mission offer a new opportunity to refine our understanding of atmospheric CO2 IAV since the satellite can measure over remote terrestrial regions and the open ocean, where traditional in situ CO2 monitoring is difficult, providing better spatial coverage compared to ground-based monitoring techniques. In this study, we analyze the IAV of column-averaged dry-air CO2 mole fraction (XCO2) from OCO-2 between September 2014 and June 2021. The amplitude of the IAV, which is calculated as the standard deviation of the time series, is up to 1.2 ppm over the continents and around 0.4 ppm over the open ocean. Across all latitudes, the OCO-2-detected XCO2 IAV shows a clear relationship with El Niño–Southern Oscillation (ENSO)-driven variations that originate in the tropics and are transported poleward. Similar, but smoother, zonal patterns of OCO-2 XCO2 IAV time series compared to ground-based in situ observations and with column observations from the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT) show that OCO-2 observations can be used reliably to estimate IAV. Furthermore, the extensive spatial coverage of the OCO-2 satellite data leads to smoother IAV time series than those from other datasets, suggesting that OCO-2 provides new capabilities for revealing small IAV signals despite sources of noise and error that are inherent to remote-sensing datasets.

The Arctic–boreal zone (ABZ) is experiencing amplified warming, actively changing biogeochemical cycling of vegetation and soils. The land-to-atmosphere fluxes of CO2 in the ABZ have the potential to increase in magnitude and feedback to the climate causing additional large-scale warming. The ability to model and predict this vulnerability is critical to preparation for a warming world, but Earth system models have biases that may hinder understanding of the rapidly changing ABZ carbon fluxes. Here we investigate circumpolar carbon cycling represented by the Community Land Model 5 (CLM5.0) with a focus on seasonal gross primary productivity (GPP) in plant functional types (PFTs). We benchmark model results using data from satellite remote sensing products and eddy covariance towers. We find consistent biases in CLM5.0 relative to observational constraints: (1) the onset of deciduous plant productivity to be late; (2) the offset of productivity to lag and remain abnormally high for all PFTs in fall; (3) a high bias of grass, shrub, and needleleaf evergreen tree productivity; and (4) an underestimation of productivity of deciduous trees. Based on these biases, we focus on model development of alternate phenology, photosynthesis schemes, and carbon allocation parameters at eddy covariance tower sites. Although our improvements are focused on productivity, our final model recommendation results in other component CO2 fluxes, e.g., net ecosystem exchange (NEE) and terrestrial ecosystem respiration (TER), that are more consistent with observations. Results suggest that algorithms developed for lower latitudes and more temperate environments can be inaccurate when extrapolated to the ABZ, and that many land surface models may not accurately represent carbon cycling and its recent rapid changes in high-latitude ecosystems, especially when analyzed by individual PFTs.

Spatial and temporal variations in atmospheric carbon dioxide (CO2) reflect large-scale net carbon exchange between the atmosphere and terrestrial ecosystems. Soil heterotrophic respiration (HR) is one of the component fluxes that drive this net exchange, but, given observational limitations, it is difficult to quantify this flux or to evaluate global-scale model simulations thereof. Here, we show that atmospheric CO2 can provide a useful constraint on large-scale patterns of soil heterotrophic respiration. We analyze three soil model configurations (CASA-CNP, MIMICS, and CORPSE) that simulate HR fluxes within a biogeochemical test bed that provides each model with identical net primary productivity (NPP) and climate forcings. We subsequently quantify the effects of variation in simulated terrestrial carbon fluxes (NPP and HR from the three soil test-bed models) on atmospheric CO2 distributions using a three-dimensional atmospheric tracer transport model. Our results show that atmospheric CO2 observations can be used to identify deficiencies in model simulations of the seasonal cycle and interannual variability in HR relative to NPP. In particular, the two models that explicitly simulated microbial processes (MIMICS and CORPSE) were more variable than observations at interannual timescales and showed a stronger-than-observed temperature sensitivity. Our results prompt future research directions to use atmospheric CO2, in combination with additional constraints on terrestrial productivity or soil carbon stocks, for evaluating HR fluxes.

The amplitude of the mean annual cycle of atmospheric CO2 is a diagnostic of seasonal surface–atmosphere carbon exchange. Atmospheric observations show that this quantity has increased over most of the Northern Hemisphere (NH) extratropics during the last 3 decades, likely from a combination of enhanced atmospheric CO2, climate change, and anthropogenic land use change. Accurate climate prediction requires accounting for long-term interactions between the environment and carbon cycling; thus, analysis of the evolution of the mean annual cycle in a fully prognostic Earth system model may provide insight into the multi-decadal influence of environmental change on the carbon cycle. We analyzed the evolution of the mean annual cycle in atmospheric CO2 simulated by the Community Earth System Model (CESM) from 1950 to 2300 under three scenarios designed to separate the effects of climate change, atmospheric CO2 fertilization, and land use change. The NH CO2 seasonal amplitude increase in the CESM mainly reflected enhanced primary productivity during the growing season due to climate change and the combined effects of CO2 fertilization and nitrogen deposition over the mid- and high latitudes. However, the simulations revealed shifts in key climate drivers of the atmospheric CO2 seasonality that were not apparent before 2100. CO2 fertilization and nitrogen deposition in boreal and temperate ecosystems were the largest contributors to mean annual cycle amplification over the midlatitudes for the duration of the simulation (1950–2300). Climate change from boreal ecosystems was the main driver of Arctic CO2 annual cycle amplification between 1950 and 2100, but CO2 fertilization had a stronger effect on the Arctic CO2 annual cycle amplitude during 2100–2300. Prior to 2100, the NH CO2 annual cycle amplitude increased in conjunction with an increase in the NH land carbon sink. However, these trends decoupled after 2100, underscoring that an increasing atmospheric CO2 annual cycle amplitude does not necessarily imply a strengthened terrestrial carbon sink.

We observe significant interannual variability in the strength of the seasonal cycle drawdown in northern midlatitudes from measurements of CO2 made by the Total Carbon Column Observing Network (TCCON) and the Greenhouse Gases Observing Satellite (GOSAT). This variability correlates with surface temperature in the boreal regions. Using TCCON measurements, we find that the slope of the relationship between the XCO2 seasonal cycle minima and boreal surface temperature is 1.2 ± 0.7 ppm K−1. Assimilations from CarbonTracker 2011 and CO2 simulations using the Simple Biosphere exchange Model (SiB) transported by GEOS-Chem underestimate this covariation. Both atmospheric transport and biospheric activity contribute to the observed covariation.

We assess the large-scale, top-down constraints on regional fossil fuel emissions provided by observations of atmospheric total column CO2, XCO2. Using an atmospheric general circulation model (GCM) with underlying fossil emissions, we determine the influence of regional fossil fuel emissions on global XCO2 fields. We quantify the regional contrasts between source and upwind regions and probe the sensitivity of atmospheric XCO2 to changes in fossil fuel emissions. Regional fossil fuel XCO2 contrasts can exceed 0.7 ppm based on 2007 emission estimates, but have large seasonal variations due to biospheric fluxes. Contamination by clouds reduces the discernible fossil signatures. Nevertheless, our simulations show that atmospheric fossil XCO2 can be tied to its source region and that changes in the regional XCO2 contrasts scale linearly with emissions. We test the GCM results against XCO2 data from the GOSAT satellite. Regional XCO2 contrasts in GOSAT data generally scale with the predictions from the GCM, but the comparison is limited by the moderate precision of and relatively few observations from the satellite. We discuss how this approach may be useful as a policy tool to verify national fossil emissions, as it provides an independent, observational constraint.

New observations of the vertically integrated CO2 mixing ratio, ⟨CO2⟩, from ground-based remote sensing show that variations in CO2⟩ are primarily determined by large-scale flux patterns. They therefore provide fundamentally different information than observations made within the boundary layer, which reflect the combined influence of large-scale and local fluxes. Observations of both ⟨CO2⟩ and CO2 concentrations in the free troposphere show that large-scale spatial gradients induce synoptic-scale temporal variations in ⟨CO2⟩ in the Northern Hemisphere midlatitudes through horizontal advection. Rather than obscure the signature of surface fluxes on atmospheric CO2, these synoptic-scale variations provide useful information that can be used to reveal the meridional flux distribution. We estimate the meridional gradient in ⟨CO2⟩ from covariations in ⟨CO2⟩ and potential temperature, θ, a dynamical tracer, on synoptic timescales to evaluate surface flux estimates commonly used in carbon cycle models. We find that simulations using Carnegie Ames Stanford Approach (CASA) biospheric fluxes underestimate both the ⟨CO2⟩ seasonal cycle amplitude throughout the Northern Hemisphere midlatitudes and the meridional gradient during the growing season. Simulations using CASA net ecosystem exchange (NEE) with increased and phase-shifted boreal fluxes better fit the observations. Our simulations suggest that climatological mean CASA fluxes underestimate boreal growing season NEE (between 45–65° N) by ~40%. We describe the implications for this large seasonal exchange on inference of the net Northern Hemisphere terrestrial carbon sink.

We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO2 (XCO2) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in XCO2 south of 25° S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use the observed correlation between free-tropospheric potential temperature and XCO2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TCCON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25° S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

Observations of gradients in the total CO2 column, 〈CO2〉, are expected to provide improved constraints on surface fluxes of CO2. Here we use a general circulation model with a variety of prescribed carbon fluxes to investigate how variations in 〈CO2〉 arise. On diurnal scales, variations are small and are forced by both local fluxes and advection. On seasonal scales, gradients are set by the north-south flux distribution. On synoptic scales, variations arise due to large-scale eddy-driven disturbances of the meridional gradient. In this case, because variations in 〈CO2〉 are tied to synoptic activity, significant correlations exist between 〈CO2〉 and dynamical tracers. We illustrate how such correlations can be used to describe the north-south gradients of 〈CO2〉 and the underlying fluxes on continental scales. These simulations suggest a novel analysis framework for using column observations in carbon cycle science.

The Total Carbon Column Observing Network (TCCON) produces precise measurements of the column average dry-air mole fractions of CO2, CO, CH4, N2O and H2O at a variety of sites worldwide. These observations rely on spectroscopic parameters that are not known with sufficient accuracy to compute total columns that can be used in combination with in situ measurements. The TCCON must therefore be calibrated to World Meteorological Organization (WMO) in situ trace gas measurement scales. We present a calibration of TCCON data using WMO-scale instrumentation aboard aircraft that measured profiles over four TCCON stations during 2008 and 2009. These calibrations are compared with similar observations made in 2004 and 2006. The results indicate that a single, global calibration factor for each gas accurately captures the TCCON total column data within error.

An automated Fourier Transform Spectroscopic (FTS) solar observatory was established in Darwin, Australia in August 2005. The laboratory is part of the Total Carbon Column Observing Network, and measures atmospheric column abundances of CO2 and O2 and other gases. Measured CO2 columns were calibrated against integrated aircraft profiles obtained during the TWP-ICE campaign in January–February 2006, and show good agreement with calibrations for a similar instrument in Park Falls, Wisconsin. A clear-sky low airmass relative precision of 0.1% is demonstrated in the CO2 and O2 retrieved column-averaged volume mixing ratios. The 1% negative bias in the FTS XCO2 relative to the World Meteorological Organization (WMO) calibrated in situ scale is within the uncertainties of the NIR spectroscopy and analysis.

Variations in atmosphere total column-mean CO 2 (XCO 2 ) collected by the National Aeronautics and Space Administration's Orbiting Carbon Observatory-2 satellite can be used to constrain surface carbon fluxes if the influence of atmospheric transport and observation errors on the data is known and accounted for. Due to sparse validation data, the portions of fine-scale variability in XCO 2 driven by fluxes, transport, or retrieval errors remain uncertain, particularly over the ocean. To better understand these drivers, we characterize variability in OCO-2 Level 2 version 10 XCO 2 from the seasonal scale, synoptic-scale (order of days, thousands of kilometers), and mesoscale (within-day, hundreds of kilometers) for 10 biomes over North America and adjacent ocean basins. Seasonal and synoptic variations in XCO 2 reflect real geophysical drivers (transport and fluxes), following large-scale atmospheric circulation and the north-south distribution of biosphere carbon uptake. In contrast, geostatistical analysis of mesoscale and finer variability shows that real signals are obscured by systematic biases across the domain. Spatial correlations in along-track XCO 2 are much shorter and spatially coherent variability is much larger in magnitude than can be attributed to fluxes or transport. We characterize random and coherent along-track XCO 2 variability in addition to quantifying uncertainty in XCO 2 aggregates across typical lengths used in inverse modeling. Even over the ocean, correlated errors decrease the independence and increase uncertainty in XCO 2 . We discuss the utility of computing geostatistical parameters and demonstrate their importance for XCO 2 science applications spanning from data reprocessing and algorithm development to error estimation and carbon flux inference., (© 2023. The Authors.)

Earth system models are intended to make long-term projections, but they can be evaluated at interannual and seasonal time scales. Although the Community Earth System Model (CESM2) showed improvements in a number of terrestrial carbon cycle benchmarks, relative to its predecessor, our analysis suggests that the interannual variability (IAV) in net terrestrial carbon fluxes did not show similar improvements. The model simulated low IAV of net ecosystem production (NEP), resulting in a weaker than observed sensitivity of the carbon cycle to climate variability. Low IAV in net fluxes likely resulted from low variability in gross primary productivity (GPP)-especially in the tropics-and a high covariation between GPP and ecosystem respiration. Although lower than observed, the IAV of NEP had significant climate sensitivities, with positive NEP anomalies associated with warmer and drier conditions in high latitudes, and with wetter and cooler conditions in mid and low latitudes. We identified two dominant modes of seasonal variability in carbon cycle flux anomalies in our fully coupled CESM2 simulations that are characterized by seasonal amplification and redistribution of ecosystem fluxes. Seasonal amplification of net and gross carbon fluxes showed climate sensitivities mirroring those of annual fluxes. Seasonal redistribution of carbon fluxes is initiated by springtime temperature anomalies, but subsequently negative feedbacks in soil moisture during the summer and fall result in net annual carbon losses from land. These modes of variability are also seen in satellite proxies of GPP, suggesting that CESM2 appropriately represents regional sensitivities of photosynthesis to climate variability on seasonal time scales., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2021. The Authors.)

Across temperate North America, interannual variability (IAV) in gross primary production (GPP) and net ecosystem exchange (NEE) and their relationship with environmental drivers are poorly understood. Here, we examine IAV in GPP and NEE and their relationship to environmental drivers using two state-of-the-science flux products: NEE constrained by surface and space-based atmospheric CO 2 measurements over 2010-2015 and satellite up-scaled GPP from FluxSat over 2001-2017. We show that the arid western half of temperate North America provides a larger contribution to IAV in GPP (104% of east) and NEE (127% of east) than the eastern half, in spite of smaller magnitude of annual mean GPP and NEE. This occurs because anomalies in western ecosystems are temporally coherent across the growing season leading to an amplification of GPP and NEE. In contrast, IAV in GPP and NEE in eastern ecosystems is dominated by seasonal compensation effects, associated with opposite responses to temperature anomalies in spring and summer. Terrestrial biosphere models in the MsTMIP ensemble generally capture these differences between eastern and western temperate North America, although there is considerable spread between models., (©2020. The Authors.)

The amplitude of the atmospheric CO 2 seasonal cycle has increased by 30 to 50% in the Northern Hemisphere (NH) since the 1960s, suggesting widespread ecological changes in the northern extratropics. However, substantial uncertainty remains in the continental and regional drivers of this prominent amplitude increase. Here we present a quantitative regional attribution of CO 2 seasonal amplification over the past 4 decades, using a tagged atmospheric transport model prescribed with observationally constrained fluxes. We find that seasonal flux changes in Siberian and temperate ecosystems together shape the observed amplitude increases in the NH. At the surface of northern high latitudes, enhanced seasonal carbon exchange in Siberia is the dominant contributor (followed by temperate ecosystems). Arctic-boreal North America shows much smaller changes in flux seasonality and has only localized impacts. These continental contrasts, based on an atmospheric approach, corroborate heterogeneous vegetation greening and browning trends from field and remote-sensing observations, providing independent evidence for regionally divergent ecological responses and carbon dynamics to global change drivers. Over surface midlatitudes and throughout the midtroposphere, increased seasonal carbon exchange in temperate ecosystems is the dominant contributor to CO 2 amplification, albeit with considerable contributions from Siberia. Representing the mechanisms that control the high-latitude asymmetry in flux amplification found in this study should be an important goal for mechanistic land surface models moving forward., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

The response of the carbon cycle in prognostic Earth system models (ESMs) contributes significant uncertainty to projections of global climate change. Quantifying contributions of known drivers of interannual variability in the growth rate of atmospheric carbon dioxide (CO 2 ) is important for improving the representation of terrestrial ecosystem processes in these ESMs. Several recent studies have identified the temperature dependence of tropical net ecosystem exchange (NEE) as a primary driver of this variability by analyzing a single, globally averaged time series of CO 2 anomalies. Here we examined how the temporal evolution of CO 2 in different latitude bands may be used to separate contributions from temperature stress, drought stress, and fire emissions to CO 2 variability. We developed atmospheric CO 2 patterns from each of these mechanisms during 1997-2011 using an atmospheric transport model. NEE responses to temperature, NEE responses to drought, and fire emissions all contributed significantly to CO 2 variability in each latitude band, suggesting that no single mechanism was the dominant driver. We found that the sum of drought and fire contributions to CO 2 variability exceeded direct NEE responses to temperature in both the Northern and Southern Hemispheres. Additional sensitivity tests revealed that these contributions are masked by temporal and spatial smoothing of CO 2 observations. Accounting for fires, the sensitivity of tropical NEE to temperature stress decreased by 25% to 2.9 ± 0.4 Pg C yr -1  K -1 . These results underscore the need for accurate attribution of the drivers of CO 2 variability prior to using contemporary observations to constrain long-term ESM responses.

We present a method to reduce the impact of source brightness fluctuations (SBFs) on spectra recorded by Fourier-transform spectrometry (FTS). Interferograms are recorded without AC coupling of the detector signal (DC mode). The SBF are determined by low-pass filtering of the DC interferograms, which are then reweighted by the low-pass, smoothed signal. Atmospheric solar absorption interferograms recorded in DC mode have been processed with and without this technique, and we demonstrate its efficacy in producing more consistent retrievals of atmospheric composition. We show that the reweighting algorithm improves retrievals from interferograms subject to both gray and nongray intensity fluctuations, making the algorithm applicable to atmospheric data contaminated by significant amounts of aerosol or cloud cover.