Regional Flux Estimates Research Articles

Impact of the horizontal resolution of GEOS-Chem on land‒ocean and tropic‒extratropic partitioning and seasonal cycle in CO2 inversion

Abstract Credible regional carbon budget estimates from atmospheric CO2 measurements rely on the accuracy of atmospheric transport models (ATMs). However, the atmospheric transport in ATMs is usually simplified and spatiotemporally averaged, leading to systematic biases in simulating the atmospheric CO2 and estimating surface CO2 fluxes. We show that forward simulations of global CO2 using an ATM, GEOS-Chem, at a native resolution of 0.5°×0.625° and a coarse resolution of 4°×5° differ significantly near the surface in the Northern Hemisphere and the polar vortex, mainly because of advection in GEOS-Chem. Comparing observing system simulation experiments that assimilate synthetic observations sampled from the forward simulations, we separate the impact of coarse-resolution GEOS-Chem on regional flux estimates. The results suggest that a significant amount of annual carbon uptake from the ocean and tropics is improperly redistributed to the land and northern and southern extratropics, respectively. In addition, these errors lead to an underestimated seasonal amplitude in the northern extratropical land and a reversed sign of the seasonal cycle in the northern extratropical ocean. The reversed sign of the seasonal cycle has also been observed in a real data assimilation experiment and several state-of-the-art inversions, suggesting that reasonable ocean flux estimates depend strongly on the accuracy of the ATM.

The Importance of Contemporaneous Wind and pCO2 Measurements for Regional Air‐Sea CO2 Flux Estimates

AbstractFew observational platforms are able to sustain direct measurements of all the key variables needed in the bulk calculation of air‐sea carbon dioxide (CO2) exchange, a capability newly established for some Uncrewed Surface Vehicles (USVs). Western boundary currents are particularly challenging observational regions due to strong variability and dangerous sea states but are also known hot spots for CO2 uptake, making air‐sea exchange quantification in this region both difficult and important. Here, we present new observations collected by Saildrone USVs in the Gulf Stream during the winters of 2019 and 2022. We compared Saildrone data across co‐located vehicles and against the Pioneer Array moorings to validate the data quality. We explored how CO2 flux estimates differ when all variables needed to calculate fluxes from the bulk formulas are simultaneously measured on the same platform, relative to the situation where in situ observations must be combined with publicly‐available data products. We systematically replaced variables in the bulk formula with those often used for local and regional flux estimates. The analysis revealed that when using the ERA‐5 reanalysis wind speed in place of in situ observations, the ocean uptake of CO2 is underestimated by 8%; this underestimate grows to 9% if the NOAA Marine Boundary Layer atmospheric CO2 product and ERA‐5 significant wave height are also used in place of in situ observations. Overall our findings point to the importance of collecting contemporaneous observations of wind speed and ocean pCO2 to reduce biases in estimates of regional CO2 flux, especially during high wind events.

Potential of using CO2 observations over India in a regional carbon budget estimation by improving the modelling system

Abstract. Devising effective national-level climate action plans requires a more detailed understanding of the regional distribution of sources and sinks of greenhouse gases. Due to insufficient observations and modelling capabilities, India's current carbon source–sink estimates are uncertain. This study uses a high-resolution Lagrangian transport model to examine the potential of available CO2 observations over India for inverse estimation of regional carbon fluxes. We use four different sites in India that vary in the measurement technique, frequency and spatial representation. These observations exhibit substantial seasonal (7.5 to 9.2 ppm) and intra-seasonal (2 to 12 ppm) variability. Our modelling framework, a high-resolution Weather Research and Forecasting Model combined with the Stochastic Time-Inverted Lagrangian Transport model (WRF–STILT), performs better in simulating seasonal (R2=0.50 to 0.96) and diurnal (R2=0.96) variability (for the Mohali station) of observed CO2 than the current-generation global models (CarboScope, CarbonTracker and ECMWF EGG4). The seasonal CO2 concentration variability in Mohali, associated with crop residue burning, is largely underestimated by the models. WRF–STILT captures the seasonal biospheric variability over Nainital better than the global models but underestimates the strength of the CO2 uptake by crops. The choice of emission inventory in the modelling framework alone leads to significant biases in simulations (5 to 10 ppm), endorsing the need for accounting for emission fluxes, especially for non-background sites. Our study highlights the possibility of using the CO2 observations from these Indian stations for deducing carbon flux information at regional (Nainital) and suburban to urban (Mohali, Shadnagar and Nagpur) scales with the help of a high-resolution model. On accounting for observed variability in CO2, the global carbon data assimilation system can benefit from the measurements from the Indian subcontinent.

Sea Surface pCO2 Response to Typhoon “Wind Pump” and Kuroshio Intrusion in the Northeastern South China Sea

The Luzon Strait (LS) is a key region for estimating carbon sources and sinks in the South China Sea (SCS) and is highly influenced by the Kuroshio Current (KC) and typhoons. Understanding the variations in the sea surface partial pressure of carbon dioxide (pCO2-sw) under the combined effects of typhoons and KC in this region is crucial for estimating local and regional changes in ocean carbon flux. Based on valuable in situ pCO2-sw and remote sensing data, this study aimed to reveal the temporal variations and the physical mechanisms of pCO2-sw variations under the comprehensive effects of both typhoons and Kuroshio Intrusion (KI) in the LS. One week after the passage of the tropical cyclone (TC) Nanmadol, the concentration in the pCO2-sw and the influencing mechanisms varied in three different regions (W1–W3) on Transect A (120°E). In the region dominated by SCS waters (W1), the average pCO2-sw increased by 5.1 μatm after TC, which was mainly due to the TC “Wind Pump” inducing strong vertical mixing, which brought dissolved inorganic carbon (DIC)-rich deeper water up to the surface. In the region affected by KC (W2 and W3), pCO2-sw decreased after the TC (−8.2 μatm and −1.8 μatm, respectively) with TC-enhanced KI because the invasion of lower pCO2-sw of Kuroshio waters inhibited the TC-induced upwelling. More significant TC-induced upwelling (W3) would alleviate the decrease in pCO2-sw caused by the TC-enhanced KI. This study is a rare case providing a better understanding of the variations in pCO2-sw under TC-enhanced KI, which provides support for regional climate change prediction and carbon flux estimation in the western boundary current regions.

Independent Bias Correction Method for Satellite Observation Data Introduced to CO<sub>2</sub> Flux Inversion

Satellite observations are expected to play an important role in studying carbon fluxes. However, it is necessary to properly remove spatiotemporal bias from these observations. In this study, we estimated the spatiotemporal bias in satellite XCO2 data by making an inversion of in-situ observations. Compared to the GOSAT XCO2 concentrations derived from this analysis, the global average difference is −1.26 ppm, with a difference of −0.76 ppm over the land and −1.54 ppm over the ocean. We then developed a method to correct the bias between the satellite XCO2 and the in-situ inversed XCO2 data. Using this bias correction method, we performed an inverse analysis using in-situ and satellite observations and found that the error relative to the independent observations decreased mainly in the middle and upper troposphere compared to the in-situ inversion. Compared to previous studies, the introduction of satellite observations also suppressed large variations in the regional CO2 flux, except in regions with few observations. After correction using the proposed method, the estimated CO2 flux can be used to provide improved estimates of regional CO2 fluxes; however, the results also show a need for increasing the CO2 observation sites and improving the analysis system.

Uncertainty in land carbon budget simulated by terrestrial biosphere models: the role of atmospheric forcing

Global estimates of the land carbon sink are often based on simulations by terrestrial biosphere models (TBMs). The use of a large number of models that differ in their underlying hypotheses, structure and parameters is one way to assess the uncertainty in the historical land carbon sink. Here we show that the atmospheric forcing datasets used to drive these TBMs represent a significant source of uncertainty that is currently not systematically accounted for in land carbon cycle evaluations. We present results from three TBMs each forced with three different historical atmospheric forcing reconstructions over the period 1850–2015. We perform an analysis of variance to quantify the relative uncertainty in carbon fluxes arising from the models themselves, atmospheric forcing, and model-forcing interactions. We find that atmospheric forcing in this set of simulations plays a dominant role on uncertainties in global gross primary productivity (GPP) (75% of variability) and autotrophic respiration (90%), and a significant but reduced role on net primary productivity and heterotrophic respiration (30%). Atmospheric forcing is the dominant driver (52%) of variability for the net ecosystem exchange flux, defined as the difference between GPP and respiration (both autotrophic and heterotrophic respiration). In contrast, for wildfire-driven carbon emissions model uncertainties dominate and, as a result, model uncertainties dominate for net ecosystem productivity. At regional scales, the contribution of atmospheric forcing to uncertainty shows a very heterogeneous pattern and is smaller on average than at the global scale. We find that this difference in the relative importance of forcing uncertainty between global and regional scales is related to large differences in regional model flux estimates, which partially offset each other when integrated globally, while the flux differences driven by forcing are mainly consistent across the world and therefore add up to a larger fractional contribution to global uncertainty.

Number of Chamber Measurement Locations for Accurate Quantification of Landscape‐Scale Greenhouse Gas Fluxes: Importance of Land Use, Seasonality, and Greenhouse Gas Type

AbstractAccurate quantification of landscape soil greenhouse gas (GHG) exchange from chamber measurements is challenging due to the high spatial‐temporal variability of fluxes, which results in large uncertainties in upscaled regional and global flux estimates. We quantified landscape‐scale (6 km2 in central Germany) soil/ecosystem respiration (SR/ER‐CO2), methane (CH4), and nitrous oxide (N2O) fluxes at stratified sites with contrasting landscape characteristics using the fast‐box chamber technique. We assessed the influence of land use (forest, arable, and grassland), seasonality (spring, summer, and autumn), soil types, and slope on the fluxes. We also evaluated the number of chamber measurement locations required to estimate landscape fluxes within globally significant uncertainty thresholds. The GHG fluxes were strongly influenced by seasonality and land use rather than soil type and slope. The number of chamber measurement locations required for robust landscape‐scale flux estimates depended on the magnitude of fluxes, which varied with season, land use, and GHG type. Significant N2O‐N flux uncertainties greater than the global mean flux (0.67 kg ha−1 yr−1) occurred if landscape measurements were done at <4 and <22 chamber locations (per km2) in forest and arable ecosystems, respectively, in summer. For CO2 and CH4 fluxes, uncertainties greater than the global median CO2‐C flux (7,500 kg ha−1 yr−1) and the global mean forest CH4‐C uptake rate (2.81 kg ha−1 yr−1) occurred at <2 forest and <6 arable chamber locations. This finding suggests that more chamber measurement locations are required to assess landscape‐scale N2O fluxes than CO2 and CH4, based on these GHG‐specific uncertainty thresholds.

Worldwide Evaluation of CAMS-EGG4 CO2 Data Re-Analysis at the Surface Level.

This study systematically examines the global uncertainties and biases in the carbon dioxide (CO2) mixing ratio provided by the Copernicus Atmosphere Monitoring Service (CAMS). The global greenhouse gas re-analysis (EGG4) data product from the European Centre for Medium-Range Weather Forecasts (ECMWF) was evaluated against ground-based in situ measurements from more than 160 of stations across the world. The evaluation shows that CO2 re-analysis can capture the general features in the tracer distributions, including the CO2 seasonal cycle and its strength at different latitudes, as well as the global CO2 trend. The emissions and natural fluxes of CO2 at the surface are evaluated on a wide range of scales, from diurnal to interannual. The results highlight re-analysis compliance, reproducing biogenic fluxes as well the observed CO2 patterns in remote environments. CAMS consistently reproduces observations at marine and remote regions with low CO2 fluxes and smooth variability. However, the model’s weaknesses were observed in continental areas, regions with complex sources, transport circulations and large CO2 fluxes. A strong variation in the accuracy and bias are displayed among those stations with different flux profiles, with the largest uncertainties in the continental regions with high CO2 anthropogenic fluxes. Displaying biased estimation and root-mean-square error (RMSE) ranging from values below one ppmv up to 70 ppmv, the results reveal a poor response from re-analysis to high CO2 mixing ratio, showing larger uncertainty of the product in the boundaries where the CAMS system misses solving sharp flux variability. The mismatch at regions with high fluxes of anthropogenic emission indicate large uncertainties in inventories and constrained physical parameterizations in the CO2 at boundary conditions. The current study provides a broad uncertainty assessment for the CAMS CO2 product worldwide, suggesting deficiencies and methods that can be used in the future to overcome failures and uncertainties in regional CO2 mixing ratio and flux estimates.

A Numerical Model for Determining Deep Methane Flux Linked to the Free Gas Zone: Application to the Ocean Drilling Program Site 995 and Implications for Regional Deep Methane Flux at the Blake Ridge

The lack of the quantification of deep dissolved methane flux prevents us from accurately understanding hydrate accumulation and distribution at a given geologic setting where vertically upward methane advection dominates the hydrate system. The upward deep methane flux was usually applied as an assumed value in many previous studies. Considering the deep methane flux changes the methane concentration in the pore water and further affects the phase transfer between the gas and aqueous phases depending on the in situ methane concentration, we link gas bubbles distribution to deep dissolved methane flux. Here, we constructed a numerical model to quantify the dissolved methane flux from depth based on the parameters related to gas bubble distribution, including the residual gas saturation in sediments and the free gas zone (FGZ) thickness. We then applied our model to ODP Site 995 at the Blake Ridge where methane was sourced from deep layers. Our model results predict an upward deep methane flux of 0.0231 mol/m2/a and the occurrence of another gas interval in deeper sediments, which are consistent with seismic data. We further explored the influence of upward methane flux on hydrate accumulation and found that the thin hydrate occurrence zone at nearby Site 994 likely resulted from a small deep methane flux. Combined with the previous conclusion of high deep methane flux at Site 997, we showed that along the Blake Ridge drilling transect the estimated deep methane fluxes decrease with increasing distance from the crest of the ridge. This approach for quantifying deep methane flux is complementary to the current hydrate accumulation model and provides new insights into the regional methane flux estimation at the Blake Ridge.

Multi‐Season Evaluation of CO2 Weather in OCO‐2 MIP Models

AbstractThe ability of current global models to simulate the transport of CO2 by mid‐latitude, synoptic‐scale weather systems (i.e., CO2 weather) is important for inverse estimates of regional and global carbon budgets but remains unclear without comparisons to targeted measurements. Here, we evaluate ten models that participated in the Orbiting Carbon Observatory‐2 model intercomparison project (OCO‐2 MIP version 9) with intensive aircraft measurements collected from the Atmospheric Carbon Transport (ACT)‐America mission. We quantify model‐data differences in the spatial variability of CO2 mole fractions, mean winds, and boundary layer depths in 27 mid‐latitude cyclones spanning four seasons over the central and eastern United States. We find that the OCO‐2 MIP models are able to simulate observed CO2 frontal differences with varying degrees of success in summer and spring, and most underestimate frontal differences in winter and autumn. The models may underestimate the observed boundary layer‐to‐free troposphere CO2 differences in spring and autumn due to model errors in boundary layer height. Attribution of the causes of model biases in other seasons remains elusive. Transport errors, prior fluxes, and/or inversion algorithms appear to be the primary cause of these biases since model performance is not highly sensitive to the CO2 data used in the inversion. The metrics presented here provide new benchmarks regarding the ability of atmospheric inversion systems to reproduce the CO2 structure of mid‐latitude weather systems. Controlled experiments are needed to link these metrics more directly to the accuracy of regional or global flux estimates.

The First Global Carbon Dioxide Flux Map Derived from TanSat Measurements

Space-borne measurements of atmospheric greenhouse gas concentrations provide global observation constraints for top-down estimates of surface carbon flux. Here, the first estimates of the global distribution of carbon surface fluxes inferred from dry-air CO2 column (XCO2) measurements by the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) are presented. An ensemble transform Kalman filter (ETKF) data assimilation system coupled with the GEOS-Chem global chemistry transport model is used to optimally fit model simulations with the TanSat XCO2 observations, which were retrieved using the Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS). High posterior error reduction (30%–50%) compared with a priori fluxes indicates that assimilating satellite XCO2 measurements provides highly effective constraints on global carbon flux estimation. Their impacts are also highlighted by significant spatiotemporal shifts in flux patterns over regions critical to the global carbon budget, such as tropical South America and China. An integrated global land carbon net flux of 6.71 ± 0.76 Gt C yr−1 over 12 months (May 2017–April 2018) is estimated from the TanSat XCO2 data, which is generally consistent with other inversions based on satellite data, such as the JAXA GOSAT and NASA OCO-2 XCO2 retrievals. However, discrepancies were found in some regional flux estimates, particularly over the Southern Hemisphere, where there may still be uncorrected bias between satellite measurements due to the lack of independent reference observations. The results of this study provide the groundwork for further studies using current or future TanSat XCO2 data together with other surface-based and space-borne measurements to quantify biosphere-atmosphere carbon exchange.

Evaluation of CMIP6 Global Climate Models for Simulating Land Surface Energy and Water Fluxes During 1979–2014

AbstractThis study examined the overall performance of the climate models in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating the key energy and water fluxes over land. For this purpose, this study selected multiple land flux products as reference data sets and assessed the global spatial means, patterns, trends, seasonal cycles, and regional mean estimates of the sensible heat (SH), latent heat (LH), net radiation (RN), runoff (RF), and precipitation (PR) simulated by 32 CMIP6 models in recent decades. The global (Antarctica, Greenland, and hot deserts are not included) mean SH, LH, RN, RF, and PR simulated by the CMIP6 models are 37.55 ± 4.81 W m−2, 49.88 ± 5.31 W m−2, 89.10 ± 4.45 W m−2, 351.31 ± 95.28 mm yr−1, and 948.35 ± 88.77 mm yr−1, respectively. The ensemble median of CMIP6 simulations (CMIP6‐MED) can provide robust estimates of global and regional land fluxes, which are within the ranges given by the reference data sets, and highly consistent spatiotemporal patterns of these fluxes. The comparison of CMIP6‐MED with the first preferred reference data sets shows that CMIP6‐MED generally overestimates the water and energy fluxes over land, except for the simulated RF and PR in the Amazon region. The most disagreements between CMIP6‐MED and the reference data sets occur in South America (particularly the Amazon region) and the Tibetan Plateau. Finally, the sources of model biases are discussed. It is suggested that current land flux products should be widely used to optimize the structures and parameters of climate models in future work.

Evaluation of Regional CO2 Mole Fractions in the ECMWF CAMS Real‐Time Atmospheric Analysis and NOAA CarbonTracker Near‐Real‐Time Reanalysis With Airborne Observations From ACT‐America Field Campaigns

AbstractThis study systematically examines the regional uncertainties and biases in carbon dioxide (CO2) mole fractions from two of the state‐of‐the‐art global CO2 analysis products, namely, the Copernicus Atmosphere Monitoring Service (CAMS) real‐time atmospheric analysis from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the CarbonTracker Near‐Real‐Time (CT‐NRT) reanalysis from the National Oceanic and Atmospheric Administration (NOAA), by evaluation against hundreds of hours of airborne in situ measurements from the summer 2016 and winter 2017 Atmospheric Carbon and Transport (ACT)‐America field campaigns. Both the CAMS and CT‐NRT analyses agree reasonably well with the independent ACT‐America airborne CO2 measurements in the free troposphere, with root‐mean‐square deviations (RMSDs) between analyses and observations generally between 1 and 2 ppm but show considerably larger uncertainties in the atmospheric boundary layer where the RMSDs exceed 8 ppm in the lowermost 1 km of the troposphere in summer. There are strong variations in accuracy and bias between seasons, and across three different subregions in the United States (Mid‐Atlantic, Midwest, and South), with the largest uncertainties in the Mid‐Atlantic region in summer. Overall, the RMSDs of the CAMS and CT‐NRT analyses against airborne data are comparable to each other and largely consistent with the differences between the two analyses. The current study provides uncertainty estimates for both analysis products over North America and suggests that these two independent estimates can be used to approximate regional CO2 analysis uncertainties. Both statistics are important in future studies in quantifying the uncertainties in regional CO2 mole fraction and flux estimates.

Sensitivity of CO2 surface flux constraints to observational coverage

AbstractInverse modeling of regional CO2 fluxes using atmospheric CO2 data is sensitive to the observational coverage of the observing network. Here we use the GEOS‐Chem adjoint model to examine the sensitivity to CO2 fluxes of observations from the in situ surface network, the Total Carbon Column Observing Network (TCCON), the Greenhouse Gases Observing Satellite (GOSAT), and the Orbiting Carbon Observatory (OCO‐2). We find that OCO‐2 has high sensitivity to fluxes throughout the tropics and Southern Hemisphere, while surface observations have high sensitivity to fluxes in the northern extratropics throughout the year. For GOSAT viewing modes, ocean glint data provide the strongest constraints on fluxes in the tropics and Southern Hemisphere during Northern Hemisphere fall and winter relative to other viewing modes. In contrast, GOSAT nadir land data offer the greater sensitivity to fluxes in these regions during Northern Hemisphere spring and summer. For OCO‐2 viewing modes, ocean glint data provided the dominant sensitivity to the surface fluxes in the northern subtropics, tropics, and Southern Hemisphere. We performed a series of inversion analyses using pseudodata and found that the varying sensitivities can result in large differences in regional flux estimates. However, combining measurements from different observing systems to exploit their complementarity may lead to a posteriori flux estimates with improved accuracy.

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as model) and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situ G0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from-7 to-0.5K in LST amplitude and from-300 to 300J m-2 K-1 s-0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

Acetone–CO enhancement ratios in the upper troposphere based on 7 years of CARIBIC data: new insights and estimates of regional acetone fluxes

Abstract. Acetone and carbon monoxide (CO) are two important trace gases controlling the oxidation capacity of the troposphere; enhancement ratios (EnRs) are useful in assessing their sources and fate between emission and sampling, especially in pollution plumes. In this study, we focus on in situ data from the upper troposphere recorded by the passenger-aircraft-based IAGOS–CARIBIC (In-service Aircraft for a Global Observing System–Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container) observatory over the periods 2006–2008 and 2012–2015. This dataset is used to investigate the seasonal and spatial variation of acetone–CO EnRs. Furthermore, we utilize a box model accounting for dilution, chemical degradation and secondary production of acetone from precursors. In former studies, increasing acetone–CO EnRs in a plume were associated with secondary production of acetone. Results of our box model question this common presumption and show increases of acetone–CO EnR over time without taking secondary production of acetone into account. The temporal evolution of EnRs in the upper troposphere, especially in summer, is not negligible and impedes the interpretation of EnRs as a means for partitioning of acetone and CO sources in the boundary layer. In order to ensure that CARIBIC EnRs represent signatures of source regions with only small influences by dilution and chemistry, we limit our analysis to temporal and spatial coherent events of high-CO enhancement. We mainly focus on North America and Southeast Asia because of their different mix of pollutant sources and the good data coverage. For both regions, we find the expected seasonal variation in acetone–CO EnRs with maxima in summer, but with higher amplitude over North America. We derive mean (± standard deviation) annual acetone fluxes of (53 ± 27) 10−13 kg m−2 s−1 and (185 ± 80) 10−13 kg m−2 s−1 for North America and Southeast Asia, respectively. The derived flux for North America is consistent with the inventories, whereas Southeast Asia acetone emissions appear to be underestimated by the inventories.

A decadal inversion of CO2 using the Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA): sensitivity to the ground-based observation network

We present an assimilation system for atmospheric carbon dioxide (CO2) using a Global Eulerian–Lagrangian Coupled Atmospheric model (GELCA), and demonstrate its capability to capture the observed atmospheric CO2 mixing ratios and to estimate CO2 fluxes. With the efficient data handling scheme in GELCA, our system assimilates non-smoothed CO2 data from observational data products such as the Observation Package (ObsPack) data products as constraints on surface fluxes. We conducted sensitivity tests to examine the impact of the site selections and the prior uncertainty settings of observation on the inversion results. For these sensitivity tests, we made five different site/data selections from the ObsPack product. In all cases, the time series of the global net CO2 flux to the atmosphere stayed close to values calculated from the growth rate of the observed global mean atmospheric CO2 mixing ratio. At regional scales, estimated seasonal CO2 fluxes were altered, depending on the CO2 data selected for assimilation. Uncertainty reductions were determined at the regional scale and compared among cases. As measures of the model–data mismatch, we used the model–data bias, root-mean-square error, and the linear correlation. For most observation sites, the model–data mismatch was reasonably small. Regarding regional flux estimates, tropical Asia was one of the regions that showed a significant impact from the observation network settings. We found that the surface fluxes in tropical Asia were the most sensitive to the use of aircraft measurements over the Pacific, and the seasonal cycle agreed better with the results of bottom-up studies when the aircraft measurements were assimilated. These results confirm the importance of these aircraft observations, especially for constraining surface fluxes in the tropics.

Large XCH<sub>4</sub> anomaly in summer 2013 over northeast Asia observed by GOSAT

Abstract. Extremely high levels of column-averaged dry-air mole fractions of atmospheric methane (XCH4) were detected in August and September 2013 over northeast Asia (∼ 20 ppb above the averaged summertime XCH4 over 2009–2012, after removing a long-term trend), as being retrieved from the Short-Wavelength InfraRed (SWIR) spectral data observed with the Thermal And Near-infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) onboard Greenhouse Gases Observing Satellite (GOSAT). Similar enhancements of XCH4 were also observed by the ground-based measurements at two Total Carbon Column Observing Network (TCCON) sites in Japan. The analysis of surface CH4 concentrations observed at three monitoring sites around the Japan archipelago suggest that the extreme increase of XCH4 has occurred in a limited area. The model analysis was conducted to investigate this anomalously high XCH4 event, using an atmospheric transport model. The results indicate that the extreme increase of XCH4 is attributed to the anomalous atmospheric pressure pattern over East Asia during the summer of 2013, which effectively transported the CH4-rich air to Japan from the strong CH4 source areas in east China. The two Japanese TCCON sites, ∼ 1000 km east–west apart each other, coincidentally located along the substantially CH4-rich air flow from east China. This analysis demonstrates the capability of GOSAT to monitor an XCH4 event on a synoptic scale. We anticipate that the synoptic information of XCH4 from GOSAT data contributes to improve our understanding of regional carbon cycle and the regional flux estimation.

Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region

Quantifying spatial and temporal patterns of carbon sources and sinks and their uncertainties across agriculture-dominated areas remains challenging for understanding regional carbon cycles. Characteristics of local land cover inputs could impact the regional carbon estimates but the effect has not been fully evaluated in the past. Within the North American Carbon Program Mid-Continent Intensive (MCI) Campaign, three models were developed to estimate carbon fluxes on croplands: an inventory-based model, the Environmental Policy Integrated Climate (EPIC) model, and the General Ensemble biogeochemical Modeling System (GEMS) model. They all provided estimates of three major carbon fluxes on cropland: net primary production (NPP), net ecosystem production (NEP), and soil organic carbon (SOC) change. Using data mining and spatial statistics, we studied the spatial distribution of the carbon fluxes uncertainties and the relationships between the uncertainties and the land cover characteristics. Results indicated that uncertainties for all three carbon fluxes were not randomly distributed, but instead formed multiple clusters within the MCI region. We investigated the impacts of three land cover characteristics on the fluxes uncertainties: cropland percentage, cropland richness and cropland diversity. The results indicated that cropland percentage significantly influenced the uncertainties of NPP and NEP, but not on the uncertainties of SOC change. Greater uncertainties of NPP and NEP were found in counties with small cropland percentage than the counties with large cropland percentage. Cropland species richness and diversity also showed negative correlations with the model uncertainties. Our study demonstrated that the land cover characteristics contributed to the uncertainties of regional carbon fluxes estimates. The approaches we used in this study can be applied to other ecosystem models to identify the areas with high uncertainties and where models can be improved to reduce overall uncertainties for regional carbon flux estimates.

Combining GOSAT XCO2 observations over land and ocean to improve regional CO2 flux estimates

AbstractWe used the GEOS‐Chem data assimilation system to examine the impact of combining Greenhouse Gases Observing Satellite (GOSAT) XCO2 data over land and ocean on regional CO2 flux estimates for 2010–2012. We found that compared to assimilating only land data, combining land and ocean data produced an a posteriori CO2 distribution that is in better agreement with independent data and fluxes that are in closer agreement with existing top‐down and bottom‐up estimates. Adding XCO2 data over oceans changed the tropical land regions from a source of 0.64 Pg C/yr to a sink of −0.60 Pg C/yr and produced a corresponding reduction in the estimated sink in northern and southern land regions by 0.49 Pg C/yr and 0.80 Pg C/yr, respectively. This highlights the importance of improved observational coverage in the tropics to better quantify the latitudinal distribution of the terrestrial fluxes. Based only on land XCO2 data, we estimated a strong source in northern tropical South America, which experienced wet conditions in 2010–2012. In contrast, with the land and ocean data, we estimated a sink for this wet region in the north, and a source for the seasonally dry regions in the south and east, which is consistent with our understanding of the impact of moisture availability on the carbon balance of the region. Our results suggest that using satellite data with a more zonally balanced observational coverage could help mitigate discrepancies in CO2 flux estimates; further improvement could be expected with the greater observational coverage provided by the Orbiting Carbon Observatory‐2.