Over the last few years, solar‐induced chlorophyll fluorescence (SIF) observations from space have emerged as a promising resource for evaluating the spatio‐temporal distribution of gross primary ...productivity (GPP) simulated by global terrestrial biosphere models. SIF can be used to improve GPP simulations by optimizing critical model parameters through statistical Bayesian data assimilation techniques. A prerequisite is the availability of a functional link between GPP and SIF in terrestrial biosphere models. Here we present the development of a mechanistic SIF observation operator in the ORCHIDEE (Organizing Carbon and Hydrology In Dynamic Ecosystems) terrestrial biosphere model. It simulates the regulation of photosystem II fluorescence quantum yield at the leaf level thanks to a novel parameterization of non‐photochemical quenching as a function of temperature, photosynthetically active radiation, and normalized quantum yield of photochemistry. It emulates the radiative transfer of chlorophyll fluorescence to the top of the canopy using a parametric simplification of the SCOPE (Soil Canopy Observation Photosynthesis Energy) model. We assimilate two years of monthly OCO‐2 (Orbiting Carbon Observatory‐2) SIF product at 0.5° (2015–2016) to optimize ORCHIDEE photosynthesis and phenological parameters over an ensemble of grid points for all plant functional types. The impact on the simulated GPP is considerable with a large decrease of the global scale budget by 28 GtC/year over the period 1990–2009. The optimized GPP budget (134/136 GtC/year over 1990–2009/2001–2009) remarkably agrees with independent GPP estimates, FLUXSAT (137 GtC/year over 2001–2009) in particular and FLUXCOM (121 GtC/year over 1990–2009). Our results also suggest a biome dependency of the SIF‐GPP relationship that needs to be improved for some plant functional types.
Key Points
We developed a process‐based SIF observation operator in a terrestrial biosphere model
We assimilated monthly OCO‐2 SIF products to optimize model photosynthesis and phenology‐related parameters
The optimized GPP is considerably reduced with spatio‐temporal patterns in closer agreement with independent products
We estimate the CO2 flux over Tropical Asia in 2009, 2010, and 2011 using Greenhouse Gases Observing Satellite (GOSAT) total column CO2(XCO2) and in situ measurements of CO2. Compared to flux ...estimates from assimilating surface measurements of CO2, GOSAT XCO2 estimates a more dynamic seasonal cycle and a large source in March–May 2010. The more dynamic seasonal cycle is consistent with earlier work by Patra et al. (2011), and the enhanced 2010 source is supported by independent upper air CO2 measurements from the Comprehensive Observation Network for Trace gases by Airliner (CONTRAIL) project. Using Infrared Atmospheric Sounding Interferometer (IASI) measurements of total column CO (XCO), we show that biomass burning CO2 can explain neither the dynamic seasonal cycle nor the 2010 source. We conclude that both features must come from the terrestrial biosphere. In particular, the 2010 source points to biosphere response to above‐average temperatures that year.
Key Points
GOSAT estimates a dynamic seasonal cycle over Tropical Asia
The GOSAT‐estimated seasonal cycle is confirmed by CONTRAIL data
IASI CO shows that the dynamism is not caused by biomass burning
The GHG-CCI project is one of several projects of the European Space Agency's (ESA) Climate Change Initiative (CCI). The goal of the CCI is to generate and deliver data sets of various ...satellite-derived Essential Climate Variables (ECVs) in line with GCOS (Global Climate Observing System) requirements. The “ECV Greenhouse Gases” (ECV GHG) is the global distribution of important climate relevant gases – atmospheric CO2 and CH4 – with a quality sufficient to obtain information on regional CO2 and CH4 sources and sinks. Two satellite instruments deliver the main input data for GHG-CCI: SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT. The first order priority goal of GHG-CCI is the further development of retrieval algorithms for near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 and XCH4, to meet the demanding user requirements. GHG-CCI focuses on four core data products: XCO2 from SCIAMACHY and TANSO and XCH4 from the same two sensors. For each of the four core data products at least two candidate retrieval algorithms have been independently further developed and the corresponding data products have been quality-assessed and inter-compared. This activity is referred to as “Round Robin” (RR) activity within the CCI. The main goal of the RR was to identify for each of the four core products which algorithms should be used to generate the Climate Research Data Package (CRDP). The CRDP will essentially be the first version of the ECV GHG. This manuscript gives an overview of the GHG-CCI RR and related activities. This comprises the establishment of the user requirements, the improvement of the candidate retrieval algorithms and comparisons with ground-based observations and models. The manuscript summarizes the final RR algorithm selection decision and its justification. Comparison with ground-based Total Carbon Column Observing Network (TCCON) data indicates that the “breakthrough” single measurement precision requirement has been met for SCIAMACHY and TANSO XCO2 (<3ppm) and TANSO XCH4 (<17ppb). The achieved relative accuracy for XCH4 is 3–15ppb for SCIAMACHY and 2–8ppb for TANSO depending on algorithm and time period. Meeting the 0.5ppm systematic error requirement for XCO2 remains a challenge: approximately 1ppm has been achieved at the validation sites but also larger differences have been found in regions remote from TCCON. More research is needed to identify the causes for the observed differences. In this context GHG-CCI suggests taking advantage of the ensemble of existing data products, for example, via the EnseMble Median Algorithm (EMMA).
•Global satellite data sets of column-averaged CO2 and CH4 have been assessed.•For the first time the quality obtained using different methods has been evaluated.•CO2 relative biases are typically approximately 1ppm relative to TCCON.•CH4 relative biases are typically in the 3–13ppb range relative to TCCON.•However, also differences have been identified which are not yet well understood.
Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data ...to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar‐induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun‐sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data‐driven products (mean R2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.
Plain Language Summary
Terrestrial plants exchange water for CO2, but there is not a direct way to measure the carbon gain and water loss at the global scale. Researchers often use eddy covariance flux tower measurements and satellite observations to infer vegetation gross primary productivity (GPP) and transpiration (T). However, flux towers with high temporal resolution are too sparsely distributed, and satellites with high spatial coverage can only detect vegetation properties indirectly, such as solar induced chlorophyll fluorescence, rather than GPP and T themselves. We bridge these two aspects in a new generation land surface model that simultaneously simulates GPP and T, as well as spectrally resolved canopy radiative transfer. We compare our model outputs directly to not only GPP and T estimations but also satellite retrievals of fluorescence and vegetation indices. We show that our new model can represent how GPP and T, as well as canopy radiative properties vary across the globe.
Key Points
Overview of Climate Modeling Alliance Land model at the global scale and comparison to existing products
Vegetation gross primary productivity, transpiration, and hyperspectral canopy radiative transfer are simulated simultaneously
Modeled fluxes and canopy reflectance and fluorescence well capture the spatial patterns across the globe compared to existing observations
Analysis systems incorporating atmospheric observations could provide a powerful tool for validating fossil fuel CO2 (ffCO2) emissions reported for individual regions, provided that fossil fuel ...sources can be separated from other CO2 sources or sinks and atmospheric transport can be accurately accounted for. We quantified ffCO2 by measuring radiocarbon (14C) in CO2, an accurate fossil-carbon tracer, at nine observation sites in California for three months in 2014-15. There is strong agreement between the measurements and ffCO2 simulated using a high-resolution atmospheric model and a spatiotemporally-resolved fossil fuel flux estimate. Inverse estimates of total in-state ffCO2 emissions are consistent with the California Air Resources Board's reported ffCO2 emissions, providing tentative validation of California's reported ffCO2 emissions in 2014-15. Continuing this prototype analysis system could provide critical independent evaluation of reported ffCO2 emissions and emissions reductions in California, and the system could be expanded to other, more data-poor regions.
Monitoring of atmospheric methane (CH4) concentrations from space‐based instruments such as the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and the Greenhouse ...Gases Observing Satellite (GOSAT) relies on observations of sunlight backscattered to space by the Earth's surface and atmosphere. Retrieval biases occur due to unaccounted scattering effects by aerosols and thin cirrus that modify the lightpath. Here, we evaluate the accuracy of two retrieval methods that aim at minimizing such scattering induced errors. The lightpath “proxy” method, applicable to SCIAMACHY and GOSAT, retrieves CH4 and carbon dioxide (CO2) simultaneously and uses CO2 as a proxy for lightpath modification. The “physics‐based” method, which we propose for GOSAT, aims at simultaneously retrieving CH4 concentrations and scattering properties of the atmosphere. We evaluate performance of the methods against a trial ensemble of simulated aerosol and cirrus loaded scenes. More than 80% of the trials yield residual scattering induced CH4 errors below 0.6% and 0.8% for the proxy and the physics‐based approach, respectively. Very few cases result in errors greater than 2% for both methods. Advantages of the proxy approach are efficient and robust performance yielding more useful retrievals than the physics‐based method which reveals some nonconvergent cases. The major disadvantage of the proxy method is the uncertainty of the proxy CO2 concentration contributing to the overall error budget. Residual errors generally correlate with particle and surface properties and thus might impact inverse modeling of CH4 sources and sinks.
Carbon dioxide (CO2) and methane (CH4) are the two most important greenhouse gases emitted by mankind. Better knowledge of the surface sources and sinks of these Essential Climate Variables (ECVs) ...and related carbon uptake and release processes is needed for important climate change related applications such as improved climate modelling and prediction. Some satellites provide near-surface-sensitive atmospheric CO2 and CH4 observations that can be used to obtain information on CO2 and CH4 surface fluxes. The goal of the GHG-CCI project of the European Space Agency's (ESA) Climate Change Initiative (CCI) is to use satellite data to generate atmospheric CO2 and CH4 data products meeting demanding GCOS (Global Climate Observing System) greenhouse gas (GHG) ECV requirements. To achieve this, retrieval algorithms are regularly being improved followed by annual data reprocessing and analysis cycles to generate better products in terms of extended time series and continuously improved data quality. Here we present an overview about the latest GHG-CCI data set called Climate Research Data Package No. 3 (CRDP3) focusing on the GHG-CCI core data products, which are column-averaged dry-air mole fractions of CO2 and CH4, i.e., XCO2 and XCH4, as retrieved from SCIAMACHY/ENVISAT and TANSO/GOSAT satellite radiances covering the time period end of 2002 to end of 2014. We present global maps and time series including initial validation results obtained by comparisons with Total Carbon Column Observing Network (TCCON) ground-based observations. We show that the GCOS requirements for systematic error (<1ppm for XCO2, <10ppb for XCH4) and long-term stability (<0.2ppm/year for XCO2, <2ppb/year for XCH4) are met for nearly all products (an exception is SCIAMACHY methane especially since 2010). For XCO2 we present comparisons with global models using the output of two CO2 assimilation systems (MACC version 14r2 and CarbonTracker version CT2013B). We show that overall there is reasonable consistency and agreement between all data sets (within ~1–2ppm) but we also found significant differences depending on region and time period.
•State-of-the-art satellite-derived data sets of atmospheric CO2 and CH4•Comparisons with TCCON ground-based observations•Critical GCOS requirements are met (with some exceptions).•Detailed comparisons with state-of-the art global CO2 assimilation systems•Overall, reasonable agreement between all CO2 data sets
We compare two conceptually different methods for determining methane column‐averaged mixing ratios from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared (SWIR) measurements. These ...methods account differently for light scattering by aerosol and cirrus. The proxy method retrieves a CO2 column which, in conjunction with prior knowledge on CO2acts as a proxy for scattering effects. The physics‐based method accounts for scattering by retrieving three effective parameters of a scattering layer. Both retrievals are validated on a 19‐month data set using ground‐based at 12 stations of the Total Carbon Column Observing Network (TCCON), showing comparable performance: for the proxy retrieval we find station‐dependent retrieval biases from −0.312% to 0.421% of a standard deviation of 0.22% and a typical precision of 17 ppb. The physics method shows biases between −0.836% and −0.081% with a standard deviation of 0.24% and a precision similar to the proxy method. Complementing this validation we compared both retrievals with simulated methane fields from a global chemistry‐transport model. This identified shortcomings of both retrievals causing biases of up to 1ings and provide a satisfying validation of any methane retrieval from space‐borne SWIR measurements, in our opinion it is essential to further expand the network of TCCON stations.
Key Points
Shortcomings of current methane retrieval algorithms are identified
For a satisfying algorithm validation the TCCON should be expanded
The UV/Vis/near infrared spectrometer SCIAMACHY on board the European ENVISAT satellite enables total column retrieval of atmospheric methane with high sensitivity to the lower troposphere. The ...vertical column density of methane is converted to column averaged mixing ratio by using carbon dioxide retrievals as proxy for the probed atmospheric column. For this purpose, we apply concurrent total column measurements of CO2 in combination with modeled column‐averaged CO2 mixing ratios. Possible systematic errors are discussed in detail while the precision error is 1.8% on average. This paper focuses on methane retrievals from January 2003 through December 2004. The measurements with global coverage over continents are compared with model results from the chemistry–transport model TM4. In the retrievals, the north‐south gradient as well as regions with enhanced methane levels can be clearly identified. The highest abundances are found in the Red Basin of China, followed by northern South America, the Gangetic plains of India and central parts of Africa. Especially the abundances in northern South America and the Red Basin are generally higher than modeled. Further, we present the seasonal variations within the investigated time period. Peak values in Asia due to rice emissions are observed from August through October. We expand earlier investigations that suggest underestimated emissions in the tropics. It is shown that these underestimations show a seasonal behavior that peaks from August through December. The global measurements may be used for inverse modeling and are thus an important step towards better quantification of the methane budget.
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