Integrated Carbon Observation System Research Articles

The Pyrenean Platform for Observation of the Atmosphere: site, long-term dataset, and science

Abstract. The Pyrenean Platform for Observation of the Atmosphere (P2OA) is a coupled plain–mountain instrumented platform in southwestern France. It is composed of two physical sites: the “Pic du Midi” mountaintop observatory (2877 m a.s.l.) and the “Centre de Recherches Atmosphériques” (600 m a.s.l). Both sites are complementarily instrumented for the monitoring of climate-relevant variables and the study of meteorological processes in a mountainous region. The scientific topics covered by P2OA include surface–atmosphere interactions in heterogeneous landscapes and complex terrain, the physics and chemistry of atmospheric trace species at a large scale, the influence of local- and regional-scale emissions and transport on the atmospheric composition, and transient luminous events above thunderstorms. With a large number of instruments and a high hosting capacity, P2OA contributes to atmospheric sciences through (i) building long-term series of atmospheric observations, (ii) hosting experimental field campaigns and instrumental tests, and (iii) educational training in atmospheric observation techniques. In this context, P2OA is part of the French component of the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS-Fr) and also contributes to the Integrated Carbon Observation System (ICOS) research infrastructure and to several European or international networks. Here, we present the complete instrumentation of P2OA and the associated datasets, give a meteorological characterization of the platform, and illustrate the potential of P2OA and its dataset with past or ongoing studies and projects.

Estimation of surface soil moisture from Sentinel-1 synthetic aperture radar imagery using machine learning method

Surface soil moisture (SM) is a crucial variable representing the water content in soil at the topmost soil layer. Accurate data of SM are essential for various purposes, including the monitoring of drought, vegetation modeling, weather forecasting, and agriculture management. Over the past decades, microwave remote sensing has been employed to estimate SM at large scales, with the Sentinel-1 satellite mission recently offering Synthetic Aperture Radar (SAR) data and enabling to estimate SM at high spatial resolution. Machine learning (ML) techniques have further enhanced these estimations, with random forest (RF) emerging as a promising method due to its strong capabilities. However, the performance of RF in estimating SM with satellite data has not yet been thoroughly evaluated across large areas. This study presents a new RF-based model for estimating SM over Europe with SAR data from Sentinel-1, along with the climate and terrain data. The predictions from the RF model were compared against the ground measurements from the International Soil Moisture Network (ISMN), Integrated Carbon Observation System (ICOS), and official SMAP/Sentinel-1 SM products. The results demonstrated that the RF model yielded accurate predictions with a Pearson's correlation coefficient (R) of 0.847 outperforming the official SMAP/Sentinel-1 product at spatial resolutions of 1 km and 3 km, which achieved R values of 0.599 and 0.616, respectively. Additionally, the impact of vegetation cover that is represented by using multiple satellite vegetation products on the RF model was investigated. Despite a moderate correlation between backscattering coefficients and vegetation cover, no correlation was found between the RF model's errors and vegetation cover. The results suggest that the RF model and Sentinel-1 SAR data can be used to provide SM estimations at high spatial resolution, characterizing the fine-scale variations in SM, to support various applications such as precision agriculture at the small, localized scales.

Potential of constructing all‐encompassing soil–plant‐atmosphere‐continuum stations and datasets from meteorological, flux, soil moisture station networks and plant‐relevant observations

AbstractThe establishment of SPAC (soil–plant‐atmosphere continuum) stations is essential for comprehensive monitoring of land‐atmosphere interactions and ecological and hydrological processes. This paper addresses the critical limitations of existing observation networks, which often rely on single‐aspect observations, resulting in insufficient data for a holistic understanding of SPAC dynamics. Specifically, SPAC stations provide critical multi‐variable observations that enhance process‐based model calibration and physical constraints and improve the empirical basis of data‐driven models. Advanced technologies such as machine learning and remote sensing are proposed to transform current weather and soil moisture stations into quasi‐SPAC sites capable of estimating carbon and water flux data. Additionally, the strategic placement of new SPAC sites in regions projected to be sensitive to future climate change and climate risks, as indicated by models such as CMIP6, is recommended. Furthermore, promoting comprehensive observational systems like Europe's Integrated Carbon Observation System (ICOS) in other regions, establishing a unified management framework and coordinating the upgrading of existing global observation networks are essential steps. Ultimately, the proposed enhancements will advance global ecological and hydrological studies, providing a more integrated and accurate understanding of the SPAC system and its responses to climate variability.

Opinion: New directions in atmospheric research offered by research infrastructures combined with open and data-intensive science

Abstract. The acquisition and dissemination of essential information for understanding global biogeochemical interactions between the atmosphere and ecosystems and how climate–ecosystem feedback loops may change atmospheric composition in the future comprise a fundamental prerequisite for societal resilience in the face of climate change. In particular, the detection of trends and seasonality in the abundance of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Therefore, easy and fast access to reliable, long-term, and high-quality observational environmental data is recognised as fundamental to research and the development of environmental forecasting and assessment services. In our opinion article, we discuss the potential role that environmental research infrastructures in Europe (ENVRI RIs) can play in the context of an integrated global observation system. In particular, we focus on the role of the atmosphere-centred research infrastructures ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure), IAGOS (In-service Aircraft for a Global Observing System), and ICOS (Integrated Carbon Observation System), also referred to as ATMO-RIs, with their capabilities for standardised collection and provision of long-term and high-quality observational data, complemented by rich metadata. The ATMO-RIs provide data through open access and offer data interoperability across different research fields including all fields of environmental sciences and beyond. As a result of these capabilities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which arise from the combination of open-access and interoperable observational data, tools, and technologies offered by data-intensive science and the emerging collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud (EOSC).

A pre-whitening with block-bootstrap cross-correlation procedure for temporal alignment of data sampled by eddy covariance systems

The eddy covariance (EC) method is a standard micrometeorological technique for monitoring the exchange rate of the main greenhouse gases across the interface between the atmosphere and ecosystems. One of the first EC data processing steps is the temporal alignment of the raw, high frequency measurements collected by the sonic anemometer and gas analyser. While different methods have been proposed and are currently applied, the application of the EC method to trace gases measurements highlighted the difficulty of a correct time lag detection when the fluxes are small in magnitude. Failure to correctly synchronise the time series entails a systematic error on covariance estimates and can introduce large uncertainties and biases in the calculated fluxes. This work aims at overcoming these issues by introducing a new time lag detection procedure based on the assessment of the cross-correlation function (CCF) between variables subject to (i) a pre-whitening based on autoregressive filters and (ii) a resampling technique based on block-bootstrapping. Combining pre-whitening and block-bootstrapping facilitates the assessment of the CCF, enhancing the accuracy of time lag detection between variables with correlation of low order of magnitude (i.e. lower than -1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-1$$\\end{document}) and allowing for a proper estimate of the associated uncertainty. We expect the proposed procedure to significantly improve the temporal alignment of the EC time-series measured by two physically separate sensors, and to be particularly beneficial in centralised data processing pipelines of research infrastructures (e.g. the Integrated Carbon Observation System, ICOS-RI) where the use of robust and fully data-driven methods, like the one we propose, constitutes an essential prerequisite.

The suitability of atmospheric oxygen measurements to constrain western European fossil-fuel CO2 emissions and their trends

Abstract. Atmospheric measurements of the O2/N2 ratio and the CO2 mole fraction (combined into the conceptual tracer “Atmospheric Potential Oxygen”, APO) over continents have been proposed as a constraint on CO2 emissions from fossil-fuel burning. Here we assess the suitability of such APO data to constrain anthropogenic CO2 emissions in western Europe, with particular focus on their decadal trends. We use an inversion of atmospheric transport to estimate spatially and temporally explicit scaling factors on a bottom-up fossil-fuel emissions inventory. Based on the small number of currently available observational records, our CO2 emissions estimates show relatively large apparent year-to-year variations, exceeding the expected uncertainty of the bottom-up inventory and precluding the calculation of statistically significant trends. We were not able to trace the apparent year-to-year variations back to particular properties of the APO data. Inversion of synthetic APO data, however, confirms that data information content and degrees of freedom are sufficient to successfully correct a counterfactual prior. Larger sets of measurement stations, such as the recently started APO observations from the Integrated Carbon Observation System (ICOS) European research infrastructure, improve the constraint and may ameliorate possible problems with local signals or with measurement or model errors at the stations. We further tested the impact of uncertainties in the O2:CO2 stoichiometries of fossil-fuel burning and land biospheric exchange and found they are not fundamental obstacles to estimating decadal trends in fossil-fuel CO2 emissions, though further work on fossil-fuel O2:CO2 stoichiometries seems necessary.

Simultaneous estimation of five temporally regular land variables at seven spatial resolutions from seven satellite data using a multi-scale and multi-depth convolutional neural network

Various satellite sensors have provided a huge amount of observations of Earth's environment at variable spatial and temporal resolutions. Many global coarse-resolution land products have been generated from single-satellite data, but global temporally regular land products at fine spatial resolutions (say 10-30 m) are scarce because of infrequent observations. An ideal inversion framework can estimate multiple global continuous land variables at different spatial resolutions by combining all sources of satellite data. This paper proposes a new framework that can estimate five land variables simultaneously from the top-of-atmosphere (TOA) reflectance acquired by seven satellite sensors based on a multi-scale and multi-depth convolutional neural network (MSDCNN). This framework enables us to estimate temporally regular land variables at as fine as 10 m spatial resolution by transforming information from satellite data at coarser spatial resolutions. The estimated land variables include Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), shortwave albedo, visible albedo, and spectral reflectance. Seven sensors that acquire data at different spatial resolutions, include Visible Infrared Imaging Radiometer Suite (VIIRS) (750 m), Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m), Fengyun-3 (FY-3) Medium Resolution Spectral Imager (MERSI) (250 m), China-Brazil Earth Resources Satellite 04 (CBERS-04) Wide Field Imager (WFI) (73 m), Landsat 8 Operational Land Imager (OLI) (30 m), Gaofen-1 (GF-1) Wide-Field-of-View (16 m) and Sentinel 2 A/B Multispectral Imager (MSI) (10 m). This framework is mainly composed of four steps. First, a Shuffled Complex Evolution (SCE) optimization method is adopted to estimate these variables from VIIRS TOA reference. Second, a joint-output random forest regression (RF) method is used to link the satellite observations and the estimated values from step 1. Third, the six-type multi-resolution satellite observations are used to downscale the VIIRS TOA reflectance to six different spatial resolutions by using the MSDCNN. Finally, the downscaled six-resolution VIIRS TOA reflectance is fed into the multiple-variable RF model to estimate land variables. The framework was validated, and the results based on high-resolution reference maps from ImagineS network and time series shortwave albedo field values from Surface Radiation (SURFRAD) and Integrated Carbon Observation System (ICOS) network show that the retrieved variables had high validation accuracy, with root mean square error (RMSE) ranges of 0.361–0.489 (LAI), 0.023–0.120 (FAPAR), 0.013–0.026 (snow-free shortwave albedo), respectively. Comparison results of the retrieved multi-scale variables with the Sentinel 2 A/B (10 m), Landsat 8 (30 m), Global LAnd Surface Satellite (GLASS, 250 m, 500 m), MODIS (500 m), and VIIRS (750 m) products show that their values were close, with RMSE ranges of 0.107–0.273 (LAI), 0.015–0.027 (FAPAR), 0.003–0.007 (shortwave albedo), 0.001–0.007 (visible albedo), and 0.003–0.025 (spectral reflectance), respectively. The results of the direct validation as well as the product intercomparison show that this novel framework has the potential to be used in estimating global land variables at various spatial scales using a variety of satellite data sources.

Note on the compatibility of ICOS, NEON, and TERN sampling designs, different camera setups for effective plant area index estimation with digital hemispherical photography

Abstract Environmental monitoring networks such as the Integrated Carbon Observation System (ICOS) in Europe, the National Ecological Observatory Network (NEON) in the U.S., or the Terrestrial Ecosystem Research Network (TERN) in Australia deploy different sampling schemes for in situ measurements. We report on the intercomparison of measurements of the canopy gap fraction with different digital hemispherical photography setups adopting ICOS, NEON, and TERN sampling schemes. The test was carried out at the Järvselja Radiation Transfer Model Intercomparison (RAMI) birch stand. Results show that spreading out sampling points which cover more of the plot is important for a good representation of the forest as a whole. The NEON tower plot layout scheme may be more prone to errors in overall canopy properties estimation than ICOS or TERN due to its compact sampling layout and should always be used in conjunction with its distributed plots. Different camera setups involving different camera operators, camera bodies, lenses and settings yield slightly varied results, and it is important to ensure that the images are taken in such a way that they would not be over or underexposed, or out of focus. As a conclusion we recommend always to carry out intercomparison measurements with old and new cameras when devices are upgraded. Our study contributes towards establishing the uncertainty and evaluating potential error budget stemming from collecting in situ measurements using different sampling schemes and camera setups.

Standards and Open Access are the ICOS Pillars: Reply to “Comments on ‘The Integrated Carbon Observation System in Europe’”

Standards and Open Access are the ICOS Pillars: Reply to “Comments on ‘The Integrated Carbon Observation System in Europe’”

The Flux You Say?: Comments on “The Integrated Carbon Observation System in Europe”

The Flux You Say?: Comments on “The Integrated Carbon Observation System in Europe”

Estimating regional fossil fuel CO2 concentrations from 14CO2 observations: challenges and uncertainties.

The direct way to estimate the regional fossil fuel CO2 surplus (ΔffCO2) at a station is by measuring the Δ14CO2 depletion compared with a respective background. However, this approach has several challenges, which are (i) the choice of an appropriate Δ14CO2 background, (ii) potential contaminations through nuclear 14CO2 emissions and (iii) masking of ΔffCO2 by 14C-enriched biosphere respiration. Here we evaluate these challenges and estimate potential biases and typical uncertainties of 14C-based ΔffCO2 estimates in Europe. We show that Mace Head (MHD), Ireland, is a representative background station for the Integrated Carbon Observation System(ICOS) atmosphere station network. The mean ΔffCO2 representativeness bias when using the MHD Δ14CO2 background for the whole observation network is of order 0.1 ± 0.3 ppm. At ICOS sites, the median nuclear contamination leads to 25% low-biased ΔffCO2 estimates if not corrected for. The ΔffCO2 masking due to 14C-enriched heterotrophic CO2 respiration can lead to similar ΔffCO2 biases as the nuclear contaminations, especially in summer. Our evaluation of all components contributing to the uncertainty of ΔffCO2 estimates reveals that, due to the small ffCO2 signals at ICOS stations, almost half of the 14C-based ΔffCO2 estimates from integrated samples have an uncertainty that is larger than 50%. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'.

Approximation of multi-year time series of XCO2 concentrations using satellite observations and statistical interpolation methods

Long-term time series of CO2 concentrations are required for studying e.g., the effects of rising CO2 concentrations on plants and ecosystems. Different methods for approximating column-averaged dry-air mole fractions of CO2 (XCO2) have been developed to substitute missing local CO2 measurements.This study presents a novel method (XCO2SAT+) for determining monthly XCO2 concentrations for selected sites, based entirely on observations from multiple satellites as well as statistical methods in the case of missing satellite data. The product currently covers a period of 16 years from 2003 to 2018. XCO2SAT+ results were validated against data of five European sites of the Total Carbon Column Observing Network (TCCON) and compared to two established XCO2 approximation methods. All three approximation methods were compared to CO2 measurements of German towers from the Integrated Carbon Observation System (ICOS).The mean absolute error (MAE) between the XCO2SAT+ method and TCCON site Karlsruhe, Germany ranges between 0.5 and 1.1 ppm (0.1–0.3%) for years 2014–2018. Mean differences between XCO2SAT+ and XCO2 approximation method CAMS-GCM shows a slightly better MAE of 0.1–0.3 ppm for CAMS-GCM at two German TCCON sites. Multi-year comparisons with ground-level ICOS measurements revealed a comparable seasonality of CO2 concentrations during the vegetation period, but underestimated high winter concentrations, a pattern also observed when comparing with CAMS-GCM. XCO2SAT+ is a suitable alternative and simple method for providing long-term time series of monthly averaged XCO2 concentrations for selected sites, expanding the methodological toolbox for in-situ CO2 research. The new method, which so far provides XCO2 concentrations, will thus serve as a first step towards providing further CO2 data sources, which could be used to further improve numerical models such as atmospheric models or crop growth models and to study CO2 impacts in agriculture and natural ecosystems.

SMEARcore – modular data infrastructure for atmospheric measurement stations

Abstract. We present the SMEARcore data infrastructure framework: a collection of modular programs and processing workflows intended for measurement stations and campaigns as a real-time data analysis and management platform. SMEARcore enables new SMEAR (Station for Measuring Ecosystem–Atmosphere Relations) stations to be integrated in a way that is consistent with existing stations and transfers the existing data curation experience to the new station. It establishes robust data pipelines that allow easier diagnosis of problems. We show practical examples of how SMEARcore is utilized at operational measurement stations. This work differs from earlier similar concepts, such as those used at stations within ACTRIS (Aerosols, Clouds and Trace Gases Research Infrastructure) and ICOS (Integrated Carbon Observation System) networks, in three important aspects: firstly, by keeping all the processing under the control of the data owners; secondly, by providing tools for making data interoperable in general instead of harmonizing a particular set of instruments; and thirdly, by being extensible to new instruments. As such it is not meant as a replacement for these infrastructures but to be used in addition to them and to bring structured data curation to more measurement stations not yet using these practices.

Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS sites

Eddy covariance sites are ideally suited for the study of extreme events on ecosystems as they allow the exchange of trace gases and energy fluxes between ecosystems and the lower atmosphere to be directly measured on a continuous basis. However, standardized definitions of hydroclimatic extremes are needed to render studies of extreme events comparable across sites. This requires longer datasets than are available from on-site measurements in order to capture the full range of climatic variability. We present a dataset of drought indices based on precipitation (Standardized Precipitation Index, SPI), atmospheric water balance (Standardized Precipitation Evapotranspiration Index, SPEI), and soil moisture (Standardized Soil Moisture Index, SSMI) for 101 ecosystem sites from the Integrated Carbon Observation System (ICOS) with daily temporal resolution from 1950 to 2021. Additionally, we provide simulated soil moisture and evapotranspiration for each site from the Mesoscale Hydrological Model (mHM). These could be utilised for gap-filling or long-term research, among other applications. We validate our data set with measurements from ICOS and discuss potential research avenues.

Triple collocation-based merging of multi-source gridded evapotranspiration data in the Nordic Region

Accurate evapotranspiration (ET) data are required for many hydro-meteorological applications. Compared with the traditional evaluation that requires in-situ measurements, the triple collocation (TC) technique estimates geophysical product errors without the need for ground truth, which is especially suitable over large areas lacking a dense in-situ network. However, violations of the zero-error cross-correlation (ECC) assumption are found to be the dominant sources of impairing the TC robustness. This study presents the first application of a TC-based merging framework that optimally considers ECC to merge multi-source gridded ET products in the Nordic Region during 2003–2018. The ECC estimates of each ET dataset pair calculated by the quadruple collocation approach are used to select the qualified triplets from four products, including FLUXCOM, Global Land Surface Satellite (GLASS), Global Land Evaporation and Amsterdam Model (GLEAM), and Penman-Monteith-Leuning Version 2 (PML-V2). Then the ET merged datasets are generated by weighting TC-based rescaled error variances of the parent datasets through least square merging. Finally, the accuracy of both the parent and the merged datasets are assessed with the Integrated Carbon Observation System (ICOS) flux data in the Nordic Region based on multiple statistical metrics. Results demonstrate that the ECC values provide intuitive evidence for filtering unqualified TC triplets. Both the absolute and relative error variances (signal-to-noise ratio) are considered for ET dataset evaluation. Overall PML-V2 has the best performance among the evaluated four products. Two merged ET datasets with the reference climatology of FLUXCOM outperform all parent products with the lowest errors by using ICOS data as reference among all sites – indicating the feasibility of TC technique for improving ET accuracy in the Nordic Region. This study also analyses the impacts of reference climatology selection on the TC merged results.

Towards a General Monitoring System for Terrestrial Primary Production: A Test Spanning the European Drought of 2018

(1) Land surface models require inputs of temperature and moisture variables to generate predictions of gross primary production (GPP). Differences between leaf and air temperature vary temporally and spatially and may be especially pronounced under conditions of low soil moisture availability. The Sentinel-3 satellite mission offers estimates of the land surface temperature (LST), which for vegetated pixels can be adopted as the canopy temperature. Could remotely sensed estimates of LST offer a parsimonious input to models by combining information on leaf temperature and hydration? (2) Using a light use efficiency model that requires only a handful of input variables, we generated GPP simulations for comparison with eddy-covariance inferred estimates available from flux sites within the Integrated Carbon Observation System. Remotely sensed LST and greenness data were input from Sentinel-3. Gridded air temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. We chose the years 2018–2019 to exploit the natural experiment of a pronounced European drought. (3) Simulated GPP showed good agreement with flux-derived estimates. During dry conditions, simulations forced with LST performed better than those with air temperature for shrubland, grassland and savanna sites. (4) This study advances the prospect for a global GPP monitoring system that will rely primarily on remotely sensed inputs.

Evolution of traceable radon emanation sources from MBq to few Bq

In the framework of the EMPIR project traceRadon, stable atmospheres with low-level radon activity concentrations have to be produced for calibrating radon detectors designed to measure outdoor air activity concentrations. The traceable calibration of these detectors at very low activity concentrations is of special interest to the radiation protection, climate observation, and atmospheric research communities.Radiation protection networks (such as the EUropean Radiological Data Exchange Platform (EURDEP)) and atmospheric monitoring networks (such as the Integrated Carbon Observation System (ICOS)) need reliable and accurate radon activity concentration measurements for a variety of reasons, including: the identification of Radon Priority Areas (RPA); improving the sensitivity and reliability of radiological emergency early warning systems (Melintescu et al., 2018); for more reliable application of the Radon Tracer Method (RTM) to estimate greenhouse gas (GHG) emissions; for improved global “baseline” monitoring of changing GHG concentrations and quantification of regional pollution transport (Chambers et al., 2016), (Chambers et al., 2018); and for evaluating mixing and transport parameterisations in regional or global chemical transport models (CTMs) (Zhang et al., 2021), (Chambers et al., 2019).To achieve this goal, low activity sources of radium with a variety of characteristics were produced using different methods. Sources ranging from MBq 226Ra down to several Bq 226Ra were developed and characterised during the evolution of production methods, and uncertainties below 2 % (k=1) were achieved through dedicated detection techniques, even for the lowest activity sources. The uncertainty of the lowest activity sources was improved using a new online measurement technique for which the source and detector were combined in the same device. This Integrated Radon Source Detector device, henceforth an IRSD, reaches a counting efficiency approaching 50 % through detection under quasi 2π sr solid-angle. At the time of this study the IRSD was already produced with 226Ra activities between 2 Bq and 440 Bq.To compare the working performance of the developed sources (i.e., to establish a reference atmosphere), study the stability of the sources, and to establish traceability to national standards, an intercomparison exercise was carried out at the PTB facility. Here we present the various source production techniques, the determination of their radium activity, and determination of their radon emanation (including assigned uncertainties). This includes details of the implementation of the intercomparison set-up, and a discussion of the results of the source characterisations.

Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling

Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

Calculation of soil water content using dielectric-permittivity-based sensors – benefits of soil-specific calibration

Abstract. Soil water content (SWC) sensors are widely used for scientific studies or for the management of agricultural practices. The most common sensing techniques provide an estimate of volumetric soil water content based on sensing of dielectric permittivity. These techniques include frequency domain reflectometry (FDR), time domain reflectometry (TDR), capacitance and even remote-sensing techniques such as ground-penetrating radar (GPR) and microwave-based techniques. Here, we will focus on frequency domain reflectometry (FDR) sensors and more specifically on the questioning of their factory calibration, which does not take into account soil-specific features and therefore possibly leads to inconsistent SWC estimates. We conducted the present study in the southwest of France on two plots that are part of the ICOS ERIC network (Integrated Carbon Observation System, European Research and Infrastructure Consortium), FR-Lam and FR-Aur. We propose a simple protocol for soil-specific calibration, particularly suitable for clayey soil, to improve the accuracy of SWC determination when using commercial FDR sensors. We compared the sensing accuracy after soil-specific calibration versus factory calibration. Our results stress the necessity of performing a thorough soil-specific calibration for very clayey soils. Hence, locally, we found that factory calibration results in a strong overestimation of the actual soil water content. Indeed, we report relative errors as large as +115 % with a factory-calibrated sensor based on the real part of dielectric permittivity and up to + 245 % with a factory-calibrated sensor based on the modulus of dielectric permittivity.

Modelling managed forest ecosystems in Sweden: An evaluation from the stand to the regional scale

Incorporation of a forest management module in the dynamic vegetation model LPJ-GUESS has allowed the study and predictions of management treatment effects on the carbon cycle and on forest ecosystem structure. In this study, LPJ-GUESS is evaluated at the regional scale against observational data from the Swedish National Forest Inventory. Simulated standing volume is compared against observations for the four most common forest types in the country. Furthermore, eddy-covariance flux measurements from the Integrated Carbon Observation System (ICOS) are used to evaluate model predictions of net ecosystem exchange (NEE), gross primary productivity (GPP) and ecosystem respiration (Reco) at the site scale. The model results suggest an adequate representation of standing volume in monocultures of Norway spruce and Scots pine for regional simulations in southern and central Sweden, after an updated parameterization of the species. For northern Sweden, the standing volume in Norway spruce monocultures was overestimated with the updated parameter values. At the stand scale, the model produced mixed results for carbon fluxes when evaluated against eddy-covariance data for two sites, one in central and one in southern Sweden. The interannual variation of GPP was well captured for the central Swedish site, but the modelled average GPP for the period 2015–2019 was overestimated by 9%. For the southern Swedish site, GPP was underestimated by 15% for the corresponding period and the simulated interannual variation was half of the observed. The seasonal estimates of modelled net ecosystem exchange (NEE) deviated from observations and the simulated standing volume was underestimated by 25% for both sites. The results highlight further potential to perform species-specific calibration to capture latitudinal gradients in key ecosystem properties, and to incorporate additional characteristics of site quality which could benefit model accuracy at the scale of individual forest stands, both regarding simulated carbon fluxes and forest stand variables.