• Energy Research

Abstract. Understanding the global carbon (C) cycle is of crucial importance to map current and future climate dynamics relative to global environmental change. A full characterization of C cycling requires detailed information on spatiotemporal patterns of surface–atmosphere fluxes. However, relevant C cycle observations are highly variable in their coverage and reporting standards. Especially problematic is the lack of integration of the carbon dioxide (CO2) exchange of the ocean, inland freshwaters and the land surface with the atmosphere. Here we adopt a data-driven approach to synthesize a wide range of observation-based spatially explicit surface–atmosphere CO2 fluxes from 2001 to 2010, to identify the state of today's observational opportunities and data limitations. The considered fluxes include net exchange of open oceans, continental shelves, estuaries, rivers, and lakes, as well as CO2 fluxes related to net ecosystem productivity, fire emissions, loss of tropical aboveground C, harvested wood and crops, as well as fossil fuel and cement emissions. Spatially explicit CO2 fluxes are obtained through geostatistical and/or remote-sensing-based upscaling, thereby minimizing biophysical or biogeochemical assumptions encoded in process-based models. We estimate a bottom-up net C exchange (NCE) between the surface (land, ocean, and coastal areas) and the atmosphere. Though we provide also global estimates, the primary goal of this study is to identify key uncertainties and observational shortcomings that need to be prioritized in the expansion of in situ observatories. Uncertainties for NCE and its components are derived using resampling. In many regions, our NCE estimates agree well with independent estimates from other sources such as process-based models and atmospheric inversions. This holds for Europe (mean ± 1 SD: 0.8 ± 0.1 PgC yr−1, positive numbers are sources to the atmosphere), Russia (0.1 ± 0.4 PgC yr−1), East Asia (1.6 ± 0.3 PgC yr−1), South Asia (0.3 ± 0.1 PgC yr−1), Australia (0.2 ± 0.3 PgC yr−1), and most of the Ocean regions. Our NCE estimates give a likely too large CO2 sink in tropical areas such as the Amazon, Congo, and Indonesia. Overall, and because of the overestimated CO2 uptake in tropical lands, our global bottom-up NCE amounts to a net sink of −5.4 ± 2.0 PgC yr−1. By contrast, the accurately measured mean atmospheric growth rate of CO2 over 2001–2010 indicates that the true value of NCE is a net CO2 source of 4.3 ± 0.1 PgC yr−1. This mismatch of nearly 10 PgC yr−1 highlights observational gaps and limitations of data-driven models in tropical lands, but also in North America. Our uncertainty assessment provides the basis for setting priority regions where to increase carbon observations in the future. High on the priority list are tropical land regions, which suffer from a lack of in situ observations. Second, extensive pCO2 data are missing in the Southern Ocean. Third, we lack observations that could enable seasonal estimates of shelf, estuary, and inland water–atmosphere C exchange. Our consistent derivation of data uncertainties could serve as prior knowledge in multicriteria optimization such as the Carbon Cycle Data Assimilation System (CCDAS) and atmospheric inversions, without over- or under-stating bottom-up data credibility. In the future, NCE estimates of carbon sinks could be aggregated at national scale to compare with the official national inventories of CO2 fluxes in the land use, land use change, and forestry sector, upon which future emission reductions are proposed.

Aunque el cultivo de arroz es una de las fuentes agrícolas más importantes de metano (CH4) y contribuye con ~8% del total de las emisiones antropogénicas globales, persisten grandes discrepancias entre las estimaciones de las emisiones globales de CH4 del cultivo de arroz (que van de 18 a 115 Tg CH4 año−1) debido a la falta de limitaciones observables. La distribución espacial de las emisiones de arrozales se ha evaluado a escalas regionales a globales mediante inventarios ascendentes y modelos de superficie terrestre sobre resolución espacial gruesa (por ejemplo, > 0,5°) o unidades espaciales (por ejemplo, zonas agroecológicas). Sin embargo, las estimaciones de flujo de CH4 de alta resolución capaces de capturar los efectos del clima local y las prácticas de gestión sobre las emisiones, así como la replicación de datos in situ, siguen siendo difíciles de producir debido a la escasez de mapas de arroz de alta resolución y a la insuficiente comprensión de los predictores de CH4. Aquí, combinamos datos de flujo de metano de arroz con arroz de 23 sitios de covarianza de remolinos globales y datos de teledetección MODIS con aprendizaje automático para 1) evaluar el rendimiento del modelo basado en datos y la importancia variable para predecir los flujos de CH4 de arroz; y 2) producir estimaciones cuadriculadas de aumento de escala de las emisiones de CH4 de arroz a una resolución de 5000 m en toda Asia monzónica, donde se cultiva ~87% del área mundial de arroz y se produce ~ 90% de la producción mundial de arroz. Nuestro modelo de bosque aleatorio logró valores de eficiencia de Nash-Sutcliffe de 0,59 y 0,69 para flujos de CH4 de 8 días y flujos de CH4 medios del sitio, respectivamente, con índices relacionados con la temperatura de la superficie terrestre, la biomasa y la disponibilidad de agua como los predictores más importantes. Estimamos que las emisiones anuales promedio de arroz con cáscara CH4 (excluida la temporada de barbecho invernal) en toda Asia monzónica son de 20.6 ± 1.1 Tg año−1 para 2001–2015, que se encuentra en el rango más bajo de las estimaciones anteriores basadas en el inventario (20–32 CH4 Tg año−1). Nuestras estimaciones también sugieren que las emisiones de CH4 del arroz con cáscara en esta región han estado disminuyendo desde 2007 hasta 2015 después de las disminuciones tanto en el área de cultivo de arroz con cáscara como en las tasas de emisión por unidad de área, lo que sugiere que las emisiones de CH4 del arroz con cáscara en el monzón de Asia probablemente no hayan contribuido al renovado crecimiento del CH4 atmosférico en los últimos años. Bien que la riziculture soit l'une des plus importantes sources agricoles de méthane (CH4) et contribue à environ8% des émissions anthropiques mondiales totales, de grands écarts subsistent entre les estimations des émissions mondiales de CH4 provenant de la riziculture (allant de 18 à 115 Tg de CH4 par an) en raison d'un manque de contraintes d'observation. La distribution spatiale des émissions de riz paddy a été évaluée à l'échelle régionale et mondiale par des inventaires ascendants et des modèles de surface terrestre sur une résolution spatiale grossière (par exemple, > 0,5°) ou des unités spatiales (par exemple, des zones agro-écologiques). Cependant, les estimations de flux de CH4 à haute résolution capables de capturer les effets du climat local et des pratiques de gestion sur les émissions, ainsi que de reproduire les données in situ, restent difficiles à produire en raison de la rareté des cartes à haute résolution du riz paddy et d'une compréhension insuffisante des prédicteurs de CH4. Ici, nous combinons les données de flux de méthane de riz paddy provenant de 23 sites mondiaux de covariance des tourbillons et les données de télédétection MODIS avec l'apprentissage automatique pour 1) évaluer les performances du modèle basé sur les données et l'importance variable pour prédire les flux de CH4 du riz ; et 2) produire des estimations maillées des émissions de CH4 du riz à une résolution de 5000 m dans toute l'Asie de la mousson, où ∼87 % de la superficie mondiale du riz est cultivée et ∼ 90 % de la production mondiale de riz se produit. Notre modèle de forêt aléatoire a atteint des valeurs d'efficacité de Nash-Sutcliffe de 0,59 et 0,69 pour les flux de CH4 sur 8 jours et les flux de CH4 moyens du site, respectivement, la température de surface du sol, la biomasse et les indices liés à la disponibilité de l'eau étant les prédicteurs les plus importants. Nous estimons que les émissions annuelles moyennes de CH4 de riz paddy (hors saison de jachère hivernale) dans toute l'Asie de la mousson sont de 20,6 ± 1,1 Tgan-1 pour 2001–2015, ce qui se situe dans la fourchette inférieure des estimations antérieures basées sur les inventaires (20–32 Tgan-1 de CH4). Nos estimations suggèrent également que les émissions de CH4 du riz paddy dans cette région ont diminué de 2007 à 2015 à la suite de baisses à la fois de la superficie cultivée en riz paddy et des taux d'émission par unité de surface, ce qui suggère que les émissions de CH4 du riz paddy dans Monsoon Asia n'ont probablement pas contribué à la croissance renouvelée du CH4 atmosphérique ces dernières années. Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years. على الرغم من أن زراعة الأرز هي واحدة من أهم المصادر الزراعية للميثان (CH4) وتساهم بنسبة 8 ٪ من إجمالي الانبعاثات العالمية البشرية المنشأ، إلا أنه لا تزال هناك اختلافات كبيرة بين تقديرات انبعاثات الميثان العالمية من زراعة الأرز (التي تتراوح من 18 إلى 115 تيراغرام من الميثان في السنة−1) بسبب نقص قيود المراقبة. تم تقييم التوزيع المكاني لانبعاثات الأرز والأرز على المستويات الإقليمية إلى العالمية من خلال قوائم الجرد التصاعدية ونماذج سطح الأرض على الدقة المكانية الخشنة (على سبيل المثال، > 0.5درجة) أو الوحدات المكانية (على سبيل المثال، المناطق الزراعية الإيكولوجية). ومع ذلك، لا تزال تقديرات تدفق الميثان عالية الدقة القادرة على التقاط آثار المناخ المحلي وممارسات الإدارة على الانبعاثات، وكذلك تكرار البيانات في الموقع، صعبة الإنتاج بسبب ندرة الخرائط عالية الدقة لأرز الأرز وعدم كفاية فهم تنبؤات الميثان. هنا، نجمع بين بيانات تدفق الميثان من الأرز والأرز من 23 موقعًا عالميًا للتباين الدوامي وبيانات الاستشعار عن بعد MODIS مع التعلم الآلي من أجل 1) تقييم أداء النموذج القائم على البيانات والأهمية المتغيرة للتنبؤ بتدفقات CH4 للأرز ؛ و 2) إنتاج تقديرات شبكية لانبعاثات CH4 للأرز بدقة 5000 متر في جميع أنحاء آسيا الموسمية، حيث تتم زراعة 87 ٪ من مساحة الأرز العالمية و 90 ٪ من إنتاج الأرز العالمي. حقق نموذجنا للغابات العشوائية قيم كفاءة ناش- سوتكليف البالغة 0.59 و 0.69 لتدفقات الميثان لمدة 8 أيام ومتوسط تدفقات الميثان في الموقع على التوالي، مع مؤشرات درجة حرارة سطح الأرض والكتلة الحيوية وتوافر المياه كأهم المؤشرات. نقدر المتوسط السنوي (باستثناء موسم الإراحة الشتوية) لانبعاثات الميثان من الأرز في جميع أنحاء آسيا الموسمية بـ 20.6 ± 1.1 تيراغرام في السنة-1 للفترة 2001–2015، وهو في النطاق الأدنى للتقديرات السابقة القائمة على المخزون (20–32 تيراغرام في السنة-1). تشير تقديراتنا أيضًا إلى أن انبعاثات الميثان من أرز الأرز في هذه المنطقة قد انخفضت من عام 2007 حتى عام 2015 بعد الانخفاضات في كل من مساحة زراعة أرز الأرز ومعدلات الانبعاثات لكل وحدة مساحة، مما يشير إلى أن انبعاثات الميثان من أرز الأرز في الرياح الموسمية في آسيا من المحتمل ألا تساهم في النمو المتجدد للميثان في الغلاف الجوي في السنوات الأخيرة.

Plants acquire carbon through photosynthesis to sustain biomass production, autotrophic respiration and production of non-structural compounds for multiple purposes1. The fraction of photosynthetic production used for biomass production, the biomass production efficiency2, is a key determinant of the conversion of solar energy to biomass. In forest ecosystems, biomass production efficiency was suggested to be related to site fertility2. Here we present a database of biomass production efficiency from 131 sites compiled from individual studies using harvest, biometric, eddy covariance, or process-based model estimates of production. The database is global, but dominated by data from Europe and North America. We show that instead of site fertility, ecosystem management is the key factor that controls biomass production efficiency in terrestrial ecosystems. In addition, in natural forests, grasslands, tundra, boreal peatlands and marshes, biomass production efficiency is independent of vegetation, environmental and climatic drivers. This similarity of biomass production efficiency across natural ecosystem types suggests that the ratio of biomass production to gross primary productivity is constant across natural ecosystems. We suggest that plant adaptation results in similar growth efficiency in high- and low-fertility natural systems, but that nutrient influxes under managed conditions favour a shift to carbon investment from the belowground flux of non-structural compounds to aboveground biomass.

Abstract Since 1750, land-use change and fossil fuel combustion has led to a 46% increase in the atmospheric carbon dioxide (CO2) concentrations, causing global warming with substantial societal consequences. The Paris Agreement aims to limit global temperature increases to well below 2°C above preindustrial levels. Increasing levels of CO2 and other greenhouse gases (GHGs), such as methane (CH4) and nitrous oxide (N2O), in the atmosphere are the primary cause of climate change. Approximately half of the carbon emissions to the atmosphere are sequestered by ocean and land sinks, leading to ocean acidification but also slowing the rate of global warming. However, there are significant uncertainties in the future global warming scenarios due to uncertainties in the size, nature, and stability of these sinks. Quantifying and monitoring the size and timing of natural sinks and the impact of climate change on ecosystems are important information to guide policy-makers’ decisions and strategies on reductions in emissions. Continuous, long-term observations are required to quantify GHG emissions, sinks, and their impacts on Earth systems. The Integrated Carbon Observation System (ICOS) was designed as the European in situ observation and information system to support science and society in their efforts to mitigate climate change. It provides standardized and open data currently from over 140 measurement stations across 12 European countries. The stations observe GHG concentrations in the atmosphere and carbon and GHG fluxes between the atmosphere, land surface, and the oceans. This article describes how ICOS fulfills its mission to harmonize these observations, ensure the related long-term financial commitments, provide easy access to well-documented and reproducible high-quality data and related protocols and tools for scientific studies, and deliver information and GHG-related products to stakeholders in society and policy.

Abstract The eddy covariance is a powerful technique to estimate the surface-atmosphere exchange of different scalars at the ecosystem scale. The EC method is central to the ecosystem component of the Integrated Carbon Observation System, a monitoring network for greenhouse gases across the European Continent. The data processing sequence applied to the collected raw data is complex, and multiple robust options for the different steps are often available. For Integrated Carbon Observation System and similar networks, the standardisation of methods is essential to avoid methodological biases and improve comparability of the results. We introduce here the steps of the processing chain applied to the eddy covariance data of Integrated Carbon Observation System stations for the estimation of final CO2, water and energy fluxes, including the calculation of their uncertainties. The selected methods are discussed against valid alternative options in terms of suitability and respective drawbacks and advantages. The main challenge is to warrant standardised processing for all stations in spite of the large differences in e.g. ecosystem traits and site conditions. The main achievement of the Integrated Carbon Observation System eddy covariance data processing is making CO2 and energy flux results as comparable and reliable as possible, given the current micrometeorological understanding and the generally accepted state-of-the-art processing methods.