The CO2 record at the Amazon Tall Tower Observatory : A new opportunity to study processes on seasonal and inter-annual scales

High-quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014-2019) of continuous, high-precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1 degrees S, 58.9 degrees W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal (Delta CO2obs) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter-annual scales, we find differences in phase between Delta CO2obs and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of Delta CO2obs. In addition, we present how the 2015-2016 El Nino-induced drought was captured by our atmospheric record as a positive 2 sigma anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of Delta CO2obs together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.Peer reviewe

The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter-annual scales

Abstract High-quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2. In this study, we present the first 6 years (2014?2019) of continuous, high-precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal () that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter-annual scales, we find differences in phase between and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of . In addition, we present how the 2015?2016 El Niño-induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales

The CO2 Human Emissions (CHE) Project: First steps towards a European operational capacity to monitor anthropogenic CO2 emissions

The Paris Agreement of the United Nations Framework Convention on Climate Change is a binding international treaty signed by 196 nations to limit their greenhouse gas emissions through ever-reducing Nationally Determined Contributions and a system of 5-yearly Global Stocktakes in an Enhanced Transparency Framework. To support this process, the European Commission initiated the design and development of a new Copernicus service element that will use Earth observations mainly to monitor anthropogenic carbon dioxide (CO2) emissions. The CO2 Human Emissions (CHE) project has been successfully coordinating efforts of its 22 consortium partners, to advance the development of a European CO2 monitoring and verification support (CO2MVS) capacity for anthropogenic CO2 emissions. Several project achievements are presented and discussed here as examples. The CHE project has developed an enhanced capability to produce global, regional and local CO2 simulations, with a focus on the representation of anthropogenic sources. The project has achieved advances towards a CO2 global inversion capability at high resolution to connect atmospheric concentrations to surface emissions. CHE has also demonstrated the use of Earth observations (satellite and ground-based) as well as proxy data for human activity to constrain uncertainties and to enhance the timeliness of CO2 monitoring. High-resolution global simulations (at 9 km) covering the whole of 2015 (labelled CHE nature runs) fed regional and local simulations over Europe (at 5 km and 1 km resolution) and supported the generation of synthetic satellite observations simulating the contribution of a future dedicated Copernicus CO2 Monitoring Mission (CO2M

Acceleration of partitioned fluid-structure interaction simulations by means of space mapping: An analysis of suitable approaches

Modelling of fluid-structure interactions (FSI) plays a key role in many engineering applications. However, due to the interaction between fluid and structure the computational cost related to high fidelity models (especially for strongly coupled systems) limits the direct use of current FSI simulation techniques in industry. Since a thorough knowledge of FSI phenomena is very important in design processes, efficient simulation techniques that combine low cost with high accuracy need to be developed. To this end, the use of the space mapping optimization technique as coupling approach for partitioned FSI simulations is investigated.Aerospace Engineerin

Extreme droughts and floods in the Amazon forest

In recent years, the Amazon forest has experienced several major droughts (2010, 2015/16) and floods (2012, 2014, 2021). Extreme events represent a threat to the Amazons important functions, but these perturbations also provide valuable insights into the underlying mechanisms. Here we studied the most recent massive drought and flood events in detail, and quantified their severity and spatiotemporal extent relative to a multi-year baseline. First, we describe the anomalous hydrological status of these events, by bringing together a large variety of data sets, including in-situ observations and reanalysis products for precipitation, discharge, vapor pressure deficit and soil moisture. During the strong El Niño conditions following the dry season of 2015, the precipitation fell below its climatological values. This was soon reflected in low discharge rates and soil moisture levels, persisting far into the year 2016 for some regions. In contrast, we find anomalously high precipitation over the northern Amazon during the first months of 2021, resulting in high discharge rates, and rising river levels that have led to massive floods in downstream regions. Finally, we quantified the impact of the 2015/16 drought on vegetation using the inverse model CarbonTacker South America (CT-SAM) and remote sensing proxies for photosynthesis. To address the uncertainty in prior emission estimates, we have used a range of different biosphere models (SiBCASA, SiB4), including a biosphere model linked to a detailed hydrological model (PCR-GLOBWB). For the fire flux we used multiple data sets (GFAS, SiBCASA-GFED4), including a modified version based on CO inversions performed with the TM5-4DVAR system. We find that photosynthesis was reduced during the 2015 drought, especially in the drier, southern part of the Amazon. This was followed by a recovery in the first months of 2016, but during the subsequent dry season a secondary impact on photosynthesis was found. The inversely derived net CO2 fluxes do not have the same high resolution as the satellite products, but when assessed over larger scales, a consistent drought signal is derived

Extreme droughts and floods in the Amazon forest

In recent years, the Amazon forest has experienced several major droughts (2010, 2015/16) and floods (2012, 2014, 2021). Extreme events represent a threat to the Amazons important functions, but these perturbations also provide valuable insights into the underlying mechanisms. Here we studied the most recent massive drought and flood events in detail, and quantified their severity and spatiotemporal extent relative to a multi-year baseline. First, we describe the anomalous hydrological status of these events, by bringing together a large variety of data sets, including in-situ observations and reanalysis products for precipitation, discharge, vapor pressure deficit and soil moisture. During the strong El Niño conditions following the dry season of 2015, the precipitation fell below its climatological values. This was soon reflected in low discharge rates and soil moisture levels, persisting far into the year 2016 for some regions. In contrast, we find anomalously high precipitation over the northern Amazon during the first months of 2021, resulting in high discharge rates, and rising river levels that have led to massive floods in downstream regions. Finally, we quantified the impact of the 2015/16 drought on vegetation using the inverse model CarbonTacker South America (CT-SAM) and remote sensing proxies for photosynthesis. To address the uncertainty in prior emission estimates, we have used a range of different biosphere models (SiBCASA, SiB4), including a biosphere model linked to a detailed hydrological model (PCR-GLOBWB). For the fire flux we used multiple data sets (GFAS, SiBCASA-GFED4), including a modified version based on CO inversions performed with the TM5-4DVAR system. We find that photosynthesis was reduced during the 2015 drought, especially in the drier, southern part of the Amazon. This was followed by a recovery in the first months of 2016, but during the subsequent dry season a secondary impact on photosynthesis was found. The inversely derived net CO2 fluxes do not have the same high resolution as the satellite products, but when assessed over larger scales, a consistent drought signal is derived

Extreme droughts and floods in the Amazon forest

In recent years, the Amazon forest has experienced several major droughts (2010, 2015/16) and floods (2012, 2014, 2021). Extreme events represent a threat to the Amazons important functions, but these perturbations also provide valuable insights into the underlying mechanisms. Here we studied the most recent massive drought and flood events in detail, and quantified their severity and spatiotemporal extent relative to a multi-year baseline. First, we describe the anomalous hydrological status of these events, by bringing together a large variety of data sets, including in-situ observations and reanalysis products for precipitation, discharge, vapor pressure deficit and soil moisture. During the strong El Niño conditions following the dry season of 2015, the precipitation fell below its climatological values. This was soon reflected in low discharge rates and soil moisture levels, persisting far into the year 2016 for some regions. In contrast, we find anomalously high precipitation over the northern Amazon during the first months of 2021, resulting in high discharge rates, and rising river levels that have led to massive floods in downstream regions. Finally, we quantified the impact of the 2015/16 drought on vegetation using the inverse model CarbonTacker South America (CT-SAM) and remote sensing proxies for photosynthesis. To address the uncertainty in prior emission estimates, we have used a range of different biosphere models (SiBCASA, SiB4), including a biosphere model linked to a detailed hydrological model (PCR-GLOBWB). For the fire flux we used multiple data sets (GFAS, SiBCASA-GFED4), including a modified version based on CO inversions performed with the TM5-4DVAR system. We find that photosynthesis was reduced during the 2015 drought, especially in the drier, southern part of the Amazon. This was followed by a recovery in the first months of 2016, but during the subsequent dry season a secondary impact on photosynthesis was found. The inversely derived net CO2 fluxes do not have the same high resolution as the satellite products, but when assessed over larger scales, a consistent drought signal is derived

Supplemental data of Global Carbon Project 2020

Supplement containing data related to the 2020 Global Carbon Budget from the Global Carbon Project. The original article is Friedlingstein et al: Global Carbon Budget 2020, Earth Syst. Sci. Data Discussions, 2020. https://doi.org/10.5194/essd-12-3269-2020. Further information is available on: http://www.globalcarbonproject.org/carbonbudget. File Global_Carbon_Budget_2020v1.0.xlsx includes the following: 1. Summary 2. Global Carbon Budget 3. Fossil fuel emissions by Fuel Type 4. Land-use change emissions 5. Ocean Sink 6. Terrestrial sink 7. Historical Budget. File National_Carbon_Emissions_2020v1.0.xlsx includes the following: 1. Summary 2. Territorial emissions 3. Consumption emissions 4. Emissions transfers 5. Country definitions 6. Disaggregation 7. Aggregatio