The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales. (26th October 2021)
- Record Type:
- Journal Article
- Title:
- The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales. (26th October 2021)
- Main Title:
- The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales
- Authors:
- Botía, Santiago
Komiya, Shujiro
Marshall, Julia
Koch, Thomas
Gałkowski, Michał
Lavric, Jost
Gomes‐Alves, Eliane
Walter, David
Fisch, Gilberto
Pinho, Davieliton M.
Nelson, Bruce W.
Martins, Giordane
Luijkx, Ingrid T.
Koren, Gerbrand
Florentie, Liesbeth
Carioca de Araújo, Alessandro
Sá, Marta
Andreae, Meinrat O.
Heimann, Martin
Peters, Wouter
Gerbig, Christoph - Abstract:
- 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 ( Δ CO 2 obs ) 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 Δ CO 2 obs 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 Δ CO 2 obs . 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 Δ CO 2 obs 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 fluxesAbstract: 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 ( Δ CO 2 obs ) 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 Δ CO 2 obs 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 Δ CO 2 obs . 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 Δ CO 2 obs 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. Abstract : By analyzing a six‐year record of CO2 mole fractions at the Amazon Tall Tower Observatory (ATTO), we found that the seasonal cycle amplitude is ~4 ppm and that the controls at seasonal and inter‐annual scales shift from local to non‐local drivers. We show that the 2015/2016 ENSO was captured as a strong positive anomaly mainly in 2016. Finally, we use a suit of biosphere models in an atmospheric transport model to simulated CO2 mole fractions at ATTO. We found that the seasonal cycle was not captured by our simulations but 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. … (more)
- Is Part Of:
- Global change biology. Volume 28:Number 2(2022)
- Journal:
- Global change biology
- Issue:
- Volume 28:Number 2(2022)
- Issue Display:
- Volume 28, Issue 2 (2022)
- Year:
- 2022
- Volume:
- 28
- Issue:
- 2
- Issue Sort Value:
- 2022-0028-0002-0000
- Page Start:
- 588
- Page End:
- 611
- Publication Date:
- 2021-10-26
- Subjects:
- atmospheric transport -- carbon dioxide -- net ecosystem exchange -- river evasion
Climatic changes -- Environmental aspects -- Periodicals
Troposphere -- Environmental aspects -- Periodicals
Biodiversity conservation -- Periodicals
Eutrophication -- Periodicals
551.5 - Journal URLs:
- http://www.blackwell-synergy.com/member/institutions/issuelist.asp?journal=gcb ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1111/gcb.15905 ↗
- Languages:
- English
- ISSNs:
- 1354-1013
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4195.358330
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20227.xml