003 Characterisation of wetland carbon emissions from the Amazon basin

Job title:

003 Characterisation of wetland carbon emissions from the Amazon basin

Company:

Centre National d’Etudes Spatiales

Job description

Scientific context: Tropical regions remain the greatest sources of uncertainties in the global carbon budget (Saunois et al., 2020; 2024). Both methane (CH4) and carbon dioxide (CO2) fluxes from wetlands and water bodies remain difficult to simulate using current biogeochemical models (McNicol et al., 2023). These emissions are set to increase considerably over the course of this century as a result of rapid climate warming (Shen et al., 2023; Yuan et al., 2024). The Amazon basin alone contributes around 15% of global wetland emissions and around 8% of total global emissions. In addition, biomass burning emissions are also highly uncertain, varying year-to-year depending on El Nino oscillations, law enforcement, and extreme events reinforced by climate change (Gatti et al., 2023). Due to the lack of ground-based measurements and a dense cloud coverage, the greenhouse gas budget of the Amazon basin and its arc of deforestation remains highly uncertain (Saunois et al., 2024), hence one of the most promising regions to utilize current and future satellite measurements (Palmer et al., 2018) combined with aircraft measurements (Wilson et al., 2021).We propose here to leverage all the existing satellite measurements able to measure CO2 and CH4 concentrations in parallel with the international campaign MAGIC-Tropics 2026, analyzed with novel atmospheric modeling tools able to resolve the tropical convection and the large spatial variability in fluxes across the region. Based on current estimates of wetland CH4 fluxes from biogeochemical models driven by remote sensing data (wetland extent, biomass, land cover,…) and greenhouse gas measurements from current and upcoming satellite missions (Sentinel-5P, MicroCarb, OCO-2/3, IASI-NG), the PhD student will develop and utilize a regional inversion system to quantify the CO2 and CH4 fluxes using a convection-explicit model (MPAS). To date, convective mass transfers require the use of convection schemes due to their low spatial resolution. We intend here to overcome this problem by using a novel high-resolution, adaptive-mesh approach. In parallel, in situ and remote sensing data collected during the MAGIC Tropics (2026) campaign will serve as an evaluation of the transport model and the satellite measurements assimilated in the inversion. The MAGIC initiative ( . fr) led by CNRS and CNES began in 2017 and has seen 6 major measurement campaigns carried out with the support of ESA, EUMETSAT and DLR. The large-scale MAGIC-Tropics-2026 campaign will take place in Brazil in summer 2026 to determine the contribution of wetlands and biomass combustion emissions to GHG balances in Brazil, and to validate space-based measurements in the tropical region. To achieve these objectives, coordinated ground (5 x EM-27), airborne (Safire, DLR, and NASA aircraft, coordinated with Aircore balloon launches) and satellite observations will be combined to cover the main tropical ecosystems.PhD research objectivesWe propose to address the following questions during this phD:1. What is the added-value of current and future satellites to constrain both CO2 and CH4 fluxes over a key Tropical region using an inversion system able to assimilate multiple observation types?2. What is the contribution of tropical wetlands to Brazil’s carbon balance (CO2 and CH4) at the end of/after the rainy season?Technical developmentsMeasurement analyses: The thesis work is based on the analysis of satellite data (TROPOMI, IASI-NG, OCO-2/3, MicroCarb) and data collected during the MAGIC Tropics 2026 campaign, in addition to existing measuring stations operated by our Brazilian partners (UFSM, USP,…), including Aircore data, eddy-covariance flux towers and concentration measurements from ground-based and aircraft flights (NASA Langley, Safire, DLR).Direct modeling analyses: The MPAS model, part of a new generation of variable-resolution atmospheric models, has been implemented and evaluated to increase the resolution over the observed zones (1-km resolution) and thus the representation of convective transport, a major source of transport errors in global inversions (Schuh et al., 2023). This adaptive-mesh modelling system will be used to explicitely resolve deep convection over the Amazon basin and beyond, thanks to its zooming capabilities.Inversion modeling analyses: The inversion analysis will be performed using a newly-developed inversion system including adaptive mesh resolution, sparse matrix, and block matrix decomposition (Che et al., 2024). This inverse approach relies on Lagrangian footprints (MPAS coupled to LPDM) to describe the relationship between the observed concentrations and the surface fluxes.For more Information about the topics and the co-financial partner (found by the lab !); contact Directeur de thèse –Then, prepare a resume, a recent transcript and a reference letter from your M2 supervisor/ engineering school director and you will be ready to apply online before March 14th, 2025 Midnight Paris time !

Expected salary

Location

Reims, Marne

Job date

Wed, 05 Feb 2025 07:41:17 GMT

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