the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Jun 2026
Quantifying canopy CO2 storage to improve long-term estimates of net ecosystem carbon exchange in the southern Amazon forest
Abstract. The Amazon rainforest plays a critical role in the global carbon cycle, yet large uncertainties remain regarding its net carbon balance. Accurate estimates of net ecosystem exchange (NEE) require accounting for the CO2 storage term, which is commonly derived from vertical CO2 profile measurements that are scarcely available at long-term flux tower sites. Here, we evaluated three approaches for estimating canopy CO2 storage at the Rebio Jaru forest site in Rondônia, Brazil: (1) a multi-point CO2 profile (MPP), (2) a single-point profile (SPP) based on a closed-path gas analyzer, and (3) a single-point eddy covariance approach (SPE) using CO2 measurements from an open-path gas analyzer. Although the methods operated simultaneously for less than two years, both simplified approaches showed strong agreement with the reference MPP method, with coefficients of determination of R2 = 0.87 for SPP and R2 = 0.79 for SPE, whereas neglecting the storage term resulted in poor agreement (R2 = 0.24). Using the SPE approach, we reconstructed a 13-year NEE time series that revealed pronounced interannual variability, with the forest generally acting as a carbon sink during La Niña conditions and shifting toward a carbon source during El Niño events and during consecutive years due to drought legacy effects. The seasonal cycle exhibited a marked carbon source peak during the transition from the dry to the wet season, likely associated with enhanced litter decomposition and increased soil respiration following the first rainfall events after prolonged dry periods. These results demonstrate that simplified single-point approaches can reliably reconstruct long-term NEE dynamics and substantially improve estimates of Amazonian carbon balance at sites where complete CO2 profile measurements are unavailable.
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Status: open (until 12 Sep 2026)
- RC1: 'Comment on egusphere-2026-3167', Anonymous Referee #1, 14 Aug 2026 reply
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- 1
This manuscript addresses an important topic, but in its current form it does not sufficiently confront the well‑documented uncertainties in Amazon nocturnal CO₂ fluxes that arise from canopy storage, advection, and flux filtering, which are large enough in many forests to overturn the inferred sign of the annual carbon balance.
It is indisputable that an estimate of the variable storage of CO₂ in the airspace below the eddy covariance sensors must be added to above‑canopy flux measurements, because CO₂ often accumulates within the canopy at night and is flushed after dawn. The manuscript proposes to simply “fill in” missing canopy CO₂ storage at a long‑term Amazon flux site by calibrating simple single‑point measurements against a short period with full CO₂ profiles, then using those calibrated single‑point methods to reconstruct storage over the full 13‑year record.
However, there has already been a study exploring methods for gap‑filling within‑canopy CO₂ storage (Iwata et al., 2005) that concluded that above‑canopy CO₂ concentration is a poor proxy for within‑canopy storage. I recommend that the authors explicitly compare their SPE/SPP approach with the gap‑filling schemes proposed by Iwata et al. (2005) and discuss under which conditions the use of above‑canopy CO₂ is justified or potentially biased.
There is another issue, more important, that the manuscript does not discuss. At night, low wind and weak turbulence commonly cause eddy covariance to underestimate respiration fluxes, even when storage is included (Zeri et al., 2014; Barr et al., 2013; Lloyd et al., 2007), which is commonly treated by applying a u* filter. The manuscript acknowledges that defining an appropriate friction‑velocity (u*) threshold is “widely recognized as one of the main” issues in nighttime EC processing, but then uses an arbitrary threshold that corresponds to the exclusion of 60% of the nocturnal data, without discussing any implications for the uncertainty in the annual totals.
I am afraid this is insufficient to treat nocturnal CO₂ storage and advection and their interaction with flux filtering, which are the dominant sources of uncertainty here. Existing work at several Amazon towers shows that nighttime treatment can alter annual fluxes by more than 4 t C ha⁻¹ yr⁻¹, and different validation approaches have produced at least a 100% difference in annual totals (Miller et al., 2004; Kruijt et al., 2004). A previous analysis at the Jaru site indicated that data gaps and filtering alone induce annual errors of up to ~32%, particularly because of terrain heterogeneity and nighttime conditions (Zeri et al., 2010). Finally, Zeri et al. (2014) reported annual fluxes on the order of 5 t C ha⁻¹ yr⁻¹ at this site, after discussion and analysis of possible uncertainties.
As these sensitivities are unusually large in these Amazonian towers, I think any claim about the sign or magnitude of the forest carbon balance that does not explicitly quantify associated uncertainties, or at least bracket some of these unresolved transport processes, should be treated as provisional and discussed in more detail. How much of the change in the fluxes in the “reconstructed” time series relative to previous output comes purely from adding canopy storage (using the SPE/SPP methods), versus from other choices (e.g. stricter QC, different filters and gap‑filling, random and systematic errors, inclusion of additional years with different climate)?
Before publication, I recommend that the authors engage more deeply with the existing literature on spatially variable storage, subcanopy drainage flows, and u*‑based nighttime corrections, and demonstrate through quantitative analyses that their main conclusions are robust to these processes.
References cited
Aubinet, M. (2008). EDDY COVARIANCE CO2 FLUX MEASUREMENTS IN NOCTURNAL CONDITIONS: AN ANALYSIS OF THE PROBLEM. Ecological Applications, 18(6), 1368–1378. https://doi.org/https://doi.org/10.1890/06-1336.1
Barr, A. et al. 2013. Use of change-point detection for friction–velocity threshold evaluation in eddy-covariance studies. Agricultural and Forest Meteorology, 171, 31-45. https://doi.org/10.1016/j.agrformet.2012.11.023
Iwata, H., et al 2005. Gap-filling measurements of carbon dioxide storage in tropical rainforest canopy airspace. Agricultural and Forest Meteorology, 132(3–4). https://doi.org/10.1016/j.agrformet.2005.08.005
Kruijt, B., et al 2004. The robustness of eddy correlation fluxes for Amazon rain forest conditions. Ecological Applications, 14, 101-113. https://doi.org/10.1890/02-6004
Lloyd, J., et al. 2007. An airborne regional carbon balance for Central Amazonia. Biogeosciences, 4, 759-768. https://doi.org/10.5194/bg-4-759-2007
Miller, S. D., et al. 2004. Biometric and Micrometeorological measurements of tropical forest carbon balance. Ecological Applications, 14(4), S114–S126. https://doi.org/10.1890/02-6005
Zeri, M., & Sá, L. D. A. (2010). The impact of data gaps and quality control filtering on the balances of energy and carbon for a Southwest Amazon forest. Agricultural and Forest Meteorology, 150(12), 1543–1552. https://doi.org/https://doi.org/10.1016/j.agrformet.2010.08.004
Zeri, M., et al. (2014). Variability of carbon and water fluxes following climate extremes over a tropical forest in southwestern amazonia. PLoS ONE, 9(2). https://doi.org/10.1371/journal.pone.0088130