the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Uncertainties in OCO-2 satellite retrievals of XCO2 limit diagnosis of transport model simulation uncertainty
Abstract. Estimating regional CO2 sources and sinks is challenging due to limited data and uncertainties in transport models. Orbiting Carbon Observatory-2 (OCO-2) overcomes measurement limits, providing CO2 variations beyond in-situ networks. This study analyses altitude-wise model-observation CO2 differences from surface to upper troposphere using aircraft observations from ATom, Amazon, and CONTRAIL campaigns over OCO-2 total column CO2 (XCO2) sampling location to characterise sources of uncertainty in MIROC4-ACTM. We show model aligns better with ATom tropospheric columns (0.03 ± 0.03 ppm) than OCO-2 XCO2 (0.2 ± 0.5 ppm), especially over oceans, highlighting the need for expanded profile measurements to characterise errors robustly. Altitude-wise comparisons reveal this differences primarily occur in the lower troposphere (0–2 km), likely due to ACTM's near-surface land CO2 flux errors. In contrast, ACTM better matches aircraft CO2 in the middle (2–5 km) and upper (5–8 km) troposphere, likely due to accurate large-scale transport representation. Over the Amazon, CO2 differences with aircraft and OCO-2 differ, likely due to a lack of regional surface sites for inversion and insufficient high-altitude profile (~4 km) not representative of XCO2. Over Asian megacity airports, which are significant emission hotspots, the model shows a large negative difference with CONTRAIL than OCO-2. This discrepancy likely hints that MIROC4-ACTM is unable to capture urban fossil CO2 emission signals at airports due to coarse resolution (~2.8° x 2.8°) and higher resolution of OCO-2 limits ability to fully capture actual emission footprints.
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RC1: 'Comment on egusphere-2024-3976', Anonymous Referee #1, 11 Feb 2025
Das et al. analyzed differences between MIROC-ACTM simulated atmospheric CO2 profiles and observations from aircraft and OCO-2. While the paper's title claims to focus on understanding how OCO-2 satellite retrieval uncertainties limit the diagnosis of transport model simulation uncertainty, the actual analysis presented does not address this question. The paper is poorly written with unclear reasoning and numerous grammatical errors. The quality does not satisfy ACP standards. I do not recommend this paper for publication based on the following major concerns:
Scope and Focus:
- Analyzing transport uncertainty requires examining tracer (e.g., CO2) vertical and meridional profiles. However, satellite retrieval algorithms have known limitations in resolving vertical details from XCO2 measurements. Using satellite vertical CO2 retrievals to analyze transport uncertainty is fundamentally limited. A meaningful study should examine how biases in vertical profiles derived from OCO-2 XCO2 could affect transport analysis (e.g., vertical mixing or PBL mixing). The manuscript fails to address these fundamental aspects of transport uncertainty analysis.
Methodological Issues:
- Analyzing model or data biases always requires establishing a 'ground truth'. This paper compares MIROC-ACTM simulated atmospheric CO2 profiles with observations from aircraft and OCO-2. However, it is not clear what is considered as the truth and what is being analyzed. The authors keep switching between these three models/products to evaluate the other two. In some cases, the authors consider MIROC-ACTM model simulation as the truth, which has certain limitations. My understanding is that the model output is the forward transport of a posterior flux, which was derived using only surface station data. However, the column-averaged model data might still be biased due to transport errors of the parent transport model and any additional bias from the inversion setup. It is fine to establish this model output as the truth, but the authors need to show that the column-average could represent the observed column-average.
- The method description part lacks many details. For example, how was the column-average calculated? How do you compare XCO2 with aircraft column-averages, considering they have different spatial coverage? Overall, the sampling strategy comparisons between different observation types need more careful consideration. The criteria for comparing different observational datasets need better justification. The statistical treatment of uncertainties requires more rigor. For example, what do you mean by variability? When you say there is a difference, is it significant?
Writing and Organization:
- The manuscript suffers from severe organizational and writing issues that significantly impair its readability and scientific credibility. The logical flow is persistently problematic, with paragraphs lacking clear structure and transitions, making it extremely difficult to follow the authors' reasoning. Most sections and paragraphs lack topic sentences to guide readers through the argument progression. The paper's overall organization is confusing, with little logical connection between sections, making it challenging to understand how different components of the analysis relate to each other. The manuscript is also filled with grammatical errors, suggesting it has not been properly proofread. The writing style lacks scientific rigor and precision - many statements are made without proper justification or clear explanation, and technical terms are used inconsistently. Here are a few examples (I cannot list all instances due to the extensive number of vague presentations and grammatical errors):
- L242: "lesser" is incorrect usage
- L244: "Negative difference" is vague; better to specify low or high
- L249: The bias referenced is undefined
- L250: "some regions" is too vague
- L269: "CO2_in-situ" is vague, as aircraft data could also be in-situ measurements
- L278-280: The focus of this sentence is unclear
- L282: "The vast part of the region" - which region?
- L349-350: "These differences are..." - this statement lacks supporting analysis
- L378-379: "This essentially..." - this statement lacks supporting evidence
- L384: "Needs further …" is an incomplete sentence
- L425-429: This content belongs in the introduction/motivation section
The paper requires thorough revision by a native English speaker and substantial reorganization to meet the standards expected of scientific publications.
Citation: https://doi.org/10.5194/egusphere-2024-3976-RC1 -
CC1: 'Comment on egusphere-2024-3976', Bharat Rastogi, 01 Mar 2025
Publisher’s note: this comment is a copy of RC2 and its content was therefore removed on 3 March 2025.
Citation: https://doi.org/10.5194/egusphere-2024-3976-CC1 -
RC2: 'Comment on egusphere-2024-3976', Bharat Rastogi, 01 Mar 2025
The paper is extremely poorly written and is unacceptable in its current form. While I believe the analyses are interesting, I highly recommend the authors to review the text and re-write as needed. Apart from the grammatical errors, there are also several errors in the introduction- which I would expect the senior authors to have addressed. I provide some examples from the Abstract and Introduction. I would be happy to re-review this paper if the manuscript is resubmitted.
Abstract
line 18: We show the model...line 20: comparisons reveal these differences
line 21: define the acronym ACTM when first used
line 24: unclear, because of course 4 km is not representative of XCO2 (which by definition is total column)
line 27: due to its course resolution and the higher resolution
Introduction
line 44: majorly responsible for global warming
line 48: chemical transport model
line 52: perhaps data sparse is better than data void
line 54: space-based satellites (not measurements). OCO-2 is a satellite that "measures" XCO2, OCO-2 itself is not a measurement
line 61: measurements have shown retrieval errors
line 64: Wunch et al., 2017 is cited for OCO b10 but this study came out before OCO-2 b10 which was released in 2020/2021.
line 66: CO2 fluxes at regional scales
line 68: Millet et al., 2007 isn't a great reference for this because observing systems and models have both tremendously improved since that study. Current precision requirements are below 1 ppm for inferring regional fluxes. See Feng et al., 2019 (JGR-A) and Rastogi et al., 2021 ACP.
line 71: Massie et al., 2021 show the impact of 3D cloud radiative impacts on XCO2 (bias). This is different from cloud effects (which could mean no data over cloudy conditions).
line 73: estimates not estimations
line 75: This is incorrect. OCO-2 has always had a DEM. Jacobs et al., 2023 found that the choice of DEM lead to large systematic errors in high latitudes for OCO-2 b10.
line 77: transport not such kind
lines 78-80: needs to be re-written
line 86: inversion estimates through...
lines 90-91: This needs to be clarified. Schuh et al., 2019 have examined the impact of transport related errors on flux estimates. Feng 2019 and Rastogi 20201 used aircraft profiles to understand combined flux and transport errors.
line 96: four specific aircraft sites
References:
Feng 2019: http://dx.doi.org/10.1029/2019JD031165
Rastogi 2021: https://doi.org/10.5194/acp-21-14385-2021
Schuh et al., 2019 10.1029/2018GB006086
Citation: https://doi.org/10.5194/egusphere-2024-3976-RC2
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