the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Jul 2024
How atmospheric CO2 can inform us on annual and decadal shifts in the biospheric carbon uptake period
Abstract. The carbon uptake period (CUP) refers to the time of each year during which the rate of photosynthetic uptake surpasses that of respiration in the terrestrial biosphere, resulting in a net absorption of CO2 from the atmosphere to the land. Since climate drivers influence both photosynthesis and respiration, the CUP offers valuable insights into how the terrestrial biosphere responds to climate variations and affects the carbon budget. Several studies have assessed large-scale changes in CUP based on seasonal metrics from CO2 mole fraction measurements. However, an in-depth understanding of the sensitivity of the CUP as derived from the CO2 mole fraction data (CUPMR) to actual changes in the CUP of the net ecosystem exchange (CUPNEE) is missing. In this study, we specifically assess the impact of (i) atmospheric transport (ii) inter-annual variability in CUPNEE (iii) regional contribution to the signals that integrate at different background sites where CO2 dry air mole fraction measurements are made. We conducted idealized simulations where we imposed known changes (∆) to the CUPNEE in the Northern Hemisphere to test the effect of the aforementioned factors in CUPMR metrics at ten Northern Hemisphere sites. Our analysis indicates a significant damping of changes in the simulated ∆CUPMR due to the integration of signals with varying CUPNEE timing across regions. CUPMR at well-studied sites such as Mauna Loa, Barrow, and Alert showed only 50 % of the applied ∆CUPNEE under non interannually-varying atmospheric transport conditions. Further, our synthetic analyses conclude that interannual variability (IAV) in atmospheric transport accounts for a significant part of the changes in the observed signals. However, even after separating the contribution of transport IAV, the estimates of surface changes in CUP by previous studies are not likely to provide an accurate magnitude of the actual changes occurring over the surface. The observed signal experiences significant damping as the atmosphere averages out non-synchronous signals from various regions.
- Preprint
(4489 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
CC1: 'The meaning of mixing ratio', Andrew Kowalski, 10 Jul 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1382/egusphere-2024-1382-CC1-supplement.pdf
-
AC1: 'Reply on CC1', Theertha Kariyathan, 15 Jul 2024
Reply on RCC1
Thank you for your insightful comments. We appreciate the opportunity to clarify the definitions and terminologies used in our manuscript.
The reviewer is correct that atmospheric CO2 measurements are reported in dry air mole fractions, excluding water vapor. And we agree that we could have more explicitly added this to the wording we use throughout the manuscript.
The TM3 model operates using the mass of dry air and the mass of specified tracers, which are then recalculated to mole fractions based on the tracer molar mass and the molar mass of dry air. So while the actual atmosphere contains both dry air and water vapor, the tracer transport modeling approach works with an atmosphere that is entirely "dry" mass (Heimann and Körner, 2003). Although this sounds like a poor approach, this is only a small source of potential errors for offline transport models, as we will explain.
In the TM3 model, the atmospheric mass is initialized as dry mass from surface pressure fields derived from the parent weather model, the Integrated Forecast System (IFS). In this conversion of surface pressure to mass we disregard the pressure contribution from lighter water vapor. The CO2 mass is determined by multiplying the air mass by the dry air mole fraction (approximately 400 ppm) and the ratio of CO2 molar mass (44 g/mol) to dry air molar mass (28.96 g/mol). The CO2 sources and sinks are then modeled by adjusting the CO2 mass accordingly, and finally converting the updated mass back to a CO2 mole fraction using the same dry air assumption (28.96/44). This consistent exclusion of water vapor mass ensures that the model results are directly comparable to the measured dry air mole fractions, and also maintains full mass balance in the CO2 budget independent of water vapor variations. Finally, the inclusion of water vapor differences between the time of introducing sources and sinks, and recording mole fractions is considered minimal due to the small variation in water vapor mole fraction (<1%) that then only affects the simulated tracer change (few ppm). For example, Lee and Weidner (2016) simulated CO2 fluxes using the GEOS-Chem Adjoint (GCA) system under two conditions: 1) assuming a dry pressure surface and 2) assuming a wet pressure surface and found a bias in the global CO2 volume mixing ratio of less than 0.1%. This result was independently confirmed using the TM5 model (unpublished), which shares a similar modeling framework with the TM3 model. This effect is also much smaller than the magnitude of changes that we aim to detect and their much larger uncertainty from interannual variability in transport (see Kariyathan et al., 2023).
The reviewer also inquired whether including water vapor mass (and its gradients) would affect the transport modeling. For advection, the mass fluxes in our offline model are based on pressure gradients derived from the Integrated Forecast System (IFS), which includes water vapor in its calculations and hence there is no reason to assume these mass-fluxes are incorrect. For turbulent motions in the planetary boundary layer, water vapor is considered when setting up the vertical diffusion constants (K), but it is not included in the calculations for molecular diffusion or Steffen flow at the leaf level. However, in large models with grid resolutions of 100-300 km and for tracers at small ambient levels, these will play a small role.
Reference
Heimann, M. and Körner, S., 2003, The Global Atmospheric Tracer Model TM3: Model Description and User’s Manual, Release 3.8a, Max-Planck-Institut für Biogeochemie, Jena, Germany, 131pp.
Kariyathan, T., Bastos, A., Reichstein, M., Peters, W., and Marshall, J., 2024, How atmospheric CO2 can inform us on annual and decadal shifts in the biospheric carbon uptake period, EGUsphere, 2024, 1-22, https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1382, DOI:10.5194/egusphere-2024-1382
Lee, M. , and Weidner, R. (2016). Jpl publication 16‐4: Surface pressure dependencies in the GEOS‐chem adjoint system and the impact of the GEOS‐5 surface pressure on CO2 model forecast. Jet Propulsion Laboratory California Institute of Technology Pasadena, California.
Citation: https://doi.org/10.5194/egusphere-2024-1382-AC1
-
AC1: 'Reply on CC1', Theertha Kariyathan, 15 Jul 2024
-
RC1: 'Comment on egusphere-2024-1382', Anonymous Referee #2, 14 Sep 2024
Review of "How atmospheric CO2 can inform us on annual and decadal shifts in the biospheric carbon uptake period" by Kariyathan et al.
The authors have conducted a sensitivity analysis of the carbon uptake period (CUP) in the atmospheric CO2 mole fraction to understand how changes in the CUP in net ecosystem exchange (NEE) are reflected in the observed atmospheric mole fraction. Using a series of model simulations, they examined how atmospheric transport and interannual variations in NEE contribute to changes in the CUP in the atmospheric CO2 mole fraction, focusing on 10 observations sites across the northern hemisphere. The authors showed that interannual variations in atmospheric transport provides a significant contribution to changes in the mole fraction CUP. They found that changes in NEE are manifested as damped variations in the CO2 mole fraction, suggesting that the use of observations of changes in the mole fraction CUP to quantify changes in NEE will likely result in an underestimate of the changes in NEE. The authors have developed an interesting approach for assessing the impact of changes in NEE on the atmospheric CO2 mole fraction that would be a valuable contribution to the literature. However, as discussed in my comments below, these results are not surprising. I have some concerns about the observation sites that were selected for the analysis and the resulting conclusions. I would like to see the authors address these concerns before the manuscript can be considered suitable for publication.
Main comments
- The authors are using 10 observation sites in the northern hemisphere to assess the sensitivity of changes in the CO2 mole fraction to variations in NEE. However, the sites that were selected are those that capture mainly background CO2. As the authors noted, the CO2 model fraction at these background sites "represent the balance between surface emissions and uptake from land and ocean… over large spatial scales," so it is not surprising that these sites will capture a damped manifestation of the changes in NEE. At the end of Section 4.2, the authors stated that "CO2 observations should preferably be interpreted following a formal inverse estimate of the corresponding surface NEE. It is then possible to account for the inter-annual variability, trends and delays imposed by the slow atmospheric mixing." However, inversions using the remote background sites also have difficulties in providing robust estimates of regional changes in NEE because of the influence of transport and mixing. This is one of the main reasons there has been significant effort focused on expanding the observing network. It is reasonable to ask how would the results of the study change if different sites were selected? For example, would Hegyhatsal (HUN) in Hungary be more sensitive to variations in changes in European NEE? Would East Trout Lake (ETL) in Canada be more sensitive to North American boreal fluxes? There is clearly a need to avoid sites that are strongly sensitive to anthropogenic emissions, but are the 10 sites used in this study the best sites for capturing changes in NEE given the well-known confounding influence of transport and mixing? As a modeling study, it seems to me that the authors have the opportunity to better evaluate the sensitivity of the existing observing network rather than that of what seems to be an arbitrary selection of 10 sites in the network.
Another issue is that the authors are using the temporal frequency of the flask measurements, but that is quite coarse – it is at best weekly. Some sites provide continuous measurements. Is there any benefit to using continuous data for capturing the variations and trends in NEE? - The analysis uses the ensemble of the first derivative (EFD) method to estimate the CUP. This approach was described in Kariyathan et al. (2023), but there is no explanation of the method in this manuscript. The reader is forced to go through Kariyathan et al. (2023) to understand what is being done. A brief description of the approach in the current manuscript would be helpful to the reader.
Other comments
- Table 1 caption: I believe the subscript refers to the CUPNEE, so the caption should read "subscript and superscript of the last character denote CUPNEE and variability (V) in transport, respectively."
- Line 90: Please change: "we used experiment ENV00 and LNV" to "we used experiments ENV00 and LNV."
- Line 100: Please list the regions over which the fluxes were aggregated.
- Lines 137-143: Should Figure 3 be referenced in the text here? I don't believe the figure is actually referenced in the manuscript.
- Figure 4 caption: On the second line, change "left panel shows" to "left panels show". On the third line form the bottom, change "right panel ((d) to (f)) shows" to "right panels ((d) to (f)) show".
- Line 218: It would be helpful to see what are the results for the other sites. Can this be presented in a separate table?
- Lines 226-227: It is unclear that it is the trend from the IAV in transport that "leads to the asymmetry". Perhaps this should be phrased as "the trend from the IAV in transport contributes to the asymmetry."
- Line 300: This description is confusing. Transport from Europe, Russia, and boreal Eurasia is westerly, so it is confusing when the authors say that "air mass transport is largely over the Northern Atlantic Ocean and does not extend into the continents in some years." During summer, because of the warm surface it is difficult for air transported from Europe and boreal Eurasia to enter the Arctic at low altitudes, so this circumpolar westerly transport occurs at lower latitudes, reducing the sensitivity of the high-latitude ZEP site to Eurasian air masses. I believe that the authors are trying to say that because of the lower latitude circumpolar transport in summer, the sensitivity of the ZEP site to variations in surface CO2 fluxes in summer is confined to the Arctic and does not extend equatorward into the continents.
Citation: https://doi.org/10.5194/egusphere-2024-1382-RC1 - The authors are using 10 observation sites in the northern hemisphere to assess the sensitivity of changes in the CO2 mole fraction to variations in NEE. However, the sites that were selected are those that capture mainly background CO2. As the authors noted, the CO2 model fraction at these background sites "represent the balance between surface emissions and uptake from land and ocean… over large spatial scales," so it is not surprising that these sites will capture a damped manifestation of the changes in NEE. At the end of Section 4.2, the authors stated that "CO2 observations should preferably be interpreted following a formal inverse estimate of the corresponding surface NEE. It is then possible to account for the inter-annual variability, trends and delays imposed by the slow atmospheric mixing." However, inversions using the remote background sites also have difficulties in providing robust estimates of regional changes in NEE because of the influence of transport and mixing. This is one of the main reasons there has been significant effort focused on expanding the observing network. It is reasonable to ask how would the results of the study change if different sites were selected? For example, would Hegyhatsal (HUN) in Hungary be more sensitive to variations in changes in European NEE? Would East Trout Lake (ETL) in Canada be more sensitive to North American boreal fluxes? There is clearly a need to avoid sites that are strongly sensitive to anthropogenic emissions, but are the 10 sites used in this study the best sites for capturing changes in NEE given the well-known confounding influence of transport and mixing? As a modeling study, it seems to me that the authors have the opportunity to better evaluate the sensitivity of the existing observing network rather than that of what seems to be an arbitrary selection of 10 sites in the network.
-
RC2: 'Comment on egusphere-2024-1382', Anonymous Referee #3, 19 Oct 2024
Summary:
The authors present a modeling study to determine how much surface flux information is contained in the atmospheric CO2 mole fraction observations. They see how well they can recover the time duration when net ecosystem exchange is uptake by the northern hemisphere biosphere from the seasonal cycle of atmospheric CO2 mole fraction observations after running surface fluxes and anomalies through an atmospheric transport model. They show that the surface flux signal information is reduced by mixing in the atmosphere.
Major comments:
- The authors motivate this study by a few published studies of changes in the atmospheric CO2 mole fraction seasonal cycle used to infer northern hemisphere surface flux changes without explicitly considering atmospheric transport. This feels like a strawman. The community already acknowledges the influence of atmospheric transport when linking concentrations to surface fluxes. That is why atmospheric inverse techniques were developed decades ago. Jin et al. (2022) provides an example of a study that uses an atmospheric transport model to separate the influence of surface fluxes and winds on the seasonal cycle at Mauna Loa without using an inverse approach. The references therein identify other studies that also make the point that atmospheric CO2 mole fractions are influenced by fluxes and winds. The authors’ most important sentence buried in the middle of the discussion is the main point of this study. “Therefore, CO2 observations should preferably be interpreted following a formal inversion estimate of the corresponding surface NEE. It is then possible to account for the inter-annual variability, trends and delays imposed by the slow atmospheric mixing.” I’m not sure what new perspective this study adds.
- Furthermore, the way that the wind influence is described as “the integration of signals with varying CUPNEE timing across regions” and “variations in the timing of CUPNEE across different regions from where the signal is integrated” and in Fig 9 is confusing. They are describing the influence of atmospheric transport or winds, but making it sound like it’s a flux synchronization issue.
- Other studies have mostly focused on CO2 seasonal cycle amplitude changes and this study’s focus on the duration of the carbon uptake period is a little unique. But this study does hypothetical forward model experiments without using the observed atmospheric CO2 mole fractions to constrain which fluxes anomalies are supported by the observations, if any. What new information about the behavior of the northern hemisphere biosphere is learned here? The authors state “Considering the {transport} reducing factor, a change of approximately 0.7 days/year might be occurring in the surface fluxes.”
- The nomenclature for the experiments makes it difficult to look at a figure and quickly interpret which experiment combination of fluxes and winds it is comparing. The reader must work very hard to understand and remember the notation. Table 1 helps, but does it have to be that complicated? It seems the paired notation of the Experiment and Delta CUPNEE columns are needed to uniquely identify the tests, especially with ENVTo and LNVTo cases.
- Misleading title? The finding was that obs can’t inform well on surface fluxes, without considering atmospheric transport.
References:
Jin, Y., Keeling, R. F., Rödenbeck, C., Patra, P. K., Piper, S. C., & Schwartzman, A. (2022). Impact of changing winds on the Mauna Loa CO2 seasonal cycle in relation to the Pacific Decadal Oscillation. Journal of Geophysical Research: Atmospheres, 127, e2021JD035892. https://doi.org/10.1029/2021JD035892
Citation: https://doi.org/10.5194/egusphere-2024-1382-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 460 | 121 | 30 | 611 | 13 | 12 |
- HTML: 460
- PDF: 121
- XML: 30
- Total: 611
- BibTeX: 13
- EndNote: 12
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1