the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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.
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CC1: 'The meaning of mixing ratio', Andrew Kowalski, 10 Jul 2024
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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
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AC1: 'Reply on CC1', Theertha Kariyathan, 15 Jul 2024
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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
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AC1: 'Reply on CC1', Theertha Kariyathan, 15 Jul 2024
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