the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Building-resolving simulations of anthropogenic and biospheric CO2 in the city of Zurich with GRAMM/GRAL
Abstract. Urban areas are significant contributors to global CO2 emissions, requiring detailed monitoring to support climate neutrality goals. This study presents a high-resolution modeling framework using GRAMM/GRAL, adapted for simulating atmospheric CO2 concentrations from anthropogenic and biospheric sources and sinks in Zurich, Switzerland. The framework resolves atmospheric concentrations at the building scale, and it employs a detailed inventory of anthropogenic emissions as well as biospheric fluxes, which were calculated using the Vegetation Photosynthesis and Respiration Model (VPRM). Instead of simulating the full dynamics of meteorology and atmospheric transport, the dispersion of CO2 is precomputed for more than 1000 static weather situations, from which the best match is selected for any point in time based on the simulated and measured meteorology in and around the city. In this way, time series over multiple years can be produced with minimal computational cost. Measurements from a dense network of mid-cost CO2 sensors are used to validate the model, demonstrating its capability to capture spatial and temporal CO2 variability. Applications to other cities are discussed, emphasizing the need for high-quality input data and tailored solutions for diverse urban environments. The work contributes to advancing urban CO2 monitoring strategies and their integration with policy frameworks.
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Status: open (until 15 May 2025)
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RC1: 'Comment on egusphere-2025-640', Anonymous Referee #1, 10 Apr 2025
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This is an interesting and impactful paper. I recommend publication after attention to the comments below.
Major Comments
- The rationale for referencing prior work seems somewhat arbitrary. There are notable recent efforts with similar observations and similar modeling that are omitted (although not combined). A more through discussion of the recent literature would help the reader put this work in appropriate context.
- It would be helpful to also include the spatial and temporal resolution of each input dataset in Table 1.
- Why are there 3 peaks in heating/hot water demand diurnal profile? I understand this is based on a simulation from CESAR-P, but what is the behavioral reason, especially for the 3-6 am peak and then the 9-12 peak? Homes then offices? Surprising to me that it’s not smoothed more in the morning
- Additional quantification of the uncertainties in each aspect of the model would be helpful.
- How should the reader thinking about the various sources of uncertainty in inversions. Presumably this approach largely aims to minimize the model representation error. Can we expect that other sources of error therefore are more important (measurement error, background error)?
- Additional discussion of whether a 10 m simulation is necessary for inverse modeling and offers significant benefit beyond lower resolution forward model would be helpful. The diurnal disagreement in Figure 15 is substantial. If this model is not replicating observed concentrations, would a lower resolution model have sufficed for inverse modeling? Some quantification of the improvement in modeled concentration of this model over others would be quite helpful or at least a discussion of how the reader might think about that question.
- For lines 427 - 438 (and Figure 16): How does the seasonal and diurnal variability in the probability density of stability classes prove that the distribution of stability classes are correct?
- I suggest adding an additional paragraph addressing the implications of this paper at the end.
Line-by-Line Edits
- Figure text is too small throughout the manuscript
- What is meant by the last line of Table 1? The line with “Example” and “how we should do.” Final line is an “example” and should be removed.
- Description Equation 1, for the unfamiliar reader, describing the units of each term would be helpful for understanding the equation
- Figure 11, is the white in the city center missing data due to the building height higher than 2 m?
- Line 36: It is unclear what “suitable measurement” means.
- Figure 5: Color key shows values of “-999” to “0.” The value -999 must be a filler for unknown values, so it should be removed or explained.
- Figure 7: Images b and c require x axis labels.
- Figure 8: How were the different vegetation patches selected as representative?
- Table 3: How was the threshold determined for match2obs selected sites.
Citation: https://doi.org/10.5194/egusphere-2025-640-RC1
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