| 08 Apr 2026
The first offline land carbon simulation over Europe driven by the atmospheric forcing of a global storm-resolving climate model
Abstract. This study is motivated by the hypothesis that the drizzle problem of coarse-resolution climate models, whereby convective precipitation preferentially falls as light precipitation rather than short-lived and intense storms, leads to low gross primary productivity (GPP). To test this hypothesis, we perform an offline land carbon simulation over Europe using a terrestrial biosphere model driven by atmospheric forcing from a global km-scale climate simulation with explicitly resolved convection. This simulation is compared with a coarse-resolution simulation derived by atmospheric forcing from a coarse-resolution climate model with parameterized convection. The km-scale forcing leads, on average, to higher GPP. We find that shorter, more intense daily precipitation events when convection is explicitly resolved allow for stronger downward shortwave radiation on rainy days, thereby enhancing photosynthesis. At the same time, differences in the precipitation climatology between the two atmospheric forcing datasets, with a deficit of precipitation over eastern Europe in the km-scale forcing, result in soil moisture falling below the wilting point and reduced GPP in that region. Consistent with these GPP changes, autotrophic respiration is larger in the km-scale simulation, whereas heterotrophic respiration is smaller, the latter due to drier conditions.
Overall, this is an interesting study. The idea that precipitation duration in km-scale climate forcing may affect shortwave radiation and land carbon fluxes is worth exploring. However, the manuscript still needs substantial additional support. At this stage, many results are based on comparisons between two model setups, but it is still not clear whether the SRM forcing is more realistic, or how much of the GPP difference can really be attributed to the treatment of convection.
1. The paragraph around L50, where the authors explain that the two forcing datasets differ in many aspects beyond convection, is important. However, it may fit better in the Methods or Discussion rather than in the Introduction. The two forcing datasets differ in more aspects than just the treatment of convection. However, acknowledging this limitation does not resolve the uncertainty in the interpretation. Since SRM and LR differ in model resolution, climate model, forcing frequency, land-surface representation, and background climate, it remains difficult to determine how much of the GPP difference is actually caused by explicitly resolved convection or shorter rainfall duration. The additional sensitivity tests should be provided to better support this attribution.
2.The reason for fixing atmospheric CO₂ at 367 ppm in both simulations is not sufficiently clear. This choice may be reasonable, but the motivation should be explained more directly.
3.The Results section mainly compares LR and SRM, and most of the results show differences between LR and SRM. This is useful, but it does not really tell us whether SRM is better or more realistic. The paper needs more quantitative evaluation, not only maps and mean differences between the two simulations. Otherwise, the results show that the two simulations are different, but not necessarily that the SRM forcing improves the land carbon simulation. The paper mentions FLUXNET, MODIS, and MsTMIP, but this is mostly a broad comparison of carbon flux magnitudes. Why not directly compare the key variables with observations or high-resolution products?
4.The increase in domain-mean GPP in SRM is quite small compared with the mean GPP value. Given that SRM and LR differ in many aspects, it is difficult to know whether this small difference really comes from the mechanism highlighted in the paper or from other differences between the two setups. A more direct test would be to focus on the specific days or events that are most relevant to the hypothesis and compare precipitation duration, shortwave radiation, and GPP against observations or high-resolution products. This would help show whether SRM actually captures the observed rainy-day relationship between rainfall, radiation, and ecosystem productivity better than LR. Without this kind of event-based evaluation, the small mean GPP difference is hard to interpret.
5.There seems to be a numerical inconsistency in Section 3.2.2. The text says that the mean Ra value is 479.4 g C m⁻² yr⁻¹ in SRM, but Table 1 shows that 479.4 is the LR value, while the SRM value is 519.2. This should be corrected.
6.In Summary section, the statement that the land surface in SRM “takes up more carbon” should be clarified.