the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Representation of the Terrestrial Carbon Cycle in CMIP6
Abstract. Improvements in the representation of the land carbon cycle in Earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) include interactive treatment of both the carbon and nitrogen cycles, improved photosynthesis, and soil hydrology. To assess the impact of these model developments on aspects of the global carbon cycle, the Earth System Model Evaluation Tool (ESMValTool) is expanded to compare CO2 concentration and emission driven historical simulations from CMIP5 and CMIP6 to observational data sets. A particular focus is on the differences in models with and without an interactive terrestrial nitrogen cycle. Overestimations of photosynthesis (gross primary productivity (GPP)) in CMIP5 were largely resolved in CMIP6 for participating models with an interactive nitrogen cycle, but remaining for models without one. This points to the importance of including nutrient limitation. Simulating the leaf area index (LAI) remains challenging with a large model spread in both CMIP5 and CMIP6. In ESMs, global mean land carbon uptake (net biome productivity (NBP)) is well reproduced in the CMIP5 and CMIP6 multi-model means. However, this is the result of an underestimation of NBP in the northern hemisphere, which is compensated by an overestimation in the southern hemisphere and the tropics. Carbon stocks remain a large uncertainty in the models. While vegetation carbon content is slightly better represented in CMIP6, the inter-model range of soil carbon content remains the same between CMIP5 and CMIP6. Overall, a slight improvement in the simulation of land carbon cycle parameters is found in CMIP6 compared to CMIP5, but with many biases remaining, further improvements of models in particular for LAI and NBP is required. Models from modeling groups participating in both CMIP phases generally perform similarly or better in their CMIP6 compared to their CMIP5 models. This improvement is not as significant in the multi-model means due to more new models in CMIP6, especially those using older versions of the Community Land Model (CLM). Emission driven simulations perform just as well as concentration driven models despite the added process-realism. Due to this we recommend ESMs in future CMIP phases to perform emission driven simulations as the standard so that climate-carbon cycle feedbacks are fully active. The inclusion of nitrogen limitation led to a large improvement in photosynthesis compared to models not including this process, suggesting the need to view the nitrogen cycle as a necessary part of all future carbon cycle models. Possible benefits when including further limiting nutrients such as phosphorus should also be considered.
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Status: final response (author comments only)
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CC1: 'EC-Earth NBP', David Wårlind, 15 Mar 2024
Why EC-Earth showed a very strong land source in December is due to that all land-use transitions is happening on the 31st of December in LPJ-GUESS. To not get a peak in CO2 concentrations on the 1st of January every year, the land-use fluxes are released to the atmosphere evenly on every day the following year in EC-Earth3-CC. But the LPJ-GUESS outputs are still following when it actually happens in the LPJ-GUESS.
Citation: https://doi.org/10.5194/egusphere-2024-277-CC1 - RC1: 'Comment on egusphere-2024-277', Christopher Reyer, 08 Apr 2024
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RC2: 'Comment on egusphere-2024-277', Anonymous Referee #2, 07 Jun 2024
The paper evaluates whether CMIP6 models are better in reproducing several selected observations as compared to CMIP5. The authors use the ESMValTool to judge model quality, and focus on LAI, GPP and NBP, and vegetation and soil carbon stocks. The period covered is 1986 to 2005, the spatial resolution is rather course (2 deg x 2 deg) by necessity. It is a pity that they exclude the years 2006-2014 for the CMIP6 models since their ability to reproduce more recent observations is also a relevant topic.
The paper is very careful in drawing clear conclusions, and the authors demonstrate that they are aware of the limitations of their data and the whole study. E.g., calling products like MTE or GLASS "observations" is problematic - for their construction, a lot of modelling is involved, and the uncertainty introduced prior to ESM runs is not easy to control. A drastic example is for soil and vegetation carbon, where in Figure 15 the observations appear as a single small star; in fact, could you quantify the uncertainty reliably, that star would probably cover a good fraction of the plane shown in Fig. 15. The models are also not constraining the values very much - with vegetation carbon differing by a factor of 3, and soil carbon by a factor of 6 between them.
In general, when comparing GPP and NBP means and their trends (Figs. 6 and 12), the ESMs are simply not doing well with a large spread and gross deviations from the observations even if the latter are quite constrained. Miraculously, the MMMs are often closer to the observations, but not always (the Christiansen 2018 study is not claiming that the MMMs are closer, only that the ensemble mean error is smaller than the errors of the individual models). However, in some cases, the authors achieve this desired property only by excluding models considered as outliers, which is merely a matter of taste, given the huge discrepancies existing anyhow. The ESMs have a very long way to go before one could consider them as "good" in the ordinary sense of modelling.
This is all not the fault of the authors who are simply reporting the current status of the climate models. The distinction between models with and without a nitrogen cycle, with or without dynamic vegetation, and either with prescribed concentrations or emission-driven makes a lot of sense. It is surprising that the effect of more "process realism" (models incorporating the N and potentially also the P cycle, have dynamic vegetation, and are emission-driven) is by and large absent, or at least do not lead to the clear conclusion to be a must-have. Yes, the GPP is reduced overall when N is a limiting nutrient as expected, but the model quality is not necessarily improved. There are still some striking differences, beginning with the notorious overestimation of LAI and than of GPP as a consequence, and so on.
The paper is rather thoroughly written, has a decent and balanced representation of the state of affairs of CMIP6 and CMIP5, and avoids bold claims about the improvements made from version 5 to 6. It can be easily accepted for publication, apart from some minor points addressed below and in particular in the attached pdf which contains 32 comments and corrections, please consider them all. They still amount only to something between "technical corrections" and "minor revisions".
Specific comments:
l. 35f: how could you know that the models show "improved climate projections" when their present-day performance is worse? Wouldn't that require to gaze into the crystal ball?
l. 359: "The models are clustered around the GLASS trend" (ref. to Fig. 6) - of course they are, but with differences of up to 300% ! In short, the models are unable to reproduce the observed GPP trend!
l. 397-402: any ideas why the EC-Earth models have such a "strange December"? Or the "popping up in random months" for MIROC? This almost sounds like bugs in the code. Could you comment on that in the paper? What do the model authors say?
Figure 10: this is really tough for the eye with so many lines on top of each other. Choose another way of presenting, or delete the Figure.
The paper is quite lengthy. A suggestion to shorten it a bit: in the main text, there are detailed legend descriptions for some Figures which are either repeated in the proper figure legend, or in one case (Fig. 18) absent from the figure legend. Delete from the main text and keep it only in the Figure legends.
l. 591f: a classical modelling dilemma is reported here: more process realism (like dynamic vegetation cover) leads to worse performance. If the main purpose is to reproduce the observations better, shouldn't one follow a parsimonious path and not include these additional processes? This is a rather sharp version of Occam's razor; but still, you are recommending to include them all ("only these models...can account for future changes"), why? This needs a proper justification.
My congratulations to a really good paper.
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