LPJ-GUESS/LSMv1.0: A next generation Land Surface Model with high ecological realism
- 1Karlsruhe Institute of Technology KIT, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), 82467 Garmisch-Partenkirchen, Germany
- 2University of Lund, Department of Physical Geography and Ecosystem Science, 223 62, Lund, Sweden
- 3Terrestrial Ecology Section, Department of Biology, Universitetsparken 15, DK-2100, Copenhagen Ø, Denmark
- 4Center for Permafrost (CENPERM), University of Copenhagen, Øster Voldgade 10, DK-1350, Copenhagen K, Denmark
- 5Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
- 6Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
- 1Karlsruhe Institute of Technology KIT, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), 82467 Garmisch-Partenkirchen, Germany
- 2University of Lund, Department of Physical Geography and Ecosystem Science, 223 62, Lund, Sweden
- 3Terrestrial Ecology Section, Department of Biology, Universitetsparken 15, DK-2100, Copenhagen Ø, Denmark
- 4Center for Permafrost (CENPERM), University of Copenhagen, Øster Voldgade 10, DK-1350, Copenhagen K, Denmark
- 5Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
- 6Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia
Abstract. Land biosphere processes are of central importance to the climate system. Specifically, biological processes interact with the atmosphere through a variety of feedback loops that modulate energy, water and CO2 fluxes between the land surface and the atmosphere across a wide range of temporal and spatial scales. Human land use and land cover modification add a further level of complexity to land-atmosphere interactions. Dynamic Global Vegetation Models (DGVMs) attempt to capture these land surface processes, and are increasingly incorporated into Earth System Models (ESMs), which makes it possible to study the coupled dynamics of the land-biosphere and the climate. In this work we describe a number of modifications to the LPJ-GUESS DGVM, aimed at enabling direct integration into an ESM. These include energy balance closure, the introduction of a sub-daily time step, a new radiative transfer scheme, and improved soil physics. The implemented modifications allow the model (LPJ-GUESS/LSM) to simulate the diurnal exchange of energy, water and CO2 between the land-ecosystem and the atmosphere. A site-based evaluation against FLUXNET2015 data shows reasonable agreement between observed and modeled sensible and latent heat fluxes. Differences in predicted ecosystem function between standard LPJ-GUESS and LPJ-GUESS/LSM vary across land cover types, but the emergent ecosystem composition and structure are consistent between the two versions. We find that the choice of stomatal conductance model has a major impact on the model's predictions. The new LSM implementation described in this work lays the foundation for using the well established LPJ-GUESS DGVM as an alternative LSM in coupled land-biosphere-climate studies, where an accurate representation of ecosystem processes is essential.
David Martín Belda et al.
Status: open (until 20 May 2022)
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RC1: 'Comment on gmd-2022-1', Anonymous Referee #1, 10 May 2022
reply
This paper describes a land surface model hooked on a well-developed vegetation dynamics model, LPJ-GUESS, mainly including the energy fluxes and radiation transfer processes of vegetation canopy and soil. Since the LPJ-GUESS model was designed to simulate vegetation dynamics at daily and yearly time steps, it requires many adaptations to simulating the sub-daily processes, such as soil temperature, ground heat flux, latent heat flux, transpiration, stomatal conductance, canopy bulk temperature, etc., as described in detail in this paper. I appreciate the efforts of transforming a traditional vegetation model (which is a very successful one) to a land surface model, where the original vegetation model plays a role of boundary conditions for the fast biophysical processes. I also like the idea of coupling the fast biophysical processes into the detailed vegetation structure, instead of the other way around, i.e., calculating the mean states of vegetation for coupling a vegetation model into land models.
The authors tested this model at 8 sites across the globe with vegetation types ranging from grasses (C3 and C4) to shrubs, woodland, and forests. They also tested different formulations of leaf conductance and soil water uptake functions. To me, the model behavior is well tested in this paper, because this paper is to show if these biophysical processes, when organized together on top of LPJ-GUESS, work reasonably well in different climate zones and vegetation types. And, these formulations have been used widely and tested extensively in other models.
As for the water (and latent heat) fluxes issue, the authors invested a lot of efforts to look for reasons with different stomatal conductance models and water uptake functions. I think an expanded discussion about the interactions between vegetation settings from the LPJ-GUESS model and physical environments derived from the new biophysical processes would be helpful. This would also be helpful for understanding the differences in productivity between the original LPJ-GUESS and it coupled with LSM. Extensively tuning the parameters is unnecessary in this paper though.
Minor comments:
1. Line 146: the unit of lambda. I think “C-1” is not necessary. The number of 2.44x10^6 has included an assumption of normal water temperature
2. Lines 380 and 388: How do the vegetation conditions vary with the actual vegetation? I think the vegetation and soil states (equilibrium or not) may affect NEE, as shown in Fig. 14 that the simulations are close to equilibrium state.
3. Line 506 section Ecosystem structure and function: They are related to the settings of the vegetation model since the structure is highly dynamic. How to make them consistent with each case and the measurement data?
4. Lines 572: measured NEE is more negative than those simulated. I think it is related to how far the vegetation is from its theoretical equilibrium state. Disturbances also play a role here. For example, at equilibrium state, an ecosystem will have a zero NEE (or fluctuated around zero) if the system has no disturbances. However, if it is equilibrated with a particular disturbance regime (e.g., a given fire frequency distribution), the system must have a negative NEE that is to counter the carbon release at disturbance events. In long-term, it is still carbon neutral. This is the pattern this paper showed in this section that observations have higher carbon sink (more negative NEE) than the simulated (with 500 years of model run).
5. Time steps of and growth (yearly) and SOM (daily): how LAI dynamics and heterotrophic respiration are calculated? Usually, LAI should be updated daily and Rh hourly (or half hourly). Are they connected with plant growth and SOM dynamics at each step, respectively? This just need to clarify. I may miss the description.
6. In discussion, for the water uptake functions and C4 grass carbon assimilation simulations, they are phenomenological equations in the model that directly link soil water availability to leaf functions. A discussion of actual plant-soil hydraulics would be helpful for understanding why they happen and why we don’t have to spend much time to tune these functions.
7. I just realized the codes of LPJ-GUESS are still not publicly available. Maybe, this question should be asked by the handling editor. Does it comply with the journal's policy?
David Martín Belda et al.
Data sets
Forcing data, evaluation data, model output and analysis scripts used in LPJ-GUESS/LSM description paper David Martin Belda https://doi.org/10.5281/zenodo.5813886
David Martín Belda et al.
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