the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Dec 2024
Modelling framework for asynchronous land-atmosphere coupling using NASA GISS ModelE and LPJ-LMfire: Design, Application and Evaluation for the 2.5ka period
Abstract. While paleoclimate simulations have been a priority for Earth system modelers over the past three decades, little attention has been paid to the period between the mid-Holocene and the Last Millennium, although this is an important period for the emergence of complex societies. Here, we consider the climate of 2500 BP (550 BCE), a period when compared to late preindustrial time, greenhouse gas concentrations were slightly lower, and orbital forcing led to a stronger seasonal cycle in high latitude insolation. To capture the influence of land cover on climate, we asynchronously coupled the NASA GISS ModelE Earth system model with the LPJ-LMfire dynamic global vegetation model. We simulated global climate and assessed our results in the context of independent paleoclimate reconstructions. We also explored a set of combinations of model performance parameters (bias and variability) and demonstrated their importance for the asynchronous coupling framework. The coupled model system shows substantial vegetation albedo feedback to climate. In the absence of a bias correction, while driving LPJ-LMfire in the coupling process, ModelE drifts towards colder conditions in the high latitudes of the Northern Hemisphere in response to land cover simulated by LPJ-LMfire. A regional precipitation response is also prominent in the various combinations of the coupled model system, with a substantial intensification of the Summer Indian Monsoon and a drying pattern over Europe. Evaluation of the simulated climate against reconstructions of temperature from multiple proxies and the isotopic composition of precipitation (δ18Op) from speleothems demonstrated the skill of ModelE in simulating past climate. A regional analysis of the simulated vegetation-climate response further confirmed the validity of this approach. The coupled model system is sensitive to the representation of shrubs and this land cover type requires particular attention as a potentially important driver of climate in regions where shrubs are abundant. Our results further demonstrate the importance of bias correction in coupled paleoclimate simulations.
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RC1: 'Comment on gmd-2024-219', Anonymous Referee #1, 31 Jan 2025
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Singh et al. “Modelling framework for asynchronous land-atmosphere coupling using NASA GISS ModelE and LPJ-LMfire: Design, Application and Evaluation for the 2.5ka period”
GENERAL
This study simulates the climate of the 2.5 ka period using the NASA GISS ModelE, which is asynchronously coupled to the dynamic vegetation model LPJ-LMfire. The authors conducted several sensitivity experiments to assess the impacts of initial land cover and bias correction on paleoclimate and ancient vegetation. Additionally, they used multiple proxy datasets to validate the simulation results. While this study represents a considerable effort in developing a modeling framework and conducting numerical simulations, the robustness of the main results is questionable, and the study's novelty remains unclear.
First, the study lacks sufficient novelty. The primary focus is on asynchronous land-atmosphere coupling between a climate model and a vegetation model. However, it is unclear why this coupling approach warrants extensive application, as it does not appear to enhance the simulated paleoclimate. As shown in Fig. 12, temperature anomalies exhibited the lowest biases in the 2.5k_PI_ctrl simulation, which did not include asynchronous land-atmosphere coupling. This finding suggests that vegetation coupling has a minimal impact on improving model performance, thereby weakening the study's novelty.
Second, the key sources of discrepancies remain unexplored. While the authors compared results from multiple experiments, they primarily noted that bias correction led to a warmer and more stabilized climate, without further investigating the underlying causes of climate variations across different simulations. In particular, the influence of initial vegetation cover, climate variability, and climate-vegetation interactions on the simulated paleoclimate should be explicitly quantified and explained. Furthermore, the δ18O validations revealed similar biases across different experimental configurations (Fig. 15), raising the question: what is the added value of employing different model configurations?
SPECIFIC
Line 40: “The coupled model system is sensitive to the representation of shrubs.” What is the underlying reason for this particular sensitivity? Is this a characteristic of this specific model, or is it a general feature of all coupled models?
Lines 176-177: How well does the LPJ model perform in simulating present-day vegetation? It would be more appropriate to validate the LPJ model using observed meteorological data and vegetation parameters rather than relying solely on PI simulations.
Line 207: What observational datasets could be used to validate the derived ‘wet days’? Additionally, models tend to overestimate the frequency of small rainfall events, which suggests that the nonlinear relationship derived from observations may not be directly applicable to model outputs.
Line 220: “Adding interannual variability”—why is this an important consideration? How do simulations differ with and without interannual variability?
Line 235: What is the connection between plant functional types (PFTs) and fire frequency? Please clarify the underlying mechanism.
Line 243: “A predefined threshold”—Is this threshold applied uniformly at the global scale, or do different grid cells use varied thresholds? How was this threshold determined?
Line 272: “climate vegetation models”—this phrase is missing an “and” (should be “climate and vegetation models”).
Line 285: “Linearly interpolating”—Are there any proxy datasets available to constrain vegetation cover for the 2.5 ka period? How would different initial conditions affect the final simulated vegetation cover? Additionally, how can the derived paleo-vegetation be evaluated?
Table 2: The reasoning behind the different simulation lengths across various experiments is unclear. What criteria were used to determine the duration of each experiment?
Figure 3: Is it reasonable to use present-day satellite-derived land cover for PI simulations? Please provide justification.
Lines 389-391: These sentences belong in the figure captions rather than the main text.
Figure 10: The differences between LPJ and GISS, as well as between PI and GS, are relatively minor. The most significant differences appear between x and BC. This conclusion should be explicitly stated and explained in the main text.
Figure 11: In some regions, differences among simulations are substantial, while in others, they are minimal. What accounts for these regional variations? What role does vegetation play in shaping these differences?
Section 5: This section should be positioned before the analysis for better logical flow.
Figure 12: Proxy data appear to be more consistent with PI simulations than with the 2.5 ka simulation. What is the purpose of bias correction or climate-vegetation coupling if it does not improve the agreement with proxy data?
Line 640: How is isotopic composition represented in the model simulations? Please provide details on its setup.
Citation: https://doi.org/10.5194/gmd-2024-219-RC1
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