the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Getting the leaves right matters for estimating temperature extremes
Gregory Duveiller
Mark Pickering
Joaquin Muñoz-Sabater
Luca Caporaso
Souhail Boussetta
Gianpaolo Balsamo
Alessandro Cescatti
Abstract. Atmospheric reanalyses combine observations and models through data assimilation techniques to provide spatio-temporally continuous fields of key surface variables. They can do so for extended historical periods whilst ensuring a coherent representation of the main Earth system cycles. ERA5, and its enhanced land surface component ERA5-Land, are widely used in Earth System science and form the flagship products of the Copernicus Climate Change Service (C3S) of the European Commission. Such land surface modelling frameworks generally rely on a state variable called leaf area index (LAI), representing the amount of leaves in a grid cell at a given time, to quantify the fluxes of carbon, water and energy between the vegetation and the atmosphere. However, the LAI within the modelling framework behind ERA5 and ERA5-Land is prescribed as a climatological seasonal cycle, neglecting any inter-annual variability and the potential consequences that this uncoupling between vegetation and atmosphere may have on the surface energy balance and the climate. To evaluate the impact of this mismatch in LAI, we analyse the corresponding effect it has on land surface temperature (LST) by comparing what is simulated to satellite observations. We characterise a hysteretic behaviour between LST biases and LAI biases that evolves differently along the year depending on the background climate. We further analyse their repercussion on the reconstructed climate during the more extreme conditions in terms of LAI deviations, with a specific focus on the 2003, 2010 and 2018 heatwaves in Europe where LST mismatches are exacerbated. We anticipate that our results will assist users of ERA5 and ERA5-Land data to understand where and when the larger discrepancies can be expected, but also guide developers towards improving the modelling framework. Finally, this study could provide a blueprint for a wider benchmarking framework for land surface model evaluation that exploits the capacity of LST to integrate the effects of both radiative and non-radiative processes affecting the surface energy.
Gregory Duveiller et al.
Status: open (until 02 Apr 2023)
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RC1: 'Comment on gmd-2022-216', Anonymous Referee #1, 27 Feb 2023
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Review of
Getting the leaves right matters for estimating temperature extremes
by Duveiller et al.
General comments:
This paper shows that the ERA5-Land product should be used with caution and that the ERA5-Land production chain should use a more modern approach for representing vegetation or include satellite-derived LAI into their LSM. I am not sure this paper has much practical value from a modelling point of view. "Getting the leaves right matters" is perfectly right but far from being a new requirement. The added-value of this work should be better explained. Interesting recommendations are given in the Discussion section. For example, the recommendation to use hourly LST data from geostationary satellites. It is well known that solutions to integrate LAI into LSMs do exist. They are not mentioned and not used in ERA5-land. Could ERA-Land incorporate interactive LAI at some stage? Assimilation of LAI observations? Why not using another more advanced LSM forced by ERA5 atmospheric variables? The joint use of LAI and LST is interesting and offers a good benchmarking framework for assessing model performance. However, this paper may give the wrong impression that LST biases are completely explained by LAI. Other factors include the absence of representation of irrigation, snow misrepresentation, altitude solar radiation bias in mountainous areas, and slope effects in complex terrain. Overall, the paper is well written and a few changes could be sufficient to address my remarks.
Recommendation: minor revisions.
Particular comments:
- L. 40: I don’t understand well the LAI definition used by the authors: “LAI is defined as half of the total green leaf area per unit horizontal ground surface area”. Why half? Because only one side of the leaf is counted? For the sake of clarity, I would recommend using this more precise definition: “LAI is the one-sided green leaf area per unit horizontal ground surface area”.
- L. 152 (GEOV2/AVHRR): The THEIA LAI data portal web page should be given. Not only the LTDR web page.
- L. 202 (GLEAM): Which satellite date are used in this version of GLEAM? Is LST used for example?
- L. 241 (“darker than”): I am not sure this can be considered as a general rule. At wintertime, wet soils might be darker than senescent vegetation. Is this represented in the model? Adding a reference showing what is occurring in the real world would be useful.
- L. 266 (Fig. 4): Figure 4 interpretation is not straightforward. This Figure does not show much more than Fig. 3 and has too many tiny sub-figures, difficult to read. The complete Figure could be moved to a Supplement and a selection of meaningful sub-figures could be left in the paper. Why not plotting the two HI and BD indices in a SM - Temperature space instead? I.e. replace Fig. 4 by the two left sub-figures of Fig. 5?
- L. 311 (Fig. 6): Connecting maps to the colour scale is difficult. I suggest plotting only 3 colour classes: one for significant positive correlation, one for significant negative correlation (significant meaning p-value < 0.01), and white for non-significant correlation.
- L. 324 (Fig. 7): The top Cfb subfigure is not readable (dark green is difficult to distinguish from the dark gray color used for ocean surfaces).
Gregory Duveiller et al.
Data sets
Dataset in support of the study "Getting the leaves right matters for estimating temperature extremes" Mark Pickering & Gregory Duveiller https://doi.org/10.5281/zenodo.6976942
Model code and software
Code in support of the study "Getting the leaves right matters for estimating temperature extremes" Gregory Duveiller & Mark Pickering https://doi.org/10.5281/zenodo.7275088
Gregory Duveiller et al.
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