the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Sep 2023
Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests
Allan Buras
Konstantin Gregor
Lucia S. Layritz
Adriana Principe
Jürgen Kreyling
Anja Rammig
Christian S. Zang
Abstract. Late-spring frost (LSF) is a critical factor influencing the functioning of temperate, forest ecosystems. Frost damage in the form of canopy defoliation impedes the ability of trees to effectively photosynthesize thereby reducing tree productivity. In recent decades, LSF frequency has increased across Europe, likely intensified by the effects of climate change. With increasing warming, many deciduous tree species have shifted towards earlier budburst and leaf development. The earlier start of the growing season not only facilitates forest productivity but also lengthens the period during which trees are most susceptible to LSF. Moreover, recent forest transformation efforts in Europe intended to increase forest resilience to climate change have focused on increasing the share of deciduous species in forests. To assess the ability of forests to remain productive under climate change, dynamic vegetation models (DVMs) have proven to be useful tools. Currently, however, most state-of-the-art DVMs do not model processes related to LSF and the associated impacts. Here, we present a novel LSF module for integration with the dynamic vegetation model LPJ-GUESS. This new model implementation, termed LPJ-GUESS-FROST, provides the ability to directly attribute impacts on simulated forest productivity dynamics to LSF. We use the example of European beech, one of the dominant deciduous species in Central Europe, to demonstrate the functioning of our novel LSF module. Using a network of tree-ring observations from past frost events we show that LPJ-GUESS-FROST can reproduce productivity reductions caused by LSF. Further, to exemplify the effects of including LSF dynamics in DVMs, we run LPJ-GUESS-FROST for a study region in southern Germany for which high-resolution climate observations are available. Here, we show that modeling LSF plays a substantial role in regulating regional NPP and biomass dynamics emphasizing the need for LSF to be more widely accounted for in DVMs.
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Benjamin F. Meyer et al.
Status: final response (author comments only)
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RC1: 'Comment on bg-2023-139', Anonymous Referee #1, 12 Oct 2023
Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests
by Meyer et al.
The paper present a new parameterization of the impact of frost on leaf development in the LPJ-GUESS model. There is an increasing evidence that a climate change paradox is that increasing surface temperature will increase the risk of late-spring frost because advance in time of bud-burst is more rapid than the date of last frost. In parallel most of land surface models do not include the impact of frost on leaf development. Then the proposed parameterization included in LPJ-GUESS is very interesting. The model is tested for Bavarian Beech forest for which two large forest events occurred in 1953 and 2011. Tree rings data are then used as a proxy of NPP which is then compared with simulations of LPJ-GUESS with and without impact of frost and the comparison shows that the new parameterization allows to simulated the observed decrease of NPP which is not the case for standard version. I also appreciate the discussion about the limit of the current approach especially the fact that
I have some questions and suggestions for improving the paper:
- From what I understood, when a frost occur the leaf phenology status is reset to 0 which means that all existing leaves are removed (there is no indication about what is done with this biomass, does it goes to litter ?). Isn’t this binary behavior not too strong ? Would it not be better to have a more continuous function which could modulate the effect of damage as a function of temperature ? This would be probably more realistic. Indeed for a not too cold temperature (around -2°C) there is probably only a partial damage when for a temperature of -8°C we can guess that damage will be generalized. Especially as micro climate into the canopy (which is not modeled), will induce during night higher leaf temperature in the inner canopy than at the top.
Also it could solve, partially, all the discussion in the paper about the choice of threshold temperature. Indeed such continuous function will let the results be less sensitive the the threshold temperature.
- Tree ring is only an indirect proxy of the simulated frost damage. Indeed it only allows to access the total NPP. Then theses tree rings (and total NPP) are influenced by all the factors during all the growing season making the width also depending of possible drought that will amplify the NPP decrease or on he opposite a very good climate conditions that can dampen it. Then why not use also remote sensing data ? Obviously it will not be possible for the 1953 event. But it will be possible for 2011. In particular remote sensing data can be a good proxy of LAI which will allow to see if the simulated decrease and lagged regrowth after frost is comparable to what is observed from satellite.
- We can see on figure 2 the for some years (for instance between 1999 and 2005) the LPJ-GUESS-FROST simulate a annual NPP which is higher than for the standard LPJ-GUESS. As in principle the effect of frost is only negative, how it can be explained ? Is it only related to the fact that, for what I understood, you do a set of patch simulations with could then gives different results ? In other words, two different simulations with LPJ-GUESS could give slightly different means ?
Citation: https://doi.org/10.5194/bg-2023-139-RC1 -
RC2: 'Comment on bg-2023-139', Anonymous Referee #2, 17 Nov 2023
Overall evaluation of the study
The manuscript by Benjamin Meyer and colleagues addresses an important physiological aspect that so far has been largely ignored in DGVMs. Given how important the effect of late spring frost can be for forest productivity and resilience to future climate change, improved understanding of the mechanisms and a quantification of the effect through vegetation models at the regional to continental scale is indispensable and long over-due. Therefore, the attempt to incorporate a mechanistic representation of LSF into a well-established DGVM such as LPJ-GUESS is a relevant and important contribution to the modeling community. Incorporating LSF effects into physiological tree representation in DGVMs is also important because tree productivity is additionally impeded by other climate change related direct and indirect effects, such as growth reduction through drought and, in the case of beech, the recently observed beech vitality loss that results from indirect drought-related effects. Ultimately, the different climate-associated effects on tree physiology and growth performance need to be combined and integrated in DGVM implementations for a realistic assessment and quantification of climate change effects.
The manuscript is written well and is easy to read and understand. My comments and thoughts are therefore mostly minor, including some questions where I would appreciate further information or clarification, plus a few editorial comments (spelling errors, grammar issues, etc.).
Detailed comments and thoughts
- 26/27: Maybe state explicitly that causally, the increased occurrence of LSF events is an indirect effect that results from a statistically earlier start of the growing season, leading to budburst in deciduous trees at times where the likelihood of frost events is still higher than later in the season.
- 35/36: Not only beech, but a generally higher share of deciduous broadleaf trees in forests, as mixed deciduous broadleaf forests are perceived as more resilient to climate change and conducive to biodiversity and habitat conservation.
- 45-47: Not to forget other aspects aside carbon sequestration, such as tree health and mortality risk. The data series of the annual forest condition survey in Germany ("Waldzustandserhebung") shows an increase in beech trees with partially to strongly defoliated crowns from 2017 onwards. In 2021 and 2022, only 16 to 21% of beeches in Germany showed no signs of crown thinning, while approx. 45% showed signs of significant crown thinning.
- 61: “In both cases, freezing damage was observed in European beech.” – Add a reference?
- 86-89: Is this the same parameterization for all cohorts within a PFT, or does LPJ-GUESS account for age-dependent variation within a PFT? Understory beeches start leaf-out earlier than mature beeches in the upper canopy layer so that they can profit from a period with reduced shading / light competition. However, this earlier start should also make them more prone to experience LSF events than the mature cohorts, with potentially adverse effects for recruitment success. This could be an aspect to be included in future versions of the model, if not accounted for yet in the LSF scheme.
- 92: Technically / physiologically speaking: LSF and the damage caused to the leaves implies a loss of stored carbon resources, i.e., a reduction in the carbon storage pool, and a re-set or partial re-set of the phenological cycle, i.e., a reduction of canopy leaf area. After having read the paper, I am not sure if the first aspect (loss of carbon resources and need of reallocation from storage reserves to replace lost leaf biomass is implemented in the model. If it is not, this is an aspect that needs to be addressed urgently, because otherwise, the size of the overall effect caused by LSF may be underestimated. Needing to withdraw carbon from storage reserves may not reflect directly on the growth performance of the consecutive growing season. However, a depletion of C-storage reserves makes trees more prone to suffer if additional disturbances occur that require carbon reserves to repair damages. For example, to repair xylem damages caused by cavitation during drought. Resulting carbon debt ultimately may increase mortality risk for affected trees.
- 96: How about partial leaf-out? Is this discrete, or continuous between 0 and 1 to allow representation of partial leaf-out? Leaf-out happens within a time span of two-three weeks, during which leaf biomass and LAI increase from zero to maximum.
- 117: This implies that LSF is a yes/no decision. It does not yet allow a quantification of the severity of the frost event (which should be a function of the below-zero temperature value, and maybe additionally the duration of the frost event, as one moderately frosty night will differ in effect size from a several-day-period with strong frost). The stochastic application for individual patches partially addresses this aspect, as far as I understand?
- 122: How is the duration of the leafless period determined in the model? An assumed constant (how long?), or a function (depending on what variables?)?
- 125/126: How long are the time series of tree ring data? Where are they made available?
- 136: Yes, but climate influence itself is composed of a variety of factors, including growing season water availability and temperature. These other climate effects contribute to the overall signal (the RWI) in combination with the frost effect. Did you attempt to address this issue? Is there a way to separate the frost effect contribution from the overall climate signal?
- 166: Did you a) detrend the 30 year slice, and b) did you do a blockwise randomization of the detrended years, or did you simply use the years as-is (that often leads to a saw-tooth behavior in the results that is not quite realistic)?
- 168/169: You used identical spin-up sequences for both LPJ-GUESS and LPJ-GUESS-FROST runs, correct?
- 175 ff: Not clear: did you model multi-species patches including beech, or monospecies-stands with beech only? It becomes clear later on in the manuscript that you simulated monospecific stands, but it would be useful to already clarify this point here in the methods description.
- 191: The direct impacts, yes. However, indirect effects (e.g., a reduction of carbon storage size due to a second leaf-out required to at least partially replace the frost-damaged foliage; an overall reduced growth performance) will put affected trees on a differing growth trajectory in consecutive years.
Figure 1: Any idea why the response of LPJ-GUESS_FROST is so much more homogeneous in 2011 than in 1953? And why it is more pronounced than in the observations in 2011, but somewhat less pronounced than in the observations in 1953? (Explanation attempt can be in discussion section).
- 219: I find that a bit surprising, given that Bavaria contains part of the Alps and therefore high-elevation territory where productivity should be considerably lower than at low elevations.
- 222/223: Bavaria-wide average loss, or average loss across frost-affected grid cells only?
- 224: “The lost productivity translates to biomass loss.” _ And a reduced carbon sink strength of affected forests, which has implications for the National GHG inventory reporting (side note).
- 226: “This biomass loss primarily affects the sapwood” - That agrees well with expectations. Did you adapt the carbon allocation scheme within LPJ-GUESS-FROST to achieve this result, or did it emerge without additional adjustment?
- 232/233: Looking at Figure 2, you actually managed to simulate frost events in a variety of years beyond these two years of special focus, which is a promising result you deserve to highlight as well.
- 243: Thirdly, the phenological representation of the leaf-out process in the model is yet a simplification of real-world processes and variability, and therefore some temporal mismatch between actual and simulated leaf-out can be expected as well. If your simulated leaf-out in 2011 was somewhat earlier than real-world leaf-out, that could explain part of the mismatch as well. Also, if the model indeed represents leaf-out as a nothing-or-all process instead of a continuous process extending over two to three weeks with leaf biomass unfolding and building up during that transition period, in which case it should also matter if a frost event happened in the earlier or later stage of leaf-out. In addition, I'd expect frost severity and frost duration to also modulate the effect size. A light frost of maybe just little below zero for one night should have a less pronounced effect than, say, a night with -5 °C or three consecutive nights with below-zero temperatures.
- 261: Do you plan to ultimately also implement LSF effects for other deciduous broadleaf species in LPJ-GUESS? It should matter in a similar way for other early-budding European species, such as maple or hornbeam. It should even affect some of the coniferous species, as their development of new shoots to some degree is also sensitive to frost. With an earlier end of winter dormancy, the trees lose their frost hardiness earlier and become more prone to LSF. Tree species at risk from late frost are primarily fir, beech, chestnut and - although late bloomers - also ash and walnut.
- 271/272: For how long exactly does the dormancy last?
- 276/277: This should also be a challenging endeavor, because it would require tree stands that experience identical environmental conditions except for experiencing frost or not. Otherwise frost effects are always integrated with additional climatic and non-climatic effects on tree ring growth.
- 278/279: Integrated over that time period, or in comparison in the final year of the simulation period?
- 291 ff: Likely, frost damage across all leaves within a canopy follows a statistical distribution, with some of the most protected leaves within the canopy suffering hardly at all and some of the most exposed leaves being lost to frost damage entirely. Integrated across all leaves within a canopy, this results in a continuous effect nature, not a yes-or-no effect nature.
- 305/306: Which poses a particual challenge for predictive modeling of future dynamics. Especially because the commonly used (downscaled) climate forcing from GCM output is unlikely to catch such regional effects.
- 312: “carbon costs for re-building the canopy should not be ignored” - Are they ignored so far?! I implicitly assumed that producing leaf tissue biomass involves reallocation from carbon from a storage pool to the new biomass (including growth respiration losses)? If this mechanism is not yet part of the model, it should be addressed sooner rather than later. Producing leaves must come at a cost, carbon-wise!
Editorial remarks
- 2: add comma after “photosynthesize”
- 18: add comma after “dynamics”
- 78: add comma after “e.g.” (and in all other places where it is used)
- 91: extend => extended
- 152: resemble => represent
- 248: “In contrast” - This is not in contrast, but points in the same direction as the preceding evidence.
- 258: “European forest were” – “European forests were”
Citation: https://doi.org/10.5194/bg-2023-139-RC2
Benjamin F. Meyer et al.
Model code and software
LPJ-GUESS Release v4.0.1 model code M. Lindeskog, A. Arneth, P. Miller, J. Nord, M. Mischurov, S. Olin, G. Schurgers, B. Smith, D. Wårlind, and past LPJ-GUESS contributors https://doi.org/10.5281/zenodo.8070582
Benjamin F. Meyer et al.
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