the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evidence of Amazon rainforest dieback in CMIP6 models
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK, EX4 4QE
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK, EX4 4QE
Abstract. Amazon forest dieback is seen as a potential tipping point under climate change. These concerns are partly based-on an early coupled climate-carbon cycle simulation, that produced unusually strong drying and warming in Amazonia. In contrast, the 5th generation Earth System Models (CMIP5) produced few examples of Amazon dieback under climate change. Here we examine results from seven 6th generation models (CMIP6) which include vegetation dynamics, and in some cases interactive forest fires. Although these models typically project increases in area-mean forest carbon across Amazonia under CO2-induced climate change, five of the seven models also produce abrupt reductions in vegetation carbon which indicate localised dieback events. The Northern South America region (NSA), which contains most of the rainforest, is especially vulnerable in the models. These dieback events, some of which are mediated by fire, are preceded by an increase in the amplitude of the seasonal cycle in near surface temperature, which is consistent with more extreme dry seasons. Based-on the ensemble mean of the detected dieback events we estimate that 7 +/-5 % of the NSA region will experience abrupt downward shifts in vegetation carbon per °C of global warming above 1.5 °C.
Isobel Parry et al.
Status: open (until 22 May 2022)
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RC1: 'Comment on egusphere-2022-82', Anonymous Referee #1, 25 Apr 2022
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In the manuscript “Evidence of Amazon rainforest dieback in CMIP6 models”, Parry et al. examined the scope of forest dieback events based on CMIP6 simulations, and proposed an early warning signal to predict the occurrence of dieback. The paper updated our understanding of future forests dieback in Amazon using new model results. The introduction is well written, my reservation is about the lack of in-depth analysis in Results and Discussions, in particular:
- The current study shows where the abrupt shift likely to happen but less on how. I am looking for some mechanistic explanations on model difference in abrupt shift (AS) identification – some are concentrated, some scattered, some none? One explanation proposed is that the larger internal variability of model led to scattered distribution of AS, perhaps need to present data to show it? In addition, do we expect to see more/less abrupt shift at regions with larger internal variability at all? Any other potential reasons that can explain inter-model variation.
- The author demonstrated that diebacks happen at places where there are higher temperature sensitivity of seasonal temperature amplitude – which is further regarded as an early-warning signal (EWS). I am curious that how early could the EWS work, or do we really see higher predictive accuracy of dieback if we use EWS. I am curious whether the ESW is the precursor for the dieback or ESW is caused by the dieback due to climate-vegetation feedback?
Other comments:
1. the reason to use 1%. It has been argued it is idealized to use 1% CO2 simulations, though it is not clear to me how that would be “ideal”.
2. Other than dieback, the authors also present other combinations of AS and trendy changes. Though they are less common, I am wondering if the authors need to provide some mechanistic explanations…or I would suggest removing those as they might be distractive.
3. Is it possible the key piece of evidence supporting ESW (Fig. 4i) mostly come from one model - TaiESM1. The model has the largest number of valid samples for the analysis, but quite few other models – Samu-UNICO, EC-Earth3, does not such the effectiveness of the ESW. How robust it is if we bootstrap model, or normalize result by valid samples. It linked back to my first major concern that why models show different results.
L96. “many abrupt shifts” – perhaps provide a more quantitative statement.
Figure 3. y axis and caption, what is cVeg? Those are good examples. Is it possible to get a scatter plot of the timings of EWS and dieback for all pixels?
L115. Regional scale means “region average”?
L175. stufy – study?
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RC2: 'Comment on egusphere-2022-82', Anonymous Referee #2, 02 May 2022
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The manuscript "Evidence of Amazon rainforest dieback in CMIP6 models" by Parry et al. explores the behaviour of vegetation carbon in the Northern South America region for a selection of Earth System Models under a particular global warming scenario. The authors perform a grid point by grid point analysis, as well as a regional averaged analysis. It is shown that of the models which exhibit abrupt shifts, they primarily show localised shifts rather than region-wide shifts. Additionally, the authors introduce a quantity to potentially be used as an early warning signal, namely the temperature seasonal cycle amplitude.
I think the work at hand has value for the wider scientific community, however I feel a few points must be addressed before the manuscript is ready for publication.
1. My main issue is in regards to the use of temperature seasonal cycle ampliture as an EWS. It is not clear to me what the criteria for the EWS would be. For instance, how much of a rise in the amplitude needs to occur to indicate an approach to a tipping point? Is the increase significant compared to other localised increases in the signal?
2. Throughout the mauscript samples are used to illustrate behviour (see for instance Figure 1h and Figure 3). It is not explained how these samples are chosen and if they exhibit characteristic behaviour of some class of grid points (e.g. those that exhibit a negative abrupt shift). Please consider being more transparent regarding the sample selection and how each sample compares to similar grid points in the respective models. This will help the reader to draw appropraite conclusions (and/or not draw incorrect conclusions) of the general model behaviour.
3. Line 122 mentions a critical threshold of CO2 but then this is not further discussed in regards to the examples shown. Can you draw any connection to the behaviour seen in the models? I would suggest to either make the discussion of this more explicit or leave it out, as it seems out of place currently.
4. There are quite a few abbreviations that are not explicity defined or explained for the reader not familiar with the data and methods. For instance, lines 56 and 57 use terms "1pctCO2" and "PIControl" which although I was able to discern what they probably refer to, it would be better for the reader if these were explained. Also when discussing sensitivity increases the units K/K are used. Is this Kelvin per Kelvin? I don't quite understand the units here.
A few technical notes:
Line 79 - The colors red and purple are mentioned with no reference to a figure.
Figure 3 caption - Should black squares be black crosses?
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RC3: 'Comment on egusphere-2022-82', Anonymous Referee #3, 16 May 2022
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General comments
Amazon forests play an important role on the Earth’s carbon cycle. Previous modelling studies have suggested that a widespread forest dieback could occur as climate changes, but this remains highly uncertain. In this study, Parry et al. investigate the risks of such dieback in more recent models, by comparing the predictions of seven CMIP6 models under increasing CO2, and identified that about 7% of the Amazon could experience dieback once global temperature exceeds 1.5°C above the historical average.
Overall, I think the research question is highly relevant and can eventually provide important insights on the likelihood of significant and persistent changes of tropical forests under climate change. However, in its current form, the manuscript lacks important analyses and discussions on the mechanisms that result (or do not result) in significant forest losses in the models, and what are the main factors that cause the different outcomes across the models. To give one example, the authors mention that some of the diebacks are mediated by fire, but from reading the manuscript it is unclear which models predicted dieback due to fire, which models predicted dieback due to physiological/hydraulic failure, or even if the models that resulted in no dieback were also the ones that did not have fire. I list other instances in the specific points below. If the authors can strengthen the discussion and analyses in this direction, I think the manuscript could become a significant scientific contribution.
Specific points
Introduction. There are many studies that investigated the risk of critical transitions specifically in the Amazon, and it may be worth including them to provide a stronger motivation for this work. As a starting point, the recent Amazon assessment report (https://www.theamazonwewant.org/amazon-assessment-report-2021/, chapter 24) has an extensive review on this subject.
L49-51. This paragraph could be expanded to provide a stronger motivation for this study. The authors could justify why the current analysis is necessary, and how this study contributes to learning something new about the future of the Amazon to climate change.
Table 1. The authors could expand this table to provide a bit more information of the simulations and models used. For example, they could provide some information about the models (e.g., which ones had fires enabled, which other mechanisms cause mortality and biomass loss), and which variants were used from each model. If citations exist for these models, the authors could add references.
Section 2.3. I found the early warning section disconnected from the introduction. In the introduction the authors describe the theory of critical transitions, critical slowing down and increase in autocorrelation, yet none of these seem to be used in the actual analysis. Was there any reason for not using these established approaches?
Section 3.1. The authors use savannah as the alternate state for forests, and this can be misleading if fire is not the driver for abrupt changes. Also, this section describes the changes across tropical South America, but the authors do not provide any insight on what causes the variability across models. Presumably they also have broad range of predicted climate, presence/absence of fires, and different approaches to simulate drought mortality. Explaining these differences could help us understanding why there was such broad range in dieback responses.
L115–118. My previous comment applies here too. The remark that models largely disagree is correct, but not very informative. I would not expect the authors to provide details about every model configuration and formulation, but I think they could explore some potential causes by looking at other model output data (at the very least precipitation, some insight on model sensitivity to CO2, and some fire and mortality-related variables if available).
Section 3.3. I am unable to see the causal link between CO2 and the abrupt shift based solely on Figure 3. If anything, in GFDL most of the shift in the amplitude of the seasonal cycle seems to occur after the abrupt transition. Also, the difference in the seasonal cycle is very large across models, with NorCPM1 remaining below 4°C for the entire century, whereas the other models show much higher amplitudes. Is it fair to treat the shifts marked in Fig. 3 the same?
Discussion: I support keeping the discussion short, but maybe the current one is a bit too short and narrow in scope. For example, the authors mention that the dieback was present in previous generations of model but not in CMIP6. Why is this the case? Also, how does this result differ from the analysis by Cox et al. (2013) (https://doi.org/10.1038/nature11882), which had already indicated lower risk of a dieback. Also, the results implied that fire is an important mechanism for dieback, and I think the discussion could emphasise this further, considering that fire activity has significantly increased recently in the Amazon. The mechanistic links between increased CO2 and dieback (and the uncertainty in these links) could be discussed in more depth too. I think addressing some of these aspects would help placing these interesting results from the CMIP6 predictions in a broader context.
Minor points
L9. I am not sure I followed this sentence. Does this mean that an additional 7% of the NSA will experience dieback for every 1°C above 1.5°C (i.e., if the temperature change is 2.5°C, dieback will occur at 14% of the NSA, if the change is 3.5°C, 21% of the NSA will suffer dieback and so on?).
L49. Quantify “fairly short observational records”
L55. Include references that describe both CMIP6 and the 1pctCO2 runs.
L67. “Unforced control run” was not described up to this point. Consider describing it in section 2.1
L78–81. This seems to be out of place, it reads more like the caption of Figure 4 (which is referred to before Figures 1–3). Perhaps rewrite this to focus on how sensitivity and dieback risk were calculated.
L87–90. This text repeats what was described in the methods section. I suggest dropping it.
L105–111. “Jumps” seems a bit too colloquial, and it is unclear how it differs from “abrupt shifts”, which is defined in the methods.
L175. “Study” is misspelt.
Figure 2. In panel (a), I suggest keeping only NSA, as this is the only specific region analysed in this study. Also, make the labels consistent with captions (e.g., use either abrupt shift or dieback shift), and define the acronyms (AS, cVeg) in the caption.
Figure 3. Why did the authors show different points for each model? Also add “W” after the last 60°.
Figure 4. I recommend adding a colour legend to the figure.
Isobel Parry et al.
Isobel Parry et al.
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