the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Pathways for avoiding ocean biogeochemical damage: Mitigation limits, mitigation options, and projections
Abstract. Anthropogenic greenhouse gas emissions cause multiple changes in the ocean and its ecosystems through climate change and ocean acidification. These changes can occur progressively with rising atmospheric carbon dioxide concentrations, but there is also the possibility of large-scale abrupt, and/or potentially irreversible changes, which would leave limited opportunity for marine ecosystems to adapt. Such changes, either progressive or abrupt, pose a threat to biodiversity, food security, and human societies. However, it remains notoriously difficult to determine exact limits of a “safe operating space” for humanity. Here, we map, for a variety of ocean impact metrics, the crossing of limits, which we define using the available literature and to represent a wide range of deviations from the unperturbed state. We assess the crossing of these limits in three future emission pathways: two climate mitigation scenarios, including an overshoot scenario, and one high-emission no-mitigation scenario. These scenarios are simulated by the latest generation of Earth system models and large perturbed-parameter ensembles with two Earth system models of intermediate complexity. Using this comprehensive model database, we estimate when and at which warming level 4 mitigation limits based on expert judgement for 14 different impact metrics are exceeded along with an assessment of uncertainties. We find that under the high-emissions scenario, the two highest limits are exceeded with high confidence for the marine heatwaves’ duration, expansion of ocean areas that are undersaturated with respect to aragonite, decreases in plankton biomass, and loss of Arctic summer sea ice extent. The probability of exceeding a given limit generally decreases clearly under low-emissions scenario. Yet, exceedance of ambitious limits related to Arctic aragonite undersaturation, plankton biomass, and Arctic summer sea ice extent are projected to be difficult to avoid (high confidence) even under the low-emissions scenario. The scenario including a temporary overshoot reduces with high confidence the risk of exceeding mitigation limits by year 2100 related to the marine heatwave duration, metabolic index, plankton biomass, Atlantic meridional overturning circulation, aragonite undersaturation, global deoxygenation, and Arctic summer sea ice extent compared to the high-emissions scenario. Our study highlights the urgent need for ambitious mitigation efforts to minimize extensive impacts and potentially irreversible changes to the world's oceans and ecosystems.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2024-2768', Pearse Buchanan, 05 Nov 2024
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RC2: 'Comment on egusphere-2024-2768', Anonymous Referee #2, 15 Nov 2024
Bourgeois et al. identified 14 different climate sensitive metrics related to physics and ocean biogeochemistry, and set criteria for 4 levels of limits based on existing literature, then analyzed a suite of ESMs and ESIMs future projections to estimate whether and when these limits are reached as the climate warms, and how this differs in response to different emission scenarios. Bias corrections are applied where necessary, and uncertainties are carefully considered. Overall, I found this analysis useful for bridging CMIP ensemble output variables and more societally relevant metrics, tipping points, and probability of occurrences, and a good contribution to the current understanding of CMIP datasets as well as future CMIP type of efforts. I have a few quibbles and requests listed below:
- The authors chose the 4 levels of limits based on literature, which is reasonable. However, some of the literature is based on observations or single events, thus it might be challenging to use CMIP models, which are mostly at coarse and intermediate resolution, to capture the observed ranges. This results that even Level 1 limit is never reached with high uncertainty for a number of analyzed metrics. I suggest that the authors use literature as a guidance, but also consider the model ensemble’s capability of capturing those ranges and changes. One thing that could be looked at is the historical period of the CMIP simulations between 1950-2015. If literature suggests that metric A has changed by xx% over this period yet the model ensemble can only capture about yy% (e.g., xx>>yy), then the authors may want to consider scaling the limits accordingly. This may help minimize the loss of signals solely due to choices made on an ad hoc basis. The authors indeed applied some levels of bias corrections for a few metrics but it was done in a systematic way.
- Line 97: “we spread evenly …” I am not clear what this means. Please clarify.
- Line 103: “... 40% of the total sea-level rise of 0.2 m today” - is the approximated SSL rise 0.2 m or 0.08 m, then? The chosen levels of limits are 0.2, 0.3, 0.4, and 0.5, so I am assuming SSL rise is about 0.2 m based on literature. Please clarify the original sentence.
- Line 117 through 125: despite shown in Table 1, the AMOC metric and its four limits are not clearly described as for other metrics.
- Line 129 “ocean acidification could lead to undersaturation and dissolution of calcium …” This is not accurate. The majority of the ocean interior has an omega value under 1 so OA actually leads to undersaturation and dissolution of calcium carbonate at shallower depths.
- The “subsurface delta O2” metric and its abbreviation are confusing as they are not consistent with the actual definition of this metric. Suggest to use “hypo(xic) delta O2” which usually refers to the volume change in the relevant research field.
- Line 175: the calculation of metabolic index needs a bit more clarification. How is it averaged across the 61 species to get a single number? Does biomass distribution matter when averaging?
- Line 200-204: all bias corrections should be described in the same place, including aragonite saturation state.
- The two ESIMS are described in great detail while the ESMs are not. I suggest the author at least list spatial resolution of the native model grids (even though outputs are regridded) and screamline the description of the two ESIMs.
- I think Fig 1 and Fig 2 can be easily combined by adding a second x-axis. I also think it is okay and even preferred to have the warming axis on a different range for each limit level.
Citation: https://doi.org/10.5194/egusphere-2024-2768-RC2
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