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  • 1
    Publication Date: 2024-02-21
    Description: Emergent constraints on carbon cycle feedbacks in response to warming and increasing atmospheric CO〈sub〉2 〈/sub〉 concentration have previously been identified in Earth system models participating in the Coupled Model Intercomparison Project (CMIP) Phase 5. Here, we examine whether two of these emergent constraints also hold for CMIP6. The spread of the sensitivity of tropical land carbon uptake to tropical warming in an idealized simulation with a 1% per year increase of atmospheric CO〈sub〉2 〈/sub〉 shows only a slight decrease in CMIP6 (−52 ± 35 GtC/K) compared to CMIP5 (−49 ± 40 GtC/K). For both model generations, the observed interannual variability in the growth rate of atmospheric CO〈sub〉2 〈/sub〉 yields a consistent emergent constraint on the sensitivity of tropical land carbon uptake with a constrained range of −37 ± 14 GtC/K for the combined ensemble (i.e., a reduction of ∼30% in the best estimate and 60% in the uncertainty range relative to the multimodel mean of the combined ensemble). A further emergent constraint is based on a relationship between CO〈sub〉2 〈/sub〉 fertilization and the historical increase in the CO〈sub〉2 〈/sub〉 seasonal cycle amplitude in high latitudes. However, this emergent constraint is not evident in CMIP6. This is in part because the historical increase in the amplitude of the CO〈sub〉2 〈/sub〉 seasonal cycle is more accurately simulated in CMIP6, such that the models are all now close to the observational constraint.
    Description: Plain Language Summary: The statistical model of so‐called emergent constraints help to better understand the sensitivity of Earth system processes in a changing climate. Here, we analyze the robustness of two previously found emergent constraints on carbon cycle feedbacks, using models from the Coupled Model Intercomparison Project (CMIP) of Phases 5 and 6. First the decrease of carbon storage in the tropics due to increasing near‐surface air temperatures, which is found to be robust on the choise of model ensemble. Giving a constraint estimate of −52 ± 35 GtC/K for CMIP6 models, being within the range of uncertainty for the previously estimated result for CMIP5. Second, the increase of carbon storage in high latitudes due to CO〈sub〉2 〈/sub〉 fertilization effect, which is found to be not evident among CMIP6 models. This is in part because the historical increase in the amplitude of the CO〈sub〉2 〈/sub〉 seasonal cycle is more accurately simulated in CMIP6, such that the models are all now close to the observational constraint.
    Description: Key Points: An emergent constraint on the sensitivity of tropical land carbon to global warming, originally derived from Coupled Model Intercomparison Project Phase 5 (CMIP5), also holds for CMIP6. The combined CMIP5 + CMIP6 ensemble gives an emergent constraint on the sensitivity of tropical land carbon to global warming of −37 ± 14 GtC/K. An emergent constraint on the fertilization feedback due to rising CO〈sub〉2 〈/sub〉 levels, previously derived, is not evident in CMIP6.
    Description: Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661
    Description: ERC
    Description: https://doi.org/10.5281/zenodo.6900341
    Description: https://doi.org/10.5281/zenodo.3387139
    Description: https://github.com/ESMValGroup
    Description: https://docs.esmvaltool.org/
    Keywords: ddc:551 ; carbon cycle ; emergent constraint ; CMIP5 ; CMIP6 ; fertilization effect ; temperature warming
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2021-07-03
    Description: The terrestrial biosphere is currently slowing down global warming by absorbing about 30% of human emissions of carbon dioxide (CO2). The largest flux of the terrestrial carbon uptake is gross primary production (GPP) defined as the production of carbohydrates by photosynthesis. Elevated atmospheric CO2 concentration is expected to increase GPP (“CO2 fertilization effect”). However, Earth system models (ESMs) exhibit a large range in simulated GPP projections. In this study, we combine an existing emergent constraint on CO2 fertilization with a machine learning approach to constrain the spatial variations of multimodel GPP projections. In a first step, we use observed changes in the CO2 seasonal cycle at Cape Kumukahi to constrain the global mean GPP at the end of the 21st century (2091–2100) in Representative Concentration Pathway 8.5 simulations with ESMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 171 ± 12 Gt C yr−1, compared to the unconstrained model range of 156–247 Gt C yr−1. In a second step, we use a machine learning model to constrain gridded future absolute GPP and gridded fractional GPP change in two independent approaches. For this, observational data are fed into the machine learning algorithm that has been trained on CMIP5 data to learn relationships between present‐day physically relevant diagnostics and the target variable. In a leave‐one‐model‐out cross‐validation approach, the machine learning model shows superior performance to the CMIP5 ensemble mean. Our approach predicts an increased GPP change in northern high latitudes compared to regions closer to the equator.
    Description: Plain Language Summary: About a quarter of human emissions of carbon dioxide (CO2) is absorbed by vegetation and soil on the Earth's surface and hence does not contribute to global warming caused by CO2 in the atmosphere. Thus, in order to better define the remaining carbon budgets left to meet particular warming targets like the 1.5°C of the Paris Agreement, it is important to accurately quantify the carbon uptake by plants in the future. Currently, this is modeled by Earth system models yet with great uncertainties. In this work, we present an alternative machine learning approach to reduce spatial uncertainties of vegetation carbon uptake in future climate projections using observations of today's conditions.
    Description: Key Points: An emergent constraint on CO2 seasonal cycle amplitude changes reduces uncertainties in global mean gross primary production projections. A machine learning model with multiple predictors can further constrain the spatial distribution of gross primary production. High‐latitude ecosystems show higher gross primary production increase over the 21st century compared to regions closer to the equator.
    Description: EC | Horizon 2020 Framework Programme 4C
    Description: EC | Horizon 2020 Framework Programme CRESCENDO
    Description: ERC Consolidator Grant SEDAL
    Description: ERC Synergy Grant USMILE
    Keywords: 551.6 ; future climate projections ; modeling
    Type: article
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