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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/esd-2018-71
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-2018-71
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 21 Nov 2018

Research article | 21 Nov 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Dynamics (ESD).

Limitations of Emergent Constraints on Multi-Model Projections: Case Study of Constraining Vegetation Productivity With Observed Greening Sensitivity

Alexander J. Winkler1,2, Ranga B. Myneni3, and Victor Brovkin1 Alexander J. Winkler et al.
  • 1Max Planck Institute for Meteorology, Bundesstrasse 53, 20146 Hamburg, Germany
  • 2International Max Planck Research School on Earth System Modelling, Bundesstrasse 53, 20146 Hamburg, Germany
  • 3Department of Earth and Environment, Boston University, Boston, Massachusetts 02215, USA

Abstract. Recent research on Emergent Constraints (EC) has delivered promising results. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in a difficult-to-measure variable (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure critically depends on, first, accurate estimation of the predictor from observations and models, and second, on a robust relationship between inter-model variations in the predictor-predictand space. We investigate issues related to these two themes in this article, using vegetation greening sensitivity to CO2 forcing during the satellite era as a predictor of change in Gross Primary Productivity (GPP) of the Northern High Latitudes region (60°N–90°N, NHL) for a doubling of pre-industrial CO2 concentration in the atmosphere. Greening sensitivity is defined as changes in annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the greening sensitivity of a large area, the NHL, from pixel-level LAImax data. This requires an investigation into uncertainties in LAImax data source and an evaluation of the spatial and temporal variability in greening sensitivity to forcing in both the data and model simulations. Second, the relationship between greening sensitivity and ΔGPP across the model ensemble depends on a strong coupling among simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time-rate of application (scenarios) and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can impair the EC estimate of the predictand (ΔGPP). Accounting for uncertainties in greening sensitivity and stability of the relation between inter-model variations results in a quantitative estimate of the uncertainty (±0.2PgCyr−1) on constrained GPP enhancement (ΔGPP=+3.4PgCyr−1) for a doubling of pre-industrial atmospheric CO2 concentration in NHL. This ΔGPP is 60% larger than the conventionally used average of model projections. The illustrated sources of uncertainty and limitations of the EC method go beyond carbon cycle research and are generally relevant for Earth system sciences.

Alexander J. Winkler et al.
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Alexander J. Winkler et al.
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Short summary
The concept of Emergent Constraints is a new method to reduce uncertainty in multi-model climate projections using historical simulations and observations. Here, we present an in-depth analysis of the applicability of the method and uncover possible limitations. Key limitations are a lacking comparability (temporal, spatial, and conceptual) between models and observations, and disagreement between models on system dynamics throughout different levels of atmospheric CO2 concentration.
The concept of Emergent Constraints is a new method to reduce uncertainty in multi-model climate...
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