01 Nov 2022
01 Nov 2022
Status: this preprint is open for discussion.

TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change

Shruti Nath1,2, Lukas Gudmundsson2, Jonas Schwaab2, Gregory Duveiller3, Steven Johan De Hertog4, Suqi Guo5, Felix Havermann5, Fei Luo6,7, Iris Manola6, Julia Pongratz5,8, Sonia Isabelle Seneviratne2, Carl Friedrich Schleussner1,9, Wim Thiery4, and Quentin Lejeune1 Shruti Nath et al.
  • 1Climate Analytics, Berlin, Germany
  • 2Institute of Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
  • 3Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
  • 4Vrije Universiteit Brussel, Department of Hydrology and Hydraulic Engineering, Brussels, Belgium
  • 5Ludwig-Maximilians-University Munich, Department of Geography, Munich, Germany
  • 6Vrije Universiteit Amsterdam, Institute for Environmental studies, Amsterdam, Netherlands
  • 7Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands
  • 8Max Planck Institute for Meteorology, Hamburg, Germany
  • 9Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys) and Geography Department, Humboldt-Universität zu Berlin, Berlin, Germany

Abstract. Society is set to experience significant land cover changes in order to achieve the temperature goals agreed upon under the Paris Agreement. Such changes carry both global implications, pertaining to the biogeochemical effects of land cover change and thus the global carbon budget, and regional/local implications, pertaining to the biogeophysical effects arising within the immediate area of land cover change. Biogeophysical effects of land cover change are of high relevance to national policy- and decision- makers and their accountance is essential towards effective deployment of land cover practices that optimises between global and regional impacts. To this end, ESM outputs that isolate the biogeophysical responses of climate to land cover changes are key in informing impact assessments and supporting scenario development exercises. Generating multiple such ESM outputs, in a manner that allows comprehensive exploration of all plausible land cover scenarios however, is computationally untenable. This study proposes a framework to agilely explore the local biogeophysical responses of climate under different land cover scenarios by means of a computationally inexpensive emulator. The emulator is novel in that it solely represents the land cover forced, biogeophysical responses of climate, and can be used as either a standalone device or supplementary to existing climate model emulators that represent greenhouse gas (GHG)- or Global Mean Temperature (GMT)- forced climate responses. We start off by modelling local minimum, mean and maximum surface temperature responses to tree cover changes by means of a month- and Earth System Model (ESM)- specific Generalised Additive Model (GAM) trained over the whole globe. 2-m air temperature responses are then diagnosed from the modelled minimum and maximum surface temperature responses using observationally derived relationships. Such a two-step procedure accounts for the different physical representations of surface temperature responses to tree cover changes under different ESMs, whilst respecting a definition of 2-m air temperature that is more consistent across ESMs and with observational datasets. In exploring new tree cover change scenarios, we employ a parametric bootstrap sampling method to generate multiple possible temperature responses, such that the uncertainty within the GAM's derived shape of the response is also quantified. The output of the final emulator is demonstrated for the SSP 1-2.6 and 3-7.0 scenarios. Relevant temperature responses are identified as those displaying a clear signal in relation to the surrounding uncertainty in shape of derived response, calculated as the "signal-to-noise" ratio between the sample set mean and sample set variability. The emulator framework developed in this study thus provides a first step towards bridging the information-gap surrounding biogeophysical implications of land cover changes, allowing for smarter land-use decision making.

Shruti Nath et al.

Status: open (until 27 Dec 2022)

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  • RC1: 'Comment on egusphere-2022-1024', Anonymous Referee #1, 05 Dec 2022 reply

Shruti Nath et al.

Shruti Nath et al.


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Short summary
The COP26 saw significant pledges made to to halt and reverse deforestation. The regional impacts of this are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards agilely exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model output representing the maximal impacts of aff/re/deforestation from which it explores plausible in-between outcomes itself.