Preprints
https://doi.org/10.5194/bg-2021-152
https://doi.org/10.5194/bg-2021-152

  17 Jun 2021

17 Jun 2021

Review status: a revised version of this preprint is currently under review for the journal BG.

Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model-data fusion framework

Stephanie G. Stettz1, Nicholas C. Parazoo2, A. Anthony Bloom2, Peter D. Blanken3, David R. Bowling4, Sean P. Burns3,5, Cédric Bacour6, Fabienne Maignan7, Brett Raczka5, Alexander J. Norton2, Ian Baker8, Mathew Williams9,10, Mingjie Shi11, Yongguang Zhang12, and Bo Qiu12 Stephanie G. Stettz et al.
  • 1Department of Earth System Science, University of California Irvine, Irvine, California, USA
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
  • 3Department of Geography, University of Colorado Boulder, Boulder, Colorado, USA
  • 4School of Biological Sciences, University of Utah, Salt Lake City, Utah, USA
  • 5National Center for Atmospheric Research, Boulder, Colorado, USA
  • 6NOVELTIS, 153 rue du Lac, 31670 Labège, France
  • 7Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
  • 8Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado, USA
  • 9School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
  • 10National Centre for Earth Observation, Edinburgh EH9 3FF, Edinburgh, UK
  • 11Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99354
  • 12International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu Province, China

Abstract. The flow of carbon through terrestrial ecosystems and the response to climate is a critical but highly uncertain process in the global carbon cycle. However, with a rapidly expanding array of in situ and satellite data, there is an opportunity to improve our mechanistic understanding of the carbon (C) cycle’s response to land use and climate change. Uncertainty in temperature limitation on productivity pose a significant challenge to predicting the response of ecosystem carbon fluxes to a changing climate. Here we diagnose and quantitatively resolve environmental limitations on growing season onset of gross primary production (GPP) using nearly two decades of meteorological and C flux data (2000–2018) at a subalpine evergreen forest in Colorado USA. We implement the CARDAMOM model-data fusion network to resolve the temperature sensitivity of spring GPP. To capture a GPP temperature limitation – a critical component of integrated sensitivity of GPP to temperature – we introduced a cold temperature scaling function in CARDAMOM to regulate photosynthetic productivity. We found that GPP was gradually inhibited at temperature below 6.0 °C (±2.6 °C) and completely inhibited below −7.1 °C (±1.1 °C). The addition of this scaling factor improved the model’s ability to replicate spring GPP at interannual and decadal time scales (r = 0.88), relative to the nominal CARDAMOM configuration (r = 0.47), and improved spring GPP model predictability outside of the data assimilation training period (r = 0.88) . While cold temperature limitation has an important influence on spring GPP, it does not have a significant impact on integrated growing season GPP, revealing that other environmental controls, such as precipitation, play a more important role in annual productivity. This study highlights growing season onset temperature as a key limiting factor for spring growth in winter-dormant evergreen forests, which is critical in understanding future responses to climate change.

Stephanie G. Stettz et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-152', Anonymous Referee #1, 12 Jul 2021
  • RC2: 'Comment on bg-2021-152', Anonymous Referee #2, 19 Sep 2021

Stephanie G. Stettz et al.

Data sets

Datasets for Study on Cold Temperature Limitation on GPP in an Evergreen Forest Stephanie Stettz http://doi.org/10.5281/zenodo.4928097

Model code and software

CARDAMOM code A. Anthony Bloom https://github.com/CARDAMOM-framework/CARDAMOM_v2.2

Stephanie G. Stettz et al.

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Short summary
Uncertainty in the response of photosynthesis to temperature poses a major challenge to predicting the response of forests to climate change. In this paper, we study how photosynthesis in a mountainous evergreen forest is limited by temperature. This study highlights that cold temperature is a key factor that controls spring photosynthesis. Including the cold temperature limitation in an ecosystem model improved its ability to simulate spring photosynthesis.
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