Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework
Stettz, S. G., Parazoo, N. C., Bloom, A. A., Blanken, P. D., Bowling, D. R., et al. (2022). Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework. Biogeosciences, doi:https://doi.org/10.5194/bg-19-541-2022
| Title | Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework |
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| Author(s) | S. G. Stettz, N. C. Parazoo, A. A. Bloom, P. D. Blanken, D. R. Bowling, Sean Burns, C. Bacour, F. Maignan, Brett Raczka, A. J. Norton, I. Baker, M. Williams, M. Shi, Y. Zhang, B. Qiu |
| Abstract | The flow of carbon through terrestrial ecosystems and the response to climate are critical but highly uncertain processes 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 poses a significant challenge to predicting the response of ecosystem carbon fluxes to a changing climate. Here we diagnose and quantitatively resolve environmental limitations on the growing-season onset of gross primary production (GPP) using nearly 2 decades of meteorological and C flux data (2000-2018) at a subalpine evergreen forest in Colorado, USA. We implement the CARbon DAta-MOdel fraMework (CARDAMOM) model-data fusion network to resolve the temperature sensitivity of spring GPP. To capture a GPP temperature limitation - a critical component of the integrated sensitivity of GPP to temperature - we introduced a cold-temperature scaling function in CARDAMOM to reg- ulate photosynthetic productivity. We found that GPP was gradually inhibited at temperatures below 6.0 degrees C (+/- 2.6 degrees C) and completely inhibited below -7.1 degrees C (+/- 1.1 degrees C). The addition of this scaling factor improved the model's ability to replicate spring GPP at interannual and decadal timescales (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. |
| Publication Title | Biogeosciences |
| Publication Date | Jan 28, 2022 |
| Publisher's Version of Record | https://dx.doi.org/https://doi.org/10.5194/bg-19-541-2022 |
| OpenSky Citable URL | https://n2t.org/ark:/85065/d7kd22gd |
| OpenSky Listing | View on OpenSky |
| CISL Affiliations | TDD, DARES |