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

Abstract

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 regulate photosynthetic productivity. We found that GPP was gradually inhibited at temperatures 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 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.

Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems

“…Interestingly, ɑ was also not affected by environmental parameters and further did not differ by site when T air was below 3.2°C (data not shown), indicating a temperature threshold for photosynthetic activity, or average temperature at which leaf out occurs (Aalto et al., 2015 ; Donnelly et al., 2019 ), for the different plant species present at all sites. Similarly, A max did not increase for temperature <7.4°C, which is similar to temperature limitations of photosynthesis found in a high‐elevation conifer forest (Stettz et al., 2022 ). With warming temperatures, we found a significant increase in ɑ with temperature, independent of site, suggesting that enzymatic activity (i.e., RuBisCo) increased with greater temperature (Moore et al., 2021 ).…”

Quantifying Northern High Latitude Gross Primary Productivity (GPP) Using Carbonyl Sulfide (OCS)

“…Given the diversity of these models, it is unlikely they share a common set of mechanisms to explain their low productivity. We do note that CARDAMOM currently does not account for cold temperature limitation in spring, which can drive low seasonal GPP amplitude in temperature limited forests including high winter GPP and low summer GPP (Stettz et al., 2021). While GOME SIF has traditionally offered a reliable measurement of GPP variability (e.g., Commane et al., 2017; Luus et al., 2017), we acknowledge previous work showing a late bias in the date of spring onset relative to tower based GPP in Alaskan boreal forests (Parazoo et al., 2018).…”

Resolving the Carbon‐Climate Feedback Potential of Wetland CO2 and CH4 Fluxes in Alaska

“…In turn, terrestrial biosphere models provide the necessary means to mechanistically represent the ecosystem responses to climate variability. Bayesian model‐data fusion (MDF) using EC data has become a valuable tool for optimizing model C states, fluxes and process parameters and provides quantitative insights on terrestrial C cycling (Bloom & Williams, 2015; Famiglietti et al., 2020; Keenan et al., 2013; Smallman et al., 2017; Stettz et al., 2021; Williams et al., 2005; Yang et al., 2021). Model‐data integration efforts also provide the C cycle mechanistic insights necessary for explicitly resolving the sensitivity of C cycling to climate and anthropogenic forcings (Bloom et al., 2020a, 2020b; Stettz et al., 2021; Yin et al., 2020).…”