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.
Highlights
Northern Hemisphere evergreen forests contribute significantly to terrestrial carbon (C) storage and exchange (Beer et al, 2010; Thurner et al, 2014)
Given the complexity of carbon–water cycle interactions during the growing season in this highly water limited ecosystem, as well as the relatively weak correlation between tower-derived spring and summer gross primary production (GPP) (r = −0.31; p = 0.20), we focus on spring GPP–temperature interactions, with the aim to resolve just one piece of the larger, complex problem of understanding changes in C uptake in a subalpine evergreen ecosystem
Assimilating only the first decade of GPP data (Half experiments) does not drastically alter model performance (Fig. S5), with only slight changes in root mean square error (RMSE) and mean bias error (MBE) ( RMSE = 0.008 g C m−2 d−1 and MBE = 0.03 g C m−2 d−1 for CARD-Half; RMSE = −0.003 g C m−2 d−1 and MBE = 0.02 g C m−2 d−1 for CARDcold-Half)
Summary
Northern Hemisphere evergreen forests contribute significantly to terrestrial carbon (C) storage and exchange (Beer et al, 2010; Thurner et al, 2014). High-latitude and highelevation evergreen forests show increasing gross primary productivity (GPP) with increasing temperature driven in large part by earlier growing seasons (Myneni et al, 1997; Randerson et al, 1999; Forkel et al, 2016; Winchell et al, 2016; Lin et al, 2017). Many subalpine forests in western North America are highly water limited, with warming and earlier snowmelt creating accumulated water deficits, increased drought stress and growing-season C uptake losses (Wolf et al, 2016; Sippel et al, 2017; Buermann et al, 2018; Goulden and Bales, 2019); these factors make subalpine forest ecosystems sensitive to the direct and indirect effects of climate change and other disturbances, including the effects of droughts, fires and insect infestations (Keenan et al, 2014; Frank et al, 2014; Knowles et al, 2015). The uncertainty in the temperature sensitivity of springtime GPP, increasing vulnerability to disturbance and GPP modeling challenges (Anav et al, 2015) create urgency to improve our ability to observe and model these ecosystems to understand how C exchange will be altered in a warming climate
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