Sustained Nonphotochemical Quenching Shapes the Seasonal Pattern of Solar‐Induced Fluorescence at a High‐Elevation Evergreen Forest

Abstract

Traditional methods of carbon monitoring in mountainous regions are challenged by complex terrain. Recently, solar‐induced fluorescence (SIF) has been found to be an indicator of gross primary production (GPP), and the increased availability of remotely sensed SIF provides an opportunity to estimate GPP across the Western United States. Although the empirical linkage between SIF and GPP is strong, the current mechanistic understanding of this linkage is incomplete and depends upon changes in leaf biochemical processes in which absorbed sunlight leads to photochemistry, heat (via nonphotochemical quenching [NPQ]), fluorescence, or tissue damage. An improved mechanistic understanding is necessary to leverage SIF observations to improve representation of ecosystem processes within land surface models. Here we included an improved fluorescence model within the Community Land Model, Version 4.5 (CLM 4.5), to simulate seasonal changes in SIF at a subalpine forest in Colorado. We found that when the model accounted for sustained NPQ, this provided a larger seasonal change in fluorescence yield leading to simulated SIF that more closely resembled the observed seasonal pattern (Global Ozone Monitoring Experiment‐2 [GOME‐2] satellite platform and a tower‐mounted spectrometer system). We found that an acclimation model based on mean air temperature was a useful predictor for sustained NPQ. Although light intensity was not an important factor for this analysis, it should be considered before applying the sustained NPQ and SIF to other cold climate evergreen biomes. More leaf‐level fluorescence measurements are necessary to better understand the seasonal relationship between sustained and reversible components of NPQ and to what extent that influences SIF.

Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops

“…S12), which indicates that ecosystem-or pathway-dependent slopes will be required to some degree for global studies in any case. Further research is needed to determine if SIF obs considerably outperforms APAR × f esc for GPP estimation in ENF ecosystems where canopy structure is stable but photosynthesis and Φ F are downregulated in winter (Magney et al, 2019;Porcar-Castell, 2011;Raczka et al, 2019).…”

Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model

“…The coupling of BETHY and SCOPE enables spatially explicit global simulations of GPP and SIF that are dependent on plant functional type (PFT). BETHY is a process-based terrestrial biosphere model, which is a key element of the CCDAS (Rayner et al, 2005;Scholze et al, 2007). Full model description details can be found elsewhere (e.g., Rayner et al, 2005;Scholze et al, 2007;Knorr et al, 2010).…”

“…BETHY is a process-based terrestrial biosphere model, which is a key element of the CCDAS (Rayner et al, 2005;Scholze et al, 2007). Full model description details can be found elsewhere (e.g., Rayner et al, 2005;Scholze et al, 2007;Knorr et al, 2010). Briefly, BETHY simulates carbon assimilation and plant and soil respiration within a full energy and water balance.…”

“…where H is calculated at the posterior (i.e., x post ). Rayner et al (2005) demonstrated how to propagate these parameter uncertainties forward through a model onto simulated quantities such as carbon fluxes. Using the Jacobian rule for probabilities, parameter uncertainties in the model parameter covariance matrix (C x 0 and C x post ) can propagate forward onto GPP using Eq.…”

“…Data assimilation provides an effective way of optimizing the input parameters and evaluating the consistency of the model with various observational data, providing insight into the model formulation as well (Rayner, 2010). The Carbon Cycle Data Assimilation System (CCDAS) is one example that has been developed to ingest multiple sources of data (Kaminski et al, 2013;Koffi et al, 2012;Rayner et al, 2005). More generally, various carbon cycle data assimilation systems have been applied using a range of observational data, including atmospheric CO 2 , soil moisture, the fraction of absorbed photosynthetically active radiation (FAPAR) and flux tower measurements (Bacour et al, 2015;Kaminski et al, 2012Kaminski et al, , 2013Kato et al, 2013;MacBean et al, 2016;Scholze et al, 2016).…”