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Abstract Remote sensing is a powerful tool for understanding and scaling measurements of plant carbon uptake via photosynthesis, gross primary productivity (GPP), across space and time. The success of remote sensing measurements can be attributed to their ability to capture valuable information on plant structure (physical) and function (physiological), both of which impact GPP. However, no single remote sensing measure provides a universal constraint on GPP and the relationships between remote sensing measurements and GPP are often site specific, thereby limiting broader usefulness and neglecting important nuances in these signals. Improvements must be made in how we connect remotely sensed measurements to GPP, particularly in boreal ecosystems which have been traditionally challenging to study with remote sensing. In this paper we improve GPP prediction by using random forest models as a quantitative framework that incorporates physical and physiological information provided by solar-induced fluorescence (SIF) and vegetation indices (VIs). We analyze 2.5 years of tower-based remote sensing data (SIF and VIs) across two field locations at the northern and southern ends of the North American boreal forest. We find (a) remotely sensed products contain information relevant for understanding GPP dynamics, (b) random forest models capture quantitative SIF, GPP, and light availability relationships, and (c) combining SIF and VIs in a random forest model outperforms traditional parameterizations of GPP based on SIF alone. Our new method for predicting GPP based on SIF and VIs improves our ability to quantify terrestrial carbon exchange in boreal ecosystems and has the potential for applications in other biomes. 

Abstract. Land surface modellers need measurable proxies toconstrain the quantity of carbon dioxide (CO2) assimilated bycontinental plants through photosynthesis, known as gross primary production(GPP). Carbonyl sulfide (COS), which is taken up by leaves through theirstomates and then hydrolysed by photosynthetic enzymes, is a candidate GPPproxy. A former study with the ORCHIDEE land surface model used a fixedratio of COS uptake to CO2 uptake normalised to respective ambientconcentrations for each vegetation type (leaf relative uptake, LRU) tocompute vegetation COS fluxes from GPP. The LRU approach is known to havelimited accuracy since the LRU ratio changes with variables such asphotosynthetically active radiation (PAR): while CO2 uptake slows underlow light, COS uptake is not light limited. However, the LRU approach hasbeen popular for COS–GPP proxy studies because of its ease of applicationand apparent low contribution to uncertainty for regional-scaleapplications. In this study we refined the COS–GPP relationship andimplemented in ORCHIDEE a mechanistic model that describes COS uptake bycontinental vegetation. We compared the simulated COS fluxes againstmeasured hourly COS fluxes at two sites and studied the model behaviour andlinks with environmental drivers. We performed simulations at a global scale,and we estimated the global COS uptake by vegetation to be −756 Gg S yr−1,in the middle range of former studies (−490 to −1335 Gg S yr−1). Basedon monthly mean fluxes simulated by the mechanistic approach in ORCHIDEE, wederived new LRU values for the different vegetation types, ranging between0.92 and 1.72, close to recently published averages for observed values of1.21 for C4 and 1.68 for C3 plants. We transported the COS using the monthlyvegetation COS fluxes derived from both the mechanistic and the LRUapproaches, and we evaluated the simulated COS concentrations at NOAA sites.Although the mechanistic approach was more appropriate when comparing tohigh-temporal-resolution COS flux measurements, both approaches gave similarresults when transporting with monthly COS fluxes and evaluating COSconcentrations at stations. In our study, uncertainties between these twoapproaches are of secondary importance compared to the uncertainties in theCOS global budget, which are currently a limiting factor to the potential ofCOS concentrations to constrain GPP simulated by land surface models on theglobal scale. 

Abstract. The uptake of carbonyl sulfide (COS) by terrestrial plants is linked tophotosynthetic uptake of CO2 as these gases partly share the sameuptake pathway. Applying COS as a photosynthesis tracer in models requires anaccurate representation of biosphere COS fluxes, but these models have notbeen extensively evaluated against field observations of COS fluxes. In thispaper, the COS flux as simulated by the Simple Biosphere Model, version 4(SiB4), is updated with the latest mechanistic insights and evaluated with siteobservations from different biomes: one evergreen needleleaf forest, twodeciduous broadleaf forests, three grasslands, and two crop fields spread overEurope and North America. We improved SiB4 in several ways to improve itsrepresentation of COS. To account for the effect of atmospheric COS molefractions on COS biosphere uptake, we replaced the fixed atmospheric COS molefraction boundary condition originally used in SiB4 with spatially andtemporally varying COS mole fraction fields. Seasonal amplitudes of COS molefractions are ∼50–200 ppt at the investigated sites with aminimum mole fraction in the late growing season. Incorporating seasonalvariability into the model reduces COS uptake rates in the late growingseason, allowing better agreement with observations. We also replaced theempirical soil COS uptake model in SiB4 with a mechanistic model thatrepresents both uptake and production of COS in soils, which improves thematch with observations over agricultural fields and fertilized grasslandsoils. The improved version of SiB4 was capable of simulating the diurnal andseasonal variation in COS fluxes in the boreal, temperate, and Mediterraneanregion. Nonetheless, the daytime vegetation COS flux is underestimated onaverage by 8±27 %, albeit with large variability across sites. On aglobal scale, our model modifications decreased the modeled COS terrestrialbiosphere sink from 922 Gg S yr−1 in the original SiB4 to753 Gg S yr−1 in the updated version. The largest decrease influxes was driven by lower atmospheric COS mole fractions over regions withhigh productivity, which highlights the importance of accounting forvariations in atmospheric COS mole fractions. The change to a different soilmodel, on the other hand, had a relatively small effect on the globalbiosphere COS sink. The secondary role of the modeled soil component in theglobal COS budget supports the use of COS as a global photosynthesis tracer. Amore accurate representation of COS uptake in SiB4 should allow for improvedapplication of atmospheric COS as a tracer of local- to global-scaleterrestrial photosynthesis. 

The boreal forest is a major contributor to the global climate system, therefore, reducing uncertainties in how the forest will respond to a changing climate is critical. One source of uncertainty is the timing and drivers of the spring transition. Remote sensing can provide important information on this transition, but persistent foliage greenness, seasonal snow cover, and a high prevalence of mixed forest stands (both deciduous and evergreen species) complicate interpretation of these signals. We collected tower‐based remotely sensed data (reflectance‐based vegetation indices and Solar‐Induced Chlorophyll Fluorescence [SIF]), stem radius measurements, gross primary productivity, and environmental conditions in a boreal mixed forest stand. Evaluation of this data set shows a two‐phased spring transition. The first phase is the reactivation of photosynthesis and transpiration in evergreens, marked by an increase in relative SIF, and is triggered by thawed stems, warm air temperatures, and increased available soil moisture. The second phase is a reduction in bulk photoprotective pigments in evergreens, marked by an increase in the Chlorophyll‐Carotenoid Index. Deciduous leaf‐out occurs during this phase, marked by an increase in all remotely sensed metrics. The second phase is controlled by soil thaw. Our results demonstrate that remote sensing metrics can be used to detect specific physiological changes in boreal tree species during the spring transition. The two‐phased transition explains inconsistencies in remote sensing estimates of the timing and drivers of spring recovery. Our results imply that satellite‐based observations will improve by using a combination of vegetation indices and SIF, along with species distribution information.

 

Abstract. For the past decade, observations of carbonyl sulfide (OCS or COS) have been investigated as a proxy for carbon uptake by plants. OCS is destroyed by enzymes that interact with CO₂ during photosynthesis, namely carbonic anhydrase (CA) and RuBisCO, where CA is the more important one. The majority of sources of OCS to the atmosphere are geographically separated from this large plant sink, whereas the sources and sinks of CO₂ are co-located in ecosystems. The drawdown of OCS can therefore be related to the uptake of CO₂ without the added complication of co-located emissions comparable in magnitude. Here we review the state of our understanding of the global OCS cycle and its applications to ecosystem carbon cycle science. OCS uptake is correlated well to plant carbon uptake, especially at the regional scale. OCS can be used in conjunction with other independent measures of ecosystem function, like solar-induced fluorescence and carbon and water isotope studies. More work needs to be done to generate global coverage for OCS observations and to link this powerful atmospheric tracer to systems where fundamental questions concerning the carbon and water cycle remain.