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Abstract The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive “greenness” of these systems. We combine continuous needle‐scale chlorophyll fluorescence measurements with tower‐based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar‐induced chlorophyll fluorescence (SIF) and solar intensity‐normalized SIF (SIF_relative) under snow‐covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near‐infrared vegetation index [NIRv] under 50% snow cover). Needle‐scale fluorescence and photochemistry strongly correlated (r² = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower‐based estimates of LUE_Pand SIF_relativewhich were well correlated across all sites (r² = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data‐driven and process‐based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems. 

Abstract Water stress regulates land‐atmosphere carbon dioxide (CO₂) exchanges in the tropics; however, its role remains poorly characterized due to the confounding roles of radiation, temperature and canopy dynamics. In particular, uncertainty stems from the relative roles of plant‐available water (supply) and atmospheric water vapor deficit (demand) as mechanistic drivers of photosynthetic carbon (C) uptake variability. Using satellite measurements of gravity, CO₂and fluorescence to constrain a mechanistic carbon‐water cycle model from 2001 to 2018, we found that the interannual variability (IAV) of water stress on photosynthetic C uptake was 52% greater than the combined effects of other factors. Surprisingly, the dominance of water stress on C uptake IAV was greater in the wet tropics (94%) than in the dry tropics (26%). Plant‐available water supply and atmospheric demand both contributed to the IAV of water stress on photosynthetic C uptake across the tropics, but the IAV of demand effects was 21% greater than the IAV of supply effects (33% greater in the wet tropics and 6% greater in the dry tropics). We found that the IAV of water stress on C uptake was 24% greater than the IAV of the combination of other factors in the net land‐atmosphere C sink in the whole tropics, 26% greater in the wet tropics, and 7% greater in the dry tropics. Given the recent trends in tropical precipitation and atmospheric humidity, our findings indicate that water stress——from both supply and demand——will likely dominate the climate response of land C sink across tropical ecosystems in the coming decades. 

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 The timing and progression of the spring thaw transition in high northern latitudes (HNL) coincides with warmer temperatures and landscape thawing, promoting increased soil moisture and growing season onset of gross primary productivity (GPP), heterotrophic respiration (HR), and evapotranspiration (ET). However, the relative order and spatial pattern of these events is uncertain due to vast size and remoteness of the HNL. We utilized satellite environmental data records (EDRs) derived from complementary passive microwave and optical sensors to assess the progression of spring transition events across Alaska and Northern Canada from 2016 to 2020. Selected EDRs included land surface and soil freeze‐thaw status, solar‐induced chlorophyll fluorescence (SIF) signifying canopy photosynthesis, root zone soil moisture (RZSM), and GPP, HR, and ET as indicators of ecosystem carbon and water‐energy fluxes. The EDR spring transition maps showed thawing as a precursor to rising RZSM and growing season onset. Thaw timing was closely associated with ecosystem activation from winter dormancy, including seasonal increases in SIF, GPP, and ET. The HR onset occurred closer to soil thawing and prior to GPP activation, reducing spring carbon (CO₂) sink potential. The mean duration of the spring transition spanned ∼6 ± 1.5 weeks between initial and final onset events. Spring thaw timing and maximum RZSM were closely related to active layer thickness in HNL permafrost zones, with deeper active layers showing generally earlier thawing and greater RZSM. Our results confirm the utility of combined satellite EDRs for regional monitoring and better understanding of the complexity of the spring transition. 

Abstract. We introduce a transformed isentropic coordinate Mθe,defined as the dry air mass under a given equivalent potential temperaturesurface (θe) within a hemisphere. Like θe, thecoordinate Mθe follows the synoptic distortions of theatmosphere but, unlike θe, has a nearly fixedrelationship with latitude and altitude over the seasonal cycle. Calculationof Mθe is straightforward from meteorological fields. Usingobservations from the recent HIAPER Pole-to-Pole Observations (HIPPO) and Atmospheric Tomography Mission (ATom) airborne campaigns, we map theCO2 seasonal cycle as a function of pressure and Mθe, whereMθe is thereby effectively used as an alternative tolatitude. We show that the CO2 seasonal cycles are more constantas a function of pressure using Mθe as the horizontal coordinatecompared to latitude. Furthermore, short-term variability inCO2 relative to the mean seasonal cycle is also smaller when the dataare organized by Mθe and pressure than when organized by latitudeand pressure. We also present a method using Mθe to computemass-weighted averages of CO2 on a hemispheric scale. Using this methodwith the same airborne data and applying corrections for limited coverage,we resolve the average CO2 seasonal cycle in the Northern Hemisphere(mass-weighted tropospheric climatological average for 2009–2018), yieldingan amplitude of 7.8 ± 0.14 ppm and a downward zero-crossing on Julianday 173 ± 6.1 (i.e., late June). Mθe may be similarlyuseful for mapping the distribution and computing inventories of anylong-lived chemical tracer. 

Abstract. The flow of carbon through terrestrial ecosystems and the response toclimate are critical but highly uncertain processes in the global carboncycle. However, with a rapidly expanding array of in situ and satellitedata, there is an opportunity to improve our mechanistic understanding ofthe carbon (C) cycle's response to land use and climate change. Uncertaintyin temperature limitation on productivity poses a significant challenge topredicting the response of ecosystem carbon fluxes to a changing climate.Here we diagnose and quantitatively resolve environmental limitations onthe growing-season onset of gross primary production (GPP) using nearly 2 decades of meteorological and C flux data (2000–2018) at a subalpineevergreen forest in Colorado, USA. We implement the CARbonDAta-MOdel fraMework (CARDAMOM) model–datafusion network to resolve the temperature sensitivity of spring GPP. Tocapture a GPP temperature limitation – a critical component of the integratedsensitivity of GPP to temperature – we introduced a cold-temperature scalingfunction in CARDAMOM to regulate photosynthetic productivity. We found thatGPP 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 improvedthe 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 dataassimilation training period (r=0.88). While cold-temperaturelimitation has an important influence on spring GPP, it does not have asignificant impact on integrated growing-season GPP, revealing that otherenvironmental controls, such as precipitation, play a more important role inannual productivity. This study highlights growing-season onset temperatureas a key limiting factor for spring growth in winter-dormant evergreenforests, which is critical in understanding future responses to climatechange.