The Western US accounts for a significant amount of the forested biomass and carbon uptake within the conterminous United States. Warming and drying climate trends combined with a legacy of fire suppression have left Western forests particularly vulnerable to disturbance from insects, fire and drought mortality. These challenging conditions may significantly weaken this region’s ability to uptake carbon from the atmosphere and warrant continued monitoring. Traditional methods of carbon monitoring are limited by the complex terrain of the Rocky Mountains that lead to complex atmospheric flows coupled with heterogeneous climate and soil conditions. Recently, solar induced fluorescence (SIF) has been found to be a strong indicator of GPP, and with the increased availability of remotely-sensed SIF, provides an opportunity to estimate GPP and ecosystem function across the Western US. Although the SIF-GPP empirical linkage is strong, the mechanistic understanding between SIF and GPP is lacking, and ultimately depends upon changes in leaf chemistry that convert absorbed radiation into photochemistry, heat (via non-photochemical quenching (NPQ)), leaf damage or SIF. Understanding of the mechanistic detail is necessary to fully leverage observed SIF to constrain model estimates of GPP and improve representation of ecosystem processes. Here, we include an improved fluorescence model within CLM 4.5 to simulate seasonal changes in SIF at a sub-alpine forest in Colorado. We find that when the model includes a representation of sustained NPQ the simulated fluorescence is much closer to the seasonal pattern of SIF observed from the GOME-2 satellite platform and a custom tower mounted spectrometer system. We also find that average air temperature may be used as a predictor of sustained NPQ when observations are not available. This relationship to air temperature is promising because it may allow for efficient spatial upscaling of SIF simulations, given widespread availability of temperature data, but not NPQ observations. Further improvements to the fluorescence model should focus upon distinguishing between the impacts of NPQ versus the de-activation of photosystems brought on by high-stress environmental conditions.

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity on the global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. Transpiration and gross primary productivity (GPP) that traditional LSMs simulate are not directly measurable from space and they are inferred from spaceborne observations using assumptions that are inconsistent with those of the LSMs, whereas canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we present the land model developed within the Climate Modeling Alliance (CliMA), which simulates global-scale GPP, transpiration, and hyperspectral canopy radiative transfer (RT). Thus, CliMA Land can predict any vegetation index or outgoing radiance, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given measurement geometry. Even without parameter optimization, the modeled spatial patterns of CliMA Land GPP, SIF, NDVI, EVI, and NIRv correlate significantly with existing observational products. CliMA Land is also very useful in its high temporal resolution, e.g., providing insights into when GPP, SIF, and NIRv diverge. Based on comparisons between models and observations, we propose ways to improve future land modeling regarding data processing and model development.

Governments restricted mobility and effectively shuttered much of the global economy in response to the COVID-19 pandemic. Six San Francisco Bay Area counties were the first region in the United States to issue a “shelter-in-place” order asking non-essential workers to stay home. Here we use CO2 observations from 35 Berkeley Environment, Air-quality and CO2 Network (BEACO2N) nodes and an atmospheric transport model to quantify changes in urban CO2 emissions due to the order. We infer hourly emissions at 900-m spatial resolution for 6 weeks before and 6 weeks during the order. We observe a 28% decrease in anthropogenic CO2 emissions during the order and show this decrease is primarily due to changes in traffic (-44%) with pronounced changes to daily and weekly cycles; non-traffic emissions show small changes (-8%). These findings provide a glimpse into a future with reduced CO2 emissions through electrification of vehicles. (submitted to GRL: 2020-07-24 16:49:19)

Solar-Induced chlorophyll Fluorescence (SIF) has previously been shown to strongly correlate with gross primary productivity (GPP), however this relationship has not yet been quantified for the recently launched TROPOspheric Monitoring Instrument (TROPOMI). Here we use a Gaussian mixture model to develop a parsimonious relationship between SIF from TROPOMI and GPP from flux towers across the conterminous United States (CONUS). The mixture model indicates the SIF-GPP relationship can be characterized by a linear model with two terms. We then estimate GPP across CONUS at 500-m spatial resolution over a 16-day moving window. We find that CONUS GPP varies by less than 4% between 2018 and 2019. However, we observe four extreme precipitation events that induce regional GPP anomalies: drought in west Texas, flooding in the midwestern US, drought in South Dakota, and drought in California. Taken together, these events account for 28% of the year-to-year GPP differences across CONUS.

Terrestrial vegetation is known to be an important sink for carbon dioxide (CO2). However, fluxes to and from vegetation are often not accounted for when studying anthropogenic CO2 emissions in urban areas. This project seeks to quantify urban biogenic fluxes in the Greater Toronto and Hamilton Area located in Southern Ontario, Canada. Toronto is Canada’s most populated city but also has a large amount of green-space, covering approximately 13 % of the city. In addition, vegetation is not evenly distributed throughout the region. We therefore expect biogenic fluxes to play an important role in the spatial patterns of CO2 concentrations and the overall local carbon budget. In order to fully understand biogenic fluxes they can be partitioned into the amount of CO2 sequestered via photosynthesis, gross primary productivity (GPP), and the amount respired by vegetation, ecosystem respiration (Reco). Solar induced chlorophyll fluorescence (SIF) measured from space has been shown to be a valuable proxy for photosynthesis and thus can be used to estimate GPP. Vegetation models, including the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM) and the SIF for Modelling Urban biogenic Fluxes (SMUrF) model, have also been used to estimate both GPP and Reco In this study we compare modelled and SIF-derived biogenic CO2 fluxes at a 500 m by 500 m resolution, to ground-based flux tower measurements in Southern Ontario to determine how well these methods estimate biogenic CO2 fluxes. This study works towards determining the importance of biogenic fluxes in the Greater Toronto and Hamilton Area. Furthermore, the results of this work may inform policy makers and city planners on how urban vegetation affects CO2 concentrations and patterns within cities.