Accurate representation of the interplay between ecosystems and climate in Earth system models (ESMs) is important for understanding and predicting changes in carbon, water, and energy cycles. The traditional land components of ESMs often rely on the categorization of plant functional types (PFTs), a method that may overlook the ecological variations within plant groups. Using the CliMA Land model with surface hyperspectral data primarily sourced from AVIRIS-NG (Airborne Visible/Infrared Imaging Spectrometer-Next Generation) during the NASA's SHIFT (Surface Biology and Geology High-Frequency Time Series) campaign, we derive leaf biogeochemical traits, and compare a trait-based land modeling approach to the commonly used PFT one. Our findings reveal significant spatial and temporal variations in traits and other related optical and photosynthetic parameters, not only within the same PFT but also across different seasons. Validation against TROPOMI Solar-Induced Fluorescence (SIF) at 740nm shows that the trait-based approach enhanced model accuracy due to better representation of phenology and spatial heterogeneity. Our findings highlight the limitations of traditional PFTbased approaches in ESMs and underscore the importance of incorporating detailed, trait-based data for a more accurate and comprehensive understanding of ecosystem functions.

Diagnosing land-atmosphere fluxes of carbon-dioxide (CO$_2$) and methane (CH$_4$), is essential for evaluating carbon-climate feedbacks. Greenhouse gas satellite missions aim to fill data gaps in regions like the humid tropics, but obtain very few valid measurements due to cloud contamination. We examined data yields from the Orbiting Carbon Observatory alongside Sentinel 2 cloud statistics. We find that the main contribution to low data yields are frequent shallow cumulus clouds. In the Amazon, the success rate in obtaining valid measurements vary from 0.1\% to 1.0\%. By far the lowest yields occur in the wet season, consistent with Sentinel 2 cloud patterns. We find that increasing the spatial resolution of observations to $\sim$200\,m would increase yields by 2-3 orders of magnitude, and allow regular measurements in the wet season. Thus, the key effective tropical greenhouse gas observations lies in regularly acquiring high-spatial resolution data, rather than more frequent low-resolution measurements.

Recently, more advanced synchronous global-scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO-2) solar-induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the simulations of both water and carbon cycles in land surface modeling. This study introduces a mechanistic representation of SIF to the Simplified Simple Biosphere Model version 4 (SSiB4) coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID). This newly developed model with the observed satellite data indicates that introducing dynamic processes can lead to substantial improvement in global carbon flux simulation. In the SSiB4/TRIFFID/SIF, four critical soil and vegetation parameters–B parameter, soil hydraulic conductivity at saturation (Ks), wilting point, and maximum Rubisco carboxylation rate (Vmax)–were identified through numerical sensitivity experiments. Among the four parameters, the B parameter has the most significant effects on both soil moisture and SIF simulations. With the optimized B parameter, both soil moisture and SIF simulations were improved substantially, with especially significant improvement for shrubs. The Ks and wilting point also affect both soil moisture and SIF but with reduced magnitude. The Vmax directly affects photosynthesis, and its modification can substantially improve the SIF simulation of needleleaf trees and C3 grasses. With all four calibrated parameters based on SMAP L3 and OCO-2 data, the root-mean-squared error (RMSE) of soil moisture and SIF simulations decreased from 0.076 to 0.063 m3/m3 and from 0.143 to 0.117 W/m2/μm/sr, respectively.

Vegetation water content (VWC) plays a key role in transpiration, plant mortality, and wildfire risk. Although land surface models now often contain plant hydraulics schemes, there are few direct VWC measurements to constrain these models at global scale. One proposed solution to this data gap is passive microwave remote sensing, which is sensitive to temporal changes in VWC. Here, we test that approach by using synthetic microwave observations to constrain VWC and surface soil moisture within the CliMA Land model. We further investigate the possible utility of sub-daily observations of VWC, which could be obtained through a satellite in geostationary orbit or combinations of multiple satellites. These high-temporal-resolution observations could allow for improved determination of ecosystem parameters, carbon and water fluxes, and subsurface hydraulics, relative to the currently available twice-daily sun-synchronous observational patterns. We find that incorporating observations at four different times in the diurnal cycle (such as could be available from two sun-synchronous satellites) provides a significantly better constraint on water and carbon fluxes than twice-daily observations do. For example, the root mean square errors (RMSE) of projected evapotranspiration and gross primary productivity during drought periods was reduced by approximately 40%, when using four-times-daily relative to twice-daily observations. Adding hourly observations of the entire diurnal cycle did not further improve the inferred parameters and fluxes. Our comparison of observational strategies may be informative in the design of future satellite missions to study plant hydraulics, as well as when using existing remotely sensed data to study vegetation water stress response.

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.

Solar-Induced Chlorophyll Fluorescence (SIF) is a powerful proxy for gross primary productivity (GPP) in Boreal ecosystems. However, SIF and GPP are fundamentally different quantities that describe distinct, but related, physiological processes. Recent work has highlighted non-linearities between SIF and GPP at finer spatial (leaf- to canopy- level) and temporal (half-hourly) scales. Therefore, questions have arisen about when, where, and why SIF is a good proxy for GPP and what the potential sources for divergence between the two are. The goal of this study is to answer two specific questions: 1) At what temporal scale is SIF a good proxy for GPP and 2) What are the predominant physical and ecophysiological drivers of nonlinearity between SIF and GPP in boreal ecosystems? We collected tower-based measurements of SIF (and other common vegetation indices) with PhotoSpec (a custom spectrometer system) and eddy-covariance GPP data at a 30-minute resolution at the Southern Old Black Spruce Site (SOBS) in Saskatchewan, CA. We applied a combination of statistical and machine learning approaches to disentangle the influence of structural/illumination effects and ecophysiological variations on the SIF signal. Our results show that at a high temporal resolution (half-hourly), SIF and GPP are predominantly dependent on photosynthetically active radiation (PAR). Therefore, the non-linear light response of GPP drives non-linearity between SIF and GPP. Additionally, canopy structure and illumination effects become important to the SIF signal at high temporal resolutions. At the seasonal timescale, SIF and GPP exhibit co-varying responses to PAR, even when accounting for changes in canopy structure. We attribute changes in the light responses of SIF and GPP to sustained photoprotection over winter which co-varies with changes in temperature. Finally, we show that the relationship between SIF and GPP has a seasonal dependence caused by small differences between the light use efficiencies of fluorescence and photosynthesis. Accounting for this seasonally variable relationship will improve the use of SIF as a proxy for GPP.

Since the 1960s, carbon cycling in the high-latitude northern forest (HLNF) has experienced dramatic changes: most of the forest is greening and net carbon uptake from the atmosphere has increased. During the same time period, the COseasonal cycle amplitude (SCA) has almost doubled. Disentangling complex processes that drive these changes has been challenging. In this study, we substitute spatial sensitivity to temperature for time to quantify the impact of temperature increase on Gross Primary Production (GPP), total ecosystem respiration (TER), the fraction of Photosynthetic Active Radiation (fPAR), and the resulted contribution of these changes in amplifying the COSCA over the HLNF since 1960s. We use the spatial heterogeneity of GPP inferred from solar-induced chlorophyll Fluorescence in combination with net ecosystem exchange (NEE) inferred from column COobservations made between 2015 and 2017 from NASA’s Orbiting Carbon Observatory -2. We find that three quarters of the spatial variations in GPP and in the fPAR absorbed by the HLNF can be explained by the spatial variation in the growing season mean temperature (GSMT). The long term hindcast captures both the magnitude and spatial variability of the trends in observed fPAR. We estimate that between 1960 and 2010, the increase in GSMT enhanced both GPP and the SCA of NEE by ~20%. The calculated enhancement of NEE due to increase in GSMT contributes 56–72% of the trend in the CO SCA at high latitudes, much larger than simulations by most biogeochemical models.

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.