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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Adjusting solar-induced fluorescence to nadir-viewing provides a better proxy for GPP

Solar-induced fluorescence (SIF) provides key information for inferring terrestrial gross primary productivity (GPP). However, there is little research on analyzing the performance of nadir-adjusted SIF (SIF nadir ) on GPP, compared to total SIF emitted by all leaves (SIF total ) for reducing the viewing angle effects and estimating GPP. Besides, there have been controversial opinions on whether SIF total is better correlated to GPP than raw SIF observations (SIF obs ). Here we systematically analyzed the relationship between raw/nadir/total SIF (i.e., SIF obs , SIF nadir and SIF total ) and GPP and investigated the underlying mechanism, using multi-angular field measurements and eddy covariance data in wheat and corn crops at sub-daily scale. We further compared the performance of SIF obs , SIF nadir and SIF total in estimating GPP using the TROPOspheric Monitoring Instrument (TROPOMI) SIF and concurrent AmeriFlux measurements at daily scale. Furthermore, the results indicate that diurnal SIF nadir has stronger correlations to GPP than SIF obs for field measurements, with the increase of mean coefficient of determination (R 2 ) by 0.05–0.07 for far-red band and 0.11–0.20 for red band. SIF nadir shows comparable performance with SIF total for both far-red and red bands. Although the viewing angle effects dominate the difference in estimating GPP between SIF nadir and SIF obs , the correlation between light use efficiency (LUE) and fesc further determines the different performance of SIF nadir and SIF total in estimating GPP. TROPOMI-based analysis further confirms that SIF nadir overall has higher correlations to AmeriFlux GPP than SIF obs for different plant functional types and shows similar performance with SIF total . Compared to SIF total , the estimation of SIF nadir independent of viewing angle effects does not require any canopy structure parameters, and thus offers promising potential for reliably estimating regional and global GPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Comparison of total emitted solar-induced chlorophyll fluorescence (SIF) and top-of-canopy (TOC) SIF in estimating photosynthesis

We report many studies have shown that solar-induced fluorescence (SIF) has a good potential to predict gross primary production (GPP) of vegetation. What we measured by remote sensing or near-surface platforms is top-of-canopy (TOC) SIF (SIF toc ), which is not necessarily equal or proportional to total emitted SIF (SIF tot ) from the entire canopy due to the (re)absorption and scattering effects. However, photosynthesis, the process that plants use to fix carbon from the atmosphere, occurs at the entire vertical canopy. Here, by using the recollision theory, we calculated SIF tot at 760 nm from the measured SIF toc , hyperspectral reflectance (R), canopy interception (i) and leaf albedo (ω l ). Among them, both SIF toc and R can be obtained from concurrent TOC spectral measurements; i and ω l in the near-infrared region can be estimated from the open access datasets with a good accuracy. Our result confirms that the measured SIF toc only accounts for a small fraction of SIF tot : the above-the-canopy sensor can only “see” on average 22.9% of SIF tot at Harvard Forest. SIF tot has the following advantages over SIF toc in estimating GPP: (1) SIF tot improves the diurnal estimate of canopy GPP, especially capable to capture the midday depression of photosynthesis which may cause the large discrepancies between SIF toc and GPP on a diurnal basis, (2) SIF tot produces a stronger correlation with GPP from plants with complex canopy structure or under sky conditions with more diffuse irradiance, and (3) the SIF tot -GPP relationship shows a stronger resilience to environmental stresses. The fluorescence escape ratio (f esc ), the ratio between SIF toc multiplied by π and SIF tot , is mostly determined by the sun-canopy-sensor geometry and leaf inclination distribution. The effect of LAI and the leaf chlorophyll concentration on f esc is marginal at the 760 nm wavelength. Our results suggest that converting SIF toc into SIF tot provides a better tool to understand and estimate GPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Can upscaling ground nadir SIF to eddy covariance footprint improve the relationship between SIF and GPP in croplands?

Ground solar-induced chlorophyll fluorescence (SIF) is important for the mechanistic understanding of the dynamics of vegetation gross primary production (GPP) at fine spatiotemporal scales. However, eddy covariance (EC) observations generally cover larger footprint areas than ground SIF observations (a bare fiber with nadir), and this footprint mismatch between nadir SIF and GPP could complicate the canopy SIF-GPP relationships. Here, we upscaled nadir SIF observations to EC footprint and investigated the change in SIF-GPP relationships after the upscaling in cropland. We included 13 site-years data in our study, with seven site-years corn, four site-years soybeans, and two site-years miscanthus, all located in the US Corn Belt. All sites’ crop nadir SIF observations collected from the automated FluoSpec2 system (a hemispheric-nadir system) were upscaled to the GPP footprint-based SIF using vegetation indices (VIs) calculated from high spatiotemporal satellite reflectance data. We found that SIF-GPP relationships were not substantially changed after upscaling nadir SIF to GPP footprint at our crop sites planted with corn, soybean, and miscanthus, with R 2 change after the upscaling ranging from -0.007 to 0.051 and root mean square error (RMSE) difference from -0.658 to 0.095 umol m –2 s –1 relative to original nadir SIF-GPP relationships across all the site-years. The variation of the SIF-GPP relationship within each species across different site-years was similar between the original nadir SIF and upscaled SIF. Different VIs, EC footprint models, and satellite data led to marginal differences in the SIF-GPP relationships when upscaling nadir SIF to EC footprint. Furthermore, our study provided a methodological framework to correct this spatial mismatch between ground nadir SIF and GPP observations for croplands and potentially for other ecosystems. Our results also demonstrated that the spatial mismatch between ground nadir SIF and GPP might not significantly affect the SIF-GPP relationship in cropland that are largely homogeneous.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating ecosystem water use efficiency and recovery dynamics during flash droughts: insights from observations and model simulations

Flash droughts (FD), rapidly emerging in a warming future, disrupt ecosystems, agriculture, and water security. Ecosystem water use efficiency (WUE), the ratio of gross primary production (GPP) to actual evapotranspiration (AET), balances carbon assimilation and water loss. FD rapidly disrupts this balance, making WUE critical for assessing plant stress and recovery. Here, this study investigates the dynamics of landscape-scale WUE, and the components of GPP and AET under FD utilizing both observed data from the Missouri Ozark AmeriFlux site (US-MOz) and version 2 of the U.S. Department of Energy’s Earth, Energy, Exascale System Model (E3SM) Land Model (ELMv2). Observations and simulations reveal GPP as dominant for WUE during earlier FD events (2005, 2007, 2012), shifting to AET in recent events (2014, 2018). This agreement indicates that the ELM can capture the shifting dynamics of GPP and AET in regulating WUE under FD conditions. However, the ELM systematically underestimates both GPP and AET and does so in a manner that does not preserve their ratio. As a result, WUE is also underestimated, suggesting that GPP is more strongly underestimated than AET. Furthermore, the ELM also underestimates the speed of GPP recovery, producing an artificially prolonged GPP recovery time following FD events. Observed environmental drivers such as vapor pressure deficit (VPD), soil moisture (SM), and predawn leaf water potential (PLWP) effectively predict WUE, but ELM primarily highlights SM, underestimating VPD’s role. This study demonstrates that relying solely on soil moisture fails to capture the rapid hydraulic recovery observed in PLWP, underscoring the necessity of integrating plant hydraulics into land surface models to improve flash drought predictability.

Evapotranspiration↗
DOE OSTI2024

Freshwater Biogeochemical Hotspots: High Primary Production and Ecosystem Respiration in Shallow Waterbodies

Abstract Ponds, wetlands, and shallow lakes (collectively “shallow waterbodies”) are among the most biogeochemically active freshwater ecosystems. Measurements of gross primary production (GPP), respiration (R), and net ecosystem production (NEP) are rare in shallow waterbodies compared to larger and deeper lakes, which can bias our understanding of lentic ecosystem processes. In this study, we calculated GPP, R, and NEP in 26 small, shallow waterbodies across temperate North America and Europe. We observed high rates of GPP (mean 8.4 g O 2 m −3 d −1 ) and R (mean −9.1 g O 2 m −3 d −1 ), while NEP varied from net heterotrophic to autotrophic. Metabolism rates were affected by depth and aquatic vegetation cover, and the shallowest waterbodies had the highest GPP, R, and the most variable NEP. The shallow waterbodies from this study had considerably higher metabolism rates compared to deeper lakes, stressing the importance of these systems as highly productive biogeochemical hotspots.

Geology↗
NASA NTRS2020

Continuity of A Global, Satellite-Based Terrestrial Primary Productivity Dataset in the VIIRS Era Achieved With Model-Data Fusion

The NASA Terra and Aqua satellites have been successfully operating for over two decades and have far exceeded their original 5‐year design life. However, the era of NASA’s Earth Observing System (EOS) may be coming to a close as early as 2023. We conducted a comprehensive calibration and validation of the MODIS MOD17 product [1,2] and the potential for continuity of multi‐decadal ecosystem gross primary productivity (GPP) and annual net primary productivity (NPP), using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensors aboard Suomi NPP and NOAA‐20. We combined an 18‐year record of eddy covariance flux tower measurements with hundreds of field measurements of NPP from the Oak Ridge National Laboratories Multi‐Biome collection to benchmark MODIS MOD17 Collection 6.1 (C61) and to develop the first terrestrial productivity estimates from VIIRS. Plant traits from the literature and the global TRY database [3,4] provide strong priors for identifying model parameters in a Bayesian model‐data fusion. As MODIS‐like observations are still needed for global environmental applications, the new VIIRS VNP17 product has the potential to extend these continuous estimates of global, terrestrial primary productivity beyond 2030.

K. Arthur Endsley↗
DOE OSTI2021

Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam G CC (70.4 days, p adj < 0.0001) and EC GPP (79.6 days, p adj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam G CC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m –2 yr –1 . Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Emerging signals of declining forest resilience under climate change

Forest ecosystems depend on their capacity to withstand and recover from natural and anthropogenic perturbations (that is, their resilience). Experimental evidence of sudden increases in tree mortality is raising concerns about variation in forest resilience, yet little is known about how it is evolving in response to climate change. Here we integrate satellite-based vegetation indices with machine learning to show how forest resilience, quantified in terms of critical slowing down indicators, has changed during the period 2000–2020. We show that tropical, arid and temperate forests are experiencing a significant decline in resilience, probably related to increased water limitations and climate variability. By contrast, boreal forests show divergent local patterns with an average increasing trend in resilience, probably benefiting from warming and CO 2 fertilization, which may outweigh the adverse effects of climate change. These patterns emerge consistently in both managed and intact forests, corroborating the existence of common large-scale climate drivers. Reductions in resilience are statistically linked to abrupt declines in forest primary productivity, occurring in response to slow drifting towards a critical resilience threshold. Approximately 23% of intact undisturbed forests, corresponding to 3.32 Pg C of gross primary productivity, have already reached a critical threshold and are experiencing a further degradation in resilience. Together, these signals reveal a widespread decline in the capacity of forests to withstand perturbation that should be accounted for in the design of land-based mitigation and adaptation plans

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

Mapping in situ eddy covariance measurements of terrestrial land–atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO 2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at −5.75 ± 0.33 Pg C yr −1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C yr −1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7×10 3 ± 0.9×10 3 km 3 globally for the years 2001–2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Nelson, Jacob A.↗
NASA NTRS2017

Angular Normalization of GOME-2 Sun-Induced Chlorophyll Fluorescence Observation as a Better Proxy of Vegetation Productivity

Sun-induced chlorophyll fluorescence (SIF) has been regarded as a promising proxy for gross primary productivity (GPP) over land. Considerable uncertainties in GPP estimation using remotely sensed SIF exist due to variations in the Sun-satellite view observation geometry that could induce unwanted variations in SIF observation. In this study, we normalize the far-red Global Ozone Monitoring Experiment-2 SIF observations on sunny days to hot spot direction (SIF(h)) to represent sunlit leaves and compute a weighted sum of SIF (SIF(t)) from sunlit and shaded leaves to represent the canopy. We found that SIF(h) is better correlated with sunlit GPP simulated by a process-based ecosystem model and SIF(t) is better correlated with the simulated total GPP than the original SIF observations. The coefficient of determination (R(exp 2)) are increased by 0.04 +/- 0.03, and 0.07 +/- 0.04 on a global average using SIF(h) and SIF(t), respectively. The most significant increases of the R(exp 2) (0.09 +/- 0.04 for SIF(t) and 0.05 +/- 0.03 for SIF(h)) appear in deciduous broadleaf forests.

Australia↗
NASA NTRS2015

Contribution of Phenological and Physiological Variations on Northern Vegetation Productivity Changes over Last Three Decades

Plant phenology and maximum photosynthetic state determine spatiotemporal variability of gross primary productivity (GPP) of vegetation. Recent warming induced impacts accelerate shifts of phenology and physiological status over Northern vegetated land. Thus, understanding and quantifying these changes are very important. Here, we investigate 1) how vegetation phenology and physiological status (maximum photosynthesis) are evolved over last three decades and 2) how such components (phenology and physiological status) contribute on inter-annual variation of the GPP during the last three decades. We utilized both long-term remotely sensed (GIMMS (Global Inventory Modeling and Mapping Studies), NDVI3g (Normalized Difference Vegetation Index 3rd generation) and MODIS (Moderate Resolution Imaging Spectroradiometer)) to extract larger scale phenology metrics (growing season start, end and duration); and productivity (i.e., growing season integrated vegetation index, GSIVI) to answer these questions. For evaluation purpose, we also introduced field-measured phenology and productivity datasets (e.g., FLUXNET) and possible remotely-sensed and modeled metrics at continental and regional scales. From this investigation, we found that onset of the growing season has advanced by 1.61 days per decade and the growing season end has delayed by 0.67 days per decade over the circumpolar region. This asymmetric extension of growing season results in a longer growing-season trend (2.96 days per decade) and widespread increasing vegetation-productivity trend (2.96 GSIVI per decade) over Northern land. However, the regionally-diverged phenology shift and maximum photosynthetic state contribute differently characterized productivity, inter-annual variability and trend. We quantified that about 50 percent, 13 percent and 6.5 percent of Northern land's inter-annual variability are dominantly controlled by the onset of the growing season, the end of the growing season and the maximum photosynthetic state, respectively. Productivity characterization over the other approximately 30 percent region has been driven by these co-dominant drivers. Our study clearly shows that regionally different contribution of phenological and physiological components on characterizing vegetation production over the last three decades.

productivity↗
NASA NTRS2009

Using LIDAR and Quickbird Data to Model Plant Production and Quantify Uncertainties Associated with Wetland Detection and Land Cover Generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Cook, Bruce D.↗
DOE OSTI2021

The response of the Amazon ecosystem to the photosynthetically active radiation fields: integrating impacts of biomass burning aerosol and clouds in the NASA GEOS Earth system model

The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon's clouds in tempering this impact. The study focuses on a 7-year period (2010–2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity – called here the aerosol diffuse radiation fertilization effect – is found to increase Amazonian gross primary production (GPP) by 2.6 % via a 3.8 % increase in diffuse PAR (DFPAR) despite a 5.4 % decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9 % (occurring in August 2010). Consequently, the net primary production (NPP) in the Amazon is increased by 1.5 %, or ~92 Tg C yr -1 – equivalent to ~37 % of the average carbon lost due to Amazon fires over the 7 years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ~0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2021

The Response of the Amazon Ecosystem to the Photosynthetically Active Radiation Fields: Integrating Impacts of Biomass Burning Aerosol and Clouds in the NASA GEOS Earth System Model

The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon’s clouds in tempering this impact. The study focuses on a seven-year period (2010-2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity—called here the aerosol diffuse radiation fertilization effect —is found to increase Amazonian Gross Primary Production (GPP) by 2.6% via a 3.8% increase in diffuse PAR (DFPAR) despite a 5.4% decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9% (occurring in August 2010). Consequently, the net primary production (NPP) in Amazon is increased by 1.5%, or ~92 Tg C a-1– equivalent to ~37% of the average carbon lost due to Amazon fires over the seven years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ~0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.

biomass burning aerosols on ecosystem productivity↗
DOE OSTI2025

Variational AutoEncoders Reveal Intensifying GPP Extremes in Continental United States based on CESM2 Simulations

Climate extremes significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under SSP5-8.5 scenario. The VAE architecture employs three dense layers and a latent space with input sequence length of 12 months, training on normalized GPP time series to reconstruct the GPP and identify anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE consistently producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. This research demonstrates the potential of deep learning approaches for extremes detection and provides a foundation for improved understanding of future carbon cycle risks under future conditions.

Sharma, Bharat [ORNL] (ORCID:0000000266982487)↗
DOE OSTI2023

Evaluating Northern Hemisphere Growing Season Net Carbon Flux in Climate Models Using Aircraft Observations

Understanding terrestrial ecosystems and their response to anthropogenic climate change requires quantification of land-atmosphere carbon exchange. However, top-down and bottom-up estimates of large-scale land-atmosphere fluxes, including the northern extratropical growing season net flux (GSNF), show significant discrepancies. We developed a data-driven metric for the GSNF using atmospheric carbon dioxide concentration observations collected during the High-Performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations and Atmospheric Tomography Mission flight campaigns. This aircraft-derived metric is bias-corrected using three independent atmospheric inversion systems. We estimate the northern extratropical GSNF to be 5.7 ± 0.3 Pg C and use it to evaluate net biosphere productivity from the Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. While the model-to-model spread in the GSNF has decreased in the CMIP6 models relative to that of the CMIP5 models, there is still disagreement on the magnitude and timing of seasonal carbon uptake with most models underestimating the GSNF and overestimating the length of the growing season relative to the observations. We also use an emergent constraint approach to estimate annual northern extratropical gross primary productivity to be 56 ± 17 Pg C, heterotrophic respiration to be 25 ± 13 Pg C, and net primary productivity to be 28 ± 12 Pg C. The flux inferred from these aircraft observations provides an additional constraint on large-scale gross fluxes in prognostic Earth system models that may ultimately improve our ability to accurately predict carbon-climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improved dryland carbon flux predictions with explicit consideration of water-carbon coupling

Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Despite their importance, dryland carbon dynamics are not well-characterized by current models. Here, we present DryFlux, an upscaled product built on a dense network of eddy covariance sites in the North American Southwest. To estimate dryland gross primary productivity, we fuse in situ fluxes with remote sensing and meteorological observations using machine learning. DryFlux explicitly accounts for intra-annual variation in water availability, and accurately predicts interannual and seasonal variability in carbon uptake. Applying DryFlux globally indicates existing products may underestimate impacts of large-scale climate patterns on the interannual variability of dryland carbon uptake. We anticipate DryFlux will be an improved benchmark for earth system models in drylands, and prompt a more sensitive accounting of water limitation on the carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Evaluating Approaches Relating Ecosystem Productivity with Desis Spectral Information

Data from the DLR Earth Sensing Imaging Spectrometer (DESIS), mounted on the International Space Station (ISS), were used to develop and test algorithms for remotely retrieving ecosystem productivity. Twenty DESIS images were used from three widely separated forested study sites representing deciduous and conifer forests. Gross primary production (GPP) values from eddy covariance flux towers at the sites were matched with DESIS spectral reflectances collected on the same days. Multiple algorithms were successful relating spectral reflectance with GPP, including: spectral vegetation indices (SVI) sensitive to chlorophyll content, SVI used in a photosynthetic light-use efficiency model framework, spectral shape characteristics through spectral derivatives and absorption feature analysis, and statistical models leading to multiband hyperspectral indices from partial least squares regression. Successful algorithms were able to achieve R2 better than 0.7 using a diverse set of observations combining data from different sites from multiple years and at multiple times during the year. The demonstrated robustness of the algorithms provides some confidence in using DESIS imagery to map spatial patterns of GPP.

K F Huemmrich↗