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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Matrix Approach to Land Carbon Cycle Modeling

Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin-up of land carbon cycle models by tens of times. The accelerated spin-up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever-increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assimilation of multiple datasets results in large differences in regional- to global-scale NEE and GPP budgets simulated by a terrestrial biosphere model

In spite of the importance of land ecosystems in offsetting carbon dioxide emissions released by anthropogenic activities into the atmosphere, the spatiotemporal dynamics of terrestrial carbon fluxes remain largely uncertain at regional to global scales. Over the past decade, data assimilation (DA) techniques have grown in importance for improving these fluxes simulated by terrestrial biosphere models (TBMs), by optimizing model parameter values while also pinpointing possible parameterization deficiencies. Although the joint assimilation of multiple data streams is expected to constrain a wider range of model processes, their actual benefits in terms of reduction in model uncertainty are still under-researched, also given the technical challenges. In this study, we investigated with a consistent DA framework and the ORCHIDEE-LMDz TBM–atmosphere model how the assimilation of different combinations of data streams may result in different regional to global carbon budgets. To do so, we performed comprehensive DA experiments where three datasets (in situ measurements of net carbon exchange and latent heat fluxes, spaceborne estimates of the normalized difference vegetation index, and atmospheric CO 2 concentration data measured at stations) were assimilated alone or simultaneously. We thus evaluated their complementarity and usefulness to constrain net and gross C land fluxes. We found that a major challenge in improving the spatial distribution of the land C sinks and sources with atmospheric CO 2 data relates to the correction of the soil carbon imbalance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Overlooked cooling effects of albedo in terrestrial ecosystems

Radiative forcing (RF) resulting from changes in surface albedo is increasingly recognized as a significant driver of global climate change but has not been adequately estimated, including by Intergovernmental Panel on Climate Change (IPCC) assessment reports, compared with other warming agents. Here, we first present the physical foundation for modeling albedo-induced RF and the consequent global warming impact (GWI Δα ). We then highlight the shortcomings of available current databases and methodologies for calculating GWI Δα at multiple temporal scales. There is a clear lack of comprehensive in situ measurements of albedo due to sparse geographic coverage of ground-based stations, whereas estimates from satellites suffer from biases due to the limited frequency of image collection, and estimates from earth system models (ESMs) suffer from very coarse spatial resolution land cover maps and associated albedo values in pre-determined lookup tables. Field measurements of albedo show large differences by ecosystem type and large diurnal and seasonal changes. As indicated from our findings in southwest Michigan, GWI Δα is substantial, exceeding the RF Δα values of IPCC reports. Inclusion of GWI Δα to landowners and carbon credit markets for specific management practices are needed in future policies. We further identify four pressing research priorities: developing a comprehensive albedo database, pinpointing accurate reference sites within managed landscapes, refining algorithms for remote sensing of albedo by integrating geostationary and other orbital satellites, and integrating the GWI Δα component into future ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Surface temperatures reveal the patterns of vegetation water stress and their environmental drivers across the tropical Americas

Vegetation is a key component in the global carbon cycle as it stores ~450 GtC as biomass, and removes about a third of anthropogenic CO2 emissions. However, in some regions, the rate of plant carbon uptake is beginning to slow, largely because of water stress. Here, we develop a new observation-based methodology to diagnose vegetation water stress and link it to environmental drivers. We used the ratio of remotely sensed land surface to near surface atmospheric temperatures (LST/Tair) to represent vegetation water stress, and built regression tree models (random forests) to assess the relationship between LST/Tair and the main environmental drivers of surface energy fluxes in the tropical Americas. We further determined ecosystem traits associated with water stress and surface energy partitioning, pinpointed critical thresholds for water stress, and quantified changes in ecosystem carbon uptake associated with crossing these critical thresholds. Additionally, we found that the top drivers of LST/Tair, explaining over a quarter of its local variability in the study region, are (1) radiation, in 58% of the study region; (2) water supply from precipitation, in 30% of the study region; and (3) atmospheric water demand from vapor pressure deficits (VPD), in 22% of the study region. Regions in which LST/Tair variation is driven by radiation are located in regions of high aboveground biomass or at high elevations, while regions in which LST/Tair is driven by water supply from precipitation or atmospheric demand tend to have low species richness. Carbon uptake by photosynthesis can be reduced by up to 80% in water-limited regions when critical thresholds for precipitation and air dryness are exceeded simultaneously, that is, as compound events. Our results demonstrate that vegetation structure and diversity can be important for regulating surface energy and carbon fluxes over tropical regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Role of Maritime Continent Land Convection on the Mean State and MJO Propagation

The Maritime Continent (MC) region is known as a ‘barrier’ in the life-cycle of the Madden-Julian oscillation (MJO). During boreal winter, the MJO detours the equatorial MC land region southward and propagates through the oceanic region. Also, about half of the MJO events that initiate over the Indian Ocean cease around the MC. The mechanism through which the MC affects MJO propagation, however, has remained unanswered. The current study investigates the MJO-MC interaction with a particular focus on the role of MC land convection. Using a global climate model that simulates both mean climate and MJO realistically, we performed two sensitivity experiments in which updraft plume radius is set to its maximum and minimum value only in the MC land grid points, making convective top deeper and shallower, respectively. In this work, our results show that MC land convection plays a key role in shaping the 3-D climatological moisture distribution around the MC through its local and non-local effects. Shallower and weaker MC land convection results in a steepening of the vertical and meridional mean moisture gradient over the MC region. The opposite is the case when MC land convection becomes deeper and stronger. The MJO’s eastward propagation is enhanced (suppressed) with the steeper (lower) mean moisture gradient. The moist static energy (MSE) budget of the MJO reveals the vertical and meridional advection of the mean MSE by MJO wind anomalies as the key processes that are responsible for the changes in MJO propagation characteristics. Our results pinpoint the critical role of the background moisture gradient on MJO propagation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Out With the Old: Empirical Trends in U.S. Land‐Based Wind Turbine Decommissioning and Repowering

A growing number of wind turbines (WTs) across the globe are now reaching or exceeding their expected service lifetime; WT decommissioning is on the rise. Accordingly, questions pertaining to WT end-of-life have risen in importance in policy and practice. Yet, research on the various factors relating to WT decommissioning is relatively sparse. Moreover, the key assumptions underpinning that prior research (e.g., the lifespan of WTs, characteristics of WTs being decommissioned, and whether the site is repowered with new WTs) have never been empirically tested across a large set of decommissioned WTs. Leveraging a uniquely comprehensive and spatially explicit dataset of decommissioned WTs in the United States, this research analyzes spatial, technological, and temporal trends in WT decommissioning and develops a novel predictive model for WT decommissioning. Our analysis pinpoints more than 12,400 WTs that have been fully decommissioned in the United States., the majority of which have been relatively old (> 30 years) and small (< 200 kW). While a WT's age alone is a good predictor of the likelihood of decommissioning, other factors such as the size of the WT and recent performance are also important and significant predictors. Most sites where decommissioning has occurred have seen subsequent repowering, with repowered plants featuring substantially fewer WTs (−86 on average) and higher rated plant capacity (+62 MW on average). Many existing WTs in the U.S. are approaching the end of their expected life with roughly 7500 being 20 or more years old. Findings can help policymakers and stakeholders begin preparing for this potential wave of future decommissioning and repowering.

Decommissioning / End-of-life↗