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DOE OSTI2024

Growth, mortality, wood density, biomass data from BIONTE inventories in Manaus, Brazil

BIONTE (BIOmass and NuTrient Experiment) is a selective logging experiment established at the Experimental Station of Tropical Forestry (EEST, aka “ZF2”) field research station in the mid 1980s in the central Amazon (Higuchi et al. 1997, Amaral et al. 2019). Led by the National Institute for Amazon Research (INPA) in Brazil, the project aimed at assessing the effects of logging intensity on forest dynamics and enabling the creation of a model of forest management for the Central Amazon. The experiment included three levels of increasing selective logging intensity and controls, with 1 hectare sample plots (12 total) located at the center of 4 hectare treatment plots. The site’s Köppen classification is tropical rainforest (Af), characterized by high temperatures and humidity, with mean annual temperatures around 27 ℃ and mean annual precipitation around 2200 mm of rain. The vegetation has a high floristic diversity, the soils of the region are poor in nutrients, and the topography is characterized by plateaus (where BIONTE is located), and also valley bottoms and slopes. The inventory (growth and mortality) and biomass data included here covers the 1990 to 2019 period, with wood density being averaged from existing datasets. This dataset includes a data file in .csv file format and a .txt file, BIONTE_mortality-rates_headers.txt, that provides descriptions for the data file headers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

AmeriFlux FLUXNET-1F CL-SDF Senda Darwin Forest

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CL-SDF Senda Darwin Forest. This is the FLUXNET version of the carbon flux data for the site CL-SDF Senda Darwin Forest produced by applying the standard ONEFlux (1F) software. Site Description - Located at the Senda Darwin Biological Station, in a old-growth rainforest. Locally named as Nordpatagonian type of forest. The station is 15 km east of Ancud, in the Chiloé Island, close to the Huicha river.

Perez-Quezada, Jorge↗

DOE OSTI2025

AmeriFlux CL-ACF Alerce Costero Forest

This is the AmeriFlux version of the carbon flux data for the site CL-ACF Alerce Costero Forest. Site Description - Tower located in an old-growth coniferous rainforest (~300 years) at the Alerce Costero National Park. The forest is locally known as Alerce type. More information at: https://jbarichivich.github.io/page_acos.html

Lara, Antonio [Universidad Austral de Chile]↗

DOE OSTI2025

AmeriFlux FLUXNET-1F CL-SDP Senda Darwin Peatland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CL-SDP Senda Darwin Peatland. This is the FLUXNET version of the carbon flux data for the site CL-SDP Senda Darwin Peatland produced by applying the standard ONEFlux (1F) software. Site Description - Located at the Senda Darwin Biological Station, in a old-growth rainforest. Locally named "pomponal" from the common name of Sphagnum, is an anthropogenic peatland, formed after the forest is cut or a forest fire. The station is 15 km east of Ancud, in the Chiloé Island, close to the Huicha river.

Perez-Quezada, Jorge [University of Chile]↗

DOE OSTI2025

AmeriFlux FLUXNET-1F CL-ACF Alerce Costero Forest

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CL-ACF Alerce Costero Forest. This is the FLUXNET version of the carbon flux data for the site CL-ACF Alerce Costero Forest produced by applying the standard ONEFlux (1F) software. Site Description - Tower located in an old-growth coniferous rainforest (~300 years) at the Alerce Costero National Park. The forest is locally known as Alerce type. More information at: https://jbarichivich.github.io/page_acos.html

Lara, Antonio [Universidad Austral de Chile]↗

DOE OSTI2025

AmeriFlux FLUXNET-1F US-xPU NEON Pu'u Maka'ala Natural Area Reserve (PUUM)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xPU NEON Pu'u Maka'ala Natural Area Reserve (PUUM). This is the FLUXNET version of the carbon flux data for the site US-xPU NEON Pu'u Maka'ala Natural Area Reserve (PUUM) produced by applying the standard ONEFlux (1F) software. Site Description - NEON's PUUM field site is located in the Pu'u Maka'ala Natural Area Reserve (NAR) on the eastern side of Hawaii’s “Big Island,” managed by the Hawaii Division of Forestry and Wildlife (DOFAW). More than 18,000 acres in size, the NAR is home to a rainforest with many native species, some of them endangered. It was established to protect some of the Big Island’s best wet native forest and unique geologic features.

Network), NEON (National Ecological Observatory [N↗

DOE OSTI2024

CLimate Impact: Determining Etiology thRough pAthways (CLDERA)

Climate impacts have broad economic, health, political, and national security ramifications. Societally relevant impacts are typically farther downstream, are the product of multiple interacting processes, and can arise over small regions and timeframes because their sources are short-term and localized. Short-term forcings (as can be seen in volcanic eruptions, climatic tipping points (e.g., the collapse of rainforests or the disappearance of sea ice), or in increasingly plausible climate interventions) fundamentally possess low signal-to-noise and could benefit from accounting for the multiple conditional processes through which a downstream impact arises. Under the Grand Challenge LDRD CLDERA (CLimate impacts: Discovering Etiology thRough pAthways), we have developed tools to enable downstream impact attribution from geographically and temporally localized source forcings in the climate. CLDERA developed methods that can distinguish how a localized source drives the climate system to respond with particular impacts. The how is embodied in pathways – the spatio-temporally evolving chain of physical processes that connects a source to a series of increasingly distant impacts. Novel analytic methods in pursuit of downstream impact attribution were developed and demonstrated on simulations and observations of the 1991 eruption of Mt. Pinatubo in the Philippines. As described within this report we have • developed stratospheric expertise and aerosol modeling capabilities in E3SM, • created original methods to detect and model pathways from source-to-impact, and • advanced climate attribution through novel methods, cases, and approaches. Further, CLDERA developed a tiered verification process consisting of controlled datasets to prototype, verify, and refine the original method development. CLDERA increased Sandia’s footprint in the climate analytics community and developed new climate collaborations whilst also creating a cadre of climate analysts at Sandia. The products from CLDERA have been extensive with a total of 9 journal articles published, 12 articles submitted and under review, and an additional 8 articles in preparation. We have produced 1750 simulated years and developed 9 code-bases. This report details these accomplishments and serves as a summary of the work completed during the CLDERA Grand Challenge.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Wind River Experimental Forest Subcanopy Tower Information Sheet

Wind River was one of three sites that collected 3d sonic anemometer data for an ICOS subcanopy observation study. The three sites were defined by the following features and terrain: a deciduous broadleaf forest in flat terrain (Lanžhot, Czech Republic), a coniferous forest in mountainous terrain (Renon, Italy), and a tall conifer forest in mountain-valley terrain (Wind River, USA). The Wind River subcanopy towers were deployed in a high LAI, old-growth evergreen conifer forest and collected approximately 11 months of data. The site is an ecologically rich temperate rainforest in the western Cascade Mountains, and the biological carbon sink and source strength has been measured since 1998 using eddy covariance on the top of a 74 m tall flux tower (currently called the Wind River NEON tower). Additionally, forest inventory records date back to the 1920s. In 2024, four subcanopy towers were installed near the Wind River NEON tower to measure wind flow in the understory canopy layer for better understanding canopy flow coupling and decoupling in the subcanopy and how this affects the interpretation of overstory fluxes. The subcanopy tower installation was done by Lawrence Livermore National Laboratory and Washington State University (WSU) with collaborations from the University of Utah and the National Ecological Observatory Network (NEON).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Final DOE-ASR Report for the Project “Using LASSO to bridge the gap between model and observations and to learn about atmospheric convection”

Atmospheric convection spans a wide range of spatial and temporal scales and involves complex interactions with the surrounding dynamic and thermodynamic environment, particularly over tropical continental regions. These processes remain a major source of uncertainty in weather and climate models, including persistent biases in the diurnal cycle of convective precipitation that directly affect estimates of climate sensitivity. Addressing these challenges requires the combined use of high-resolution observations and cloud-resolving modeling frameworks. In this context, the DOE Atmospheric Radiation Measurement (ARM) program’s Large-Eddy Simulation ARM Symbiotic Simulation and Observation (LASSO) activity provides a powerful platform that pairs comprehensive observations with numerical simulations to enable process-level understanding of atmospheric convection. Within this context, this Research and Development Partnership Pilot (RDPP) project was designed to initiate and expand DOE ARM/ASR research capacity at minority-serving institutions, while advancing scientific understanding of convective processes over the Amazon rainforest. Consistent with the RDPP mission, the project emphasized partnership development, training, and workforce capacity building alongside exploratory research activities. On the scientific side, the project produced two peer-reviewed journal articles, and one manuscript currently under review (see list in section 3.1). Together, these studies combine long-term ARM observations and cloud-resolving and convection-permitting modeling to investigate the environmental controls on the shallow-to-deep convective transition during the Amazon wet season. The results demonstrate the central role of early-day moisture preconditioning and large-scale dynamical forcing in regulating isolated deep convection, provide mechanistic insight into convective evolution, and establish physically informed modeling frameworks for future sensitivity experiments. These scientific outcomes are described in sections 2.1 to 2.3 and were disseminated in 8 conference presentations (see section 3.2) and 5 invited talks (see section 3.3), reflecting broad engagement with our community. Equally important, the project achieved its RDPP capacity-building objectives (see section 2.4). A sustained research partnership was established among the University of Maryland, Baltimore County (UMBC), Morgan State University (MSU), and Howard University (HU), and extended to include collaboration with Pacific Northwest National Laboratory (PNNL). The project organized multiple multi-day training events focused on ARM data, LASSO simulations, and quantitative analysis methods, directly engaging students, postdoctoral researchers, and faculty across institutions. These activities broadened participation in ASR research and led to independent adoption of LASSO workflows by students beyond the immediate project team. Finally, the project successfully positioned the participating institutions to pursue future DOE research. Preliminary scientific results, coupled with strengthened partnerships and technical capacity, enabled the submission of follow-on proposals to DOE ASR funding opportunities. In this way, the project fulfilled the RDPP goal of seeding durable research capacity and laying the foundation for larger-scale, sustained engagement with DOE ARM and ASR programs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Techno-Economic Analysis of Geologically Connected Seawater Air Conditioning (GeoSWAC) Concept for District Cooling at the University of Puerto Rico at Rio Piedras

At the University of Puerto Rico at Rio Piedras, a central chilled water plant supplies cooling to several campus buildings, contributing significantly to electricity demand during daytime peak hours, particularly in the summer months. These operational challenges are exacerbated by Puerto Rico's tropical rainforest climate and a power grid vulnerable to frequent disruptions caused by hurricanes and tropical storms. This study presents a techno-economic analysis of the existing chilled water plant serving four representative campus buildings and introduces a conceptual alternative: the Geologically connected Seawater Air Conditioning (GeoSWAC) system. GeoSWAC leverages stable low temperatures of deep ocean water (~1 km depth), hydraulically connected to an inland well, to deliver cooling without the use of vapor-compression refrigeration. Using modeled annual cooling loads and chiller performance data, capital costs, energy consumption, and levelized cost of cooling (LCOC) were evaluated for both systems. While GeoSWAC showed higher capital costs than the chiller-based scenario, operational costs were significantly lower at $26k-$53k annually, resulting in a lower LCOC between $2.3/MWh and $8.2/MWh compared to $30.2/MWh-$33.6/MWh for the chiller scenario. These results suggest that the GeoSWAC system offers a promising, low-energy, and climate-resilient alternative for large-scale cooling in tropical coastal environments, with significant potential to reduce peak electricity demand and improve long-term system reliability.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Deep-learning-derived planetary boundary layer height from conventional meteorological measurements

Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Environmental controls on isolated convection during the Amazonian wet season

The Amazon rainforest is a vital component of the global climate system, influencing the hydrological cycle and tropical circulation. However, understanding and modeling the evolution of convection in this region remain a scientific challenge. Here, we assess the environmental conditions associated with shallow, congestus, and isolated deep convection days during the wet season (December to April), employing measurements from the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment and large-scale wind fields from the constrained variational analysis. Composites of deep days show moister than average conditions below 3 km early in the morning. Analyzing the water budget at the surface through observations only, we estimated the water vapor convergence term as a residual of the water balance closure. Convergence remains nearly zero during the deep days until early afternoon (13:00 LST), when it becomes a dominant factor in the water budget. At 14:00 LST, the deep days experience a robust upward large-scale vertical velocity, especially above 4 km, which supports the shallow-to-deep convective transition occurring around 16:00–17:00 LST. In contrast, shallow and congestus days exhibit drier pre-convective conditions, along with diurnal water vapor divergence and large-scale subsidence that extend from the surface to the lower free troposphere. Moreover, afternoon precipitation exhibits the strongest linear correlation (0.6) with large-scale vertical velocity, nearly double the magnitude observed for other environmental factors, even moisture, at different levels and periods of the day. Precipitation also exhibits a moderate increase with low-level wind shear, while upper-level shear has a relatively minor negative impact on convection.

Environmental Sciences & Ecology↗

DOE OSTI2024

Deep-Learning-derived Boundary Layer Height from Meteorological Data over the SGP, GOAMAZON, CACTI

The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Techno-Economic Analysis of Geologically Connected Seawater Air Conditioning (GeoSWAC) Concept for District Cooling at the University of Puerto Rico at Rio Piedras: Preprint

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Intense formation of secondary ultrafine particles from Amazonian vegetation fires and their invigoration of deep clouds and precipitation

New particle formation (NPF) in fire smoke is thought to be unlikely due to large condensation and coagulation sinks that scavenge molecular clusters. We analyze aircraft measurements over the Amazon and find that fires significantly enhance NPF and ultrafine particle (UFP < 50 nm diameter) numbers compared to background conditions, contrary to previous understanding. We identify that the nucleation of dimethylamine with sulfuric acid, which is aided by the formation of extremely low volatility organics in biomass-burning smoke, can overcome the large condensation and coagulation sinks and explain aircraft observations. We show that freshly formed clusters rapidly grow to UFP sizes through biomass-burning secondary organic aerosol formation, leading to a 10-fold increase in UFP number concentrations. Here, we find a contrasting effect of UFPs on deep convective clouds compared to the larger particles from primary emissions for the case investigated here. UFPs intensify the deep convective clouds and precipitation due to increased condensational heating, while larger particles delay and reduce precipitation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Hot droughts in the Amazon provide a window to a future hypertropical climate

Tropical forests represent the warmest and wettest of Earth’s biomes, but with continued anthropogenic warming, they will be pushed to climate states with no current analogue. Droughts in the tropics are already becoming more intense as they occur at successively higher temperatures. Here, in this study, we synthesize multiple datasets to assess the effects of hot droughts on a central Amazon forest. First, a more than 30-year record of annually resolved forest demographic data from a selective logging experiment showed higher tree mortality during intense droughts, particularly among fast-growing pioneer species with low wood density. Second, analysis of ecophysiological field measurements from the 2015 and 2023 El Niño droughts identified a soil moisture threshold beyond which transpiration rates rapidly declined. As rainless days beyond this threshold continued, drought conditions intensified, increasing the potential for tree mortality from hydraulic failure and carbon starvation. Third, analyses from the Coupled Model Intercomparison Project Phase 6 demonstrated that under high-emission scenarios, a large area of tropical forest will shift to a hotter ‘hypertropical’ climate by 2100. Last, under a hypertropical climate, temperature and moisture conditions during typical dry season months will more frequently exceed identified drought mortality thresholds, elevating the risk of forest dieback. Present-day hot droughts are harbingers of this emerging climate, offering a window for studying tropical forests under expected extreme future conditions.

drought↗

DOE OSTI2024

Methane-cycling microbial communities from Amazon floodplains and upland forests respond differently to simulated climate change scenarios

Seasonal floodplains in the Amazon basin are important sources of methane (CH 4 ), while upland forests are known for their sink capacity. Climate change effects, including shifts in rainfall patterns and rising temperatures, may alter the functionality of soil microbial communities, leading to uncertain changes in CH 4 cycling dynamics. To investigate the microbial feedback under climate change scenarios, we performed a microcosm experiment using soils from two floodplains (i.e., Amazonas and Tapajós rivers) and one upland forest. We employed a two-factorial experimental design comprising flooding (with non-flooded control) and temperature (at 27 °C and 30 °C, representing a 3 °C increase) as variables. We assessed prokaryotic community dynamics over 30 days using 16S rRNA gene sequencing and qPCR. These data were integrated with chemical properties, CH 4 fluxes, and isotopic values and signatures. In the floodplains, temperature changes did not significantly affect the overall microbial composition and CH 4 fluxes. CH 4 emissions and uptake in response to flooding and non-flooding conditions, respectively, were observed in the floodplain soils. By contrast, in the upland forest, the higher temperature caused a sink-to-source shift under flooding conditions and reduced CH 4 sink capability under dry conditions. The upland soil microbial communities also changed in response to increased temperature, with a higher percentage of specialist microbes observed. Floodplains showed higher total and relative abundances of methanogenic and methanotrophic microbes compared to forest soils. Isotopic data from some flooded samples from the Amazonas river floodplain indicated CH 4 oxidation metabolism. This floodplain also showed a high relative abundance of aerobic and anaerobic CH 4 oxidizing Bacteria and Archaea. Taken together, our data indicate that CH 4 cycle dynamics and microbial communities in Amazonian floodplain and upland forest soils may respond differently to climate change effects. We also highlight the potential role of CH 4 oxidation pathways in mitigating CH 4 emissions in Amazonian floodplains.

16S rRNA sequencing↗

DOE OSTI2024

Metaanalysis of liana and tree functional traits

The objectives of this project were (i) to determine how tropical trees and lianas differed in terms of their functional traits, and (ii) to parameterize a computational model of tree-liana competition. We carried out a meta-analysis of tree and liana functional traits in order to achieve these goals. First, we downloaded functional trait data from the TRY database during November and December 2019. Traits of interest included leaf, wood, and root functional traits. We included only angiosperm tree and liana species that are found in tropical biomes. We then computed the species average for each trait. The results are included in “TRY_traits_metaanalysis.csv”. We also conducted a second meta-analysis focused on the hydraulic traits of tropical trees and lianas. We used Google Scholar and Web of Science to identify papers that contained hydraulic trait values. The papers that we found were all published between 1997-2019. As with our TRY-based meta-analysis, we included only angiosperm tree and liana species that are found in the tropics, and we computed species averages. The results are contained in the file “hydraulic_traits_metaanalysis.csv”. Both files are in csv format, so they can be read with any plain text editor, as well as programs like R or Excel.

54 ENVIRONMENTAL SCIENCES↗