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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multiscale Interactions between Local Short- and Long-Term Spatio-Temporal Mechanisms and Their Impact on California Wildfire Dynamics

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes; the interplay between fire weather index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks; and geographical variations in the relationship between the FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. The findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r > 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ -0.7, p ≤ 0.05).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

Leaf Functional Traits in Relation to Species Composition in an Arctic–Alpine Tundra Grassland

The relict arctic–alpine tundra provides a natural laboratory to study the potential impacts of climate change and anthropogenic disturbance on tundra vegetation. The Nardus stricta-dominated relict tundra grasslands in the Krkonoše Mountains have experienced shifting species dynamics over the past few decades. Changes in species cover of the four competing grasses—Nardus stricta, Calamagrostis villosa, Molinia caerulea, and Deschampsia cespitosa—were successfully detected using orthophotos. Leaf functional traits (anatomy/morphology, element accumulation, leaf pigments, and phenolic compound profiles), were examined in combination with in situ chlorophyll fluorescence in order to shed light on their respective spatial expansions and retreats. Our results suggest a diverse phenolic profile in combination with early leaf expansion and pigment accumulation has aided the expansion of C. villosa, while microhabitats may drive the expansion and decline of D. cespitosa in different areas of the grassland. N. stricta—the dominant species—is retreating, while M. caerulea did not demonstrate significant changes in territory between 2012 and 2018. We propose that the seasonal dynamics of pigment accumulation and canopy formation are important factors when assessing potential “spreader” species and recommend that phenology be taken into account when monitoring grass species using remote sensing.

Lena Hunt↗
DOE OSTI2023

Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

Geology↗
DOE OSTI2025

Enhancing carbon storage through proactively managing fire-prone coniferous mountain forests

Mountain ecosystems typically serve as carbon (C) sinks. However, studies also suggest that they could be C sources due to climate warming, drought and insect-related mortality, wildfires, and management actions. We applied the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), a process-based dynamic vegetation-ecosystem model, to investigate the role of ecosystem management in C storage under Mediterranean climate over the 21 st century. We modified LPJ-GUESS to include implementing mechanical thinning by vegetation size classes, components, and types along with a new mechanistic fire-occurrence model that accounts for wind speed and lightning ignition. Simulations show that mechanical thinning or prescribed fire performed 5-20 years in advance of a high-severity wildfire reduced direct wildfire C emissions by 38-66 %. Our results also show that long-term management actions repeated every 5-20 years, including thinning relatively small trees (diameters up to 7 inches or ∼178 mm), can maintain stable C levels in the forest and lower dead-fuel amounts. We found that, although prescribed fire mitigated wildfire severity, ecosystem C storage from reduced wildfire emissions can be outweighed by the added emissions from the prescribed fire themselves. Thinning plus removing and sequestering the thinned biomass can ensure that forests act as net C sinks through the end of the 21 st century. However, addition of prescribed fire is needed to reduce understory and lower the projected extent of high-severity wildfire. Achieving the competing goals of reducing wildfire and making the Sierra Nevada long-term C sink can be advanced through carefully coordinated thinning, sequestration of thinned biomass, and prescribed fire.

Climate solutions↗
NASA NTRS2024

The INAF Campo Imperatore Observatory in Abruzzo (Italy) as an Earth Observation Facility for the Study of Venus Night Airglows (VNAs)

The National Institute for Astrophysics (INAF) Campo Imperatore Observatory is located in the Gran Sasso mountains in Abruzzo, Italy, at an altitude of 2150 m above the sea level. The atmospheric transparency, along with the climatic conditions - especially in winter - have always made it a suitable site for observations in the near infrared (1 - 2 micron). The Campo Imperatore Observatory is equipped with the AZT-24 reflecting telescope, Ritchey-Chrétien configuration, with an aperture of 1.1 m. The new motorization system – currently in the commissioning phase - will allow great pointing and tracking accuracy (<0.1 arcsec), as well as an uncommonly fast-tracking speed (the mechanics is designed to reach 3 degrees/second). With this facility we are starting the ADvanced VENus’ Night Airglows Near-infrared Telescope (ADVENANT) project, which aims to observe and study Venus’ nightside airglows (VNAs).

P. D'Incecco↗
NASA NTRSDate not supplied

TPSAS-NF1676L-31979-DND

The rapid, and in many cases, unprecedented changes in the Arctic climate system observed since 1979 are having devastating impacts on both natural and human systems. These changes are marked by warming Arctic temperatures, rapid declines in sea ice and snow cover, permafrost thaw, mountain glacier melt, and Greenland Ice Sheet mass loss. A common thread through each of these changes is the impact on the surface energy budget. However, our understanding of the controls on and variability of the Arctic surface energy budget is incomplete and this contributes to the significant biases in the climate models. To better understand the controls on the variability of the Arctic surface energy budget and CMIP5 model biases, we use an atmospheric regime-based perspective of the Arctic surface energy budget decomposing variability by atmospheric dynamic and thermodynamic properties.

Patrick C. Taylor↗
NASA NTRS2001

Using Climate Models to Evaluate Mechanisms of Glacial Inception

The initiation and subsequent growth of an ice sheet or large glacier is based on two primary factors: 1. Most fundamentally, a region must exist with a positive net snow accumulation, that is, cold season snowfall exceeds warm season snowmelt. Because snow can melt very rapidly, in a practical sense this probably means that little or no snow melt should occur in the warm season (mountain glaciers being one possible exception). 2. When sufficient ice builds in a region with a positive net snow accumulation, the ice will flow into adjoining regions with a negative mass balance. Feedbacks can also then arise between the emerging ice sheet and the overall climate, which, among other effects, may cause the mass balance in that region to turn positive. A key question is the relative importance of these two factors. In particular, is it possible for a large lowland region to experience a positive mass balance, such that the ice sheet can arise largely 'in-situ'? Or instead are uplands necessary, such that essentially mountain glaciers form first, and then, under the right conditions, grow and coalesce, eventually spreading out into the lowlands? This is probably the single most fundamental question to be addressed in the modeling of glacial inception. Other key questions then focus on how the (upland or low-land) positive mass balance is obtained at some times, but not others (the ice sheets are not continuously present). For Northern Hemisphere ice sheets in particular, what climatic conditions can lead to abundant winter snowfall in the Canadian Arctic and northern Labrador in conjunction with cool summertime conditions? Are both required, or will cool summer conditions alone suffice? Conversely, are a few years of abnormally heavy snowfall all that is required to trigger glacial inception? A major need at present is for carefully constructed climate model studies aimed at addressing these questions. A successful strategy will almost certainly require more than just a global model; while the global climate model might be necessary to properly simulate large-scale forcing, such models have insufficient spatial resolution to adequately address the roles of topography and the nature of the land surface. Necessary also is the use of a high-resolution regional climate model (in conjunction with a global model). Possible forcing mechanisms of Pleistocene ice ages are well known (e.g., orbital forcing; CO2 fluctuations) but we must understand and be able to successfully model the actual processes involved in glacial inception before we can fully understand the true roles played by these forcing mechanisms.

Oglesby, Robert J.↗
NASA NTRS2001

The Effect of Regional Climate Variability on Outbreak of Bartonellosis Epidemics in Peru

Bartonellosis is a vector-borne, highly fatal, emerging infectious disease, which has been known in the Peruvian Andes since the early 1600s and has continued to be a problem in many mountain valleys in Peru and other Andean South American countries. The causative bacterium, Bartonella bacilliformis (Bb), is believed to be transmitted to humans by bites of the sand fly Lutzomyia verrucarum. According to available medical records, the transmission of infection often occurs in river valleys of the Andes Mountains at an altitude between 800 and 3500 meters above sea level. It shows a seasonal pattern, which usually begins to rise in December, peaks in February and March, and is at its lowest from July until November. The epidemics of bartonellosis also vary interannually, occurring every four to eight years, and appear to be associated with the El Nino cycle. In response to the National Oceanic and Atmospheric Administration (NOAA) announcement on climate variability and human health, which was constructed to stimulate integrated multidisciplinary research in the area of climate variability and health interactions, we have conducted a study to investigate the relationship between the El Nino induced regional climate variation and the outbreak of bartonellosis epidemics in Peru. Two test sites, Caraz and Cusco, were selected for this study. According to reports, Caraz has a long-standing history of endemic transmission and Cusco, which is located about five degrees poleward of Caraz, had no recorded epidemics until the most recent 1997/1998 El Nino event. The goal of this study is to clarify the relative importance of climatic risk factors for each area that could be predicted in advance, thus allowing implementation of cost-effective control measures, which would reduce disease morbidity and mortality.

Zhou, Jia-Yu↗
DOE OSTI2021

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (R eco ). However, accurate estimation of ET and R eco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance measurements, physically based model simulation results, meteorological forcings, and remote-sensing datasets to estimate ET and R eco in high space–time resolution. HPM relies on a deep learning algorithm and long short-term memory (LSTM) and requires only air temperature, precipitation, radiation, normalized difference vegetation index (NDVI), and soil temperature (when available) as input variables. We tested and validated HPM estimation results in different climate regions and developed four use cases to demonstrate the applicability and variability of HPM at various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North America. To test the limitations and performance of the HPM approach in mountainous watersheds, an expanded use case focused on the East River Watershed, Colorado, USA. The results indicate HPM is capable of identifying complicated interactions among meteorological forcings, ET, and R eco variables, as well as providing reliable estimation of ET and R eco across relevant spatiotemporal scales, even in challenging mountainous systems. The study documents that HPM increases our capability to estimate ET and R eco and enhances process understanding at sparsely monitored watersheds.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1984

Tectonic, volcanic, and climatic geomorphology study of the Sierras Pampeanas Andes, northwestern Argentina

A proposed analysis of Shuttle Imaging Radar-B (SIR-B) data extends current research in the Sierras Pampeanas and the Puna of northwestern Argentina to the determination - by the digital analysis of mountain-front sinuousity - of the relative age and amount of fault movement along mountain fronts of the late-Cenozoic Sierras Pampeanas basement blocks; the determination of the age and history of the boundary across the Andes at about 27 S latitude between continuing volcanism to the north and inactive volcanism to the south; and the determination of the age and extent of Pleistocene glaciation in the High Sierras, as well as the comparative importance of climatic change and tectonic movements in shaping the landscape. The integration of these studies into other ongoing geology projects contributes to the understanding of landform development in this active tectonic environment and helps distinguish between climatic and tectonic effects on landforms.

Bloom, A. L.↗
NASA NTRS2005

Prototype Cryospheric Experimental Synthetic Aperture Radiometer (CESAR)

Present satellite microwave radiometers typically have a coarse spatial resolution of several kilometers or more. This is only adequate only over homogenous areas. Significantly enhanced spatial resolution is critically important to reduce the uncertainty of estimated cryospheric parameters in heterogeneous and climatically-sensitive areas. Examples include: (1) dynamic sea ice areas with frequent lead and polynya developments and variable ice thicknesses, (2) mountainous areas that require improved retrieval of snow water equivalent, and (3) melting outlet glacier or ice shelf areas along the coast of Greenland and Antarctica. For these situations and many others, an Earth surface spot size of no more than 100 m is necessary to retrieve the information needed for significant new scientific progress, including the synthesis of field observations with satellite observations with high confidence.

Hilliard, Lawrence M.↗
DOE OSTI2022

Warming Response of Deep Soil Carbon (LDRD Final Report)

The overarching objective of this LDRD project was to determine the vulnerability of deep soil organic carbon (SOC) to warming in the Sierra Nevada region. Deep soils (>30 cm) store more than 70% of global SOC, and increased SOC decomposition and CO 2 emissions caused by warming are potentially large climate change feedbacks. According to the Intergovernmental Panel on Climate Change, temperatures are expected to increase by 4°C by the year 2100, warming the land and underlying soil, and making understanding of how warming will influence deep SOC storage and persistence critical to projecting the land carbon sink. However, uncertainty remains in our process-level understanding and ability to quantify how projected warming will impact the stability of carbon in deep soils. We investigated warming effects on deep (up to 16 m) SOC stability across climate, vegetation, and soil mineralogy gradients in California. We sampled soils from the surface to bedrock (down to 16 meters) at four sites representing vastly different ecosystems across the Northern and Southern Sierra Nevada mountains. This study quantified the response of SOC concentration, distribution, and vulnerability to warming using a soil incubation experiment to warm the whole soil profile and radiocarbon and stable isotopes to assess which pools are vulnerable to loss under warming. Our results refine our understanding of the terrestrial carbon cycle by revealing the vulnerability of deep SOC to future changes in climate and how minerology may influence that vulnerability.

58 GEOSCIENCES↗
DOE OSTI2022

Variability in observed stable water isotopes in snowpack across a mountainous watershed in Colorado

In this study, isotopic information from 81 snowpits was collected over a 5-year period in a large, Colorado watershed. Data spans gradients in elevation, aspect, vegetation, and seasonal climate. They are combined with overlapping campaigns for water isotopes in precipitation and snowmelt, and a land-surface model for detailed estimates of snowfall and climate at sample locations. Snowfall isotopic inputs, describe the majority of δ18O snowpack variability. Aspect is a secondary control, with slightly more enriched conditions on east and north facing slopes. This is attributed to preservation of seasonally enriched snowfall and vapour loss in the early winter. Sublimation, expressed by decreases in snowpack d-excess in comparison to snowfall contributions, increases at low elevation and when seasonal temperature and solar radiation are high. At peak snow accumulation, post-depositional fractionation appears to occur in the top 25 ± 14% of the snowpack due to melt-freeze redistribution of lighter isotopes deeper into the snowpack and vapour loss to the atmosphere during intermittent periods of low relative humidity and high windspeed. Relative depth of fractionation increases when winter daytime temperatures are high and winter precipitation is low. Once isothermal, snowpack isotopic homogenization and enrichment was observed with initial snowmelt isotopically depleted in comparison to snowpack and enriching over time. The rate of δ18O increase (d-excess decrease) in snowmelt was 0.02‰ per day per 100-m elevation loss. Isotopic data suggests elevation dictates snowpack and snowmelt evolution by controlling early snow persistence (or absence), isotopic lapse rates in precipitation and the ratio of energy to snow availability. Hydrologic tracer studies using stable water isotopes in basins of large topographic relief will require adjustment for these elevational controls to properly constrain stream water sourcing from snowmelt.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

climate projection↗
NASA NTRS2016

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km by 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

general circulation model (GCM)↗
DOE OSTI2026

Historic climate, cosmogenic 10Be, denudation-rate, and geospatial datasets from the Pikes Peak region, Colorado, USA

This data package contains geographic information system (GIS) layers and tabular datasets associated with the study of elevation-dependent denudation rates on Pikes Peak in the Front Range of the Rocky Mountains, Colorado, USA. The package includes GIS layers used to produce the study-area map, including sample locations, sample watershed boundaries, the Pikes Peak batholith, Pleistocene glacier extent, weather station locations, and elevation and hillshade rasters, together with comma-separated value (CSV) tables and matching CSV data dictionaries. These mapped layers provide the geographic framework for interpreting denudation patterns across the Pikes Peak region and for relating sample locations to watershed geometry, bedrock setting, glacial history, and nearby climate stations. The first group of tables reports climate and geospatial context for the study area. These files include station-based temperature and precipitation data used to characterize elevational gradients in mean annual climate and monthly climate seasonality, sample locations, denudation-rate and topographic metrics, fixed frost-cracking model parameters, frost-cracking intensity and precipitation-frequency metrics, and stream-power inversion results. Together, these data provide the basis for evaluating how denudation varies with elevation, climate, and landscape form across sampled catchments on Pikes Peak. The second group of tables reports cosmogenic nuclide and erosion-model results used in the denudation analysis. Included files contain accelerator mass spectrometry (AMS) measurements for in situ-produced cosmogenic beryllium-10 (10Be), including sample identifiers, measured 10Be:9Be ratios, analytical uncertainties, carrier mass, quartz mass, blank corrections, blank-group statistics, and calculated 10Be concentrations and uncertainties. Additional tables summarize stream-power-law inversion results for sampled catchments, including optimized model parameters, predicted erosion rates, residual metrics, channel-pixel counts, and convergence status, as well as regression equations and summary statistics used to evaluate relationships among elevation, climate, frost cracking, precipitation forcing, and denudation rate. The package contains GIS files, comma-separated value files (.csv), Microsoft Excel files (.xlsx), CSV data dictionaries, a file-level metadata table, and a readme text file.

10Be cosmogenic nuclides↗
DOE OSTI2020

Evaluation of Timber Mountain Recharge and Groundwater Flow in Relation to Pahute Mesa-Oasis Valley Flow System

The purpose of this study was to evaluate the effect of Timber Mountain recharge on groundwater flow in the surrounding Pahute Mesa-Oasis Valley groundwater basin using several different methodologies. These methodologies included examining climatic and soil moisture data, estimating groundwater recharge by chloride mass balance, interpreting water chemical and isotopic data, and modeling groundwater flow along faults.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Strategic Siting of Direct Air Capture Facilities in the United States

Direct air capture (DAC) systems that capture carbon dioxide (CO 2 ) directly from the atmosphere are garnering considerable attention for their potential role as negative emission technologies in achieving net-zero CO 2 emission goals. Common DAC technologies are based either on liquid–solvent (L-DAC) or solid–sorbent (S-DAC) to capture CO 2 . A comprehensive multi-factor comparative economic analysis of the deployment of L-DAC and S-DAC facilities across various United States (U.S.) cities is presented in this paper. The analysis considers the influence of various factors on the favorability of DAC deployment, including local climatic conditions such as temperature, humidity, and CO 2 concentrations; the availability of energy sources to power the DAC system; and costs for the transport and storage of the captured CO 2 along with the consideration of the regional market and policy drivers. The deployment analysis in over 70 continental U.S. cities shows that L-DAC and S-DAC complement each other spatially, as their performance and operational costs vary in different climates. L-DAC is more suited to the hot, humid Southeast, while S-DAC is preferrable in the colder, drier Rocky Mountain region. Strategic deployment based on regional conditions and economics is essential for promoting the commercial adoptability of DAC, which is a critical technology to meet the CO 2 reduction targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗