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DOE OSTI2024

Connecting Global Modes of Variability to Climate in High Mountain Asia

Oscillations in global modes of variability (MoVs) form global teleconnections that affect regional climate variability and modify the potential for severe and damaging weather conditions. Understanding the link between certain MoVs and regional climate can improve the ability to more accurately predict environmental conditions that impact human life and health. In this study, we explore the connection between different MoVs, including the Arctic oscillation (AO), Eurasian teleconnection, Indian Ocean dipole (IOD), North Atlantic oscillation (NAO), and El Niño southern oscillation (Nino34), with winter and summer climates in the High Mountain Asia (HMA) region, including geopotential height at 250 hPa (z250), 2 m air temperature (T2M), total precipitation (PRECTOT), and fractional snow cover area (fSCA). Relationships are explored for the same monthly period between the MoVs and the climate variables, and a lagged correlation analysis is used to investigate whether any relationship exists at different time lags. We find that T2M has a negative correlation with the Eurasian teleconnection in the Inner Tibetan Plateau and central China in both winter and summer and a positive correlation in western China in summer. PRECTOT has a positive correlation with all MoVs in most regions in winter, especially with the IOD, and a negative correlation in summer, especially with the Eurasian teleconnection. Snow cover in winter is positively correlated with most indices throughout many regions in HMA, likely due to wintertime precipitation also being positively correlated with most indices. Generally, the AO and NAO show similar correlation patterns with all climate variables, especially in the winter, possibly due to their oscillations being so similar. Furthermore, the AO and NAO are shown to be less significant in explaining the variation in HMA climate compared to other MoVs such as the Eurasian teleconnection. Overall, our results identify different time windows and specific regions within HMA that exhibit high correlations between climate and MoVs, which might offer additional predictability of the MoVs as well as of climate and weather patterns in HMA and throughout the globe.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Metagenome‐Assembled Genomes for Oligotrophic Nitrifiers From a Mountainous Gravelbed Floodplain

Riparian floodplains are important regions for biogeochemical cycling, including nitrogen. Here, we present MAGs from nitrifying microorganisms, including ammonia-oxidising archaea (AOA) and comammox bacteria from Slate River (SR) floodplain sediments (Crested Butte, CO, US). Additionally, we explore MAGs from potential nitrite-oxidising bacteria (NOB) from the Nitrospirales. AOA diversity in SR is lower than observed in other western US floodplain sediments and Nitrosotalea-like lineages such as the genus TA-20 are the dominant AOA. No ammonia-oxidising bacteria (AOB) MAGs were recovered. Microorganisms from the Palsa-1315 genus (clade B comammox) are the most abundant ammonia-oxidizers in SR floodplain sediments. Established NOB are conspicuously absent; however, we recovered MAGs from uncultured lineages of the NS-4 family (Nitrospirales) and Nitrospiraceae that we propose as putative NOB. Nitrite oxidation may be carried out by organisms sister to established Nitrospira NOB lineages based on the genomic content of uncultured Nitrospirales clades. Nitrifier MAGs recovered from SR floodplain sediments harbour genes for using alternative sources of ammonia, such as urea, cyanate, biuret, triuret and nitriles. In conclusion, the SR floodplain therefore appears to be a low ammonia flux environment that selects for oligotrophic nitrifiers.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Effects of spatial resolution on WRF v3.8.1 simulated meteorology over the central Himalaya

The sensitive ecosystem of the central Himalayan (CH) region, which is experiencing enhanced stress from anthropogenic forcing, requires adequate atmospheric observations and an improved representation of the Himalaya in the models. However, the accuracy of atmospheric models remains limited in this region due to highly complex mountainous topography. This article delineates the effects of spatial resolution on the modeled meteorology and dynamics over the CH by utilizing the Weather Research and Forecasting (WRF) model extensively evaluated against the Ganges Valley Aerosol Experiment (GVAX) observations during the summer monsoon. The WRF simulation is performed over a domain (d01) encompassing northern India at 15 km x 15 km resolution and two nests (d02 at 5 km x 5 km and d03 at 1 km x 1 km) centered over the CH, with boundary conditions from the respective parent domains. WRF simulations reveal higher variability in meteorology, e.g., relative humidity (RH = 70.3%-96.1%) and wind speed (WS = 1.1-4.2m s(-1)), compared to the ERA-Interim reanalysis (RH D 80.0%-85.0%, WS D 1.2-2.3m s(-1)) over northern India owing to the higher resolution. WRF-simulated temporal evolution of meteorological variables is found to agree with balloon-borne measurements, with stronger correlations aloft (r = 0.44-0.92) than those in the lower troposphere (r = 0.18-0.48). The model overestimates temperature (warm bias by 2.8 degrees C) and underestimates RH (dry bias by 6.4 %) at the surface in d01. Model results show a significant improvement in d03 (P = 827.6 hPa, T = 19.8 degrees C, RH = 92.3 %), closer to the GVAX observations (P = 801.4 hPa, T = 19.5 degrees C, RH = 94.7 %). Interpolating the output from the coarser domains (d01, d02) to the altitude of the station reduces the biases in pressure and temperature; however, it suppresses the diurnal variations, highlighting the importance of well-resolved terrain (d03). Temporal variations in near-surface P, T, and RH are also reproduced by WRF in d03 to an extent (r>0:5). A sensitivity simulation incorporating the feedback from the nested domain demonstrates the improvement in simulated P, T, and RH over the CH. Our study shows that the WRF model setup at finer spatial resolution can significantly reduce the biases in simulated meteorology, and such an improved representation of the CH can be adopted through domain feedback into regional-scale simulations. Interestingly, WRF simulates a dominant easterly wind component at 1 km x 1 km resolution (d03), which is missing in the coarse simulations; however, the frequency of southeasterlies remains underestimated. The model simulation implementing a highresolution (3 s) topography input (SRTM) improved the prediction of wind directions; nevertheless, further improvements are required to better reproduce the observed local-scale dynamics over the CH.

Singh, Jaydeep↗

DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Applying MALDI-TOF MS to resolve morphologic and genetic similarities between two Dermacentor tick species of public health importance

Abstract Hard ticks (Acari: Ixodidae) have been historically identified by morphological methods which require highly specialized expertise and more recently by DNA-based molecular assays that involve high costs. Although both approaches provide complementary data for tick identification, each method has limitations which restrict their use on large-scale settings such as regional or national tick surveillance programs. To overcome those obstacles, the matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been introduced as a cost-efficient method for the identification of various organisms, as it balances performance, speed, and high data output. Here we describe the use of this technology to validate the distinction of two closely relatedDermacentortick species based on the development of the first nationwide MALDI-TOF MS reference database described to date. The dataset obtained from this protein-based approach confirms that tick specimens collected from United States regions west of the Rocky Mountains and identified previously asDermacentor variabilisare the recently described species,Dermacentor similis. Therefore, we propose that this integrative taxonomic tool can facilitate vector and vector-borne pathogen surveillance programs in the United States and elsewhere.

Science & Technology - Other Topics↗

DOE OSTI2022

Geologic Carbon Storage of Anthropogenic CO2 under the Colorado Plateau in Emery County, Utah

Geologic Carbon Storage (GCS) is a promising technology for storing large volumes of anthropogenic CO2 effectively and permanently. Numerical simulations are an integral part of site selection and characterization for any potential GCS site. As part of the DOE-funded CarbonSAFE Rocky Mountains Phase I project, a regional GCS analysis was undertaken to understand the efficacy of storing CO2 emissions from the power generation and heavy industry in central Utah’s favorable geology. In this study, the injection of CO2 for geologic storage was simulated in the Navajo Sandstone Formation in Emery County, Utah. Carbon dioxide was sourced from regional power generation stations and heavy industries throughout Utah, with an emphasis on emissions reduction at the Hunter Power Plant near Castle Dale, Utah. A simulation grid was extracted from the project’s geological model encompassing an area around Price, Huntington, and Castle Dale in central Utah. The Navajo Sandstone Member of the Glen Canyon Group was the target of CO2 injection with the overlying Carmel formation providing the primary seal. A suite of simulations was performed assessing the viability of this area for permanent CO2 storage. Results indicate that the area can not only store 46 million metric tons of anthropogenic CO2, meeting the project goals, but this area has the capacity to securely store at least 1.3 billion tons of CO2, suggesting the injection site and surrounding geology are suitable locations for commercial-scale GCS.

58 GEOSCIENCES↗

DOE OSTI2022

Impacts of Large-Scale Urbanization and Irrigation on Summer Precipitation in the Mid-Atlantic Region of the United States

This study investigates how urbanization and irrigation in the United States east of the Rocky Mountains affect summer precipitation in the Mid-Atlantic region (MAR) using convection-permitting Weather Research and Forecasting model simulations with/without urbanization or irrigation. In this work, a feature tracking algorithm is used to identify precipitation from mesoscale convective systems (MCSs), isolated deep convection (IDC), and non-convective systems (NC). Overall, urbanization suppresses all three types of precipitation in the MAR by reducing water vapor content and convective available potential energy, while irrigation enhances IDC and NC precipitation but suppresses MCS precipitation. Examination of the MCS and IDC initiation locations indicates that irrigation suppresses MAR precipitation that is produced by MCSs initiated in the Great Plains and Midwest (GP) but enhances MAR precipitation from MCSs initiated locally within the region. Irrigation induces a mid-level cyclonic circulation anomaly centering in the Southeast, hindering the eastward propagation and development of MCSs from the GP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Constraining Physical Understanding of Aerosol Loading, Biogeochemistry, and Snowmelt Hydrology from Hillslope to Watershed Scale in the East River Scientific Focus Area

The mountain snowpack is a critical component of regional hydrology, ecology, biogeochemistry, and climate in the Western US. This project leveraged the East River Scientific Focus Area (SFA), as an outdoor laboratory to address a significant gap in our understanding of the mountain snowpack; namely, how atmospheric constituent deposition on snowpack influences snow energy balance and nutrient/chemical cycling, and how snowmelt timing and intensity exerts controls on emergent biogeochemical and ecohydrologic behavior. This aligned with East River SFA goals to integrate landscape scale measurements and physical based modeling tools to improve understanding of controls on runoff production, ecohydrology, biogeochemical cycling, and land surface energy partitioning in high mountain watersheds. The project included observations of snowpack at multiple scales, measurements of snow and deposited aerosols properties, in situ time series of surface energy balance, and spatially distributed, process-based snowmelt modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Remote effects of Tibetan Plateau spring land temperature on global subseasonal to seasonal precipitation prediction and comparison with effects of sea surface temperature: the GEWEX/LS4P Phase I experiment

The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Surface Quantitative Precipitation Estimates (SQUIRE) of Snow Water Equivalent from the Surface Atmospheric Integrated Field Laboratory

The upper Colorado River basin is the primary source of water for 40 million people. With declining snowpack in the basin, forecasting hydrological budgets in the Southwest United States is more important than ever. However, due in part, to a lack of reliable observations of precipitation in complex terrain, hydrological models struggle to assess and forecast snowpack snow water equivalent (SWE) in the upper Colorado River basin (UCRB). Therefore, the need for more reliable SWE forecasts in the UCRB motivated the U.S. Department of Energy Atmospheric Radiation Measurement Facility’s Surface Atmospheric Integrated Field Laboratory (SAIL) that occurred from June 2021 to June 2023. During SAIL, the X-band precipitation radar from Colorado State University conducted volume scans sampling the precipitation properties over the UCRB. The ARM facility developed a gridded Surface Quantitative Precipitation Estimates (SQUIRE) product from the radar observations. To do this, various daily SWE estimates from radar using the radar reflectivity factor Z e and specific differential phase K dp were compared against ground-based precipitation gauges. SWE in precipitation calculated from Wolfe and Snider’s S–Z e estimator was in best agreement with the rain gauges for the days when SWE < 12 mm. For days with SWE > 12 mm, the WSR-88D Intermountain West relationship had the best agreement with the precipitation gauges. Airborne snow depth observations show that SQUIRE captures regions of orographic enhancement in the mountains to the west and northwest of the SAIL study area, indicating that the scientific community should focus on understanding and ultimately simulating orographic atmospheric precipitation processes to improve UCRB snowpack SWE assessment and forecasting.

Hydrology↗

DOE OSTI2020

CACTI Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed a large number of instruments to a region nearby the Sierra de Córdobas mountains in Argentina as part of the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign (1). During this campaign, four radars were installed at a site outside of Villa Yacanto as shown in Figure 1. As part of a post-campaign effort to improve the usability of these data, a significant activity was undertaken towards the calibration, correction, and improvement of the data quality of these radar datastreams. This process in ARM nomenclature is referred to as generating a “b1” datastream. While these “b1” standards may imply different corrections or standards for various ARM instruments, for radars it refers to a datastream that has been calibrated (and cross-calibrated), including a serious effort to deliver the highest-quality (well-characterized) data possible. This report details (i) the status/quality of the original “a1” (raw) data, (ii) the corrections and calibrations that are applied to generate these b1 datastreams, (iii) the details of the applied algorithms and how radar offset/calibration numbers were determined for the eventual corrections, and (iv) the new and flexible plug-in-based processing system designed during CACTI for radar b1 activities (current, future) that interfaces with ARM’s Data Integrator (ADI) and high-performance computing (HPC) system.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Sensitivity of tropical orographic precipitation to wind speed with implications for future projections

Abstract. Some of the rainiest regions on Earth lie upstream of tropical mountains, where the interaction of prevailing winds with orography produces frequent precipitating convection. Yet the response of tropical orographic precipitation to the large-scale wind and temperature variations induced by anthropogenic climate change remains largely unconstrained. Here, we quantify the sensitivity of tropical orographic precipitation to background cross-slope wind using theory, idealized simulations, and observations. We build on a recently developed theoretical framework that characterizes the orographic enhancement of seasonal mean precipitation, relative to upstream regions, as a response of convection to cooling and moistening of the lower free troposphere by stationary orographic gravity waves. Using this framework and convection-permitting simulations, we show that higher cross-slope wind speeds deepen the penetration of the cool and moist gravity wave perturbation upstream of orography, resulting in a mean rainfall increase of 20 % (m s−1)−1 to 30 % (m s−1)−1 increase in cross-slope wind speed. Additionally, we show that orographic precipitation in five tropical regions exhibits a similar dependence on changes in cross-slope wind at both seasonal and daily timescales. Given next-century changes in large-scale winds around tropical orography projected by global climate models, this strong scaling rate implies wind-induced changes in some of Earth's rainiest regions that are comparable with any produced directly by increases in global mean temperature and humidity.

Nicolas, Quentin (ORCID:000000024116973X)↗

DOE OSTI2020

Assessing Shifts in Regional Hydroclimatic Conditions of U.S. River Basins in Response to Climate Change over the 21st Century

Characterization of shifts in regional hydroclimatic conditions helps reduce negative consequences on agriculture, environment, economy, society, and ecosystem. This study assesses shifts in regional hydroclimatic conditions across the conterminous United States in response to climate change over the 21 st Century. The hydrological responses of five downscaled climate models from the Multivariate Adaptive Constructed Analogs (MACA) dataset ranging from the driest to wettest and least warm to hottest were simulated using the Variable Infiltration Capacity (VIC) model. Shifts in regional hydroclimatic conditions at 8-digit hydrologic unit scale (HUC8) were evaluated by the magnitude and direction of movements in the Budyko space. HUC8 river basins were then clustered into seven unique hydroclimatic behavior groups using the K-means method. A tree classification method was proposed to illustrate the relationships between hydroclimatic behavior groups and regional characteristics. The results indicate that hydroclimatic responses may vary from a river basin to another, but basins in the same neighborhood follow a similar movement in the Budyko space. The systematic hydroclimatic behavior of river basins is highly associated with their regional landform, climate, and ecosystem characteristics. Most HUC8s with Mountain, Plateau and Basin landform types will likely experience less arid conditions. However, most HUC8s with Plain landform type behave differently according to the regional ecosystem and climate. This study provides a potential roadmap of shifts in regional hydroclimatic conditions of U.S. river basins, which can be used to improve regional preparedness and ability of various sectors to mitigate or adapt to the impacts of future hydroclimate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Evaluation and Optimization of Snow Albedo Scheme in Noah-MP Land Surface Model Using In Situ Spectral Observations in the Colorado Rockies

The Biosphere-Atmosphere Transfer Scheme (BATS) ground snow albedo algorithm is commonly used in land-surface models (LSM), weather forecasting and research applications. This study addresses key uncertainties in BATS simulated ground snow albedo within the Noah-MP LSM framework through evaluation and optimization of the Noah-MP BATS ground snow albedo formulation using 2-band (visible and near-infrared (NIR)) in situ albedo observations at Rocky Mountain field stations. The Noah-MP BATS ground snow albedo scheme is extremely sensitive to its input parameters. Namely, an ensemble generated by varying BATS input parameters within potentially plausible ranges provides an average daily range (maximum ensemble member minus minimum ensemble member) of ground snow albedo exceeding 0.45 in visible and NIR bands. Parameter optimization improves agreement between simulated and in situ observed ground snow albedo in visible, NIR and broadband spectrums. Importantly, optimized parameters result in reduced biases relative to observed fresh-snow albedo and better agreement with observed albedo decay. Our analysis across different sites supports that the optimized BATS ground snow albedo parameters are appropriate to transfer in space and time, at least within the region studied (the central-southern Rocky Mountains). The primary error source remaining after parameter optimization is that observed fresh-snow albedo is highly variable, particularly in the NIR spectrum, whereas BATS fresh-snow albedo is constant, an issue which requires further investigation. This study shows significant correlations between observed fresh-snow albedo and surface meteorological conditions (e.g., downward shortwave radiation and temperature) which can support future model development that attempts to include a time-varying formulation for fresh-snow albedo.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

The detection and attribution of extreme reductions in vegetation growth across the global land surface

Abstract Negative extreme anomalies in vegetation growth (NEGs) usually indicate severely impaired ecosystem services. These NEGs can result from diverse natural and anthropogenic causes, especially climate extremes (CEs). However, the relationship between NEGs and many types of CEs remains largely unknown at regional and global scales. Here, with satellite‐derived vegetation index data and supporting tree‐ring chronologies, we identify periods of NEGs from 1981 to 2015 across the global land surface. We find 70% of these NEGs are attributable to five types of CEs and their combinations, with compound CEs generally more detrimental than individual ones. More importantly, we find that dominant CEs for NEGs vary by biome and region. Specifically, cold and/or wet extremes dominate NEGs in temperate mountains and high latitudes, whereas soil drought and related compound extremes are primarily responsible for NEGs in wet tropical, arid and semi‐arid regions. Key characteristics (e.g., the frequency, intensity and duration of CEs, and the vulnerability of vegetation) that determine the dominance of CEs are also region‐ and biome‐dependent. For example, in the wet tropics, dominant individual CEs have both higher intensity and longer duration than non‐dominant ones. However, in the dry tropics and some temperate regions, a longer CE duration is more important than higher intensity. Our work provides the first global accounting of the attribution of NEGs to diverse climatic extremes. Our analysis has important implications for developing climate‐specific disaster prevention and mitigation plans among different regions of the globe in a changing climate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

CACTI ARM Aerial Facility Measurements of Ice Nucleating Particles

The dataset comprises measures, using Colorado State University's Ice Spectrometer (IS, an immersion freezing device with a range from 0°C down to -26 to -29°C) of atmospheric ice nucleating particle (INP) concentrations taken on the Atmospheric Radiation Measurement (ARM) program Aerial Facilty (AAF) G-1 aircraft. INP measurements on the G-1 were collected from varied altitudes on different flights over the region of the Sierras de Córdoba mountain range of north-central Argentina, centred over ARM's Mobile Facility (AMF-1) near Villa Yacanto, where ground-based INP measures were being taken concurrently. Both studies took place as part of the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Experiment. A filter sampling system was deployed on the G-1 to collect aerosol particles for post-processing of INPs collected on filters (measuring their immersion freezing ability) once returned to Colorado State University (CSU). Filter holders used were pre-cleaned aluminum in-line units loaded with pre-cleaned and pre-sterilized 47 mm dia. Nuclepore polycarbonate filters (0.2 mm pore size). Filters were drawn for varied times, resulting in varied volumes collected (51 to 1667 SL). Mass flow rate was recorded in real-time so that total sampled volume (at standard temperature and pressure) could be determined. A total of 34 sample filters were collected over the IOP, including 5 blanks. Filters were stored at -20°C freezer prior to frozen return to Colorado State University (CSU). . Processing to obtain spectra of INP number concentration active via the immersion freezing mechanism versus temperature was conducted using CSU's IS instrument (McCluskey et al., 2018). For measurment of INPs, collected aerosol particles were re-suspended in 7 mL of 0.02 µm-filtered deionized water. Aliquots of each suspension, and serial dilutions, were dispensed into trays which were fit into aluminum blocks in the IS. Samples are cooled at 0.33°C min-1 and the freezing temperatures of wells recorded automatically. Cumulative INP concentrations were determined by first calculating the INPs per mL of suspension based on Vali (1971) and then converting to concentration per standard liter of air using the proportion of the total liquid sample dispensed and the air sample volumes. Aliquots of suspensions from selected samples were also heat treated (95°C for 20 min) to denature and deactivate biological INPs, and digested in 10% H2O2 at 95°C under UV-B to remove all organic carbon INPs. McCluskey, C. S., J. Ovadnevaite, M. Rinaldi, J. Atkinson, F. Belosi, D. Ceburnis, S. Marullo, T. C. J. Hill, U. Lohmann, Z. A. Kanji, C. O’Dowd, S. M. Kreidenweis, P. J. DeMott, 2018: Marine and Terrestrial Organic Ice Nucleating Particles in Pristine Marine to Continentally-Influenced Northeast Atlantic Air Masses, Journal of Geophysical Research: Atmospheres, 123, 6196–6212, https://doi.org/10.1029/2017JD028033. Vali, G., 1971: Quantitative evaluation of experimental results on the heterogeneous freezing nucleation of supercooled liquids. J. Atmos. Sci., 28, 402–409.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluating Variable‐Resolution CESM Over China and Western United States for Use in Water‐Energy Nexus and Impacts Modeling

Abstract Climate models are critical tools to study earth system processes and are often further downscaled to provide refined‐resolution data sets for regional water‐energy impact analyses. In this study, the Variable‐Resolution Community Earth System Model (VR‐CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR‐CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR‐CESM simulates +2°C warm bias in the low‐elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR‐CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water‐energy studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

54 ENVIRONMENTAL SCIENCES↗