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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

SPRUCE Manual Phenology Observations and Photographs Beginning in 2011

This dataset contains periodic (most often weekly during the active season) observations of the phenology of vegetation bud swell, leaf out, leaf off, flowering, fruiting and the nature of snow cover and ice presence from the 2011-2023 growing seasons. These observations are supplemented by the collection of images from periodic photographs of the experimental plots (e.g., panoramic photographs) or images within plots (e.g., pictures of vegetation development or community status within defined ground footprints such as flux collars). A summary of vegetation phenology and snow and ice presence form 2011-2016 is also provided. Related data sets are also available that summarize site phenology (see Related Datasets). This dataset contains 336 phenology notes files in *.pdf format, 2284 phenology photos in *.jpg format, and three companion files containing phenology summaries, methodological, other contextual information. Early observations were conducted throughout the S1-Bog with an emphasis on the southern end of the bog. Observations collected since the spring of 2013 have focused on the southern end of the S1-Bog and the plots 2 through 21 of the SPRUCE experiment. Beginning in 2015 those observations included assessments within the enclosure space surrounding plots 4, 6, 8, 10, 11, 13, 16, 17, 19 and 20. These observations are ongoing, and this dataset will be appended annually.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2021

Soil Temperature and Moisture, Council Road Mile Marker 71, Seward Peninsula, Alaska, beginning 2016

Daily averages of soil temperature and moisture measured once every hour at different heights, as well as daily averages of hourly measured snow depths located at Intensive Monitoring Stations at Council Road Mile Marker 71 site. Deployed at each site is an Onset HOBO U30 data logger with five smart temperature sensors and three smart soil moisture sensors (10HS). Three sites (CN_IS_4, CN_IS_6A, CN_IS_7) are equipped with a snow depth sensor. Data are retrieved annually since 2016. Contains 53 *.CSV files including a file inventory list by year. Data files have header rows, NaN fields indicate invalid or missing data, and negative vertical offsets are above ground. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Surface Meteorology at Teller Mile 47 Watershed, Seward Peninsula, Alaska, Ongoing from 2018

Meteorological data are currently being collected at one location at the Teller Mile 47 (TL_MM47) Research Basin Site, Seward Peninsula (N64 58' 36.918", W166 12' 32.67", 67 meters above sea level). The site was installed and initial measurements started in September 2018 and it has operated continuously since then. The meteorological station is co-located with a continuous snow depth sensor and two soil pits for subsurface temperature and moisture measurements. These data are being collected to better understand the energy dynamics above the active layer and permafrost. They complement in-situ snow and soil measurements also at this location. The data could also be used as supporting measurements for other research and modeling activities. There are 35 comma separated value format (*.csv) files provided, where each file contains the full data for an individual parameter (e.g. air temperature at 1.5 meters above the ground surface (teller_m47_air_temperature_150cm_ags_Avg.csv) or soil temperature 20 centimeters below ground surface (teller_m47_dry_soil_pit_temperature_20cmbgs_Avg.csv)) plus the time in Universal Coordinated Time (UTC) and Alaska Standard Time (UTC time minus nine hours). The site was installed and initial measurements started in September 2018. It has been operated continuously since. Primary data gaps are due to battery failure or sensor failure. These data are being collected to better understand the surface energy dynamics above the active layer and permafrost. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 10-year research effort (2012-2022) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy?s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy?s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Digital camera imagery for vegetation phenology, Seward Peninsula, Alaska, 2019-2021

Time lapse camera images from Kougarok mile marker (MM) 64 and Teller MM 27 NGEE-Arctic field sites on the Seward Peninsula, Alaska, captured from July 2019 to August 2021. Thirty three Wingscape Timelapse Pro cameras were deployed targeting patches of low and tall shrubs (including Alnus sp. and Salix sp.) and general vegetation and landscape views. Images from were recorded at hourly intervals from 11 AM to 2 PM, continuously for 25 months and capture vegetation phenology, snow accumulation and snow melt events. This data package includes images (*.jpg), organized by site and camera ID, and metadata with details of the cameras used, number of images recorded, start and end dates, GPS locations and example fields of view. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Colorado East River Community Observatory Data Collection

Abstract The U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow‐dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long‐term and spatially‐extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross‐disciplinary process‐based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological, genomic, biogeochemical, climate, vegetation, geological, and remote sensing data, which combined with model inputs and outputs comprise a collection of datasets and value‐added products within a mountainous watershed that span multiple spatiotemporal scales, compartments, and life zones. Within 5 years of collection, these datasets have revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over 70 ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Projecting climate change in South America using variable‐resolution Community Earth System Model: An application to Chile

Abstract We introduce variable‐resolution enabled Community Earth System Model (VR‐CESM) results simulating historical and future climate conditions at 28 km over South America and 14 km over the Andes. Three 30‐year simulations are performed: a historic (1985–2014), a near future (2030–2059), and an end‐century (2070–2099) simulation under the RCP8.5 scenario. Historic results compare favourably to several temperature and precipitation reanalysis products, though local biases are present, particularly during austral summer. Future simulations highlight broad warming patterns (+3–6°C by end‐century) and heterogeneous precipitation responses across South America that qualitatively agree with prior modelling efforts. Our results reveal that the interaction between temperature and precipitation changes produce shifts in several Köppen–Geiger climates. Notable changes include the near‐elimination of the Andean Tundra or Alpine climates, a 15% decrease in Tropical Rainforests and a Tropical Savannah expansion of 20%. To provide a regionally focused analysis of projected climate change and to illustrate the benefits of variable resolution modelling, we analyse changes in the magnitude and trend in seasonal and daily temperature and precipitation in Chile. We also examined several metrics [e.g., snow water equivalent (SWE), temperatures on wet days, and days below 0°C] to evaluate potential impacts of climate change on the Chilean cryosphere between the end‐of‐century and historic periods, finding wide‐ranging indications of cryospheric decline. These changes are interpreted through reductions in the timing (1–2.5 months earlier peak SWE) and magnitude (200–1,000 mm SWE decreases) of water stored as snow in the Andes, a 10–30% decrease in number of cool season wet days with temperatures below 1°C, and 50–200 fewer days (annually) with minimum temperatures below 0°C. Our aim in producing a high‐resolution dataset of climate projections from VR‐CESM is to support analyses of climate change throughout South America but especially in vulnerable montane regions and to provide additional results for comparison with previous, ongoing, and upcoming modelling efforts.

variable-resolution global climate models↗
DOE OSTI2021

The influence of tillage and fertilizer on the flux and source of nitrous oxide with reference to atmospheric variation using laser spectroscopy

Nitrous oxide (N 2 O) is the third most important long-lived greenhouse gas and agriculture is the largest source of N 2 O emissions. Curbing N 2 O emissions requires understanding influences on the flux and sources of N 2 O. We measured flux and evaluated microbial sources of N 2 O using site preference ( S P ; the intramolecular distribution of 15 N in N 2 O) in flux chambers from a grassland tilling and agricultural fertilization experiments and atmosphere. We identified values greater than that of the average atmosphere to reflect nitrification and/or fungal denitrification and those lower than atmosphere as increased denitrification. Our spectroscopic approach was based on an extensive calibration with 18 standards that yielded S P accuracy and reproducibility of 0.7 ‰ and 1.0 ‰, respectively, without preconcentration. Chamber samples from the tilling experiment taken ~ monthly over a year showed a wide range in N 2 O flux (0–1.9 g N 2 O-N ha -1 d -1 ) and S P (- 1.8 to 25.1 ‰). Flux and S P were not influenced by tilling but responded to sampling date. Large fluxes occurred in October and May in no-till when soils were warm and moist and during a spring thaw, an event likely representing release of N 2 O accumulated under snow cover. These high fluxes could not be ascribed to a single microbial process as S P differed among chambers. However, the year-long S P and flux data for no-till showed a slight direct relationship suggesting that nitrification increased with flux. The comparative data in till showed an inverse relationship indicating that high flux events are driven by denitrification. Corn ( Zea mays ) showed high fluxes and S P values indicative of nitrification ~ 4 wk after fertilization with subsequent declines in S P indicating denitrification. Although there was no effect of fertilizer treatment on flux or S P in switchgrass ( Panicum virgatum) , high fluxes occurred ~1 month after fertilization. In both treatments, S P was indicative of denitrification in many instances, but evidence of nitrification/fungal denitrification also prevailed. At 2 m atmospheric N 2 O S P had a range of 31.1 ‰ and 14.6 ‰ in the grassland tilling and agricultural fertilization experiments, respectively. These data suggest the influence of soil microbial processes on atmospheric N 2 O and argue against the use of the global average atmospheric S P in isotopic modeling approaches.

Environmental Sciences & Ecology↗
DOE OSTI2025

Uncertainty quantification and optimization of precipitating hydrometeor parameters for winter precipitation in a cloud microphysics scheme

The precipitating hydrometeor parameters used in cloud microphysics schemes carry inherent uncertainties. The quantification of these uncertainties, together with parameter optimization, can significantly improve precipitation forecasts. This study investigates the effects of 13 parameters in the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme, which define the hydrometeor characteristics such as fall velocity–diameter and mass–diameter relationships, as well as the shape parameter of the drop size distribution for precipitating particles such as rain, snow, and graupel on simulated winter precipitation. A comparison between the model's pre-defined parameters and observations from the International Collaborative Experiments for the PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign reveals that the fall velocity–diameter relationship for rain, the mass–diameter relationships for snow and graupel, and the shape parameters for all precipitating particles in the WDM6 scheme deviate from the median values observed by the two-dimensional video disdrometer (2DVD). To quantify parameter sensitivities, a perturbed parameter ensemble (PPE) of 256 simulations was conducted within parameter ranges constrained by 2DVD observations for three winter precipitation cases. Bayesian optimization was then applied to identify parameter sets that minimized the root mean square error (RMSE) for each case, achieving reductions of up to 30.2 %. These results demonstrate that ensemble-based uncertainty quantification and parameter optimization can help identify key parameters and provide a pathway to improving precipitation simulation performance. In addition, measurement sites can be strategically selected based on regions that show high sensitivity to variations in hydrometeor characteristic parameters.

Bayesian optimization↗
DOE OSTI2024

Coal-derived electrically conductive asphalt pavements for snow/ice melting: From laboratory to field

Timely removal of ice and snow from roads is critical to safe, fast, and uninterrupted transportation networks in cold regions. Constructing electrically conductive asphalt pavements to melt the ice and snow on the roads through resistive eating is an emerging alternative technology to traditional snow/ice removal approaches such as utilizing snowplow machines and deicing chemicals. Carbon-based fibers and fillers including carbon fiber and graphite have been widely reported to make electrically conductive hot mix asphalt mixtures for pavement snow/ice-melting applications. This study aimed to develop and demonstrate a novel type of electrically conductive asphalt pavements for snow/ice-melting, which utilizes electrically conductive cold mix asphalt (CMA) mixtures incorporating coal-derived carbon-based coke aggregate as resistive heating elements. Both laboratory experiments and field tests were conducted to investigate the electrical, mechanical, and thermal properties of such electrically conductive asphalt mixtures and pavements. The laboratory experiment results indicated that the electrically conductive CMA mixtures incorporating coke aggregate had sufficiently high electrical conductivity and satisfactory mechanical performance and the pavement prototype slab utilizing a thin layer of such CMA mixtures could successfully raise the pavement surface temperatures to 8.3–11.7 °C rom a low temperature of –5 °C with an input power density of 473 W/m 2 . The field test results showed that the full-scale coal-derived electrically conductive asphalt pavements were able to increase the pavement surface temperatures when electricity was applied, but the magnitude of temperature increase was highly dependent on the power density. Furthermore, it is promising to use coke aggregate to construct coal-derived electrically conductive asphalt pavements for snow/ice melting.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Elevation distributed micro-climatology data in a coastal glaciated watershed

Air temperature, ground temperature and relative humidity data were collected in a longitudinal transect of the Nooksack watershed at varying elevations from 500 to 1800 m above sea level. Data were collected by anchoring sensors from trees above winter snow levels and shaded from direct solar radiation. Paired sensors were also buried 3 cm under ground near each air temperature sensor to determine snow absence or presence. Select sites included relative humidity sensors to indicate whether precipitation was occurring. Data were collected every 3-4 h from December 2015 to Sept 2018 (with ongoing collection). Code for analysis of daily mean, minimum, maximum, and temperature change with elevation (lapse rates) are available on Github (https://doi.org/10.5281/zenodo.3239539). The sensor download and intermediate data products are available on HydroShare at (http://www.hydroshare.org/resource/222e832d3df24dea9bae9bbeb6f4219d) with publicly accessible visualization available from the Nooksack Observatory at data.cuahsi.org. Hydrologic models are generally structured with a single annual average lapse rate parameter which assumes a linear temperature gradient with elevation. The daily data (2016-2018) is used as part of ongoing studies on the non-linear dynamics and temporal variability of temperature with elevation to improve assessments of watershed function and salmon habitat.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Projected changes in extreme streamflow and inland flooding in the mid-21st century over Northeastern United States using ensemble WRF-Hydro simulations

Study region: Northeastern United States (NEUS). Study focus: We investigate the potential impacts of climate change on precipitation, streamflow, and inland flooding in the NEUS during the mid-21st century. Dynamically downscaled climate projections from three global climate models for historical (1995-2004) and future (2045-2054) periods under business-as-usual scenarios were used to force the hydrologic model WRF-Hydro at 200-meter resolution and create ensemble hydrologic simulations. Additionally, an extreme value model was developed to project the risks associated with low-frequency hydrologic events. New hydrological insights for the region: Results from four major watersheds indicate a significantly wetter regime in winter months and potential drier conditions during late spring to early summer. Discharges in fall are projected to decrease in the northern watersheds and increase toward the south. Extreme flow and water depths resulting from extreme inland flooding are projected to increase by 5-20% and > 100%, respectively. The extent of the total flooded area is likely to be 20% greater by the mid-century. These increased risks can be attributed to (i) an approximate 25% increase in decadal mean and > 40% increase in decadal extreme precipitation intensity, (ii) up to 30% lower snow availability and 5-25% higher evapotranspiration throughout the year, and (iii) a projected 5% increase in soil moisture in all seasons except summer. Furthermore, rapid snow melting in winter will likely cause an earlier peak flow in the rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Systematic Evaluation of Atmospheric Forcing, Surface Datasets, and Mesh Effects on Kilometer-Scale Land Surface and River Modeling

Earth system models are advancing toward kilometer-scale resolution to capture local climate impacts and extremes. High-resolution land and river modeling depends on multiple factors, including mesh, surface datasets, and atmospheric forcing, but their relative effects at kilometer scales remain unquantified. We evaluated five Energy Exascale Earth System Model land and river configurations over the Mid-Atlantic region using two mesh (1/8° structured versus variable-resolution unstructured mesh), two surface datasets (default versus newly developed), and three atmospheric forcings (NLDAS2, MSWX, GSWP). Evaluation against satellite, reanalysis, and in situ benchmarks across water, energy, and carbon cycles quantifies how these factors affect model performance. Forcing selection produces the largest bias reductions (12-99% across variables), followed by surface datasets (7-75%) and mesh (up to 21%). Forcing effects vary by variable, with MSWX reducing biases for snow water equivalent, evapotranspiration, albedo, temperature, and gross primary productivity, GSWP for snow cover and runoff, and NLDAS for soil moisture and streamflow. The use of newly developed surface datasets improves gross primary productivity (58% bias reduction) and evapotranspiration but increase soil moisture and albedo biases due to current modeling limitations. Variable-resolution unstructured mesh improves the simulation of small-basin streamflow through better capturing drainage networks, though mesh minimally affects other land variables. These findings provide important guidance for high-resolution modeling development and actionable science.

Land and River modeling↗
DOE OSTI2023

How does humidity data impact land surface modeling of hydrothermal regimes at a permafrost site in Utqia$\mathrm{\dot{g}}$vik, Alaska?

Humidity is a basic and crucial meteorological indicator commonly measured in several forms, including specific humidity, relative humidity, and absolute humidity. These different forms can be inter-derived based on the saturation vapor pressure (SVP). In past decades, dozens of formulae have been developed to calculate the SVP with respect to, and in equilibrium with, liquid water and solid ice surfaces, but many prior studies use a single function for all temperature ranges, without considering the distinction between over the liquid water and ice surfaces. These different approaches can result in humidity estimates that may impact our understanding of surface-subsurface thermal-hydrological dynamics in cold regions. In this study, we compared the relative humidity (RH) downloaded and calculated from four data sources in Alaska based on five commonly used SVP formulas. These RHs, along with other meteorological indicators, were then used to drive physics-rich land surface models at a permafrost-affected site. Here, we found that higher values of RH (up to 40%) were obtained if the SVP was calculated with the over-ice formulation when air temperatures were below freezing, which could lead to a 30% maximum difference in snow depths. The choice of whether to separately calculate the SVP over an ice surface in winter also produced a significant range (up to 0.2 m) in simulated annual maximum thaw depths. The sensitivity of seasonal thaw depth to the formulation of SVP increases with the rainfall rate and the height of above-ground ponded water, while it diminishes with warmer air temperatures. These results show that RH variations based on the calculation of SVP with or without over-ice calculation meaningfully impact physically-based predictions of snow depth, sublimation, soil temperature, and active layer thickness. Under particular conditions, when severe flooding (inundation) and cool air temperatures are present, care should be taken to evaluate how humidity data is estimated for land surface and earth system modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Simulating a Mesoscale Convective System Using WRF with a New Spectral Bin Microphysics: Part 1: Hail vs Graupel

A modified Fast Spectral Bin Microphysics scheme (FSBM-2) embedded into the Weather Research and Forecasting Model (WRF) is used to simulate a mesoscale deep convective system observed during the Midlatitude Continental Convective Clouds Experiment (MC3E). FSBM-2 uses modified source codes as compared to the current FSBM (FSBM-1). In contrast to FSBM-1, FSBM-2 can simulate hail of several cm in diameter and includes additional processes such as spontaneous breakup of raindrops and aerosol regeneration by drop evaporation. It is shown that allowing large hail particles of diameters exceeding about 1 cm substantially increases the agreement between the simulated and observed squall-line structures both in the convective and stratiform regions. In contrast, if graupel particles are used to represent high-density hydrometeors in convective areas, the ratio of convective-to-stratiform areas diverges from the ratio seen in observations and maximum radar reflectivities are substantially underestimated. Analysis of snow size distributions in the stratiform area shows an important link between the ice crystals formed by homogeneous freezing in the convective area to ice particle number concentration in the stratiform region. Simulated raindrop size distributions in the stratiform area below the melting level from FSBM-2 show a good agreement with observations. The regeneration of CCN by droplet evaporation and detrainment of these CCN to the stratiform region increases the concentration of CCN there up to 8-9 km altitude; the additional CCN penetrate clouds and produce new droplets, leading to some convection intensification. Key words: Mesoscale convective systems (MCS), squall lines, spectral bin microphysics, large hail, in-cloud nucleation, size distributions of hydrometeors in MCS.

Shpund, Kobby↗
DOE OSTI2020

Future Warming and Intensification of Precipitation Extremes: A “Double Whammy” Leading to Increasing Flood Risk in California

This study focuses on quantifying future anthropogenic changes in surface runoff associated with extreme precipitation in California's Sierra Nevada. The method involves driving a land surface model with output from a high resolution regional atmospheric simulation of the most extreme atmospheric rivers (ARs). AR events were selected from an ensemble of global climate model simulations of historical and late 21st century climate under the “high-emission” RCP8.5 scenario. Average precipitation during the future ARs increases by ~25% but a much lower proportion falls as snow. The resulting future runoff increase is dramatic—nearly 50%, reflecting both the precipitation increase and simultaneous conversion of snow to rain. The “double whammy” impact on runoff is largest in the 2,000–2,500 m elevation band, where the snowfall loss and precipitation increase are both especially large. Overall, this huge increase in runoff during the most extreme AR events could present major flood control challenges for the region.

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↗