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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Dual-Polarization Radar Fingerprints of Precipitation Physics: A Review

This article reviews how precipitation microphysics processes are observed in dual-polarization radar observations. These so-called “fingerprints” of precipitation processes are observed as vertical gradients in radar observables. Fingerprints of rain processes are first reviewed, followed by processes involving snow and ice. Then, emerging research is introduced, which includes more quantitative analysis of these dual-polarization radar fingerprints to obtain microphysics model parameters and microphysical process rates. New results based on a detailed rain shaft bin microphysical model are presented, and we conclude with an outlook of potentially fruitful future research directions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Investigating Mountain Watershed Headwater‐To‐Groundwater Connections, Water Sources, and Storage Selection Behavior With Dynamic‐Flux Particle Tracking

Abstract Climate change will impact mountain watershed streamflow both directly—with changing precipitation amounts and variability—and indirectly—through temperature shifts altering snowpack, melt, and evapotranspiration. To understand how these complex processes will affect ecosystem functioning and water resources, we need tools to distinguish connections between water sources (rain/snowmelt), groundwater storage, and exit fluxes (streamflow/evapotranspiration), and to determine how these connections change seasonally and as climate shifts. Here, we develop novel watershed‐scale approaches to understand water source, storage, and exit flux connections using a dynamic‐flux particle tracking model (EcoSLIM) applied in California's Cosumnes Watershed, which connects the Sierra Nevada and Central Valley. This work develops new visualizations and applications to provide mechanistic understanding that underpins the interpretation of isotopic field data at watershed scales to distinguish sources, flow paths, residence times, and storage selection. In our simulations, streamflow comes primarily from snow‐derived water while evapotranspiration generally comes from rain. Most streamflow starts above 1,000 m while evapotranspiration is sourced relatively evenly across the watershed and is generally younger than streamflow. Modeled streamflow consists primarily of water sourced from precipitation in the previous 5 years but before the current water year, while ET consists primarily of water from precipitation in the current water year. ET, and to a lesser extent streamflow, are both younger than water in groundwater storage. However, snowmelt‐derived streamflow preferentially discharges older water from snow‐derived storage. Dynamic‐flux particle tracking and new approaches presented here enable novel model‐tracer comparisons in large‐scale watersheds to better understand watershed behavior in a changing climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A New Method for Ice-Ice Aggregation in the Adaptive Habit Model

In this paper, a novel methodology for modeling ice-ice aggregation is presented. This methodology combines a modified hydrodynamic collection algorithm with bulk aggregate characteristic information from an offline simulator that collects ice particles, namely, the Ice Particle and Aggregate Simulator, and has been implemented into the Adaptive Habit Microphysics scheme in the Weather Research and Forecasting Model. Aggregates, or snow, are formed via collection of cloud ice particles, where initial ice characteristics and the resulting geometry determine aggregate characteristics. Upon implementation, idealized squall-line simulations are performed to examine the new methodology in comparison with commonly used bulk microphysics schemes. It is found that the adaptive habit aggregation parameterization develops snow and reduces ice mass and number concentrations compared to other schemes. The development of aggregates through the new methodology cascades into other interesting effects, including enhancements in ice and snow growth, as well as homogeneous freezing. Further microphysical analyses reveal varying sensitivities, where snow processes are most sensitive to the new parameterization, followed by ice, then cloud, rain, and graupel processes. Further, the new scheme results in enhancements in surface precipitation due to the persistence of snow at lower altitudes. This persistence is a result of shape-dependent melting and sublimation, increasing the residence time. Moreover, these low-level enhancements are reflected in increases in radar reflectivity at the surface and its spatial distribution. Finally, the ability to predict snow shape and density allows for the simulation of polarimetric radar quantities, resulting in signature enhancements compared to schemes that do not consider spatial and temporal variations in snow shape and density.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Aerosol-deep convection interaction based on joint cell-thermal tracking in Large Eddy Simulations during the TRACER campaign

In cumulus clouds, aerosol concentrations control cloud droplet concentrations, modifying cloud radiative properties, precipitation processes, and cloud electrification. However, mechanisms of aerosol-deep convection interactions are not well understood due to complex cloud dynamics and microphysics. We investigate the interaction of aerosols with isolated deep convection using Large Eddy Simulations of two cases during the TRacking Aerosol Convection interactions ExpeRiment (TRACER) near Houston, Texas, using a joint cell-thermal tracking algorithm. Cumulus thermals are droplet generators, since supersaturation and droplet nucleation coincide with thermal centers, where the strongest updrafts occur. Primary ice crystal formation does not take place inside thermals, but at layers where previous thermals detrained moisture. As subsequent thermals containing supercooled droplets penetrate these layers, hail and graupel form at or near these thermals. Higher aerosol concentrations result in higher droplet concentrations that suppress drizzle, delay warm rain processes, and transport more moisture aloft. This increases snow and ice amount, as well as graupel and hail, leading to more lightning. Polluted thermals initiate at slightly higher altitudes, and are slightly larger and faster, suggesting a weak invigoration. We also find more thermals per cell, but fewer isolated cells, since convection is more aggregated and intense, especially near the end of the 24 h simulation. Non-linear mesoscale feedback likely triggered by temperature and moisture responses to aerosol-thermal interactions causes the aggregation. Time-lagged aerosol-reinitialization experiments show that the mesoscale response is the predominant forcing for the invigoration. These changes happen within one day, on a smaller scale than previously suggested.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data and scripts associated with the manuscript evaluating the hydrologic responses of the Pacific Northwest watersheds to wildfires (v2)

This data package is associated with the publication “Evaluating Post-fire Watershed Response to Varying Burn Severity and Precipitation Regimes Using Fully-distributed and Integrated Hydrologic Models” submitted to Journal of Hydrology (Li et al. 2025). In this study, we employed the Advanced Terrestrial Simulator (ATS), an integrated watershed model that couples surface flow, subsurface flow, and canopy biophysical processes, to investigate post-fire hydrologic responses in a few selected watersheds with varying burn severity.The data package contains the required input data (meteorological forcing, Leaf Area Index, wildfire burn severities, etc.) to run the model, configuration files, the Jupyter notebooks in Python to pre-process and post-process data, the figures in the manuscript, and the modeling output files. The variables include watershed-averaged evapotranspiration, watershed-averaged surface/subsurface/canopy water content, and river discharge at watershed outlet.The data package contains a file-level metadata that lists and describes all the files contained in the data package (ATS_flmd.csv), a data dictionary file that defines columns headers across all csv files contained in the data package (ATS_dd.csv), a data package level readme file (the current file), and four zipped folders.The ‘data’ folder provides data needed to run the model in .h5, .i2s, .xyz, .shp, and .exo formats. The sub-folders are for each data types. The ‘model’ folder provides input files (.xml format) and essential model outputs. Each sub-folder provides the files from each simulated watershed. The ‘notebooks’ folder provides the Jupyter notebooks (.ipynb format) for pre- and post- processing model files, and for producing the figures in the manuscript. The ‘figures’ folder provides the figures associated with manuscript in .pdf and .png formats.The ‘model’ folder and the ‘data’ folder have been split into 5GB-large pieces using the Linux command ‘split -b 5120m model.zip model.zip.’ and ‘split -b 5120m data.zip data.zip.’, respectively. They can be merged back using the Linux command ‘cat model.zip.* > model.zip’ and ‘cat data.zip.* > data.zip’, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions

Machine learning (ML) models, and Long Short-Term Memory (LSTM) networks in particular, have demonstrated remarkable performance in streamflow prediction and are increasingly being used by the hydrological research community. However, most of these applications do not include uncertainty quantification (UQ). ML models are data driven and can suffer from large extrapolation errors when applied to changing climate/environmental conditions. UQ is required to quantify the influence of data noises on model predictions and avoid overconfident projections in extrapolation. In this work, we integrate a novel UQ method, called PI3NN, with LSTM networks for streamflow prediction. PI3NN calculates Prediction Intervals by training 3 Neural Networks. It can precisely quantify the predictive uncertainty caused by the data noise and identify out-of-distribution (OOD) data in a non-stationary condition to avoid overconfident predictions. We apply the PI3NN-LSTM method in the snow-dominant East River Watershed in the western US and in the rain-driven Walker Branch Watershed in the southeastern US. Results indicate that for the prediction data which have similar features as the training data, PI3NN precisely quantifies the predictive uncertainty with the desired confidence level; and for the OOD data where the LSTM network fails to make accurate predictions, PI3NN produces a reasonably large uncertainty indicating that the results are not trustworthy and should avoid overconfidence. PI3NN is computationally efficient, robust in performance, and generalizable to various network structures and data with no distributional assumptions. It can be broadly applied in ML-based hydrological simulations for credible prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Surface heat flux and its association with the MJO in the tropical western Pacific using ARM observations

The Madden-Julian oscillation (MJO) is a major atmospheric phenomenon in the tropics that moves eastward every 20 to 100 days. It brings heavy rain and strong winds, influencing extreme weather events far beyond the tropics – including flooding, hurricanes, tornadoes and heavy snow in the United States. Therefore, an improved understanding of the MJO is critical for enhancing weather forecasts and supporting better decision-making for communities, emergency managers, and the private sector. Past studies, based on short-term observations or long-term model data, have emphasized the dominant role of atmospheric humidity in driving the MJO. Our research, using long-term observations (2000-2014) from three U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) sites in the tropical western Pacific (Manus, Nauru, and Darwin) confirms those past findings. However, we also show that atmospheric temperature, especially in the mid and upper troposphere, also plays a key role in the MJO’s evolution from its quiet (suppressed) to convective (active) phase. These findings provide valuable insights into how the MJO evolves and may help evaluate and improve weather and climate models. Our study also examined how the exchange of heat between the Earth's surface and the atmosphere, called surface heat flux, interacts with the MJO. This flux includes components such as latent heat (related to evaporation), sensible heat (related to temperature difference between surface and atmosphere), and radiation. We found that modulation in MJO convection is well connected to surface heat flux over the tropical western Pacific. One surface heat flux component often overlooked is the sensible heat flux due to precipitation (Q P ). Since falling raindrops are typically cooler than the land or ocean surface, they can cool the surface and affect local weather. To test the impact of Q P on convection during MJO, we incorporated it into a weather model, and ran simulations for two periods: April 2009 (when the MJO was active) and June 2006 (when it was not). Including Q P reduced errors in simulating the daily peak of rainfall—by 83% during the strong MJO and 23% during the inactive phase. It also improved the timing of peak daily rainfall and reduced the overall precipitation error by about 10%. These improvements were especially noticeable during periods of heavy rainfall. Our results suggest that even small heat exchanges from rainfall can play a significant role in shaping local weather. Accounting for these effects, particularly in regions like the tropical islands, can lead to more accurate simulations of the MJO-associated precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Energy‐Water Asynchrony Principally Determines Water Available for Runoff From Snowmelt in Continental Montane Forests

ABSTRACT Changes in the volume, rate, and timing of the snowmelt water pulse have profound implications for seasonal soil moisture, evapotranspiration (ET), groundwater recharge, and downstream water availability, especially in the context of climate change. Here, we present an empirical analysis of water available for runoff using five eddy covariance towers located in continental montane forests across a regional gradient of snow depth, precipitation seasonality, and aridity. We specifically investigated how energy‐water asynchrony (i.e., snowmelt timing relative to atmospheric demand), surface water input intensity (rain and snowmelt), and observed winter ET (winter AET) impact multiple water balance metrics that determine water available for runoff (WAfR). Overall, we found that WAfR had the strongest relationship with energy‐water asynchrony (adjusted r 2 = 0.52) and that winter AET was correlated to total water year evapotranspiration but not to other water balance metrics. Stepwise regression analysis demonstrated that none of the tested mechanisms were strongly related to the Budyko‐type runoff anomaly (highest adjusted r 2 = 0.21). We, therefore, conclude that WAfR from continental montane forests is most sensitive to the degree of energy‐water asynchrony that occurs. The results of this empirical study identify the physical mechanisms driving variability of WAfR in continental montane forests and are thus broadly relevant to the hydrologic management and modelling communities.

Webb, Ryan William↗
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 OSTI2021

Impact of a New Cloud Microphysics Parameterization on the Simulations of Mesoscale Convective Systems in E3SM

Abstract Mesoscale convective systems (MCSs) are one of the most climatically significant forms of convection because of their large role in water and energy cycles. The mesoscale features associated with MCS are difficult to represent in climate models because the relevant dynamics and physics are absent or poorly represented with coarse model resolution (∼100 km). Using a regionally refined model (RRM) with 0.25° grid spacing embedded in the Energy Exascale Earth System Model (E3SM), we explore the impact of cloud microphysics parameterizations on the simulation of precipitation, particularly MCS precipitation over the contiguous United States. The Predicted Particle Properties (P3) cloud microphysics scheme has been modified and implemented into E3SM to overcome the limitations of the default Morrison and Gettelman (MG2) scheme in which rimed precipitating ice particles (graupel/hail) are absent and frozen particles are artificially partitioned into cloud ice and snow. We show that P3 improves the simulation of precipitation statistics including frequency distribution compared with MG2 with a limited effect on the diurnal cycle. P3 predicts higher hourly rain rates, resulting in 20% more MCSs and a higher total MCS precipitation (4.4%) compared to MG2, agreeing better with observations. The improvements with P3 mainly result from improved representations of ice microphysics, which not only produces higher rain rates through melting but also leads to a stronger large‐scale ascending motion by releasing more latent heating. This study suggests that improving microphysics parameterization is important for simulating MCS precipitation as future climate model resolutions continue to increase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Opportunistic Short‐Term Water Uptake Dynamics by Subalpine Trees Observed via In Situ Water Isotope Measurements

Abstract Variations in tree water sources are important to understand in semi‐arid ecosystems because climatic shifts towards lower snowpack and increased drought affect water availability in subalpine forests of the western US. Here, we use daily in situ measurements of stable isotopes ( 2 H & 18 O) in soil and tree stem water, soil matric potential and sap flow to study tree water uptake dynamics. We instrumented three soil profiles down to 90 cm, as well as three aspen and engelmann spruce trees near Gothic, Colorado, in the East River watershed. We observed the fate of natural isotopic variations in rainfall, soil, and plants from June to October 2022, and in August 2023 we conducted a 2 H labeled irrigation experiment. Our observations showed that all studied aspen trees compensated for water scarcity in the shallow soil by shifting the dominant water source at 60(±20) cm to ⅔ of uptake from 90 cm within a few days of a dry period. Both species relied on snowmelt stored in the subsoil to sustain transpiration. Intense rainfall caused the plant water uptake to shift partially to top soil layers within 2 days. Spruce transpiration was lower and relied more on snowmelt, because rainfall infiltration was low in the spruce stand due to high canopy interception. Our findings highlight the important role of snowmelt stored in the deep soil layers for subalpine forest drought response and the dominant fate of monsoonal rainfall to become transpiration rather than recharging groundwater and streams in the Upper Colorado River. Plain Language Summary There is a need to understand how trees in mountainous regions respond to dry conditions that lead to water scarcity, because climate projections suggest that such conditions will become more frequent in the future. Here we present a novel data set of measurements of daily stable isotopes of water across soil profiles and in tree stems of aspen and spruce. Our data show that when the upper soil dried out, aspen trees shifted to using water from deeper layers (beneath 60 cm) to keep transpiring. For spruce trees the uptake pattern is less clear, but both types of trees mainly used snowmelt stored in the deeper soil layers to survive the dry summer. After heavy rain, aspen and spruce trees switched to using water from the top 20 cm of soil. However, for spruce, only some rain reached the soil because the dense tree canopy intercepted it, so spruce trees stayed more dependent on snowmelt and used less water overall. This study shows how important deep snowmelt water is for helping forests survive dry periods and suggests that most summer rain is quickly used by trees rather than replenishing streams and groundwater in the headwaters of the Colorado River. Key Points Tree water resources changed within a few days from snow dominated to higher share of rainfall as soils wetted up after a dry period Compensatory plant water uptake by aspen from the deep layer (90 cm), while uptake from soil depths that became drier (60 cm) declined Strong differences between water sources and availability beneath aspen and spruce, respectively

Sprenger, Matthias↗
DOE OSTI2026

A Comprehensive Calibration Framework for the Northwest River Forecast Center

We present a comprehensive framework developed by the Northwest River Forecast Center for calibrating hydrologically diverse basins. The framework includes models for snow, soil moisture, routing, channel loss, and consumptive use. Data inputs include a wide range of open-access datasets for meteorology, land use, topography, and land cover. The framework uses conceptual hydrologic models to handle basins with various hydrologic regimes including rain-driven and snowmelt-dominated basins. We also develop a flexible automatic calibration system that can handle numerous unobservable model parameters in a computationally efficient manner. A single-basin automatic calibration run can typically be completed on a modern laptop in under 10 min. We found that model performance metrics for this new approach match the quality of the NWRFC's previous labor-intensive manual calibrations. The model performance also rivals that of a state-of-the-art deep learning model at a fraction of the computational cost. This framework presents a new standard for the quality of calibrations possible with lumped conceptual hydrologic models, combining careful data curation, an objective calibration framework, and expert local knowledge. In addition, we have made software packages available for the entire suite of National Weather Service River Forecast System models, including SAC-SMA, SNOW-17, and Lag-K. These modern interfaces are intended to increase accessibility and facilitate future research.

Forecasting↗
DOE OSTI2023

A numerical simulation of CCN impacts on weather modification efficiency

Aerosols affect development of clouds and precipitation by serving as cloud condensation nuclei (CCN) and ice nuclei (IN). Considering the dramatically changing ambient aerosol concentration, it is important to examine the potential “side effect” of aerosol pollution on precipitation enhancement by weather modification. In this study, the cloud seeding was performed on a precipitation event in Beijing in the summer of 2008, which is simulated by the NSSL two-moment cloud scheme of the Weather Research and Forecasting (WRF) model. Sensitivity tests were conducted by modifying the ambient aerosol concentration and the ice crystal seeding amount to investigate the cloud seeding efficacy in different CCN concentration scenarios. There was a slight difference in the precipitation distribution between the simulations with two ambient CCN concentrations: the northern precipitation center in polluted scenario was weaker and the southern center was stronger. Compared with normal CCN scenario, the cloud liquid water mass and ice crystal mass in the severe pollution scenario is larger, and the total contents of snow and graupel were not sensitive to the CCN concentration. With the same amount of man-made ice crystals seeding, the precipitation enhancement was quite different under different CCN conditions. The higher the CCN concentration usually leads to stronger precipitation suppression. As CCN concentration increase, the deposition growth of snow, auto-conversion and accretion of ice crystals to snow were weakened, as well as the conversion of melting snow and graupel into rainwater.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Model files for estimating snow dynamics and stable water isotopes across the East River, CO.

A coupled hydrologic and snowpack stable water isotope model is used to assesses controls on isotopic inputs across the East River, Colorado, a large, mountainous basin. The hydrologic model uses the semi-empirical, spatially distributed and publicly available U.S. Geological Survey numerical code Precipitation-Modelling Runoff System (PRMS). Water and energy are tracked daily through the atmosphere, canopy and subsurface at a 100-m grid resolution. The isotope mass balance model follows previous work by Ala-aho et al. (2017) to track stable isotopes entering the soil system as snowmelt or rain. Water stores and fluxes needed for the isotope model use hydrologic model output for each timestep and model modeled grid location. This data package provides all files related to the hydrologic model (executable, input and output files), the isotopic model calibration and the historic isotopic model (water years 2015-2020). The isotope model source code and executable are provided. The file "readme.txt" includes information on the file structure, steps to run the model, and description of included folders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Euler–Lagrange stochastic modeling of droplet breakup and impact in supersonic flight

Blunt bodied aircraft traveling supersonically in weather environments may be damaged by impacts with water droplets and other airborne particles, such as snow and ice. Prior to an impact, these particles will encounter a bow shock that causes a discontinuity in their relative velocity with the gas phase, which can lead droplets to breakup into smaller droplets. These smaller droplets are more easily diverted from colliding with the blunt body due to their significantly reduced inertia relative to the initial rain droplets. One-way coupled Euler–Lagrange simulations are used to study the dynamics of droplets approaching a blunt body in steady two dimensional and axi-symmetric flow fields using a stochastic version of the Taylor analogy breakup model for the breakup dynamics. Ultimately, the dominant mechanism determining engineering quantities of interest was observed to be a competition between breakup time and the time available for a droplet to reach the body after encountering the bow shock. At Mach numbers 2, 3, and 6, the competition between these mechanisms was the dominant factor determining the momentum transfer to the blunt body via droplet collisions, which can be well characterized by a scaling relation.

Briney, Sam (ORCID:0000000179061081)↗
DOE OSTI2020

Polarimetric Radar Characteristics of Simulated and Observed Intense Convective Cores for a Midlatitude Continental and Tropical Maritime Environment

This study contrasts midlatitude continental and tropical maritime deep convective cores using polarimetric radar observables and retrievals from selected deep convection episodes during the MC3E and TWPICE field campaigns. The continental convective cores produce stronger radar reflectivities throughout the profiles, while maritime convective cores produce more positive differential reflectivity Z dr and larger specific differential phase K dp above the melting level. Hydrometeor identification retrievals revealed the presence of large fractions of rimed ice particles (snow aggregates) in the continental (maritime) convective cores, consistent with the Z dr and K dp observations. The regional cloud-resolving model simulations with bulk and size-resolved bin microphysics are conducted for the selected cases, and the simulation outputs are converted into polarimetric radar signals and retrievals identical to the observational composites. Both the bulk and the bin microphysics reproduce realistic land and ocean (L-O) contrasts in reflectivity, polarimetric variables of rain drops, and hydrometeor profiles, but there are still large uncertainties in describing Z dr and K dp of ice crystals associated with the ice particle shapes/orientation assumptions. Sensitivity experiments are conducted by swapping background aerosols between the continental and maritime environments, revealing that background aerosols play a role in shaping the distinct L-O contrasts in radar reflectivity associated with raindrop sizes, in addition to the dominant role of background thermodynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Formation and fate of freshwater on an ice floe in the Central Arctic

The melt of snow and sea ice during the Arctic summer is a significant source of relatively fresh meltwater. The fate of this freshwater, whether in surface melt ponds or thin layers underneath the ice and in leads, impacts atmosphere–ice–ocean interactions and their subsequent coupled evolution. Here, we combine analyses of datasets from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (June–July 2020) for a process study on the formation and fate of sea ice freshwater on ice floes in the Central Arctic. Our freshwater budget analyses suggest that a relatively high fraction (58 %) is derived from surface melt. Additionally, the contribution from stored precipitation (snowmelt) outweighs by 5 times the input from in situ summer precipitation (rain). The magnitude and rate of local meltwater production are remarkably similar to those observed on the prior Surface Heat Budget of the Arctic Ocean (SHEBA) campaign, where the cumulative summer freshwater production totaled around 1 m during both. A relatively small fraction (10 %) of freshwater from melt remains in ponds, which is higher on more deformed second-year ice (SYI) compared to first-year ice (FYI) later in the summer. Most meltwater drains laterally and vertically, with vertical drainage enabling storage of freshwater internally in the ice by freshening brine channels. In the upper ocean, freshwater can accumulate in transient meltwater layers on the order of 0.1 to 1 m thick in leads and under the ice. The presence of such layers substantially impacts the coupled system by reducing bottom melt and allowing false bottom growth; reducing heat, nutrient, and gas exchange; and influencing ecosystem productivity. Regardless, the majority fraction of freshwater from melt is inferred to be ultimately incorporated into the upper ocean (75 %) or stored internally in the ice (14 %). Terms such as the annual sea ice freshwater production and meltwater storage in ponds could be used in future work as diagnostics for global climate and process models. For example, the range of values from the CESM2 climate model roughly encapsulate the observed total freshwater production, while storage in melt ponds is underestimated by about 50 %, suggesting pond drainage terms as a key process for investigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Uncertainty of 21st Century western U.S. snowfall loss derived from regional climate model large ensemble

Abstract The western United States is dependent on winter snowfall over its major mountain ranges, which gradually melts each year, serving as a natural reservoir for water resources. In a future warmer climate, much of this snowfall could be replaced by rain, making it more challenging to capture and store water. In this study, we utilize an ensemble of dynamically downscaled simulations forced by 14 global climate models (GCMs). These GCMs project wildly different futures, in terms of both temperature and precipitation change, producing significant uncertainty in snowfall projections. Here we exploit the robust statistics of the downscaled ensemble, and diagose the sensitivity of end-of-century snowfall loss across the region to both warming and regional wetting/drying in the driving GCM. The windward slopes of the Sierra Nevada and Cascades are particularly sensitive to warming (losing ~ 15% annual snowfall per degree warming), with little influence of precipitation. By contrast, snowfall loss in the inter-mountain west is less sensitive to warming (~ 5% K −1 ), but is significantly offset/exacerbated by precipitation changes (~ 0.5% snow per 1% precipitation). Combining such sensitivities with the warming and regional precipitation signals in the full CMIP6 ensemble, we can fully quantify likely snowfall loss and its uncertainty at any location, for any emissions scenario. We find that the western U.S. as a whole will lose 34 ± 8% of its total volumetric snowfall by end-of-century under the high-emissions SSP3-7.0 scenario, but 25 ± 6% and 17 ± 6% under the lower-emissions SSP2-4.5 and SSP1-2.6 scenarios.

Norris, Jesse (ORCID:0000000289883326)↗