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At least 145 records · Page 8
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DOE OSTI2021

Porosity-Permeability Evolution During Simultaneous Mineral Dissolution and Precipitation

Permeability in reactive porous media evolves in complex ways that are hard to predict. Macroscopic empirical equations are often used to estimate permeability evolution but fail to reflect the impact of pore scale reactions on permeability. This work aims to understand the evolution of permeability in systems where mineral dissolution and precipitation occur simultaneously. Pore network models are used for permeability simulation of a Paluxy sandstone using pore and pore-throat size distributions from the analysis of X-ray CT images. Pore and pore-throat radii are increased and decreased to reflect the effects of dissolution and precipitation reactions. Varying spatial distributions of reactions are simulated and the resulting porosity and permeability values compared with commonly used macroscopic equations. It is observed that when dissolution occurs at the inlet and precipitation at the outlet, porosity increases while permeability decreases. Similar results are also observed in the respective opposite scenario where precipitation occurs at the inlet and dissolution at the outlet. When dissolution and precipitation reactions are randomly distributed throughout the network, porosity increases with little change in permeability that is not captured by the empirical equations. When reactions are controlled by pore and pore-throat sizes, such as dissolution occurring in pores and pore-throats of small size and precipitation in pores and pore-throats of larger size, porosity and permeability decrease. When precipitation occurs in pores and pore-throats of smaller size and dissolution in pores and pore-throats of larger size, porosity and permeability increase and is described relatively well by the Verma-Pruess porosity-permeability equation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Potential Chemical Impacts of Subsurface CO 2 : An Integrated Experimental and Numerical Assessment for a Case Study of the Ogallala Aquifer

Leakage from geologic CO 2 sequestration (GCS) reservoirs to overlying underground sources of drinking water (USDW) is a tangible risk. This study is an integrated assessment that combines column experiments and reactive transport simulations of sediments sampled from the Ogallala aquifer above an active commercial-scale GCS site (the Farnsworth Unit in northern Texas). Experimental and simulation results suggest that carbonate mineral (calcite and dolomite) dissolution is the most significant reaction following CO 2 intrusion, and is also the dominant source of trace metal release. Cation exchange is another key mechanism controlling trace metal release by cation interference. Most of the trace metals, including Ba, Sr, As, Pb, and Zn, show a short-term release and quickly drop to the baseline values, suggesting low risk to the overlying USDW quality. Other trace metals, such as Mn and U, exhibit a tangible increase of their concentrations in the beginning, and drop to a higher level compared to the baseline, which may become a potential concern of long-term USDW quality change with CO2introduction. This study provides a comprehensive example of combining laboratory experiments and simulations for assessment of CO 2 -sediment interactions with combined release mechanisms in shallow groundwater aquifers. Data presented here provides useful insights for quantitative risk assessment and effective public education regarding CO 2 geological sequestration, and trace metal reactive transport studies in shallow groundwater aquifers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Diurnal Dynamics of Gross Primary Productivity Using Observations From the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite‐R Series at an Oak Savanna Ecosystem

Gross primary productivity (GPP) is the largest flux in the global carbon cycle and satellite-based GPP estimates have long been used to study the trends and interannual variability of GPP. With recent updates to geostationary satellites, we can now explore the diurnal variability of GPP at a comparable spatial resolution to polar-orbiting satellites and at temporal frequencies comparable to eddy covariance (EC) tower sites. We used observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellite-R series (GOES-R) to test the ability of subdaily satellite data to capture the shifts in the diurnal course of GPP at an oak savanna EC site in California, USA that is subject to seasonal soil moisture declines. We compared three methods to estimate GPP: (a) a light-use efficiency model, (b) a linear relationship between the product of near-infrared reflectance of vegetation and photosynthetically active radiation (LIN-NIR v P) and EC tower GPP, and (c) a light response curve (LRC-NIR v P) between NIR v P and EC GPP. The LRC-NIR v P achieved the lowest mean absolute error for winter (2 µmol CO 2 m –2 s –1 ), spring (2.51 µmol CO 2 m –2 s –1 ), summer (1.43 µmol CO 2 m –2 s –1 ), and fall (1.35 µmol CO 2 m –2 s –1 ). The ecosystem experienced the largest shift in daily peak GPP in relation to the peak of incoming solar radiation toward the morning hours during the dry summers. The LRC-NIRvP and the light-use efficiency model were in agreement with these patterns of a shift in peak daily GPP toward the morning hours during summer. Our results can help develop diurnal estimates of GPP from geostationary satellites that are sensitive to fluctuating environmental conditions during the day.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using Complex Probability Amplitudes to Simulate Solute Transport in Composite Porous Media

Probability amplitudes are fundamental to quantum mechanics and offer robust descriptions of complicated systems, which have allowed physicists to explain behaviors inaccessible to classical physics. This article ponders how some of the same conceptual underpinnings of the mathematics used for modeling quantum systems might be applied to subsurface water resources problems and speculates how these tools could facilitate applications on quantum computers. A probability amplitude-based model for describing advective-dispersive transport in porous media using linear operators is investigated. The proposed complex valued model decomposes spreading into two “sub-continuum partial dispersion” coefficients, and this recovers classical spreading when the sum of these coefficients is the Fickian dispersion coefficient. However, the probability amplitudes have a manyto-one relationship to a probability distribution, so it embeds a level of heterogeneity into seemingly equivalent functions. Two propagators with different sub-continuum coefficients may have the same macroscopic behavior when either is considered in isolation, but when they act on the other the system’s behavior changes. Additionally, differences in the amplitudes cause a reduction in spreading as velocity correlations are disrupted, despite both propagators having identical dispersion coefficients, and this cannot be achieved using classical methods without changing the dispersion coefficient. The main point is that these amplitude-based models offer a way to embed information about the system into the propagators, instead of just “averaging it out” when making an upscaled model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Potential Vorticity Dynamics Explain How Extratropical Oceans and the Arctic Modulate Wintertime Land–Temperature Variations

Temperature variations across the continental northern hemisphere at the interdecadal scale are thought to be remotely modulated by oceanic internal climate variability and the Arctic. Nevertheless, further elucidating the dynamics is essential for clarifying ongoing debates. We show that potential vorticity (PV) dynamics provide a concise explanation for these teleconnections. Our findings demonstrate that extratropical oceans and the Arctic can remotely modulate the wintertime continental temperature variations by stimulating PV anomalies, which are constrained by climatological PV gradients and jet streams. A causal explanation includes anomalous temperature and precipitation over oceans and the Arctic inducing local PV anomalies via diabatic heating. Subsequently, meridional and downstream advection distributes the anomalous PV to remote land regions where the climatological PV gradients are strong, that is, involving PV fronts, as well as jet streams; therefore, PV fronts and jet streams jointly indicate land regions that are largely and frequently impacted. However, land can also modulate other regions through the same mechanism when variations over land occur in advance. Clear causality depends on which factor is independent, while interactions among those regions may convolute the causality, thereby causing further debates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Long-Term Changes in Concentration and Yield of Riverine Dissolved Silicon From the Poles to the Tropics

Riverine exports of silicon (Si) influence global carbon cycling through the growth of marine diatoms, which account for ~25% of global primary production. Climate change will likely alter river Si exports in biome-specific ways due to interacting shifts in chemical weathering rates, hydrologic connectivity, and metabolic processes in aquatic and terrestrial systems. Nonetheless, factors driving long-term changes in Si exports remain unexplored at local, regional, and global scales. We evaluated how concentrations and yields of dissolved Si (DSi) changed over the last several decades of rapid climate warming using long-term data sets from 60 rivers and streams spanning the globe (e.g., Antarctic, tropical, temperate, boreal, alpine, Arctic systems). We show that widespread changes in river DSi concentration and yield have occurred, with the most substantial shifts occurring in alpine and polar regions. The magnitude and direction of trends varied within and among biomes, were most strongly associated with differences in land cover, and were often independent of changes in river discharge. These findings indicate that there are likely diverse mechanisms driving change in river Si biogeochemistry that span the land-water interface, which may include glacial melt, changes in terrestrial vegetation, and river productivity. Finally, trends were often stronger in months outside of the growing season, particularly in temperate and boreal systems, demonstrating a potentially important role of shifting seasonality for the flux of Si from rivers. Our results have implications for the timing and magnitude of silica processing in rivers and its delivery to global oceans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Disentangling the Relative Drivers of Seasonal Evapotranspiration Across a Continental-Scale Aridity Gradient

Evapotranspiration (ET) is a significant ecosystem flux, governing the partitioning of energy at the land surface. Understanding the seasonal pattern and magnitude of ET is critical for anticipating a range of ecosystem impacts, including drought, heat-wave events, and plant mortality. Here, in this study, we identified the relative controls of seasonal variability in ET, and how these controls vary among ecosystems. We used overlapping AmeriFlux and PhenoCam time series at a daily timestep from 20 sites to explore these linkages (# site-years >100), and our study area covered a broad climatological aridity gradient in the U.S. and Canada. We focused on disentangling the most important controls of bulk surface conductance (G s ) and evaporative fraction (EF = LE/[H + LE]), where LE and H represent latent and sensible heat fluxes, respectively. Specifically, we investigated how vegetation phenology varied in importance relative to meteorological variables (vapor pressure deficit and antecedent precipitation) as a driver of G s and EF using path analysis, a framework for quantifying and comparing the causal linkages among multiple response and explanatory variables. Our results revealed that the drivers of G s and EF seasonality varied significantly between energy- and water-limited ecosystems. Specifically, precipitation had a much higher effect in water-limited ecosystems, while seasonal patterns in canopy greenness emerged as a stronger control in energy-limited ecosystems. Given that phenology is expected to shift under future climate, our findings provide key information for understanding and predicting how phenology may impact 21st-century hydroclimate regimes and the surface-energy balance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Roles of Tidal Marshes in the Estuarine Biochemical Processes: A Numerical Modeling Study

Observations suggest that the existence of tidal marsh can alter the oxygen and nutrient dynamics in adjacent water bodies, but assessing the impacts of large tidal marshes on an estuary is challenging. In this study, we use a modeling approach to investigate the roles of tidal marshes on the estuarine biochemical processes. The marsh model, which simulates the ecological functions of marshes at seasonal and annual time-scales, is embedded inside an unstructured-grid three-dimensional hydrodynamic and eutrophication model (SCHISM-ICM). This modeling system simulates the growth and metabolism of the tidal marshes and links biological processes to nutrient dynamics in the water column and sediment. This model dynamically simulates nutrient recycling and physical transport of the materials between marshes and open water through wetting-drying processes. This coupled model system is validated and successfully applied to the York River Estuary. Model results suggest that tidal marshes influence the local diurnal dissolved oxygen (DO) cycle by exporting dissolved organic carbon and high sediment oxygen demand in the marsh system through the tidal exchange. The high deposition rates of organics and diurnal DO cycle enhance the sediment release of phosphorus. On the other hand, marshes tend to decrease dissolved inorganic nitrogen in the water column by settling particulate nutrients and enhancing the denitrification process. The study demonstrates that tidal marshes exert substantial impacts on the estuarine biochemical processes. The developed tidal marsh model enhances eutrophication modeling and advances the understanding of the feedback effects between marsh biogeochemistry and estuarine eutrophication processes on a systemic scale.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Dinitrogen Emissions Dominate Nitrogen Gas Emissions From Soils With Low Oxygen Availability in a Moist Tropical Forest

Lowland tropical forest soils are relatively N rich and are the largest global source of N 2 O (a powerful greenhouse gas) to the atmosphere. Despite the importance of tropical N cycling, there have been few direct measurements of N 2 (an inert gas that can serve as an alternate fate for N 2 O) in tropical soils, limiting our ability to characterize N budgets, manage soils to reduce N 2 O production, or predict the future role that N limitation to primary productivity will play in buffering against climate change. We collected soils from across macro‐ and micro‐topographic gradients that have previously been shown to differ in O 2 availability and trace gas emissions. We then incubated these soils under oxic and anoxic headspaces to explore the relative effect of soil location versus transient redox conditions. No matter where the soils came from, or what headspace O 2 was used in the incubation, N 2 emissions dominated the flux of N gas losses. In the macrotopography plots, production of N 2 and N 2 O were higher in low O 2 valleys than on more aerated ridges and slopes. In the microtopography plots, N 2 emissions from plots with lower mean soil O 2 (5%–10%) were greater than in plots with higher mean soil O 2 (10%–20%). We estimate an N gas flux of ∼37 kg N/ha/yr from this forest, 99% as N 2 . These results suggest that N 2 fluxes may have been systematically underestimated in these landscapes, and that the measurements we present call for a reevaluation of the N budgets in lowland tropical forest ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Rate of Canopy Development Modulates the Link Between the Timing of Spring Leaf Emergence and Summer Moisture

Shifts in phenological timing have important implications for ecosystem processes, with spring leaf emergence as a dominant control of carbon, water, and energy cycling. Phenological events are predominantly determined by weather and climate, therefore dynamic in time and sensitive to climate feedbacks. Improving our understanding of how ecosystems respond to changes in phenological timing will enhance our ability to assess summer soil water availability, since the timing of spring leaf emergence may lead to soil moisture deficits later in the growing season. We leveraged data from five AmeriFlux towers in central and eastern United States to investigate the extent spring leaf emergence (i.e., start of spring, SoS ) influences rates at which forest canopies develop and how this impacts summer soil moisture ( θ JJA ) variability. Our results indicate that ecosystem processes, specifically gross primary production ( GPP ) and evapotranspiration ( ET ), exhibit compensatory responses to varying leaf emergence; with delayed spring-onset, the canopy developed more quickly, resulting in rapid GPP and ET increases, consistent across sites. Nonetheless, early SoS is a relatively good indicator for potential summer soil water deficits, particularly when it occurs together with meteorological conditions (i.e., lower-than-average precipitation, hot summer temperatures) that contribute to soil water deficits. When these meteorological conditions coincide with early SoS , θ JJA deficits are exacerbated. To the extent that these extreme conditions occur more frequently under future climate scenarios, the dynamics of spring phenology and hydroclimate may play an increasingly important role in portending the likelihood of summer water deficits, which are projected to become more severe.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs can Approach State-Of-The-Art Hydrologic Prediction Accuracy

Predictions of hydrologic variables across the entire water cycle have significant value for water resources management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly insurmountable performance in modeling rainfall runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here, we show that differentiable, learnable, process-based models (called δ models here) can approach the performance level of LSTM for the intensively observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process-based model's modules. Without using an ensemble or post-processor, δ models can obtain a median Nash-Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing data set, compared to 0.748 from a state-of-the-art LSTM model with the same setup. For another forcing data set, the difference is even smaller: 0.715 versus 0.722. Meanwhile, the resulting learnable process-based models can output a full set of untrained variables, for example, soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and can later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Testing Hidden Assumptions of Representativeness in Reach-Scale Studies of Hyporheic Exchange

Field studies of hyporheic exchange in mountain systems are often conducted using short study reaches and a limited number of observations. It is common practice to assume these study reaches represent hyporheic exchange at larger scales or different sites and to infer general relationships among potential causal mechanisms from the limited number of observations. However, these assumptions of representativeness are rarely tested. In this study, we develop numerical models from four segments of mountain streams in different geomorphologic settings and extract shorter reaches to test how representative exchange metrics are in shorter reaches compared to their reference segments. We also map the locations of the representative reaches to determine if a pattern exists based on location. Finally, we compare variance of these shorter within-site reaches to 29 additional reaches across the same basin to understand the impacts of inferring causal mechanisms, for example, the expectation that wide and narrow valley bottoms will yield different hyporheic exchange patterns. Our results show that the location and length strategy of the study reach must be considered before assuming an exchange metric to be representative of anything other than the exact segment studied. Further, it is necessary to quantify within and between site variations before making causal inferences based on observable characteristics, such as valley width or stream morphology. Our findings have implications for future field practices and how those practices are translated into models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An Evaluation of Dynamical Downscaling Methods Used to Project Regional Climate Change

In the past decade, dynamical downscaling using “pseudo-global-warming” (PGW) techniques has been applied frequently to project regional climate change. Such techniques generate signals by adding mean global climate model (GCM)-simulated climate change signals in temperature, moisture, and circulation to lateral and surface boundary conditions derived from reanalysis. An alternative to PGW is to downscale GCM data directly. This technique should be advantageous, especially for simulation of extremes, since it incorporates the GCM's full spectrum of changing synoptic-scale dynamics in the regional solution. Here, we test this assumption, by comparing simulations in Europe and Western North America. We find that for warming and changes in temperature extremes, PGW often produces similar results to direct downscaling in both regions. For mean and extreme precipitation changes, PGW generally also performs surprisingly well in many cases. Moisture budget analysis in the Western North America domain reveals why. Large fractions of the downscaled hydroclimate changes arise from mean changes in large-scale thermodynamics and circulation, that is, increases in temperature, moisture, and winds, included in PGW by design. The one component PGW may have difficulty with is the contribution from changes in synoptic-scale variability. When this component is large, PGW performance could be degraded. Global analysis of GCM data shows there are regions where it is large or dominant. Hence, our results provide a road map to identify, through GCM analyses, the circumstances when PGW would not be expected to accurately regionalize GCM climate signals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Sea–Level Rise Impacts on Tidal Marshes and Estuarine Biogeochemical Processes

We used a numerical model to investigate the effects of sea-level rise (SLR) on the biogeochemical processes in the York River Estuary with extensive tidal marshes. The fully-coupled hydrodynamic-water quality-marsh model accounts for the spatial and temporal variations of physical-biogeochemical interactions between the tidal marshes and surrounding waters. This study focuses on an SLR scenario where the vertical accretion of tidal marshes keeps pace with the rising sea levels. Results show that SLR amplifies the tidal range and prolongs flooding duration, which results in enhanced porewater exchanges of materials between the tidal marshes and the surrounding waters. The increased availability of shallow-water habitats and enhanced light utilization in the shallow areas under SLR promote phytoplankton production (PP) in the shallow-water regions of the York River. Consequently, the organic carbon in the open water is fueled by the contributions from shallow waters and the enhanced export of organic carbon from the marshes. The change in the dissolved oxygen (DO) budget in the York River Estuary is attributed to changes in water column respiration, net metabolism of the benthic layer, reaeration, PP, and increased stratification under SLR. The net DO flux out of the York River increases at the York River mouth. Diel DO variation, especially in the marshes in the upper estuary, promotes phosphorus release from the sediment. The changes in dissolved nitrogen under SLR are relatively minimal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Understanding the Shift of Drivers of Soil Erosion and Sedimentation Based on Regional Process-Based Modeling in the Mississippi River Basin During the Past Century

Soil erosion and sedimentation problems remain a major water quality concern for making watershed management policies in the Mississippi River Basin (MRB). It is unclear whether the observed decreasing trend of stream suspended sediment loading to the mouth of the MRB over the last eight decades truly reflects a decline in upland soil erosion in this large basin. Here, for this work, we improved a distributed regional land surface model, the Dynamic Land Ecosystem Model, to evaluate how climate and land use changes have impacted soil erosion and sediment yield over the entire MRB during the past century. Model results indicate that total sediment yield significantly increased during 1980–2018, despite no significant increase in annual precipitation and runoff. The increased soil erosion and sediment yield are mainly driven by intensified extreme precipitation (EP). Spatially, we found notable intensified EP events in the cropland-dominated Midwest region, resulting in a substantial increase in soil erosion and sediment yield. Land use change played a critical role in determining sediment yield from the 1910s to the 1930s, thereafter, climate variability increasingly became the dominant driver of soil erosion, which peaked in the 2010s. This study highlights the increasing influences of extreme climate in affecting soil erosion and sedimentation, thus, water quality. Therefore, existing forest and cropland Best Management Practices should be revisited to confront the impacts of climate change on water quality in the MRB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Large‐Scale Statistically Meaningful Patterns (LSMPs) Associated With Precipitation Extremes Over Northern California

Abstract We analyze large‐scale statistically meaningful patterns (LSMPs) that precede extreme precipitation (PEx) events over Northern California (NorCal). We find LSMPs by applying k‐means clustering to the two leading principal components of daily 500 hPa geopotential height anomalies two days before the onset, from October to March during 1948–2015. Statistical significance testing based on Monte Carlo simulations suggests a minimum of four statistically distinguished LSMP clusters. The four LSMP clusters are characterized as Northwest continental negative height anomaly, Eastward positive “Pacific‐North American Pattern (PNA),” Westward negative “PNA,” and Prominent Alaskan ridge. These four clusters, shown in multiple variables, evolve very differently and have differing links to the Arctic and tropical Pacific regions. Using binary forecast skill measures and a new copula‐based framework for predicting PEx events, we find LSMP indices that are useful predictors of NorCal PEx events, with moisture‐based variables being the best predictors of PEx events at least 6 days before the onset, and the lower atmospheric variables being better than their upper atmospheric counterparts any day in advance tested. To ensure statistical rigor, the LSMPs analyzed here (with the modified acronym) include local tests of both significance and consistency, which are not always featured in the literature on large‐scale meteorological patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Examining the Robustness of Weakened Orographic Influence on Precipitation in Downscaled Climate Projections Over the Western US

Assessing local climate change impacts often requires downscaling coarse global climate model (GCM) output to finer resolution. Two main approaches exist: dynamical downscaling using high-resolution regional climate models, and statistical downscaling based on historical relationships between large-scale and local variables. In a recent analysis of five dynamically downscaled simulations over the western United States, Koszuta et al. (2024, https://doi.org/10.1029/2023gl107298) found that warming weakens orographic influence on winter precipitation, damping increases on windward slopes and amplifying them in rain-shadowed regions. Here we show that this effect is robust across seasons and multiple dynamically downscaled ensembles, and is more pronounced at higher model resolutions. However, it is absent in projections from a widely used statistical model (LOCA2), even when trained on high-resolution future simulations (LOCA2-Hybrid). This highlights a key limitation of many statistical downscaling methods: their preservation of parent GCM trends, which usually fail to capture emergent changes in orographic precipitation patterns.

54 ENVIRONMENTAL SCIENCES↗