Results for “climate model testing” — Page 11

DOE OSTI2023

Comparing the cooling effectiveness of operationalisable urban surface combination scenarios for summer heat mitigation

Extreme summer heat in cities exacerbates the vulnerability of urban communities to heatwaves. Vegetative and reflective urban surfaces can help reduce urban heat. This study investigated the impacts of urban trees, green roofs and cool roofs on heat mitigation during average and extreme summer conditions in temperate oceanic Melbourne, Australia. We simulated the city climate using ‘The Air Pollution Model’ (TAPM) at a 1 km spatial resolution over 10 years, which according to our review of the literature, was the most prolonged period for simulation in Melbourne. During a widespread heatwave event, some of the tested scenarios with combined surface parameters could reduce the extreme values of the energy budget components- sensible heat, latent heat, and storage heat fluxes up to seasonal averages compared to the existing situation for Melbourne (control). The scenario with the highest (reasonable maximum) ground-level vegetation, green roofs, and cool roofs could reduce air temperatures up to 2.4 °C. The simulations suggest that a combined strategy with vegetative and high-albedo surfaces will deliver higher effectiveness with maximum cooling benefits and cost-effectiveness than individual strategies in cities. These results suggest the importance of collaborative strategic planning of urban surfaces to make cities healthier, sustainable, and liveable.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) sea spray organic aerosol emissions – implementation in a global climate model and impacts on clouds

Sea spray aerosol is one of the major sources of atmospheric particulate matter globally. It has increasingly been recognized that organic matter derived from ocean biological precursors contributes significantly to the composition of submicron sea spray and may modify sea spray aerosol impacts on clouds and climate. This paper describes the implementation of the OCEANFILMS (Organic Compounds from Ecosystems to Aerosols: Natural Films and Interfaces via Langmuir Molecular Surfactants) parameterization for sea spray organic aerosol emissions in a global Earth system model, the Energy Exascale Earth System Model (E3SM). OCEANFILMS is a physically based model that links sea spray chemistry with ocean biogeochemistry using a Langmuir partitioning approach. We describe the implementation details of OCEANFILMS within E3SM, compare simulated aerosol fields with observations, and investigate impacts on simulated clouds and climate. Four sensitivity cases are tested, in which organic emissions either strictly add to or strictly replace sea salt emissions (in mass and number) and are either fully internally or fully externally mixed with sea salt. The simulation with internally mixed, added organics agrees reasonably well with observed seasonal cycles of organic matter in marine aerosol and has been selected as the default configuration of the E3SM. In this configuration, sea spray organic aerosol contributes an additional source of cloud condensation nuclei, adding up to 30 cm -3 to Southern Ocean boundary-layer cloud condensation nuclei concentrations (supersaturation = 0.1 %). The addition of this new aerosol source strengthens shortwave radiative cooling by clouds by -0.36 W m -2 in the global annual mean and contributes more than -3.5 W m -2 to summertime zonal mean cloud forcing in the Southern Ocean, with maximum zonal mean impacts of about -4 W m -2 around 50–60°S. This is consistent with a previous top-down, satellite-based empirical estimate of the radiative forcing by sea spray organic aerosol over the Southern Ocean. Through its mechanistic approach, OCEANFILMS offers a path towards improved understanding of the feedbacks between ocean biology, sea spray organic matter, and climate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Sensitivity of meteorological-forcing resolution on hydrologic variables

Projecting the spatiotemporal changes in water resources under a no-analog future climate requires physically based integrated hydrologic models which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, are typically only resolved on the order of tens to hundreds of kilometers, while hydrologic variables of interest (e.g., discharge and groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution hydrologic model is forced with various resolutions of meteorological forcing (0.5 to 40.5 km) generated by a dynamical downscaling analysis from the regional climate model Weather Research and Forecasting (WRF). The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare undammed river basin and is used here as a test bed to illustrate potential impacts of various resolutions of meteorological forcing on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that the errors in spatial distribution patterns impact land surface processes and can be delayed in time. Localized biases in groundwater levels can be as large as 5–10 m and 3 m in surface water. Most hydrologic variables reveal that biases are seasonally and spatially dependent, which can have serious implications for model calibration and ultimately water management decisions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Developing a Composite Vacuum Insulated Panel (VIP) Insulation/Vinyl Siding Composite Technology for Retrofitting Residential Walls

This paper reports on a project that has attempted to seize on an opportunity to take a giant leap forward in commercializing a technology that would address a key opportunity to achieve energy savings in the existing residential housing stock. It is typically very difficult to increase the R-value of the walls in existing residential housing as doing so from the inside is disruptive and there have not been good solutions to increase thermal envelope performance from the exterior through adding continuous insulation due to the additional work required to fit existing architectural features to the new wall thickness and to reclad the façade. A high thermal resistivity or R-value per inch product that can be used in these applications offers an excellent solution.This paper summarizes a three-year research project to a produce vacuum insulated panel (VIP)/vinyl siding that would have sufficient R-value to meet the continuous insulation requirements of the 2018 International Energy Conservation Code (IECC, 2018) in all climate zones and has a much thinner profile that will facilitate its application to existing residential homes without the need for expensive retrimming of the architectural details. The paper will supply information on the development of the technology, its thermal modelling, testing, voice of user sessions to solicit input from architects, designers, manufacturers, contractors, and installers, and a techno-economic analysis to gauge its competitiveness with existing product options.

Desjarlais, Andre Omer↗

DOE OSTI2022

Developing a Composite Vacuum Insulated Panel (VIP) Insulation/Vinyl Siding Composite Technology for Retrofitting Residential Walls

This paper reports on a project that has attempted to seize on an opportunity to take a giant leap forward in commercializing a technology that would address a key opportunity to achieve energy savings in the existing residential housing stock. It is typically very difficult to increase the R-value of the walls in existing residential housing as doing so from the inside is disruptive and there have not been good solutions to increase thermal envelope performance from the exterior through adding continuous insulation due to the additional work required to fit existing architectural features to the new wall thickness and to reclad the facade. A high thermal resistivity or R-value per inch product that can be used in these applications offers an excellent solution. This paper summarizes a three-year research project to a produce vacuum insulated panel (VIP)/vinyl siding that would have sufficient R-value to meet the continuous insulation requirements of the 2018 International Energy Conservation Code (IECC, 2018) in all climate zones and has a much thinner profile that will facilitate its application to existing residential homes without the need for expensive retrimming of the architectural details. The paper will supply information on the development of the technology, its thermal modelling, testing, voice of user sessions to solicit input from architects, designers, manufacturers, contractors, and installers, and a techno-economic analysis to gauge its competitiveness with existing product options.

Desjarlais, Andre↗

DOE OSTI2021

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

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

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

NETL Direct Air Capture Center

In 2022 Congress authorized the $25M National Energy Technology Laboratory Direct Air Capture Center. The facility will be specifically targeted at accelerating the commercialization of technologies beyond the conceptual stage which have not yet reached full pilot-scale (TRL 3 to 6). The ability to operate over a wide range of conditions will help the developers to understand how their technologies respond in different climates, from summer to winter and arid to tropical. NETL recognizes that DAC technology is still early in its evolution, and a wide variety of highly varied technologies are being explored. The Center will be designed with substantial flexibility to accommodate the rapidly evolving technological landscape. Testing systems at three scales will be included: lab-scale systems designed to examine the long-term stability of DAC materials, bench-scale module testing systems capable of probing flow dynamics, and small pilot-scale skid rooms able to test prototype DAC units under a wide variety of climate conditions. The NETL DAC Center will feature dedicated engineering, technician, scientific and logistical support for experimental system design, installation, operation of experiments, and interpretation of results for projects including but not limited to kinetics of absorption/desorption, thermal duty, and post-mortem elemental characterization to understand degradation. The NETL DAC Center projects will also have access to dedicated process modeling and analysis to evaluate the technoeconomic aspects of new technologies. NETL’s Systems Engineering and Analysis (SEA) team, the Carbon Capture Simulation Initiative (CCSI), and the Institute for the Design of Advanced Energy Systems (IDAES) are all potential collaborators with NETL’s testing partners from industry, academia, and other research institutions. NETL also has world class capabilities in device scale modeling (i.e., MFIX) that will be available to help design more efficient contactors and optimize unique internal configurations that can be realized by advanced manufacturing. The talk will cover the design and capabilities of the center, the planned availability, and feedback will be solicited from the audience on DAC testing needs.

Luebke, David↗

DOE OSTI2023

Accelerating commercialization of direct air capture technology

In 2022 Congress authorized the $25M National Energy Technology Laboratory Direct Air Capture Center. The facility will be specifically targeted at accelerating the commercialization of technologies beyond the conceptual stage which have not yet reached full pilot-scale (TRL 3 to 6). The ability to operate over a wide range of conditions will help the developers to understand how their technologies respond in different climates, from summer to winter and arid to tropical. NETL recognizes that DAC technology is still early in its evolution, and a wide variety of highly varied technologies are being explored. The Center will be designed with substantial flexibility to accommodate the rapidly evolving technological landscape. Testing systems at three scales will be included: lab-scale systems designed to examine the long-term stability of DAC materials, bench-scale module testing systems capable of probing flow dynamics, and small pilot-scale skid rooms able to test prototype DAC units under a wide variety of climate conditions. The NETL DAC Center will feature dedicated engineering, technician, scientific and logistical support for experimental system design, installation, operation of experiments, and interpretation of results for projects including but not limited to kinetics of absorption/desorption, thermal duty, and post-mortem elemental characterization to understand degradation. The NETL DAC Center projects will also have access to dedicated process modeling and analysis to evaluate the technoeconomic aspects of new technologies. NETL’s Systems Engineering and Analysis (SEA) team, the Carbon Capture Simulation Initiative (CCSI), and the Institute for the Design of Advanced Energy Systems (IDAES) are all potential collaborators with NETL’s testing partners from industry, academia, and other research institutions. NETL also has world class capabilities in device scale modeling (i.e., MFIX) that will be available to help design more efficient contactors and optimize unique internal configurations that can be realized by advanced manufacturing. The talk will cover the design and capabilities of the center, the planned availability, and feedback will be solicited from the audience on DAC testing needs.

Luebke, David↗

DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Advancing Organized Convection Representation in the Unified Model: Implementing and Enhancing Multiscale Coherent Structure Parameterization

To address the effect of stratiform latent heating on meso- to large-scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top-heavy stratiform latent heating from under-resolved organized convection in general circulation models. We couple the MCSP with a mass-flux convection scheme (CoMorph-A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed-phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective-stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden-Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature-dependent convective-stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Overview of the Nephele Perturbed Parameter Ensemble for Aerosol‐Cloud Interactions in E3SMv3

Aerosol-cloud interactions (aci) are the leading source of uncertainty in inferring climate sensitivity from the historical record. Earth system models (ESMs) struggle to represent aci because the processes responsible for these phenomena occur at much finer time and space scales than can be resolved by any ESM. Observational constraints provide key benchmarks to test ESMs, but cannot be used alone to fully understand aci processes except in very specific cases where causality is controlled; some degree of modeling is required to infer aci and estimate radiative forcing. Here, we generate and characterize a perturbed parameter ensemble (PPE) in version 3 of the Energy Exascale ESM (E3SMv3). We perturb 25 parameters that govern aci processes over 250 members and integrate the model over present-day and preindustrial aerosol emissions. We find that the process representation in E3SMv3 is flexible and can generate global-mean effective radiative forcings due to aci (ERFaci) ranging from −3.0 to +0.9 W m −2 . The positive ERFaci values simulated by a portion of the PPE are implausible and result from parameter combinations that produce unrealistic top-of-atmosphere energy fluxes. While global-mean cloud droplet number concentration always increases in response to anthropogenic aerosol, cloud liquid water path can both increase and decrease, suggesting that precipitation suppression is not the only aerosol-cloud adjustment represented by E3SMv3. Analysis of which processes control liquid cloud adjustment in the PPE points toward stratiform precipitation processes and aerosol activation, which is consistent with many previous ESMs, as well as the new two-moment convective cloud microphysics in E3SMv3.

Nugent, Jacqueline M. [Univ. of Wyoming, Laramie, ↗

DOE OSTI2022

Empower Wall: Active insulation system leveraging additive manufacturing and model predictive control

Buildings are one of the largest energy consumers worldwide, using large amounts of energy during their construction and for climate control during operation. Active insulation systems (AIS) have been shown to reduce the energy needed for climate control in buildings by dynamically regulating the heat transferred between a building’s interior and exterior. Infrastructure-scale additive manufacturing (AM) has the potential to reduce the resources needed for building construction. Combining these two technologies into a single building envelope would create a path towards more sustainable buildings. A test was conducted for the Federal Energy Management Program (FEMP) Energy Exchange training and trade show, in August 2021, to investigate a new building envelope design, termed the Empower Wall, that utilized an AIS and was constructed using AM. Model predictive control was implemented to manage operation of the Empower Wall in concert with the existing HVAC system. Finally, the prototype system demonstrated that the Empower Wall lowered total energy consumption and reduced the cost of energy used.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Deep-Learning-derived Boundary Layer Height from Meteorological Data over the SGP, GOAMAZON, CACTI

The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Deep-learning-derived planetary boundary layer height from conventional meteorological measurements

Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A new method for diagnosing effective radiative forcing from aerosol–cloud interactions in climate models

Aerosol–cloud interactions (ACIs) are a leading source of uncertainty in estimates of the historical effective radiative forcing (ERF). One reason for this uncertainty is the difficulty in estimating the ERF from aerosol–cloud interactions (ERFaci) in climate models, which typically requires multiple calls to the radiation code. Most commonly used methods also cannot disentangle the contributions from different processes to ERFaci. Here, we develop a new, computationally efficient method for estimating the shortwave (SW) ERFaci from liquid clouds using histograms of monthly averaged cloud fraction partitioned by cloud droplet effective radius (r e ) and liquid water path (LWP). Multiplying the histograms with SW cloud radiative kernels gives the total SW ERFaci from liquid clouds, which can be decomposed into contributions from the Twomey effect, LWP adjustments, and cloud fraction (CF) adjustments. We test the method with data from five CMIP6-era models, using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument simulator to generate the histograms. Our method gives similar total SW ERFaci estimates to other established methods in regions of prevalent liquid cloud and indicates that the Twomey effect, LWP adjustments, and CF adjustments have contributed -0.34 ± 0.23, -0.22 ± 0.13, and -0.09 ± 0.11 W m -2 , respectively, to the effective radiative forcing of the climate since 1850 in the ensemble mean (95 % confidence). These results demonstrate that widespread adoption of a MODIS r e –LWP joint histogram diagnostic would allow the SW ERFaci and its components to be quickly and accurately diagnosed from climate model outputs, a crucial step for reducing uncertainty in the historical ERF.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

The impact of forest-controlled snow variability on late-season streamflow varies by climatic region and forest structure

Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest-snow variability has on streamflow. Here to test how much forest-controlled snow variability influences streamflow, a tiling parameterization based on classifications from high-resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest-snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north- and south-facing forest edges. This tiled parameterization was used to test where and when detailed forest-snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non-tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late-season streamflow decreased by only 3%–4% compared to the non-tiled model. In Jemez, NM, the tiled model decreased late-season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late-season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north-facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late-season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest-SWE variability had an effect on late-season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

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

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

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

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

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

54 ENVIRONMENTAL SCIENCES↗