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DOE OSTI2025

Quantification of regional net CO 2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO 2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO 2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO 2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO 2 concentrations and airborne observations and then isolate the CO 2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO 2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO 2 flux estimates.

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 OSTI2025

Atmospheric processing and aerosol aging responsible for observed increase in absorptivity of long-range-transported smoke over the southeast Atlantic

Biomass burning aerosol (BBA) from agricultural fires in southern Africa contributes about one-third of the global carbonaceous aerosol load. These particles have strong radiative effects in the southeast Atlantic (SEA), which depend in part on the radiative contrast between the aerosol layer in the free troposphere (FT) and the underlying cloud layer. However, there is large disagreement in model estimates of aerosol-driven climate forcing due to uncertainties in the vertical distribution, optical properties, and life cycle of these particles. This study applies a novel method combining remote sensing observations with regional model outputs to investigate the aging of the BBA and its impact on the optical properties during transatlantic transport from emission sources in Africa to the SEA. Results show distinct variations in extinction Ångström exponent (EAE) and single-scattering albedo (SSA) as aerosols age. Near the source, fresh aerosols are characterized by low mean SSA (0.84) and high EAE (1.85), indicating smaller, highly absorbing particles. By isolating marine contributions from the total column during BBA transport across the SEA, our analysis reveals an initial decrease in BBA absorptivity, with mean FT SSA of 0.87 after 6–7 d, followed by increased absorptivity with mean FT SSA of 0.84 after 10 d, suggesting enhanced absorption due to chemical aging. These findings indicate that BBA becomes more absorbing during extended transport across the SEA, with implications for reducing model uncertainties. Our remote-sensing-based results agree well with previous in situ studies and offer new insights into aerosol–radiation interactions and the energy balance over the SEA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Occurrence of seeding multi-layer clouds in the Arctic from ground-based observations

Studies of Arctic clouds often focus on low-level single-layer clouds (SLCs). Here, we use combined observations of soundings and cloud radar during the MOSAiC, ACSE, and AO2018 research cruises as well as from long-term observations at Ny-Ålesund, Svalbard and Utqiagvik, Alaska to investigate the occurrence of SLCs and multi-layer clouds (MLCs) in the Arctic and to assess the rate of ice-crystal seeding in cold MLCs. MOSAiC observations show cloudy conditions in between 70 % and 90 % of sounding-radar cases. SLCs show occurrence rates of 30 % to 40 % with the highest value of 45 % during October. Cold MLCs are most abundant from November to June (40 % to 55 % of cases). Seeding occurs in about half to two thirds of the identified cold MLCs during MOSAiC for which the sub-saturated layer extends between 100 and 1000 m. The seeding rate increases by as much as 20 percentage points as the assumed size of the falling ice crystals is increased from 100 to 400 µm. The observations reveal a stable rate of cloud-free conditions of around 20 % over the covered latitude range. Cloud occurrence during MOSAiC and at Ny-Ålesund in July, when the geographical distance between observations was minimal, shows reasonable agreement. Comparisons of MOSAiC and other research cruises to the central Arctic also indicate consistent occurrence rates of different cloud types despite the likely effect of year-to-year variability. The comparison of data from ship campaigns and land sites suggests that the latter are not necessarily a good indicator of cloud occurrence in the high Arctic.

Achtert, Peggy [Leipzig Univ. (Germany); Meteorolo↗
DOE OSTI2026

Evaluation of UKESM aerosol size and composition using ATom measurements indicates missing marine aerosol formation mechanisms

Atmospheric aerosols influence climate through their interactions with radiation and clouds, yet large uncertainties remain in their simulation by global models. This study evaluates the United Kingdom Earth System Model version 1.1 (UKESM1.1) using global-scale aircraft observations from the Atmospheric Tomography (ATom) mission, focusing on aerosol lifecycle processes in the remote marine atmosphere. We assess model performance in simulating aerosol precursor vapours, number size distributions, chemical composition, and environmental conditions. Several process improvements are tested, including sulfuric acid-ammonia nucleation, ammonium nitrate scheme, methanesulfonic acid condensation, and low-temperature isoprene-derived secondary organic aerosol formation. Model biases differ significantly between the upper troposphere (UT) and the marine boundary layer (MBL). In the UT, UKESM1.1 overestimates nucleation and Aitken mode particles while underestimating accumulation mode, indicating insufficient growth. In the MBL, the model overestimates primary aerosols (e.g. seasalt) and precursor gases but underestimates nucleation and Aitken mode particles, even after incorporating updated nucleation and ammonium nitrate scheme. The persistence of low aerosol number concentrations, despite overestimated precursors, suggests missing formation pathways likely involving other species such as iodine, amines, and organic vapours. These limitations result in an unbalanced cloud condensation nuclei budget that over-relies on primary emissions. Sensitivity tests reveal that model outputs are strongly influenced by dimethyl sulfide emissions and vapour condensation schemes. Our results highlight the need for future model development to prioritise mechanistic representation of currently missing aerosol sources, rather than relying on empirical tuning, to improve aerosol-climate interaction estimates.

He, Xu-Cheng [Univ. of Cambridge (United Kingdom);↗
DOE OSTI2026

Meteorological drivers of the low-cloud radiative feedback pattern effect and its uncertainty

The radiative feedback pattern effect remains a large source of uncertainty for both projections of future trends and interpretations of past trends in global temperature. The pattern effect is defined as the difference in feedbacks between transient and long-term simulations, and past work shows that is primarily attributed to changes in the marine low-cloud radiative feedback. Here we use low cloud meteorological kernels to map out both the primary cloud controlling factors through which changing surface temperature patterns drive changes in low-cloud feedback, as well as the sources of model spread. We find that the pattern effect is almost entirely driven by changes in estimated inversion strength (EIS) in the Southern Hemisphere, particularly in the South East Pacific and Southern Ocean. In both past and future simulations, inter-model spread is primarily caused by model differences in the sensitivity of low clouds to the environmental conditions, rather than differences in the simulated evolution of environmental conditions.

58 GEOSCIENCES↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2025

Multi-seasonal measurements of the ground-level atmospheric ice-nucleating particle abundance on the North Slope of Alaska

Atmospheric ice-nucleating particles (INPs) are an important subset of aerosol particles that are responsible for the heterogeneous formation of ice crystals. INPs modulate the arctic cloud phase (liquid vs. ice), resulting in implications for radiative feedbacks. The number of arctic INP studies investigating specific INP episodes or sources increased recently. However, existing studies are based on short-duration field data, and long-term datasets are lacking. Continuous, long-term measurements are key to determining the abundance and variability of ambient arctic INPs and constraining aerosol–cloud interactions, e.g., to verify and/or improve simulations of mixed-phase clouds. Here, we present a new long-duration INP dataset from the Arctic: 2 years of predominantly immersion-mode INP concentrations (n INP ) measured continuously at the National Oceanic and Atmospheric Administration's Barrow Atmospheric Baseline Observatory (BRW) on the North Slope of Alaska. A portable ice nucleation experiment chamber (PINE-03), which simulates adiabatic expansion cooling, was used to directly measure the ground-level INP abundance with an approximately 12 min time resolution from October 2021 to December 2023. We document PINE-03 n INP measurements as well as estimated ice nucleation active surface site density (n s ) over a wide range of heterogeneous freezing temperatures from −16 to −31 °C from which we introduce new season-specific parameterizations suitable for modeling mixed-phase clouds. Collocated aerosol and meteorological data were analyzed to assess the correlation between ambient n INP , air mass origin region, and meteorological variability. Our findings suggest (1) very high freezing efficiency of INPs across the measured temperatures (n s ≈ 2×10 8 –10 10 m −2 for −16 to −31 °C), which is a factor of 10–1000 times greater efficiency as compared to that found in the previous mid-latitude INP measurements in fall using the same instrument; (2) surprisingly high n INP (≥ 1 L −1 at −25 °C) for the examined temperatures throughout the year that were not measured by PINE-03 at other sites; and (3) high n INP in spring, possibly related to arctic haze episodes. Relatively low concentrations of aerosol surface area and contrasting high-INP concentrations at BRW relative to mid-latitude sites are the possible reasons for the observed high freezing efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results

Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO 2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Urban weather modeling using WRF: linking physical assumptions, code implementation, and observational needs

The Weather Research and Forecasting (WRF) model includes urban schemes that simulate the influence of urban surfaces on the atmosphere using parameterizations for flux, and radiative exchanges. Three core schemes – the Bulk urban parameterization, Single-Layer Urban Canopy Model (SLUCM), and Multi-Layer Urban Canopy Model (MLUCM) – represent increasing levels of complexity. Although the parameterizations within these urban schemes are described in the literature, their specific implementation remains poorly documented, thus slowing down model development efforts. This manuscript provides a roadmap to the three urban schemes in WRF version 4.5.2, presenting equations using the same symbols as in the model code, along with references to code lines, and including graphics and explanations that connect the code to its physical foundations. Our thorough review of the urban parameterizations implemented in WRF version 4.5.2 highlighted a handful of parameters that may introduce discontinuities in simulations: (i) in the SLUCM, a 1 mm h −1 rain rate threshold is employed to switch between two minimum moisture availability parameterizations, thus impacting latent heat flux calculations; (ii) in the SLUCM a threshold is used to partition shortwave radiation into direct and diffuse components; (iii) in all three urban schemes, the bulk Richardson number is employed to select the similarity function, which influences the vertical distribution of heat and momentum. We also identified a highly simplified treatment of the radiative balance on roof surfaces. The implications of these simplifications can be assessed through targeted observations across relevant conditions, including varying precipitation rates, cloud cover, and transitions between stability regimes. Furthermore, the widespread application of the Monin-Obukhov similarity theory in these urban schemes warrants model evaluation under highly stable and unstable conditions and in heterogeneous urban settings with variable land cover and building heights on scales finer than model resolution. To address these challenges, we offer guidance on observational strategies, emphasizing the need for multi-parametric measurements to capture potential compensating biases and multi-height measurements that align with the levels where quantities are diagnostic and prognosed in the model (i.e., the lowest atmospheric level of the WRF model). Finally, our inspection of the code revealed implementation bugs that have now been corrected in WRF versions 4.6.0 and 4.6.1. Sensitivity tests over the Atlanta urban area show that these corrections affect surface temperatures, underscoring the importance of performing rigorous documentation and verification of the implementation of parameterizations in model code.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Best practices in software development for robust and reproducible geoscientific models based on insights from the Global Carbon Budget's dynamic vegetation models

Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Gregor, Konstantin [Technical Univ. of Munich (Ger↗
DOE OSTI2026

CMIP7 data request: land and land ice priorities and opportunities

The Land and Land Ice Theme in the Coupled Model Intercomparison Project Phase 7 (CMIP7) represents the current understanding of physical processes in land surface ecosystems, hydrology, cryosphere, and their physical interactions with other Earth system components. Simulations from Earth system models (ESMs) could provide crucial information for assessing planetary safety, such as critical tipping elements, and be used to inform climate risks for improving climate impact assessments and policy decisions. This paper presents a collaborative effort to identify scientific opportunities in the Land and Land Ice Theme of the CMIP7 Data Request. The proposed opportunities build upon advances in ESMs, including new freshwater system and land ice processes being included in CMIP7, as well as the scientific community's demand for high-frequency and sub-grid-scale land surface outputs. In total, 25 variable groups that contain 716 variables have been identified to be potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology. Key reflections from this data request effort include advocacy for closer engagement between the user community and modeling groups, reduction in the technical barriers to tracking existing parameters and defining new variables, and more streamlined variable management. These will be essential to enhance the usability and reliability of CMIP7 outputs for climate and Earth system research and applications to a broad audience that relies on the CMIP7 endeavor.

Li, Yue [Univ. of California, Los Angeles, CA (Uni↗
DOE OSTI2026

Process-oriented evaluation of quasi-stationary Rossby waves and their impact on surface air temperature extremes in dynamical downscaling over North America

Quasi-stationary Rossby waves are a crucial component of the general circulation and play a significant role in regional water and energy cycles, as well as in extreme events. However, process-oriented evaluation for Rossby waves is rarely performed for dynamical downscaling simulations. To close this gap, we evaluate three classes of dynamical downscaling approaches, with a focus on quasi-stationary Rossby waves and their impact on surface air temperature over North America during Northern Hemisphere summer. The three classes of models differ in the way large-scale forcing is provided: a limited-area model (LAM) constrained only by lateral boundary conditions, represented by RegCM4 from the North American branch of the Coordinated Regional Downscaling Experiment (NA-CORDEX), a LAM with spectral nudging to maintain consistency in large-scale dynamics with the forcing data, represented by the Weather Research and Forecasting (WRF) model simulation in NA-CORDEX, and a global variable-resolution model with smoothly varying grid spacings, represented by the Community Atmosphere Model version 5.4, with the Model for Prediction Across Scales (MPAS) as its dynamical core (CAM-MPAS). With no constraints on the atmospheric dynamics, CAM-MPAS exhibits several mean biases in the upper-level circulations over the Pacific Coast region: a weaker subtropical jet, a northward-shifted mid-latitude jet, and an overestimated southerly flow. With the lateral boundary constraint alone, RegCM4 also exhibits weaker jets and overestimated southerly winds off the West Coast. Rossby ray theory reveals that those wind biases direct incoming Rossby waves northward. The erroneously routed Rossby waves distort the relationship between the accumulation of wave activity over the US West Coast and surface temperature anomalies over the Southern Great Plains, which emerges approximately 4 d after the convergence of wave-activity flux in the ERA-Interim reanalysis. Furthermore, the response of heatwaves to the extreme wave activity flux is not reproduced by the two models, a serious drawback as a dynamical downscaling framework is expected to connect large-scale forcing to local-scale phenomena. The WRF model employing spectral nudging is largely free from the aforementioned problems. A pair of sensitivity simulations suggests that spectral nudging is the key to improving the dynamics of quasi-stationary Rossby waves and their impact on surface air temperature. Our results also demonstrate the effectiveness of Rossby wave diagnostics that allow for realistic background flows for assessing the credibility of dynamical downscaling over North America, where incoming Rossby waves propagate through complex circulation patterns before traveling across the continent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Canopy structure modulates the sensitivity of subalpine forest stands to interannual snowpack and precipitation variability

A declining spring snowpack is expected to have widespread effects on montane and subalpine forests in western North America and across the globe. The way that tree water demands respond to this change will have important impacts on forest health and downstream water subsidies. Here, we present data from a network of sap velocity sensors and xylem water isotope measurements from three common tree species (Picea engelmannii, Abies lasiocarpa and Populus tremuloides) across a hillslope transect in a subalpine watershed in the Upper Colorado River basin. We use these data to compare tree- and stand-level responses to the historically high spring snowpack but low summer rainfall of 2019 against the low spring snowpack but high summer rainfall amounts of 2021 and 2022. From the sap velocity data, we found that only 40 % of the trees showed an increase in cumulative transpiration in response to the large snowpack year (2019), illustrating the absence of a common response to interannual spring snowpack variability. The trees that increased water use during the year with the large spring snowpack were all found in dense canopy stands – irrespective of species – while trees in open-canopy stands were more reliant on summer rains and, thus, more active during the years with modest snow and higher summer rain amounts. Using the sap velocity data along with supporting measurements of soil moisture and snow depth, we propose three mechanisms that lead to stand density modulating the tree-level response to changing seasonality of precipitation: Topographically mediated convergence zones have consistent access to recharge from snowmelt which supports denser stands with high water demands that are more reliant and sensitive to changing snow. Interception of summer rain in dense stands reduces the throughfall of summer rain to surface soils, limiting the sensitivity of the dense stands to changes in summer rain. Shading in dense stands allows the snowpack to persist deeper into the growing season, providing high local reliance on snow during the fore-summer (early-summer) drought period. Combining data generated from natural gradients in stand density, like this experiment, with results from controlled forest-thinning experiments can be used to develop a better understanding of the responses of forested ecosystems to futures with reduced spring snowpack.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Annual carbon dioxide flux over seasonal sea ice in the Canadian Arctic

Continuous measurements of carbon dioxide (CO 2 ) flux were collected from a 10 m eddy covariance tower in a coastal-marine environment in the Canadian Arctic Archipelago over the course of a 17-month period. The extended length of data collection resulted in a unique dataset that includes measurements from two spring melt and summer seasons and one autumn freeze-up. These field observations were used to verify findings from previous theoretical and laboratory experiments investigating air-sea gas exchange in connection with sea ice. The results corroborated previous findings showing that thick ice cover under winter conditions acts as a barrier to gas exchange. In the spring, CO 2 fluxes were downward (uptake) in both the presence of melt ponds and during ice break-up. However, diurnal cycles were present throughout the early spring melt period, corresponding to the opposing influences of freezing and melting at the ice surface. Fluxes measured during melt periods confirmed previous laboratory tank measurements that showed a gas transfer coefficient of melting ice of 0.4 mol m −2 d −1 atm −1 . Open water CO 2 fluxes showed outgassing in early summer and uptake in mid-to-late summer, tied closely to trends in surface water temperature and its effect on the partial pressure of CO 2 in the water. The autumn period of the field campaign represents the first eddy covariance CO 2 fluxes measured over naturally forming sea ice. Our measurements showed mean upward fluxes (outgassing) of 1.1±1.5 mmol m −2 d −1 associated with the freezing of ice – the same order of magnitude found by previous laboratory tank experiments. However, peak flux periods during ice formation had measured fluxes that were a factor of 3 higher than the tank experiments, suggesting the importance of natural conditions (e.g., wind) on air-ice gas exchange. Conducting an Arctic-wide extrapolation we estimate CO 2 outgassing from the freezing period to be a counterbalance equivalent to 5 to 15 % of the magnitude of the estimated Arctic CO 2 sink. Overall, there was no evidence of dramatically enhanced gas exchange in marginal ice conditions as proposed by previous studies. Although the different seasons showed active CO 2 exchange, there was a balance between upward and downward fluxes at this specific location, resulting in a small net CO 2 uptake over the annual cycle of −0.3 g C m −2 .

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

Seventh Oregon Climate Assessment

Consistent with its charge under Oregon House Bill 3543, the Oregon Climate Change Research Institute (OCCRI) conducts a biennial assessment of the state of climate change science, including biological, physical, and social science, as it relates to Oregon and the likely effects of climate change on Oregon. This seventh Oregon Climate Assessment, which builds on the previous assessments, is structured with the goal of supporting the state's mitigation planning for natural hazards and implementation of the Oregon Climate Change Adaptation Framework.

17 WIND ENERGY↗