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At least 19 records

Model Data Archive for Manuscript Titled "Evaluation of a Coupled Surface–Subsurface Hydrologic Model Using Dense Water‑Level Sensors in a Mixed Urban–Rural Watershed"

This archive provides scripts, input files, and datasets used for the implementation and evaluation of a fully coupled surface–subsurface hydrologic model in the Neches River Basin, southeast Texas. The study uses the Advanced Terrestrial Simulator (ATS) to simulate coupled surface–subsurface hydrologic processes over a mixed urban–rural watershed and evaluates model performance using a dense network of 136 in situ water-level sensors, nine U.S. Geological Survey (USGS) stream gauges, and SSEBop-derived evapotranspiration estimates during the period October 2014–June 2024. The workflow is implemented primarily in Python 3 using the Watershed Workflow package. The Jupyter notebooks can be executed using open-source software such as Anaconda JupyterLab or Visual Studio Code. Other data files include TXT, CSV, XML, SHP, TIF, NetCDF, HDF5, and ExodusII files, which can be processed using the provided Python scripts. ATS input files are provided in XML format and can be edited using any commonly used text editor. This archive contains: *Scripts and input files used to generate the ATS model setup, including watershed discretization, mesh generation, parameter mapping, and model configuration. *Jupyter notebooks used for preprocessing observational data, evaluating streamflow, water levels, and evapotranspiration, computing performance metrics, and generating the figures presented in the manuscript. *ATS simulation outputs and processed observational datasets, including OneRain and DD6 water-level sensors, USGS streamflow observations, GIS data, and supporting spatial datasets used throughout the study.

Dense water-level sensor network↗

Understanding processes that control dust spatial distributions with global climate models and satellite observations

Dust aerosol is important in modulating the climate system at local and global scales, yet its spatiotemporal distributions simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate the spatiotemporal variations of dust extinction profiles and dust optical depth (DOD) simulated by the Community Earth System Model version 1 (CESM1) and version 2 (CESM2), the Energy Exascale Earth System Model version 1 (E3SMv1), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) against satellite retrievals from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging SpectroRadiometer (MISR). We find that CESM1, CESM2, and E3SMv1 underestimate dust transport to remote regions. E3SMv1 performs better than CESM1 and CESM2 in simulating dust transport and the northern hemispheric DOD due to its higher mass fraction of fine dust. CESM2 performs the worst in the Northern Hemisphere due to its lower dust emission than in the other two models but has a better dust simulation over the Southern Ocean due to the overestimation of dust emission in the Southern Hemisphere. DOD from MERRA-2 agrees well with CALIOP DOD in remote regions due to its higher mass fraction of fine dust and the assimilation of aerosol optical depth. The large disagreements in the dust extinction profiles and DOD among CALIOP, MODIS, and MISR retrievals make the model evaluation of dust spatial distributions challenging. Our study indicates the importance of representing dust emission, dry/wet deposition, and size distribution in GCMs in correctly simulating dust spatiotemporal distributions.

54 ENVIRONMENTAL SCIENCES↗

Application of spatial Poisson process models to air mass thunderstorm rainfall

Eight years of summer storm rainfall observations from 93 stations in and around the 154 sq km Walnut Gulch catchment of the Agricultural Research Service, U.S. Department of Agriculture, in Arizona are processed to yield the total station depths of 428 storms. Statistical analysis of these random fields yields the first two moments, the spatial correlation and variance functions, and the spatial distribution of total rainfall for each storm. The absolute and relative worth of three Poisson models are evaluated by comparing their prediction of the spatial distribution of storm rainfall with observations from the second half of the sample. The effect of interstorm parameter variation is examined.

Eagleson, P. S.↗

Estimating Spatial Distribution Impacts of Rooftops Solar PV on Dynamic Hosting Capacity Evaluation for a Real Distribution Feeder

This paper models the sensitivity of dynamic hosting capacity analysis to the spatial distribution of distributed PV scenarios, random, close and far from substation deployments, at various penetration levels on a real distribution feeder, in a quasi-static time series (QSTS) power flow simulation using high resolution time-aware metrics. These spatial distribution scenarios were chosen to capture a wide range of potential operation impacts of these deployments using a set of thermaland voltage-based metrics such as instantaneous violations and moving averages for both thermal and voltage constraints. This study uses actual load and PV data up-scaled from 1-hour time step to 1-minute resolution to fully characterize the interaction between the daily changes in load and PV output, and their impacts on distribution system operations.

distribution system↗

Cometary atmospheres: Modeling the spatial distribution of observed neutral radicals

Progress on modeling the spatial distributions of cometary radicals is described. The Monte Carlo particle-trajectory model was generalized to include the full time dependencies of initial comet expansion velocities, nucleus vaporization rates, photochemical lifetimes and photon emission rates which enter the problem through the comet's changing heliocentric distance and velocity. The effect of multiple collisions in the transition zone from collisional coupling to true free flow were also included. Currently available observations of the spatial distributions of the neutral radicals, as well as the latest available photochemical data were re-evaluated. Preliminary exploratory model results testing the effects of various processes on observable spatial distributions are also discussed.

Combi, M. R.↗

Sensitivity evaluation of the Kudryavtsev permafrost model

Modeling is an important way to assess current and future permafrost spatial distribution and dynamics, especially in data poor areas like the Arctic region. Here, we evaluate a physics-based analytical model, Kudryavtsev's active layer model, which is widely used because it has relatively few data requirements. This model was recently incorporated into a component modeling toolbox, allowing for coupled modeling of permafrost and geomorphic processes over geological timescales. However, systematic quantitative assessment of the influence of its controlling parameters on permafrost temperature and active layer thickness predictions has not been undertaken before. In this work, we investigate the sensitivity of the Kudryavtsev's active layer model by Monte Carlo simulations to generate probability distributions for input parameters and compare predictions with a comprehensive benchmark dataset of in-situ permafrost observations over entire Alaska. Predicted permafrost surface temperature is highly dependent on mean annual air temperature (r = 0.78 on average), annual temperature amplitude (–0.41), and winter-averaged snow thickness (0.30). Uncertainty of predicted permafrost temperature is relatively small (RMSE = 1 °C), when air temperature and snow depth are well constrained. Similarly, RMSE between simulated and observed ALT at stations is ~0.08 m. However, under given air temperature and snow conditions, soil water content bias can significantly affect modeled active layer thickness (RMSE = 0.1 m or 40% of the observed active layer thickness). If soil water content has a large bias, improvements in other parameters may not significantly improve the active layer predictions of the Kudryavtsev's model.

54 ENVIRONMENTAL SCIENCES↗

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km 2 . The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km 2 , the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.

DTVGM↗

Evaluating Global Emission Inventories of Biogenic Bromocarbons

Emissions of halogenated very short-lived substances (VSLS) are poorly constrained. However, their inclusion in global models is required to simulate a realistic inorganic bromine (Bry) loading in both the troposphere, where bromine chemistry perturbs global oxidizing capacity, and in the stratosphere, where it is a major sink for ozone (O3). We have performed simulations using a 3-D chemical transport model (CTM) including three top-down and a single bottom-up derived emission inventory of the major brominated VSLS bromoform (CHBr3) and dibromomethane (CH2Br2). We perform the first concerted evaluation of these inventories, comparing both the magnitude and spatial distribution of emissions. For a quantitative evaluation of each inventory, model output is compared with independent long-term observations at National Oceanic and Atmospheric Administration (NOAA) ground-based stations and with aircraft observations made during the NSF (National Science Foundation) HIAPER Pole-to-Pole Observations (HIPPO) project. For CHBr3, the mean absolute deviation between model and surface observation ranges from 0.22 (38 %) to 0.78 (115 %) parts per trillion (ppt) in the tropics, depending on emission inventory. For CH2Br2, the range is 0.17 (24 %) to 1.25 (167 %) ppt. We also use aircraft observations made during the 2011 Stratospheric Ozone: Halogen Impacts in a Varying Atmosphere (SHIVA) campaign, in the tropical western Pacific. Here, the performance of the various inventories also varies significantly, but overall the CTM is able to reproduce observed CHBr3 well in the free troposphere using an inventory based on observed sea-to-air fluxes. Finally, we identify the range of uncertainty associated with these VSLS emission inventories on stratospheric bromine loading due to VSLS (Br(VSLS/y)). Our simulations show Br(VSLS/y) ranges from approximately 4.0 to 8.0 ppt depending on the inventory. We report an optimized estimate at the lower end of this range (approximately 4 ppt) based on combining the CHBr3 and CH2Br2 inventories which give best agreement with the compilation of observations in the tropics.

bromocarbons↗

Assessment of global and regional tectonic models for faulting in the ancient terrains of Mars

The structural mapping of tectonic landforms on Mars has provided a great deal of information regarding the planet's tectonic history. The present paper has the objective to test models of regional and global fracturing by evaluating the spatial distribution and orientation of the ancient heavily cratered terrain (HCT) structural landforms. Attention is given to background information concerning earlier studies, landform selection and analysis, rose diagrams, tests for Tharsis generation, and a calculation of thermal stress in the early Martian lithosphere. The contribution of classical Tharsis-centered tectonism to the production of HCT structural landforms was found to be very small. Few structural landforms investigated are either perpendicular to calculated principal stress trajectories or radial to Tharsis.

Schultz, R. A.↗

Data for Post-Access Report: Tidal Energy Resource Characterization and Model Validation via the Assessment of Cost-effective Spatially Distributed Current Measurements from microFloat Swarms

This project evaluated how high-resolution, spatially distributed field data can be used to refine and validate site-scale hydrodynamic simulations of tidal channels. Use of such spatially-distributed field observations or site-scale hydrodynamic simulations will be needed for producing accurate predictions of tidal energy production over larger arrays of tidal turbines. The data contained in this archive includes field observations of water velocity from a series of short surveys in Agate Pass taken on August 20th, 2020 using station-keeping ADCP (acoustic doppler current profiler) measurements, drifting downward-looking ADCPs, and microFloats, gathered by researchers at APL-UW under a previous project. It also includes high-resolution hydrodynamic model runs for the site during that time generated under TEAMER support. An explanatory file (.pdf format) describes the survey and field data structure. Processed data is stored in .mat format. Survey summary: start time, number of floats, water level. microFloat data: - Cleaned data for each float in survey, - Time series of position (lat., lon.), depth (relative to surface), and horizontal velocity (u, v) Drifting ADCP data: - Cleaned data for each drifting ADCP in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) Stationary ADCP data: - Cleaned data for each station-keeping data set in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) FVCOM (Finite Volume Coastal Ocean Model) model outputs: Water level and velocity, as well as derived variables (e.g., power density) from the final refined model simulations for the Agate Pass subdomain. Contained in NetCDF and ASCII files for the duration of August 18th and 20th 2020 field surveys. Additional scripts for processing the data and generating the figures in the report are included.

16 TIDAL AND WAVE POWER↗

Spatial quantification of maps or images - Cell size or pixel size implications

The paper discusses spatial quantification of maps or images. Spatial accuracies evaluated by mapping and inventory measures were observed with a range of cell sizes for a 10.36 sq km soil survey segment. The distribution of interboundary distances in a map was evaluated as unique, characterizing information; experimentally observed relationships between accuracy and cell size and interboundary distance distribution parameters and cell size led to a model of the process of spatial quantization. Grid positioning effects were evaluated and were significant only at the level of individual mapping units; two generations of 'universal' process models were derived, implemented, and evaluated.

Wehde, M. E.↗

Feeding Ten Billion People Is Possible Within Four Terrestrial Planetary Boundaries

Global agriculture puts heavy pressure on planetary boundaries, posing the challenge to achieve future food security without compromising Earth system resilience. On the basis of process-detailed, spatially explicit representation of four interlinked planetary boundaries (biosphere integrity, land-system change, freshwater use, nitrogen flows) and agricultural systems in an internally consistent model framework, we here show that almost half of current global food production depends on planetary boundary transgressions. Hotspot regions, mainly in Asia, even face simultaneous transgression of multiple underlying local boundaries. If these boundaries were strictly respected, the present food system could provide a balanced diet (2,355 kcal per capita per day) for 3.4 billion people only. However, as we also demonstrate, transformation towards more sustainable production and consumption patterns could support 10.2 billion people within the planetary boundaries analysed. Key prerequisites are spatially redistributed cropland, improved water–nutrient management, food waste reduction and dietary changes. Adoption of the Sustainable Development Goals by all nations in 2015 is the first ever commitment to a world development path that safeguards the stability of the Earth system as a prerequisite for meeting universal human standards1. The longstanding challenge of achieving food security through sustainable agriculture is particularly acute in this context as world agriculture is a leading cause for the current transgressions of multiple planetary boundaries (PBs) globally and regionally2–5. The PB framework is a comprehensive scientific attempt to synoptically define our planet’s biogeophysical limits to anthropogenic interference. It suggests bounds to nine interacting processes that together delineate a Holocene-like Earth system state. The Holocene is chosen as the reference state as it is the only period known to provide a safe operating space for a world population of several billion people, and according to a precautionary principle, the PBs are set in sufficient distance from processes that may critically undermine Earth system resilience and global sustainability. A challenging question, thus, is whether human development goals such as food security can be met while maintaining multiple PBs along with their subglobal manifestations. Further PB transgressions could jeopardize the chances of providing sufficient food for a world population projected to be wealthier and reach >9 billion by 2050. This conundrum portrays a tradeoff between Earth’s biophysical carrying capacity and humankind’s rising food demand, calling in response for radical rethinking of food production and consumption patterns6–9. Yield gap closures, avoidance of excessive input use, shifts towards less resource-demanding diets, food waste reductions and efficient international trade are crucial options for sustainably increasing the food supply10–15. For example, enhancing water-use efficiency on irrigated and rain-fed farms can triple or quadruple crop yields in low-performing systems, suggesting possible global gains of >20% (ref. 16). Even higher gains appear feasible through globally optimized configurations of the land-use pattern17, and cutting food losses by half could generate food for another billion people18. Thus, collective large-scale implementation of such options could sustain food for a further growing world population19. Yet achieving this within a safe operating space as defined by PBs requires not only a halt to but actually a reversal of existing PB transgressions. Previous studies suggest that such a reconciliation might be possible, but these were based on aggregate representations of PBs (not accounting for the spatial patterns of limits, transgressions and interactions) or considered only one boundary in isolation17,20–23. Here, we systematically quantify to what extent current food production depends on local to global transgressions of the PBs for biosphere integrity, land-system change, freshwater use and nitrogen (N) flows, along with the potential of a range of solutions to avoid these transgressions and still increase food supply (Table 1). To this end, we configured an internally consistent process-based model of the terrestrial biosphere including agriculture (LPJmL) with multiple spatially distributed PBs and their interactions. LPJmL is among the longest-established and best-evaluated biosphere models, showing robust performance regarding simulation of, for example, carbon, water and crop yield dynamics (Supplementary Figs. 1 and 2 and Supplementary Table 1; see ref. 24 for a comprehensive benchmarking and Supplementary Methods for more detail on model evaluations). In principle following established definitions4, we refine the computation of some PBs with respect to their regional patterns and interactions (Methods), providing globally gridded precautionary limits to human interference with the Earth system at a level of great detail. In particular, we account for the evidence that many PBs need to be represented spatially explicitly4 to cover their

Gerten, Dieter↗

Implications of increased spatial and trophic overlap between juvenile Pacific salmon and Sablefish in the northern California Current

Abstract Objective The study was designed to assess long-term variability in the distribution of juvenile Pacific salmon Oncorhynchus spp. and Sablefish Anoplopoma fimbria. The study also evaluated whether Sablefish and Pacific salmon shared food resources and looked to characterize Sablefish during an understudied period of their life cycle. Methods To meet the objectives, the study used data from 26 years of surface trawls conducted in Oregon and Washington coastal waters (1998–2023). Spatial–temporal models were used to measure changes in abundance and distribution of Pacific salmon and Sablefish along with covariates of ocean temperature. The study evaluated trophic characteristics of Pacific salmon and Sablefish from 2020 for differences. The temporal variation in size and diets of Sablefish were also analyzed, along with energy density of fish caught in 2020. Result The spatial–temporal model demonstrated that there has been a nearshore expansion of juvenile Sablefish over the past 26 years that was correlated with increased ocean temperature. The nearshore expansion of Sablefish resulted in increased spatial and trophic overlap with juvenile Pacific salmon. While feeding in nearshore waters, juvenile Sablefish demonstrated competitive feeding advantages over juvenile Pacific salmon during a critical phase of salmonid early marine life history. Juvenile Sablefish exhibited significant ontogenetic diet and energetic shifts, and even the smallest (68–80 mm fork length) were piscivorous. Conclusions If juvenile Sablefish numbers continue to increase relative to Pacific salmon, they could exert more competitive pressure, especially if food resources become limited. Pacific salmon may experience adverse effects from competition, regardless of whether or not juvenile Sablefish, which have recently expanded into nearshore waters, successfully recruit to the adult population.

Daly, Elizabeth A. (ORCID:0000000195334457)↗

A Spatially Explicit Individual-Based Modeling Approach to Evaluate the Cumulative Effects of Wind Energy Development on the Greater Sage-Grouse: Sensitivity Analysis and Validation of Key Parameters

To address the need for an approach to evaluate cumulative ecological impacts of wind energy development, particularly those on critical wildlife habitats and species, the authors developed a proof-of-concept, landscape-based, spatially explicit individual-based modeling (IBM) framework for populations of the greater sage-grouse (Centrocercus urophasianus) in Albany County, Wyoming, based on published and other available information on the life history of the species. This sage-grouse IBM estimates the spatial and temporal movements of sage-grouse based on modeled effects of habitat characteristics and developments on sage-grouse condition, survivorship, and reproduction. After completing the initial version of the model, we conducted a series of sensitivity analyses of key model parameters and validation of lek occurrences and spatial distributions to better understand performance of the model relative to observed values of important life history characteristics. The parameter sensitivity analysis was conducted as a series of one-factor-at-a-time tests in which the values of selected parameters were individually varied to evaluate the corresponding effects on model output. We examined the model’s response to changes in parameter values to gain insight into the overall robustness of the model, identify key driver parameters, and initiate qualitative validation. Five functions selected for sensitivity analyses were (1) the rate of change in body condition in relationship to habitat suitability index (HSI) of occupied habitat (condition change rate), (2) the effect of competition between individuals with overlapping home ranges expressed as a percentage reduction in HSI (competition factor), (3) value of condition at which survivorship probability is equal to zero (zero survivorship condition), (4) the relationship between female body condition and clutch size, and (5) the relationship between female body condition and nest success. We varied the parameter values to be tested for the sensitivity analysis around the original value used in the model (i.e., tested values were higher and lower than the model’s original value) to examine the effect of this variation on the predicted output of the model. Model outputs evaluated in the sensitivity analysis included (1) predicted age-class distribution (as measured by the percentage of immature individuals within the population); (2) predicted sex ratio; (3) predicted age-sex class distribution (percent of population in each of six age-sex classes, i.e., male and female juveniles, yearlings, and adults); (4) lifespan; (5) population size; and (6) spatial distribution. Of the five parameters analyzed, condition change rate, survivorship, clutch size, and nest success did not substantially affect any of the evaluated model outputs. For the predicted age-class distribution, the percentage of immature individuals predicted by the model for these four parameters ranged from 61.9% to 65.0%, which was reasonable with respect to the reference values of 51.4 to 57.8% in published studies. The ratio of females to males ranged from 1.09 to 1.14, which was comparable to the reference values of 1.2 to 3.0 presented in an existing study. The predicted mean lifespan ranged from 1.62 to 1.74 years, which appears to be reasonable with respect to the reference values of 0.9 to 1.1 years for sharp-tail grouse and greater prairie chickens presented by published studies. The predicted population size ranged from 1,877 to 3,140. This prediction would be reasonable with respect to our estimate of 5,000 yearlings and adults, which was based on the USFWS estimate for the state of Wyoming scaled to the number of leks in the county. The predicted spatial distribution was not substantially affected by the parameter values examined. In our sensitivity analyses, the only tested parameter that had a noticeable effect on model output was competition factor. Over the range of values tested for this parameter, predicted population size exhibited the largest range (from 1,833 to 4,737) of all parameters tested. As the competition factor increased, the predicted population size decreased. This reflects the effect competition had on the number of individuals that could co occur in high-quality habitat patches. Despite this apparently significant effect, the range of values appears to be reasonable with respect to our estimate of 5,000 yearlings and adults, which was based on the USFWS estimate for the state of Wyoming scaled to the number of leks in the county.

17 WIND ENERGY↗

Expected occurrence of wildlife in US Atlantic offshore wind areas

Offshore wind energy has entered a pivotal phase of development for the U.S. Atlantic Outer Continental Shelf (OCS), a region that supports critical habitats, migratory corridors and flyways for many marine species. Assessing where and when marine wildlife occurs is a crucial first step in developing a risk assessment framework to evaluate potential risks and impacts of offshore wind development. In this study, we perform this initial assessment by evaluating the expected occurrence of marine mammal, seabird and sea turtle taxa in areas of interest to identify patterns and potential areas of concern. Specifically, this work depicts the expected monthly density of 84 marine species and taxa within each of the 29 active wind energy lease areas plus a 10 km buffer to account for nearby activity. We then compare these densities to subregional thresholds, evaluated as the 90th percentile of the subregion’s monthly density, to provide comparisons across the shelf region. This analysis synthesizes the most recent spatial distribution models of 31 marine mammal taxa (26 species and 5 guilds), 49 seabird species and 4 sea turtle species to provide a unified evaluation of the major marine wildlife in the region. Out of the 84 species and taxa analyzed, 56 exhibit levels of expected density in wind energy areas that exceed the corresponding 90th percentile subregional threshold at some point throughout the year. These results represent an initial assessment in the broader Occurrence, Exposure, Response, and Consequence (OERC) framework, originally developed by the U.S. Navy for marine species risk assessments. These results offer valuable guidance to marine spatial planners, management agencies and offshore wind developers on the expected locations and timing of interaction risk to wildlife species in or near wind energy areas across the region.

17 WIND ENERGY↗

The Distribution of Snow Black Carbon observed in the Arctic and Compared to the GISS-PUCCINI Model

In this study, we evaluate the ability of the latest NASA GISS composition-climate model, GISS-E2- PUCCINI, to simulate the spatial distribution of snow BC (sBC) in the Arctic relative to present-day observations. Radiative forcing due to BC deposition onto Arctic snow and sea ice is also estimated. Two sets of model simulations are analyzed, where meteorology is linearly relaxed towards National Centers for Environmental Prediction (NCEP) and towards NASA Modern Era Reanalysis for Research and Applications (MERRA) reanalyses. Results indicate that the modeled concentrations of sBC are comparable with presentday observations in and around the Arctic Ocean, except for apparent underestimation at a few sites in the Russian Arctic. That said, the model has some biases in its simulated spatial distribution of BC deposition to the Arctic. The simulations from the two model runs are roughly equal, indicating that discrepancies between model and observations come from other sources. Underestimation of biomass burning emissions in Northern Eurasia may be the main cause of the low biases in the Russian Arctic. Comparisons of modeled aerosol BC (aBC) with long-term surface observations at Barrow, Alert, Zeppelin and Nord stations show significant underestimation in winter and spring concentrations in the Arctic (most significant in Alaska), although the simulated seasonality of aBC has been greatly improved relative to earlier model versions. This is consistent with simulated biases in vertical profiles of aBC, with underestimation in the lower and middle troposphere but overestimation in the upper troposphere and lower stratosphere, suggesting that the wet removal processes in the current model may be too weak or that vertical transport is too rapid, although the simulated BC lifetime seems reasonable. The combination of observations and modeling provides a comprehensive distribution of sBC over the Arctic. On the basis of this distribution, we estimate the decrease in snow and sea ice albedo and the resulting radiative forcing. We suggest that the albedo reduction due to BC deposition presents significant space-time variations, with highest mean reductions of 1.25% in the Russian Arctic, which are much larger than those in other Arctic regions (0.39% to 0.64 %). The averaged value over the Arctic north of 66degN is 0.4-0.6% during spring, leading to regional surface radiative forcings of 0.7, 1.1 and 1.0Wm(exp−2) in spring 2007, 2008 and 2009, respectively.

Dou, T.↗

Impacts of aerosol size-dependent below-cloud scavenging on tropospheric aerosol in the NASA GEOS model

Cloud scavenging is the major sink for a suite of tropospheric aerosols, and thus largely affects the global distribution and lifetime of aerosols as well as the formation of clouds. Simplified model parameterizations of cloud scavenging of aerosols substantially contribute to large uncertainties in the simulated aerosol burdens, spatial distributions, lifetimes, as well as aerosol radiative forcing. Here we evaluate the wet scavenging scheme in the current NASA GEOS model, develop more physically-based parameterizations of below-cloud scavenging, and assess the impacts of the new scavenging parameterizations on the GEOS simulations of aerosols. The current below-cloud scavenging (BCS; washout) scheme uses a first-order removal with a constant scavenging coefficient. We replaced the current scheme with a size-dependent parameterization of Croft et al.(Atmos. Chem. Phys., 2009; hereafter referred to as C09). The C09 BCS parameterizations calculate aerosol below-cloud scavenging coefficients based on a size-dependent collision efficiency between aerosol particles and raindrops (or snow crystals). A look-up table of compiled collision efficiencies provides values for sixty aerosol sizes and nine rain flux rates. Snow-aerosol collision efficiencies are a function of the sixty aerosol sizes only. We assume a log-normal size distribution for aerosols (bulk) in GOCART. The collision efficiencies are then calculated online using a bilinear interpolation from the look-up table for the given aerosol sizes and precipitation flux rates in the model.

aerosols↗