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DOE OSTI2023

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076 ) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Meteorology & Atmospheric Sciences↗

DOE OSTI2024

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

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

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

Water Ice Cloud Feedbacks over the North Polar Residual Cap at Moderate Obliquity

Several global climate modeling studies have now shown that water ice clouds can warm the surface 10s of K at moderate obliquities [1,2,3]. Significant greenhouse warming occurs because the predicted clouds are optically thick, the cloud particles are large enough to efficiently interact with infrared radiation, and the clouds either form at or are transported to high altitudes where the atmosphere is cold. Radiativedynamic feedbacks play a critical role in producing the conditions needed for a strong cloud greenhouse. Two feedbacks have been identified: one involves atmospheric warming by clouds aloft at lower latitudes. These clouds are generally associated with the global Hadley circulation. The second feedback involves clouds that form over the North Polar Residual Cap (NPRC) during summer. These clouds are more closely associated with the regional polar circulation. We focus here on the second of these feedbacks with the goal of understanding the details of the interactions between sublimation, cloud formation and transport in the north polar region. We show that these feedbacks strongly control the wetness of the atmosphere and the strength of the cloud greenhouse at moderate obliquity.

Kahre, M. A.↗

DOE OSTI2023

Continental United States climate projections based on thermodynamic modification of historical weather

Abstract Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980–2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020–2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1999

Arctic Summer Ice Processes

The primary objective of this study is to estimate the flux of heat and freshwater resulting from sea ice melt in the polar seas. The approach taken is to examine the decay of sea ice in the summer months primarily through the use of spaceborne Synthetic Aperture Radar (SAR) imagery. The improved understanding of the dynamics of the melt process can be usefully combined with ice thermodynamic and upper ocean models to form more complete models of ice melt. Models indicate that more heat is absorbed in the upper ocean when the ice cover is composed of smaller rather than larger floes and when there is more open water. Over the course of the summer, floes disintegrate by physical forcing and heating, melting into smaller and smaller sizes. By measuring the change in distribution of floes together with open water over a summer period, we can make estimates of the amount of heating by region and time. In a climatic sense, these studies are intended to improve the understanding of the Arctic heat budget which can then be eventually incorporated into improved global climate models. This work has two focus areas. The first is examining the detailed effect of storms on floe size and open water. A strong Arctic low pressure storm has been shown to loosen up the pack ice, increase the open water concentration well into the pack ice, and change the distribution of floes toward fewer and smaller floes. This suggests episodic melting and the increased importance of horizontal (lateral) melt during storms. The second focus area is related to an extensive ship-based experiment that recently took place in the Arctic called Surface Heat Budget of the Arctic (SHEBA). An icebreaker was placed purposely into the older pack ice north of Alaska in September 1997. The ship served as the base for experimenters who deployed extensive instrumentation to measure the atmosphere, ocean, and ice during a one-year period. My experiment will be to derive similar measurements (floe size, open water, temporal change) using spaceborne SAR data obtained during the summer of 1998, and compare these results with an ocean and ice model of summer melt. Additional information is contained in the original.

Holt, Benjamin↗

DOE OSTI2020

Understanding cloud droplet spectral dispersion effect using empirical and semi-analytical parameterizations in NCAR CAM5.3

Five parameterizations of cloud droplet spectral shape are implemented in a global climate model to investigate the dispersion effect and aerosol indirect effect (AIE). We design a series of experiments by modifying the microphysical cloud scheme of NCAR CAM5.3 (Community Atmospheric Model Version 5.3). We employ four empirical (Martin94, RLiu03, PengL03, Liu08) and one semi-analytical (LiuLi15) expressions for cloud droplet spectral shape parameters. Analysis focuses on the instantaneous differences in the simulated cloud microphysical properties and the comparison between model output and satellite data. The results show that RLiu03, PengL03 and LiuLi15 produce wider droplet spectrum and faster autoconversion rate, but Liu08 has a narrower droplet spectrum and slower autoconversion rate than the default parameterization (Martin94) in CAM5.3. Global dispersion effects caused by the five parameterizations modify the aerosol indirect effect by -10% (counteract) to 13% (strengthen). The simulated AIEs and dispersion effects exhibit noticeably spatial inhomogeneity. In the sensitive regions of AIE (Southeast Asia, North Pacific and west coast of South America), we decompose the response of shortwave cloud forcing to the change in droplet number for analysis. The varying dispersion effects can be explained by different responses of cloud properties in different spectral parameterizations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2001

Validation of Satellite Aerosol Retrievals from AERONET Ground-Based Measurements

Accurate and comprehensive assessment of the parameters that control key atmospheric and biospheric processes including assessment of anthropogenic effects on climate change is a fundamental measurement objective of NASA's EOS program (King and Greenstone, 1999). Satellite assessment programs and associated global climate models require validation and additional parameterization with frequent reliable ground-based observations. A critical and highly uncertain element of the measurement program is characterization of tropospheric aerosols requiring basic observations of aerosols optical and microphysical properties. Unfortunately as yet we do not know the aerosol burden man is contributing to the atmosphere and thus we will have no definitive measure of change for the future. This lack of aerosol assessment is the impetus for some of the EOS measurement activities (Kaufman et al., 1997; King et al., 1999) and the formation of the AERONET program (Holben et al., 1998). The goals of the AERONET program are to develop long term monitoring at globally distributed sites providing critical data for multiannual trend changes in aerosol loading and optical properties with the specific goal of providing a data base for validation of satellite derived aerosol optical properties. The AERONET program has evolved into an international federated network of approximately 100 ground-based remote sensing monitoring stations to characterize the optical and microphysical properties of aerosols.

Holben, Brent↗

DOE OSTI2024

Project Title: Adaptive Vertical Grid Enhancement for E3SM (Final Technical Report)

During the first phase of the project (2018/05-2021/05), we proved the hypothesis that, without tuning, high vertical resolution improves the representation of unresolved, parameterized low altitude clouds such as stratocumulus clouds in global climate models. We evaluated the effectiveness of the newly developed computational framework, Framework for Improvement by Vertical Enhancement (FIVE) by implementing it into E3SM. FIVE aims to improve representation of the target cloud, low level cloud for this project, with use of a locally high vertical resolution for computation of selected processes. FIVE was assessed via direct comparison among E3SM, E3SM-FIVE, and satellite data as well as a newly developed ensemble artificial neural network methodology. We demonstrated that E3SM-FIVE remarkably improves marine stratocumulus representation. To advance the FIVE methodology, we took a first step toward the adaptive vertical grid in the FIVE method by modifying the E3SM single column model. For future application of FIVE to the global storm resolving model with a few km horizontal grid resolution, we developed the vertical-meridional two-dimensional (2D) Hadley circulation model, which allows one to run simulations with much higher vertical resolution and significantly longer duration than current three-dimensional global storm resolving model. During the second phase of the project (2021/05-2023/11; period for this technical report), we sought a way to appropriately transform FIVE to a regionalized version of FIVE, which allows one to select locations with/without FIVE. In parallel, we demonstrated that E3SM-FIVE with a 25-km mesh significantly reduced the bias associated with coastal stratocumulus. Furthermore, we showed that the same improvement was able to be obtained with the use of both the Regional Refined Model (RRM) targeted to one of the stratocumulus regions with 25-km resolution and FIVE. We explored a new implementation strategy of FIVE with our 2D Hadley circulation model. In parallel we applied the 2D Hadley circulation model to study the double intertropical convergence zone bias with storm resolving model resolution and aerosol-cloud interactions within the Hadley circulation with near large eddy simulation resolution. Last, we have been investigating Arctic mixed-phase stratocumulus clouds with large eddy simulations in order to improve their representation in global models. During the course of the entire project, 6 journal articles were published, and 3 manuscripts are currently in preparation. We gave a total of 30 presentations at various domestic and international conferences. In addition we gave 6 seminar talks at universities and a national laboratory. E3SM- FIVE is available as a branch of E3SM.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

Global Single and Multiple Cloud Classification with a Fuzzy Logic Expert System

An unresolved problem in remote sensing concerns the analysis of satellite imagery containing both single and multiple cloud layers. While cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget, most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. Coakley (1983) used a spatial coherence method to determine whether a region contained more than one cloud layer. Baum et al. (1995) developed a scheme for detection and analysis of daytime multiple cloud layers using merged AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High-resolution Infrared Radiometer Sounder) data collected during the First ISCCP Regional Experiment (FIRE) Cirrus 2 field campaign. Baum et al. (1995) explored the use of a cloud classification technique based on AVHRR data. This study examines the feasibility of applying the cloud classifier to global satellite imagery.

Welch, Ronald M.↗

NASA NTRSDate not supplied

A Neural Network Parametrization of Volumetric Cloud Fraction Profiles Using Satellite Observations and MERRA-2 Reanalysis Meteorological Data

Clouds play a crucial role in regulating the hydrologic cycle and Earth's radiative energy budget, yet they are often poorly represented in global climate models (GCMs). This study applies deep machine learning techniques to develop a physical parameterization of volumetric cloud fraction (VCF), the fraction of a 3-D grid volume occupied by clouds using satellite lidar-radar measurements. The neural network (NN) captures the complicated relationships between observed VCF profiles and collocated meteorological variables from MERRA-2 reanalysis data. Our results show that the NN model, particularly a sequence-to-sequence long short-term memory (LSTM) network with a sixfactor loss function, effectively learns the underlying cloud physical processes. The NN model outperforms MERRA-2 reanalysis in representing low-level clouds in tropical and subtropical regions and low- and middle-level clouds over midlatitude storm-track regions, and also improves VCF histograms. These improvements are reflected in the vertical distributions of zonally, meridionally, and globally averaged VCFs, geographic distributions of low-, middle-, and high-level clouds, and seasonal variations in monthly-mean VCF. Furthermore, the NN predictions effectively capture the El Niño-Southern Oscillation (ENSO) effects and other interannual variations. The NN parameterization is further evaluated through a sensitivity analysis, in which a single predictor is perturbed at a time. This reveals that relative humidity (RH) is the dominant factor influencing variations in globally averaged VCF at low and middle altitudes, followed by temperature. At higher altitudes, temperature becomes the primary driver of VCF through its effect on RH. Changes in wind components had minimal impact on globally averaged VCF.

Shan Zeng↗

DOE OSTI2024

Modeled estimates of permafrost hydrology and related fields for pan-Arctic region over the period 1980-2100

This dataset contains gridded estimates of total runoff (mm/day); subsurface runoff (mm/day); snow water equivalent (SWE, mm/day); vertical upward water flux of combined surface evaporation, evapotranspiration, and snow sublimation (mm/day); and soil temperature (C) across the pan-Arctic basin from simulations of the Permafrost Water Balance Model (PWBM v4). These data were generated to allow researchers to advance understanding of the changes unfolding in the Arctic's terrestrial water cycle under the influences of climate warming, hydrological cycle intensification, and permafrost thaw. The dataset will enable researchers to address important questions spanning multiple disciplines. For example, how will rising river flows exported to the coast impact critical zone ecosystems, near-shore ice dynamics, and ocean biogeochemical processes. Spatial resolution is 25x25 km with N=39926 grid cells spanning the domain. Three forcing datasets were used to drive simulations at an implicit daily time step over the period 1980-2100. Daily air temperature, precipitation, and wind speed data from the WATCH Forcing Data methodology applied to ERA5 reanalysis (W5E5) were used for meteorological forcings over the period 1980-2019. Simulations for the period 1980-2100 used meteorological data from two global climate models; IPSL-CM6A-LR and MPI-ESM1-2-HR. The PWBM hydrological outputs are organized by grid and month in gzipped ASCII text files within each archived tar file. Soil temperature is organized by grid cell and day of year from DOY 205 to DOY 260. No special software is required to work with these data. The package contains spatial domain and river basin information files. These data are most useful for analyses of the spatial and temporal variations in terrestrial hydrology and soil thermal dynamics. A manuscript describing the model simulations and associated analysis has been accepted for publication in The Cryosphere (Rawlins and Karmalkar, 2024).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Improving GCM Predictability of Mixed-Phase Clouds and Aerosol Interactions at High Latitudes with ARM Observations

The goal of this project is to improve the predictability of mixed-phase clouds and aerosol interactions in the Community Atmosphere Model version 6 (CAM6) through comparison with the ARM observations. There are three main objectives of the proposed study: (1) Improve the representation of ice microphysical processes in mixed-phase clouds; (2) Test the performance of ice microphysics in CESM-CAM6 with the ARM observations in northern and southern high latitudes; and (3) Examine mixed-phase cloud microphysics-aerosol-turbulence-radiation interactions in CESM-CAM6. In this project, we have (1) Improved the representation of ice microphysical processes in mixed-phase clouds in CESM-CAM6 by implementing the marine organic aerosol (MOA) and treating the ice nucleating particles (INPs) from MOA and its impacts on mixed-phase clouds. We made a first attempt to represent different secondary ice production (SIP) mechanisms in a GCM (CESM2-CAM6). We found that misrepresentation of these ice formation processes in global climate models (GCMs) leads to too weak negative cloud feedback over the Southern Ocean (SO) and too high climate sensitivity in the models. In addition to ice formation processes, we improved the treatment of ice depositional growth through the Wegener–Bergeron–Findeisen (WBF) process by considering the subgrid heterogeneous distributions between liquid droplets and ice crystals in mixed-phase clouds; (2) Tested the performance of our improved representations of ice microphysics with the ARM observations at high latitudes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2015

Projections of Temperature-Attributable Premature Deaths in 209 U.S. Cities Using a Cluster-Based Poisson Approach

Background: A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. Methods: We used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100. Results: We found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April - September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October-March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city. Conclusions: We found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change.

Schwartz, Joel D.↗

DOE OSTI2023

The Role of In-Cloud Wet Removal in Simulating Aerosol Vertical Profiles and Cloud Radiative Forcing

Among the physical processes controlling aerosol vertical profiles, in-cloud wet removal is of utmost importance while its representation in global climate models (GCMs) is crude. In this study, we implement into the Community Atmosphere Model version 6 (CAM6) a physically based aerosol wet removal parameterization scheme that explicitly treats aerosol activation, removal and resuspension. Evaluation against in-situ observations shows that the default scheme substantially overestimates the upper tropospheric black carbon (BC) and sea salt mass. Our physically based scheme reduces BC and sea salt mass by a factor of 10 and 1,000, respectively, in better agreement with observations. Also, the new scheme slightly increases number of aerosol particles between 12 nm and 4.8 μm in diameter, thereby mitigating the aerosol number underestimation in the default scheme. Our new scheme reduces the overestimation of coarse-mode aerosol (0.5–4.8 μm) number. Overall, the aerosol property changes (mass decrease and number increase) reduce the cloud condensation nuclei (CCN) concentration at low supersaturation (i.e., 0.02% and 0.1%), and increase CCN at high supersaturations (i.e., 0.5% and 1%). Consequently, the global annual mean cloud liquid water path increases by 1.89 g m -2 and the ice water path increases by 0.51 g m -2 . The global annual mean shortwave, longwave, and net cloud radiative forcing change by -1.06, 0.57, and -0.48 W m -2 , respectively. Further improvement is needed to reflect the real physics that the removal efficiencies for aerosol mass and number are disproportionate and to advect cloud-borne (activated) aerosols for a complete aerosol lifecycle.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Aerosol characteristics at the Southern Great Plains site during the HI-SCALE campaign

Abstract. Large uncertainties exist in global climate model predictions of radiative forcing due to insufficient understanding and simplified numerical representation of cloud formation and cloud–aerosol interactions. The Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the DOE's Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in north-central Oklahoma to provide a better understanding of land–atmosphere interactions, aerosol and cloud properties, and the influence of aerosol and land–atmosphere interactions on cloud formation. The HI-SCALE campaign consisted of two intensive observational periods (IOPs) (April–May and August–September, 2016), during which coincident measurements were conducted both on the G-1 aircraft platform and at the SGP ground site. In this study we focus on the observations at the SGP ground site. An Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and an Ionicon proton-transfer-reaction mass spectrometer (PTR-MS) were deployed, characterizing chemistry of non-refractory aerosol and trace gases, respectively. Contributions from various aerosol sources, including biogenic and biomass burning emissions, were retrieved using factor analysis of the AMS data. In general, the organic aerosols at the SGP site was highly oxidized, with oxygenated organic aerosol (OOA) identified as the dominant factor for both the spring and summer IOP though more aged in spring. Cases of isoprene-epoxydiol-derived secondary organic aerosol (IEPOX SOA) and biomass burning events were further investigated to understand additional sources of organic aerosol. Unlike other regions largely impacted by IEPOX chemistry, the IEPOX SOA at SGP was more highly oxygenated, likely due to the relatively weak local emissions of isoprene. Biogenic emissions appear to largely control the formation of organic aerosol (OA) during the HI-SCALE campaign. Potential HOM (highly oxygenated molecule) chemistry likely contributes to the highly oxygenated feature of aerosols at the SGP site, with impacts on new particle formation and global climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Long-term variability in immersion-mode marine ice-nucleating particles from climate model simulations and observations

Abstract. Ice-nucleating particles (INPs) in the Southern Ocean (SO) atmosphere have significant impacts on cloud radiative and microphysical properties. Yet, INP prediction skill in climate models remains poorly understood, in part because of the lack of long-term measurements. Here we show, for the first time, how model-simulated INP concentrations compare with year-round INP measurements during the Macquarie Island Cloud Radiation Experiment (MICRE) campaign from 2017–2018. We simulate immersion-mode INP concentrations using the Energy Exascale Earth System Model version 1 (E3SMv1) by combining simulated aerosols with recently developed deterministic INP parameterizations and the native classical nucleation theory (CNT) for mineral dust in E3SMv1. Because MICRE did not collect aerosol measurements of super-micron particles, which are more effective ice nucleators, we evaluate the model's aerosol fields at other high-latitude sites using long-term in situ observations of dust and sea spray aerosol. We find that the model underestimates dust and overestimates sea spray aerosol concentrations by 1 to 2 orders of magnitude for most of the high-latitude sites in the Southern Hemisphere. We next compare predicted INP concentrations with concentrations of INPs collected on filter samples (typically for 2 or 3 d) and processed offline using the Colorado State University ice spectrometer (IS) in immersion freezing mode. We find that when deterministic parameterizations for both dust and sea spray INPs are used, simulated INPs are within a factor of 10 of observed INPs more than 60 % of the time during summer. Our results also indicate that the E3SM's current treatment of mineral dust immersion freezing in the SO is impacted by compensating biases – an underprediction of dust amount was compensated by an overprediction of its effectiveness as INPs. We also perform idealized droplet freezing experiments to quantify the implications of the time-dependent behavior assumed by the E3SM's CNT-parameterization and compare with the ice spectrometer observations. We find that the E3SM CNT 10 s diagnostic used in this study is a reasonable approximation of the exact formulation of CNT, when applied to ice spectrometer measurements in low-INP conditions similar to Macquarie Island. However, the linearized 10 s diagnostic underestimates the exact formula by an order of magnitude or more in places with high-INP conditions like the Sahara. Overall, our findings suggest that it is important to correct the biases in E3SM's simulated dust life cycle and update E3SM's INP parameterizations. INP prediction errors of 2 to 3 orders of magnitude can have considerable impacts on the simulated cloud and radiative properties in global climate models. On comparing INP concentrations during MICRE against ship-based campaigns, Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) and Antarctic Circumnavigation Expedition (ACE), we find that INPs from the latter are significantly higher only in regions closer to Macquarie Island. This alludes to the fact that physical, chemical and biological processes affecting INP concentrations as stimulated by the island could be partly responsible for the high INP concentrations observed at Macquarie Island during the MICRE campaign. Therefore, improvements to both aerosol simulation and INP parameterizations are required to adequately simulate INPs and their cloud impacts in E3SM. It will be helpful to include a parallel measurement of the size-resolved aerosol composition and explore opportunities for long-term measurement platforms in future field campaigns studying INP sources in remote marine regions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2001

Satellite Remote Sensing of the Liquid Water Sensitivity in Water Clouds

In estimation of the aerosol indirect effect, cloud liquid water path is considered either constant (Twomey effect) or increasing with enhanced droplet number concentrations (drizzle-suppression effect, or Albrecht effect) if cloud microphysics is the prevailing mechanism during the aerosol-cloud interactions. On the other hand, if cloud thermodynamics and dynamics are considered, the cloud liquid water path may be decreased with increasing droplet number concentration, which is predicted by model calculations and observed in ship-track and urban influence studies. This study is to examine the different responses of cloud liquid water path to changes of cloud droplet number concentration. Satellite data (January, April, July and October 1987) are used to retrieve the cloud liquid water sensitivity, defined as the changes of liquid water path versus changes of column droplet number concentrations. The results of a global survey reveal that 1) in at least one third of the cases the cloud liquid water sensitivity is negative, and the regional and seasonal variations of the negative liquid water sensitivity are consistent with other observations; 2) cloud droplet sizes are always inversely proportional to column droplet number concentrations. Our results suggest that an increase of cloud droplet number concentration leads to reduced cloud droplet size and enhanced evaporation, which weakens the coupling between water clouds and boundary layer in warm zones, decreases water supply from surface and desiccates cloud liquid water. Our results also suggest that the current evaluations of negative aerosol indirect forcing by global climate models (GCM), which are based on Twomey effect or Albrecht effect, may be overestimated.

Han, Qing-Yuan↗

NASA NTRS1995

Managing Complexity in Multidisciplinary Visualization

As high performance computing technology progresses, computational simulations are becoming more advanced in their capabilities. In the computational aerosciences domain, single discipline steady-state simulations computed on a single grid are far from the state-of-the-art. In their place are complex, time-dependent multidisciplinary simulations that attempt to model a given geometry more realistically. The product of these multidisciplinary simulations is a massive amount of data stored in different formats, grid topologies, units of measure, etc., as a result of the differences in the simulated physical domains. In addition to the challenges posed by setting up and performing the simulation, additional challenges exist in analyzing computational results. Visualization plays an important role in the advancement of multidisciplinary simulations. To date, visualization has been used to aid in the interpretation of large amounts of simulation data. Because the human visual system is effective in digesting a large amount of information presented graphically, visualization has helped simulation scientists to understand complex simulation results. As these simulations become even more complex, integrating several different physical domains, visualization will be critical to digest the massive amount of information. Another important role for visualization is to provide a common communication medium from which the domain scientists can use to develop, debug, and analyze their work. Multidisciplinary analyses are the next step in simulation technology, not only in computational aerosciences, but in many other areas such as global climate modeling. Visualization researchers must understand and work towards the challenges posed by multidisciplinary simulation scenarios. This paper addresses some of these challenges, describing technologies that must be investigated to create a useful visualization analysis tool for domain scientists.

Miceli, Kristina D.↗