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DOE OSTI2021

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗

DOE OSTI2022

Diurnal cycle of precipitation over global monsoon systems in CMIP6 simulations

Accurate simulation of the diurnal cycle of precipitation over different climate regimes, including global monsoon systems, is an ongoing challenge for current state-of-the-art climate models. Here, in this study, the diurnal cycles of summertime precipitation from 21 CMIP6 historical simulations are evaluated with the NASA IMERG data via the diurnal harmonic amplitude and phase from the Fourier analysis in seven regional monsoon systems: Northern and Southern Africa monsoons, South Asia monsoon, East Asia monsoon, Australia monsoon, and North and South America monsoons. Particularly, we focus on four subregions including ocean, coastal ocean, land, and coastal land considering the different underlying physical processes. It is found that the CMIP6 historical simulations perform fairly well on reproducing the diurnal cycle of precipitation over ocean and coastal ocean, while the simulated diurnal harmonic phase and amplitude over land and coastal land show larger biases and model spread compared to biases over ocean and coastal ocean. The simulation of precipitation diurnal cycle is substantially improved in the multi-model mean of both CMIP6 historical and CMIP6 AMIP simulations compared with that of CMIP5 historical simulations. But the model bias of precipitation diurnal cycle is in general similar between CMIP6 AMIP and CMIP6 historical simulations, which suggests that the impact of the interactive ocean on the simulated diurnal precipitation averaged over a large domain is generally minor. The influence of model resolution on the diurnal cycle is shown in mixed directions across various subregions, and significant improvement from the low- to high-resolution models is only notable over coastal ocean.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

Evaluation of an Impedance Model for Perforates Including the Effect of Bias Flow

A new bias flow impedance model is developed for perforated plates from basic principles using as little empiricisms as possible. A quality experimental database was used to determine the predictive validity of the model. Results show that the model performs better for higher (15%) rather than lower (5%) percent open area (POA) samples. Based on the least squares ratio of numerical vs. experimental results, model predictions were on average within 20% and 30% for the higher and lower (POA), respectively. It is hypothesized on the work of other investigators that at lower POAs the higher fluid velocities in the perforate's orifices start forming unsteady vortices, which is not accounted for in our model. The numerical model, in general also underpredicts the experiments. It is theorized that the actual acoustic C(sub D) is lower than the measured raylometer C(sub D) used in the model. Using a larger C(sub D) makes the numerical model predict lower impedances. The frequency domain model derived in this paper shows very good agreement with another model derived using a time domain approach.

Betts, J. F.↗

DOE OSTI2025

Improved Representations of Longwave Surface Emissivity to Reduce Surface and Atmospheric Heating Biases in Earth System Models

Abstract Many Earth system models (ESMs) approximate surface emissivity as a broadband constant. This approximation reduces the computational burden, yet omits the spectral structure of emissivity and atmospheric absorption. Neglecting spectral variation in surface emission introduces biases in longwave (LW) atmospheric fluxes and heating. Biases are strongest over surfaces with strongly varying emissivity and minimal atmospheric opacity. We examine these biases over water, ice, and snow surfaces. We partition spectral emissivity into the 16 spectral bands utilized by a single‐column atmospheric radiative transfer model (RRTMG_LW) commonly used in ESMs. We quantify flux and heating biases introduced by broadband assumptions relative to the spectrally resolved case for standard atmospheric profiles over each surface type. Current assumptions tend to overestimate upwelling surface fluxes; for example, the greybody assumption overestimates flux by 1.6 W/m 2 (0.52%) at the bottom of a mid‐latitude winter atmosphere over ice, and by 2.33 (1.0%) at the top of atmosphere. The blackbody assumption tends to artificially cool Earth's surface, stabilizing the lower troposphere. Interestingly, the optimal broadband emissivity can deviate from the Planck‐weighted mean by up to 3% depending on surface type and atmospheric profile. We investigate bias sensitivity to surface temperature, cloud water path, and atmospheric water vapor. Bias is most sensitive to water vapor content, and least sensitive to cloud water path. Lastly, we show that a modified greybody method with updated broadband values can reduce total surface flux bias up to 1.69 , comparable to a five‐band approach and at a fraction of the computational cost.

Manzo, L. [Department of Earth System Science Univ↗

NASA NTRS1992

Galaxy clusters and cold dark matter - A low-density unbiased universe?

Large-scale simulations of a universe dominated by cold dark matter (CDM) are tested against two fundamental properties of clusters of galaxies: the cluster mass function and the cluster correlation function. We find that standard biased CDM models are inconsistent with these observations for any bias parameter b. A low-density, low-bias CDM-type model, with or without a cosmological constant, appears to be consistent with both the cluster mass function and the cluster correlations. The low-density model agrees well with the observed correlation function of the Abell, Automatic Plate Measuring Facility (APM), and Edinburgh-Durham cluster catalogs. The model is in excellent agreement with the observed dependence of the correlation strength on cluster mean separation, reproducing the measured universal dimensionless cluster correlation. The low-density model is also consistent with other large-scale structure observations, including the APM angular galaxy-correlations, and for lambda = 1-Omega with the COBE results of the microwave background radiation fluctuations.

Bahcall, Neta A.↗

NASA NTRS2014

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Cohen, Charlie↗

NASA NTRS2001

Modeling of Kink-Shaped Carbon-Nanotube Schottky Diode with Gate Bias Modulation

A model is proposed for the recent gate-voltage (V(sub G)) modulation experiment of a kink-shaped carbon nanotube (NT) Schottky diode. Since larger V(sub G) increases both the forward and the reverse turn-on voltages of the diode in the experiment, we show that: (1) the rectification must occur at the kink where the metallic and the semiconducting NTs meet, and not at the electrode contact, and (2) the semiconducting NT must be n-type. The turn-on voltages are derived analytically as a function of V(sub G) with the electrode contact contribution and a good agreement is obtained with the experimental data.

Yamada, Toshishige↗

DOE OSTI2023

Hyper Suprime-Cam Year 3 results: Cosmology from cosmic shear power spectra

Here, we measure weak lensing cosmic shear power spectra from the 3-year galaxy shear catalog of the Hyper Suprime-Cam (HSC) Subaru Strategic Program imaging survey. The shear catalog covers 416 deg 2 of the northern sky, with a mean i-band seeing of 0.59 arcsec and an effective galaxy number density of 15 arcmin —2 within our adopted redshift range. With an i-band magnitude limit of 24.5 mag, and four tomographic redshift bins spanning 0.3 ≤ z ph ≤ 1.5 based on photometric redshifts, we obtain a high-significance measurement of the cosmic shear power spectra, with a signal-to-noise ratio of approximately 26.4 in the multipole range 300 < ℓ < 1800. The accuracy of our power spectrum measurement is tested against realistic mock shear catalogs, and we use these catalogs to get a reliable measurement of the covariance of the power spectrum measurements. We use a robust blinding procedure to avoid confirmation bias, and model various uncertainties and sources of bias in our analysis, including point spread function systematics, redshift distribution uncertainties, the intrinsic alignment of galaxies and the modeling of the matter power spectrum. For a flat Λ CDM model, we find S 8 ≡ σ 8 (Ω m /0.3) 0.5 = ${0.776}_{—0.033}^{+0.032}$, which is in excellent agreement with the constraints from the other HSC Year 3 cosmology analyses, as well as those from a number of other cosmic shear experiments. This result implies a ~2σ-level tension with the Planck 2018 cosmology. We study the effect that various systematic errors and modeling choices could have on this value, and find that they can shift the best-fit value of S 8 by no more than ~ 0.5σ, indicating that our result is robust to such systematics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Data used for Figure 3 of the Nature Reviews Earth and Environment (NREE) paper: "A low-to-no snow future and its impacts on water resources in the western United States

In order to synthesize western United States snowpack projections, this dataset contains the results of 18 peer-review journal articles over three periods of interest (2025-2049, 2050-2074, and 2075-2099) and over 4 mountain ranges (Cascades, Sierra Nevada, Rockies, Wasatch/Uinta) in addition to western-US wide projections. Distinction is made by model type (Earth System Models, bias-corrected statistically downscaled Earth System Models, and regional climate models). RCP4.5 and RCP8.5 emission scenarios are considered. Percent snow water equivalent (SWE) loss considers 1 April, peak SWE and/or seasonal SWE. Heterogeneity in projected snowpack changes exists across mountain ranges and for different modeling approaches, but generally indicate agreement in decreases by the end of the century.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1992

On the nature of bias and defects in the software specification process

Implementation bias in a specification is an arbitrary constraint in the solution space. This paper describes the problem of bias. Additionally, this paper presents a model of the specification and design processes describing individual subprocesses in terms of precision/detail diagrams and a model of bias in multi-attribute software specifications. While studying how bias is introduced into a specification we realized that software defects and bias are dual problems of a single phenomenon. This was used to explain the large proportion of faults found during the coding phase at the Software Engineering Laboratory at NASA/GSFC.

Straub, Pablo A.↗

NASA NTRS2012

A Tropospheric Emission Spectrometer HDO/H2O Retrieval Simulator for Climate Models

Retrievals of the isotopic composition of water vapor from the Aura Tropospheric Emission Spectrometer (TES) have unique value in constraining moist processes in climate models. Accurate comparison between simulated and retrieved values requires that model profiles that would be poorly retrieved are excluded, and that an instrument operator be applied to the remaining profiles. Typically, this is done by sampling model output at satellite measurement points and using the quality flags and averaging kernels from individual retrievals at specific places and times. This approach is not reliable when the model meteorological conditions influencing retrieval sensitivity are different from those observed by the instrument at short time scales, which will be the case for free-running climate simulations. In this study, we describe an alternative, categorical approach to applying the instrument operator, implemented within the NASA GISS ModelE general circulation model. Retrieval quality and averaging kernel structure are predicted empirically from model conditions, rather than obtained from collocated satellite observations. This approach can be used for arbitrary model configurations, and requires no agreement between satellite-retrieved and model meteorology at short time scales. To test this approach, nudged simUlations were conducted using both the retrieval-based and categorical operators. Cloud cover, surface temperature and free-tropospheric moisture content were the most important predictors of retrieval quality and averaging kernel structure. There was good agreement between the D fields after applying the retrieval-based and more detailed categorical operators, with increases of up to 30 over the ocean and decreases of up to 40 over land relative to the raw model fields. The categorical operator performed better over the ocean than over land, and requires further refinement for use outside of the tropics. After applying the TES operator, ModelE had D biases of 8 over ocean and 34 over land compared to TES D, which were less than the biases using raw model D fields.

Field, R. D.↗

NASA NTRS2014

Analyzing Source Apportioned Methane in Northern California During DISCOVER-AQ-CA Using Airborne Measurements and Model Simulations

This study analyzes source apportioned methane (CH4) emissions and atmospheric concentrations in northern California during the Discover-AQ-CA field campaign using airborne measurement data and model simulations. Source apportioned CH4 emissions from the Emissions Database for Global Atmospheric Research (EDGAR) version 4.2 were applied in the 3-D chemical transport model GEOS-Chem and analyzed using airborne measurements taken as part of the Alpha Jet Atmospheric eXperiment over the San Francisco Bay Area (SFBA) and northern San Joaquin Valley (SJV). During the time period of the Discover-AQ-CA field campaign EDGAR inventory CH4 emissions were 5.30 Gg/day (Gg 1.0 109 grams) (equating to 1.9 103 Gg/yr) for all of California. According to EDGAR, the SFBA and northern SJV region contributes 30 of total emissions from California. Source apportionment analysis during this study shows that CH4 concentrations over this area of northern California are largely influenced by global emissions from wetlands and local/global emissions from gas and oil production and distribution, waste treatment processes, and livestock management. Model simulations, using EDGAR emissions, suggest that the model under-estimates CH4 concentrations in northern California (average normalized mean bias (NMB) -5 and linear regression slope 0.25). The largest negative biases in the model were calculated on days when hot spots of local emission sources were measured and atmospheric CH4 concentrations reached values 3.0 parts per million (model NMB -10). Sensitivity emission studies conducted during this research suggest that local emissions of CH4 from livestock management processes are likely the primary source of the negative model bias. These results indicate that a variety, and larger quantity, of measurement data needs to be obtained and additional research is necessary to better quantify source apportioned CH4 emissions in California and further the understanding of the physical processes controlling them.

Methane↗

NASA NTRS2023

Upper Arctic Ocean Properties and Relationships with Sea Ice in CMIP6 Historical Simulations

While current-generation CMIP and OMIP models have clear biases in their upper Arctic Ocean hydrography, it is less clear how these biases impact the models' ability to simulate the observed Arctic sea ice mean state and trends. In this study we seek to quantify cross-relationship between sea ice and ocean states in CMIP6 historical simulations and identify common model behaviors. Multi-model mean (MMM) simulations exhibit accelerated changes in the ice and ocean system since the late 20th century. Underlying the MMM is strong inter-model variation in the simulated ice and ocean mean states and their temporal variability including trends. Despite such inter-model differences, all models show a similar ratio between sea ice reduction and upper ocean warming such that models with higher ocean warming also have higher SIE reduction and vice versa. Our results also highlight the urgent needs of reliable Arctic Ocean observations or data products in order to better contextualize modeling results.

Upper Arctic Ocean↗

NASA NTRS2023

A Performance Analysis on Soil Dielectric Models Over Organic Soils in Alaska for Passive Microwave Remote Sensing of Soil Moisture

Passive microwave remote sensing of soil moisture (SM) requires a physically based dielectric model that quantitatively converts the volumetric SM into the soil bulk dielectric constant. Mironov 2009 is the dielectric model used in the operational SM retrieval algorithms of the NASA Soil Moisture Active Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions. However, Mironov 2009 suffers a challenge in deriving SM over organic soils, as it does not account for the impact of soil organic matter (SOM) on the soil bulk dielectric constant. To this end, we presented a comparative performance analysis of nine advanced soil dielectric models over organic soil in Alaska, four of which incorporate SOM. In the framework of the SMAP single-channel algorithm at vertical polarization (SCA-V), SM retrievals from different dielectric models were derived using an iterative optimization scheme. The skills of the different dielectric models over organic soils were reflected by the performance of their respective SM retrievals, which was measured by four conventional statistical metrics, calculated by comparing satellite-based SM time series with in-situ benchmarks. Overall, SM retrievals of organic-soil-based dielectric models tended to overestimate, while those from mineral-soil-based models displayed dry biases. All the models showed comparable values of unbiased root-mean-square error (ubRMSE) and Pearson Correlation (R), but Mironov 2019 exhibited a slight but consistent edge over the others. An integrated consideration of the model inputs, the physical basis, and the validated accuracy indicated that the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM < 15%) and organic soils (SOM ≥ 15%) would be the preferred option.

Soil Moisture↗

DOE OSTI2021

Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

Abstract Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Observations of the Age of Air from the Northern Hemisphere Midlatitude Surface

Understanding and modeling the interhemispheric transport (IHT) of trace gases and aerosols is important for interpreting past (and projecting future) changes in atmospheric composition. One IHT measure -- the mean age since air was last at the Northern Hemisphere (NH) midlatitude surface – represents a fundamental property of tropospheric transport that can be estimated from measurements of sulfur hexafluoride (SF6). Here we use SF6 measurements obtained from the recent NASA Atmospheric Tomography Mission (ATom) to construct observational estimates of the mean age throughout the troposphere. We compare the ATom based ages, which span all four seasons during the period 2016-2018 over both the Pacific and Atlantic Oceans, with previous in situ-based estimates obtained from the HIAPER Pole‐to‐Pole Observations (HIPPO) project, which are available only for the Pacific. In order to interpret the observed variations in mean age and their relationship with the large-scale flow we use simulations of the Goddard Earth Observing System (GEOS) general circulation model, constrained with meteorological fields derived from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), performed over the entire MERRA-2 period (including ATom). We confirm results from previous studies showing that the model ages are biased old compared to the observational estimates. However, we also show that this model bias is reduced in more recent years, which we interpret in terms of changes in the latitudinal distribution of SF6 emissions over the 2000s.

interhemispheric transport (IHT)↗

DOE OSTI2023

Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations

There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variability and vertical variations in microphysical processes in low-level warm clouds. In this study, the warm-cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017–2018. The 8-month single-column model (SCM) simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary layer cloud structure, major macrophysical properties, and their transition. The improvement in warm-cloud properties from the Community Atmospheric Model 5 and 6 (CAM5 to CAM6) physics can be found through comparison with the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft-observed ones in the summer but are overestimated by a factor of 2 in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations in droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of subgrid variations in cloud properties in CAM6 cloud microphysics, which further suppresses the scheme's sensitivity to individual warm-rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces an aerosol indirect effect that is too strong. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1995

Measurements of noise in Josephson-effect mixers

We present new heterodyne receiver results obtained at 100 GHz using resistively-shunted Nb and NbN tunnel junctions. In addition, we have carried out accurate measurements of the available noise power of these devices at the L-band (1.5 GHz) IF (infrasonic) frequency. Both the heterodyne and the output noise measurements show that the noise of these devices can be a factor of five or more higher than that predicted by the simple current-biased RSJ model. The noise approaches the appropriate thermal or thermal and shot noise limits for bias voltages where the nonlinearity is not strong (i.e., V greater than I(sub C)R(sub N)), but as expected from the RSJ model, can be significantly higher at the low voltages where the mixers are typically biased. The bias voltage dependence of the noise shows structure which is associated with resonances in the RF embedding circuit. Surprisingly, we find that changes in the high-frequency (100 GHz) impedance presented to the junction can dramatically affect the magnitude and voltage dependence of the low-frequency (1.5 GHz) noise. This emphasizes the necessity of very closely matching the junction to free space over a wide frequency range.

NOISE MEASUREMENT↗