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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets

Publication For a thorough description of the methods, see the peer-reviewed paper: Jones, A.D., Rastogi, D., Vahmani, P. et al. Continental United States climate projections based on thermodynamic modification of historical weather. Sci Data 10, 664 (2023). https://doi.org/10.1038/s41597-023-02485-5 Overview The IM3 / HyperFACETS climate simulations provide 40-year historical (1980-2019) as well as four 80-year future simulations (2020-2099) over the U.S. The future simulations are split into near (2020-2059) and far future (2060-2099) segments. The future scenarios span a range of plausible changes in future climate (both Global Circulation Model (GCM) and Representative Concentration Pathways/Shared Socioeconomic Pathway (RCP/SSP) dimensions). The simulations provide climate variables with high spatiotemporal resolution (25 hourly variables and 207 3-hourly variables at 12 km2). The datasets are generated using dynamical downscaling with the WRF (Weather Research and Forecasting) model (version 4.2.1) and therefore preserve physical consistency across variables. WRF is a state-of-the-art, fully compressible, non-hydrostatic, mesoscale numerical weather prediction model. WRF is coupled with an urban canopy model (UCM), which resolves urban surfaces. The future scenarios were developed using a thermodynamic global warming approach where past events are replayed under a range of future warming conditions. These scenarios therefore provide a perspective on potential increases in extreme event intensity, geographic scope, and duration, with previously non-extreme conditions potentially crossing new thresholds to be considered extreme by today's standards. This approach is not intended to estimate future changes in extreme event frequency that might result from changes in large-scale atmospheric dynamics. This dataset has NOT been bias corrected. A bias corrected version of selected variables is under development and will be released here when available. Scenarios Files Data for each scenario is provided in weekly NetCDF files. 25 variables are available at hourly resolution, and 207 variables are available at three-hourly resolution. Spatial resolution is 12km and spans the conterminous United States (CONUS), including some areas of Canada and Mexico, resulting in a grid of 424 by 299 cells. The spatial projection is a Lambert Conformal Conic with the following proj-string: "+proj=lcc +lat_0=40.0000076293945 +lon_0=-97 +lat_1=30 +lat_2=45 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs". The available scenarios and simulation periods are listed below: historical | 1980 - 2019 rcp45cooler | 2020 - 2059 rcp45cooler | 2060 - 2099 rcp45hotter | 2020 - 2059 rcp45hotter | 2060 - 2099 rcp85cooler | 2020 - 2059 rcp85cooler | 2060 - 2099 rcp85hotter | 2020 - 2059 rcp85hotter | 2060 - 2099 * The first year (1979, 2019, and 2059) of data within each scenario represents a model warmup period and should not be used. These are located in the `spinup_files` directory. Historical year 2020 is considered an extra year of data beyond the simulation period and can be found in the `additional_files` directory. For information on specific variables and a more in-depth discussion of methodology, please refer to the data landing page at https://tgw-data.msdlive.org. Delta Warming Files The global and CONUS warming deltas for each scenario are provided in degrees Celsius annually and monthly. Restart Files Yearly restart files are provided for each scenario which can be used to restart the WRF model at a particular point in time. Spinup Files The first year of data within each simulation period represents a model warmup period and should not be used. The files are provided here for the sake of reproducibility. Additional Files Additional years of data are provided as an extension of the historic simulation.

Jones, Andrew D.↗
DOE OSTI2025

Comparing the DES-SN5YR and Pantheon+ SN cosmology analyses: investigation based on ‘evolving dark energy or supernovae systematics’?

Recent cosmological analyses measuring distances of type Ia supernovae (SNe Ia) and baryon acoustic oscillations (BAO) have all given similar hints at time-evolving dark energy. To examine whether underestimated SN Ia systematics might be driving these results, Efstathiou (2025) compared overlapping SN events between Pantheon+ and DES-SN5YR (20 per cent SNe are in common), and reported evidence for an $\sim$0.04 mag offset between the low- and high-redshift distance measurements of this subsample of events. If this offset is arbitrarily subtracted from the entire DES-SN5YR sample, the preference for evolving dark energy is reduced. In this paper, we show that this offset is mostly due to different corrections for Malmquist bias between the two samples; therefore, an object-to-object comparison can be misleading. Malmquist bias corrections differ between the two analyses for several reasons. First, DES-SN5YR used an improved model of SN Ia luminosity scatter compared to Pantheon+ but the associated scatter-model uncertainties are included in the error budget. Secondly, improvements in host mass estimates in DES-SN5YR also affected SN standardized magnitudes and their bias corrections. Thirdly, and most importantly, the selection functions of the two compilations are significantly different, hence the inferred Malmquist bias corrections. Even if the original scatter model and host properties from Pantheon+ are used instead, the evidence for evolving dark energy from CMB, DESI BAO Year 1 and DES-SN5YR is only reduced from 3.9$\sigma$ to 3.3$\sigma$, consistent with the error budget. Finally, in this investigation, we identify an underestimated systematic uncertainty related to host galaxy property uncertainties, which could increase the final DES-SN5YR error budget by 3 per cent. In conclusion, we confirm the validity of the published DES-SN5YR results.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRSDate not supplied

Characterization of OCO-2 and ACOS-GOSAT biases and errors for flux estimates

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~1.1 ppm, while having 2-3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5-10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

Susan S Kulawik↗
DOE OSTI2026

High-Resolution WRF-Based Downscaling of Earth System Model Projections for Energy Applications across CONUS

Evaluating energy resources under future scenarios requires meteorological information that adequately resolves regional-scale variability and is suitable for regional energy system studies. Although Earth system model (ESM) outputs provide essential large-scale context, their coarse resolution and inherent biases limit direct use in energy system applications. This work presents a high-resolution dynamical downscaling framework using the Weather Research and Forecasting (WRF) model to generate energy-relevant regional fields for future scenarios over the contiguous United States (CONUS). The framework first identifies an optimal WRF configuration through sensitivity experiments, then evaluates raw and bias-corrected ESM initial and boundary conditions, with soil moisture and soil temperature bias correction implemented as an integral component of the bias-corrected ESM atmospheric forcing prior to WRF dynamical downscaling to improve land-atmosphere interactions. Simulations performed at 4-km resolution show that uncorrected ESM forcing leads to systematically dry and cold soil states, which propagate into elevated near-surface air temperature and solar irradiance biases, particularly during summer for the period 2000-2014. Incorporating bias-corrected atmospheric forcing together with soil state bias correction substantially reduces these errors and improves the representation of surface energy processes in WRF simulations. The results highlight the importance of bias-aware initialization strategies in high-resolution dynamical downscaling for future energy system analysis and planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2016

Improvement in the Characterization of MODIS Subframe Difference

MODIS is a key instrument of NASA's Earth Observing System. It has successfully operated for 16+ years on the Terra satellite and 14+ years on the Aqua satellite, respectively. MODIS has 36 spectral bands at three different nadir spatial resolutions, 250m (bands 1-2), 500m (bands 3-7), and 1km (bands 8-36). MODIS subframe measurement is designed for bands 1-7 to match their spatial resolution in the scan direction to that of the track direction. Within each 1 km frame, the MODIS 250 m resolution bands sample four subframes and the 500 m resolution bands sample two subframes. The detector gains are calibrated at a subframe level. Due to calibration differences between subframes, noticeable subframe striping is observed in the Level 1B (L1B) products, which exhibit a predominant radiance-level dependence. This paper presents results of subframe differences from various onboard and earth-view data sources (e.g. solar diffuser, electronic calibration, spectro-radiometric calibration assembly, Earth view, etc.). A subframe bias correction algorithm is proposed to minimize the subframe striping in MODIS L1B image. The algorithm has been tested using sample L1B images and the vertical striping at lower radiance value is mitigated after applying the corrections. The subframe bias correction approach will be considered for implementation in future versions of the calibration algorithm.

Li, Yonghong↗
DOE OSTI2026

High-Resolution ESM Projections for Energy Applications Over the CONUS

Assessing energy resources under future scenarios requires high-resolution meteorological information that is physically consistent and suitable for regional-scale analysis. While Earth system model (ESM) projections provide valuable large-scale information, their coarse resolution and systematic biases limit direct applicability for energy system modeling and planning. In this study, we develop a high-resolution dynamical downscaling framework based on the Weather Research and Forecasting (WRF) model to translate global-scale ESM data into energy-relevant regional projections over the contiguous United States (CONUS). The framework identifies an optimized WRF configuration through numerical experiments and evaluates raw and bias-corrected ESM initial and boundary conditions, with soil moisture (SM) and soil temperature (ST) bias correction implemented as an integral part of the bias-corrected ESM forcing to improve land-atmosphere coupling prior to WRF dynamical downscaling. Using an optimized WRF configuration at 4-km resolution, we show that raw ESM forcing introduces systematic dry and cold soil biases that propagate into pronounced warm biases in near-surface air temperature and positive biases in solar irradiance, particularly during summer. Applying bias-corrected atmospheric forcing together with bias-corrected SM and ST substantially reduces these downstream biases and improves the surface energy balance and near-surface atmospheric fields. These results demonstrate that bias-aware treatment of initial conditions is critical for producing high-resolution downscaled projections suitable for energy system modeling and planning applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2008

Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimations over Continental South America

This paper describes a comprehensive assessment of a new high-resolution, high-quality gauge-satellite based analysis of daily precipitation over continental South America during 2004. This methodology is based on a combination of additive and multiplicative bias correction schemes in order to get the lowest bias when compared with the observed values. Inter-comparisons and cross-validations tests have been carried out for the control algorithm (TMPA real-time algorithm) and different merging schemes: additive bias correction (ADD), ratio bias correction (RAT) and TMPA research version, for different months belonging to different seasons and for different network densities. All compared merging schemes produce better results than the control algorithm, but when finer temporal (daily) and spatial scale (regional networks) gauge datasets is included in the analysis, the improvement is remarkable. The Combined Scheme (CoSch) presents consistently the best performance among the five techniques. This is also true when a degraded daily gauge network is used instead of full dataset. This technique appears a suitable tool to produce real-time, high-resolution, high-quality gauge-satellite based analyses of daily precipitation over land in regional domains.

Vila, Daniel↗
DOE OSTI2023

Redshifting galaxies from DESI to JWST CEERS: Correction of biases and uncertainties in quantifying morphology

Observations of high-redshift galaxies with unprecedented detail have now been rendered possible with the James Webb Space Telescope (JWST). However, accurately quantifying their morphology remains uncertain due to potential biases and uncertainties. To address this issue, we used a sample of 1816 nearby DESI galaxies, with a stellar mass range of 10 9.75 - 11.25 M ⊙ , to compute artificial images of galaxies of the same mass located at 0.75 ≤ z ≤ 3 and observed at rest-frame optical wavelength in the Cosmic Evolution Early Release Science (CEERS) survey. We analyzed the effects of cosmological redshift on the measurements of Petrosian radius (R p ), half-light radius (R 50 ), asymmetry (A), concentration (C), axis ratio (q), and Sérsic index (n). Our results show that R p and R 50 , calculated using non-parametric methods, are slightly overestimated due to PSF smoothing, while R 50 , q, and n obtained through fitting a Sérsic model does not exhibit significant biases. By incorporating a more accurate noise effect removal procedure, we improve the computation of A over existing methods, which often overestimate, underestimate, or lead to significant scatter of noise contributions. Due to PSF asymmetry, there is a minor overestimation of A for intrinsically symmetric galaxies. However, for intrinsically asymmetric galaxies, PSF smoothing dominates and results in an underestimation of A, an effect that becomes more significant with higher intrinsic A or at lower resolutions. Moreover, PSF smoothing also leads to an underestimation of C, which is notably more pronounced in galaxies with higher intrinsic C or at lower resolutions. We developed functions based on resolution level, defined as R p /FWHM, for correcting these biases and the associated statistical uncertainties. Applying these corrections, we measured the bias-corrected morphology for the simulated CEERS images and we find that the derived quantities are in good agreement with their intrinsic values – except for A, which is robust only for angularly large galaxies where R p /FWHM ≥ 5. Our correction functions can be applied to other surveys, offering valuable tools for future studies.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

Number-counts slope estimation in the presence of Poisson noise

The slope determination of a power-law number flux relationship in the case of photon-limited sampling. This case is important for high-sensitivity X-ray surveys with imaging telescopes, where the error in an individual source measurement depends on integrated flux and is Poisson, rather than Gaussian, distributed. A bias-free method of slope estimation is developed that takes into account the exact error distribution, the influence of background noise, and the effects of varying limiting sensitivities. It is shown that the resulting bias corrections are quite insensitive to the bias correction procedures applied, as long as only sources with signal-to-noise ratio five or greater are considered. However, if sources with signal-to-noise ratio five or less are included, the derived bias corrections depend sensitively on the shape of the error distribution.

Schmitt, Juergen H. M. M.↗
DOE OSTI2021

Redshift evolution of the underlying type Ia supernova stretch distribution

The detailed nature of type Ia supernovae (SNe Ia) remains uncertain, and as survey statistics increase, the question of astrophysical systematic uncertainties arises, notably that of the evolution of SN Ia populations. We study the dependence on redshift of the SN Ia SALT2.4 light-curve stretch, which is a purely intrinsic SN property, to probe its potential redshift drift. The SN stretch has been shown to be strongly correlated with the SN environment, notably with stellar age tracers. We modeled the underlying stretch distribution as a function of redshift, using the evolution of the fraction of young and old SNe Ia as predicted using the SNfactory dataset, and assuming a constant underlying stretch distribution for each age population consisting of Gaussian mixtures. We tested our prediction against published samples that were cut to have marginal magnitude selection effects, so that any observed change is indeed astrophysical and not observational in origin. In this first study, there are indications that the underlying SN Ia stretch distribution evolves as a function of redshift, and that the age drifting model is a better description of the data than any time-constant model, including the sample-based asymmetric distributions that are often used to correct Malmquist bias at a significance higher than 5σ. The favored underlying stretch model is a bimodal one, composed of a high-stretch mode shared by both young and old environments, and a low-stretch mode that is exclusive to old environments. The precise effect of the redshift evolution of the intrinsic properties of a SN Ia population on cosmology remains to be studied. The astrophysical drift of the SN stretch distribution does affect current Malmquist bias corrections, however, and thereby the distances that are derived based on SN that are affected by observational selection effects. We highlight that this bias will increase with surveys covering increasingly larger redshift ranges, which is particularly important for the Large Synoptic Survey Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Improved Treatment of Host-galaxy Correlations in Cosmological Analyses with Type Ia Supernovae

Improving the use of Type Ia supernovae (SNe Ia) as standard candles requires a better approach to incorporate the relationship between SNe Ia and the properties of their host galaxies. Using a spectroscopically confirmed sample of ~1600 SNe Ia, we develop the first empirical model of underlying populations for SNe Ia light-curve properties that includes their dependence on host-galaxy stellar mass; we find a significant correlation between stretch population and stellar mass (99.9% confidence) and a weaker correlation between color and stellar mass (90% confidence). These populations are important inputs to simulations that are used to model selection effects and correct distance biases within the BEAMS with Bias Correction (BBC) framework. Here we improve BBC to also account for SNe Ia-host correlations, and we validate this technique on simulated data samples. Here, we recover the input relationship between SNe Ia luminosity and host-galaxy stellar mass (the mass step, γ) with a bias of 0.004 ±0.001 mag, which is a factor of 5 improvement over previous methods that have a γ bias of ~0.02 ± 0.001 mag. We adapt BBC for a novel dust-based model of intrinsic brightness variations, which results in a greatly reduced mass step for data (γ = 0.017 ± 0.008) and for simulations (γ = 0.006 ± 0.007). Analyzing simulated SNe Ia, the biases on the dark energy equation of state, w, vary from Δw = 0.006(5) to 0.010(5) with our new BBC method; these biases are significantly smaller than the 0.02(5) w bias using previous BBC methods that ignore SNe Ia-host correlations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A machine learning approach for efficient multi-dimensional integration

Many physics problems involve integration in multi-dimensional space whose analytic solution is not available. The integrals can be evaluated using numerical integration methods, but it requires a large computational cost in some cases, so an efficient algorithm plays an important role in solving the physics problems. We propose a novel numerical multi-dimensional integration algorithm using machine learning (ML). After training a ML regression model to mimic a target integrand, the regression model is used to evaluate an approximation of the integral. Then, the difference between the approximation and the true answer is calculated to correct the bias in the approximation of the integral induced by ML prediction errors. Because of the bias correction, the final estimate of the integral is unbiased and has a statistically correct error estimation. Three ML models of multi-layer perceptron, gradient boosting decision tree, and Gaussian process regression algorithms are investigated. The performance of the proposed algorithm is demonstrated on six different families of integrands that typically appear in physics problems at various dimensions and integrand difficulties. The results show that, for the same total number of integrand evaluations, the new algorithm provides integral estimates with more than an order of magnitude smaller uncertainties than those of the VEGAS algorithm in most of the test cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Fair Bagging Boosting Models [SWR-24-38]

Fair Bagging Boosting Models is a software implementation of a framework for building, measuring bias and correcting bias in 3 popular forest machine learning models: gradient boosted trees (GBT), random forest (RF), and XGBoost models, using the XGBoost library. The framework takes advantage of the flexibility in XGBoost library to represent gradient boosted tree and random forest models, as well as the ability to use custom loss function.

Ugirumurera, Juliette↗
NASA NTRS2013

Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

Roberts, J. Brent↗
DOE OSTI2024

The National Climate Database (NCDB): An Unbiased 100-Year Dataset for PV Modeling

In this study, we develop a statistical technique to downscale the future projection of solar irradiance for photovoltaics (PV) energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the downscaling method, a summary of the 100-year dataset from 2001-2100, and future extension of this research.

bias correction↗
NASA NTRS2012

Improving the Accuracy of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

The Air Force Weather Agency (AFWA) -- NASA blended snow-cover product, called ANSA, utilizes Earth Observing System standard snow products from the Moderate- Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE with SWE values calculated from snow depths reported at ~1500 National Climatic Data Center (NCDC) co-op stations in the Lower Great Lakes Basin. Compared to station data, the ANSA significantly underestimates SWE in densely-forested areas. We use two methods to remove some of the bias observed in forested areas to reduce the root-mean-square error (RMSE) between the ANSA- and station-derived SWE. First, we calculated a 5- year mean ANSA-derived SWE for the winters of 2005-06 through 2009-10, and developed a five-year mean bias-corrected SWE map for each month. For most of the months studied during the five-year period, the 5-year bias correction improved the agreement between the ANSA-derived and station-derived SWE. However, anomalous months such as when there was very little snow on the ground compared to the 5-year mean, or months in which the snow was much greater than the 5-year mean, showed poorer results (as expected). We also used a 7-day running mean (7DRM) bias correction method using days just prior to the day in question to correct the ANSA data. This method was more effective in reducing the RMSE between the ANSA- and co-op-derived SWE values, and in capturing the effects of anomalous snow conditions.

Hall, Dorothy K.↗
DOE OSTI2022

Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects

Abstract. A previous study on the use of nudging in E3SM Atmosphere Model version 1 (EAMv1) had an unresolved issue; i.e., a simulation nudged to EAMv1's own meteorology showed non-negligible deviations from the free-running baseline simulation over some of the subtropical marine stratocumulus and trade cumulus regions. Here, we demonstrate that the deviations can be substantially reduced by (1) changing where the nudging tendency is calculated in the time integration loop of a nudged EAM simulation so as to improve consistency with the free-running baseline and (2) increasing the frequency of the constraining data so as to better capture strong sub-diurnal variations. The fact that modification (2) improves the climate representativeness of the nudged simulations has motivated us to investigate whether the use of newer reanalysis products with higher data frequency can help improve nudged hindcast simulations by better capturing the observed weather events. To answer this question, we present simulations conducted at EAMv1's standard horizontal resolution (approximately 1∘) with nudging towards 6-hourly ERA-Interim reanalysis or 6-hourly, 3-hourly, or hourly ERA5 reanalysis. These simulations are evaluated against the climatology of free-running EAMv1 simulations as well as reanalyses, satellite retrievals, and in situ measurements from the Atmospheric Radiation Measurement user facility. For the 1∘ EAMv1 simulations, we recommend using the relocated nudging tendency calculation and the ERA5 reanalysis at 3-hourly or higher frequency. Simulations used for estimating the anthropogenic aerosol effects often use nudging to help discern signal from noise. The sensitivity of such estimates to the configuration of nudging is investigated in EAMv1, again using the standard 1∘ horizontal resolution. We find that, when estimating the global mean effects, the frequency of constraining data has relatively small impacts, while the choice of nudged variables can change the results substantially. The nudging of air temperature (in addition to horizontal winds) has two non-negligible effects. First, when the constraining data come from reanalysis, the nudging-induced mean bias correction can cause significant changes in the simulated clouds and hence substantially different estimates of the aerosol effects. The impact of the mean bias correction on ice cloud formation has been noted in previous studies and is also seen in EAMv1. For applications like ours, where the preferred configurations of nudging are those capable of providing results consistent with the multi-year free-running simulations, the consequence of the mean bias correction is undesirable. The second important impact of temperature nudging is a significant suppression of adjustments to aerosol forcing, which also causes changes in the estimated aerosol effects. This effect can be seen in simulations nudged to either reanalysis or EAM's own meteorology. These results suggest that nudging horizontal winds but not temperature is a better choice for estimating the anthropogenic aerosol effects.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled Processes

Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

land data↗