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NASA NTRS2017

The Effects of Vegetative Type, Edges, Fire History, Rainfall and Management in Fire-Maintained Ecosystems

The combined effects of repeated fires, climate, and landscape features (e.g., edges) need greater focus in fire ecology studies, which usually emphasize characteristics of the most recent fire and not fire history. Florida scrub-jays are an imperiled, territorial species that prefer medium (1.2-1.7 m) shrub heights. We measured short, medium, and tall habitat quality states annually within 10 ha grid cells that represented potential territories because frequent fires and vegetative recovery cause annual variation in habitat quality. We used multistate models and model selection to test competing hypotheses about how transition probabilities between states varied annually as functions of environmental covariates. Covariates included vegetative type, edges, precipitation, openings (gaps between shrubs), mechanical cutting, and fire characteristics. Fire characteristics not only included an annual presenceabsence of fire covariate, but also fire history covariates: time since the previous fire, the maximum fire-free interval, and the number of repeated fires. Statistical models with support included many covariates for each transition probability, often including fire history, interactions and nonlinear relationships. Tall territories resulted from 28 years of fire suppression and habitat fragmentation that reduced the spread of fires across landscapes. Despite 35 years of habitat restoration and prescribed fires, half the territories remained tall suggesting a regime shift to a less desirable habitat condition. Measuring territory quality states and environmental covariates each year combined with multistate modeling provided a useful empirical approach to quantify the effects of repeated fire in combinations with environmental variables on transition probabilities that drive management strategies and ecosystem change.

Breininger, David R.↗

DOE OSTI2022

A data science approach for analysis and reconstruction of spinodal-like composition fields in irradiated FeCrAl alloys

A statistical method for the analysis of continuously distributed data representative of composition fluctuations in irradiated FeCrAl alloys acquired using Energy Dispersive X-ray Spectroscopy (EDS) method is presented. Using probability distribution functions, direct and cross-covariances between the elemental compositions, the effects of alloy composition and irradiation dose were investigated on the spatial distribution and length scale of composition fluctuations at the nanoscale. We have observed that, for neutron-irradiated FeCrAl alloys, the distribution of Fe and Cr followed a left-skewed and right-skewed distribution, respectively for all (average) alloy compositions and irradiation doses. The analysis also revealed enhanced spatial gradients in the elemental compositions at higher irradiation dose. Direct and cross-covariance estimates of the experimental data were also utilized for reconstruction of composition data through fitting it to a parametric form of the covariance functions. Linear Model of Coregionalization was used to determine the parameters of the covariance functions. Subsequently, a spectral method was utilized for simulating a realization of the alloy compositions. Close correspondence was observed between the experimental and the reconstructed data which was analyzed using probability distribution functions and covariance functions. Composition space of the experimental and reconstructed data and dislocation velocities as a function of applied stress and line directions over the entire composition maps were also examined.

36 MATERIALS SCIENCE↗

DOE OSTI2023

DESI mock challenge: constructing DESI galaxy catalogues based on FastPM simulations

Together with larger spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI), the precision of large-scale structure studies and thus the constraints on the cosmological parameters are rapidly improving. Therefore, one must build realistic simulations and robust covariance matrices. We build galaxy catalogues by applying a halo occupation distribution (HOD) model upon the FastPM simulations, such that the resulting galaxy clustering reproduces high-resolution N-body simulations. While the resolution and halo finder are different from the reference simulations, we reproduce the reference galaxy two-point clustering measurements – monopole and quadrupole – to a precision required by the DESI Year 1 emission line galaxy sample down to non-linear scales, i.e. $k\lt 0.5\, h\, \mathrm{Mpc}^{-1}$ or $s\gt 10\, \mathrm{Mpc}\, h^{-1}$. Furthermore, we compute covariance matrices based on the resulting FastPM galaxy clustering – monopole and quadrupole. We study for the first time the effect of fitting on Fourier conjugate (e.g. power spectrum) on the covariance matrix of the Fourier counterpart (e.g. correlation function). We estimate the uncertainties of the two parameters of a simple clustering model and observe a maximum variation of 20 per cent for the different covariance matrices. Nevertheless, for most studied scales the scatter is between 2 and 10 per cent. Consequently, using the current pipeline we can precisely reproduce the clustering of N-body simulations and the resulting covariance matrices provide robust uncertainty estimations against HOD fitting scenarios. We expect our methodology will be useful for the coming DESI data analyses and their extension for other studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Measurement of the 239 Pu$(n, f$) prompt fission neutron spectrum from 10 keV to 10 MeV induced by neutrons of energy 1–20 MeV

Although the prompt fission neutron spectrum (PFNS) is an essential component of neutron-driven systems that has been measured for decades, there are still multiple glaring unknowns regarding the PFNS of major actinides in the fission neutron incident energy range, specifically with regard to multichance fission and pre-equilibrium neutron emission processes. The only impactful experimental 239 Pu PFNS measurements included in recent nuclear data evaluations were measured over a limited outgoing neutron energy range at thermal and 1.5-MeV average incident neutron energy, while other potentially impactful measurements have been shown to contain errors that resulted in either large uncertainty increases or in complete exclusion from nuclear data evaluation. Here, we report a measurement of the 239 Pu PFNS over a wide range of incident neutron energy (1–20 MeV) and three orders of magnitude in outgoing neutron energy (0.01–10 MeV) resulting from the Chi-Nu experiment at the Los Alamos Neutron Science Center. These results are the combination of separate PFNS measurements in the same experimental area, one using a Li-glass and the other a liquid scintillator detector array. Covariances between all PFNS data points from each detector and within each incident energy range were generated between all other data in both detector arrays and within all other incident neutron energy bins, yielding a single covariance matrix for all 1300 PFNS data points reported here. These covariances are based on a thorough assessment of systematic bias and uncertainties associated with the measurement, PFNS extraction technique, combination of data from each detector type, and other aspects of the analysis. The existence of covariances between PFNS data points in different incident neutron energy ranges yielded covariances between average PFNS energy values at each incident energy to be reported here as well, which allowed for firm statements to be made regarding a shape of a purely experimental mean PFNS energy trend for the first time. Although minor PFNS shape differences exist between the results reported here and recent nuclear data evaluations, the ENDF/B-VIII.0 and JEFF-3.3 PFNS evaluations agree reasonably well with the present results from 1-to 10-MeV incident neutron energy, which spans the well-measured 1.5-MeV incident neutron energy PFNS from Lestone and Shores as well as the onset of second-chance fission. However, while the pre-equilibrium component of the PFNS above 12-MeV incident neutron energy roughly agrees in position and magnitude with ENDF/B-VIII.0 and JEFF-3.3, clear differences relating to the relative magnitude of third-chance fission PFNS features are present in the PFNS shape and in the mean PFNS energy trends.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Simulation and Reynolds-averaged Navier-Stokes modeling of a three-component Rayleigh-Taylor mixing problem with thermonuclear burn

Rayleigh-Taylor mixing in the presence of a third component with intermediate density is investigated through three-dimensional large-eddy simulation (LES) with a high-order compact finite-difference code. Two configurations are considered: (1) a symmetric configuration in which the Atwood number between the heavy and intermediate components matches the Atwood number between the intermediate and light components and (2) an asymmetric configuration in which the Atwood number between the heavy and intermediate components is an order of magnitude greater than the Atwood number between the intermediate and light components. Mass fraction covariances are extracted, and proposed Reynolds-averaged Navier-Stokes (RANS) closures for density-specific-volume and density-mass-fraction covariances are evaluated in an a priori fashion. Additionally, a multicomponent extension of the k - Φ - L - a - V RANS model [Morgan, Phys. Rev. E 104, 015107 (2021)] is presented which includes model equations for the upper-triangular elements of the mass fraction covariance matrix. This model, referred to as the k - Φ - L - a - C model, is compared against results from LES and against other RANS models. Profiles of average mass fraction, mass-fraction covariance, and density-specific-volume covariance obtained with the k - Φ - L - a - C model are found to agree well with LES data. Finally, the impact of three-component turbulent mixing on average reaction rate is investigated in both premixed and nonpremixed cases by heating the mixing layer and allowing it to undergo thermonuclear (TN) burn. A closure model for average reaction rate is proposed for use with the k - Φ - L - a - C model, and when this model is applied, improved agreement is obtained between LES and RANS in total TN neutron production.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Changes in leaf economic trait relationships across a precipitation gradient are related to differential gene expression in a C 4 perennial grass

Summary The leaf economics spectrum (LES) describes a suite of functional traits that consistently covary at large spatial and taxonomic scales. Despite its importance at these larger scales, few studies have examined the major drivers of intraspecific variation in the LES – phenotypic plasticity and standing genetic variation. Using experimental precipitation manipulations, we examined whether covariation among leaf economics traits and selection on leaf economics traits and trait combinations change as diverse genotypes of the widespread perennial grass Panicum virgatum are exposed to differences in precipitation. We also used RNA‐Seq to examine whether groups of co‐expressed genes that align with leaf economics traits function in processes hypothesized to underlie the LES. Water availability impacted leaf economics trait covariation in important ways – covariation between leaf economics traits and selection on covariation between traits (i.e. correlational selection) tended to be strongest when water availability was high. Additionally, many genes associated with leaf economics traits functioned in processes that may explain how the LES originates, such as chloroplasts, cell walls, and nitrogen metabolism. Water availability is likely an important modulator of selection and evolution of the LES in P. virgatum that can be better understood by examining gene expression.

Heckman, Robert W. [Department of Integrative Biol↗

NASA NTRS1971

Optimal filtering in the presence of unmodeled time correlated driving disturbances.

In many realistic data filtering problems, the cross correlation of the state estimation error and the state forcing function is unknown due to the poor knowledge of the time history of the forcing function. In this paper, the conservative and minimal approximation to the cross correlation terms is presented. It requires only the knowledge of the estimation error covariance and the forcing function covariance, with the choice of an associated free parameter left to the user. If the estimation error covariance and/or the forcing function covariance are bounded from above but not known exactly, the cross correlation approximation using those upper bounds remains conservative. This cross correlation approximation leads to a conservative approximation to the estimation error covariance matrix differential equation between measurement times. The free parameter is determined as the analytic solution to an associated optimal control problem. The procedure is expanded to include discrete linear measurement incorporation.

Fraser, D. C.↗

NASA NTRS1989

Analysis of modified SMI method for adaptive array weight control

An adaptive array is applied to the problem of receiving a desired signal in the presence of weak interference signals which need to be suppressed. A modification, suggested by Gupta, of the sample matrix inversion (SMI) algorithm controls the array weights. In the modified SMI algorithm, interference suppression is increased by subtracting a fraction F of the noise power from the diagonal elements of the estimated covariance matrix. Given the true covariance matrix and the desired signal direction, the modified algorithm is shown to maximize a well-defined, intuitive output power ratio criterion. Expressions are derived for the expected value and variance of the array weights and output powers as a function of the fraction F and the number of snapshots used in the covariance matrix estimate. These expressions are compared with computer simulation and good agreement is found. A trade-off is found to exist between the desired level of interference suppression and the number of snapshots required in order to achieve that level with some certainty. The removal of noise eigenvectors from the covariance matrix inverse is also discussed with respect to this application. Finally, the type and severity of errors which occur in the covariance matrix estimate are characterized through simulation.

Dilsavor, R. L.↗

NASA NTRS1994

On the Lighthill relationship and sound generation from isotropic turbulence

In 1952, Lighthill developed a theory for determining the sound generated by a turbulent motion of a fluid. With some statistical assumptions, Proudman applied this theory to estimate the acoustic power of isotropic turbulence. Recently, Lighthill established a simple relationship that relates the fourth-order retarded time and space covariance of his stress tensor to the corresponding second-order covariance and the turbulent flatness factor, without making statistical assumptions for a homogeneous turbulence. Lilley revisited Proudman's work and applied the Lighthill relationship to evaluate directly the radiated acoustic power from isotropic turbulence. After choosing the time separation dependence in the two-point velocity time and space covariance based on the insights gained from direct numerical simulations, Lilley concluded that the Proudman constant is determined by the turbulent flatness factor and the second-order spatial velocity covariance. In order to estimate the Proudman constant at high Reynolds numbers, we analyzed a unique data set of measurements in a large wind tunnel and atmospheric surface layer that covers a range of the Taylor microscale based on Reynolds numbers 2.0 x 10(exp 3) less than or equal to R(sub lambda) less than or equal to 12.7 x 10(exp 3). Our measurements demonstrate that the Lighthill relationship is a good approximation, providing additional support to Lilley's approach. The flatness factor is found between 2.7 - 3.3 and the second order spatial velocity covariance is obtained. Based on these experimental data, the Proudman constant is estimated to be 0.68 - 3.68.

Zhou, YE↗

NASA NTRS1996

Two Reduced Resolution Filter Approaches to Data Assimilation

In this paper we evaluate the performance of two reduced resolution filter approaches to data assimilation. The main distinction between these approaches is in the manner they propagate error covariances. Both account for error covariances in a space with dimension m smaller than the model's state vector dimension n. In the first approach the m dimensional error covariance matrix is interpolated to the n-dimensional space and propagated with the n-dimensional dynamics. In the second approach the low-dimensional error covariance matrix is propagated by a dynamical operator generated in the m-dimensional space. Our experiments indicate that the first approach provides a more reliable simplified scheme for error covariance propagation than the second approach.

Todling, Ricardo↗

NASA NTRS1999

Some Continuum Aspects of Data Assimilation

A long-sought goal in data assimilation is to build more fully the time dimension into the data assimilation process. Truly four-dimensional data assimilation requires evolving second-moment information, namely the estimation error covariance, along with the state estimate itself. The continuum evolution equations for the estimation error covariance constitute a system of partial differential equations in six space dimensions, forced by a model error covariance and by the observations themselves. The high dimensionality of this system poses a difficult computational problem. Numerous methods have been proposed for approximate, discrete solution of this system of equations. Four-dimensional variational schemes solve these equations indirectly, while schemes based on Kalman filtering solve the equations more directly, usually on a small subspace of the full six-dimensional space. In both cases, most proposed solution methods are not derived from the continuum covariance evolution equations themselves. Instead, most methods simply inherit the discretization used for evolving the state estimate, either in the form of a discrete tangent linear model or, in the case of ensemble schemes, in the form of a fully nonlinear discrete model. In this lecture we show that solutions of the continuum covariance evolution equations possess simple properties that are not readily satisfied upon discretization of the equations. we give numerical examples illustrating that "inherited" discretizations sometimes fail to produce meaningfully accurate solutions. Finally, we suggest alternative discretization methods that may yield more faithful approximate solutions of the continuum problem.

Cohn, Stephen E.↗

NASA NTRS2002

A Wavelet Based Suboptimal Kalman Filter for Assimilation of Stratospheric Chemical Tracer Observations

A suboptimal Kalman filter system which evolves error covariances in terms of a truncated set of wavelet coefficients has been developed for the assimilation of chemical tracer observations of CH4. The truncation is carried out in such a way that the resolution of the error covariance, is reduced only in the zonal direction, where gradients are smaller. Assimilation experiments which last 24 days, and used different degrees of truncation were carried out. These reduced the covariance, by 90, 97 and 99 % and the computational cost of covariance propagation by 80, 93 and 96 % respectively. The difference in both error covariance and the tracer field between the truncated and full systems over this period were found to be not growing in the first case, and a growing relatively slowly in the later two cases. The largest errors in the tracer fields were found to occur in regions of largest zonal gradients in the tracer field.

Auger, Ludovic↗

NASA NTRS2003

On the Use of the Log-Normal Particle Size Distribution to Characterize Global Rain

Although most parameterizations of the drop size distributions (DSD) use the gamma function, there are several advantages to the log-normal form, particularly if we want to characterize the large scale space-time variability of the DSD and rain rate. The advantages of the distribution are twofold: the logarithm of any moment can be expressed as a linear combination of the individual parameters of the distribution; the parameters of the distribution are approximately normally distributed. Since all radar and rainfall-related parameters can be written approximately as a moment of the DSD, the first property allows us to express the logarithm of any radar/rainfall variable as a linear combination of the individual DSD parameters. Another consequence is that any power law relationship between rain rate, reflectivity factor, specific attenuation or water content can be expressed in terms of the covariance matrix of the DSD parameters. The joint-normal property of the DSD parameters has applications to the description of the space-time variation of rainfall in the sense that any radar-rainfall quantity can be specified by the covariance matrix associated with the DSD parameters at two arbitrary space-time points. As such, the parameterization provides a means by which we can use the spaceborne radar-derived DSD parameters to specify in part the covariance matrices globally. However, since satellite observations have coarse temporal sampling, the specification of the temporal covariance must be derived from ancillary measurements and models. Work is presently underway to determine whether the use of instantaneous rain rate data from the TRMM Precipitation Radar can provide good estimates of the spatial correlation in rain rate from data collected in 5(sup 0)x 5(sup 0) x 1 month space-time boxes. To characterize the temporal characteristics of the DSD parameters, disdrometer data are being used from the Wallops Flight Facility site where as many as 4 disdrometers have been used to acquire data over a 2 km path. These data should help quantify the temporal form of the covariance matrix at this site.

Meneghini, Robert↗

NASA NTRS2013

Spatial Statistical Data Fusion (SSDF)

As remote sensing for scientific purposes has transitioned from an experimental technology to an operational one, the selection of instruments has become more coordinated, so that the scientific community can exploit complementary measurements. However, tech nological and scientific heterogeneity across devices means that the statistical characteristics of the data they collect are different. The challenge addressed here is how to combine heterogeneous remote sensing data sets in a way that yields optimal statistical estimates of the underlying geophysical field, and provides rigorous uncertainty measures for those estimates. Different remote sensing data sets may have different spatial resolutions, different measurement error biases and variances, and other disparate characteristics. A state-of-the-art spatial statistical model was used to relate the true, but not directly observed, geophysical field to noisy, spatial aggregates observed by remote sensing instruments. The spatial covariances of the true field and the covariances of the true field with the observations were modeled. The observations are spatial averages of the true field values, over pixels, with different measurement noise superimposed. A kriging framework is used to infer optimal (minimum mean squared error and unbiased) estimates of the true field at point locations from pixel-level, noisy observations. A key feature of the spatial statistical model is the spatial mixed effects model that underlies it. The approach models the spatial covariance function of the underlying field using linear combinations of basis functions of fixed size. Approaches based on kriging require the inversion of very large spatial covariance matrices, and this is usually done by making simplifying assumptions about spatial covariance structure that simply do not hold for geophysical variables. In contrast, this method does not require these assumptions, and is also computationally much faster. This method is fundamentally different than other approaches to data fusion for remote sensing data because it is inferential rather than merely descriptive. All approaches combine data in a way that minimizes some specified loss function. Most of these are more or less ad hoc criteria based on what looks good to the eye, or some criteria that relate only to the data at hand.

Braverman, Amy J.↗

NASA NTRS2013

Impact of Gender on Pharmocokinetics of Intranasal Scopolamine

Introduction: An intranasal gel dosage formulation of scopolamine (INSCOP) was developed for the treatment of Space Motion Sickness (SMS), which is commonly experienced by astronauts during space missions. The bioavailability and pharmacokinetics (PK) were evaluated under IND guidelines. Since information is lacking on the effect of gender on the PK of Scopolamine, we examined gender differences in PK parameters of INSCOP at three dose levels of 0.1, 0.2 and 0.4 mg. Methods: Plasma scopolamine concentrations as a function of time data were collected from twelve normal healthy human subjects (6 male/6 female) who participated in a fully randomized double blind crossover study. The PK parameters were derived using WinNonlin. Covariate analysis of PK profiles was performed using NONMEN and statistically compared using a likelihood ratio test on the difference of objective function value (OFV). Statistical significance for covariate analysis was set at P<0.05(ΔOFV=3.84). Results: No significant difference in PK parameters between male and female subjects was observed with 0.1 and 0.2 mg doses. However, CL and Vd were significantly different between male and female subjects at the 0.4 mg dose. Results from population covariate modeling analysis indicate that a onecompartment PK model with first-order elimination rate offers best fit for describing INSCOP concentration-time profiles. The inclusion of sex as a covariate enhanced the model fitting (ΔOFV=-4.1) owing to the genderdependent CL and Vd differences after the 0.4 mg dose. Conclusion: Statistical modeling of scopolamine concentration-time data suggests gender-dependent pharmacokinetics of scopolamine at the high dose level of 0.4 mg. Clearance of the parent compound was significantly faster and the volume of distribution was significantly higher in males than in females, As a result, including gender as a covariate to the pharmacokinetic model of scopolamine offers the best fit for PK modeling of the drug at dose of 0.4 mg or higher.

Putcha, L.↗

NASA NTRS2018

The Effects of Vegetative Type, Edges, Fire History, Rainfall and Management in Fire-Maintained Habitat

The combined effects of fire history, climate, and landscape features (e.g., edges) on habitat specialists need greater focus in fire ecology studies, which usually only emphasize characteristics of the most recent fire. Florida scrub-jays are an imperiled, territorial species that prefer medium (1.2-1.7 m) shrub heights, which are dynamic because of frequent fires. We measured short, medium, and tall habitat quality states annually within 10 ha grid cells (that represented potential territories) because fires and vegetative recovery cause annual variation in habitat quality. We used multistate models and model selection to test competing hypotheses about how transition probabilities vary between states as functions of environmental covariates. Covariates included vegetative type, edges (e.g., roads, forests), precipitation, openings (gaps between shrubs), mechanical cutting, and fire characteristics. Fire characteristics not only included an annual presence/absence of fire covariate, but also fire history covariates: time since the previous fire, the longest fire-free interval, and the number of repeated fires. Statistical models with support included many covariates for each transition probability, often including fire history, interactions and nonlinear relationships. Tall territories resulted from 28 years of fire suppression and habitat fragmentation that reduced the spread of fires across landscapes. Despite 35 years of habitat restoration and prescribed fires, half the territories remained tall suggesting a regime shift to a less desirable habitat condition. Edges reduced the effectiveness of fires in setting degraded scrub and flatwoods into earlier successional states making mechanical cutting an important tool to compliment frequent prescribed fires.

Breininger, David R.↗

NASA NTRS2018

Atmosphere-Ocean Coupled Data Assimilation Using NASA GEOS: Estimation of Air-Sea Interface State Variables

Air-sea interface variables, such as the skin Sea Surface Temperature (SST) are essential for atmosphere-ocean coupling. In the NASA GMAO Data Assimilation System (DAS), the skin SST and 3-D atmospheric state are jointly estimated [1]. This presentation is focused on the prior or background error covariance that is used in this analysis. The GEOS DAS uses an ensemble-variational assimilation strategy. In that, specification of a climatological background (CB) error covariance for SST relies on the NOAA's OI SST, with estimates of standard deviation and correlation length scales based on weekly analyses of the bulk SST at 1 degree resolution. However, present analysis system is striving to resolve SST diurnal variability with six hourly analyses and assimilates a vast number of in-situ and satellite observations. The first part of this presentation re-derives the CB error covariance using OSTIA SST analyses and illustrates the impact of this update on assimilating satellite observations. In a hybrid assimilation system the CB error covariances are appended with a flow-dependent background error covariance estimate implied by the underlying ensemble. The second part of this presentation refers to: a. treatment of the skin SST in the ensemble members, b. corresponding ensemble spread, and c. impact of these additions on the data assimilation system. [1] S. Akella, et al. (2017), doi:10.1002/qj.2988

Akella, Santha↗

NASA NTRS2020

Untangling causality in midlatitude aerosol–cloud adjustments

Aerosol–cloud interactions represent the leading uncertainty in our ability to infer climate sensitivity from the observational record. The forcing from changes in cloud albedo driven by increases in cloud droplet number (Nd) (the first indirect effect) is confidently negative and has narrowed its probable range in the last decade, but the sign and strength of forcing associated with changes in cloud macrophysics in response to aerosol (aerosol–cloud adjustments) remain uncertain. This uncertainty reflects our inability to accurately quantify variability not associated with a causal link flowing from the cloud microphysical state to the cloud macrophysical state. Once variability associated with meteorology has been removed, covariance between the liquid water path (LWP) averaged across cloudy and clear regions (here characterizing the macrophysical state) and Nd (characterizing the microphysical) is the sum of two causal pathways linking Nd to LWP: Nd altering LWP (adjustments) and precipitation scavenging aerosol and thus depleting Nd. Only the former term is relevant to constraining adjustments, but disentangling these terms in observations is challenging. We hypothesize that the diversity of constraints on aerosol–cloud adjustments in the literature may be partly due to not explicitly characterizing covariance flowing from cloud to aerosol and aerosol to cloud. Here, we restrict our analysis to the regime of extratropical clouds outside of low-pressure centers associated with cyclonic activity. Observations from MAC-LWP (Multisensor Advanced Climatology of Liquid Water Path) and MODIS are compared to simulations in the Met Office Unified Model (UM) GA7.1 (the atmosphere model of HadGEM3-GC3.1 and UKESM1). The meteorological predictors of LWP are found to be similar between the model and observations. There is also agreement with previous literature on cloud-controlling factors finding that increasing stability, moisture, and sensible heat flux enhance LWP, while increasing subsidence and sea surface temperature decrease it. A simulation where cloud microphysics are insensitive to changes in Nd is used to characterize covariance between Nd and LWP that is induced by factors other than aerosol–cloud adjustments. By removing variability associated with meteorology and scavenging, we infer the sensitivity of LWP to changes in Nd. Application of this technique to UM GA7.1 simulations reproduces the true model adjustment strength. Observational constraints developed using simulated covariability not induced by adjustments and observed covariability between Nd and LWP predict a 25 %–30 % overestimate by the UM GA7.1 in LWP change and a 30 %–35 % overestimate in associated radiative forcing.

Daniel T. McCoy↗