Results for “Reproducibility” — Page 76

NASA NTRS2020

Bias in CMIP6 Models as Compared to Observed Regional Dimming and Brightening

Anthropogenic aerosol emissions have increased considerably over the last century, but climate effects and quantification of the emissions are highly uncertain as one goes back in time. This uncertainty is partly due to a lack of observations in the pre-satellite era, making the observations we do have before 1990 additionally valuable. Aerosols suspended in the atmosphere scatter and absorb incoming solar radiation and thereby alter the Earth's surface energy balance. Previous studies show that Earth system models (ESMs) do not adequately represent surface energy fluxes over the historical era. We investigated global and regional aerosol effects over the time period 1961–2014 by looking at surface downwelling shortwave radiation (SDSR). We used observations from ground stations as well as multiple experiments from eight ESMs participating in the Coupled Model Intercomparison Project Version 6 (CMIP6). Our results show that this subset of models reproduces the observed transient SDSR well in Europe but poorly in China. We suggest that this may be attributed to missing emissions of sulfur dioxide in China, sulfur dioxide being a precursor to sulfate, which is a highly reflective aerosol and responsible for more reflective clouds. The emissions of sulfur dioxide used in the models do not show a temporal pattern that could explain observed SDSR evolution over China. The results from various aerosol emission perturbation experiments from DAMIP, RFMIP and AerChemMIP show that only simulations containing anthropogenic aerosol emissions show dimming, even if the dimming is underestimated. Simulated clear-sky and all-sky SDSR do not differ greatly, suggesting that cloud cover changes are not a dominant cause of the biased SDSR evolution in the simulations. Therefore we suggest that the discrepancy between modeled and observed SDSR evolution is partly caused by erroneous aerosol and aerosol precursor emission inventories. This is an important finding as it may help interpret whether ESMs reproduce the historical climate evolution for the right or wrong reason.

surface downwelling shortwave radiation (SDSR)↗

NASA NTRS2020

Structure and Parameter Uncertainty in Centennial Projections of Forest Community Structure and Carbon Cycling

Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multi‐decadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100 years of forest regrowth following stand‐replacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub‐model–parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100 years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under‐predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant–soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED‐2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models.

canopy radiative transfer↗

NASA NTRS2021

Bias-Correcting Carbon Fluxes Derived From Land-Surface Satellite Data for Retrospective and Near Real-Time Assimilation Systems

The ability to monitor and understand natural and anthropogenic variability in atmospheric carbon dioxide(CO2) is a growing need of many stakeholders across the world. Systems that assimilate satellite observations, given their short latency and dense spatial coverage, into high resolution global models are valuable, if not essential, tools for addressing this need. A notable drawback of modern assimilation systems is the long latency of many vital in-put datasets, e.g., inventories, in situ measurements, and re-processed remote-sensing data can trail the current date by months to years. This paper describes techniques for bias-correcting surface fluxes derived from satellite observations of the Earth’s surface to be consistent with constraints from inventories and in situ CO2 datasets. The techniques are applicable in both short-term forecasts and retrospective simulations, thus taking advantage of the coverage and short latency of satellite data while reproducing the major features of long-term inventory and in situ records. Our approach begins with a standard collection of diagnostic fluxes which incorporate a variety of remote-sensing driver data, viz. vegetation indices, fire radiative power, and nighttime lights. We then apply an empirical sink so that global budgets of the diagnostic fluxes match given atmospheric and oceanic growth rates for each year. This step removes coherent, systematic flux errors that produce biases inCO2which mask the signals an assimilation system hopes to capture. Depending on the simulation mode, the empirical sink uses different choices of atmospheric growth rates: estimates based on observations in retrospective mode and projections based on seasonal fore-casts of sea surface temperature in forecasting mode. The retrospective fluxes, when used in simulations with NASA’s Goddard Earth Observing System (GEOS), reproduce marine boundary layer measurements with comparable skill to those using fluxes from a modern inversion system. The forecasted fluxes show promising accuracy in their application to the analysis of changes in the carbon cycle as they occur.

CO2↗

NASA NTRS2021

Performance of JAXA’s SGLI standard ocean color products for oceanic to coastal waters: chlorophyll a concentration and light absorption coefficients of colored dissolved organic matter

We present the performance of JAXA’s SGLI standard algorithms for estimating chlorophyll a (chl a) concentration and the light absorption coefcients of colored dissolved organic matter (CDOM) using recently compiled high-quality data obtained from oceanic to coastal waters. Prior to the evaluation of the algorithms, we frst compare two forward models (Gordon et al. in J Geophys Res 93(D9):10909-10924, 1988: G88 and Park and Ruddick in Appl Opt 44(7):1236–1249, 2005: PR05) using a wide range of inherent optical properties (IOPs) to (1) examine if the water refectance is appropriately reproduced and (2) correct measured refectance in terms of its bidirectionality. Based on a good reproducibility of water refectance using the PR05, the optimized IOPs are further used for explaining errors in estimates of chl a concentration and CDOM absorption when using the SGLI, the GSM (Maritorena et al. in Appl Opt 41:2705–2714, 2002), and the QAA (Lee et al. in Appl Opt 41:5755, 2002) inversion algorithms. Results show that the mean error for estimating chl a concentration using the SGLI algorithm is 110% for our dataset. Although this error is lower than that of the GSM and the QAA algorithms, a signifcant underestimate at chl a higher than 2.0 mg m−3 is observed, which is further shown by independent match-up analyses. Another SGLI CDOM product includes the mean error of 50% and shows deviation at high CDOM range (>1.0 per m). A similar trend is observed for the GSM but not for the QAA when a global relationship of CDOM to colored detrital matter is applied. The sources of errors are discussed for potentially improving the retrievals

Second-generation GLobal Imager (SGLI)↗

NASA NTRSDate not supplied

Modeling the Crystallization of Martian Igneous Compositions

The martian crust is dominated by basalts and ultramafic rocks [1]. Understanding the crystallization sequence of martian igneous rocks is critical for constraining the petrologic and geochemical evolution of the crust and mantle. Experimental studies have investigated the parental liquid compositions and crystallization behavior using martian meteorite compositions [2-7], as well as compositions measured by rovers on the martian surface [8,9]. While experiments deliver crucial results, they are often limited by time and resources. Therefore, modeling approaches are an ideal tool to complement experimental results. The application of the MELTS software package [10, 11], to calculate the crystallization of martian compositions, reproduces important compositional details of magma crystallization but significant uncertainties regarding the pressure of the olivine-pyroxene multiple saturation point have been reported [12], and in the temperatures of initial crystallization [12, 13]. Recently, a new melting model, MAGMARS, has been shown to reproduce martian primary melts more accurately [14]. Here, we constrain the applicability of Gibbs free energy minimization software Perple_X [15] for martian compositions by comparing our modeled results to experimentally derived crystallization paths for two martian compositions.

J Semprich↗

NASA NTRS2022

Advances and Challenges in Observations and Modeling of the Global-Sun Dynamics and Dynamo

Computational heliophysics has shed light on the fundamental physical processes inside the Sun, such as the differential rotation, meridional circulation, and dynamo-generation of magnetic fields. However, despite the substantial advances, the current results of 3D MHD simulations are still far from reproducing helioseismic inferences and surface observations. The reason is the multi-scale nature of the solar dynamics, covering a vast range of scales, which can-not be solved with the current computational resources. In such a situation, significant progress has been achieved by the mean-field approach, based on the separation of small-scale turbulence and large-scale dynamics. The mean-field simulations can reproduce solar observations, qualitatively and quantitatively, and uncover new phenomena. However, they do not reveal the complex physics of large-scale convection, solar magnetic cycles, and the magnetic self-organization that causes sunspots and solar eruptions. Thus, developing a synergy of these approaches seems to be a necessary but very challenging task.

SMD↗

NASA NTRS2022

The Simulation and Subseasonal Forecasting of Hydrological Variables: Insights from a Simple Water Balance Model

Past work has shown that a land surface model’s (LSM’s) implicit (not explicitly coded) relationships between soil moisture and both evapotranspiration (ET) and runoff largely determine the LSM’s hydrological behavior. Here we estimate the relationships that appear to be operating in the real world and compare them to those of the LSM component of a state-of-the-art Earth system model (ESM). The two sets of relationships are determined by calibrating them within a simple water balance model (WBM): once using stream gauge observations from small, unregulated rivers over the eastern half of the U.S., and once using the runoffs generated by the LSM as part of a state-of-the-art atmospheric reanalysis. Hydrological simulations and subseasonal hydrological forecasts performed with the two calibrated versions of the WBM provide two key results. First, the version calibrated to the LSM-generated runoffs does successfully reproduce, to first order, the hydrological behavior of the full LSM within its ESM environment. Second, of the two WBM versions, the one calibrated to the observations reproduces more accurately a broad collection of fully independent streamflow observations as well as a similarly broad collection of in-situ soil moisture measurements. Taken together, the two results suggest that the observations-calibrated ET and runoff efficiency functions do successfully represent, at least to some degree, soil moisture controls over hydrological variability in Nature and can serve as potentially useful targets for further LSM development.

Water Balance Model↗

NASA NTRS2022

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

Bayesian Deep Learning↗

NASA NTRSDate not supplied

Developing a Vision for Maturing the Heliophysics Infrastructure towards Open Science: The DIARieS Analysis Ecosystem

In the dawn of open science and the upcoming requirements, we speak about the existing state of Heliophysics infrastructure and detail the evolution required to address capability or interconnection shortcomings. Such a daunting barrier calls for an analysis ecosystem with multi-faceted capability. We propose such an ecosystem, called DIARieS, to be built upon five conceptual pillars: Discovery, Implementation, Analysis, Reproducibility, and Sharing of results. The combination of these concepts in a single platform will enable users to more intuitively combine recent advances in technology to create ‘DIARieS’ of their workflows, which can be easily made open to others in the community. The DIARieS ecosystem will also increase our efficiency by streamlining our various workflow processes, including automatic incorporation of the impending requirements of open science. The various components of the ecosystem will simplify software installation and data implementation, including automatically generated citation lists based on the components included. Automatic containerization and version control of the ecosystem will make the custom workflows easily reproducible. Employing widget technology will ease the difficulty of producing publication and commercial quality visualizations and applying common analyses techniques. Incorporating multiple technologies will streamline the various sharing methods common in our work environments today. Overall, the totality of capabilities to be offered by this analysis ecosystem will drastically simplify the application of open science principles to our work in addition to improving our efficiency and ease of collaboration. This talk summarizes a vision of the proposed ecosystem, which is described in more detail in Ringuette et al. (2022: https://doi.org/10.1016/j.asr.2022.05.012).

infrastructure↗

NASA NTRSDate not supplied

High Speed Data Investigation of an LOX/LNG Combustor Assisted by Non-linear Acoustic Simulations

During testing of a regenerative cooled liquid oxygen/liquid natural gas combustor large amplitude combustion instabilities were observed in half of the series conducted. These instabilities produced an unsteady pressure Root Mean Square (RMS) value greater than the chamber steady state pressure. Quick look investigations of the test data presented some standard indications of non-linear tangential instability such as a non-sinusoidal time series, and larger peaks than trough. Also observed in the data quick look were some atypical findings including a first harmonic amplitude larger than the fundamental, and numerous side bands on the fundamental and subsequent harmonics. A simplified model of the combustor was created using a commercially available finite element software. The first tangential acoustic mode was then simulated using a non-linear wave equation in conjunction with a time explicit discontinuous Gelarkin flow solver. Numerous simulations were evaluated providing valuable insight and reproducing the atypical findings in the data. Side bands were created through non-linear signal modulation like an AM radio. This happens due to the non-linear steepening and relaxing that occurs in large amplitude oscillations. The simulation was also able to reproduce a signal with a first harmonic amplitude larger than the fundamental. Investigating the pressure signal at multiple locations across the chamber head end revealed radial dependent frequency content. A literature review of the topic revealed corroboration of these effect in idealized geometries.

Combustion Stability↗

NASA NTRSDate not supplied

Hybrid-Electric Aero-Propulsion Controls Laboratory: Overview and Capability

A hardware-in-the-loop (HIL) laboratory is developed to investigate control technologies for electrified aircraft propulsion (EAP). The laboratory emulates a propulsion system by reproducing the mechanical shaft interface to the electrical power system in hardware. The experimental electric power system includes supercapacitor energy storage and a dynamically variable electrical load. A novel method of scaling power and inertia is provided in software to accurately reproduce the transient, off-design turbomachinery performance dynamics without including actual turbomachinery. An overview of how the HIL system can accommodate a broad range of EAP architectures, including power extraction and insertion, with capability for transient energy management is described. The real-time system operates in the 100kW power class and is instrumented to emulate turbomachinery – power system interactions. The platform is an agile, flexible laboratory for low-cost, risk-reduction development and testing of propulsion control, operability, and energy management technologies.

Controls↗

NASA NTRSDate not supplied

Hybrid-Electric Aero-Propulsion Controls Laboratory: Overview and Capability

A hardware-in-the-loop (HIL) laboratory is developed to investigate control technologies for electrified aircraft propulsion (EAP). The laboratory emulates a propulsion system by reproducing the mechanical shaft interface to the electrical power system in hardware. The experimental electric power system includes supercapacitor energy storage and a dynamically variable electrical load. A novel method of scaling power and inertia is provided in software to accurately reproduce the transient, off-design turbomachinery performance dynamics without including actual turbomachinery. An overview of how the HIL system can accommodate a broad range of EAP architectures, including power extraction and insertion, with capability for transient energy management is described. The real-time system operates in the 100kW power class and is instrumented to emulate turbomachinery – power system interactions. The platform is an agile, flexible laboratory for low-cost, risk-reduction development and testing of propulsion control, operability, and energy management technologies.

controls↗

NASA NTRSDate not supplied

High Speed Data Investigation of an LOX/LNG Combustor Assisted by Non-linear Acoustic Simulations

During testing of a regenerative cooled liquid oxygen/liquid natural gas combustor large amplitude combustion instabilities were observed in half of the series conducted. These instabilities produced an unsteady pressure Root Mean Square (RMS) value greater than the chamber steady state pressure. Quick look investigations of the test data presented some standard indications of non-linear tangential instability such as a non-sinusoidal time series, and larger peaks than trough. Also observed in the data quick look were some atypical findings including a first harmonic amplitude larger than the fundamental, and numerous side bands on the fundamental and subsequent harmonics. A simplified model of the combustor was created using a commercially available finite element software. The first tangential acoustic mode was then simulated using a non-linear wave equation in conjunction with a time explicit discontinuous Gelarkin flow solver. Numerous simulations were evaluated providing valuable insight and reproducing the atypical findings in the data. Side bands were created through non-linear signal modulation like an AM radio. This happens due to the non-linear steepening and relaxing that occurs in large amplitude oscillations. The simulation was also able to reproduce a signal with a first harmonic amplitude larger than the fundamental. Investigating the pressure signal at multiple locations across the chamber head end revealed radial dependent frequency content. A literature review of the topic revealed corroboration of these effect in idealized geometries.

Combustion Stability↗

NASA NTRSDate not supplied

Improving “Domain-Relevant Metadata Requirements” for Supporting Open-Source Science Initiative

Implementation of the NASA Open-Source Science Initiative (OSSI) requires sharing of all relevant information to ensure “open reproducible science” [1]. However, there are several challenges in applying the OSSI to airborne field campaigns focused on atmospheric composition, which often involve a wide variety of in-situ measurements for trace gases, aerosol and cloud properties, meteorological parameters, and radiation fields. To ensure open reproducibility from airborne field campaigns, it is essential to obtain detailed measurement descriptions, which include the detection principle, sample procedure and treatment, and data processing and correction method. The challenge is that some information, e.g., sampling procedure and treatment, may be instrument-specific and campaign or platform-dependent. The data processing may also involve empirical corrections which may evolve over time. In addition, these details (especially operation- or campaign-specific ones) are often not given in journal publications. Given these issues, there is a need to leverage and improve the current “domain-relevant metadata requirements” to represent the measurement description in standardized metadata. These requirements can then facilitate systematic collection of measurement specific metadata and serve as a foundation to develop tools for making the information accessible and data more interoperable and usable or reusable. Here we show a review of existing metadata collections, use cases, and needs for new standards.

Sean Leavor↗

NASA NTRSDate not supplied

Developing a Vision for Maturing the Heliophysics Infrastructure towards Open Science

In the dawn of open science and the upcoming requirements, we speak about the existing state of Heliophysics infrastructure and detail the evolution required to address capability or interconnection shortcomings. Such a daunting barrier calls for an analysis ecosystem with multi-faceted capability. We propose such an ecosystem, called DIARieS, to be built upon five conceptual pillars: Discovery, Implementation, Analysis, Reproducibility, and Sharing of results. The combination of these concepts in a single platform will enable users to more intuitively combine recent advances in technology to create ‘DIARieS’ of their workflows, which can be easily made open to others in the community. The DIARieS ecosystem will also increase our efficiency by streamlining our various workflow processes, including automatic incorporation of the impending requirements of open science. The various components of the ecosystem will simplify software installation and data implementation, including automatically generated citation lists based on the components included. Automatic containerization and version control of the ecosystem will make the custom workflows easily reproducible. Employing widget technology will ease the difficulty of producing publication and commercial quality visualizations and applying common analyses techniques. Incorporating multiple technologies will streamline the various sharing methods common in our work environments today. Overall, the totality of capabilities to be offered by this analysis ecosystem will drastically simplify the application of open science principles to our work in addition to improving our efficiency and ease of collaboration.

Infrastructure↗

NASA NTRS2020

Interpreting Solar Wind Turbulent Spectra beyond Taylor’s Hypothesis

In this Letter we apply a methodology, recently proposed by Bourouaine & Perez (BP19), to interpret solar wind turbulent power spectra beyond the Taylor approximation (TA). The turbulent power spectra were measured using Helios spacecraft data near 0.6 au. We use the model proposed in BP19 to reproduce the field-perpendicular power spectrum E(k(sub ⊥)) of antisunward Alfvénic fluctuations in the plasma frame (where k(sub ⊥) is the field-perpendicular wavenumber) from the corresponding measured frequency power spectrum P(sub sc)(ω,θ(sub b)) along the sampling angle ,θ(sub b), which is the angle between the local magnetic field and the sampling direction. Here ω = 2πf and f is the frequency of the time signal. Interestingly enough, we found that for all corresponding measured frequency power spectrum P(sub sc)(ω,θ(sub b)) the reproduced field-perpendicular power spectrum E(k(sub ⊥)) is the same and independent of the considered sampling angle θ(sub b). This finding is consistent with the fact that the analyzed turbulence is strong and highly anisotropic with k(sub ∥) ≪ k(sub ⊥) (where k(sub ∥) is the field-parallel wavenumber). Furthermore, for this specific time signal we found that the commonly used TA is still approximately valid with the important difference that a broadening in k(sub ⊥) for each angular frequency ω is present. This broadening can be described in the context of the methodology proposed in BP19.

Solar magnetic fields↗

NASA NTRS2022

Ensemble Simulations of the 2012 July 12 Coronal Mass Ejection with the Constant-turn Flux Rope Model

Flux-rope-based magnetohydrodynamic modeling of coronal mass ejections (CMEs) is a promising tool for prediction of the CME arrival time and magnetic field at Earth. In this work, we introduce a constant-turn flux rope model and use it to simulate the 2012 July 12 16:48 CME in the inner heliosphere. We constrain the initial parameters of this CME using the graduated cylindrical shell (GCS) model and the reconnected flux in posteruption arcades. We correctly reproduce all the magnetic field components of the CME at Earth, with an arrival time error of approximately 1 hr. We further estimate the average subjective uncertainties in the GCS fittings by comparing the GCS parameters of 56 CMEs reported in multiple studies and catalogs. We determined that the GCS estimates of the CME latitude, longitude, tilt, and speed have average uncertainties of 5°.74, 11°.23, 24°.71, and 11.4%, respectively. Using these, we have created 77 ensemble members for the 2012 July 12 CME. We found that 55% of our ensemble members correctly reproduce the sign of the magnetic field components at Earth. We also determined that the uncertainties in GCS fitting can widen the CME arrival time prediction window to about 12 hr for the 2012 July 12 CME. On investigating the forecast accuracy introduced by the uncertainties in individual GCS parameters, we conclude that the half-angle and aspect ratio have little impact on the predicted magnetic field of the 2012 July 12 CME, whereas the uncertainties in longitude and tilt can introduce relatively large spread in the magnetic field predicted at Earth.

Talwinder Singh↗

NASA NTRS2023

Earth-System-Model Evaluation of Cloud and Precipitation Occurrence for Supercooled and Warm Clouds Over the Southern Ocean's Macquarie Island

Over the remote Southern Ocean (SO), cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud-top heights < ∼ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016–March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5∘ S, 158.9∘ E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from the liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ∼ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud-top temperatures > 0 ∘C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC2) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of spaceborne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ∼ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ∼ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs.

cloud feedbacks↗