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DOE OSTI2024

Observational constraint on a feedback from supercooled clouds reduces projected warming uncertainty

The increase of carbon-dioxide-doubling-induced warming (climate sensitivity) in the latest climate models is primarily attributed to a larger extratropical cloud feedback. This is thought to be partly driven by a greater ratio of supercooled liquid-phase clouds to all clouds, termed liquid phase ratio. We use an instrument simulator approach to show that this ratio has increased in the latest climate models and is overestimated rather than underestimated as previously thought. In our analysis of multiple models, a greater ratio corresponds to stronger negative cloud feedback, in contradiction with single-model-based studies. We trace this unexpected result to a cloud feedback involving a shift from supercooled to warm clouds as climate warms, which corresponds to greater cloud amount and optical depth and weakens the extratropical cloud feedback. Better constraining this ratio in climate models – and thus this supercooled cloud feedback – impacts their climate sensitivities by up to 1 °C and reduces inter-model spread.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

On the Role of Airborne Ice Nucleating Particles in Primary and Secondary Ice Formation Processes in Convective Midlatitude Clouds

Abstract Formation of ice in mixed-phase clouds may proceed via primary nucleation and secondary ice production (SIP). Primary nucleation involves the presence of ice nucleating particles (INPs), while SIP follows primary nucleation, resulting in ice crystal number concentrations N ice exceeding the number of INPs. The ice formation pathways in young congestus clouds are not well understood nor are the environmental conditions favorable for the onset of SIP. Coincident airborne measurements of INPs and N ice collected during the Secondary Production of Ice in Cumulus Experiment (SPICULE) campaign over the U.S. central Great Plains are reported for young congestus clouds. Number concentrations of INPs and N ice at temperatures between −10° and −20°C were used to categorize whether SIP was active in each analyzed cloud pass. Observational data suggested that fragmentation of freezing droplets (FFDs) was occurring and the most likely SIP mechanism for ice enhancement during the early stages of the sampled clouds, where higher cloud-base temperatures and stronger updrafts produced conditions favorable for the onset of FFD. Simple model simulations of a congestus cloud were used to compare predictions of N ice from newly implemented SIP mechanisms (including FFD) alongside those from the existing Hallett–Mossop (HM) mechanism. Although predicted N ice , when all SIP mechanisms were active, was in good agreement with observations, the dominant SIP mechanism in the model was predicted to be HM. The rate of heterogeneous freezing in raindrops was likely inhibiting realistic FFD rates in the simulated cloud. These discrepancies between the observations and simulations underscore the need for extended laboratory studies to formulate improved model representations of SIP in convective clouds.

Patnaude, Ryan J. [Department of Atmospheric Scien↗

DOE OSTI2019

Mechanisms Behind the Extratropical Stratiform Low-Cloud Optical Depth Response to Temperature in ARM Site Observations

Ground-based observations from three middle- and high-latitude sites managed by the U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program are used to determine the sensitivity of the low-cloud optical depth to temperature and to test whether observations support mechanisms previously proposed to affect the optical depth feedback. Analysis of cloud optical depth retrievals support previous satellite findings that the optical depth decreases or stays constant with increases in temperature when the cloud is warm but increases when the cloud is cold. The cloud liquid water path sensitivity to warming largely explains the optical depth sensitivity at all sites.Mechanisms examined in this study involve the temperature dependence of (a) the moist-adiabatic lapse rate, (b) cloud phase partitioning, (c) drying efficiency of cloud top mixing, (d) cloud top inversion strength,and (e) boundary layer decoupling. Mechanism (a) is present across all clouds and explains 30% to 50%of the increase in liquid water path with warming at temperatures below 0°C. However, the cloud's adiabaticity, the ratio between the liquid water path and its theoretical maximum, is at least as important and determines how the liquid water path sensitivity to temperature varies with temperature. At temperatures below 0°C, the adiabaticity increases with warming, and the data support mechanism (b).At warmer temperatures, the adiabaticity decreases with warming, overwhelming mechanism (a) and resulting in the liquid water path decreasing with warming. This adiabaticity decrease arises primarily because of mechanism (d), and to a lesser degree because of mechanism (e). No evidence is found supporting mechanism (c)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

CMIP5 Climate Models Overestimate Cooling by Volcanic Aerosols

We compare projections of the observed hemispherical mean surface temperature (HadCRUT4.6.0.0) and the ensemble mean of CMIP5 climate models' simulations on a set of standard regression model forcing variables. We find that the volcanic aerosol regression coefficients of the CMIP5 simulations are consistently significantly larger (by 40–49%) than the volcanic aerosol coefficients of the observed temperature. The probability that the observed differences are caused just by chance is much less than 0.01. The overestimate is due to the climate models' response to volcanic aerosol radiative forcing. The largest overestimate occurs in the winter season of each hemisphere. We hypothesize that the models' parameterization of aerosol-cloud interactions within ice and mixed phase clouds is a likely source of this discrepancy. Furthermore, the models significantly underestimate the effect of solar variability on temperature for both hemispheres.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Effects of Cloud Liquid-Phase Microphysical Processes in Mixed-Phase Cumuli Over the Tibetan Plateau

Numerical simulations often overpredict precipitation over the Tibetan Plateau (TP). To examine the factors causing precipitation overprediction, different parameterizations of liquid-phase microphysical processes (accretion, autoconversion, and entrainment-mixing) are implemented into the Morrison microphysics scheme to simulate a TP precipitation event in summer with the Weather Research and Forecasting (WRF) model. The general spatial distribution and temporal trend of precipitation are captured by all simulations, but the precipitation rate is overpredicted. The results from sensitivity experiments suggest that compared to other examined liquid-phase processes, the accretion process is more important in precipitation simulation over the TP region. Further investigation with the Heidke skill scores reveals that accretion parameterization that takes into account the raindrop size produces the most accurate results in terms of the total surface precipitation. This parameterization suppresses spurious accretion and does not produce liquid-phase precipitation until cloud droplets are big enough. Here, it is also confirmed that increasing the model resolution can reduce precipitation overprediction. Results from the case study are confirmed by the use of a one-month simulation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Evaluation of the CAM6 Climate Model Using Cloud Observations at McMurdo Station, Antarctica

A comparative analysis between observational data from McMurdo Station, Antarctica and the Community Atmosphere Model version 6 (CAM6) simulation is performed focusing on cloud characteristics and their thermodynamic conditions. Ka-band Zenith Radar (KAZR) and High Spectral Resolution Lidar (HSRL) retrievals are used as the basis of cloud fraction and cloud phase identifications. Radiosondes released at 12-hour increments provide atmospheric profiles for evaluating the simulated thermodynamic conditions. Our findings show that the CAM6 simulation consistently overestimates (underestimates) cloud fraction above (below) 3 km in four seasons of a year. Normalized by total in-cloud samples, ice and mixed phase occurrence frequencies are underestimated and liquid phase frequency is overestimated by the model at cloud fractions above 0.6, while at cloud fractions below 0.6 ice phase frequency is overestimated and liquid-containing phase frequency is underestimated by the model. The cloud fraction biases are closely associated with concurrent biases in relative humidity (RH), i.e., high (low) RH biases above (below) 2 km. Frequencies of correctly simulating ice and liquid-containing phase increase when the absolute biases of RH decrease. Cloud fraction biases also show a positive correlation with RH biases. Water vapor mixing ratio biases are the primary contributor to RH biases, and hence, likely a key factor controlling the cloud biases. Finally, this diagnosis of the evident shortfalls of representations of cloud characteristics in CAM6 simulation at McMurdo Station brings new insight in improving the governing model physics therein.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Use of Remote Sensing and In-Situ Observations to Develop and Evaluate Improved Representations of Convection and Clouds for the ACME Model

The overachieving goal of the whole CMDV-MCS project is to improve understanding of warm season continental convection and to develop treatments of convection and microphysics capable of representing mesoscale convective systems (MCSs) features in large-scale models. Our tasks for this project contributing to the overachieving goal include: (1) Improve the ice nucleation formulation for MG2 and P3 cloud microphysics schemes; (2) Improve the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM; and (3) Test the performance of improved ice microphysics in E3SM with observation data. In this project, we have (1) Improved the ice nucleation parameterization for MG2 and P3 in E3SM by implementing two advanced empirical parameterizations with connection to aerosols. The two deterministic heterogeneous ice nucleation parameterizations (i.e., DeMott et al., 2015; Niemand et al., 2012) were merged with the MG2 and P3 cloud microphysics schemes in E3SM. Long-term simulations were conducted to examine the impacts of these new parameterizations on simulated cloud properties; (2) Improved the treatment of subgrid dynamics and thermodynamics driving the ice nucleation in E3SM. We evaluated the double Gaussian PDF of vertical velocity simulated by the Cloud Layers Unified By Binormals (CLUBB) and the sub-column vertical velocity sampled from the Subgrid Importance Latin Hypercube Sampler (SILHS) in E3SM. We introduced the vertical velocity variance induced by topographic gravity waves for ice nucleation and droplet activation; and (3) Tested the performance of improved ice microphysics in E3SM with observation data. We tested the new treatments of ice nucleation in the single column model (SCM) mode for the stratiform mixed-phase clouds observed during 9-10 October 2004 in the DOE ARM Mixed-Phase Arctic Cloud Experiment (M-PACE) and for the convective clouds observed on 20 May 2011 in the Midlatitude Continental Convective Clouds Experiment (MC3E). Modeled ice nucleating particles (INPs) concentrations were compared against observations collected around the globe.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2024

Observational Constraint on A Feedback From Supercooled Clouds Reduces Projected Warming Uncertainty

Climate and Earth system modelling↗

NASA NTRS2018

Simulations of Arctic Mixed-Phase Boundary Layer Clouds: Advances in Understanding and Outstanding Questions

An overview of mixed-phase boundary layer cloud simulations emphasizes what detailed studies reveal about the level of understanding of microphysical processes, using analogous liquid-phase boundary layer clouds as a reference for the dynamical conditions. We focus primarily on modeling results from three major field campaign case studies, spanning a range of liquid water path, aerosol loading, cloud temperatures, and active processes (including drizzle, aggregation, and riming). Simulations with detailed microphysics are able to reproduce basic aspects of all three case studies, including continuous ice formation within the context of a well-mixed liquid-cloud-topped layer. However, simulations constrained with measurements of ice nuclei generally underpredict the observed amount of ice present, indicating a possible role for ice multiplication that is poorly established. Assumed ice properties may also bear a significant influence on the water vapor budget and reflectivity properties, but remain imprecisely quantified on a case study basis.

Arctic clouds; Mixed-phase clouds; Mixed-phase mic↗

NASA NTRS2001

Cloud Macro- and Microphysical Properties Derived from GOES over the ARM SGP Domain

Cloud macrophysical properties like fractional coverage and height Z(sub c) and microphysical parameters such as cloud liquid water path (LWP), effective droplet radius r(sub e), and cloud phase, are key factors affecting both the radiation budget and the hydrological cycle. Satellite data have been used to complement surface observations from Atmospheric Radiation Measurements (ARM) by providing additional spatial coverage and top-of-atmosphere boundary conditions of these key parameters. Since 1994, the Geostationary Operational Environmental Satellite (GOES) has been used for deriving at each half-hour over the ARM Southern Great Plains (SGP) domain: cloud amounts, altitudes, temperatures, and optical depths as well as broadband shortwave (SW) albedo and outgoing longwave radiation at the top of the atmosphere. A new operational algorithm has been implemented to increase the number of value-added products to include cloud particle phase and effective size (r(sub e) or effective ice diameter D(sub e)) as well as LWP and ice water path. Similar analyses have been performed on the data from the Visible Infrared Scanner (VIRS) on the Tropical Rainfall Measuring Mission satellite as part of the Clouds and Earth's Radiant Energy System project. This larger suite of cloud properties will enhance our knowledge of cloud processes and further constrain the mesoscale and single column models using ARM data as a validation/initialization resource. This paper presents the results of applying this new algorithm to GOES-8 data taken during 1998 and 2000. The global VIRS results are compared to the GOES SGP results to provide appropriate context and to test consistency.

Minnis, P.↗

DOE OSTI2017

Cloud feedback mechanisms and their representation in global climate models

Cloud feedback—the change in top-of-atmosphere radiative flux resulting from the cloud response to warming—constitutes by far the largest source of uncertainty in the climate response to CO 2 forcing simulated by global climate models (GCMs). In this paper, we review the main mechanisms for cloud feedbacks, and discuss their representation in climate models and the sources of intermodel spread. Global-mean cloud feedback in GCMs results from three main effects: (1) rising free-tropospheric clouds (a positive longwave effect); (2) decreasing tropical low cloud amount (a positive shortwave [SW] effect); (3) increasing high-latitude low cloud optical depth (a negative SW effect). These cloud responses simulated by GCMs are qualitatively supported by theory, high-resolution modeling, and observations. Rising high clouds are consistent with the fixed anvil temperature (FAT) hypothesis, whereby enhanced upper-tropospheric radiative cooling causes anvil cloud tops to remain at a nearly fixed temperature as the atmosphere warms. Tropical low cloud amount decreases are driven by a delicate balance between the effects of vertical turbulent fluxes, radiative cooling, large-scale subsidence, and lower-tropospheric stability on the boundary-layer moisture budget. High-latitude low cloud optical depth increases are dominated by phase changes in mixed-phase clouds. Finally, the causes of intermodel spread in cloud feedback are discussed, focusing particularly on the role of unresolved parameterized processes such as cloud microphysics, turbulence, and convection.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

Collapse and fragmentation of rotating, adiabatic clouds

A numerical hydrodynamics code has been used to calculate the collapse of rotating, adiabatic clouds. The three-dimensional nature of the calculation allows the clouds to fragment in the dynamic collapse phase. Clouds with adiabatic exponent of 7/5 and initial cos(2 phi) density variations fragment into binary systems if the initial ratio of thermal to gravitational energy is small (about 0.05). Clouds with higher thermal energy, however, damp the density variation and form near-equilibrium ellipsoids, with ratios of rotational to gravitational energy less than the critical value for dynamic growth of nonaxisymmetry in Maclaurin spheroids. Even with an adiabatic pressure law, dynamic fragmentation of a collapsing cloud is possible, implying for star formation theory that the low thermal energy fragments produced in isothermal collapse calculations may undergo a subsequent dynamic fragmentation in the nonisothermal regime.

Boss, A. P.↗

DOE OSTI2019

Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations

A negative extratropical shortwave cloud feedback driven by changes in cloud optical depth is a feature of global climate models (GCMs). A robust positive trend in observed liquid water path (LWP) over the last two decades across the warming Southern Ocean supports the negative shortwave cloud feedback predicted by GCMs. This feature has been proposed to be due to transitions from ice to liquid with warming. To gain insight into the shortwave cloud feedback we examine extratropical cyclone variability and the response of extratropical cyclones to transient warming in GCM simulations. Multi-Sensor Advanced Climatology Liquid Water Path (MAC-LWP) microwave observations of cyclone properties from the period 1992–2015 are contrasted with GCM simulations, with horizontal resolutions ranging from 7 km to hundreds of kilometers. We find that inter-cyclone variability in LWP in both observations and models is strongly driven by the moisture flux along the cyclone's warm conveyor belt (WCB). Stronger WCB moisture flux enhances the LWP within cyclones. This relationship is replicated in GCMs, although its strength varies substantially across models. It is found that more than 80 % of the enhancement in Southern Hemisphere (SH) extratropical cyclone LWP in GCMs in response to a transient 4 K warming can be predicted based on the relationship between the WCB moisture flux and cyclone LWP in the historical climate and their change in moisture flux between the historical and warmed climates. Further, it is found that that the robust trend in cyclone LWP over the Southern Ocean in observations and GCMs is consistent with changes in the moisture flux. We propose two cloud feedbacks acting within extratropical cyclones: a negative feedback driven by Clausius–Clapeyron increasing water vapor path (WVP), which enhances the amount of water vapor available to be fluxed into the cyclone, and a feedback moderated by changes in the life cycle and vorticity of cyclones under warming, which changes the rate at which existing moisture is imported into the cyclone. Both terms contribute to increasing LWP within the cyclone. While changes in moisture flux predict cyclone LWP trends in the current climate and the majority of changes in LWP in transient warming simulations, a portion of the LWP increase in response to climate change that is unexplained by increasing moisture fluxes may be due to phase transitions. The variability in LWP within cyclone composites is examined to understand what cyclonic regimes the mixed-phase cloud feedback is relevant to. At a fixed WCB moisture flux cyclone LWP increases with increasing sea surface temperature (SST) in the half of the composite poleward of the low and decreases in the half equatorward of the low in both GCMs and observations. Cloud-top phase partitioning observed by the Atmospheric Infrared Sounder (AIRS) indicates that phase transitions may be driving increases in LWP in the poleward half of cyclones.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Primary and secondary ice production: interactions and their relative importance

A discrepancy of up to 5 orders of magnitude between ice crystal and ice nucleating particle (INP) number concentrations was found in the measurements, indicating the potentially important role of secondary ice production (SIP) in the clouds. However, the interactions between primary and SIP processes and their relative importance remain unexplored. In this study, we implemented five different ice nucleation schemes as well as physical representations of SIP processes (i.e., droplet shattering during rain freezing, ice-ice collisional break-up, and rime splintering) in the Community Earth System Model version 2 (CESM2). We ran CESM2 in the single column mode for model comparisons with the DOE Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE) observations. We found that the model experiments with aerosol-aware ice nucleation schemes and SIP processes yield the best simulation results for the M-PACE single-layer mixed-phase clouds. We further investigated the relative importance of ice nucleation and SIP to ice number and cloud phase as well as interactions between ice nucleation and SIP in the M-PACE single-layer mixed-phase clouds. Our results show that SIP contributes 80 % to the total ice formation and transforms ~30 % of pure liquid-phase clouds simulated in the model experiments without considering SIP into mixed-phase clouds. The SIP is not only a result of ice crystals produced from ice nucleation, but also competes with the ice nucleation by reducing the number concentrations of cloud droplets and cloud-borne dust INPs. Conversely, strong ice nucleation also suppresses SIP by glaciating mixed-phase clouds and thereby reducing the amount of precipitation particles (rain and graupel).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Seasonal Variation of Dust Aerosol Vertical Distribution in Arctic Based on Polarized Micropulse Lidar Measurement

This study investigates the seasonal variation of dust aerosol vertical distribution using polarized Micropulse lidar (MPL) measurements at the Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) observatory from January 2013 to September 2017. For the first time, multi-year aerosol backscatter coefficients are retrieved at the ARM NSA site from MPL measurements and are consistent with co-located high spectral resolution lidar (HSRL) measurements. The high-quality aerosol backscatter coefficient retrievals are used to derive the particle depolarization ratio (PDR) at the wavelength of 532 nm, which is used to identify the presence of dust aerosols. The annual cycles of the vertical distributions of dust backscatter coefficient and PDR and dust aerosol optical depth (DAOD) show that aerosol loading has a maximum in late winter and early spring but a minimum in late summer and early autumn. Vertically, dust aerosol occurs in the entire troposphere in spring and winter and in the low and middle troposphere in summer and autumn. Because dust aerosols are effective ice nuclei, the seasonality of dust aerosol vertical distribution has important implications for the Arctic climate through aerosol–cloud–radiation interactions, primarily through impacting mixed-phase cloud processes.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Clutter mitigation, multiple peaks, and high-order spectral moments in 35 GHz vertically pointing radar velocity spectra

Abstract. This study presents and applies three separate processing methods to improve high-order moments estimated from 35 GHz (Ka band) vertically pointing radar Doppler velocity spectra. The first processing method removes Doppler-shifted ground clutter from spectra collected by a US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program Ka-band zenith pointing radar (KAZR) deployed at Oliktok Point (OLI), Alaska. Ground clutter resulted from multiple pathways through antenna side lobes and reflections off a rotating scanning radar antenna located 2 m away from KAZR, which caused Doppler shifts in ground clutter returns from stationary targets 2.5 km away. After removing clutter in the recorded velocity spectra, the second processing method identifies multiple separate and sub-peaks in the spectra and estimates high-order moments for each peak. Multiple peaks and high-order moments were estimated for both original 2 and 15 s averaged spectra. The third processing step improves the spectrum variance, skewness, and kurtosis estimates by removing velocity variability due to turbulent broadening during 15 s averaging intervals. Applying the multiple peak processing to Doppler velocity spectra during liquid-only clouds can identify cloud and drizzle particles and during mixed-phase clouds can identify liquid cloud and frozen hydrometeors. Consistent with previous studies, this work found that spectrum skewness assuming only a single spectral peak was a good indicator of two hydrometeor populations (for example, cloud and drizzle particles) being present in the radar pulse volume. Yet, after dividing the spectrum into multiple peaks, velocity spectrum skewness for individual peaks is near zero, indicating nearly symmetric peaks. This suggests that future studies should use velocity skewness of single-peak spectra as an indicator of possible multiple hydrometeor populations and then use multiple-peak moments for quantitative studies. Three future activities will continue this work. First, KAZR spectra from several ARM sites have been processed and are available in the ARM archive as a principal investigator (PI) product. ARM programmers are evaluating these processing methods as part of future multiple-peak products generated by ARM. Third, MATLAB code generating the Oliktok Point products has been uploaded as supplemental material for public dissemination.

47 OTHER INSTRUMENTATION↗

NASA NTRS1981

Multichannel scanning radiometer for remote sensing cloud physical parameters

A multichannel scanning radiometer developed for remote observation of cloud physical properties is described. Consisting of six channels in the near infrared and one channel in the thermal infrared, the instrument can observe cloud physical parameters such as optical thickness, thermodynamic phase, cloud top altitude, and cloud top temperature. Measurement accuracy is quantified through flight tests on the NASA CV-990 and the NASA WB-57F, and is found to be limited by the harsh environment of the aircraft at flight altitude. The electronics, data system, and calibration of the instrument are also discussed.

Curran, R. J.↗

NASA NTRS2016

Cloud Regimes as a Tool for Systematic Study of Various Aerosol-Cloud-Precipitation Interactions

Systematic changes of clouds and precipitation are notoriously difficult to ascribe to aerosols. This presentation will showcase yet one more attempt to at least credibly detect the signal of aerosol-cloud-precipitation interactions. We surmise that the concept of cloud regimes (CRs) is appropriate to conduct such an investigation. Previous studies focused on what we call here dynamical CRs, and while we continue to adopt those too for our analysis, we have found that a different way of organizing cloud systems, namely via microphysical regimes is also promising. Our analysis relies on MODIS Collection 6 Level-3 data for clouds and aerosols, and TRMM-TMPA data for precipitation. The regimes are derived by applying clustering analysis on MODIS joint histograms, and once each grid cell is assigned a regime, aerosol and precipitation data can be spatiotemporally matched and composited by regime. The composites of various cloud and precipitation variables for high (upper quartile of distribution) and low (lower quartile) aerosol loadings can then be contrasted. We seek evidence of aerosol effects both in regimes with large fractions of deep ice-rich clouds, as well as regimes where low liquid phase clouds dominate. Signals can be seen, especially when the analysis is broken by land-ocean and when additional filters are applied, but there are of course caveats which will be discussed.

cloud regimes↗