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DOE OSTI2025

Extratropical Cloud Feedback Constrained by Cloud Sources and Sinks in Cyclones

Constraining cloud feedback in global climate models (GCMs) using observations is important for establishing accurate predictions of future climate. Uncertainty in shortwave cloud feedback (SW FB ) dominates uncertainty in total cloud feedback. Recent studies show a shift toward more positive extratropical SW FB in the latest generations of GCMs leading to the emergence of very high equilibrium climate sensitivity (ECS). In this study, we use precipitation efficiency and albedo susceptibility to constrain liquid water path (LWP) response to warming and SW FB in the Southern Ocean (SO; 50°–80°S). We analyze precipitation in extratropical cyclones (ECs) to learn about extratropical condensed water sink processes, combined with observations of clouds and moisture convergence, and use the analysis to better understand and constrain SW FB . We utilize a perturbed parameter ensemble (PPE) hosted in the Community Atmosphere Model, version 6 (CAM6), to provide a constraint on SW FB based on observations from Clouds and the Earth’s Radiant Energy System (CERES) and Multisensor Advanced Climatology of LWP (MAC-LWP). We apply Gaussian process regression to emulate the model response to all parameters perturbed in the PPE. Confronting the emulator output with observations provides a new estimated response of Earth to global warming. Furthermore, our new estimates of SO LWP reduce the PPE range by 66%–72%, which results in a shortwave cloud radiative effect estimated range that is 27%–34% less than the PPE range. Observations suggest a more positive SO SW FB than the Community Earth System Model, version 2 (CESM2), and consequently do not reject the high climate sensitivity GCMs emerging from the Coupled Model Intercomparison Project phase 6 (CMIP6).

Atmosphere↗
DOE OSTI2025

Comparison of CERES SYN1deg Radiative Fluxes with Those Derived from Observations at the ARM ENA Site

Profiles of radiative fluxes simulated from thermodynamic and cloud observations made at the Atmospheric Radiation Measurement (ARM) eastern North Atlantic (ENA) site for a 6-yr period are termed as ENARad. ENARad radiative fluxes are compared to those from the Clouds and the Earth’s Radiant Energy System (CERES) 1°-resolution synoptic product (SYN1deg)-simulated radiative flux profiles as well as the CERES instrument observed top-of-the-atmosphere (TOA) fluxes and ground site broadband radiometer measurements. Monthly average differences between ENARad and surface radiometer reported fluxes and differences between ENARad, SYN1deg, and observed fluxes at TOA were statistically insignificant. SYN1deg significantly overestimated surface downwelling shortwave flux by 12 ± 52 W m −2 and surface downwelling longwave flux by 5 ± 20 W m −2 on monthly time scales. Such overestimations were traced to a moister and warmer subcloud layer, a drier cloud layer, and a moister and colder above-cloud-free troposphere in the ancillary thermodynamic and cloud properties used by SYN1deg than observed. Similarly, low-cloud coverage, boundaries, and liquid water paths utilized by SYN1deg were also significantly higher than observed. Intramodel differences in the hourly values of shortwave fluxes exceeded 100 W m−2 at the TOA and the surface. These differences were also due to inaccuracies in the representation of low-cloud properties within the SYN1deg product relative to those determined by ENA ARM instrumentation and used as ENARad ancillary data. Results presented are relevant to investigations employing the CERES SYN1deg data product, studies that estimate radiative fluxes from surface-based or satellite-borne observations, and comparative analyses of radiative fluxes derived using different methodological approaches.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Novel Method for Diagnosing Land–Atmosphere Coupling Sensitivity in a Single-Column Model

The response of boundary layer properties and cloudiness to changes in surface evaporative fraction (EF) is investigated in a single-column model to quantify the locally coupled impact of subgrid surface variations on the atmosphere during summer. Sensitive coupling days are defined when the model atmosphere exhibits large variations across a range of EFs centered on the analyzed value. Coupling sensitivity exists as both positive feedback (cloudiness increases with EF) and negative feedback (clouds increase with decreasing EF) regimes. The positive regime manifests in shallow convection situations, which are capped by a strengthened inversion and subsidence, restricting the vertical extent of convection to just above the boundary layer. Surfaces with larger EF (greater surface latent heat flux) can inject more moisture into the vertically confined system, lowering the cloud base and an increasing cloud liquid water path (LWP). Negative feedback regimes tend to manifest when large-scale deep convection, such as from mesoscale convective systems and fronts, is advected through the domain, where convection strengthens over surfaces with a lower EF (greater surface sensible heat flux). The invigoration of these systems by the land surface leads to an increase in LWP through strengthened updrafts and stronger coupling between the boundary layer and the free atmosphere. These results apply in the absence of heterogeneity-induced mesoscale circulations, providing a one-dimensional dynamical perspective on the effect of surface heterogeneity. Finally, this study provides a framework of intermediate complexity, lying between parcel theory and high-resolution coupled land–atmosphere modeling, and therefore isolates the relevant first-order processes in land–atmosphere interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Technique for Estimating Liquid Droplet Diameter and Liquid Water Content in Stratocumulus Clouds Using Radar and Lidar Measurements

This paper describes a technique for estimating the liquid water content (LWC) and a characteristic particle diameter in stratocumulus clouds using radar and lidar observations. The uncertainty in LWC estimate from radar and lidar measurements is significantly reduced once the characteristic particle diameter is known. The technique is independent of the drop size distribution. It is applicable for a broad range of W-band reflectivity Z between -30 and 0 dBZ and all values of lidar backscatter β observations. No partitioning of cloud or drizzle is required on the basis of an arbitrary threshold of Z as in prior studies. A method for estimating droplet diameter and LWC was derived from the electromagnetic simulations of radar and lidar observations. In situ stratocumulus cloud and drizzle probe spectra were input to the electromagnetic simulation. The retrieved droplet diameter and LWC were validated using in situ measurements from the southeastern Pacific Ocean. The retrieval method was applied to radar and lidar measurements from the northeastern Pacific. Uncertainty in the retrieved droplet diameter and LWC that are due to the measurement errors in radar and lidar backscatter measurements are 7% and 14%, respectively. The retrieved LWC was validated using the concurrent G-band radiometer estimates of the liquid water path.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Separating Cloud and Drizzle Signals in Radar Doppler Spectra Using a Parametric Time Domain Method

The ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s -1 , respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m -2 with the benchmark microwave method.

Engineering↗
DOE OSTI2020

Assessment of Precipitating Marine Stratocumulus Clouds in the E3SMv1 Atmosphere Model: A Case Study from the ARM MAGIC Field Campaign

This paper presents a process-oriented evaluation of precipitating stratocumulus and its transition to cumulus in version 1 of the Energy Exascale Earth System Model (E3SMv1) using comprehensive case-study observations from a field campaign of the Atmospheric Radiation Measurement program (ARM). The E3SMv1 single-column model (SCM) of the marine boundary layer and its low clouds and precipitation are compared to observations including subcloud drizzle retrievals from a combination of Doppler radar and lidar backscatter measurements. Additionally, the SCM is also compared to a large-eddy simulation (LES) of the same case. The combination of advanced remote sensing observations and LES is a powerful framework to evaluate the physical parameterizations of large-scale models. Given the observed large-scale environment, the E3SMv1 SCM realistically represents the evolution of clouds and boundary layer structure during the stratocumulus-to-cumulus transition. The model well simulates the liquid water path and its diurnal cycle in the stratocumulus period as well as the two-layer vertical thermodynamic structure and lower cloud fraction in the transition period. E3SMv1’s success in simulating the cloud in the stratocumulus period permitted examination of its precipitation processes. Here problems were identified with E3SMv1 producing an unrealistically small subcloud precipitation fraction, an unrealistic double peak in the vertical profiles of precipitation mass, and drizzle that evaporates too close to the surface. Further model diagnostics determined that these unrealistic characteristics resulted from an overly long microphysics time step and an unrealistic parameterization of the precipitation fraction. These results imply that careful consideration of these issues is needed in order to better simulate precipitation processes in marine stratocumulus.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Droplet Number Concentration Value-Added Product

The ndrop_mfrsr value-added product (VAP) provides an estimate of the cloud droplet number concentration of overcast water clouds retrieved from cloud optical depth from the multi-filter rotating shadowband radiometer (MFRSR) instrument and liquid water path (LWP) retrieved from the microwave radiometer (MWR). When cloud layer information is available from vertically pointing lidar and radars in the Active Remote Sensing of Clouds (ARSCL) product, the VAP also provides estimates of the adiabatic LWP and an adiabatic parameter (beta) that indicates how divergent the LWP is from the adiabatic case. quality control (QC) flags (qc_drop_number_conc), an uncertainty estimate (drop_number_conc_toterr), and a cloud layer type flag (cloud_base_type) are useful indicators of the quality and accuracy of any given value of the retrieval. Examples of these major input and output variables are given in sample plots in section 6.0.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

ARM data-oriented metrics and diagnostics package for climate model evaluation (ARM-DIAGS-V3) version 3

A Python-based metrics and diagnostics package is currently being developed by the ARM Infrastructure Team at Lawrence Livermore National Laboratory to facilitate the use of long-term high frequency measurements from the ARM program in evaluating the regional climate simulation of clouds, radiation, precipitation, and aerosols. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The CMIP model data sets are also included in the package to enable model inter-comparison as demonstrated in Zhang et al. (2018) and Zhang et al. (2020). The mean of the CMIP model can be served as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, aerosol optical depth, and radiative fluxes, with plan to extend to more fields, such as the evaluation of model simulated aerosol physicochemical properties and cloud microphysics properties. Process-oriented diagnostics focusing on aerosol, cloud, and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. In addition to the Southern Great Plains (SGP), North Slope of Alaska (NSA) and Tropical Western Pacific (TWP) atmospheric observatories in the ARMDIAGS version 2.0, the version 3.0 package have extended to the data collected at the ARM Eastern North Atlantic (ENA) atmospheric observatory and the Observation and Modeling of the Green Ocean Amazon (GOAMAZON) field campaign. The metrics and diagnostics package are currently built upon standard Python libraries and additional Python packages developed by DOE (CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first provide an overview of major metrics in section 2. The input data, which constitutes the core content of the metrics and diagnostics package, is summarized in section 3. A user's guide documenting the workflow/structure of the version 3.0 codes and including step-by-step instruction for running the package is described in section 4.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

EPCAPE Stratiform Cloud Analysis for Radiative Properties Experiment (SCARPE) Field Campaign Report

A unique attribute of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) is the availability of an elevated secondary measurement site just 5 km from the main U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) deployment on Scripps Pier. Mount Soledad, at 250 m elevation, offers the ability to frequently measure aerosol chemical and microphysical properties within the low-level Eastern Pacific coastal stratiform clouds that the AMF is observing from below. Based on the EPCAPE experiment design to deploy three major systems on Mount Soledad—the scanning radars, the Russell aerosol mobile laboratory, and an ARM microwave radiometer (MWR)—an additional plan was made to deploy a suite of Eppley Laboratory radiometers at this site to measure the radiative response to instantaneous changes in observed aerosol properties and MWR retrieved cloud liquid water path (LWP). This supplemental field campaign was proposed as the Stratiform Cloud Analysis for Radiative Properties Experiment (SCARPE).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Observationally-based relations among water cycle parameters during snowfall events in the Upper Colorado River Basin

Cold-season precipitation predictability within the complex terrain of the Upper Colorado River Basin is vital for water resource management across many western states, as the Colorado River serves as a primary source of water for over 40 million people in the Western United States and Mexico. This study uses the remote sensing measurements of such water cycle parameters as the liquid equivalent snowfall rate, S, and accumulation, A, vertically integrated amounts of supercooled cloud liquid and ice expressed as liquid water path(LWP) and ice water path (IWP), respectively, and vertically integrated water vapor IWV.

Matrosov, Sergey [CIRES University of Colorado Bou↗
DOE OSTI2026

Final report: Insights from ARM observations into aerosol processing and transport by extratropical cyclones and aerosol effects on cyclone clouds

This project advanced understanding of aerosol–cloud interactions in extratropical cyclones using observations from the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) observatory and ACE-ENA field campaigns, along with satellite datasets and numerical modeling. We have quantified the susceptibility of frontal clouds to aerosols, showing that this susceptibility is similar to that in non-frontal clouds despite substantial (factor 2) differences in droplet concentrations between these cloud types, and also significant differences in cloud albedo. A key finding was that Aitken-mode (here, 60-100nm diameter) aerosols play a disproportionately important role in droplet activation in frontal clouds, and concentrations of these particles are frequently strongly biased in climate models. Activation of aerosols depends strongly on updraft speeds, and via collaboration with Argonne National Laboratory we contributed to quantifying these better with the ARM radar wind profiler at the ENA site. Our studies in the North Atlantic have analogs in the Southern Ocean, and to draw these out we examined data from the MARCUS and SOCRATES field campaigns. We identified new particle formation within extratropical cyclones as a potentially important source of cloud condensation nuclei in these pristine marine environments. By combining ARM observations with perturbed parameter ensemble simulations, we demonstrated that surface observations can effectively constrain aerosol–cloud adjustments and reduce uncertainty in simulated cloud liquid water path responses by approximately 15%, yielding stronger constraints on historical aerosol cooling effects. The work further established precipitation processes as a dominant control on aerosol–cloud interactions and Earth system predictability.

Gordon, Hamish [Department of Chemical Engineering↗
DOE OSTI2026

3D Cloud Aerosol Precipitation Experiment at kennaook Cape Grim (3D-CAPE-k) Field Campaign Report

The objective of this campaign was to complement the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s Cloud And Precipitation Experiment at kennaook (CAPE-k) in northwest Tasmania by deploying scanning, fixed pointing angle, and in situ aerosol, cloud, and precipitation remote-sensing instruments during the last six months of the experiment. These instruments provided a three-dimensional (3D) context to the second ARM Mobile Facility (AMF2) vertically pointing observations collected at the kennaook Cape Grim (KCG) site and captured a portion of the life cycle of the clouds before and after the passage over the AMF2 vertical column. These 3D cloud measurements will be used in the near future to 1) investigate how the vertical profiles of aerosol and cloud properties are representative of the broader area and how these properties evolve during the portion of the cloud life cycle captured by the scanning instruments, 2) quantify how accurate the cloud fraction derived with assumptions from vertically pointing observations are compared with direct 3D cloud fraction measurements, 3) evaluate cloud fraction and liquid water path in the Australian Community Climate and Earth-System Simulator–Convective-scale (ACCESS-C) forecast model, and 4) evaluate aerosol and cloud products from the European Space Agency/Japanese Aerospace Exploration Agency (ESA/JAXA) Earth Cloud Aerosol Radiation Explorer (EarthCARE) mission using the scanning measurements, offering more chances of exact collocation, thereby complementing the statistical approach we are planning to employ with AMF2 observations for that same purpose.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Assessing Cloud and Precipitation Properties on Temporal and Spatial Scales Using LASSO Simulations over ENA

Low clouds and precipitation representation remain a major source of uncertainty in Earth System Models (ESMs), particularly due to challenges in representing their sub-grid variability and scale-dependent sampling. This study evaluates the performance of preliminary simulations from the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project over the Eastern North Atlantic (ENA), with a focus on liquid water path (LWP), ice water path (IWP), cloud fraction (CF), and surface precipitation simulated across closed-cell, open-cell, and transitional cloud regimes. Using LES (100 m horizontal grid spacing) driven by ERA5 and MERRA-2 reanalyses, we assess the representativeness of ground-based point observations by analyzing their correspondence to model-resolved spatial and temporal means. Results suggest that observational sampling of at least 6 hours is required to achieve consistency with domain-scale averages, in particular for observations that exhibit pronounced sub-grid heterogeneity, such as precipitation. ERA5-forced simulations exhibit improved spatial coherence and agreement with domain-averaged quantities when compared to MERRA-2 runs, with performance discrepancies largest for convective cloud conditions due to differences in forcing fidelity and temporal resolution. These findings highlight the importance of regime-aware model evaluation strategies and potentially demonstrate how LES can inform observation-model comparison practices and the development of cloud and precipitation parameterizations in ESMs.

Liang, Jiakun [University of Hawai'i at Manoa] (OR↗
DOE OSTI2023

Surface impacts and associated mechanisms of a moisture intrusion into the Arctic observed in mid-April 2020 during MOSAiC

Distinct events of warm and moist air intrusions (WAIs) from mid-latitudes have pronounced impacts on the Arctic climate system. We present a detailed analysis of a record-breaking WAI observed during the MOSAiC expedition in mid-April 2020. By combining Eulerian with Lagrangian frameworks and using simulations across different scales, we investigate aspects of air mass transformations via cloud processes and quantify related surface impacts. The WAI is characterized by two distinct pathways, Siberian and Atlantic. A moist static energy transport across the Arctic Circle above the climatological 90th percentile is found. Observations at research vessel Polarstern show a transition from radiatively clear to cloudy state with significant precipitation and a positive surface energy balance (SEB), i.e., surface warming. WAI air parcels reach Polarstern first near the tropopause, and only 1–2 days later at lower altitudes. In the 5 days prior to the event, latent heat release during cloud formation triggers maximum diabatic heating rates in excess of 20 K d -1 . For some poleward drifting air parcels, this facilitates strong ascent by up to 9 km. Based on model experiments, we explore the role of two key cloud-determining factors. First, we test the role moisture availability by reducing lateral moisture inflow during the WAI by 30%. This does not significantly affect the liquid water path, and therefore the SEB, in the central Arctic. The cause are counteracting mechanisms of cloud formation and precipitation along the trajectory. Second, we test the impact of increasing Cloud Condensation Nuclei concentrations from 10 to 1,000 cm -3 (pristine Arctic to highly polluted), which enhances cloud water content. Resulting stronger longwave cooling at cloud top makes entrainment more efficient and deepens the atmospheric boundary layer. Finally, we show the strongly positive effect of the WAI on the SEB. This is mainly driven by turbulent heat fluxes over the ocean, but radiation over sea ice. The WAI also contributes a large fraction to precipitation in the Arctic, reaching 30% of total precipitation in a 9-day period at the MOSAiC site. However, measured precipitation varies substantially between different platforms. Therefore, estimates of total precipitation are subject to considerable observational uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Influences of Cloud Microphysics on the Components of Solar Irradiance in the WRF-Solar Model

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Impact of biomass burning aerosols on radiation, clouds, and precipitation over the Amazon: relative importance of aerosol–cloud and aerosol–radiation interactions

Biomass burning (BB) aerosols can influence regional and global climate through interactions with radiation, clouds, and precipitation. Here, we investigate the impact of BB aerosols on the energy balance and hydrological cycle over the Amazon Basin during the dry season. We performed simulations with a fully coupled meteorology–chemistry model, the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), for a range of different BB emission scenarios to explore and characterize nonlinear effects and individual contributions from aerosol–radiation interactions (ARIs) and aerosol–cloud interactions (ACIs). The ARIs of BB aerosols tend to suppress low-level liquid clouds by local warming and increased evaporation and to facilitate the formation of high-level ice clouds by enhancing updrafts and condensation at high altitudes. In contrast, the ACIs of BB aerosol particles tend to enhance the formation and lifetime of low-level liquid clouds by providing more cloud condensation nuclei (CCN) and to suppress the formation of high-level ice clouds by reducing updrafts and condensable water vapor at high altitudes (>8 km). For scenarios representing the lower and upper limits of BB emission estimates for recent years (2002–2016), we obtained total regional BB aerosol radiative forcings of –0.2 and 1.5 W m –2 , respectively, showing that the influence of BB aerosols on the regional energy balance can range from modest cooling to strong warming. We find that ACIs dominate at low BB emission rates and low aerosol optical depth (AOD), leading to an increased cloud liquid water path (LWP) and negative radiative forcing, whereas ARIs dominate at high BB emission rates and high AOD, leading to a reduction of LWP and positive radiative forcing. In all scenarios, BB aerosols led to a decrease in the frequency of occurrence and rate of precipitation, caused primarily by ACI effects at low aerosol loading and by ARI effects at high aerosol loading. The dependence of precipitation reduction on BB aerosol loading is greater in a strong convective regime than under weakly convective conditions. Overall, our results show that ACIs tend to saturate at high aerosol loading, whereas the strength of ARIs continues to increase and plays a more important role in highly polluted episodes and regions. This should hold not only for BB aerosols over the Amazon, but also for other light-absorbing aerosols such as fossil fuel combustion aerosols in industrialized and densely populated areas. The importance of ARIs at high aerosol loading highlights the need for accurately characterizing aerosol optical properties in the investigation of aerosol effects on clouds, precipitation, and climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Constraining the Twomey effect from satellite observations: issues and perspectives

Abstract. The Twomey effect describes the radiative forcing associated with a change in cloud albedo due to an increase in anthropogenic aerosol emissions. It is driven by the perturbation in cloud droplet number concentration (ΔNd, ant) in liquid-water clouds and is currently understood to exert a cooling effect on climate. The Twomey effect is the key driver in the effective radiative forcing due to aerosol–cloud interactions, but rapid adjustments also contribute. These adjustments are essentially the responses of cloud fraction and liquid water path to ΔNd, ant and thus scale approximately with it. While the fundamental physics of the influence of added aerosol particles on the droplet concentration (Nd) is well described by established theory at the particle scale (micrometres), how this relationship is expressed at the large-scale (hundreds of kilometres) perturbation, ΔNd, ant, remains uncertain. The discrepancy between process understanding at particle scale and insufficient quantification at the climate-relevant large scale is caused by co-variability of aerosol particles and updraught velocity and by droplet sink processes. These operate at scales on the order of tens of metres at which only localised observations are available and at which no approach yet exists to quantify the anthropogenic perturbation. Different atmospheric models suggest diverse magnitudes of the Twomey effect even when applying the same anthropogenic aerosol emission perturbation. Thus, observational data are needed to quantify and constrain the Twomey effect. At the global scale, this means satellite data. There are four key uncertainties in determining ΔNd, ant, namely the quantification of (i) the cloud-active aerosol – the cloud condensation nuclei (CCN) concentrations at or above cloud base, (ii) Nd, (iii) the statistical approach for inferring the sensitivity of Nd to aerosol particles from the satellite data and (iv) uncertainty in the anthropogenic perturbation to CCN concentrations, which is not easily accessible from observational data. This review discusses deficiencies of current approaches for the different aspects of the problem and proposes several ways forward: in terms of CCN, retrievals of optical quantities such as aerosol optical depth suffer from a lack of vertical resolution, size and hygroscopicity information, non-direct relation to the concentration of aerosols, difficulty to quantify it within or below clouds, and the problem of insufficient sensitivity at low concentrations, in addition to retrieval errors. A future path forward can include utilising co-located polarimeter and lidar instruments, ideally including high-spectral-resolution lidar capability at two wavelengths to maximise vertically resolved size distribution information content. In terms of Nd, a key problem is the lack of operational retrievals of this quantity and the inaccuracy of the retrieval especially in broken-cloud regimes. As for the Nd-to-CCN sensitivity, key issues are the updraught distributions and the role of Nd sink processes, for which empirical assessments for specific cloud regimes are currently the best solutions. These considerations point to the conclusion that past studies using existing approaches have likely underestimated the true sensitivity and, thus, the radiative forcing due to the Twomey effect.

54 ENVIRONMENTAL SCIENCES↗