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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

The impact of neglecting ice phase on cloud optical depth retrievals from AERONET cloud mode observations

Clouds present many challenges to climate modelling. To develop and verify the parameterisations needed to allow climate models to represent cloud structure and processes, there is a need for high-quality observations of cloud optical depth from locations around the world.Retrievals of cloud optical depth are obtainable from radiances measured by Aerosol Robotic Network (AERONET) radiometers in “cloud mode” using a two-wavelength retrieval method. However, the method is unable to detect cloud phase, and hence assumes that all of the cloud in a profile is liquid.This assumption has the potential to introduce errors into long-term statistics of retrieved optical depth for clouds that also contain ice.Using a set of idealised cloud profiles we find that, for optical depths above 20, the fractional error in retrieved optical depth is a linear function of the fraction of the optical depth that is due to the presence ofice cloud (“ice fraction”). Clouds that are entirely ice have positive errors with magnitudes of the order of 55 % to 70 %. We derive a simple linear equation that can be used as a correction at AERONET sites where ice fraction can be independently estimated. Using this linear equation, we estimate the magnitude of the error for a set of cloud profiles from five sites of the Atmospheric Radiation Measurement programme. The dataset contains separate retrievals of ice and liquid retrievals; hence ice fraction can be estimated. The magnitude of the error at each location was related to the relative frequencies of occurrence in thick frontal cloud at the mid-latitude sites and of deep convection at the tropical sites – that is, of deep cloud containing both ice and liquid particles. The long-term mean optical depth error at the five locations spans the range 2–4, which we show to be small enough to allow calculation of top-of-atmosphere flux to within 10 % and surface flux to about 15 %.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

A method for retrieving cloud optical depth ( τ c ) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τ c produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle ( θ 0 ), τ c , solar pixel angle/scattering angle ( θ s ), and pixel zenith angle/view angle ( θ z ). The effects of these parameters are described and the functions for radiance, I λ τ c , θ 0 , θ s , θ z , and RBR τ c , θ 0 , θ s , θ z are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τ c , where RBR increases with τ c up to about τ c = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured I λ meas θ s , θ z , in addition to RBR meas θ s , θ z , to obtain a unique solution for τ c . The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τ c values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τ c RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI have an RMSE of 2.2, which is well within the uncertainty of the MWR. In conclusion, the procedure developed here provides a foundation to test and develop other cloud detection algorithms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Constraining the low-cloud optical depth feedback at middle and high latitudes using satellite observations

The increase in cloud optical depth with warming at middle and high latitudes is a robust cloud feedback response found across all climate models. This study builds on results that suggest the optical depth response to temperature is timescale invariant for low-level clouds. The timescale invariance allows one to use satellite observations to constrain the models' optical depth feedbacks. Three passive-sensor satellite retrievals are compared against simulations from eight models from the Atmosphere Model Intercomparison Project (AMIP) of the 5th Coupled Model Intercomparison Project (CMIP5). This study confirms that the low-cloud optical depth response is timescale invariant in the AMIP simulations, generally at latitudes higher than 40°. Compared to satellite estimates, most models overestimate the increase in optical depth with warming at the monthly and interannual timescales. Many models also do not capture the increase in optical depth with estimated inversion strength that is found in all three satellite observations and in previous studies. The discrepancy between models and satellites exists in both hemispheres and in most months of the year. A simple replacement of the models' optical depth sensitivities with the satellites' sensitivities reduces the negative shortwave cloud feedback by at least 50% in the 40°–70°S latitude band and by at least 65% in the 40°–70°N latitude band. Furthermore, based on this analysis of satellite observations, we conclude that the low-cloud optical depth feedback at middle and high latitudes is likely too negative in climate models.

58 GEOSCIENCES↗
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 OSTI2017

Thin ice clouds in the Arctic: cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

Multiband downwelling thermal measurements of zenith sky radiance, along with cloud boundary heights, were used in a retrieval algorithm to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. Ground-based thermal infrared (IR) radiances for 150 semitransparent ice clouds cases were acquired at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80° N, 86° W). We analyzed and quantified the sensitivity of downwelling thermal radiance to several cloud parameters including optical depth, effective particle diameter and shape, water vapor content, cloud geometric thickness and cloud base altitude. A lookup table retrieval method was used to successfully extract, through an optimal estimation method, cloud optical depth up to a maximum value of 2.6 and to separate thin ice clouds into two classes: (1) TIC1 clouds characterized by small crystals (effective particle diameter ≤ 30 µm), and (2) TIC2 clouds characterized by large ice crystals (effective particle diameter > 30 µm). The retrieval technique was validated using data from the Arctic High Spectral Resolution Lidar (AHSRL) and Millimeter Wave Cloud Radar (MMCR). Inversions were performed over three polar winters and results showed a significant correlation ( R 2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of TIC1 and TIC2 clouds. A partial validation relative to an algorithm based on high spectral resolution downwelling IR radiance measurements between 8 and 21µm was also performed. It confirms the robustness of the optical depth retrieval and the fact that the broadband thermal radiometer retrieval was sensitive to small particle (TIC1) sizes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Cloud optical depth and liquid effective radius retrievals from SPHOT zenith radiances

A new three-channel (440, 870, and 1640 nm) retrieval algorithm for cloud optical depth has recently been developed by Christine Chiu and her co-workers [Chiu et al., 2012]. This VAP is to implement the three-channel cloud optical depth retrieval algorithm as an ARM operational VAP. First, simultaneously retrieved cloud optical depth and effective radius datasets will be generated by using the pre-calibrated zenith radiance measurements taken from the ARM Sun-Photometer (SPHOT) and the NASA AERONET and Satellite-based surface albedo derived from MODSI. Finally, the algorithm produces uncertainties along with the retrieval products. This VAP outputs a daily NetCDF file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Differences in Ice Cloud Optical Depth From CALIPSO and Ground-Based Raman Lidar at the ARM SGP and TWP Sites

Ice cloud column optical depths from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite and ground-based Raman lidars (RLs) at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (TWP) sites are compared for 8 and 4 years, respectively. The cloudy mean ice cloud column optical depths from CALIPSO Version 4 data product with a horizontal resolution of 5 km are 78% (63%) larger than those from the RLs with a resolution of 10 min/30 m at SGP (TWP) for collocated transparent profiles. The main difference at SGP is caused by the lidar ratio that for CALIPSO is related to its treatment of the multiple scattering factor. The main difference at TWP is caused by the averaging resolution. Large differences in the optical depth distribution between the CALIPSO and RL are found for small optical depths at both sites, which are due to optically very thin clouds only detectable by the RLs. The differences in ice cloud column optical depth distributions and their mean values between the CALIPSO and RL can largely be reconciled over both sites after accounting for the averaging resolutions, lidar ratios along with the CALIPSO multiple scattering factor treatment, sensitivity to optically very thin ice clouds, and definition of transparent profiles. This work also examines in detail the lidar ratios that are directly observed by the RLs, which does not support the temperature-dependent parameterizations of ice cloud lidar ratio and multiple scattering factor used in CALIPSO's Version 4 data product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Biogenic Aerosols – Effects on Climate and Clouds. Cloud Optical Depth (COD) Sensor Three-Waveband Spectrally-Agile Technique (TWST) Field Campaign Report

This report describes the data collected by the Three-Waveband Spectrally-agile Technique (TWST) sensor deployed at Hyytiälä, Finland from 16 July to 31 August 2014 as a guest on the Biogenic Aerosols Effects on Climate and Clouds (BAECC) campaign. These data are currently available from the Atmospheric Radiation Measurement (ARM) Data Archive website and consists of Cloud Optical Depth (COD) measurements for the clouds directly overhead approximately every second (with some dropouts described below) during the daylight periods. A good range of cloud conditions were observed from clear sky to heavy rainfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Understanding and Constraining the Midlatitude Cloud Optical Depth Feedback in Climate Models (Final Report)

The objectives of this investigation are to provide a deeper understanding of the brightening of middle and high latitude clouds that occurs in many climate models as the climate warms, and to better understand why models give varying estimates of the strength of this negative feedback. The investigation may also provide new information on why models are deficient in their simulation of the present day clouds in these regions. The analysis will reveal which models simulate current clouds most realistically in the regions of interest, and relate this to the strength of the cloud feedbacks in the models. This will improve the uncertainty quantification of climate simulations and will provide theoretical understanding potentially leading to increased reliability of climate change projections. This will enhance the predictive understanding of the Earth system by analyzing the natural and anthropogenic components of global climate change in models. The primary methods involve the study of the clouds in the suite of CMIP5 model climate simulations and comparison of clouds in climate simulations with data from satellite measurement systems, with a particular emphasis on mixed-phase cloud physics, for which ice and water are present simultaneously. State-of-the-art climate model feedback analysis will be combined with new and more comprehensive observations of clouds available from modern measurement systems. Novel use of model simulations in combination with observations enables a deeper understanding of the role of clouds in climate change. Metrics that facilitate model-to-model and model-to-observation comparisons related to mixed-phase cloud physics will be developed and used to highlight challenges and opportunities for improving the state-of-the-science models. This investigation is a collaboration between the University of Washington, where the data work is concentrated and LLNL where the model data and the tools and expertise to do cloud feedback analysis are located. The grant supported the work of 3 different graduate students at different times (McCoy, Wall and Berry) Post-doctoral researcher Blaž Gasparini was also partially supported on this grant and led our collaboration with PNNL scientists. PNNL scientists also collaborated in the work of graduate student Wall (Po-Lun Ma). The main results of our research efforts are reported in 6 papers and 1 book chapter that were supported by this grant.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

To address significant gaps in ground-based radar coverage and rain gauge networks in the U.S., geostationary satellite quantitative precipitation estimates (QPEs) such as the Self-Calibrating Multivariate Precipitation Retrievals (SCaMPR) can be used to fill in both the spatial and temporal gaps of ground-based measurements. Additionally, with the launch of GOES-R, the temporal resolution of satellite QPEs may be comparable to that of Weather Service Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every five minutes. However, while satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations particularly during convective events. Deep Convective Systems (DCSs) have large cloud shields with similar brightness temperatures (BTs) over nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between BTs and precipitation rates often suffer from large errors because anvil regions (little/no precipitation) cannot be distinguished from rain-cores (heavy precipitation) using only BTs. However, a combination of BTs and optical depth (τ) has been found to reduce overestimates of precipitation in anvil regions. A new rain mask algorithm incorporating both τ and BTs has been developed, and its application to the existing SCaMPR algorithm was evaluated. Here, the performance of the modified SCaMPR was evaluated using traditional skill scores and a more detailed analysis of performance in individual DCS components by utilizing the Feng et al. (2012) classification algorithm. SCaMPR estimates with the new rain mask applied benefited from significantly reduced overestimates of precipitation in anvil regions and overall improvements in skill scores.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Optical Instruments Synergy in Determination of Optical Depth of Thin Clouds

Optically thin clouds have a strong radiative effect and need to be represented accurately in climate models. Cloud optical depth of thin clouds was retrieved using high resolution digital photography, lidar, and a radiative transfer model. The Doppler Lidar was operated at 1.5 μm, minimizing return from Rayleigh scattering, emphasizing return from aerosols and clouds. This approach examined cloud structure on scales 3 to 5 orders of magnitude finer than satellite products, opening new avenues for examination of cloud structure and evolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Application of an Online-Coupled Regional Climate Model, WRF-CAM5, over East Asia for Examination of Ice Nucleation Schemes: Part II. Sensitivity to Heterogeneous Ice Nucleation Parameterizations and Dust Emissions

Aerosol particles can affect cloud microphysical properties by serving as ice nuclei (IN). Large uncertainties exist in the ice nucleation parameterizations (INPs) used in current climate models. In this Part II paper, to examine the sensitivity of the model predictions to different heterogeneous INPs, WRF-CAM5 simulation using the INP of Niemand et al. (N12) [1] is conducted over East Asia for two full years, 2006 and 2011, and compared with simulation using the INP of Meyers et al. (M92) [2], which is the original INP used in CAM5. M92 calculates the nucleated ice particle concentration as a function of ice supersaturation, while N12 represents the nucleated ice particle concentration as a function of temperature and the number concentrations and surface areas of dust particles. Compared to M92, the WRF-CAM5 simulation with N12 produces significantly higher nucleated ice crystal number concentrations (ICNCs) in the northern domain where dust sources are located, leading to significantly higher cloud ice number and mass concentrations and ice water path, but the opposite is true in the southern domain where temperatures and moistures play a more important role in ice formation. Overall, the simulation with N12 gives lower downward shortwave radiation but higher downward longwave radiation, cloud liquid water path, cloud droplet number concentrations, and cloud optical depth. The increase in cloud optical depth and the decrease in downward solar flux result in a stronger shortwave and longwave cloud forcing, and decreases temperature at 2-m and precipitation. Changes in temperature and radiation lower surface concentrations of OH, O3, SO42−, and PM2.5, but increase surface concentrations of CO, NO2, and SO2 over most of the domain. By acting as cloud condensation nuclei (CCN) and IN, dust particles have different impacts on cloud water and ice number concentrations, radiation, and temperature at 2-m and precipitation depending on whether the dominant role of dust is CCN or IN. These results indicate the importance of the heterogeneous ice nucleation treatments and dust emissions in accurately simulating regional climate and air quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

SCOPE: Spectral cloud optical property estimation using real-time GOES-R longwave imagery

The output of ground-based, solar power generation systems is strongly dependent on cloud cover, which is the main contributor to solar power variability and uncertainty. Cloud optical properties are typically over-simplified in forecasting applications due to the lack of realtime, accurate estimates. In this work, we introduce a method, the Spectral Cloud Optical Property Estimation (SCOPE), for estimating cloud optical properties directly from high-resolution (5-min, 2 km) imagery from Geostationary Operational Environmental Satellite (GOES)-R, which is the newest generation of the GOES system. The SCOPE method couples a two-stream, spectrally resolved radiative model with the longwave GOES-R sensor output to simultaneously estimate the cloud optical depth, cloud top height, and cloud thickness during both day and night at 5-min intervals. The accuracy of SCOPE is evaluated using one year (2018) of downwelling longwave (DLW) radiation measurements from the Surface Radiation Budget Network, which consists of seven sites spread across climatically diverse regions of the contiguous United States. During daytime clear-sky periods, SCOPE predicts DLW within instrument uncertainty (10 W m -2 ) for four of the seven locations, with the remaining locations yielding errors of the order of 11.2, 17.7, and 20.2 W m -2 . For daytime cloudy-sky, daytime all-sky (clear or cloudy), and nighttime all-sky periods, SCOPE achieves root mean square error values of 23.0–34.5 W m -2 for all seven locations. These results, together with the low-latency of the method (1 s per sample), show that SCOPE provides a viable solution to real-time, accurate estimation of cloud optical properties for both day and night.

42 ENGINEERING↗
DOE OSTI2016

Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability

A large number of processes are involved in the chain from emissions of aerosol precursor gases and primary particles to impacts on cloud radiative forcing. Those processes are manifest in a number of relationships that can be expressed as factors dlnX/dlnY driving aerosol effects on cloud radiative forcing. These factors include the relationships between cloud condensation nuclei (CCN) concentration and emissions, droplet number and CCN concentration, cloud fraction and droplet number, cloud optical depth and droplet number, and cloud radiative forcing and cloud optical depth. The relationship between cloud optical depth and droplet number can be further decomposed into the sum of two terms involving the relationship of droplet effective radius and cloud liquid water path with droplet number. These relationships can be constrained using observations of recent spatial and temporal variability of these quantities. However, we are most interested in the radiative forcing since the preindustrial era. Because few relevant measurements are available from that era, relationships from recent variability have been assumed to be applicable to the preindustrial to present-day change. Our analysis of Aerosol Comparisons between Observations and Models (AeroCom) model simulations suggests that estimates of relationships from recent variability are poor constraints on relationships from anthropogenic change for some terms, with even the sign of some relationships differing in many regions. Proxies connecting recent spatial/temporal variability to anthropogenic change, or sustained measurements in regions where emissions have changed, are needed to constrain estimates of anthropogenic aerosol impacts on cloud radiative forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Cirrus feedback on interannual climate fluctuations

Cirrus clouds are not only important in determining the current climate, but also play an important role in climate change and variability. Analysis of satellite observations shows that the amount and altitude of cirrus clouds (optical depth <3.6, cloud top pressure <440 hPa) increase in response to inter-annual surface warming. Thus, cirrus clouds are likely to act as a positive feedback on short-term climate fluctuations, by reducing the planet’s ability to radiate longwave radiation to space in response to planetary surface warming. Using cirrus cloud radiative kernels, the magnitude of cirrus feedback is estimated to be 0.20±0.21W/m 2 /°C, which is comparable to the surface albedo feedback. Most of the cirrus feedback comes from increasing cloud amount in the tropical tropopause layer (TTL) and subtropical upper troposphere.

58 GEOSCIENCES↗