355-nm direct-detection Doppler wind lidar for vertical atmospheric motion measurement

A compact and simple 355-nm direct-detection Doppler wind lidar (DDDWL) was developed to measure the line-of-sight (LOS) wind speed of the background atmosphere from atmospheric molecule return signals with and without aerosols and clouds. A receiver design with a Fabry-Perot etalon interferometer (FPEI) without an inside deposited step coating or fiber coupling is considered for the DDDWL using the double-edge technique. The receiver with the double-edge technique uses a FPEI and wedge prism to form a double-edge filter. The development of the double-edge filter in this combination is, to the best of our knowledge, an improvement at 355-nm wavelength. Considerations for the DDDWL receiver with a FPEI revealed that a full-angle light beam divergence into the FPEI and a working FPEI aperture are significant factors for the receiver design. Preliminary experimental evaluation demonstrated that the DDDWL had the potential of LOS wind speed measurements with a random error of less than 1 m/s when the signal-to-noise ratio was approximately 300. The DDDWL-measured vertical LOS wind speed profile was consistent with that of a 2-µm coherent Doppler wind lidar within the measurement error range. The preliminary experimental LOS wind measurement results demonstrated the capability of the DDDWL to measure low LOS wind speeds.

An Investigation of the Ice Cloud Detection Sensitivity of Cloud Radars Using the Raman Lidar at the ARM SGP Site

The ice cloud detection sensitivity of the millimeter cloud radar (MMCR) and the Ka-band Zenith radar (KAZR) is investigated using a collocated Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains site. Only profiles that are transparent to the RL with ice clouds only are considered in this study. The MMCR underestimates the RL ice cloud optical depth (COD) by 20%. The MMCR detects no ice clouds in 37% of the profiles. These profiles where ice cloud goes undetected by the MMCR typically contain very optically thin clouds, with a mean RL ice COD of 0.03. Higher ice cloud detection sensitivity is found for the KAZR, which underestimates the RL ice COD by 15%. The decrease in the ice COD bias for the KAZR compared to the MMCR is largely due to a decrease in the ice COD bias for the situation where the transparent profiles with ice clouds are detected by both the RL and cloud radar. The climatic net ice cloud radiative effects (CREs) from the RL at the top of the atmosphere (TOA) and the surface are 3.2 W m−2 and −0.6 W m−2, respectively. The ice CREs at the TOA and surface are underestimated for the MMCR by 0.7 W m−2 and 0.16 W m−2 (21% and 29%) and underestimated for the KAZR by 0.6 W m−2 and 0.14 W m−2 (17% and 24%). The ice clouds undetected by the cloud radars led to underestimating the climatic net cloud heating rates below 150 hPa by about 0–0.04 K day−1.

Diverse cloud radiative effects and global surface temperature simulations induced by different ice cloud optical property parameterizations

The representation of ice cloud optical properties in climate models has long been a difficult problem. Very different ice cloud optical property parameterization schemes developed based on very different assumptions of ice particle shape habits, particle size distributions, and surface roughness conditions, are used in various models. It is not clear as to how simulated climate variables are affected by the ice cloud optical property parameterizations. A total of five ice cloud optical property parameterization schemes, including three based on the ice habit mixtures suitable for general ice clouds, mid-latitude synoptic ice clouds, and tropical deep convective ice clouds, and the other two based on single ice habits (smooth hexagonal column and severely roughened column aggregate), are developed under a same framework and are implemented in the National Center for Atmospheric Research Community Atmospheric Model version 5. A series of atmosphere-only climate simulations are carried out for each of the five cases with different ice parameterizations. The differences in the simulated top of the atmosphere shortwave and longwave cloud radiative effects (CREs) are evaluated, and the global averaged net CRE differences among different cases range from - 1.93 to 1.03 Wm-2. The corresponding changes in simulated surface temperature are found to be most prominent on continental regions which amount to several degrees in Kelvin. Our results indicate the importance of choosing a reasonable ice cloud optical property parameterization in climate simulations.

Dual-Frequency Radar Retrievals of Snowfall Using Random Forest

The microphysical parameters of snowfall directly impact hydrological and atmospheric models. During the International Collaborative Experiment hosted at the Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018), dual-frequency radar retrievals of particle size distribution (PSD) parameters were produced and assessed over complex terrain. The NASA Dual-frequency Dual-polarized Doppler Radar (D3R) and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometer observations were used to develop retrievals. The conventional look-up table method (LUT) and random forest method (RF) were applied to the disdrometer data to develop retrievals for the volume-weighted mean diameter (Dm), the shape factor (mu), the normalized intercept parameter (Nw), the ice water content (IWC), and the snowfall rate (S). Evaluations were performed between the D3R radar and disdrometer observations using these two methods. The mean errors of the retrievals based on the RF method were small compared with those of the LUT method. The results indicate that the RF method is a promising way of retrieving microphysical parameters, because this method does not require any assumptions about the PSD of snowfall.

Application of M5 Model Tree in Passive Remote Sensing of Thin Ice Cloud Microphysical Properties in Terahertz Region

This article presents a new method for retrieving the Ice Water Path (IWP), the median volume equivalent sphere diameter (Dme) of thin ice clouds (IWP < 100 g/m2, Dme < 80 μm) in the Terahertz band. The upwelling brightness temperature depressions caused by the ice clouds at 325.15, 448.0, 664.0 and 874.0 GHz channels are simulated by the Atmospheric Radiative Transfer Simulator (ARTS). The simulated forward radiative transfer models are taken as historical data for the M5 model tree algorithm to construct a set of piecewise functions which represent the relation of simulated brightness temperature depressions and IWP. The inversion results are optimized by an empirical relation of the IWP and the Dme for thin ice clouds which is summarized by previous studies. We inverse IWP and Dme with the simulated brightness temperature and analyze the inversion performance of selected channels. The 874.4 ± 6.0 GHz channel provides the most accurate results, because of the strong brightness temperature response to the change of IWP in the forward radiative transfer model. In order to improve the thin ice clouds IWP and Dme retrieval accuracy at the middle-high frequency channels in Terahertz band, a dual-channel inversion method was proposed that combines the 448.0± 3.0 GHz and 664.0 ± 4.2 GHz channel. The error analysis shows that the results of the 874.4 ± 6.0 GHz channel and the dual-channel inversion are reliable, and the IWP inversion results meet the error requirement range proposed by previous studies.

Radar-lidar ratio for ice crystals of cirrus clouds

Simultaneous measurement of lidar and radar signals returned from the same cirrus clouds is a prospective method for retrieving the cloud microphysics, i.e. size and shape of the ice crystals constituting the clouds. In this study, the ratio of the backscattered signals of lidar and radar called the radar-lidar ratio has been calculated for the first time for typical shapes of ice crystals and wide distribution of the crystals over their sizes. It is shown that it is the lidar-radar ratio that is most sensitive to crystal sizes while the lidar depolarization ratio is most sensitive to crystal shapes.

A Decadal Global Climatology of Ice Cloud Fraction with Their Microphysical and Optical Properties Inferred from the CALIPSO and Reanalysis Data

In the present study, the spatiotemporal and vertical distributions of ice cloud properties and their association with meteorological variables are analyzed for the period 2007–2016 using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Modern Era Retrospective-Analysis for Research (MERRA-2) reanalysis observations. The distribution of ice cloud fraction (ICF) with its peak does not overlap with that of the ice water content (IWC) peak during daytime and nighttime due to the sampling bias. Moreover, the vertical distributions of mean IWC exhibited a vaguely “sharp thorn” at an altitude of ~4 km in all seasons at the location of about ±40°, which can be caused by the artifacts. Furthermore, it is noted that different ice cloud optical depth (ICOD) presents significant changes observed in their diurnal variations in the heights of peaks. The maximum diurnal difference of ice cloud properties occurs in the tropical regions of the North Hemisphere (NH) during summer. We also investigated the relation between ICOD and the meteorological variables and found that the ICOD values are dependent on the meteorological parameters.

Characteristics of Snow Particle Size Distribution in the PyeongChang Region of South Korea

Snow particle size distribution (PSD) information is important in understanding the microphysics and quantitative precipitation estimation over complex terrain. Measurement and interpretation of the snow PSDs is a topic of active research. This study investigates snow PSDs during 3 year of observations from Parsivel2 disdrometers and precipitation imaging packages (PIP) at five different sites in the PyeongChang region of South Korea. Variabilities in the values of the density of snow (ρ), snowfall rate (S), and ice water content (IWC) are studied. To further understand the characteristics of snow PSD at different density and snowfall rate, the snow particle size distribution measurements are divided into six classes based on the density values of snowfall and five classes based on snowfall rates. The mean shape factors (Dm, log10Nw, and μ) of normalized gamma distribution are also derived based on different density and snowfall rate classes. The Dm decreases and log10Nw and μ increase as the density increases. The Dm and log10Nw increase and μ decreases with the increase of snowfall rate. The power-law relationship between ρ and Dm is obtained and the relationship between S and IWC is also derived.

Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2)

The cloud liquid water path (LWP), ice water path (IWP), and precipitation simulated with uniform- and variable-resolution numerical experiments using the Model for Prediction Across Scales (MPAS) are compared against Clouds and the Earth's Radiant Energy System (CERES) and Tropical Rainfall Measuring Mission data. Our comparison between monthly-mean model diagnostics and satellite data focuses on the convective activity regions of the tropical Pacific Ocean, extending from the Tropical Eastern Pacific Basin where trade wind boundary layer clouds develop to the Western Pacific Warm Pool characterized by deep convective updrafts capped with extended upper-tropospheric ice clouds. Using the scale-aware Grell-Freitas (GF) and Multiscale Kain-Fritsch (MSKF) convection schemes in conjunction with the Thompson cloud microphysics, uniform-resolution experiments produce large biases between simulated and satellite-retrieved LWP, IWP, and precipitation. Differences in the treatment of shallow convection lead the LWP to be strongly overestimated when using GF, while being in relatively good agreement when using MSKF compared to CERES data. Over areas of deep convection, uniform- and variable-resolution experiments overestimate the IWP with both MSKF and GF, leading to strong biases in the top-of-the-atmosphere longwave and shortwave radiation relative to satellite-retrieved data. Mesh refinement over the Western Pacific Warm Pool does not lead to significant improvement in the LWP, IWP, and precipitation due to increased grid-scale condensation and upward vertical motions. Results underscore the importance of evaluating clouds, their optical properties, and the top-of-the-atmosphere radiation budget in addition to precipitation when performing mesh refinement global simulations.

Evaluation of the Microphysical Assumptions within GPM-DPR Using Ground-Based Observations of Rain and Snow

The Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) provides an opportunity to investigate hydrometeor properties. Here, an evaluation of the microphysical framework used within the GPM-DPR retrieval was undertaken using ground-based disdrometer measurements in both rain and snow with an emphasis on the evaluation of snowfall retrieval. Disdrometer measurements of rain show support for the two separate prescribed relations within the GPM-DPR algorithm between the precipitation rate (R) and the mass weighted mean diameter ( D m ) with a mean absolute percent error ( M A P E ) on R of 29% and 47% and a mean bias percentage ( M B P ) of − 6% and − 20% for the stratiform and convective relation, respectively. Ground-based disdrometer measurements of snow show higher MAPE and MBP values in the retrieval of R, at 77% and − 52% , respectively, compared to the stratiform rain relation. An investigation using the disdrometer-measured fall velocity and mass in the calculation of R and D m illustrates that the variability found in hydrometeor mass causes a poor correlation between R and D m in snowfall. The results presented here suggest that R − D m retrieval is likely not optimal in snowfall, and other retrieval techniques for R should be explored.

Three Dimensional Radiative Effects in Passive Millimeter/Sub-Millimeter All-sky Observations

This study was conducted to quantify the errors prompted by neglecting three-dimensional (3D) effects, i.e., beam-filling and horizontal photon transport effects, at millimeter/sub-millimeter wavelengths. This paper gives an overview of the 3D effects that impact ice cloud retrievals of both current and proposed (Ice Cloud Imager) satellite instruments operating at frequencies of ≈186.3 and ≈668 GHz. The 3D synthetic scenes were generated from two-dimensional (2D) CloudSat (Cloud Satellite) observations over the tropics and mid-latitudes using a stochastic approach. By means of the Atmospheric Radiative Transfer Simulator (ARTS), three radiative transfer simulations were carried out: one 3D, one independent beam approximation (IBA), and one-dimensional (1D). The comparison between the 3D and IBA simulations revealed a small horizontal photon transport effect, with IBA simulations introducing mostly random errors and a slight overestimation (below 1 K). However, performing 1D radiative transfer simulations results in a significant beam-filling effect that increases primarily with frequency, and secondly, with footprint size. For a sensor footprint size of 15 km, the errors induced by neglecting domain heterogeneities yield root mean square errors of up to ≈4 K and ≈13 K at 186.3 GHz and 668 GHz, respectively. However, an instrument operating at the same frequencies, but with a much smaller footprint size, i.e., 6 km, is subject to smaller uncertainties, with a root mean square error of ≈2 K at 186.3 GHz and ≈7.1 K at 668 GHz. When designing future satellite instruments, this effect of footprint size on modeling uncertainties should be considered in the overall error budget. The smallest possible footprint size should be a priority for future sub-millimeter observations in light of these results.

Evaluation and bias correction of global climate models in the CMIP5 over the Indian Ocean region

Global climate model (GCM) simulations driven by various emission scenarios are widely used for the projections of future climate change. In this study, an assessment was carried out by using 35 GCMs under Coupled Model Intercomparison Project (CMIP5) in reproducing the present day wind speed changes over six selected regions in the Indian Ocean region based on altimetry-measured merged wind speed product in the Indian Ocean. The relative ranking of the GCMs is performed based on the evaluation of the CMIP5 historical simulations for the period 1993-2005. The skill level of GCMs in representing the various metrics such as annual mean, mean seasonal cycle, linear trend, correlation coefficient, and seasonal standard deviations was accounted for the relative ranking of the GCMs. The models CMCC-CESM, HadGEM2-ES, and GFDL-ESM2G are found to be better for the Arabian Sea region. The GCM products such as HadCM3, CSIRO-Mk3.6.0, HadGEM2-CC, HadGEM2-AO, and MIROC5 were noticed better for the Bay of Bengal (BoB) region. Large bias in wind speed (~ 3 m/s) is observed for the head BoB and the Southern Ocean region. Bias corrections for the present-day Representative Concentration Pathway (RCP) simulations (2006-2016) were performed based on quantile mapping (QM) method, and the present-day wind changes are also compared with observations. The findings from study recommend that suitable bias correction for different GCMs is an essential pre-requisite for climate change studies.

Interpretation of lidar ratio and depolarization ratio of ice clouds using spaceborne high-spectral-resolution polarization lidar

The backscattering coefficient (β), lidar ratio (S), and depolarization ratio (δ) of ice particles were estimated over a wide range of effective radii to interpret spaceborne 355-nm high-spectral-resolution lidar data from the ATLID sensor onboard the EarthCARE satellite. Five randomly oriented ice particle shapes (3D ice) and two quasi-horizontally oriented particle types (2D ice) were analyzed using five effective angles. The size dependence of β, S, and δ was examined using physical optics and geometrical optics integral equation methods. Differences in β for the same effective radius and ice water content among particle types exceeded one order of magnitude. S-δ relations are useful for inferring ice particle habit and orientation using ATLID data from EarthCARE.

A Review of Ice Cloud Optical Property Models for Passive Satellite Remote Sensing

The current wealth of spaceborne passive and active measurements from ultraviolet to the infrared wavelengths provides an unprecedented opportunity to construct ice cloud bulk optical property models that lead to consistent ice cloud property retrievals across multiple sensors and platforms. To infer the microphysical and radiative properties of ice clouds from these satellite measurements, the general approach is to assume an ice cloud optical property model that implicitly assumes the habit (shape) and size distributions of the ice particles in these clouds. The assumption is that this ice optical property model will be adequate for global retrievals. In this review paper, we first summarize the key optical properties of individual particles and then the bulk radiative properties of their ensemble, followed by a review of the ice cloud models developed for application to satellite remote sensing. We illustrate that the random orientation condition assumed for ice particles is arguably justified for passive remote sensing applications based on radiometric measurements. The focus of the present discussion is on the ice models used by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Clouds and Earth’s Radiant Energy System (CERES) science teams. In addition, we briefly review the ice cloud models adopted by the Polarization and Directionality of the Earth’s Reflectance (POLDER) and the Himawari-8 Advanced Himawari Imager (AHI) for ice cloud retrievals. We find that both the MODIS Collection 6 ice model and the CERES two-habit model result in spectrally consistent retrievals.

Anthropogenic Aerosol Indirect Effects in Cirrus Clouds

We have implemented a parameterization for forming ice in large-scale cirrus clouds that accounts for the changes in updrafts associated with a spectrum of waves acting within each time step in the model. This allows us to account for the frequency of homogeneous and heterogeneous freezing events that occur within each time step of the model and helps to determine more realistic ice number concentrations as well as changes to ice number concentrations. The model is able to fit observations of ice number at the lowest temperatures in the tropical tropopause but is still somewhat high in tropical latitudes with temperatures between 195°K and 215°K. The climate forcings associated with different representations of heterogeneous ice nuclei (IN or INPs) are primarily negative unless large additions of IN are made, such as when we assumed that all aircraft soot acts as an IN. However, they can be close to zero if it is assumed that all background dust can act as an INP irrespective of how much sulfate is deposited on these particles. Our best estimate for the forcing of anthropogenic aircraft soot in this model is -0.2 ± 0.06 W/m2, while that from anthropogenic fossil/biofuel soot is -0.093 ± 0.033 W/m2. Natural and anthropogenic open biomass burning leads to a net forcing of -0.057 ± 0.05 W/m2.

Calibration Changes to Terra MODIS Collection-5 Radiances for CERES Edition 4 Cloud Retrievals

Previous research has revealed inconsistencies between the Collection 5 (C5) calibrations of certain channels common to the Terra and Aqua MODerate-resolution Imaging Spectroradiometers (MODIS). To achieve consistency between the Terra and Aqua MODIS radiances used in the Clouds and the Earth's Radiant Energy System (CERES) Edition 4 (Ed4) cloud property retrieval system, adjustments were developed and applied to the Terra C5 calibrations for channels 1-5, 7, 20, and 26. These calibration corrections were developed independently of those used for MODIS Collection 6 (C6) data, which became available after the CERES Ed4 processing had commenced. The comparisons demonstrate that the corrections applied to the Terra C5 data for CERES Edition 4 generally resulted in Terra-Aqua radiance consistency that is as good as or better than that of the C6 datasets. The C5 adjustments resulted in more consistent Aqua and Terra cloud property retrievals than seen in the previous CERES edition. Other calibration artifacts were found in one of the corrected channels and in some of the uncorrected thermal channels after Ed4 began. Where corrections were neither developed nor applied, some artifacts are likely to have been introduced into the Ed4 cloud property record. For example, the degradation in the Aqua MODIS 0.65-μm channel in both the C5 and C6 datasets affects trends in cloud optical depth retrievals. Thus, despite the much-improved consistency achieved for the Terra and Aqua datasets in Ed4, the CERES Ed4 cloud property datasets should be used cautiously for cloud trend studies because of those remaining calibration artifacts.

Microphysical variability of Amazonian deep convective cores observed by CloudSat and simulated by a multi-scale modeling framework

Recently launched cloud observing satellites provide information about the vertical structure of deep convection and its microphysical characteristics. In this study, CloudSat reflectivity data is stratified by cloud type, and the contoured frequency by altitude diagrams reveal a double-arc structure in deep convective cores (DCCs) above 8 km. This suggests two distinct hydrometeor modes (snow versus hail/graupel) controlling variability in reflectivity profiles. The day-night contrast in the double arcs is about four times larger than the wet-dry season contrast. Using QuickBeam, the vertical reflectivity structure of DCCs is analyzed in two versions of the Superparameterized Community Atmospheric Model (SP-CAM) with single-moment (no graupel) and double-moment (with graupel) microphysics. Double-moment microphysics shows better agreement with observed reflectivity profiles; however, neither model variant captures the double-arc structure. Ultimately, the results show that simulating realistic DCC vertical structure and its variability requires accurate representation of ice microphysics, in particular the hail/graupel modes, though this alone is insufficient.

Physical model for multiple scattered space-borne lidar returns from clouds

A practical model for determining the time-dependent lidar attenuated backscattering coefficient β was developed for application to global lidar data. An analytical expression for the high-order phase function was introduced to reduce computational cost for simulating the angular distribution of the multiple scattering irradiance. The decay rate of the multiple scattering backscattered irradiance was expressed by incorporating the dependence on the scattering angle and the scattering order based on the path integral approach. The estimated β over time and the actual range showed good agreement with Monte Carlo simulations for vertically homogeneous and inhomogeneous cloud profiles, resulting in about 15% mean relative error corresponding to 4 times improved accuracy against the Ornstein-Fürth Gaussian approximation method.

Diurnal Variation of Tropical Ice Cloud Microphysics: Evidence from Global Precipitation Measurement Microwave Imager (GPM-GMI) Polarimetric Measurements

The diurnal variation of tropical ice clouds has been well observed and examined in terms of the occurring frequency and total mass but rarely from the viewpoint of ice microphysical parameters. It accounts for a large portion of uncertainties in evaluating ice clouds' role on global radiation and hydrological budgets. Owing to the advantage of precession orbit design and paired polarized observations at a high-frequency microwave band that is particularly sensitive to ice particle microphysical properties, three years of polarimetric difference (PD) measurements using the 166 GHz channel of Global Precipitation Measurement Microwave Imager (GPM-GMI) are compiled to reveal a strong diurnal cycle over tropical land (30°S-30°N) with peak amplitude varying up to 38%. Since the PD signal is dominantly determined by ice crystal size, shape, and orientation, the diurnal cycle observed by GMI can be used to infer changes in ice crystal properties. Moreover, PD change is found to lead the diurnal changes of ice cloud occurring frequency and total ice mass by about 2 hours, which strongly implies that understanding ice microphysics is critical to predict, infer, and model ice cloud evolution and precipitation processes.

Impact of aerosols on ice crystal size

The interactions between aerosols and ice clouds represent one of the largest uncertainties in global radiative forcing from pre-industrial time to the present. In particular, the impact of aerosols on ice crystal effective radius (R _ei), which is a key parameter determining ice clouds' net radiative effect, is highly uncertain due to limited and conflicting observational evidence. Here we investigate the effects of aerosols on R _ei under different meteorological conditions using 9-year satellite observations. We find that the responses of R _ei to aerosol loadings are modulated by water vapor amount in conjunction with several other meteorological parameters. While there is a significant negative correlation between R _ei and aerosol loading in moist conditions, consistent with the "Twomey effect" for liquid clouds, a strong positive correlation between the two occurs in dry conditions. Simulations based on a cloud parcel model suggest that water vapor modulates the relative importance of different ice nucleation modes, leading to the opposite aerosol impacts between moist and dry conditions. When ice clouds are decomposed into those generated from deep convection and formed in situ, the water vapor modulation remains in effect for both ice cloud types, although the sensitivities of R _ei to aerosols differ noticeably between them due to distinct formation mechanisms. The water vapor modulation can largely explain the difference in the responses of R _ei to aerosol loadings in various seasons. A proper representation of the water vapor modulation is essential for an accurate estimate of aerosol-cloud radiative forcing produced by ice clouds.

A simulation of ice cloud particle size, humidity, and temperature measurements from the TWICE CubeSat

This paper describes a forward radiative transfer model and retrieval system (FMRS) for the Tropospheric Water and cloud ICE (TWICE) CubeSat instrument. We use the FMRS to simulate radiances for the TWICE's 14 millimeter- and submillimeter-wavelength channels for a tropical atmospheric state produced by a Weather Research and Forecasting model simulation. We also perform simultaneous retrievals of cloud ice particle size, ice water content (IWC), water vapor content (H2O), and temperature from the simulated TWICE radiances using the FMRS. We show that the TWICE instrument is capable of retrieving ice particle size in the range of ~50-1000 μm in mass mean effective diameter with approximately 50% uncertainty. The uncertainties of other retrievals from TWICE are about 1 K for temperature, 50% for IWC, and 20% for H2O.

An Improved Convective Ice Parameterization for the NASA GISS Global Climate Model and Impacts on Cloud Ice Simulation

Partitioning of convective ice into precipitating and detrained condensate presents a challenge for GCMs since partitioning depends on the strength and microphysics of the convective updraft. It is an important issue because detrainment of ice from updrafts influences the development of stratiform anvils, impacts radiation, and can affect GCM climate sensitivity. Recent studies have shown that the CMIP5 configurations of the Goddard Institute for Space Studies (GISS) GCM simulated upper-tropospheric ice water content (IWC) that exceeded an estimated upper bound by a factor of 2. Partly in response to this bias, a new GCM parameterization of convective cloud ice has been developed that incorporates new ice particle fall speeds and convective outflow particle size distributions (PSDs) from the NASA African Monsoon Multidisciplinary Analyses (NAMMA), NASA Tropical Composition, Cloud and Climate Coupling (TC4), DOE ARM-NASA Midlatitude Continental Convective Clouds Experiment (MC3E), and DOE ARM Small Particles in Cirrus (SPARTICUS) field campaigns. The new parameterization assumes a normalized gamma PSD with two novel developments: no explicit assumption for particle habit in the calculation of mass distributions, and a formulation for translating ice particle fall speeds as a function of maximum diameter into fall speeds as a function of melted-equivalent diameter. Two parameters (particle volume- and projected area-weighted equivalent diameter) are diagnosed as a function of temperature and IWC in the convective plume, and these parameters constrain the shape and scale of the normalized gamma PSD. The diagnosed fall speeds and PSDs are combined with the GCM's parameterized convective updraft vertical velocity to partition convective updraft condensate into precipitating and detrained components. A 5-yr prescribed sea surface temperature GCM simulation shows a 30%-50% decrease in upper-tropospheric deep convective IWC, bringing the tropical and global mean ice water path into closer agreement with CloudSat best estimates.

Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations. Part I: Forward model, error analysis, and information content

An optimal estimation (OE) retrieval method is developed to infer three ice cloud properties simultaneously: optical thickness (τ), effective radius (r_eff ), and cloud-top height (h). This method is based on a fast radiative transfer (RT) model and infrared (IR) observations from the MODerate resolution Imaging Spectroradiometer (MODIS). This study conducts thorough error and information content analyses to understand the error propagation and performance of retrievals from various MODIS band combinations under different cloud/atmosphere states. Specifically, the algorithm takes into account four error sources: measurement uncertainty, fast RT model uncertainty, uncertainties in ancillary datasets (e.g., atmospheric state), and assumed ice crystal habit uncertainties. It is found that the ancillary and ice crystal habit error sources dominate the MODIS IR retrieval uncertainty and cannot be ignored. The information content analysis shows that, for a given ice cloud, the use of four MODIS IR observations is sufficient to retrieve the three cloud properties. However, the selection of MODIS IR bands that provide the most information and their order of importance varies with both the ice cloud properties and the ambient atmospheric and the surface states. As a result, this study suggests the inclusion of all MODIS IR bands in practice since little a priori information is available.