Quantifying the Low Bias of CALIPSO's Column Aerosol Optical Depth Due to Undetected Aerosol Layers

The CALIOP data processing scheme only retrieves extinction profiles in those portions of the return signal where cloud or aerosol layers have been identified by the CALIOP layer detection scheme. In this study we use two years of CALIOP and MODIS data to quantify the aerosol optical depth of undetected weakly backscattering layers. Aerosol extinction and column-averaged lidar ratio is retrieved from CALIOP Level 1B (Version 4) profile using MODIS AOD as a constraint over oceans from March 2013 to February 2015. To quantify the undetected layer AOD (ULA), an unconstrained retrieval is applied globally using a lidar ratio of 28.75 sr estimated from constrained retrievals during the daytime over the ocean. We find a global mean ULA of 0.031 0.052. There is no significant difference in ULA between land and ocean. However, the fraction of undetected aerosol layers rises considerably during daytime, when the large amount of solar background noise lowers the signal to noise ratio (SNR). For this reason, there is a difference in ULA between day (0.036 0.066) and night (0.025 0.021). ULA is larger in the northern hemisphere and relatively larger at high latitudes. Large ULA for the Polar Regions is strongly related to the cases where the CALIOP Level 2 Product reports zero AOD. This study provides an estimate of the complement of AOD that is not detected by lidar, and bounds the CALIOP AOD uncertainty to provide corrections for science studies that employ the CALIOP Level 2 AOD

Lidar Ratios for Dust Aerosols Derived From Retrievals of CALIPSO Visible Extinction Profiles Constrained by Optical Depths from MODIS-Aqua and CALIPSO/CloudSat Ocean Surface Reflectance Measurements

CALIPSO's (Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations) analysis algorithms generally require the use of tabulated values of the lidar ratio in order to retrieve aerosol extinction and optical depth from measured profiles of attenuated backscatter. However, for any given time or location, the lidar ratio for a given aerosol type can differ from the tabulated value. To gain some insight as to the extent of the variability, we here calculate the lidar ratio for dust aerosols using aerosol optical depth constraints from two sources. Daytime measurements are constrained using Level 2, Collection 5, 550-nm aerosol optical depth measurements made over the ocean by the MODIS (Moderate Resolution Imaging Spectroradiometer) on board the Aqua satellite, which flies in formation with CALIPSO. We also retrieve lidar ratios from night-time profiles constrained by aerosol column optical depths obtained by analysis of CALIPSO and CloudSat backscatter signals from the ocean surface

Thermal infrared dust optical depth and coarse-mode effective diameter retrieved from collocated MODIS and CALIOP observations

In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 &micro;m (DAOD10&mu;m) and the coarse-mode dust effective diameter (Deff) over global oceans. The accuracy of the Deff retrieval is assessed by comparing retrieved Deff with the in-situ measured dust particle size distributions (PSDs) from the AER-D, SAMUM-2 and SALTRACE field campaigns through case studies. The new DAOD10&mu;m retrievals were evaluated first through comparisons with the collocated DAOD10.6&mu;m retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al. 2022). The pixel-to-pixel comparison of the two retrievals indicates a good agreement (R~0.7) and a significant reduction of (~50 %) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (5&deg;&times;2&deg;) mean DAOD10um retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R = 0.9), Indian Ocean (IO; R = 0.8) and North Pacific (NP; R = 0.7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse mode dust Deff over global oceans. We found that dust Deff over IO and NP are up to 20 % smaller than that over NA. Over NA in summer, we found a ~50 % reduction of the number of retrievals with Deff &gt; 5 &mu;m from 15&deg; W to 35&deg; W and a stable trend of Deff average at 4.4 &mu;m from 35&deg; W throughout the Caribbean Sea (90&deg; W). Over NP in spring, only ~5 % of retrieved pixels with Deff &gt; 5 &mu;m are found from 150&deg; E to 180&deg;, while the mean Deff remains stable at 4.0 &mu;m throughout eastern NP. To our best knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust long-wave radiative effects and coarse mode dust particle size in models.</p

Optimal estimation for global ground-level fine particulate matter concentrations

We develop an optimal estimation (OE) algorithm based on top-of-atmosphere reflectances observed by the MODIS satellite instrument to retrieve near-surface fine particulatematter (PM2.5). The GEOS-Chem chemical transport model is used to provide prior information for the Aerosol Optical Depth (AOD) retrieval and to relate total column AOD to PM2.5. We adjust the shape of the GEOS-Chem relative vertical extinction profiles by comparison with lidar retrievals from the CALIOP satellite instrument. Surface reflectance relationships used in the OE algorithm are indexed by land type. Error quantities needed for this OE algorithm are inferred by comparison with AOD observations taken by a worldwide network of sun photometers (AERONET) and extended globally based upon aerosol speciation and cross correlation for simulated values, and upon land type for observational values. Significant agreement in PM2.5 is found over North America for 2005 (slope = 0.89; r = 0.82; 1-σ error = 1 μg/m3 + 27%), with improved coverage and correlation relative to previous work for the same region and time period, although certain subregions, such as the San Joaquin Valley of California are better represented by previous estimates. Independently derived error estimates of the OE PM2.5 values at in situ locations over North America (of ±(2.5 μg/m3 + 31%) and Europe of ±(3.5 μg/m3 + 30%) are corroborated by comparison with in situ observations, although globally (error estimates of (3.0 μg/m3 + 35%), may be underestimated. Global population-weighted PM2.5 at 50% relative humidity is estimated as 27.8 μg/m3 at 0.1° × 0.1° resolution

Novel aerosol extinction coefficients and lidar ratios over the ocean from CALIPSO–CloudSat: evaluation and global statistics

Aerosol extinction coefficients (σa) and lidar ratios (LRs) are retrieved over the ocean from CALIPSO's Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) attenuated backscatter profiles by solving the lidar equation constrained with aerosol optical depths (AODs) derived by applying the Synergized Optical Depth of Aerosols (SODA) algorithm to ocean surface returns measured by CALIOP and CloudSat's Cloud Profiling Radar. σa and LR are retrieved for two independent scenarios that require somewhat different assumptions: (a) a single homogeneous atmospheric layer (1L) for which the LR is constant with height and (b) a vertically homogeneous layer with a constant LR overlying a marine boundary layer with a homogenous LR fixed at 25&thinsp;sr (two-layer method, 2L). These new retrievals differ from the standard CALIPSO version 4.1 (V4) product, as the CALIOP–SODA method does not rely on an aerosol classification scheme to select LR. CALIOP–SODA σa and LR are evaluated using airborne high-spectral-resolution lidar (HSRL) observations over the northwest Atlantic. CALIOP–SODA LR (1L and 2L) positively correlates with its HSRL counterpart (linear correlation coefficient r&gt;0.67), with a negative bias smaller than 17.4&thinsp;% and a good agreement for σa (r≥0.78) with a small negative bias (≤|-9.2%|). Furthermore, a global comparison of optical depths derived by CALIOP–SODA and CALIPSO V4 reveals substantial discrepancies over regions dominated by dust and smoke (0.24), whereas Aqua's Moderate resolution Imaging Spectroradiometer (MODIS) and SODA AOD regional differences are within 0.06. Global maps of CALIOP–SODA LR feature high values over littoral zones, consistent with expectations of continental aerosol transport offshore. In addition, seasonal transitions associated with biomass burning from June to October over the southeast Atlantic are well reproduced by CALIOP–SODA LR.</p

Estimating the Direct Radiative Effect of Absorbing Aerosols Overlying Marine Boundary Layer Clouds in the Southeast Atlantic Using MODIS and CALIOP

Absorbing aerosols such as smoke strongly absorb solar radiation, particularly at ultraviolet and visible/near-infrared (VIS/NIR) wavelengths, and their presence above clouds can have considerable implications. It has been previously shown that they have a positive (i.e., warming) direct aerosol radiative effect (DARE) when overlying bright clouds. Additionally, they can cause biased passive instrument satellite retrievals in techniques that rely on VIS/NIR wavelengths for inferring the cloud optical thickness (COT) and effective radius (re) of underlying clouds, which can in turn yield biased above-cloud DARE estimates. Here we investigate Moderate Resolution Imaging Spectroradiometer (MODIS) cloud optical property retrieval biases due to overlying absorbing aerosols observed by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and examine the impact of these biases on above-cloud DARE estimates. The investigation focuses on a region in the southeast Atlantic Ocean during August and September (2006-2011), where smoke from biomass burning in southern Africa overlies persistent marine boundary layer stratocumulus clouds. Adjusting for above-cloud aerosol attenuation yields increases in the regional mean liquid COT (averaged over all ocean-only liquid clouds) by roughly 6%; mean re increases by roughly 2.6%, almost exclusively due to the COT adjustment in the non-orthogonal retrieval space. It is found that these two biases lead to an underestimate of DARE. For liquid cloud Aqua MODIS pixels with CALIOP-observed above-cloud smoke, the regional mean above-cloud radiative forcing efficiency (DARE per unit aerosol optical depth (AOD)) at time of observation (near local noon for Aqua overpass) increases from 50.9Wm(sup-2)AOD(sup-1) to 65.1Wm(sup-2)AOD(sup -1) when using bias-adjusted instead of nonadjusted MODIS cloud retrievals

Cloud condensation nuclei concentrations from spaceborne lidar measurements – Methodology and validation

Aerosol-cloud interactions are the most uncertain component of the anthropogenic radiative forcing. A substantial part of this uncertainty comes from the limitations of currently used spaceborne CCN proxies that (i) are column integrated and do not guarantee vertical co-location of aerosols and clouds, (ii) have retrieval issues over land, and (iii) do not account for aerosol hygroscopicity. A possible solution to overcome these limitations is to use height-resolved measurements of the spaceborne lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. This thesis presents a novel CCN retrieval algorithm based on Optical Modelling of CALIPSO Aerosol Microphysics (OMCAM) that is designed particularly for CALIPSO lidar measurements, along with its validation with airborne and surface in-situ measurements. \noindent OMCAM uses a set of normalized size distributions from the CALIPSO aerosol model and modifies them to reproduce the CALIPSO measured aerosol extinction coefficient. It then uses the modified size distribution and aerosol type-specific CCN parameterizations to estimate the number concentration of CCN (nCCN) at different supersaturations. The algorithm accounts for aerosol hygroscopicity by using the kappa parametrization. Sensitivity studies suggest that the uncertainty associated with the output nCCN may range between a factor of 2 and 3. OMCAM-estimated aerosol number concentrations (ANCs) and nCCN are validated using temporally and spatially co-located in-situ measurements. In the first part of validation, the airborne observations collected during the Atmospheric Tomography (ATom) mission are used. It is found that the OMCAM estimates of ANCs are in good agreement with the in-situ measurements with a correlation coefficient of 0.82, an RMSE of 247.2 cm-3, and a bias of 44.4 cm-3. The agreement holds for all aerosol types, except for marine aerosols, in which the OMCAM estimates are about an order of magnitude smaller than the in-situ measurements. An update of the marine model in OMCAM improve the agreement significantly. In the second part of validation, the OMCAM-estimated ANC and nCCN are compared to measurements from seven surface in-situ stations covering a variety of aerosol environments. The OMCAM-estimated monthly nCCN are found to be in reasonable agreement with the in-situ measurements with a 39 % normalized mean bias and 71 % normalized mean error. Combining the validation studies, the algorithm outputs are found to be consistent with the co-located in-situ measurements at different altitude ranges over both land and ocean. Such an agreement has not yet been achieved for spaceborne-derived CCN concentrations and demonstrates the potential of using CALIPSO lidar measurements for inferring global 3D climatologies of CCN concentrations related to different aerosol types.:1 Introduction . . . . . . . . . . . . . . . 1 1.1 Background: Aerosols in the climate system . . . . . . . . . . . . . . . . . 1 1.1.1 Aerosol-induced effective radiative forcing . . . . . . . . . . . . . . 3 1.1.2 Significance of aerosol-cloud interactions . . . . . . . . . . . . . . . 3 1.2 Observation-based ACI studies . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 In-situ studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.2 Spaceborne studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Spaceborne CCN proxies and their limitations . . . . . . . . . . . . . . . . 8 1.4 CCN concentrations from lidars . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5 Objective: CCN from spaceborne lidar . . . . . . . . . . . . . . . . . . . . 11 2 Paper 1: Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements . . . . . . . . . . . . . . . 15 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2 Data and retrievals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1 CALIPSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.2 MOPSMAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.3 POLIPHON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3.1 Aerosol size distribution . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3.2 Aerosol hygroscopicity . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.3 CCN parameterizations . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.4 Application of OMCAM to CALIPSO retrieval . . . . . . . . . . . 23 2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.4.1 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.4.2 Comparison with POLIPHON . . . . . . . . . . . . . . . . . . . . . 30 2.4.3 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5 Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3 Paper 2: Evaluation of aerosol number concentrations from CALIPSO with ATom airborne in situ measurements . . . . . . . . . . . . . . . 39 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2 Data, retrievals, and methods . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.2.1 ATom 3.2.2 CALIOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.2.3 Aerosol number concentration from CALIOP . . . . . . . . . . . . 44 3.2.4 Data matching and comparison . . . . . . . . . . . . . . . . . . . . 48 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.1 Example cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.2 General findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4 Paper 3: Assessment of CALIOP-derived CCN concentrations by in situ surface measurements . . . . . . . . . . . . . . . 65 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4.2 Data and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2.1 In situ observations . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2.2 CALIOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2.3 Comparison Methodology . . . . . . . . . . . . . . . . . . . . . . . 71 4.3 Comparison of CCN Concentrations . . . . . . . . . . . . . . . . . . . . . . 73 4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5 Summary and conclusions . . . . . . . . . . . . . . . 79 6 Outlook . . . . . . . . . . . . . . . 83 References . . . . . . . . . . . . . . . 88 List of Abbreviations . . . . . . . . . . . . . . . 107 List of Variables . . . . . . . . . . . . . . . 109 List of Figures . . . . . . . . . . . . . . . 111 List of Tables . . . . . . . . . . . . . . . 113 A List of Publications . . . . . . . . . . . . . . . 115 B Acknowledgements . . . . . . . . . . . . . . . 11

A Novel Method for Estimating Shortwave Direct Radiative Effect of Above-Cloud Aerosols Using CALIOP and MODIS Data

This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP and MODIS data. It accounts for the overlapping of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure. Effects of sub-grid scale cloud and aerosol variations on DRE are accounted for. It is computationally efficient through using grid-level cloud and aerosol statistics, instead of pixel-level products, and a pre-computed look-up table in radiative transfer calculations. We verified that for smoke over the southeast Atlantic Ocean the method yields a seasonal mean instantaneous shortwave DRE that generally agrees with more rigorous pixel-level computation within 4. We have also computed the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global ocean based on 4 yr of CALIOP and MODIS data. We found that the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol is mainly driven by the optical depth of the underlying clouds