Abstract. Transatlantic dust from North Africa is the largest intercontinental aerosol flux on Earth, affecting radiative forcing, cloud processes, and biogeochemical cycling, yet the inter-model spread of dust transport remains large in previous AeroCom experiments. With the progress in 17 AeroCom Phase III (P3) models, this study evaluates the transatlantic dust cycle for 2010 against AERONET inversions, satellite products (MODIS, CALIOP, MISR, IASI, POLDER, and MODIS-TIR), DustCOMM, CAMS, MERRA-2, in-situ records, and GPCP precipitation across subregions from North Africa to the Caribbean. We found that AeroCom P3 narrows mid-visible dust optical depth (DOD) diversity to 30–45 % across the transport domain relative to AeroCom P2 (38–59 %), yet this convergence masks compensatory biases, as models with lower (or higher) mass loading may have higher (or lower) mass extinction efficiency (MEE), yielding similar DOD despite a large spread of mass representations. Compared with observational estimates, model biases intensify with transport distance, with median DOD underestimated by ~50 % and ~70 % over mid- and long-range regions, and loss frequencies exceed satellite estimates by a factor of 2–3. Such biases remain unresolved even with super-coarse extensions, indicating that constraining mid-visible DOD alone is inadequate for the dust cycle. The excess removal implicates deficiencies in gravitational settling, wet scavenging, and vertical transport parameterizations. This systematic underestimation could propagate into dust radiative forcing, cloud-dust interactions, and nutrient delivery to Atlantic and Amazonian ecosystems. Progress requires size-resolved benchmarking (DOD at 550 nm and 10 µm with size-segregated mass), non-spherical settling treatments, and mechanisms sustaining coarse particles aloft.

Active sensors such as Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Cloud Profiling Radar (CPR) aboard CloudSat are known to have great advantages in examining cloud vertical structures. However, these sensors were designed for a relatively short lifespan (;3 yr), and trends using a combination of CALIPSO and CloudSat (CALCS) have yet to be explored. When clouds detected by CALCS are filtered for known sensitivity differences, the trends from the merged CALCS agree well with Moderate Resolution Imaging Spectroradiometer (MODIS) cloud trends from 2008 to 2017. Both CALCS and MODIS capture common features, although there are differences in high-level cloud altitudes. These trends are decomposed into El Ni & ntilde;o-Southern Oscillation (ENSO) and non-ENSO components using a regression model with the multivariate ENSO index (MEI). The non-ENSO component is related to the cloud amount increase over the Arctic and the upward shift of high clouds (i.e., rising high clouds) over 60 degrees S-60 degrees N. The rising high clouds are further verified using MODIS measurements for the extended period from 2005 to 2022. Even though altitudes of high clouds rose, their cloud temperatures remained similar, supporting the fixed anvil temperature (FAT) hypothesis.

After 17 years of highly successful operations, the CALIPSO satellite mission, a partnership between NASA and CNES, ended in August 2023, leaving behind a long and very stable measurement record that has helped to transform our understanding of climate, weather, and air quality. The CALIPSO instrument suite included a two-wavelength (532 and 1064 nm), depolarization-sensitive profiling lidar for clouds and aerosols, an imaging infrared radiometer optimized for better understanding cirrus optical and microphysical properties, and a wide field of view camera to provide meteorological context. Thus far, over 4300 peer-reviewed publications have been produced from the CALIPSO data set. CALIPSO was truly a pathfinder mission, not only with its unprecedented ability to probe the vertical structure of the Earth’s atmosphere, but also in the development of sophisticated new algorithms for retrieving the spatial and optical properties of aerosols and clouds. Throughout the mission development and operational phases, the CALIPSO team relied heavily on aerosol and cloud measurements from ground-based and airborne sensors to aid in the design and revision of fully automated retrieval algorithms, the assessment of data products, as well as with the evaluation of instrument performance and calibration. This presentation highlights the impressive breadth of validation support the CALIPSO team received from the European field measurements community across all data processing levels. CALIPSO Level 1 (L1) data report geolocated and fully calibrated lidar backscatter profiles. Because L1 is the wellspring for all subsequent data products, rigorous calibration validation is absolutely essential. While NASA’s high spectral resolution lidar validated the CALIPSO calibration over North America (Rogers et al., 2011), earlier studies conducted by European research institutes (e.g., Mamouri et al., 2009; Mona et al., 2009; Pappalardo et al., 2010) were critically important in establishing the global reliability of the CALIPSO L1 data. Similarly, European investi­gators were at the forefront in assessing the geophysical variables reported in the CALIPSO Level 2 (L2) products. Extensive Raman lidar measurements from SAMUN-2 and SALTRACE (Wandinger et al., 2010; Tesche et al., 2013; Haarig et al., 2017) sparked years of vigorous, highly rewarding discussion about regional variations in CALIPSO’s dust lidar ratio assignments. For clouds, combined in-situ and airborne lidar measurements of cirrus provided key insights into the effects of ice crystal morphology on CALIPSO retrievals of cirrus extinction coefficients (Mioche et al., 2010). The literature is replete with additional, highly compelling L2 assessments of aerosols, clouds, and the algorithms used to derive their properties. Finally, Level 3 (L3) delivers a climatological rather than instantaneous data representation by aggregating L2 variables onto uniform space-time grids. Leveraging years of EARLINET observations, Papagiannopoulos et al. (2016) conducted a comprehensive assessment of the monthly mean vertical distributions of aerosol occurrence frequency and optical properties reported in CALIPSO’s L3 tropospheric aerosol product.

Clouds play important roles in weather, climate, and the global water cycle. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft has measured global vertical profiles of clouds and aerosols in the Earth’s atmosphere since June 2006. CALIOP provides vertically resolved information on cloud occurrence, thermodynamic phase, and properties. We describe version 1.0 of a monthly gridded ice cloud product derived from over 12 years of global, near-continuous CALIOP measurements. The primary contents are monthly vertically resolved histograms of ice cloud extinction coefficient and ice water content (IWC) retrievals. The CALIOP Level 3 Ice Cloud product is built from the CALIOP Version 4.20 Level 2 5 km Cloud Profile product that, relative to previous versions, features substantial improvements due to more accurate lidar backscatter calibration, better extinction coefficient retrievals, and a temperature-sensitive parameterization of IWC. The gridded ice cloud data are reported as histograms, which provides data users with the flexibility to compare CALIOP’s retrieved ice cloud properties with those from other instruments with different measurement sensitivities or retrieval capabilities. It is also convenient to aggregate monthly histograms for seasonal, annual, or decadal trend and climate analyses. This CALIOP gridded ice cloud product provides a unique characterization of the global and regional vertical distributions of optically thin ice clouds and deep convection cloud tops, and it should provide significant value for cloud research and model evaluation. A DOI has been issued for the product: https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00 (Winker et al., 2018).

Since the first Global Energy and Water Exchanges cloud assessment a decade ago, existing cloud property retrievals have been revised and new retrievals have been developed. The new global long-term cloud datasets show, in general, similar results to those of the previous assessment. A notable exception is the reduced cloud amount provided by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Science Team, resulting from an improved aerosol–cloud distinction. Height, opacity and thermodynamic phase determine the radiative effect of clouds. Their distributions as well as relative occurrences of cloud types distinguished by height and optical depth are discussed. The similar results of the two assessments indicate that further improvement, in particular on vertical cloud layering, can only be achieved by combining complementary information. We suggest such combination methods to estimate the amount of all clouds within the atmospheric column, including those hidden by clouds aloft. The results compare well with those from CloudSat-CALIPSO radar–lidar geometrical profiles as well as with results from the International Satellite Cloud Climatology Project (ISCCP) corrected by the cloud vertical layer model, which is used for the computation of the ISCCP-derived radiative fluxes. Furthermore, we highlight studies on cloud monitoring using the information from the histograms of the database and give guidelines for: (1) the use of satellite-retrieved cloud properties in climate studies and climate model evaluation and (2) improved retrieval strategies.

Selected for development in 1998 and launched together with CloudSat in 2006, the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission terminated science operations in the summer of 2023 after completing 17 years of on-orbit observations. As one of NASA's Earth System Science Pathfinder missions, CALIPSO was truly a pathfinder. CALIPSO observations provided a new perspective on clouds and aerosol and have not only met but far exceeded the original objectives of the mission. Many unanticipated findings and data applications have been discovered along the way. Flying with many other remote sensing instruments, as part of the A-train constellation, stimulated the discovery of numerous retrieval synergies between lidar and other sensors. This paper describes how the CALIPSO mission came to be, discusses some of the early choices made by the CALIPSO team that shaped the mission, and some of the challenges facing the team in developing the first-ever global climatologies of aerosol and cloud based on lidar observations.

Abstract. Clouds play important roles in weather, climate, and the global water cycle. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft has measured global vertical profiles of clouds and aerosols in the Earth’s atmosphere since June 2006. CALIOP provides vertically resolved information on cloud occurrence, thermodynamic phase, and properties. We describe Version 1.0 of a monthly gridded ice cloud product derived from over ten years of global, near-continuous CALIOP measurements. The primary contents are monthly, vertically resolved histograms of ice cloud extinction coefficient and ice water content (IWC) retrievals. The CALIOP Level 3 Ice Cloud Product Version 1.0 is built from the CALIOP Version 4.20 Level 2 5-km Cloud Profile product, which, relative to previous versions, features substantial improvements due to more accurate lidar backscatter calibration, better extinction coefficient retrievals, and a temperature-sensitive parameterization of IWC. The gridded ice cloud data are reported as histograms, which provides data users with the flexibility to compare CALIOP’s retrieved ice cloud properties with those from other instruments with different measurement sensitivities or retrieval capabilities. It is also convenient to aggregate monthly histograms for seasonal, annual or decadal trend and climate analyses. This CALIOP gridded ice cloud product provides a unique characterization of the global and regional vertical distributions of optically thin ice clouds and deep convection cloud tops and should provide significant value for cloud research and model evaluation. A DOI has been issued for the product: https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00 (Winker et al., 2018).

The Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) satellite has been making near-global measurements of clouds and aerosols since mid-June 2006. Among the properties reported in CALIPSO data products are estimates of total column optical depth (COD) obtained by integrating the retrieved extinction coefficients of detected particulates (i.e. aerosols and clouds) from 36 km to the surface. However, due to algorithm detection limits and instrument sensitivities, particulates can go undetected when the particulate loading is especially diffuse or in regions where the signal is attenuated by overlying layers. Because extinction is not calculated where features are not detected, these undetected particulates can introduce low biases into the reported COD. Lidar ratio assumptions used in the extinction retrievals can also introduce errors. To minimize these biases, future releases of the CALIPSO lidar data products will implement an ocean-derived column optical depth (ODCOD) retrieval. The following paper briefly describes the algorithm, the current status of the algorithm development and verification efforts, and preliminary comparisons to collocated COD estimates derived using other retrieval schemes and obtained from other sensors.

Since mid-2016, the frequency of low energy laser pulses emitted by the CALIPSO lidar has been slowly increasing due to pressure losses in the canister housing the laser. While originally confined primarily to the South Atlantic Anomaly (SAA) region, these low energy pulses now occur intermittently around the globe. Low energy pulses can cause calibration biases and degrade the science quality of level 2 retrievals. We describe a new low energy mitigation (LEM) algorithm that will be implemented incrementally in future versions of the CALIOP data processing to identify and reject affected profiles during calibration and feature detection. The LEM algorithm effectively eliminates low energy calibration biases, improves level 2 retrievals, and minimizes level 2 data loss.

Using the so-called Hu diagram relating layer-integrated depolarization ratio (δv) and layer-integrated attenuated backscatter at 532 nm (γ′532), the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar can effectively discriminate between liquid water clouds, clouds composed of randomly oriented ice (ROI) crystals, and ice clouds that contain some fraction of horizontally oriented ice (HOI) crystals. A recently proposed Hu diagram update splits the original water region into a more compact pure liquid regime and two mixed-phase regions. We propose an extended Hu diagram that additionally identifies mixed-phase clouds by incorporating perfectly colocated microphysical index (βeff) and effective diameter (De) retrievals in the thermal infrared window obtained from the CALIPSO imaging infrared radiometer (IIR). First, using the βeff index, we identify regions of the Hu diagram which are seemingly associated with mixed-phase clouds. Second, we characterize opaque water clouds in the updated Hu diagram. Water clouds with top temperatures lower than 238 K, when ice is expected in the upper portion of the cloud, fall distinctly into a proposed mixed-phase region. IIR De varies as expected with lidar ratio in the tighter liquid water region and is larger due to the presence of ice in the mixed-phase region. Finally, we show that the HOI class clusters into two main families of δv-γ′532 relationships, suggesting mixed-phase clouds with smaller De and pure ice clouds with larger De, which is confirmed by Monte Carlo simulations published in the literature.

Global observations are necessary to characterize a variety of aerosol environmental impacts. Satellite sensors are a critical part of this effort, but retrievals are challenged by the diversity of aerosol sources, composition, sizes, and the frequent occurrence of mixtures of different aerosol types. The nature of aerosol effects also depends strongly on the vertical distribution of the aerosol. Global observations of aerosol column optical depth have been available from a number of passive satellite sensors for several decades, but only during daytime and in cloud-free skies. This chapter discusses new capabilities provided by lidar profiling observations from the CALIPSO satellite and how these observations have been used to improve understanding of the impacts of atmospheric aerosols on air quality and climate. This improved understanding has come from direct use of CALIPSO observations, by combining CALIPSO observations with other satellite sensors and by using CALIPSO observations to evaluate and improve models.

Atmospheric aerosol has substantial impacts on climate, air quality and biogeochemical cycles, and its concentrations are highly variable in space and time. A key variability to evaluate within models that simulate aerosol is the vertical distribution, which influences atmospheric heating profiles and aerosol–cloud interactions, to help constrain aerosol residence time and to better represent the magnitude of simulated impacts. To ensure a consistent comparison between modeled and observed vertical distribution of aerosol, we implemented an aerosol lidar simulator within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSPv2). We assessed the attenuated total backscattered (ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1). The simulator performs the computations at the same vertical resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), making use of aerosol optics from the E3SMv1 model as inputs and assuming that aerosol is uniformly distributed horizontally within each model grid box. The simulator applies a cloud masking and an aerosol detection threshold to obtain the ATB and SR profiles that would be observed above clouds by CALIOP with its aerosol detection capability. Our analysis shows that the aerosol distribution simulated at a seasonal timescale is generally in good agreement with observations. Over the Southern Ocean, however, the model does not produce the SR maximum as observed in the real world. Comparison between clear-sky and all-sky SRs shows little differences, indicating that the cloud screening by potentially incorrect model clouds does not affect the mean aerosol signal averaged over a season. This indicates that the differences between observed and simulated SR values are due not to sampling errors, but to deficiencies in the representation of aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.

The NASA Langley Research Center (LaRC) has designed the Clio High Spectral Resolution Lidar (HSRL) instrument concept for NASA's Atmosphere Observing System (AOS). The AOS mission is being developed by NASA in response to the National Academy of Sciences Decadal Survey for Earth Observations from Space and addresses two of five core science foci recommended through the Decadal Survey process: a focus on aerosol impacts on climate and air quality and a focus on global hydrological cycle and cloud-climate feedbacks. The AOS mission implementation, which follows the NASA Aerosol-Cloud-Convection-Precipitation (ACCP) Study recommendations, includes several instruments deployed in two orbital planes, one inclined and one polar. Clio is designed for deployment to the polar orbital plane along with a Doppler radar, microwave radiometer, polarimeter, long-wave IR imaging radiometer, and aerosol and water vapor limb sounders and contributes to both the aerosol and cloud science foci of the mission. Clio capabilities would provide major advances in aerosol and cloud measurements over those available from past spaceborne cloud/aerosol lidars in terms of accuracy, precision, sensitivity, and information content.

The accurate classification of aerosol types injected into the stratosphere is important to properly characterize their chemical and radiative impacts within the Earth climate system. The updated stratospheric aerosol subtyping algorithm used in the version 4.5 (V4.5) release of the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) level 2 data products now delivers more comprehensive and accurate classifications than its predecessor. The original algorithm identified four aerosol subtypes for layers detected above the tropopause: volcanic ash, smoke, sulfate/other, and polar stratospheric aerosol (PSA). In the revised algorithm, sulfates are separately identified as a distinct, homogeneous subtype, and the diffuse, weakly scattering layers previously assigned to the sulfate/other class are recategorized as a fifth “unclassified” subtype. By making two structural changes to the algorithm and revising two thresholds, the V4.5 algorithm improves the ability to discriminate between volcanic ash and smoke from pyrocumulonimbus injections, improves the fidelity of the sulfate subtype, and more accurately reflects the uncertainties inherent in the classification process. The 532 nm lidar ratio for volcanic ash was also revised to a value more consistent with the current state of knowledge. This paper briefly reviews the previous version of the algorithm (V4.1 and V4.2) then fully details the rationale and impact of the V4.5 changes on subtype classification frequency for specific events where the dominant aerosol type is known based on the literature. Classification accuracy is best for volcanic ash due to its characteristically high depolarization ratio. Smoke layers in the stratosphere are also classified with reasonable accuracy, though during the daytime a substantial fraction are misclassified as ash. It is also possible for mixtures of ash and sulfate to be misclassified as smoke. The V4.5 sulfate subtype accuracy is less than that for ash or smoke, with sulfates being misclassified as smoke about one-third of the time. However, because exceptionally tenuous layers are now assigned to the unclassified subtype and the revised algorithm levies more stringent criteria for identifying an aerosol as sulfate, it is more likely that layers labeled as this subtype are in fact sulfate compared to those assigned the sulfate/other classification in the previous data release.

Aerosol optical properties depend on wavelength as well as both mixing ratios and size distributions of components that make up a particular type of aerosol. This study examines impacts on direct aerosol radiative effect (DARE) for desert, clean maritime, and polluted maritime aerosol types over the ocean when their optical properties are determined by various combinations of observations made by active (i.e., lidar) and passive (e.g., shortwave spectrometer) satellite sensors. Spectral optical properties are perturbed by altering mixing ratios of components that define aerosol types with assumptions that components within an aerosol type are fixed and only one aerosol type is present in the atmosphere. When 532 nm depolarization ratio from the lidar is used to identify desert aerosol, the uncertainty in the mean DARE due to spectral optical property variabilities is 10%. When the 532 nm depolarization and lidar ratios are used to identify clean and polluted maritime aerosols, uncertainties in mean DARE are, respectively, 4 and 18%. When scattering optical thicknesses are also known to within ± 3% at four passive imager wavelengths (340 nm, 546 nm, 966 nm, and 1,657 nm), uncertainty in the polluted maritime DARE decreases to 8%. Uncertainties in the instantaneous top-of-atmosphere (TOA) reflected irradiances derived from observed broadband radiances and angular distribution models are also estimated. When TOA irradiances are derived solely from the nadir view, their uncertainties can be reduced if aerosol type can be identified and aerosol type dependence is considered in the radiance to irradiance conversion. This is especially so for aerosols with a large fraction of nonspherical particles, such as desert aerosols.

The features detected in monolayer atmospheric columns sounded by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and classified as cloud or aerosol layers by the CALIOP version 4 (V4) cloud and aerosol discrimination (CAD) algorithm are reassessed using perfectly collocated brightness temperatures measured by the Imaging Infrared Radiometer (IIR) aboard the same satellite. Using the IIR's three wavelength measurements of layers that are confidently classified by the CALIOP CAD algorithm, we calculate two-dimensional (2-D) probability distribution functions (PDFs) of IIR brightness temperature differences (BTDs) for different cloud and aerosol types. We then compare these PDFs with 1-D radiative transfer simulations for ice and water clouds and dust and marine aerosols. Using these IIR 2-D BTD signature PDFs, we develop and deploy a new IIR-based CAD algorithm and compare the classifications obtained to the results reported by the CALIOP-only V4 CAD algorithm. IIR observations are shown to be able to identify clouds with a good accuracy. The IIR cloud identifications agree very well with layers classified as confident clouds by the V4 CAD algorithm (88 %). More importantly, simultaneous use of IIR information reduces the ambiguity in a notable fraction of "not confident" V4 cloud classifications. 28 % and 14 % of the ambiguous V4 cloud classifications are reclassified more appropriately as confident cloud layers through the use of the IIR observations in the tropics and in the midlatitudes, respectively. IIR observations are of relatively little help in deriving high-confidence classifications for most aerosols, as the low altitudes and small optical depths of aerosol layers yield IIR signatures that are similar to those of clear skies. However, misclassifications of aerosol layers, such as dense dust or elevated smoke layers, by the V4 CAD algorithm can be corrected to cloud layer classification by including IIR information. 10 %, 16 %, and 6 % of the ambiguous V4 dust, polluted dust, and tropospheric elevated smoke, respectively, are found to be misclassified cloud layers by the IIR measurements.