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

Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements

The satellite-based cloud condensation nuclei (CCN) proxies used to quantify the aerosolcloud interactions (ACIs) are column integrated and do not guarantee the vertical co-location of aerosols and clouds. This has encouraged the use of height-resolved measurements of spaceborne lidars for ACI studies and led to advancements in lidar-based CCN retrieval algorithms. In this study, we present a comparison between the number concentration of CCN (nCCN) derived from ground-based in situ and spaceborne lidar cloud-aerosol lidar with orthogonal polarization (CALIOP) measurements. On analysing their monthly time series, we found that about 88% of CALIOP nCCN estimates remained within a factor of 1.5 of the in situ measurements. Overall, the CALIOP estimates of monthly nCCN were in good agreement with the in situ measurements with a normalized mean error of 71%, normalized mean bias of 39% and correlation coefficient of 0.68. Based on our comparison results, we point out the necessary measures that should be considered for global nCCN retrieval. Our results show the competence of CALIOP in compiling a global height- and type-resolved nCCN dataset for use in ACI studies

Decline of signal transduction by phospholipase Cγ1 in IMR 90 human diploid fibroblasts at high population doubling levels

AbstractDuring cellular senescence in vitro, the cells do not respond mitogenically to serum growth factors at high population doubling levels. Phospholipase C activity in low PDL IMR 90 cells showed a 4.7-fold stimulation in response to 10% serum compared to 3.3-fold in high PDL cells when measured in whole cell extracts. Immunoaffinity purified tyrosine phosphorylated protein fraction showed a greater increase (5.2-fold) in phospholipase C activity in low PDL than high PDL cells (2.1-fold) in response to serum. Serum stimulated PLCγ1 activity was diminished in high PDL cells. Immunokinase assay of PLCγ1 immunoprecipitates from serum stimulated IMR 90 fibroblasts suggested that diminished enzymatic activity in high PDL cells is not due to less receptor coupled tyrosine phosphorylated PLCγ1 enzyme. Serum stimulated [3H]thymidine incorporation into DNA declined in parallel with the activity of PLCγ1, suggesting that its activation might play significant roles in this in vitro model for cellular senescence

Type of Deepbite in Orthodontic Treated Patients

Deep bite is a common malocclusion that can occur in both children and adults. It is defined as an excessive vertical overlap of the upper teeth on the labial surface of the lower teeth when the teeth are in centric occlusion. Deep bite can be classified according to its origin (dental or skeletal), function (true or pseudo), and extent (incomplete or complete). The aetiological factors of deep bite include inherent factors (tooth morphology, skeletal pattern, and malocclusion) and acquired factors (muscular habit, change in tooth position, loss of posterior supporting tooth, and lateral tongue thrusting habit). Deep bite is more common in some racial groupings than others, and it is associated with compromising periodontal health of maxillary anteriors and the palatal tissue. This study found that 58% of orthodontic patients had deep bite, and 71% of those had incomplete deep bite. Females were more likely to have deep bite than males

Shallow Cumulus Cloud Fields Are Optically Thicker When They Are More Clustered

Shallow trade cumuli over subtropical oceans are a persistent source of uncertainty in climate projections. Mesoscale organization of trade cumulus clouds has been shown to influence their cloud radiative effect (CRE) through cloud cover. We investigate whether organization can explain CRE variability independently of cloud cover variability. By analyzing satellite observations and high-resolution simulations, we show that increased clustering leads to geometrically thicker clouds with larger domain-averaged liquid water paths, smaller cloud droplets, and consequently, larger cloud optical depths. The relationships between these variables are shaped by the mixture of deep cloud cores and shallower interstitial clouds or anvils that characterize cloud organization. Eliminating cloud cover effects, more clustered clouds reflect up to 20 W/m$^2$ more instantaneous shortwave radiation back to space

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

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