UNIVERSITY of CALIFORNIA, SAN DIEGO Observational Estimates Of
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UNIVERSITY OF CALIFORNIA, SAN DIEGO Observational estimates of planetary albedo changes due to anthropogenic effects A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Oceanography by Kristina Pistone Committee in charge: Veerabhadran Ramanathan, Chair Jay Barlow Joel Norris Lynn Russell Mark Thiemens 2014 Copyright Kristina Pistone, 2014 All rights reserved. The dissertation of Kristina Pistone is approved, and it is acceptable in quality and form for publication on mi- crofilm and electronically: Chair University of California, San Diego 2014 iii DEDICATION For science. iv TABLE OF CONTENTS Signature Page . iii Dedication . iv Table of Contents . v List of Abbreviations . vii List of Symbols . viii List of Constants . ix List of Figures . x List of Tables . xiii Acknowledgements . xiv Vita and Publications . xvi Abstract of the Dissertation . xvii Chapter 1 Introduction . 1 1.1 The Earth's albedo . 3 1.2 The cryosphere and albedo: Arctic sea ice retreat . 4 1.3 Clouds, aerosols, and their interactions . 6 1.3.1 Cloud radiative forcing . 6 1.3.2 Aerosol effects on clouds . 8 1.3.3 Absorbing aerosol and the Indian Ocean . 13 Chapter 2 Observational determination of the albedo decrease caused by vanishing Arctic sea ice . 15 Chapter 3 Field observations of Indian Ocean trade cumulus clouds . 37 3.1 The tropical atmosphere and the northern Indian Ocean . 37 3.2 The CARDEX campaign: instruments and data . 42 3.2.1 Maldives Climate Observatory{ Hanimaadhoo . 42 3.2.2 Microwave radiometer (MWR) . 45 3.2.3 UAV flight data . 53 3.3 Trade cumulus observations and theory . 59 3.3.1 Mechanics of trade cumulus formation . 59 3.3.2 Thermodynamic characteristics of the tropical In- dian Ocean atmosphere . 60 v 3.3.3 Previous studies of cumulus . 61 3.3.4 Cloud liquid water and atmospheric water vapor . 64 3.3.5 Cloud albedo: radiative forcing of clouds . 65 3.3.6 Calculation of additional atmospheric parameters . 66 3.4 Observations of CARDEX cloud properties and atmospheric structure . 68 3.4.1 Aerosol and meteorological properties . 68 3.4.2 Observed CARDEX cumulus clouds . 75 3.5 Conclusion . 78 Chapter 4 Effects of aerosol variability on Indian Ocean trade cumulus . 81 4.1 Atmospheric regime as indicated by total-column water va- por content . 82 4.1.1 Observed distinctions between dry and wet atmo- spheric conditions . 85 4.1.2 Large-scale contrasts between high and low water vapor conditions . 90 4.2 Aerosol-cloud interactions: history and mechanisms . 91 4.2.1 Previous studies of aerosol-cloud interactions . 93 4.2.2 Observed aerosol-cloud correlations during CARDEX 94 4.3 Case study: cloud liquid water under low versus high aerosol conditions . 95 4.3.1 Surface and boundary layer characteristics . 96 4.3.2 Relative dependence of LWP on atmospheric tem- perature and humidity . 108 4.3.3 Factors influencing atmospheric water content . 112 4.3.4 Large-scale variability between low and high pollu- tion cases . 114 4.3.5 Correlation between large-scale aerosol and temper- ature and humidity patterns . 121 4.3.6 Aerosol effects on atmospheric humidity . 127 4.4 Conclusion . 128 Chapter 5 Conclusions . 131 5.1 Summary . 131 5.2 The global climate context and future directions . 133 Appendix A Arctic: Discussion of data uncertainty . 135 Appendix B CARDEX: Supplementary figures and discussion . 138 References . 154 vi LIST OF ABBREVIATIONS AOD Aerosol Optical Depth AR5 Fifth Assessment Report of the IPCC BC Black carbon CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations CARDEX Cloud, Aerosol, Radiative forcing, Dynamics EXperiment CERES Clouds and Earth's Radiant Energy System CPC Condensation particle counter ECMWF European Center for Medium-range Weather Forecasts HYSPLIT HYbrid Single-Particle Lagrangian Integrated Trajectory model INDOEX The Indian Ocean Experiment IPCC Intergovernmental Panel on Climate Change LCL Lifting condensation level LEF Latent energy flux LWC Liquid water content (g/m3) LWP Liquid water path (g/m2) MAC Maldives Autonomous unmanned aerial vehicle Campaign MCOH Maldives Climate Observatory{ Hanimaadhoo MODIS MODerate resolution Imaging Spectroradiometer MPL Micropulse lidar MVT Maldives local time (UTC+5) MWR Microwave radiometer PBL Planetary boundary layer (also BL) PWV Precipitable water vapor (kg/m2) RH Relative humidity (%) SSM/I Special Sensor Microwave Imager TMI TRMM Microwave Imager TOA Top of atmosphere TRMM Tropical Rainfall Measuring Mission UAV Unmanned Aerial Vehicle vii LIST OF SYMBOLS α Albedo αcs Clear-sky albedo αts Total-sky albedo d Divergence fc Cloud fraction Γ Atmospheric lapse rate (K/km) θ Potential temperature θe Equivalent potential temperature θv Virtual potential temperature Nd Cloud droplet number concentration p Pressure qv Specific humidity ρ Density ρd Density of dry air re Cloud droplet effective radius τ Optical thickness T Temperature Tb Brightness temperature Tv Virtual temperature wv Water vapor mixing ratio ! Pressure vertical velocity (Pa/s) z Altitude zcb Cloud-base altitude zcld Cloud thickness zct Cloud-top altitude zP BL Boundary layer height viii LIST OF CONSTANTS cpd 1004 J/(K kg) (specific heat capacity of dry air at constant pressure) hPa hectopascals (102 Pascals) p0 100000 Pa (reference pressure) Rd 287.104 J/(K kg) (specific gas constant for dry air) Rv 461.51 J/(K kg) (specific gas constant for water vapor) ix LIST OF FIGURES Figure 1.1: The Earth's energy budget (from Stephens et al. 2012). 2 Figure 1.2: Visualization of the decline in Arctic sea ice between 1979 and 2012. 5 Figure 1.3: Relative radiative forcing of anthropogenic climate components compared with preindustrial levels, following the IPCC AR5. 8 Figure 1.4: Schematic representing the modifications to the cloud system with the introduction of aerosol. 10 Figure 2.1: Satellite measurements of September sea ice concentration, clear- sky albedo, and the change over the time period of CERES observations. 18 Figure 2.2: Monthly-averaged and change in sea ice concentration and clear- sky albedo, March through August and October. 19 Figure 2.3: Map of the six regions. 20 Figure 2.4: Monthly-mean CERES clear-sky planetary albedo versus SSM/I sea ice cover. 21 Figure 2.5: Seasonal cycle of ice albedo from CERES compared with in-situ surface observations. 23 Figure 2.6: Regional albedo versus ice cover. 24 Figure 2.7: Sea ice versus clear-sky albedo for observations versus model output for all regions of the Arctic. 25 Figure 2.8: Seasonal cycle of regional CERES ice albedo. 26 Figure 2.9: Clear- and all-sky planetary albedos measured (2000-2011) and reconstructed (1979-1999). 27 Figure 2.10: Linear fit of the sea ice-clear sky albedo relationship for each month of data. 28 Figure 2.11: Seasonal cycle of TOA radiative forcing. 29 Figure 2.12: CCSM4 Eastern Pacific albedo versus ice cover. 31 Figure 3.1: Trade cumulus clouds in the Northern Indian Ocean, overlaid with a schematic of the Hadley circulation. 38 Figure 3.2: Map of the study location highlighting MCOH. 40 Figure 3.3: Time series of aerosol parameters observed during CARDEX. 43 Figure 3.4: Schematic of the measurement operations during the CARDEX campaign. 44 Figure 3.5: Illustration of the MWR operational interface. 49 Figure 3.6: MWR retrieved channel brightness temperatures. 51 Figure 3.7: Liquid water path measured during CARDEX. 52 Figure 3.8: Precipitable water vapor measured during CARDEX. 53 Figure 3.9: Comparison of CARDEX PWV from the MWR, AERONET sun photometer, and TRMM TMI satellite instrument. 54 x Figure 3.10: UAV flights during CARDEX and the range of atmospheric wa- ter vapor and pollution conditions observed. 57 Figure 3.11: Previous descriptions of small cumulus according to the literature. 63 Figure 3.12: Average values measured at MCOH, and MAC4 vertical profiles over the entire CARDEX period. 69 Figure 3.13: Frequency distributions of parameters measured at MCOH dur- ing CARDEX. 70 Figure 3.14: Observed surface winds at MCOH during CARDEX. 71 Figure 3.15: Total-column cloud LWP measured during CARDEX. 72 Figure 3.16: Diurnal variability at MCOH. 74 Figure 3.17: Number of observed clouds by average-peak LWP. 76 Figure 3.18: Vertical position of clouds observed by the MWR. 77 Figure 3.19: Summary of mean trade cumulus cloud and atmospheric prop- erties and thermodynamic structure observed in CARDEX. 78 Figure 4.1: Distributions of MCOH variables on minute-resolution for low versus high water vapor. 86 Figure 4.2: Cloud LWP for wet versus dry cases. 87 Figure 4.3: Temperature and relative humidity vertical profiles from the MAC4 aircraft for individual wet and dry flights. 88 Figure 4.4: NOAA HYSPLIT back trajectories comparing the air mass his- tories of wet versus dry atmospheric conditions. 89 Figure 4.5: ECMWF temperature, relative humidity, subsidence, and di- vergence for wet versus dry days. 92 Figure 4.6: Dependence of liquid water path on aerosol concentration. 95 Figure 4.7: Profiles of CPC, temperature, and relative humidity from MAC4 for low and high pollution cases. 97 Figure 4.8: H-L differences in measured MAC4 profiles of CPC, tempera- ture, and relative humidity. 98 Figure 4.9: Characteristics of Case L versus Case H conditions. 99 Figure 4.10: Frequency distributions for low versus high pollution cases. 100 Figure 4.11: MAC4 vertical profiles for low (green) and high (red) pollution cases. 101 Figure 4.12: ECMWF reanalysis fields for low versus high pollution cases, dry days only. 104 Figure 4.13: Adiabatic temperature profiles and CLWC profiles for the cases described in the text. 110 Figure 4.14: Altitude of cloud retrievals by MAC6 under low (green) and high (red) pollution cases.