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DTPQDT02019019641.pdf

To the University of Wyoming The members of the Committee approve the thesis of Yang Shi presented on 05/08/2019. Dr. Xiaohong Liu, Chairperson Dr. Suresh Muknahallipatna, Outside Member Dr. Zachary J. Lebo Dr. Paul DeMott, External Member APPROVED Dr. Bart Geerts, Department, Head, Department of Atmospheric Science Dr. Michael Pishko, Dean, College of Engineering and Applied Science 1 Shi, Yang, Dust Radiative Effects on Climate by Glaciating Mixed-Phase Clouds, MS, Department of Atmospheric Science, 05/2019. Mineral dust plays an important role in the primary formation of ice crystals in mixed-phase clouds by acting as ice nucleating particles INPs. It can influence the cloud phase transition and radiative forcing of mixed-phase clouds, both of which are crucial to the global energy budget and climate. In this study, we investigate the dust indirect effects on mixed-phase clouds through heterogeneous ice nucleation with the US Department of Energy Exascale Earth System Model E3SM. Dust and INP concentrations simulated from two versions of E3SM with three ice nucleation parameterizations were evaluated against observations in the Northern Hemisphere NH. Constrained by these observations, E3SM shows that dust INPs induce a global mean net cloud radiative effect of 0.06 to 0.25 W m -2 with the predominant warming appearing in the NH midlatitudes. However, a cooling effect is found in the Arctic due to reduced longwave cloud forcing. DUST RADIATIVE EFFECTS ON CLIMATE BY GLACIATING MIXED-PHASE CLOUDS by Yang Shi A thesis submitted to the University of Wyoming in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in ATMOSPHERIC SCIENCE Laramie, Wyoming 05/2019 ProQuest Number All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages,these will be noted. Also, if material had to be removed, a note will indicate the deletion. ProQuest Published by ProQuest LLC . Copyright of the Dissertation is held by the Author. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code Microform Edition ProQuest LLC. ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, MI 48106 - 1346 13883904 13883904 2019 ii COPYRIGHT PAGE 2019, Yang Shi iii ACKNOWLEDGEMENT Yang Shi would like to thank Dr. Xiaohong Liu for the help and support in advising her study for the Master of Science Degree, and thank Dr. Zachary Lebo, Dr. Suresh Muknahallipatna, and Dr. Paul DeMott for serving as the members in her committee. She would like to thank Z. Wang, K. Yang, and T, Luo for providing the CALIPSO dust extinction data, P. J. DeMott for providing the CFDC INP data at Barrow and Storm Peak, A. Bertram for providing the MOUDI-DFT INP data at Alert, K. Zhang for assisting with the coding developments in E3SM, P.-L. Ma for providing MERRA2 data, and H. Brown and M. Wu for their useful comments. This work is supported by the US Department of Energy DOE as part of the Climate Model Development and Validation – Mesoscale Convective System project CMDV-MCS and DOE’s Atmospheric System Research Program grant DE-SC0014239. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The E3SM source code is available on GitHub iv TABLE OF CONTENTS COPYRIGHT PAGE ii ACKNOWLEDGEMENT . iii TABLE OF CONTENTS . iv LIST OF TABLES v LIST OF FIGURES . vi 1 Introduction 1 2 E3SM Parameterizations and Experiments 4 2.1 EAM v1 and v0 4 2.2 Ice nucleation parameterizations . 7 2.3 Model experiments 9 3 Results 12 3.1 Dust extinction vertical profiles 12 3.2 INP concentration 18 3.3 Dust glaciation impacts on mixed-phase clouds 24 4 Discussion and Summary . 41 5 Future work 43 Reference 45 v LIST OF TABLES Table 1. Comparison of EAM v1 and EAM v0 configurations. Abbreviations ZM1995 – deep convection parameterization of Zhang and McFarlane 1995; PB2009 – shallow convection and turbulence parameterization of Park and Bretherton 2009; PBR2014 – cloud macrophysics parameterization of Park et al. 2014; CLUBB – unified turbulence, shallow convection, and cloud macrophysics parameterization of Golaz et al. 2002 and Larson et al. 2002; MG1 – stratiform cloud microphysics parameterization of Morrison and Gettelman 2008; MG2 – stratiform cloud microphysics parameterization of Gettelman and Morrison 2015; MAM3 – three-mode version of the modal aerosol model of Liu et al. 2012; MAM4_MOM – four-mode version of the modal aerosol model of Liu et al. 2016 with marine organic aerosol model of Burrows et al. 2018. Table 2. Experiments conducted in this study. Table 3. INP datasets used for this study. Table 4. Annual mean global average longwave cloud forcing, shortwave cloud forcing, and net cloud forcing change due to the introduction of dust immersion freezing. All the listed experiments are compared with corresponding tests that turn off dust immersion freezing i.e. CNTv1 is compared with CNTv1_x0. First number in parenthesis is the average from 30N to 70N. Second number in parenthesis is the average from 70N to 90N. Units are W m -2 . vi LIST OF FIGURES Figure 1. Vertical profiles of mean dust extinction km -1 at different latitudinal bands in Northern Hemisphere from CALIPSO retrieval Luo et al., 2015a, 2015b, and two model simulations EAM v1 and EAM v0. CNTv1 and CNTv0 are used to represent EAM v1 and EAM v0 results, because the ice nucleation parameterizations have little impact on dust spatial distribution. Figure 2. Zonal averaged dust wet removal rate in the Northern Hemisphere for EAM v1 and EAM v0. The dust wet removal rate is equal to the dust wet deposition flux divided by dust total column burden. Figure 3. Monthly mean modeled dust surface concentration comparison with observations at the Alert site 82.39N, 62.3W Fan et al., 2013. Observed dust concentrations dark blue line, with gray shade representing 1 standard deviation are estimated from Al in weekly filter samples from 2000 to 2006. Figure 4. Global soil erodibility map for EAM v0 and v1. The regions with no dust emission is colored by white. Figure 5. Comparison of predicted vs. observed INP concentrations at high latitudes of the Northern Hemisphere for a CNTv1, b NIEv1, c DEMv1, d CNTv0, e NIEv0, and f DEMv0. Uncertainties for observations are shown on selected data points. Solid line in each panel represents 11 comparison, while dashed lines outline 1 order differences. The scatters are colored by observed temperatures. The unit for INP concentration is L -1 . Figure 6. Same as Figure 5, except for the US continent. Figure 7. Same as Figure 5, except for north China. Figure 8. Change from DEMv1_x0 to DEMv1_x10 for annual-mean a liquid water path LWP, b ice water path IWP, c shortwave cloud forcing SWCF, d longwave cloud forcing LWCF, and e net cloud forcing. LWP includes both cloud liquid and rain, while IWP includes both cloud ice and snow. Figure 9. Annual mean cloud fration differences between DEMv1_x10 and DEMv1_x0 for a low clouds, b mid-level clouds, c high clouds, and d total clouds. vii Figure 10. Zonal average temperature difference between DEMv1_x10 and DEMv1_x0. Unit K. Figure 11. Annual mean differences between DEMv1_x10 and DEMv1_x0 for a low- tropospheric stability LTS, and relatitve humidity RH at b 300 hPa, c 500 hPa, and d 850 hPa. LTS is defined as the potential temperature difference between the pressure level of 700 hPa and 1000 hPa following Xie et al. 2013. Figure 12. Changes in shortwave cloud forcing SWCF, top row, longwave cloud forcing LWCF, middle row, and net cloud forcing bottom row between DEMv1_x10 and DEMv1_x0 at different seasons. Figure 13. Change from DEMv1_x0 to DEMv1_x10 for annual-mean a downwelling surface shortwave flux, b downwelling surface longwave flux, and c downwelling surface net flux. Figure 14. Change from DEMv1_x0_B10 to DEMv1_x10_B10 for a liquid water path LWP, b ice water path IWP, c shortwave cloud forcing SWCF, d longwave cloud forcing LWCF, and e net cloud forcing. LWP includes both cloud liquid and rain, while IWP includes both cloud ice and snow. Descriptions about DEMv1_x0_B10 and DEMv1_x10_B10 are listed in Table 2, Part 2. Figure 15. Annual mean liquid containing cloud fraction CLDTOT_CAL_LIQ and ice containing cloud fraction CLDTOT_CAL_ICE difference between modeled results DEMv1 and DEMv1_x10 and CALIPSO-GOCCP at the Northern Hemisphere from 30N to 90N. Figure 16. Annual mean liquid containing cloud fraction CLDTOT_CAL_LIQ and ice containing cloud fraction CLDTOT_CAL_ICE difference between modeled results DEMv1_B10 and DEMv1_x10_B10 and CALIPSO-GOCCP at the Northern Hemisphere from 30N to 90N. 1 Chapter 1 Introduction Mixed-phase clouds containing both liquid and ice cover approximately 34 of the globe Wang, 2013 and are frequently observed at high latitudes and in the midlatitudes storm tracks Shupe, 2011; Zhao Findeisen, 1938; Wegener, 1911. The resulting large ice crystals fall quickly to initiate precipitation and change the mixed-phase cloud lifetime. These ice microphysical processes can also influence the partitioning between liquid and ice and are consequently crucial for mixed-phase cloud radiative forcing. Heterogeneous ice nucleation is hypothesized to occur via four different mechanisms Vali et al., 2015; Kanji et al., 2017. Below water saturation, water vapor can deposit on dry INPs directly 2 to initiate ice formation deposition nucleation. Above water saturation, freezing of a supercooled droplet is induced either by INPs immersed inside the droplets immersion freezing or by the collision between INPs and the droplets contact freezing. Freezing can also be initiated concurrently with the formation of the droplets on cloud condensation nuclei CCN, namely condensation freezing Marcolli, 2014; Wagner et al., 2016. The immersion and/or condensation freezing are hard to distinguish and are suggested to be the dominant mechanism for primary ice formation in mixed-phase clouds Prenni et al., 2009; de Boer et al., 2011; Westbrook and Illingworth, 2013. Due to the lack of understanding about its mechanism, representing heterogeneous ice nucleation processes in numerical models is challenging. Several parameterizations have been developed, which can be divided into two types those following the stochastic hypothesis and those following the singular or deterministic hypothesis. The stochastic hypothesis holds that heterogeneous ice nucleation is a function of time, the representation of which often involves application of classical nucleation theory CNT Chen et al., 2008; Hoose et al., 2010; Niedermeier et al., 2011. The singular hypothesis does not quantify time-dependent ice nucleation behavior, but instead only its temperature dependence. Early singular approaches relate the INP number concentration to temperature and humidity e.g. Meyers et al., 1992, while more recent ones consider aerosol properties e.g. DeMott et al., 2010; DeMott et al., 2015; Niemand et al., 2012. Mineral dust is generally recognized as the most efficient type of INP in the atmosphere at temperatures colder than -10C Hoose Murray et al., 2012. This implies the potentially important role of dust indirect effects on mixed-phase clouds, thereby affecting cloud radiative forcing and climate. Many advanced dust-specific ice nucleation parameterizations in 3 mixed-phase cloud regimes have been recently developed e.g., DeMott et al., 2015; Hoose et al., 2010; Niemand et al., 2012. Some of them have been implemented in GCMs to enable dust and mixed-phase cloud interactions e.g., DeMott et al., 2010; Wang et al., 2014; Wang the differences between which are summarized in Table 1. EAM v0 basically uses the same physics as the Community Atmosphere Model version 5 CAM5 Neale et al., 2012. It uses the spectral element dynamical core on a cubed-sphere mesh with 30 vertical layers that cover the atmosphere from the Earth’s surface to about 2 hPa. The key parameterizations for physical processes in EAM v0 include deep convection Zhang and McFarlance, 1995, shallow convection and turbulence Park and Bretherton, 2009, cloud macrophysics Park et al., 2014 and microphysics Morrison and Gettelman, 2008, and aerosol microphysics Liu et al., 2012. We also note that the default setting of EAM v0 tunes down the WBF process artificially by a factor of 10 with the intention to increase cloud liquid in mixed- phase clouds. EAM v1 uses the same dynamical core spectral element as EAM v0 but increases the vertical resolution to 72 layers, which extends the model top to about 0.1 hPa. Comparing with EAM v0, EAM v1 uses the Cloud Layers Unified by Binormals parameterization CLUBB Golaz et al., 2002; Larson et al., 2002; Bogenschutz et al., 2013 to replace the separate shallow convection, turbulent transport and cloud macrophysics schemes in EAM v0. EAM v1 also includes an updated cloud microphysics scheme Gettelman and Morrison, 2015 and a four-mode version of the modal aerosol model Liu et al., 2016 that considers marine organic aerosols Burrows et al., 2018. 5 Though EAM v1 uses an improved version of the aerosol model, the treatments for dust are generally the same in the two model versions. Dust is carried in two size modes at 0.1-1.0 m accumulation mode and 1.0-10.0 m coarse mode. The dust emission parameterization follows Zender et al. 2003, and the dust emission mass fraction for accumulation and coarse modes are 3.2 and 96.8, respectively. Moreover, EAM v1’s default setting also has the artificial tuning factor 10 to the WBF process. A description of EAM v1 with more details can be found at the E3SM website https//e3sm.org/model/e3sm-model-description/v1-description/v1-atmosphere/. 6 Table 1. Comparison of EAM v1 and EAM v0 configurations. Abbreviations ZM1995 – deep convection parameterization of Zhang and McFarlane 1995; PB2009 – shallow convection and turbulence parameterization of Park and Bretherton 2009; PBR2014 – cloud macrophysics parameterization of Park et al. 2014; CLUBB – unified turbulence, shallow convection, and cloud macrophysics parameterization of Golaz et al. 2002 and Larson et al. 2002; MG1 – stratiform cloud microphysics parameterization of Morrison and Gettelman 2008; MG2 – stratiform cloud microphysics parameterization of Gettelman and Morrison 2015; MAM3 – three-mode version of the modal aerosol model of Liu et al. 2012; MAM4_MOM – four-mode version of t

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