AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Evaluating Aerosol Property Predictions from E3SM Using Recent ARM Field Campaign Measurements in Continental and Tropical Environments
JEROME FAST, Po-Lun Ma, John Shilling, ManishKumar Shrivastava, Jason Tomlinson, Jian Wang, Rahul Zaveri, Alla Zelenyuk, Pacific Northwest National Laboratory
Abstract Number: 591 Working Group: Remote and Regional Atmospheric Aerosol
Abstract We use the DOE’s new climate model, the Energy Exascale Earth System Model (E3SM), to simulate the evolution of aerosol populations during two recent Atmospheric Radiation Measurement (ARM) field campaigns: HI-SCALE and GoAmazon. The Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) campaign was conducted in north-central Oklahoma during spring and late summer Intensive Observation Periods (IOPs) of 2016 near the ARM Southern Great Plains (SGP) site. The Green Ocean Amazon (GoAmazon) campaign was conducted in the vicinity of Manaus during 2014 and 2015, with IOPs during the wet and dry season of 2014. Both campaigns had long-term ground measurements to complement more detailed aerosol property and precursor instrumentation onboard the G-1 aircraft during the IOPs to characterize aerosol mass, composition of single particles and bulk particle populations, and size distribution aloft. We use these measurements to evaluate predicted aerosol properties and their multi-day and seasonal variations made by the four-mode version of the Model Aerosol Model (MAM4) that is part of E3SM. In addition to quantifying model performance, we identify likely reasons for biases and their potential effect on aerosol-radiation-cloud-precipitation interactions. We also focus on secondary organic aerosol formation (SOA) processes since they are often the largest fraction of aerosol mass in both campaigns. The performance metrics between the two campaigns will be compared to assess how well the SOA treatment in MAM4 represents both continental and tropical environments. The global model predictions will also be compared with more detailed aerosol populations simulated by the regional WRF-Chem model that includes a sectional aerosol treatment and a more complex representation of SOA.