Mixed phase clouds, consisting of ice particles and super cooled liquid droplets, predominantly found in Arctic, are poorly represented in various climate models. A unique tethered balloon campaign carrying 4π radiometer, a cloud particle imager, and a meteorological package on board was conducted in Ny-Ålesund, Norway, located high in the Arctic at 78.91°N, 11.91°E during May-June 2008. The radiometric and cloud observations at 500nm and 800nm were collected during a month long experimental campaign. A state-of-the-art discrete ordinate radiative transfer model was used to estimate optimal cloud optical properties. These optical properties are contrasted with those of the cloud parameterization in the new NOAA-GFDL AM4 model that is presently being used in the Coupled Model Intercomparison Project 6 (CMIP6). The improved simulation of cloud optical properties will aid in understanding cloud radiation feedbacks and better representation of clouds in global climate model.

This study aims to create observation-based cloud radiative kernel (CRK) datasets and evaluate them by direct comparison of CRK and the CRK-derived cloud feedback datasets. Based on the International Satellite Cloud Climatology Project (ISCCP) H datasets, we calculate CRKs (called ISCCP-FH or FH CRKs) as 2D joint function/histogram of cloud optical depth and cloud top pressure for shortwave, longwave, and their sum, Net, at the top of atmosphere (TOA), as well as, for the first time, at the surface (SFC) and in the atmosphere (ATM). All the FH CRKs are physically plausible. The direct comparison shows that FH agrees reasonably well with three other TOA CRK datasets. With cloud fraction change (CFC) datasets of the same histogram for doubled-CO2 simulation from 10 CFMIP1 models, we derive all the TOA, SFC and ATM cloud feedback using the FH CRKs. Our TOA cloud feedback is highly similar to the previous counterparts. Based on the comparison for the 4 CRK datasets and the 10 CFC datasets, we estimate the uncertainty budget for the CRK-derived cloud feedback, which shows that the CFC-associated uncertainty contributes > 98.5% of the total cloud feedback uncertainty while CRK’s is very small. Our preliminary evaluation also shows that some near-zero/small cloud feedback in the TOA-alone feedback indeed results from the compensation of sizable cloud feedback of the SFC and ATM feedback, demonstrating how the SFC- and ATM-CRK derived cloud feedback can be valuable in revealing some significant surface and atmospheric cloud feedback whose sum appears insignificant in TOA-alone feedback.

Numeracy is the ability to understand and interpret numbers. The numeracy skills are required and relevant across all disciplines and especially in STEM majors. The diverse group of underrepresented students from two-year community college was assessed on the knowledge of basic units of measurement and unit conversions in this study. The student performance was assessed in a lesson plan administered during The Numeracy Infusion for College Educators (NICE) Program at Bronx Community College. The students were assessed to identify different units; to be able to perform basic unit conversions; to recognize the scale of bigger and smaller units; and associated vocabulary. The learning goals aimed at developing thinking skills in reading graphs, trends or patterns and develop a habit of comparing the values of measurement in one unit system to another system of units. The students were provided tools to remember the increasing and decreasing trends in the scale of units. The real-life examples were incorporated into the learning process. The assessment was administered at different stages of learning in this lesson plan. This study will present the assessment results of lesson plan administered to a diverse group of underrepresented students from various majors in community college education. The student performance assessment in this lesson plan highlights the urgency to incorporate tools and techniques for numeracy skills across the curriculum in various disciplines of higher education.