Presented at the GHRSST XXIII international science team meeting, 27 June-1 July 2022, online and in-person (Barcelona). #GHRSST23 Short abstract As the PO.DAAC (and the rest of the NASA earth science data portfolio) migrates GHRSST and other datasets to the AWS cloud with its enterprise level data discovery, access and services capabilities (see abstract: Li et al., PO.DAAC Cloud Data Ecosystem - Part 1: Search, Access and Services) new opportunities (and challenges) are emerging for the scientific and applications user community. In this presentation we detail some of the emerging science analysis capabilities that a user in the cloud can leverage. This will be demonstrated through a series of jupyter notebook workflows that run and manipulate data directly in the cloud using many of the capabilities and services from Part 1, and other standard python/AWS/Pangeo project utilities and customized code. Examples include workflows that perform spatial/temporal matchups of satellite SST to in situ data, interdisciplinary matchups at the land/sea coastal boundary (e.g., Amazon River outflow), long time series ECCO ocean model analyses and several others that are made available as ready-to-run tutorials from the public PO.DAAC github site. These tutorials have been developed over the past year in support of various NASA cloud data workshops and hackathons to introduce the concept of performing scientific analysis directly in the cloud with little need to download input data; only the results after cloud computation. Examples of cloud computing costs will also be presented as this should not be a significant blocker for usage of cloud data. Related resources PO.DAAC Cloud Data Ecosystem – Part 1: Search, Access and Services. Poster by Wen-Hao Li, Edward M Armstrong, Jorge Vazquez @NASAJPL https://zenodo.org/record/7119594