In the face of a changing climate, the understanding, predictions and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, a reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support the next generation of climate services.

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.

The COVID-19 pandemic led to a widespread reduction in aerosol emissions. Anecdotal effects on air quality and visibility were widely reported. Less known are the impacts on the planetary energy balance. Using satellite observations and climate model simulations, we 15 study the underlying mechanisms of the large, precipitous decreases in solar clear-sky reflection (3.8 W m-2 or 7%) and aerosol optical depth (0.16 or 32%) over the East Asian Marginal Seas in March 2020. By separating the impacts due to meteorology and emissions in the model simulations, we find that about one-third of the anomalies can be attributed to pandemic-related emission reductions, and the rest to weather variability and long-term emission trends. The 20 current observational and modeling capabilities will be critical for monitoring, understanding, and predicting the radiative forcing and climate impacts of the ongoing crisis.