The prediction of future climate changes is one of the most complex problems undertaken by the scientific community. Although scientists have been striving to better understand the physical basis of the climate system behavior and to improve climate models, the overall uncertainty in projecting future climate has not been reduced (e.g., from the IPCC 2007 to 2013). With the rapid increase of complexity in Earth system models, reducing uncertainties and increasing reliability of climate projections becomes an extremely challenging task. Since uncertainties always exist in climate models, interpreting model simulations and quantifying uncertainty is key to understanding and modeling atmospheric, land, ocean, and socio-economic phenomena and processes. Meanwhile, climate change adaption and impact communities rely on climate models to provide climate change information. Such information, if not accurate, should be provided with well-quantified uncertainty. Uncertainty Quantification (UQ) is a fundamental challenge in numerical simulations of Earth’s weather and climate. It entails much more than attaching defensible error bars to predictions. In recent years, formal methods of verification, validation, and UQ employed in other simulation problems have been applied to climate simulations. The topics to be discussed in this workshop will include many aspects of UQ in climate modeling, such as identifying sources of uncertainty, describing uncertainty associated with input parameters, evaluating model uncertainty through validation against observations, model comparison between numerical and/or analytical solutions, and upscaling/downscaling, as well as quantifying uncertainty through both forward modeling (sensitivity analyses) and inverse modeling (optimization/calibration) in all components of climate and integrated Earth system models at various spatial and temporal scales. The workshop is also aimed at providing both theoretical lectures and hands-on sessions on the theory and application of the various UQ methods and approaches, such as sensitivity analysis, construction of surrogate models and response surfaces, input parameter calibration studies, forward propagation of uncertainties, and assessment of model discrepancies and structural uncertainties. Supervised by the directors and lecturers, participants will be encouraged to design, complete and report on short research projects during the event.