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.

Recent mass loss from ice sheets and ice shelves is now persistent and prolonged enough that it impacts downstream oceanographic conditions. To demonstrate this, we use an ensemble of coupled GISS-E2.1-G simulations forced with historical estimates of anomalous freshwater, in addition to other climate forcings, from 1990 through 2019. In this ensemble there are detectable differences in zonal-mean sea surface temperatures (SST) and sea ice in the Southern Ocean, and in regional sea level around Antarctica and in the western North Atlantic. These impacts mostly improve the model’s representation of historical changes, including reversing the forced trends in Southern Ocean surface temperature and Antarctic sea ice. The changes in SST may have implications for estimates of the SST pattern effect on climate sensitivity and for cloud feedbacks. We conclude that the changes are sufficiently large that these drivers should be included in all-forcing historical simulations in coupled model intercomparisons.

Coral oxygen isotopes (δ18O) from the central equatorial Pacific provide monthly-resolved records of El Niño-Southern Oscillation (ENSO) activity over past centuries to millennia. However, calibration studies using in situ data to assess the relative contributions of warming and freshening to coral δ18O records are exceedingly rare. Furthermore, the fidelity of coral δ18O records under the most severe thermal stress events is difficult to assess. Here, we present six coral δ18O records and in situ temperature, salinity, and seawater δ18O data from Kiritimati Island (2°N, 15°W) spanning the very strong 2015/16 El Niño event. Local sea surface temperature (SST) anomalies of +2.4±0.4°C and seawater δ18O anomalies of -0.19±0.02‰ contribute to the observed coral δ18O anomalies of -0.58±0.05‰, consistent with a ~70% contribution from SST and ~30% from seawater δ18O. Our results demonstrate that Kiritimati coral δ18O records can provide reliable reconstructions even during the largest class of El Niño events.

We examine multiple factors in the representation of satellite-retrieved atmospheric temperature diagnostics in historical simulations of climate change during the satellite era (specifically 1979-2021) using GISS ModelE contributions to the Coupled Model Intercomparison Project (Phase 6) (CMIP6). The tropospheric and stratospheric trends in these diagnostics are affected by greenhouse gases (notably carbon dioxide and ozone), coupling with the ocean, volcanic aerosols, solar activity and compositional and dynamic feedbacks. We explore the impacts of internal variability, changing forcing specifications, composition interactivity, the quality of the stratospheric circulation, vertical resolution, and possible impacts of the mis-specification of volcanic aerosol optical depths. Overall trends and patterns over the satellite period are well captured, but discrepancies at all levels exist and have multiple distinct causes. We find that stratospheric comparisons (using Stratospheric Sounding Unit (SSU) retrievals and successor instruments) are most affected by variations in the representation of ozone depletion and feedbacks, followed by the volcanic signals. Tropospheric skill (using the Microwave Sounding Unit (MSU) retrievals) is affected by the trends in ocean temperature and tropospheric aerosols, but also by the representation of stratospheric processes through the impact of the Brewer-Dobson circulation on the height of the tropical tropopause. We do not find evidence of a systematic problem in the model climate sensitivity.