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.

In August 2017, a smoke plume from wildfires in British Columbia and the Northwest Territories recirculated and persisted over northern Canada for over two weeks. We compared a full-factorial set of NASA Goddard Institute for Space Studies ModelE simulations of the plume to satellite retrievals of aerosol optical depth and carbon monoxide, finding that ModelE performance was dependent on the model configuration, and more so on the choice of injection height approach, aerosol scheme and biomass burning emissions estimates than to the choice of horizontal winds for nudging. In particular, ModelE simulations with free-tropospheric smoke injection, a mass-based aerosol scheme and high fire NOx emissions led to unrealistically high aerosol optical depth. Using paired simulations with fire emissions excluded, we estimated that for 16 days over an 850 000 km2 region, the smoke decreased planetary boundary layer heights by between 253 m and 547 m, decreased downward shortwave radiation by between 52 Wm-2 and 172 Wm-2, and decreased surface temperature by between 1.5 oC and 4.9 oC, the latter spanning an independent estimate from operational weather forecasts of a 3.7 oC cooling. The strongest surface climate effects were for ModelE configurations with more detailed aerosol microphysics that led to a stronger first indirect effect.

We assess the effective radiative forcing due to ozone-depleting substances using models participating in the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). A large inter-model spread in this globally averaged quantity necessitates an “emergent constraint” approach whereby we link the radiative forcing to the amount of ozone depletion simulated during 1979-2000, excluding two volcanically perturbed periods. During this period ozone-depleting substances were increasing, and several merged satellite-based climatologies document the ensuing decline of total-column ozone. We use these analyses to come up with effective radiative forcing magnitudes. For all of these satellite climatologies we find an effective radiative forcing outside or on the edge of the previously published “likely” range given by the 5th Assessment Report of IPCC, implying an offsetting effect of ozone depletion and/or other atmospheric feedbacks of -0.4 to -0.25 Wm-2, which is in absolute terms is larger than the previous best estimate of -0.15 Wm-2.

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.

We quantify the impacts of halogenated ozone-depleting substances (ODSs), methane, N2O, CO2, and short-lived ozone precursors on total and partial ozone column changes between 1850 and 2014 using CMIP6 Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) simulations. We find that whilst substantial ODS-induced ozone loss dominates the stratospheric ozone changes since the 1970s, the increases in short-lived ozone precursors and methane lead to increases in tropospheric ozone since the 1950s that make increasingly important contributions to total column ozone (TCO) changes. Our results show that methane impacts stratospheric ozone changes through its reaction with atomic chlorine leading to ozone increases, but this impact will decrease with declining ODSs. The N2O increases mainly impact ozone through NOx-induced ozone destruction in the stratosphere, having an overall small negative impact on TCO. CO2 increases lead to increased global stratospheric ozone due to stratospheric cooling. However, importantly CO2 increases cause TCO to decrease in the tropics. Large interannual variability obscures the responses of stratospheric ozone to N2O and CO2 changes. Substantial inter-model differences originate in the models’ representations of ODS-induced ozone depletion. We find that, although the tropospheric ozone trend is driven by the increase in its precursors, the stratospheric changes significantly impact the upper tropospheric ozone trend through modified stratospheric circulation and stratospheric ozone depletion. The speed-up of stratospheric overturning (i.e. decreasing age of air) is driven mainly by ODS and CO2; increases. Changes in methane and ozone precursors also modulate the cross-tropopause ozone flux.

Biases in aerosol optical depths (AOD) and land surface albedos in the AeroCom models are manifested in the top-of-atmosphere (TOA) clear-sky reflected shortwave (SW) fluxes. Biases in the SW fluxes from AeroCom models are quantitatively related to biases in AOD and land surface albedo by using their radiative kernels. Over ocean, AOD contributes about 25% to the 60°S-60°N mean SW flux bias for the multi-model mean (MMM) result. Over land, AOD and land surface albedo contribute about 40% and 30%, respectively, to the 60°S-60°N mean SW flux bias for the MMM result. Furthermore, the spatial patterns of the SW flux biases derived from the radiative kernels are very similar to those between models and CERES observation, with the correlation coefficient of 0.6 over ocean and 0.76 over land for MMM using data of 2010. Satellite data used in this evaluation are derived independently from each other, consistencies in their bias patterns when compared with model simulations suggest that these patterns are robust. This highlights the importance of evaluating related variables in a synergistic manner to provide an unambiguous assessment of the models, as results from single parameter assessments are often confounded by measurement uncertainty. We also compare the AOD trend from three models with the observation-based counterpart. These models reproduce all notable trends in AOD (i.e. decreasing trend over eastern United States and increasing trend over India) except the decreasing trend over eastern China and the adjacent oceanic regions due to limitations in the emission dataset.