Glacier mass loss is one of the main contributors to sea-level rise and poses challenges for future water resources. Refining glacier projections and sources of uncertainty thus supports climate adaptation and mitigation. Here we explicitly quantify the impact of internal climate variability and climate data bias adjustment methods on regional and global glacier projections through 2100 for various emissions scenarios. Uncertainty from internal climate variability is comparable to climate model structural uncertainty in the coming decades at the regional level, but is not a major source of uncertainty in centennial global glacier projections. Bias adjustment options (method and time period) greatly impact projections at regional and glacier scales, but have a smaller impact (~4% of global glacier mass at 2100, relative to 2020) at global scales. In some regions, internal climate variability is larger than climate model structural uncertainty for the entirety of the 21st century, and bias adjustment options can more than double the regional uncertainty by 2100. At the glacier scale, bias adjustments can lead to differences in projected decadal and centennial mass loss of up to 50%, although these greatest differences are associated with the smallest (<1 km2) glaciers. Overall, internal climate variability and climate data bias adjustment methods are important to consider, especially in regional applications, to better estimate uncertainty in future sea-level rise and water resources availability.

Pacific decadal variability (PDV), low-frequency changes in Pacific sea surface temperatures (SSTs), significantly impacts global climate. However, disentangling anthropogenic effects upon PDV is challenging since both vary on similar time scales. Using single-forcing climate model large ensembles, we find that anthropogenic forcing primarily drives a spatially-varying pattern of mean-state change in North Pacific SST that project onto leading PDV patterns, principally the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO). In fact, when the trend is determined by the model ensemble mean, there is no forced change of the PDV modes. However, analysis of single model realizations, where the mean-state trend cannot be cleanly identified, suggests an apparent anthropogenic change in NPGO decadal variability. This suggests that observed PDV responses to anthropogenic forcing may be erroneously convolved with the background trend pattern. Therefore, correctly determining the mean-state trend is a necessary precursor for identifying forced changes to PDV.

The mechanisms underlying decadal variability in Arctic sea ice remain actively debated. Here we show that variability in boreal biomass burning (BB) emissions strongly influences simulated Arctic sea ice on multi-decadal timescales. In particular, we find that a strong acceleration in sea ice decline in the early 21st century in the Community Earth System Model version 2 (CESM2) is related to increased variability in prescribed CMIP6 BB emissions through summertime aerosol-cloud interactions. Furthermore, we find that more than half of the reported improvement in sea ice sensitivity to CO2 emissions and global warming from CMIP5 to CMIP6 can be attributed to the increased BB variability, at least in the CESM. These results highlight a new kind of uncertainty that needs to be considered when incorporating new observational data into model forcing, while also raising questions about the role of BB emissions on the observed Arctic sea ice loss.

Model dependence in simulated responses to stratospheric aerosol injection (SAI) is a major uncertainty surrounding the potential implementation of this solar climate intervention strategy. We identify large differences in the aerosol mass latitudinal distributions between two recently produced climate model SAI large ensembles, despite using similar climate targets and controller algorithms, with the goal of understanding the drivers of such differences. Using a hierarchy of recently produced simulations, we identify three main contributors including: 1) the rapid adjustment of clouds and rainfall to elevated levels of carbon dioxide, 2) the associated low-frequency dynamical responses in the Atlantic Meridional Overturning Circulation, and 3) the contrasts in future climate forcing scenarios. Each uncertainty is unlikely to be significantly narrowed over the likely timeframe of a potential SAI deployment if a 1.5C target is to be met. The results thus suggest the need for significant flexibility in climate intervention deployment in order to account for these large uncertainties in the climate system response.

Multiple 50-member ensemble simulations with the Community Earth System Model version 2 are performed to estimate the coupled climate responses to the 2019-2020 Australian wildfires and COVID-19 pandemic policies. The climate response to the pandemic is found to be weak generally, with net top-of-atmosphere radiative anomalies of +0.23+-0.14 W m/2driving a gradual global warming of 0.05+-0.04 K by the end of 2022. While regional anomalies are detectible in aerosol burdens and clear-sky radiation, few significant anomalies exist in other fields due to internal variability. In contrast, the simulated response to Australian wildfires is a strong and rapid cooling, peaking at -0.95+-0.15 W m/2 in late 2019 with an anomalous global cooling of 0.06+-0.04 K by mid-2020. Transport of fire aerosols throughout the Southern Hemisphere increases albedo and drives a strong interhemispheric radiative contrast, with simulated responses that are consistent generally with those to a Southern Hemisphere volcanic eruption.