Plant phenology regulates the carbon cycle and land-atmosphere coupling. Currently, climate models often disagree with observations on the seasonal cycle of vegetation growth, partially due to how spring onset is measured and simulated. Here we use both thermal and leaf area index (LAI) based indicators to characterize spring onset in CMIP6 models. Although the historical timing varies considerably across models, most agree that spring has advanced in recent decades and will continue to arrive earlier with future warming. Across the Northern Hemisphere for the periods 1950-2014, 1981-2014, and 2015-2099, thermal-based indicators estimate spring advances of -0.7±0.2, -1.4±0.4, and -2.4±0.7 days/decade, while LAI-based indicators estimate -0.4±0.3, -0.1±0.3, and -1±1.1 days/decade. Thereby, LAI-based indicators exhibit later spring and weaker trends toward earlier onset, leading to index-related uncertainty being as large or larger than model uncertainty for a given index. Reconciling index-related discrepancies is therefore critical for understanding future changes in spring onset.

Hydroclimate variability of the southwest United States (SWUS) is influenced by the tropical Pacific Ocean, particularly the phase of the El Niño/Southern Oscillation (ENSO), via teleconnections, which are expected to be altered by climate change. However, natural variability in this teleconnection has not been robustly quantified, complicating the detection of anthropogenic climate change. Here, we use a linear inverse model (LIM) to quantify natural variability in the ENSO-SWUS teleconnection. The LIM yields realistic teleconnection patterns with century-scale variability in teleconnection strength comparable to simulations from the Last Millennium Ensemble project and the Coupled Model Intercomparison Project Phases 5 and 6. The variability quantified by the LIM illuminates two aspects of our understanding of ENSO and its impacts: the inherent statistics of the observable system can produce century-long periods of nonsignificant correlation, and detecting changes in ENSO-related hydroclimate variability is challenging in a changing climate.