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Abstract Editors note: For easy download the posted pdf of the State of the Climate in 2023 is a low-resolution file. A high-resolution copy of the report is available by clickinghere. Please be patient as it may take a few minutes for the high-resolution file to download. 

Abstract Climatic change is dramatically altering phenology but generalities regarding tempo and mode of response remain limited. Here we present a general model framework incorporating spring temperature, velocity of spring warming, and species’ thermal requirements for predicting phenological response to warming. A key prediction of this framework is that species active earlier in the season and located in warmer regions where spring temperature velocity is lowest show strongest sensitivity to climatic change and greatest advancement in response to warming. We test this prediction using plant phenology datasets collected in the 1850s and 2010s. Our results strikingly confirm model predictions, showing that while temperature sensitivity is higher in regions with low temperature velocity, the greatest realized change in phenological onset is northern areas where warming rates have been fastest. Our framework offers enhanced utility for predicting phenological sensitivity and responsiveness in temperate regions and across multiple plant species and potentially other groups. 

Abstract The number and diversity of phenological studies has increased rapidly in recent years. Innovative experiments, field studies, citizen science projects, and analyses of newly available historical data are contributing insights that advance our understanding of ecological and evolutionary responses to the environment, particularly climate change. However, many phenological data sets have peculiarities that are not immediately obvious and can lead to mistakes in analyses and interpretation of results. This paper aims to help researchers, especially those new to the field of phenology, understand challenges and practices that are crucial for effective studies. For example, researchers may fail to account for sampling biases in phenological data, struggle to choose or design a volunteer data collection strategy that adequately fits their project’s needs, or combine data sets in inappropriate ways. We describe ten best practices for designing studies of plant and animal phenology, evaluating data quality, and analyzing data. Practices include accounting for common biases in data, using effective citizen or community science methods, and employing appropriate data when investigating phenological mismatches. We present these best practices to help researchers entering the field take full advantage of the wealth of available data and approaches to advance our understanding of phenology and its implications for ecology.