Keynote: Dist. Prof. Mark D Schwartz

State Library of Victoria

Continental to Global Scale Assessment of Spring Phenological Change

Professor Mark D Schwartz, Distinguished Professor of Geography, University of Wisconsin-Milwaukee

Phenological research provides valuable approaches for understanding Earth systems interactions and facilitating global change studies. As a simple expression of seasonal biology, phenology offers another independent measure (along with climate records and remote sensing observations) of the extent and impact of climate change. However, phenological data are still not collected and recorded in spatially comprehensive and comparable ways around the world. Thus for now, phenological models can allow simulation of general plant responses, facilitating testing of broad hypotheses in locations and at times when actual phenological data are not available, but with more detail than possible when using remote sensing-derived measures.

One set of phenological models that have been successfully applied to assess impacts of climate change on the onset of the spring growing season across temperate regions around the Northern Hemisphere are the Spring Indices (SI). This suite of metrics includes several sub-models and associated measures, all of which can be calculated using daily maximum/minimum surface temperatures and latitude. SI models process weather data into a form mimicking the spring growth of many plants that are not water limited, but are responsive to temperature increases. This paper summarizes recent work using longer and denser station network data since 1900 and more recent high-resolution spatially gridded data across the continental USA, which has shown: 1) the SI onset of spring growing earlier overall since the late 1950s; 2) regional differences in the Western and Southeastern USA; and 3) spatial aspects of the large inter-annual variability of spring’s onset in recent years. Finally, the latest developments from on-going work will be presented that uses gridded air temperature data and SI to provide near real-time monitoring and long-lead forecasting of the onset of the growing season.