The climatologies underlying interannual variations and secular trends in spring onset are poorly understood. Spring onset can have important hydrological and ecological consequences, including changes in the timing of snowmelt and snowmelt runoff, in timing of plant and animal phenologies and their interactions, in ecosystem fluxes, and in the probabilities of ecological disturbances such as fire and insect outbreaks. Any ability to predict spring onset would find myriad applications. We used Spring Indices (SI) developed from cloned lilac phenological data to evaluate the climatologies and impacts of spring onset variations and trends. The SI models represent seasonally integrated changes in temperature can be generated at any location that has daily maximum–minimum temperature time series. We performed principal component analysis (PCA) on an SI dataset from western North America to identify large-scale patterns of variability in the onset of spring and used observational climate data to assess the physical mechanisms and dynamics associated with those patterns. We also repeated a previous analysis of large fire occurrence (the number of fires exceeding 400 ha in a given year) in the western U.S. that used center of mass in streamflow (i.e., the snowmelt pulse) as the spring onset indicator, this time using SI.
Results/Conclusions
At least two significant and independent modes of climate variability control the timing of spring throughout much of the West. The first shows a subcontinental trend towards earlier springs and is associated most strongly with warm March temperatures. In addition to the long-term trend, there is a strong correspondence between early springs in this mode and the positive phase of the Pacific North American (PNA) pattern. The second mode of spring variability exhibits a north-south dipole and correlates strongly with conditions in the tropical Pacific. Our analyses suggest that knowledge of large-scale patterns during the antecedent winter could help forecast the onset of spring and related hydrological and ecological phenomena. For example, the SI/fire comparison showed strong associations between SI at weather stations, particularly those in the Central Rockies/Colorado Plateau and large fire frequency in the northern, central and southern Rockies, as well as in the Sierra Nevada, but less so in southern California and the Black Hills. Though they have their own peculiar biases and shortcomings, phenological models such as SI may be particularly useful in predicting climate change impacts on fire and other phenomena, and could offer more precision and better lead time in fire forecasting.
