Significant challenges to actionable management decisions lie in uncertainties over changing temperature, precipitation, and snow dynamics, how these changes vary spatially, and their implication for ecological and human populations, communities, ecosystems, and landscapes. There are many broad trends which have been observed across the region that can help inform decision making at local levels. However, it is critical to use caution when trying to make inference form data or research representing distant or disparate species or systems. There are many spatial, temporal, and ecological gaps in knowledge and data that limit our understanding of phenological responses to climate throughout much of the Northwest region, especially arid regions which also have low human population density but represent important ecological and natural resource regions which are undergoing significant changes.
The ecological and biogeographic consequences of climate change depend on ecoregional differences in preexisting environmental and management conditions, rates and magnitude of climate change drivers such as warming, precipitation – including drought and snowmelt, and CO2 concentrations. Ecoregional characterization and classification is often based on patterns and composition of biotic and abiotic components of ecosystems (Nolin 2012). Therefore, an ecoregional approach could be useful for assessing phenological patterns and responses at large scales. However, this approach is still challenging for in the extreme heterogeneity of the Northwest CSC region as it includes four Level II and 15 Level III EPA Ecoregions (Figure 3), many of which have little data, research, or monitoring (see Part III: Citizen Science and Open Source Data Campaigns and Part IV: The USA-NPN National Phenology Network & Database for details on current citizen science and monitoring programs). Therefore, we can expect significant variation across this biogeographically complex mosaic of ecoregions. This challenge is then exacerbated by the relative lack of information in the NW, relative to other regions such as the Northeast, on phenological trends for most species within a given ecosystem or ecoregion, the underlying biological mechanisms of phenological response, and the complex cascading interactions of differential species responses within communities.
The ecological and biogeographic consequences of climate change depend on ecoregional differences in preexisting environmental and management conditions, rates and magnitude of climate change drivers such as warming, precipitation – including drought and snowmelt, and CO2 concentrations. Ecoregional characterization and classification is often based on patterns and composition of biotic and abiotic components of ecosystems (Nolin 2012). Therefore, an ecoregional approach could be useful for assessing phenological patterns and responses at large scales. However, this approach is still challenging for in the extreme heterogeneity of the Northwest CSC region as it includes four Level II and 15 Level III EPA Ecoregions (Figure 3), many of which have little data, research, or monitoring (see Part III: Citizen Science and Open Source Data Campaigns and Part IV: The USA-NPN National Phenology Network & Database for details on current citizen science and monitoring programs). Therefore, we can expect significant variation across this biogeographically complex mosaic of ecoregions. This challenge is then exacerbated by the relative lack of information in the NW, relative to other regions such as the Northeast, on phenological trends for most species within a given ecosystem or ecoregion, the underlying biological mechanisms of phenological response, and the complex cascading interactions of differential species responses within communities.
Figure 4. Level II and Level III EPA Ecoregions encompassed by the Northwest Climate Science Center
The Northwest Dipole
When we ask, “what are the climate change impacts on phenology in Region X?”, it is critical to recognize that there are biogeographic biases in the distribution of scientific research and literature on phenology and climate change. In large part, research and monitoring and thus published research studies, especially using ground-based methods (i.e. not satellite remote sensing) has focused on temperate deciduous ecosystems of the eastern US and Europe. Furthermore, analyses that utilize satellite remote sensing are temporally limited to assessing patterns and trends within the past 20 years.
Biogeographic phenology research is generally biased in focus on the northeast US, with large data gaps in the northwest (and southwest) are important when we consider spatial variation in climate and phenological response. One robust regional response to interannual climate variability is the trend in the coterminous United States of an apparent dipole pattern with ‘‘centers of action’’ in the northwest Cascade and Great Lakes regions (Ault et al. 2015). This dipole patterns appears to be generated by different climatological processes operating in these regions. In the Northwest, spring temperatures and thus spring bloom and leaf out of plants are strongly correlated with the Pacific Decadal Oscillation (Ault et al. 2015), a climate index based upon patterns of variation in sea surface temperature of the North Pacific from 1900 to present. Positive PDO phases favor early spring and negative phases favor later spring; the negative PDO phase during recent years (through 2013) is consistent with the positive (later spring) trends the Northwest has experienced (Ault et al. 2015). The PDO may currently be suppressing or even counteracting the warming trend of the Northeast; however, it should not be assumed that this trend toward later springs will continue, especially considering recent extremely early springs such as 2015.
When we ask, “what are the climate change impacts on phenology in Region X?”, it is critical to recognize that there are biogeographic biases in the distribution of scientific research and literature on phenology and climate change. In large part, research and monitoring and thus published research studies, especially using ground-based methods (i.e. not satellite remote sensing) has focused on temperate deciduous ecosystems of the eastern US and Europe. Furthermore, analyses that utilize satellite remote sensing are temporally limited to assessing patterns and trends within the past 20 years.
Biogeographic phenology research is generally biased in focus on the northeast US, with large data gaps in the northwest (and southwest) are important when we consider spatial variation in climate and phenological response. One robust regional response to interannual climate variability is the trend in the coterminous United States of an apparent dipole pattern with ‘‘centers of action’’ in the northwest Cascade and Great Lakes regions (Ault et al. 2015). This dipole patterns appears to be generated by different climatological processes operating in these regions. In the Northwest, spring temperatures and thus spring bloom and leaf out of plants are strongly correlated with the Pacific Decadal Oscillation (Ault et al. 2015), a climate index based upon patterns of variation in sea surface temperature of the North Pacific from 1900 to present. Positive PDO phases favor early spring and negative phases favor later spring; the negative PDO phase during recent years (through 2013) is consistent with the positive (later spring) trends the Northwest has experienced (Ault et al. 2015). The PDO may currently be suppressing or even counteracting the warming trend of the Northeast; however, it should not be assumed that this trend toward later springs will continue, especially considering recent extremely early springs such as 2015.
Figure 6. Linear trends in the first leaf (left) and first bloom (right) indices over two different periods: (top) 1979–2013 and (bottom) 1920–2013. The boxes show the centers of action (refer to section 4c) in the Northwest Cascades (43°–48°N, 122°–110°W) and Great Lakes (38°–45°N, 90°–75°W). The longer period map shows much more uniform warming. Large-scale climate mechanisms that generate these patterns may be different in the two regions (Figure used with permission by T. Ault, adapted from (Ault et al. 2015).