Wed, Aug 17, 2022: 10:00 AM-10:15 AM
520E
Background/Question/MethodsClimate change represents a key challenge to the sustained provisioning of forest ecosystem goods and services. The use of standardized model projections to quantify potential climatic novelty relative to historical variability is increasingly used to infer climate change implications on ecosystem response. However, current assessments incorporating annualized projections are not well suited for detecting changes that may occur over different seasons. Explicitly considering seasonal change in future projections could help in developing sound management practices for building climate resiliency of eastern US forestlands. For example, building such knowledge base could help in the identification of unique risks to tree regeneration and growth manifesting at different times in the annual cycle and facilitate the selection of tree species for assisted range expansion. Using climatic novelty, we examined seasonal multivariate climate departures at monthly resolution for mid- (2046-2065) and late-century (2081-2099) time periods to anticipate potential differences in seasonal change for 55 eastern US National Forests (NFs). We used three climate variables for each month, mean daily maximum and minimum temperature and total precipitation, from an ensemble of six global change models under the RCP 8.5 emissions scenario, standardized against baseline conditions around the turn of the 21st century (1979-2010).
Results/ConclusionsWe mapped climate departures for each month according to the Mahalanobis distance and its standardization on the Chi distribution (σd). Climate departures were generally greater during summer relative to winter months for both time periods, indicating increasing projected novelty during the critical growing season for eastern US forests. For example, ~90% of NFs displayed σd exceeding two standard deviations by mid-century July (µ σd = 2.56), whereas only six NFs exceeded similar values by end-century January (µ σd = 1.29). Spring and autumn transition periods were more variable, including the Upper Midwest and Mid-Atlantic regions showing smaller departures relative to others. Projected monthly temperatures outpaced precipitation changes relative to historical variability (e.g., end-century July: µ σdTmax = 4.52; µ σdPPT = -0.17), inferring a stronger influence on climatic novelty. Differences in projected novelty can be explained by differences in seasonal variability, with historically-variable winters and less-variable summers generally contributing to smaller and larger departures, respectively. By explicitly examining seasonal change, our study improves predictions of climate change effects on forest ecosystems. These results help identify opportunities and risks (e.g., heightened exposure to summer drought) that may be masked from annualized projections and facilitate adaptive forest management in a changing climate.
Results/ConclusionsWe mapped climate departures for each month according to the Mahalanobis distance and its standardization on the Chi distribution (σd). Climate departures were generally greater during summer relative to winter months for both time periods, indicating increasing projected novelty during the critical growing season for eastern US forests. For example, ~90% of NFs displayed σd exceeding two standard deviations by mid-century July (µ σd = 2.56), whereas only six NFs exceeded similar values by end-century January (µ σd = 1.29). Spring and autumn transition periods were more variable, including the Upper Midwest and Mid-Atlantic regions showing smaller departures relative to others. Projected monthly temperatures outpaced precipitation changes relative to historical variability (e.g., end-century July: µ σdTmax = 4.52; µ σdPPT = -0.17), inferring a stronger influence on climatic novelty. Differences in projected novelty can be explained by differences in seasonal variability, with historically-variable winters and less-variable summers generally contributing to smaller and larger departures, respectively. By explicitly examining seasonal change, our study improves predictions of climate change effects on forest ecosystems. These results help identify opportunities and risks (e.g., heightened exposure to summer drought) that may be masked from annualized projections and facilitate adaptive forest management in a changing climate.