Reconstructed storm tracks reveal three centuries of changing moisture delivery to North America

Moisture delivery to western North America is closely linked to variability in the westerly storm tracks of midlatitude cyclones, which are, in turn, modified by larger-scale features such as the El Niño–Southern Oscillation system. Instrumental and modeling data suggest that extratropical storm tracks may be intensifying and shifting poleward due to anthropogenic climate change, but it is difficult to separate recent trends from natural variability because of the large amount of decadal and longer variation in storm tracks and their limited instrumental record. We reconstruct cool-season, midlatitude Pacific storm-track position and intensity from 1693 to 1995 CE using existing tree-ring chronologies along with a network of newly developed chronologies from the U.S. Pacific Northwest, where small variations in storm-track position can have a major influence on hydroclimate patterns. Our results show high interannual-to-multidecadal variability in storm-track position and intensity over the past 303 years, with spectral signatures characteristic of tropical and northern Pacific influences. Comparison with reconstructions of precipitation and tropical sea surface temperature confirms the relationship between shifting drought patterns in the Pacific Northwest and storm-track variability through time and demonstrates the long-term influence of El Niño. These results allow us to place recent storm-track changes in the context of decadal and multidecadal fluctuations across the long-term record, showing that recent changes in storm-track intensity likely represent a warming-related increase amplified by natural decadal variability

Persistence of pressure patterns over North America and the North Pacific since AD 1500

Changes in moisture delivery to western North America are largely controlled by interrelated, synoptic-scale atmospheric pressure patterns. Long-term records of upper-atmosphere pressure and related circulation patterns are needed to assess potential drivers of past severe droughts and evaluate how future climate changes may impact hydroclimatic systems. Here we develop a tree-ring-based climate field reconstruction of cool-season 500 hPa geopotential height on a 2° × 2° grid over North America and the North Pacific to AD 1500 and examine the frequency and persistence of preinstrumental atmospheric pressure patterns using Self-Organizing Maps. Our results show extended time periods dominated by a set of persistent upper-air pressure patterns, providing insight into the atmospheric conditions leading to periods of sustained drought and pluvial periods in the preinstrumental past. A striking shift from meridional to zonal flow occurred at the end of the Little Ice Age and was sustained for several decades

The opposing transcriptional functions of Sin3a and c-Myc are required to maintain tissue homeostasis.

How the proto-oncogene c-Myc balances the processes of stem-cell self-renewal, proliferation and differentiation in adult tissues is largely unknown. We explored c-Myc's transcriptional roles at the epidermal differentiation complex, a locus essential for skin maturation. Binding of c-Myc can simultaneously recruit (Klf4, Ovol-1) and displace (Cebpa, Mxi1 and Sin3a) specific sets of differentiation-specific transcriptional regulators to epidermal differentiation complex genes. We found that Sin3a causes deacetylation of c-Myc protein to directly repress c-Myc activity. In the absence of Sin3a, genomic recruitment of c-Myc to the epidermal differentiation complex is enhanced, and re-activation of c-Myc-target genes drives aberrant epidermal proliferation and differentiation. Simultaneous deletion of c-Myc and Sin3a reverts the skin phenotype to normal. Our results identify how the balance of two transcriptional key regulators can maintain tissue homeostasis through a negative feedback loop

Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm

Classifying land cover is perhaps the most common application of remote sensing, yet classification at frequent temporal intervals remains a challenging task due to radiometric differences among scenes, time and budget constraints, and semantic differences among class definitions from different dates. The automatic adaptive signature generalization (AASG) algorithm overcomes many of these limitations by locating stable sites between two images and using them to adapt class spectral signatures from a high-quality reference classification to a new image, which mitigates the impacts of radiometric and phenological differences between images and ensures that class definitions remain consistent between the two classifications. We refined AASG to adapt stable site identification parameters to each individual land cover class, while also incorporating improved input data and a random forest classifier. In the Research Triangle region of North Carolina, our new version of AASG demonstrated an improved ability to update existing land cover classifications compared to the initial version of AASG, particularly for low intensity developed, mixed forest, and woody wetland classes. Topographic indices were particularly important for distinguishing woody wetlands from other forest types, while multi-seasonal imagery contributed to improved classification of water, developed, forest, and hay/pasture classes. These results demonstrate both the flexibility of the AASG algorithm and the potential for using it to produce high-quality land cover classifications that can utilize the entire temporal range of the Landsat archive in an automated fashion while maintaining consistent class definitions through time

Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes

Earth’s hydroclimatic variability is increasing, with changes in the frequency of extreme events that may negatively affect forest ecosystems. We examined possible consequences of changing precipitation variability using tree rings in the conterminous United States. While many growth records showed either little evidence of precipitation limitation or linear relationships to precipitation, growth of some species (particularly those in semiarid regions) responded asymmetrically to precipitation such that tree growth reductions during dry years were greater than, and not compensated by, increases during wet years. The U.S. Southwest, in particular, showed a large increase in precipitation variability, coupled with asymmetric responses of growth to precipitation. Simulations suggested roughly a twofold increase in the probability of large negative growth anomalies across the Southwest resulting solely from 20th century increases in variability of cool-season precipitation. Models project continued increases in precipitation variability, portending future growth reductions across semiarid forests of the western United States.Strategic Environmental Research and Development Program (SERDP) [RC18-1322]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Atmospheric teleconnection influence on North American land surface phenology

Short-term forecasts of vegetation activity are currently not well constrained due largely to our lack of understanding of coupled climate-vegetation dynamics mediated by complex interactions between atmospheric teleconnection patterns. Using ecoregion-scale estimates of North American vegetation activity inferred from remote sensing (1982-2015), we examined seasonal and spatial relationships between land surface phenology and the atmospheric components of five teleconnection patterns over the tropical Pacific, north Pacific, and north Atlantic. Using a set of regression experiments, we also tested for interactions among these teleconnection patterns and assessed predictability of vegetation activity solely based on knowledge of atmospheric teleconnection indices. Autumn-to-winter composites of the Southern Oscillation Index (SOI) were strongly correlated with start of growing season timing, especially in the Pacific Northwest. The two leading modes of north Pacific variability (the Pacific-North American, PNA, and West Pacific patterns) were significantly correlated with start of growing season timing across much of southern Canada and the upper Great Lakes. Regression models based on these Pacific teleconnections were skillful predictors of spring phenology across an east-west swath of temperate and boreal North America, between 40 degrees N-60 degrees N. While the North Atlantic Oscillation (NAO) was not strongly correlated with start of growing season timing on its own, we found compelling evidence of widespread NAO-SOI and NAO-PNA interaction effects. These results suggest that knowledge of atmospheric conditions over the Pacific and Atlantic Oceans increases the predictability of North American spring phenology. A more robust consideration of the complexity of the atmospheric circulation system, including interactions across multiple ocean basins, is an important step towards accurate forecasts of vegetation activity.NSF Paleo Perspectives on Climate Change (P2C2) grant [1304422]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Globally Consistent Patterns of Asynchrony in Vegetation Phenology Derived From Optical, Microwave, and Fluorescence Satellite Data

Climate change is impacting vegetation phenology with important implications and feedbacks to global carbon, water, and nutrient cycling. Satellite remote sensing can monitor multiple aspects of land surface phenology and its sensitivity to climate. Normalized difference vegetation index (NDVI) tracks vegetation chlorophyll content. Vegetation optical depth (VOD) tracks the total water content of aboveground biomass. Solar-induced chlorophyll fluorescence (SIF) more directly approximates vegetation gross photosynthesis. Yet it remains unclear how these different indices, representing independent vegetation development processes, covary over the course of a growing season and across the global domain. To address this gap, we synthesize information from all three indices and enable a deeper understanding of seasonal phenology that goes beyond seasonal photosynthesis. We derive and evaluate 9-year average timing of start of growing season (SOS), peak of growing season (POS), and end of growing season (EOS) for each of these satellite indices globally. We found SOS occurs relatively synchronously, but they become increasingly asynchronous as growing season progresses, such that POS and EOS from SIF occurred first, followed by NDVI and finally by VOD. In contrast to the relatively quick and synchronous start-of-season transition, senescence appears to be a relatively prolonged transition, beginning with reduced photosynthetic activity, then greenness/chlorophyll, and finally plant water content. Comparisons with gross primary productivity from eddy covariance flux tower observations confirm that SIF most closely tracks seasonal photosynthesis. NDVI, VOD, and SIF provide independent, complementary information on seasonal vegetation transitions and reveal new insights into the complex underlying functional and structural processes that control vegetation growth and senescence.6 month embargo; first published: 14 June 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Scale dependency of lidar‐derived forest structural diversity

Abstract Lidar‐derived forest structural diversity (FSD) metrics—including measures of forest canopy height, vegetation arrangement, canopy cover (CC), structural complexity and leaf area and density—are increasingly used to describe forest structural characteristics and can be used to infer many ecosystem functions. Despite broad adoption, the importance of spatial resolution (grain and extent) over which these structural metrics are calculated remains largely unconsidered. Often researchers will quantify FSD at the spatial grain size of the process of interest without considering the scale dependency or statistical behaviour of the FSD metric employed. We investigated the appropriate scale of inference for eight lidar‐derived spatial metrics—CC, canopy relief ratio, foliar height diversity, leaf area index, mean and median canopy height, mean outer canopy height, and rugosity (RT)‐‐representing five FSD categories—canopy arrangement, CC, canopy height, leaf area and density, and canopy complexity. Optimal scale was determined using the representative elementary area (REA) concept whereby the REA is the smallest grain size representative of the extent. Structural metrics were calculated at increasing canopy spatial grain (from 5 to 1000 m) from aerial lidar data collected at nine different forested ecosystems including sub‐boreal, broadleaf temperate, needleleaf temperate, dry tropical, woodland and savanna systems, all sites are part of the National Ecological Observatory Network within the conterminous United States. To identify the REA of each FSD metric, we used changepoint analysis via segmented or piecewise regression which identifies significant changepoints for both the magnitude and variance of each metric. We find that using a spatial grain size between 25 and 75 m sufficiently captures the REA of CC, canopy arrangement, canopy leaf area and canopy complexity metrics across multiple forest types and a grain size of 30–150 m captures the REA of canopy height metrics. However, differences were evident among forest types with higher REA necessary to characterize CC in evergreen needleleaf forests, and canopy height in deciduous broadleaved forests. These findings indicate the appropriate range of spatial grain sizes from which inferences can be drawn from this set of FSD metrics, informing the use of lidar‐derived structural metrics for research and management applications