Peer review report 2 On “Reconstruction of a 253 year-long mast record of European beech revealsits association with large scale temperature variability and no long-term trend in mast frequencies”

2015 ◽  
Vol 201 ◽  
pp. 281-282
Author(s):  
Anonymous
Keyword(s):  
Peer Review ◽  
Large Scale ◽  
European Beech ◽  
Temperature Variability ◽  
Term Trend ◽  
Scale Temperature ◽  
Long Term Trend

scholarly journals Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niloufar Nouri ◽  
Naresh Devineni ◽  
Valerie Were ◽  
Reza Khanbilvardi
Keyword(s):  
Population Density ◽  
Large Scale ◽  
Southern Oscillation ◽  
The United States ◽  
Anthropogenic Factors ◽  
Climate Variables ◽  
Term Trend ◽  
Cyclical Variability ◽  
Long Term Trend

AbstractThe annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

scholarly journals On the Maximum Observed Wind Speed in a Randomly Sampled Hurricane

2010 ◽  
Vol 23 (5) ◽  
pp. 1262-1265 ◽  
Author(s):  
Andrew R. Solow
Keyword(s):  
Wind Speed ◽  
Large Scale ◽  
Ocean Warming ◽  
Hurricane Intensity ◽  
Term Trend ◽  
Long Term Trend ◽  
Observational Record

Abstract There is considerable interest in detecting a long-term trend in hurricane intensity possibly related to large-scale ocean warming. This effort is complicated by the paucity of wind speed measurements for hurricanes occurring in the early part of the observational record. Here, results are presented regarding the maximum observed wind speed in a sparsely randomly sampled hurricane based on a model of the evolution of wind speed over the lifetime of a hurricane.

scholarly journals Variation in Leaf Morphological Traits of European Beech and Norway Spruce Over Two Decades in Switzerland

2022 ◽  
Vol 4 ◽  
Author(s):  
Joachim Zhu ◽  
Anne Thimonier ◽  
Sophia Etzold ◽  
Katrin Meusburger ◽  
Peter Waldner ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Morphological Traits ◽  
European Beech ◽  
Temporal Analysis ◽  
Legacy Effects ◽  
Term Trend ◽  
Long Term Trend ◽  
Needle Mass ◽  
Leaf Morphological Traits

Leaf morphological traits (LMTs) of forest trees have been observed to vary across space and species. However, long-term records of LMTs are scarce, due to a lack of measurements and systematic leaf archives. This leaves a large gap in our understanding of the temporal dynamics and drivers of LMT variations, which may help us understand tree acclimation strategies. In our study, we used long-term LMT measurements from foliar material collections of European beech (Fagus sylvatica) and Norway spruce (Picea abies), performed every second year from 1995 to 2019 on the same trees within the Swiss Long-term Forest Ecosystem Research Program LWF. The 11 study plots (6 beech, 4 spruce, and 1 mixed) are distributed along gradients of elevation (485–1,650 m a.s.l.), mean annual precipitation (935–2142 mm), and mean annual temperature (3.2–9.8°C). The investigated LMTs were (i) leaf or needle mass, (ii) leaf area or needle length, and (iii) leaf mass per area or needle mass per length. We combined this unique data set with plot variables and long-term data on potential temporal drivers of LMT variations, including meteorological and tree trait data. We used univariate linear regressions and linear mixed-effects models to identify the main spatial and temporal drivers of LMT variations, respectively. For beech LMTs, our temporal analysis revealed effects of mast year and crown defoliation, and legacy effects of vapor pressure deficit and temperature in summer and autumn of the preceding year, but no clear long-term trend was observed. In contrast, spruce LMTs were mainly driven by current-year spring conditions, and only needle mass per length showed a decreasing long-term trend over the study period. In temporal models, we observed that LMTs of both species were influenced by elevation and foliar nutrient concentrations, and this finding was partly confirmed by our spatial analyses. Our results demonstrate the importance of temporal analysis for determining less recognized drivers and legacy effects that influence LMTs, which are difficult to determine across space and species. The observed differences in the temporal drivers of beech and spruce LMTs suggest differences in the adaptation and acclimation potential of the two species.

scholarly journals Bayesian probabilistic forecasting with large-scale educational trend data: a case study using NAEP

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
David Kaplan ◽  
Mingya Huang
Keyword(s):  
United States ◽  
Large Scale ◽  
Population Level ◽  
The United States ◽  
The State ◽  
Probabilistic Forecasting ◽  
Term Trend ◽  
Trend Data ◽  
Long Term Trend

AbstractOf critical importance to education policy is monitoring trends in education outcomes over time. In the United States, the National Assessment of Educational Progress (NAEP) has provided long-term trend data since 1970; at the state/jurisdiction level, NAEP has provided long-term trend data since 1996. In addition to the national NAEP, all 50 states and United States jurisdictions participate in the state NAEP assessment. Thus, NAEP provides important monitoring and forecasting information regarding population-level academic performance of relevance to national and international goals. However, an inspection of NAEP trend reports shows that relatively simple trend plots are provided; and although these plots are important for communicating general trend information, we argue that much more useful information can be obtained by adopting a Bayesian probabilistic forecasting point of view. The purpose of this paper is to provide a Bayesian probabilistic forecasting workflow that can be used with large-scale assessment trend data generally, and to demonstrate that workflow with an application to the state NAEP assessments.

NAEP Trends: Main NAEP vs. Long-Term Trend

2010 ◽  
Author(s):  
Albert E. Beaton ◽  
James R. Chromy
Keyword(s):  
Term Trend ◽  
Long Term Trend
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