scholarly journals Supplementary material to "A regional evaluation of the influence of climate change on long term trends in chlorophyll-a in large Italian lakes from satellite data"

2020 ◽  
Author(s):  
Gary Free ◽  
Mariano Bresciani ◽  
Monica Pinardi ◽  
Nicola Ghirardi ◽  
Giulia Luciani ◽  
...  
Keyword(s):  
Climate Change ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Long Term Trends ◽  
Supplementary Material

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

scholarly journals Forest insects and climate change: long-term trends in herbivore damage

2013 ◽  
Vol 3 (12) ◽  
pp. 4183-4196 ◽  
Author(s):  
Maartje J. Klapwijk ◽  
György Csóka ◽  
Anikó Hirka ◽  
Christer Björkman
Keyword(s):  
Climate Change ◽  
Herbivore Damage ◽  
Forest Insects ◽  
Long Term Trends

scholarly journals Examining the Climatology of Shortwave Radiation in the Northeastern United States

2017 ◽  
Vol 56 (10) ◽  
pp. 2869-2881
Author(s):  
Janel Hanrahan ◽  
Alexandria Maynard ◽  
Sarah Y. Murphy ◽  
Colton Zercher ◽  
Allison Fitzpatrick
Keyword(s):  
Climate Change ◽  
United States ◽  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Predictor Variable ◽  
Shortwave Radiation ◽  
Northeastern United States ◽  
Weather Patterns ◽  
Long Term Trends

AbstractAs demand for renewable energy grows, so does the need for an improved understanding of renewable energy sources. Paradoxically, the climate change mitigation strategy of fossil fuel divestment is in itself subject to shifts in weather patterns resulting from climate change. This is particularly true with solar power, which depends on local cloud cover. However, because observed shortwave radiation data usually span a decade or less, persistent long-term trends may not be identified. A simple linear regression model is created here using diurnal temperature range (DTR) during 2002–15 as a predictor variable to estimate long-term shortwave radiation (SR) values in the northeastern United States. Using an extended DTR dataset, SR values are computed for 1956–2015. Statistically significant decreases in shortwave radiation are identified that are dominated by changes during the summer months. Because this coincides with the season of greatest insolation and the highest potential for energy production, financial implications may be large for the solar energy industry if such trends persist into the future.

scholarly journals Supplementary material to "Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations"

2018 ◽  
Author(s):  
Daniel T. McCoy ◽  
Paul R. Field ◽  
Gregory S. Elsaesser ◽  
Alejandro Bodas-Salcedo ◽  
Brian H. Kahn ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Extratropical Cyclones ◽  
Cloud Feedbacks ◽  
Supplementary Material

Long-term trends in seasonal plankton dynamics in Lake Mead (Nevada-Arizona, USA) and implications for climate change

Hydrobiologia ◽  
2018 ◽  
Vol 822 (1) ◽  
pp. 85-109 ◽  
Author(s):  
John R. Beaver ◽  
Janet E. Kirsch ◽  
Claudia E. Tausz ◽  
Erin E. Samples ◽  
Thomas R. Renicker ◽  
...  
Keyword(s):  
Climate Change ◽  
Lake Mead ◽  
Plankton Dynamics ◽  
Long Term Trends

scholarly journals Analysis of Long-Term Trends of Annual and Seasonal Rainfall in the Awash River Basin, Ethiopia

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1498 ◽  
Author(s):  
Solomon Mulugeta ◽  
Clifford Fedler ◽  
Mekonen Ayana
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Winter Rainfall ◽  
Adaptation Measures ◽  
Awash River Basin ◽  
Long Term Trends ◽  
Increasing Trends

With climate change prevailing around the world, understanding the changes in long-term annual and seasonal rainfall at local scales is very important in planning for required adaptation measures. This is especially true for areas such as the Awash River basin where there is very high dependence on rain- fed agriculture characterized by frequent droughts and subsequent famines. The aim of the study is to analyze long-term trends of annual and seasonal rainfall in the Awash River Basin, Ethiopia. Monthly rainfall data extracted from Climatic Research Unit (CRU 4.01) dataset for 54 grid points representing the entire basin were aggregated to find the respective areal annual and seasonal rainfall time series for the entire basin and its seven sub-basins. The Mann-Kendall (MK) test and Sen Slope estimator were applied to the time series for detecting the trends and for estimating the rate of change, respectively. The Statistical software package R version 3.5.2 was used for data extraction, data analyses, and plotting. Geographic information system (GIS) package was also used for grid making, site selection, and mapping. The results showed that no significant trend (at α = 0.05) was identified in annual rainfall in all sub-basins and over the entire basin in the period (1902 to 2016). However, the results for seasonal rainfall are mixed across the study areas. The summer rainfall (June through September) showed significant decreasing trend (at α ≤ 0.1) over five of the seven sub-basins at a rate varying from 4 to 7.4 mm per decade but it showed no trend over the two sub-basins. The autumn rainfall (October through January) showed no significant trends over four of the seven sub-basins but showed increasing trends over three sub-basins at a rate varying from 2 to 5 mm per decade. The winter rainfall (February through May) showed no significant trends over four sub-basins but showed significant increasing trends (at α ≤ 0.1) over three sub-basins at a rate varying from 0.6 to 2.7 mm per decade. At the basin level, the summer rainfall showed a significant decreasing trend (at α = 0.05) while the autumn and winter rainfall showed no significant trends. In addition, shift in some amount of summer rainfall to winter and autumn season was noticed. It is evident that climate change has shown pronounced effects on the trends and patterns of seasonal rainfall. Thus, the study contribute to better understanding of climate change in the basin and the information from the study can be used in planning for adaptation measures against a changing climate.

Long-term trends in ocean chlorophyll: update from a Bayesian hierarchical space-time model

2020 ◽  
Author(s):  
Claudie Beaulieu ◽  
Matthew Hammond ◽  
Stephanie Henson ◽  
Sujit Sahu
Keyword(s):  
Climate Change ◽  
Coupled Model ◽  
Climate Change Impacts ◽  
Space Time ◽  
Time Model ◽  
Bayesian Hierarchical ◽  
Color Sensors ◽  
Long Term Trends ◽  
Space Time Model

<p>Assessing ongoing changes in marine primary productivity is essential to determine the impacts of climate change on marine ecosystems and fisheries. Satellite ocean color sensors provide detailed coverage of ocean chlorophyll in space and time, now with a combined record length of just over 20 years. Detecting climate change impacts is hindered by the shortness of the record and the long timescale of memory within the ocean such that even the sign of change in ocean chlorophyll is still inconclusive from time-series analysis of satellite data. Here we use a Bayesian hierarchical space-time model to estimate long-term trends in ocean chlorophyll. The main advantage of this approach comes from the principle of ”borrowing strength” from neighboring grid cells in a given region to improve overall detection. We use coupled model simulations from the CMIP5 experiment to form priors to provide a “first guess” on observational trend estimates and their uncertainty that we then update using satellite observations. We compare the results with estimates obtained with the commonly used vague prior, reflecting the case where no independent knowledge is available.  A global average net positive chlorophyll trend is found, with stronger regional trends that are typically positive in high and mid latitudes, and negative at low latitudes outside the Atlantic. The Bayesian hierarchical model used here provides a framework for integrating different sources of data for detecting trends and estimating their uncertainty in studies of global change.</p>

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