scholarly journals Nutrient dilution and climate cycles underlie declines in a dominant insect herbivore

2020 ◽  
Vol 117 (13) ◽  
pp. 7271-7275 ◽  
Author(s):  
Ellen A. R. Welti ◽  
Karl A. Roeder ◽  
Kirsten M. de Beurs ◽  
Anthony Joern ◽  
Michael Kaspari
Keyword(s):  
Large Scale ◽  
Plant Biomass ◽  
Southern Oscillation ◽  
Chemical Pollution ◽  
Elemental Content ◽  
Insect Herbivores ◽  
Foliar Concentrations ◽  
Long Term Trends ◽  
Underlying Mechanisms

Evidence for global insect declines mounts, increasing our need to understand underlying mechanisms. We test the nutrient dilution (ND) hypothesis—the decreasing concentration of essential dietary minerals with increasing plant productivity—that particularly targets insect herbivores. Nutrient dilution can result from increased plant biomass due to climate or CO2 enrichment. Additionally, when considering long-term trends driven by climate, one must account for large-scale oscillations including El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). We combine long-term datasets of grasshopper abundance, climate, plant biomass, and end-of-season foliar elemental content to examine potential drivers of abundance cycles and trends of this dominant herbivore. Annual grasshopper abundances in 16- and 22-y time series from a Kansas prairie revealed both 5-y cycles and declines of 2.1–2.7%/y. Climate cycle indices of spring ENSO, summer NAO, and winter or spring PDO accounted for 40–54% of the variation in grasshopper abundance, mediated by effects of weather and host plants. Consistent with ND, grass biomass doubled and foliar concentrations of N, P, K, and Na—nutrients which limit grasshopper abundance—declined over the same period. The decline in plant nutrients accounted for 25% of the variation in grasshopper abundance over two decades. Thus a warming, wetter, more CO2-enriched world will likely contribute to declines in insect herbivores by depleting nutrients from their already nutrient-poor diet. Unlike other potential drivers of insect declines—habitat loss, light and chemical pollution—ND may be widespread in remaining natural areas.

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.

Oceanography of the New Zealand subantarctic region

2021 ◽  
Author(s):  
◽  
Aitana Forcén-Vázquez
Keyword(s):  
New Zealand ◽  
Interannual Variability ◽  
Water Column ◽  
Large Scale ◽  
Southern Oscillation ◽  
Complex Structure ◽  
Positive Trend ◽  
Intermediate Water ◽  
Subantarctic Region

<p>Subantarctic New Zealand is an oceanographycally dynamic region with the Subtropical Front (STF) to the north and the Subantarctic Front (SAF) to the south. This thesis investigates the ocean structure of the Campbell Plateau and the surrounding New Zealand subantarctic, including the spatial, seasonal, interannual and longer term variability over the ocean properties, and their connection to atmospheric variability using a combination of in-situ oceanographic measurements and remote sensing data.  The spatial and seasonal oceanographic structure in the New Zealand subantarctic region was investigated by analysing ten high resolution Conductivity Temperature and Depth (CTD) datasets, sampled during oceanographic cruises from May 1998 to February 2013. Position of fronts, water mass structure and changes over the seasons show a complex structure around the Campbell Plateau combining the influence of subtropical and subantarctic waters.  The spatial and interannual variability on the Campbell Plateau was described by analysing approximately 70 low resolution CTD profiles collected each year in December between 2002 and 2009. Conservative temperature and absolute salinity profiles reveal high variability in the upper 200m of the water column and a homogeneous water column from 200 to 600m depth. Temperature variability of about 0.7 °C, on occasions between consecutive years, is observed down to 900m depth. The presence of Subantarctic Mode Water (SAMW) on the Campbell Plateau is confirmed and Antarctic Intermediate Water (AAIW) reported for the first time in the deeper regions around the edges of the plateau.  Long-term trends and variability over the Campbell Plateau were investigated by analysing satellite derived Sea Level Anomalies (SLA) and Sea Surface Temperature (SST) time series. Links to large scale atmospheric processes are also explored through correlation with the Southern Oscillation Index (SOI) and Southern Annular Mode (SAM). SST shows a strong seasonality and interannual variability which is linked to local winds, but no significant trend is found. The SLA over the Campbell Plateau has increased at a rate of 5.2 cm decade⁻¹ in the last two decades. The strong positive trend in SLA appears to be a combination of the response of the ocean to wind stress curl (Ekman pumping), thermal expansion and ocean mass redistribution via advection amongst others.  These results suggest that the variability on the Campbell Plateau is influenced by the interaction of the STF and the SAF. The STF influence reaches the limit of the SAF over the western Campbell Plateau and the SAF influence extends all around the plateau. Results also suggest different connections between the plateau with the surrounding oceans, e.g., along the northern edge with the Bounty Trough and via the southwest edge with the SAF. A significant correlation with SOI and little correlation with SAM suggest a stronger response to tropically driven processes in the long-term variability on the Campbell Plateau.  The results of this thesis provide a new definitive assessment of the circulation, water masses and variability of the Campbell Plateau on mean, annual, and interannual time scales which will support research in other disciplines such as palaeoceanography, fisheries management and climate.</p>

scholarly journals West African Summer Monsoon Precipitation Variability as Represented by Reanalysis Datasets

Climate ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 111
Author(s):  
Kwesi Akumenyi Quagraine ◽  
Francis Nkrumah ◽  
Cornelia Klein ◽  
Nana Ama Browne Klutse ◽  
Kwesi Twentwewa Quagraine
Keyword(s):  
Summer Monsoon ◽  
Southern Oscillation ◽  
West African Monsoon ◽  
West African ◽  
Positive Trend ◽  
In Situ Data ◽  
Dry Conditions ◽  
Long Term Trends

Focusing on West Africa, a region riddled with in situ data scarcity, we evaluate the summer monsoon monthly rainfall characteristics of five global reanalysis datasets: ERA5, ERA-Interim, JRA-55, MERRA2, and NCEP-R2. Their performance in reproducing the West African monsoon (WAM) climatology, interannual variability, and long-term trends for the main monsoon months are compared to gauge-only and satellite products. We further examine their ability to reproduce teleconnections between sea surface temperatures and monsoon rainfall. All reanalyses are able to represent the average rainfall patterns and seasonal cycle; however, regional biases can be marked. ERA5, ERA-Interim, and NCEP-R2 underestimate rainfall over areas of peak rainfall, with ERA5 showing the strongest underestimation, particularly over the Guinea Highlands. The meridional northward extent of the monsoon rainband is well captured by JRA-55 and MERRA2 but is too narrow in ERA-Interim, for which rainfall stays close to the Guinea Coast. Differences in rainband displacement become particularly evident when comparing strong El Niño Southern Oscillation (ENSO) years, where all reanalyses except ERA-Interim reproduce wetter Sahelian conditions for La Niña, while overestimating dry conditions at the coast except for NCEP-R2. Precipitation trends are not coherent across reanalyses and magnitudes are generally overestimated compared to observations, with only JRA-55 and NCEP-R2 displaying the expected positive trend in the Sahel. ERA5 generally outperforms ERA-Interim, highlighting clear improvements over its predecessor. Ultimately, we find the strengths of reanalyses to strongly vary across the region.

Large-scale, long-term trends in British river macroinvertebrates

2012 ◽  
Vol 18 (7) ◽  
pp. 2184-2194 ◽  
Author(s):  
Ian P. Vaughan ◽  
Steve J. Ormerod
Keyword(s):  
Large Scale ◽  
Long Term Trends

scholarly journals Reply to “Comment on ‘Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue’”

2014 ◽  
Vol 27 (4) ◽  
pp. 1821-1825 ◽  
Author(s):  
Douglas Maraun
Keyword(s):  
Large Scale ◽  
Weather Forecasting ◽  
Small Scale ◽  
Quantile Mapping ◽  
Spatiotemporal Structure ◽  
Weather Forecasts ◽  
Long Term Trends ◽  
Climate Signals ◽  
Local Variability

Abstract In his comment, G. Bürger criticizes the conclusion that inflation of trends by quantile mapping is an adverse effect. He assumes that the argument would be “based on the belief that long-term trends and along with them future climate signals are to be large scale.” His line of argument reverts to the so-called inflated regression. Here it is shown, by referring to previous critiques of inflation and standard literature in statistical modeling as well as weather forecasting, that inflation is built upon a wrong understanding of explained versus unexplained variability and prediction versus simulation. It is argued that a sound regression-based downscaling can in principle introduce systematic local variability in long-term trends, but inflation systematically deteriorates the representation of trends. Furthermore, it is demonstrated that inflation by construction deteriorates weather forecasts and is not able to correctly simulate small-scale spatiotemporal structure.

scholarly journals Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2020 ◽  
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Global Scale ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

scholarly journals Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

2021 ◽  
Vol 13 (5) ◽  
pp. 2001-2023
Author(s):  
Zhen Zhang ◽  
Etienne Fluet-Chouinard ◽  
Katherine Jensen ◽  
Kyle McDonald ◽  
Gustaf Hugelius ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Amazon Basin ◽  
Southern Oscillation ◽  
Microwave Remote Sensing ◽  
Annual Maximum ◽  
Time Step ◽  
Wetland Area ◽  
Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area are a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary in their wetland definition, causing substantial disagreement between and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed the global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset at a ∼ 25 km resolution at the Equator (0.25∘) at a monthly time step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at a coarse resolution with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We excluded all permanent water bodies (e.g., lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0×106 km2 (13.0 Mkm2), which can be divided into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M shows good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Basin lowlands and West Siberian lowlands, with Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetland products. By evaluating the temporal variation in WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño–Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at https://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

scholarly journals Challenges and Opportunities in Coastal Shoreline Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Kristen D. Splinter ◽  
Giovanni Coco
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Shoreline Change ◽  
Sandy Beaches ◽  
Water Levels ◽  
Current State ◽  
Challenges And Opportunities ◽  
Future Challenges ◽  
Shoreline Prediction

Sandy beaches comprise approximately 31% of the world's ice-free coasts. Sandy coastlines around the world are continuously adjusting in response to changing waves and water levels at both short (storm) and long (climate-driven, from El-Nino Southern Oscillation to sea level rise) timescales. Managing this critical zone requires robust, advanced tools that represent our best understanding of how to abstract and integrate coastal processes. However, this has been hindered by (1) a lack of long-term, large-scale coastal monitoring of sandy beaches and (2) a robust understanding of the key physical processes that drive shoreline change over multiple timescales. This perspectives article aims to summarize the current state of shoreline modeling at the sub-century timescale and provides an outlook on future challenges and opportunities ahead.

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