scholarly journals The Influence of Abiotic Factors on an Invasive Pest of Pulse Crops,Sitona lineatus(L.) (Coleoptera: Curculionidae), in North America

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
O. Olfert ◽  
R. M. Weiss ◽  
H. A. Cárcamo ◽  
S. Meers
Keyword(s):  
General Circulation ◽  
Abiotic Factors ◽  
Simulation Models ◽  
General Circulation Models ◽  
Invasive Pest ◽  
Significant Shift ◽  
Climate Data ◽  
Bioclimatic Model ◽  
The Usa ◽  
Ecoclimatic Index

Pea leaf weevil,Sitona lineatus(L.), native to Europe and North Africa, has been introduced into many other countries around the world, including the USA and Canada. Adults are oligophagous pests on leguminaceous plants.Sitona lineatuswas first recorded in Canada in 1997, near Lethbridge, Alberta. Since then, it has spread north in Alberta and west into Saskatchewan in 2007. Bioclimatic simulation models were used to predict the distribution and extent of establishment ofS. lineatusin Canada based on its current geographic range, phenology, relative abundance, and empirical data. The study identified areas in Canada that are at risk for future establishment ofS. lineatusand developed a better understanding of climate effects. Climate change projections (General Circulation Models) were then imposed on the bioclimatic model ofS. lineatus. Bioclimatic model output varied for each of the three General Circulation Models. In terms of suitability for pest establishment (Ecoclimatic Index), the NCAR273 CCSM climate data resulted in the most significant shift northward.

scholarly journals PLASIM-ENTSem: a spatio-temporal emulator of future climate change for impacts assessment

2013 ◽  
Vol 6 (2) ◽  
pp. 3349-3380 ◽  
Author(s):  
P. B. Holden ◽  
N. R. Edwards ◽  
P. H. Garthwaite ◽  
K. Fraedrich ◽  
F. Lunkeit ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
Coupled Model ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Climate Impacts ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Data ◽  
Spatio Temporal

Abstract. Many applications in the evaluation of climate impacts and environmental policy require detailed spatio-temporal projections of future climate. To capture feedbacks from impacted natural or socio-economic systems requires interactive two-way coupling but this is generally computationally infeasible with even moderately complex general circulation models (GCMs). Dimension reduction using emulation is one solution to this problem, demonstrated here with the GCM PLASIM-ENTS. Our approach generates temporally evolving spatial patterns of climate variables, considering multiple modes of variability in order to capture non-linear feedbacks. The emulator provides a 188-member ensemble of decadally and spatially resolved (~ 5° resolution) seasonal climate data in response to an arbitrary future CO2 concentration and radiative forcing scenario. We present the PLASIM-ENTS coupled model, the construction of its emulator from an ensemble of transient future simulations, an application of the emulator methodology to produce heating and cooling degree-day projections, and the validation of the results against empirical data and higher-complexity models. We also demonstrate the application to estimates of sea-level rise and associated uncertainty.

Bioclimatic assessment of abiotic factors affecting relative abundance and distribution of wheat stem sawfly (Hymenoptera: Cephidae) in western Canada

2018 ◽  
Vol 151 (1) ◽  
pp. 16-33
Author(s):  
Owen Olfert ◽  
Ross M. Weiss ◽  
Haley Catton ◽  
Héctor Cárcamo ◽  
Scott Meers
Keyword(s):  
North America ◽  
Geographic Distribution ◽  
Relative Abundance ◽  
General Circulation ◽  
General Circulation Models ◽  
Simulation Software ◽  
Economic Losses ◽  
Western Canada ◽  
Wheat Stem Sawfly ◽  
Ecoclimatic Index

AbstractWheat stem sawfly, Cephus cinctus Norton (Hymenoptera: Cephidae), indigenous to North America, quickly adapted from host native grasses to wheat crops (Triticum Linnaeus (Poaceae)) with expansion of agriculture on the Great Plains of North America. Bioclimatic simulation tools, such as Climex, predict the potential geographic distribution and establishment of insects in ecosystems, based on climate. The ecoclimatic index, a measure of ecological suitability, integrates potential population growth with stresses to produce estimates of relative abundance. This simulation software was used to develop a bioclimate model for C. cinctus in western Canada. Results fostered a better understanding of how C. cinctus responded to selected climate variables. Two general circulation models were then applied to assess the response of C. cinctus populations to future climate. Relative to current climate, predicted changes in C. cinctus distribution and relative abundance were greatest for 2030, with a small further increase for 2070. Across the Prairies and Boreal Plains Ecozones, changes in ecoclimatic index were greater than in geographic distribution. Both general circulation models indicated most of this area would be categorised as very favourable. This suggests that the potential for pest populations could expand into areas that do not currently experience economic losses associated with C. cinctus.

Beneficial impacts of climate change on pastoral and broadacre agriculture in cool-temperate Tasmania

2014 ◽  
Vol 65 (2) ◽  
pp. 194 ◽  
Author(s):  
D. C. Phelan ◽  
D. Parsons ◽  
S. N. Lisson ◽  
G. K. Holz ◽  
N. D. MacLeod
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
General Circulation Models ◽  
Annual Rainfall ◽  
Wheat Crop ◽  
Daily Maximum ◽  
Climate Data ◽  
Diverse Range ◽  
Wheat Production ◽  
Elevated Atmospheric Co2

Although geographically small, Tasmania has a diverse range of regional climates that are affected by different synoptic influences. Consequently, changes in climate variables and climate-change impacts will likely vary in different regions of the state. This study aims to quantify the regional effects of projected climate change on the productivity of rainfed pastoral and wheat crop systems at five sites across Tasmania. Projected climate data for each site were obtained from the Climate Futures for Tasmania project (CFT). Six General Circulation Models were dynamically downscaled to ~10-km grid cells using the CSIRO Conformal Cubic Atmospheric Model under the A2 emissions scenario for the period 1961–2100. Mean daily maximum and minimum temperatures at each site are projected to increase from a baseline period (1981–2010) to 2085 (2071–2100) by 2.3–2.7°C. Mean annual rainfall is projected to increase slightly at all sites. Impacts on pasture and wheat production were simulated for each site using the projected CFT climate data. Mean annual pasture yields are projected to increase from the baseline to 2085 largely due to an increase in spring pasture growth. However, summer growth of temperate pasture species may become limited by 2085 due to greater soil moisture deficits. Wheat yields are also projected to increase, particularly at sites presently temperature-limited. This study suggests that increased temperatures and elevated atmospheric CO2 concentrations are likely to increase regional rainfed pasture and wheat production in the absence of any significant changes in rainfall patterns.

High-resolution isotopic simulations from ECHAM6-wiso nudged with ERA5 reanalyses: new products for isotopic model-data comparisons

2020 ◽  
Author(s):  
Alexandre Cauquoin ◽  
Martin Werner
Keyword(s):  
General Circulation ◽  
Ice Cores ◽  
General Circulation Models ◽  
Global Network ◽  
Climate Data ◽  
Isotopic Data ◽  
Surrounding Water ◽  
Weather Forecasts ◽  
The Impact ◽  
Water Isotope

<p>For several decades, the comparison of climate data with results from water isotope-enabled Atmosphere General Circulation Models (AGCMs) significantly helped to a better understanding of the processes ruling the water cycle, which is one of the main drivers of the climate variability. For the modern period, the use of AGCMs nudged with weather forecasts reanalyses is a powerful way to obtain model outputs under the same weather conditions than at the sampling time of the observations.</p><p>Here we present new isotopic simulations results from ECHAM6-wiso [1] nudged with the last reanalyses dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5 [2], at different spatial resolutions over the period 1979-2018. Model results are evaluated against isotopic data compilations, including GNIP (Global Network of Isotopes in Precipitation [3]), speleothems [4], ice cores datasets and water vapor measurements. To quantify the impact of these reanalyses on our simulations, we also performed nudged simulations with the previous model version ECHAM5-wiso [5] by using ERA5 data and its predecessor ERA-Interim [6].</p><p>These new simulation products could be a useful contribution to the isotopic data community for the interpretation of their water isotope records and for the exploration of the mechanisms controlling the variability of the surrounding water isotopic composition.</p><p> </p><p>[1] Cauquoin et al., Clim. Past, <strong>15</strong>, 1913–1937, https://doi.org/10.5194/cp-15-1913-2019, 2019.</p><p>[2] Copernicus Climate Change Service (C3S), 2017.</p><p>[3] IAEA, the GNIP Database, available at: https://nucleus.iaea.org/wiser.</p><p>[4] Comas-Bru et al., Clim. Past, <strong>15</strong>, 1557–1579, https://doi.org/10.5194/cp-15-1557-2019, 2019.</p><p>[5] Werner et al., Geosci. Model Dev., <strong>9</strong>, 647–670, https://doi.org/10.5194/gmd-9-647-2016, 2016.</p><p>[6] Dee et al., Q. J. R. Meteorol. Soc., <strong>137</strong>, 553–597, https://doi.org/10.1002/qj.828, 2011.</p>

scholarly journals The ability of a GCM-forced hydrological model to reproduce global discharge variability

2010 ◽  
Vol 7 (1) ◽  
pp. 687-724 ◽  
Author(s):  
F. C. Sperna Weiland ◽  
L. P. H. van Beek ◽  
J. C. J. Kwadijk ◽  
M. F. P. Bierkens
Keyword(s):  
General Circulation ◽  
Hydrological Model ◽  
General Circulation Models ◽  
The Arctic ◽  
Climate Data ◽  
Mean Values ◽  
Ensemble Mean ◽  
Runoff Variability ◽  
Abstract Data ◽  
Impacts Of Climate Change

Abstract. Data from General Circulation Models (GCMs) are often used in studies investigating hydrological impacts of climate change. However GCM data are known to have large biases, especially for precipitation. In this study the usefulness of GCM data for hydrological studies was tested by applying bias-corrected daily climate data of the 20CM3 control experiment from an ensemble of twelve GCMs as input to the global hydrological model PCR-GLOBWB. Results are compared with discharges calculated from a model run based on a reference meteorological dataset constructed from the CRU TS2.1 data and ERA-40 reanalysis time-series. Bias-correction was limited to monthly mean values as our focus was on the reproduction of runoff variability. The bias-corrected GCM based runs resemble the reference run reasonably well, especially for rivers with strong seasonal patterns. However, GCM derived discharge quantities are overall too low. Furthermore, from the arctic regimes it can be seen that a few deviating GCMs can bias the ensemble mean. Moreover, the GCMs do not well represent intra- and inter-year variability as exemplified by a limited persistence. This makes them less suitable for the projection of future runoff extremes.

scholarly journals PLASIM-ENTSem v1.0: a spatio-temporal emulator of future climate change for impacts assessment

2014 ◽  
Vol 7 (1) ◽  
pp. 433-451 ◽  
Author(s):  
P. B. Holden ◽  
N. R. Edwards ◽  
P. H. Garthwaite ◽  
K. Fraedrich ◽  
F. Lunkeit ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
Coupled Model ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Climate Impacts ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Data ◽  
Spatio Temporal

Abstract. Many applications in the evaluation of climate impacts and environmental policy require detailed spatio-temporal projections of future climate. To capture feedbacks from impacted natural or socio-economic systems requires interactive two-way coupling, but this is generally computationally infeasible with even moderately complex general circulation models (GCMs). Dimension reduction using emulation is one solution to this problem, demonstrated here with the GCM PLASIM-ENTS (Planet Simulator coupled with the efficient numerical terrestrial scheme). Our approach generates temporally evolving spatial patterns of climate variables, considering multiple modes of variability in order to capture non-linear feedbacks. The emulator provides a 188-member ensemble of decadally and spatially resolved (~ 5° resolution) seasonal climate data in response to an arbitrary future CO2 concentration and non-CO2 radiative forcing scenario. We present the PLASIM-ENTS coupled model, the construction of its emulator from an ensemble of transient future simulations, an application of the emulator methodology to produce heating and cooling degree-day projections, the validation of the simulator (with respect to empirical data) and the validation of the emulator (with respect to high-complexity models). We also demonstrate the application to estimates of sea-level rise and associated uncertainty.

scholarly journals Conjunction of wavelet-entropy and SOM clustering for multi-GCM statistical downscaling

2018 ◽  
Vol 50 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Aida Hosseini Baghanam ◽  
Vahid Nourani ◽  
Mohammad-Ali Keynejad ◽  
Hassan Taghipour ◽  
Mohammad-Taghi Alami
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Large Scale ◽  
General Circulation Models ◽  
Reanalysis Data ◽  
Self Organizing Map ◽  
Climate Data ◽  
Wavelet Entropy ◽  
Som Clustering ◽  
Artificial Neural Network Ann

Abstract Important issues in statistical downscaling of general circulation models (GCMs) is to select dominant large-scale climate data (predictors). This study developed a predictor screening framework, which integrates wavelet-entropy (WE) and self-organizing map (SOM) to downscale station rainfall. WEs were computed as the representatives of predictors and fed into the SOM to cluster the predictors. SOM-based clustering of predictors according to WEs could lead to physically meaningful selection of the dominant predictors. Then, artificial neural network (ANN) as the statistical downscaling method was developed. To assess the advantages of different GCMs, multi-GCM ensemble approach was used by Can-ESM2, BNU-ESM, and INM-CM4 GCMs. Moreover, NCEP reanalysis data were used to calibrate downscaling model as well for comparison purposes. The calibration, validation, and projection of the proposed model were performed during January 1951 to December 1991, January 1992 to December 2005 and January 2017 to December 2100, respectively. The proposed data screening model could reduce the dimensionality of data and select appropriate predictors for generalizing future rainfall. Results showed better performance of ANN than multiple linear regression (MLR) model. The projection results yielded 29% and 21% decrease of rainfall at the study area for 2017–2050 under RCPs 4.5 and 8.5, respectively.

Simulated impact of climate change on hydrology of multiple watersheds using traditional and recommended snowmelt runoff model methodology

2016 ◽  
Vol 7 (4) ◽  
pp. 665-682 ◽  
Author(s):  
Emile Elias ◽  
Albert Rango ◽  
Caitriana M. Steele ◽  
John F. Mejia ◽  
Ruben Baca ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
General Circulation Models ◽  
Future Application ◽  
Climate Data ◽  
Snowmelt Runoff ◽  
Change Analysis ◽  
The Impact ◽  
Runoff Model ◽  
Impacts Of Climate Change

For more than two decades researchers have utilized the snowmelt runoff model (SRM) to test the impacts of climate change on streamflow of snow-fed systems. SRM developers recommend a parameter shift during simulations of future climate, but this is often omitted. Here we show the impact of this omission on model results. In this study, the hydrological effects of climate change are modeled over three sequential years with typical and recommended SRM methodology. We predict the impacts of climate change on water resources of five subbasins of an arid region. Climate data are downscaled to weather stations. Period change analysis gives temperature and precipitation changes for 55 general circulation models which are then subsampled to produce four future states per basin. Results indicate an increase in temperature between 3.0 and 6.2 °C and an 18% decrease to 26% increase in precipitation. Without modifications to the snow runoff coefficient (cS), mean results across all basins range from a reduction in total volume of 21% to an increase of 4%. Modifications to cS resulted in a 0–10% difference in simulated annual volume. Future application of SRM should include a parameter shift representing the changed climate.

scholarly journals Spatio-Temporal Analysis of Historical and Future Climate Data in the Texas High Plains

2020 ◽  
Vol 12 (15) ◽  
pp. 6036
Author(s):  
Yong Chen ◽  
Gary W. Marek ◽  
Thomas H. Marek ◽  
Dana O. Porter ◽  
Jerry E. Moorhead ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
General Circulation Models ◽  
Quality Data ◽  
High Plains ◽  
Climate Change Scenarios ◽  
Climate Data ◽  
Air Temperatures ◽  
Texas High Plains ◽  
Spatio Temporal

Agricultural production in the Texas High Plains (THP) relies heavily on irrigation and is susceptible to drought due to the declining availability of groundwater and climate change. Therefore, it is meaningful to perform an overview of possible climate change scenarios to provide appropriate strategies for climate change adaptation in the THP. In this study, spatio-temporal variations of climate data were mapped in the THP during 2000–2009, 2050–2059, and 2090–2099 periods using 14 research-grade meteorological stations and 19 bias-corrected General Circulation Models (GCMs) under representative concentration pathway (RCP) scenarios RCP 4.5 and 8.5. Results indicated different bias correction methods were needed for different climatic parameters and study purposes. For example, using high-quality data from the meteorological stations, the linear scaling method was selected to alter the projected precipitation while air temperatures were bias corrected using the quantile mapping method. At the end of the 21st century (2090–2099) under the severe CO2 emission scenario (RCP 8.5), the maximum and minimum air temperatures could increase from 3.9 to 10.0 °C and 2.8 to 8.4 °C across the entire THP, respectively, while precipitation could decrease by ~7.5% relative to the historical (2000–2009) observed data. However, large uncertainties were found according to 19 GCM projections.

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