scholarly journals Including Host Availability and Climate Change Impacts on the Global Risk Area of Carpomya pardalina (Diptera: Tephritidae)

2021 ◽  
Vol 9 ◽  
Author(s):  
Yujia Qin ◽  
Yuan Zhang ◽  
Anthony R. Clarke ◽  
Zihua Zhao ◽  
Zhihong Li
Keyword(s):  
Invasive Species ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Western China ◽  
Risk Modeling ◽  
Invasion Pathways ◽  
Risk Area ◽  
Climate Conditions ◽  
Host Availability

Fruit flies are a well-known invasive species, and climate-based risk modeling is used to inform risk analysis of these pests. However, such research tends to focus on already well-known invasive species. This paper illustrates that appropriate risk modeling can also provide valuable insights for flies which are not yet “on the radar.” Carpomya pardalina is a locally important cucurbit-infesting fruit fly of western and central Asia, but it may present a risk to other temperate countries where melons are grown. MaxEnt models were used to map the risk area for this species under historical and future climate conditions averaged from three global climate models under two shared socio-economic pathways in 2030 and 2070 from higher climate sensitivity models based on the upcoming 2021 IPCC sixth assessment report. The results showed that a total of 47.64% of the world’s land mass is climatically suitable for the fly; it could establish widely around the globe both under current and future climates with host availability. Our MaxEnt modeling highlights particularly that Western China, Russia, and other European countries should pay attention to this currently lesser-known melon fly and the melons exported from the present countries. The current and expanding melon trade could offer direct invasion pathways to those regions. While this study offers specific risk information on C. pardalina, it also illustrates the value of applying climate-based distribution modeling to species with limited geographic distributions.

Future ant invasions in France

2014 ◽  
Vol 41 (2) ◽  
pp. 217-228 ◽  
Author(s):  
CLEO BERTELSMEIER ◽  
FRANCK COURCHAMP
Keyword(s):  
Invasive Species ◽  
Potential Distribution ◽  
Climate Models ◽  
Global Climate ◽  
Climatic Conditions ◽  
Global Climate Models ◽  
Potential Range ◽  
Invasive Ants ◽  
Distribution Models ◽  
Wasmannia Auropunctata

SUMMARYAnts are among the worst invasive species, and can have tremendous negative impacts on native biodiversity, agriculture, estates, property and human health. Invasive ants are extremely difficult to control, and thus early detection is essential to prevent ant invasions, in particular through surveillance efforts at ports of entry. This paper assesses the potential distribution of 14 of the worst invasive ant species in France, under current and future climatic conditions. Consensus species distribution models, using five different modelling techniques, three global climate models and two CO2 emission scenarios, indicated that France presented suitable areas for 10/14 species, including five listed on the Invasive Species Specialist Group's selection of the world's 100 worst invasive species. Among these 10 species, eight were predicted to increase their potential range with climate change. Areas with the highest concentration of potential invaders were mainly located along the coastline, especially in the south-west of France, but all departments appeared to be climatically suitable for at least two invasive species. A ranking of climatic suitability per species for 17 major airports and 14 maritime ports indicated that the ports of entry with the highest suitability were located in Biarritz, Toulon and Nice, and the species with the greatest potential distribution in France were Lasius neglectus and Linepithema humile, followed by Solenopsis richteri, Pheidole megacephala and Wasmannia auropunctata.

Going with the trend: forecasting seasonal climate conditions under climate change

2021 ◽  
Author(s):  
Yawen Shao ◽  
Quan J. Wang ◽  
Andrew Schepen ◽  
Dongryeol Ryu
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Daily Maximum ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Conditions ◽  
Seasonal Climate

AbstractFor managing climate variability and adapting to climate change, seasonal forecasts are widely produced to inform decision making. However, seasonal forecasts from global climate models are found to poorly reproduce temperature trends in observations. Furthermore, this problem is not addressed by existing forecast post-processing methods that are needed to remedy biases and uncertainties in model forecasts. The inability of the forecasts to reproduce the trends severely undermines user confidence in the forecasts. In our previous work, we proposed a new statistical post-processing model that counteracted departures in trends of model forecasts from observations. Here, we further extend this trend-aware forecast post-processing methodology to carefully treat the trend uncertainty associated with the sampling variability due to limited data records. This new methodology is validated on forecasting seasonal averages of daily maximum and minimum temperatures for Australia based on the SEAS5 climate model of the European Centre for Medium-Range Weather Forecasts. The resulting post-processed forecasts are shown to have proper trends embedded, leading to greater accuracy in regions with significant trends. The application of this new forecast post-processing is expected to boost user confidence in seasonal climate forecasts.

Ranking general circulation models for India using TOPSIS

2014 ◽  
Vol 6 (2) ◽  
pp. 288-299 ◽  
Author(s):  
K. Srinivasa Raju ◽  
D. Nagesh Kumar
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Performance Indicator ◽  
Global Climate ◽  
Evaluation Criteria ◽  
General Circulation Models ◽  
Skill Score ◽  
Global Climate Models ◽  
Climate Conditions ◽  
Grid Points

Eleven general circulation models/global climate models (GCMs) – BCCR-BCCM2.0, INGV-ECHAM4, GFDL2.0, GFDL2.1, GISS, IPSL-CM4, MIROC3, MRI-CGCM2, NCAR-PCMI, UKMO-HADCM3 and UKMO-HADGEM1 – are evaluated for Indian climate conditions using the performance indicator, skill score (SS). Two climate variables, temperature T (at three levels, i.e. 500, 700, 850 mb) and precipitation rate (Pr) are considered resulting in four SS-based evaluation criteria (T500, T700, T850, Pr). The multicriterion decision-making method, technique for order preference by similarity to an ideal solution, is applied to rank 11 GCMs. Efforts are made to rank GCMs for the Upper Malaprabha catchment and two river basins, namely, Krishna and Mahanadi (covered by 17 and 15 grids of size 2.5° × 2.5°, respectively). Similar efforts are also made for India (covered by 73 grid points of size 2.5° × 2.5°) for which an ensemble of GFDL2.0, INGV-ECHAM4, UKMO-HADCM3, MIROC3, BCCR-BCCM2.0 and GFDL2.1 is found to be suitable. It is concluded that the proposed methodology can be applied to similar situations with ease.

scholarly journals An analysis of the risk of cocoa moniliasis occurrence in Brazil as the result of climate change

2012 ◽  
Vol 38 (1) ◽  
pp. 30-35 ◽  
Author(s):  
Wanderson Bucker Moraes ◽  
Waldir Cintra de Jesus Júnior ◽  
Leonardo de Azevedo Peixoto ◽  
Willian Bucker Moraes ◽  
Edson Luiz Furtado ◽  
...  
Keyword(s):  
Climate Change ◽  
Potential Risk ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Future Scenarios ◽  
Intergovernmental Panel ◽  
Climate Conditions ◽  
Third Assessment Report ◽  
Impacts Of Climate Change

The aim of this study was to evaluate the potential risk of moniliasis occurrence and the impacts of climate change on this disease in the coming decades, should this pathogen be introduced in Brazil. To this end, climate favorability maps were devised for the occurrence of moniliasis, both for the present and future time. The future scenarios (A2 and B2) focused on the decades of 2020, 2050 and 2080. These scenarios were obtained from six global climate models (GCMs) made available by the third assessment report of Intergovernmental Panel on Climate Change (IPCC). Currently, there are large areas with favorable climate conditions for moniliasis in Brazil, especially in regions at high risk of introduction of that pathogen. Considering the global warming scenarios provided by the IPCC, the potential risk of moniliasis occurrence in Brazil will be reduced. This decrease is predicted for both future scenarios, but will occur more sharply in scenario A2. However, there will still be areas with favorable climate conditions for the development of the disease, particularly in Brazil's main producing regions. Moreover, pathogen and host alike may undergo alterations due to climate change, which will affect the extent of their impacts on this pathosystem.

scholarly journals The Influence of Climate Model Biases on Projections of Aridity and Drought

2016 ◽  
Vol 29 (4) ◽  
pp. 1269-1285 ◽  
Author(s):  
Darren L. Ficklin ◽  
John T. Abatzoglou ◽  
Scott M. Robeson ◽  
Anna Dufficy
Keyword(s):  
United States ◽  
Climate Models ◽  
Climate Model ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Precipitation Amount ◽  
Severity Index ◽  
Global Climate Models ◽  
Drought Severity ◽  
Climate Conditions

Abstract Global climate models (GCMs) have biases when simulating historical climate conditions, which in turn have implications for estimating the hydrological impacts of climate change. This study examines the differences in projected changes of aridity [defined as the ratio of precipitation (P) over potential evapotranspiration (PET), or P/PET] and the Palmer drought severity index (PDSI) between raw and bias-corrected GCM output for the continental United States (CONUS). For historical simulations (1950–79) the raw GCM ensemble median has a positive precipitation bias (+24%) and negative PET bias (−7%) compared to the bias-corrected output when averaged over CONUS with the most acute biases over the interior western United States. While both raw and bias-corrected GCM ensembles project more aridity (lower P/PET) for CONUS in the late twenty-first century (2070–99), relative enhancements in aridity were found for bias-corrected data compared to the raw GCM ensemble owing to positive precipitation and negative PET biases in the raw GCM ensemble. However, the bias-corrected GCM ensemble projects less acute decreases in summer PDSI for the southwestern United States compared to the raw GCM ensemble (from 1 to 2 PDSI units higher), stemming from biases in precipitation amount and seasonality in the raw GCM ensemble. Compared to the raw GCM ensemble, bias-corrected GCM inputs not only correct for systematic errors but also can produce high-resolution projections that are useful for impact analyses. Therefore, changes in hydroclimate metrics often appear considerably different in bias-corrected output compared to raw GCM output.

scholarly journals Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures

2018 ◽  
Vol 4 (1/2) ◽  
pp. 37-52
Author(s):  
Rasmus E. Benestad ◽  
Bob van Oort ◽  
Flavio Justino ◽  
Frode Stordal ◽  
Kajsa M. Parding ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Principal Component ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Climate Conditions ◽  
Probability Of Occurrence

Abstract. A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 ∘C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.

Extreme Events in High Resolution CMCC Regional and Global Climate Models

2011 ◽  
Author(s):  
Enrico Scoccimarro ◽  
Silvio Gualdi ◽  
Antonella Sanna ◽  
Edoardo Bucchignani ◽  
Myriam Montesarchio
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models

scholarly journals Global climatology and trends in convective environments from ERA5 and rawinsonde data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Mattia Marchio ◽  
Harold E. Brooks
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Global Climate Models ◽  
Convective Precipitation ◽  
The Past ◽  
The Tropics ◽  
Rawinsonde Data

AbstractGlobally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

scholarly journals Regions of intensification of extreme snowfall under future warming

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lennart Quante ◽  
Sven N. Willner ◽  
Robin Middelanis ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Observational Data ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Average Intensity ◽  
High Latitudes ◽  
Future Warming

AbstractDue to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.

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