Climate change projections of maximum temperatures for southwest Iraq using statistical downscaling

2021 ◽  
Author(s):  
WH Hassan
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Change Projections ◽  
Maximum Temperatures

scholarly journals Climate change projections of boreal summer precipitation over tropical America by using statistical downscaling from CMIP5 models

2017 ◽  
Vol 12 (12) ◽  
pp. 124011 ◽  
Author(s):  
Reiner Palomino-Lemus ◽  
Samir Córdoba-Machado ◽  
Sonia Raquel Gámiz-Fortis ◽  
Yolanda Castro-Díez ◽  
María Jesús Esteban-Parra
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Summer Precipitation ◽  
Cmip5 Models ◽  
Boreal Summer ◽  
Climate Change Projections ◽  
Tropical America

scholarly journals Climate change projections of temperature and precipitation in Chile based on statistical downscaling

2020 ◽  
Vol 54 (9-10) ◽  
pp. 4309-4330 ◽  
Author(s):  
Daniela Araya-Osses ◽  
Ana Casanueva ◽  
Celián Román-Figueroa ◽  
Juan Manuel Uribe ◽  
Manuel Paneque
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Change Projections ◽  
Temperature And Precipitation

scholarly journals Evaluation of statistical downscaling methods for climate change projections over Spain: present conditions with perfect predictors

2021 ◽  
Author(s):  
Alfonso Hernanz ◽  
Juan Andrés García‐Valero ◽  
Marta Domínguez ◽  
Petra Ramos‐Calzado ◽  
María A. Pastor‐Saavedra ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Change Projections

scholarly journals Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change

2014 ◽  
Vol 10 (9) ◽  
pp. 20140576 ◽  
Author(s):  
Collin Storlie ◽  
Andres Merino-Viteri ◽  
Ben Phillips ◽  
Jeremy VanDerWal ◽  
Justin Welbergen ◽  
...  
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Environmental Data ◽  
Weather Data ◽  
Daily Maximum ◽  
Biologically Relevant ◽  
Species Vulnerability ◽  
Vulnerability To Climate Change ◽  
Thermal Regimes ◽  
Maximum Temperatures

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km 2 study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes.

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