Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi

2020 ◽  
Author(s):  
R. Manzanas ◽  
L. Fiwa ◽  
C. Vanya ◽  
H. Kanamaru ◽  
J. M. Gutiérrez
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Climate Change Projections ◽  
Bias Adjustment

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 Impact of climate change on the blue water footprint of agriculture on a regional scale

2018 ◽  
Vol 19 (1) ◽  
pp. 52-59 ◽  
Author(s):  
Huiping Huang ◽  
Yuping Han ◽  
Dongdong Jia
Keyword(s):  
Climate Change ◽  
Temporal Change ◽  
Water Footprint ◽  
Regional Scale ◽  
Maximum Temperature ◽  
Agricultural Water ◽  
Climate Change Projections ◽  
Change Models ◽  
Average Value

Abstract In the case study of Tangshan city, Hebei Province, China, this paper analyzes the temporal change of the blue agricultural water footprint (WF) during 1991–2016 and discusses the applicability of different climate change models during 2017–2050. Results show effective rainfall, wind speed and maximum temperature are leading factors influencing the blue agricultural WF. Relative error analysis indicates that the HadGEM2-ES model is the most applicable for climate change projections in the period of 2017–2050. Agricultural blue WF is about 1.8 billion m3 in RCP2.6, RCP4.5 and RCP8.5 emission scenarios, which is almost equal to the average value during 1991–2016.

scholarly journals Refining Climate Change Projections for Organisms with Low Dispersal Abilities: A Case Study of the Caspian Whip Snake

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e91994 ◽  
Author(s):  
Tiberiu C. Sahlean ◽  
Iulian Gherghel ◽  
Monica Papeş ◽  
Alexandru Strugariu ◽  
Ştefan R. Zamfirescu
Keyword(s):  
Climate Change ◽  
Climate Change Projections ◽  
Dispersal Abilities ◽  
Whip Snake

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 On the need of bias adjustment for more plausible climate change projections of extreme heat

2021 ◽  
Author(s):  
Maialen Iturbide ◽  
Ana Casanueva ◽  
Joaquín Bedia ◽  
Sixto Herrera ◽  
Josipa Milovac ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Heat ◽  
Climate Change Projections ◽  
Bias Adjustment

scholarly journals Demography linked to climate change projections in an ecoregional case study: integrating forecasts and field data

Ecosphere ◽  
2014 ◽  
Vol 5 (7) ◽  
pp. art85 ◽  
Author(s):  
B. C. Mclaughlin ◽  
C. N. Morozumi ◽  
J. MacKenzie ◽  
A. Cole ◽  
S. Gennet
Keyword(s):  
Climate Change ◽  
Field Data ◽  
Climate Change Projections
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