High-resolution and bias-corrected CMIP5 projections for climate change impact assessments

Navarro-Racines, Carlos Eduardo; Tarapues, Jaime; Thornton, Philip K.; Jarvis, Andy; Ramírez-Villegas, Julián

doi:10.1038/s41597-019-0343-8

Cited by 296 publications

(183 citation statements)

References 102 publications

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“…All 19 bioclimatic variables were downloaded in the baseline period 1970-2000. The same variables projected to 2050, under the IPCC RCP 8.5 scenario, were gathered using CCAFS's downscaled delta method (Navarro-Racines et al, 2020). This projection averages 33 different global climatic models at a 1 km 2 resolution.…”

Section: Climate Sensitivity In the Espeletia Complexmentioning

confidence: 99%

Climate Vulnerability Assessment of the Espeletia Complex on Páramo Sky Islands in the Northern Andes

et al. 2020

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Section: Climate Sensitivity In the Espeletia Complexmentioning

confidence: 99%

Climate Vulnerability Assessment of the Espeletia Complex on Páramo Sky Islands in the Northern Andes

et al. 2020

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“…We downloaded climate data from CRU TS v. 4.03 (0.5° × 0.5° grid; Harris et al., 2014, 2020). We calculated monthly climate variables at 1 km 2 resolution by interpolation of complex multivariate data from climate data (CRU TS v. 4.03) using thin‐plate smoothing splines for each period (1950s, 1980s and 2010s) following the downscaling method provided in CCAFS (The CGIAR Research Program on Climate Change, Agriculture and Food Security; Navarro‐Racines et al., 2020). We then derived the bioclimate variables from the monthly climate variables (1km 2 ) using ‘dismo’ package in R. We also calculated an annual heat‐moisture index (AHM) by Bio1 (annual mean temperature) and Bio12 (annual precipitation) using ‘raster’ package in R ( Formula : AHM = [Bio1*0.1 + 10]/[Bio12*0.001]) (Wang et al., 2012).…”

Section: Methodsmentioning

confidence: 99%

Mechanisms underlying altitudinal and horizontal range contraction: The western black crested gibbon

Yang

Shi

et al. 2020

Journal of Biogeography

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Aim Species ranges in mountain areas may shift both horizontally and altitudinally, resulting from climate change and anthropogenic impact. Two hypotheses (the abundant centre hypothesis and the contagion hypothesis) have been proposed to account for patterns of horizontal range contraction. However, undulating topograph causes a mosaic of unsuitable habitats, which may complicate the spatial pattern of range contraction. We develop a framework incorporating horizontal and altitudinal range contraction patterns of a species living in mountain areas, to better understand the underlying mechanisms of species range contraction. Location China, Northern Laos and Northern Vietnam Taxon Western black crested gibbon, Nomascus concolor Methods We collected occurrence data of the gibbons from various sources and modelled their potential distribution range in the 1950s, 1980s and 2010s, using ecological niche modelling. We compared distances from the centre point of the potential range in the 1950s to centre points of the largest 100 patches in the 1950s and the 2010s to understand the patterns of horizontal range contraction. We also calculated potential distributions within different altitudinal ranges for six populations in each period to understand the patterns of altitudinal range contraction. Results Potential horizontal distribution of the gibbons decreased by 69% from the 1950s to the 2010s. The centre point of the 100 largest patches in the 2010s was further apart than in the 1950s, supporting the contagion hypothesis. No populations extended their range to higher altitude, suggesting climate change did not have a profound effect on altitudinal shifts in gibbon range. All populations lost a substantial proportion of their ranges at lower altitudes (500–1,500 m) but to different degrees, suggesting that populations experienced different anthropogenic pressures. Main conclusions Anthropogenic threats including human population increase, agricultural expansion and hunting, were more likely than climate change to have caused range contraction in western black‐crested gibbons. This study highlights the importance of studying horizontal and altitudinal range contraction simultaneously.

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“…For many scientific applications, the representation of the temporal and spatial variability of temperature and precipitation is extremely important 14 . The gap between these spatial scales is often bridged by applying a delta change method 15,16 to current-time climate data that is available at high spatial resolution of ca. 1-20 km e.g.…”

Section: Background and Summarymentioning

confidence: 99%

High-resolution monthly precipitation and temperature time series from 2006 to 2100

et al. 2020

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the CHELSA algorithm 17 , which provides a more accurate representation of temperature and precipitation in highly complex terrain. Data based on the CHELSA algorithm 17 have already been used to infer e.g. ecological niches 24 , soil nutrients 25,26 , or to assess climate change impacts of forests 27 , insect pests 28 , and biodiversity 29. Mean monthly maximum daily temperatures and mean monthly minimum daily temperatures have been downscaled using the delta change method based on the high-spatial-resolution data taken from CHELSA V.1.2. Monthly precipitation sums have been downscaled by applying the CHELSA algorithm directly on bias corrected GCM data. The CHELSA algorithm allows for representing the effects from changing wind patterns and boundary layer conditions in the process of downscaling, and therefore allows for a better estimation of the km-scale changes in precipitation patterns under future climate projections. Methods Selection of global circulation models (GCMs). Projected future climate variables were taken from four global circulation models (GCMs) driven by two scenarios of representative concentration pathways (RCPs) in a factorial manner. The four selected models originate from the CMIP5 collection of model runs used in IPCC's 5th Assessment Report 30 (IPCC 2013). GCMs are, however, often based on similar code which consequently results in similar output 31,32. We therefore chose models that show a low amount of interdependence to allow for a good representation of uncertainty among available climate projections. Model selection was performed to reduce model interdependence in ensembles (see ref. 32).

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High-resolution and bias-corrected CMIP5 projections for climate change impact assessments

Cited by 296 publications

References 102 publications

Climate Vulnerability Assessment of the Espeletia Complex on Páramo Sky Islands in the Northern Andes

Climate Vulnerability Assessment of the Espeletia Complex on Páramo Sky Islands in the Northern Andes

Mechanisms underlying altitudinal and horizontal range contraction: The western black crested gibbon

High-resolution monthly precipitation and temperature time series from 2006 to 2100

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