MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
Abstract Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets availa...
Published in: | Scientific Data |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Springer Science and Business Media LLC
2017
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1038/sdata.2017.78 http://www.nature.com/articles/sdata201778.pdf http://www.nature.com/articles/sdata201778 |
id |
crspringernat:10.1038/sdata.2017.78 |
---|---|
record_format |
openpolar |
spelling |
crspringernat:10.1038/sdata.2017.78 2023-05-15T14:09:56+02:00 MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling C. Vega, Greta Pertierra, Luis R. Olalla-Tárraga, Miguel Ángel 2017 http://dx.doi.org/10.1038/sdata.2017.78 http://www.nature.com/articles/sdata201778.pdf http://www.nature.com/articles/sdata201778 en eng Springer Science and Business Media LLC https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 CC-BY Scientific Data volume 4, issue 1 ISSN 2052-4463 Library and Information Sciences Statistics, Probability and Uncertainty Computer Science Applications Education Information Systems Statistics and Probability journal-article 2017 crspringernat https://doi.org/10.1038/sdata.2017.78 2022-01-04T09:48:43Z Abstract Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe. Article in Journal/Newspaper Antarc* Antarctic Antarctica Springer Nature (via Crossref) Antarctic The Antarctic Scientific Data 4 1 |
institution |
Open Polar |
collection |
Springer Nature (via Crossref) |
op_collection_id |
crspringernat |
language |
English |
topic |
Library and Information Sciences Statistics, Probability and Uncertainty Computer Science Applications Education Information Systems Statistics and Probability |
spellingShingle |
Library and Information Sciences Statistics, Probability and Uncertainty Computer Science Applications Education Information Systems Statistics and Probability C. Vega, Greta Pertierra, Luis R. Olalla-Tárraga, Miguel Ángel MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
topic_facet |
Library and Information Sciences Statistics, Probability and Uncertainty Computer Science Applications Education Information Systems Statistics and Probability |
description |
Abstract Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe. |
format |
Article in Journal/Newspaper |
author |
C. Vega, Greta Pertierra, Luis R. Olalla-Tárraga, Miguel Ángel |
author_facet |
C. Vega, Greta Pertierra, Luis R. Olalla-Tárraga, Miguel Ángel |
author_sort |
C. Vega, Greta |
title |
MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_short |
MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_full |
MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_fullStr |
MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_full_unstemmed |
MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
title_sort |
merraclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling |
publisher |
Springer Science and Business Media LLC |
publishDate |
2017 |
url |
http://dx.doi.org/10.1038/sdata.2017.78 http://www.nature.com/articles/sdata201778.pdf http://www.nature.com/articles/sdata201778 |
geographic |
Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
genre |
Antarc* Antarctic Antarctica |
genre_facet |
Antarc* Antarctic Antarctica |
op_source |
Scientific Data volume 4, issue 1 ISSN 2052-4463 |
op_rights |
https://creativecommons.org/licenses/by/4.0 https://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/sdata.2017.78 |
container_title |
Scientific Data |
container_volume |
4 |
container_issue |
1 |
_version_ |
1766281949849059328 |