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| n | \"affiliation\": \"WSL\", \"affiliation_03\": \"\", \"given_name\": | n | \"email\": \"dirk.karger@wsl.ch\", \"given_name\": \"Dirk Nikolaus \", |
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| | | \"given_name\": \"Niklaus E.\", \"identifier\": \"\", \"name\": |
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| | | \"University of Zurich\", \"affiliation_02\": \"\", |
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| \"michael.kessler@systbot.uzh.ch\", \"affiliation_02\": \"\"}]", | | \"michael.kessler@systbot.uzh.ch\", \"given_name\": \"Michael\", |
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| "author_email": null, | | "author_email": null, |
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| "date": "[{\"date\": \"2017-07-21\", \"date_type\": \"created\", | | "date": "[{\"date\": \"2017-07-21\", \"date_type\": \"created\", |
| n | | n | \"end_date\": \"\"}, {\"date\": \"2021-06-01\", \"date_type\": |
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| "doi": "10.5061/dryad.kd1d4", | | "doi": "10.5061/dryad.kd1d4", |
| "extras": [ | | "extras": [ |
| { | | { |
| "key": "License", | | "key": "License", |
| "value": "CC BY0" | | "value": "CC BY0" |
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| information not available.\", \"institution_url\": \"\"}]", | | information not available.\", \"institution_url\": \"\"}]", |
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| "license_title": "Other (Specified in the description)", | | "license_title": "Creative Commons Zero - No Rights Reserved (CC0 |
| | | 1.0)", |
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| "maintainer": "{\"affiliation\": \"WSL\", \"identifier\": \"\", | | "maintainer": "{\"affiliation\": \"WSL\", \"email\": |
| \"given_name\": \"Dirk Nikolaus \", \"name\": \"Karger\", \"email\": | | \"dirk.karger@wsl.ch\", \"given_name\": \"Dirk Nikolaus \", |
| \"dirk.karger@wsl.ch\"}", | | \"identifier\": \"\", \"name\": \"Karger\"}", |
| "maintainer_email": null, | | "maintainer_email": null, |
| "metadata_created": "2018-07-03T12:25:50.259890", | | "metadata_created": "2018-07-03T12:25:50.259890", |
| n | "metadata_modified": "2021-06-10T12:10:58.151815", | n | "metadata_modified": "2021-06-10T12:12:52.206505", |
| "name": "chelsa-climatologies", | | "name": "chelsa-climatologies", |
| "notes": "High-resolution information on climatic conditions is | | "notes": "High-resolution information on climatic conditions is |
| essential to many applications in environmental and ecological | | essential to many applications in environmental and ecological |
| sciences. Here we present the CHELSA (Climatologies at high resolution | | sciences. Here we present the CHELSA (Climatologies at high resolution |
| for the earth\u2019s land surface areas) data of downscaled model | | for the earth\u2019s land surface areas) data of downscaled model |
| output temperature and precipitation estimates of the ERA-Interim | | output temperature and precipitation estimates of the ERA-Interim |
| climatic reanalysis to a high resolution of 30\u2009arc\u2009sec. The | | climatic reanalysis to a high resolution of 30\u2009arc\u2009sec. The |
| temperature algorithm is based on statistical downscaling of | | temperature algorithm is based on statistical downscaling of |
| atmospheric temperatures. The precipitation algorithm incorporates | | atmospheric temperatures. The precipitation algorithm incorporates |
| orographic predictors including wind fields, valley exposition, and | | orographic predictors including wind fields, valley exposition, and |
| boundary layer height, with a subsequent bias correction. The | | boundary layer height, with a subsequent bias correction. The |
| resulting data consist of a monthly temperature and precipitation | | resulting data consist of a monthly temperature and precipitation |
| climatology for the years 1979\u20132013. We compare the data derived | | climatology for the years 1979\u20132013. We compare the data derived |
| from the CHELSA algorithm with other standard gridded products and | | from the CHELSA algorithm with other standard gridded products and |
| station data from the Global Historical Climate Network. We compare | | station data from the Global Historical Climate Network. We compare |
| the performance of the new climatologies in species distribution | | the performance of the new climatologies in species distribution |
| modelling and show that we can increase the accuracy of species range | | modelling and show that we can increase the accuracy of species range |
| predictions. We further show that CHELSA climatological data has a | | predictions. We further show that CHELSA climatological data has a |
| similar accuracy as other products for temperature, but that its | | similar accuracy as other products for temperature, but that its |
| predictions of precipitation patterns are better.\r\n\r\nData Variable | | predictions of precipitation patterns are better.\r\n\r\nData Variable |
| Codes:\r\n\r\n- Bio1 = Annual Mean Temperature\r\n- Bio2 = Mean | | Codes:\r\n\r\n- Bio1 = Annual Mean Temperature\r\n- Bio2 = Mean |
| Diurnal Range\r\n- Bio3 = Isothermality\r\n- Bio4 = Temperature | | Diurnal Range\r\n- Bio3 = Isothermality\r\n- Bio4 = Temperature |
| Seasonality\r\n- Bio5 = Max Temperature of Warmest Month\r\n- Bio6 = | | Seasonality\r\n- Bio5 = Max Temperature of Warmest Month\r\n- Bio6 = |
| Min Temperature of Coldest Month\r\n- Bio7 = Temperature Annual | | Min Temperature of Coldest Month\r\n- Bio7 = Temperature Annual |
| Range\r\n- Bio8 = Mean Temperature of Wettest Quarter\r\n- Bio9 = Mean | | Range\r\n- Bio8 = Mean Temperature of Wettest Quarter\r\n- Bio9 = Mean |
| Temperature of Driest Quarter\r\n- Bio10 = Mean Temperature of Warmest | | Temperature of Driest Quarter\r\n- Bio10 = Mean Temperature of Warmest |
| Quarter\r\n- Bio11 = Mean Temperature of Coldest Quarter\r\n- Bio12 = | | Quarter\r\n- Bio11 = Mean Temperature of Coldest Quarter\r\n- Bio12 = |
| Annual Precipitation\r\n- Bio13 = Precipitation of Wettest Month\r\n- | | Annual Precipitation\r\n- Bio13 = Precipitation of Wettest Month\r\n- |
| Bio14 = Precipitation of Driest Month\r\n- Bio15 = Precipitation | | Bio14 = Precipitation of Driest Month\r\n- Bio15 = Precipitation |
| Seasonality\r\n- Bio16 = Precipitation of Wettest Quarter\r\n- Bio17 = | | Seasonality\r\n- Bio16 = Precipitation of Wettest Quarter\r\n- Bio17 = |
| Precipitation of Driest Quarter\r\n- Bio18 = Precipitation of Warmest | | Precipitation of Driest Quarter\r\n- Bio18 = Precipitation of Warmest |
| Quarter\r\n- Bio19 = Precipitation of Coldest | | Quarter\r\n- Bio19 = Precipitation of Coldest |
| Quarter\r\n\r\n \r\n__Paper Citation:__\r\n > _Karger DN. et al. | | Quarter\r\n\r\n \r\n__Paper Citation:__\r\n > _Karger DN. et al. |
| Climatologies at high resolution for the earth\u2019s land surface | | Climatologies at high resolution for the earth\u2019s land surface |
| areas, Scientific Data, 4, 170122 (2017) [doi: | | areas, Scientific Data, 4, 170122 (2017) [doi: |
| 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._", | | 10.1038/sdata.2017.122](https://doi.org/10.1038/sdata.2017.122)._", |
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| "organization": { | | "organization": { |
| "approval_status": "approved", | | "approval_status": "approved", |
| "created": "2018-05-31T14:24:18.890912", | | "created": "2018-05-31T14:24:18.890912", |
| "description": "The dynamic macroecology group studies different | | "description": "The dynamic macroecology group studies different |
| aspects of ecology on large spatial and temporal scales, from the | | aspects of ecology on large spatial and temporal scales, from the |
| Pleistocene to the Anthropocene, related to the question: \u201cHow do | | Pleistocene to the Anthropocene, related to the question: \u201cHow do |
| species and their traits change in space and time, particularly with | | species and their traits change in space and time, particularly with |
| global change?\u201d", | | global change?\u201d", |
| "id": "8bdea9a0-ea8c-424f-8c27-a4a1f0c55f02", | | "id": "8bdea9a0-ea8c-424f-8c27-a4a1f0c55f02", |
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| "is_organization": true, | | "is_organization": true, |
| "name": "dynamic-macroecology", | | "name": "dynamic-macroecology", |
| "state": "active", | | "state": "active", |
| "title": "Dynamic Macroecology", | | "title": "Dynamic Macroecology", |
| "type": "organization" | | "type": "organization" |
| }, | | }, |
| "owner_org": "8bdea9a0-ea8c-424f-8c27-a4a1f0c55f02", | | "owner_org": "8bdea9a0-ea8c-424f-8c27-a4a1f0c55f02", |
| "private": false, | | "private": false, |
| n | "publication": "{\"publisher\": \"Dryad Digital Repository\", | n | "publication": "{\"publication_year\": \"2021\", \"publisher\": |
| \"publication_year\": \"2017\"}", | | \"EnviDat\"}", |
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| "related_publications": "", | | "related_publications": "", |
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| "relationships_as_subject": [], | | "relationships_as_subject": [], |
| "resource_type": "dataset", | | "resource_type": "dataset", |
| "resource_type_general": "dataset", | | "resource_type_general": "dataset", |
| "resources": [ | | "resources": [ |
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| "created": "2021-06-08T14:23:10.701887", | | "created": "2021-06-08T14:23:10.701887", |
| "description": "Version 2.1", | | "description": "Version 2.1", |
| "doi": "", | | "doi": "", |
| "format": "geotiff", | | "format": "geotiff", |
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| "id": "ee935f48-b3da-432d-961d-f815289e476f", | | "id": "ee935f48-b3da-432d-961d-f815289e476f", |
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| "name": "CHELSA V2.1 (current)", | | "name": "CHELSA V2.1 (current)", |
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| "state": "active", | | "state": "active", |
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| "https://envicloud.wsl.ch/#/?prefix=chelsa%2Fchelsa_V2%2GLOBAL", | | "https://envicloud.wsl.ch/#/?prefix=chelsa%2Fchelsa_V2%2GLOBAL", |
| "url_type": null | | "url_type": null |
| }, | | }, |
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| "created": "2018-07-03T12:28:32.847549", | | "created": "2018-07-03T12:28:32.847549", |
| "description": "Climatologies are the mean monthly sum of | | "description": "Climatologies are the mean monthly sum of |
| precipitation in the years 1979\u20132013 for each month. As slight | | precipitation in the years 1979\u20132013 for each month. As slight |
| errors in the precipitation sums can accumulate over time, we applied | | errors in the precipitation sums can accumulate over time, we applied |
| an additional bias correction step using the GPCC Climatology Version | | an additional bias correction step using the GPCC Climatology Version |
| 2015. We used the cells at which stations are present, calculated the | | 2015. We used the cells at which stations are present, calculated the |
| bias between the annual accumulations and the GPCC climatology, and | | bias between the annual accumulations and the GPCC climatology, and |
| used a multilevel B-spline interpolation of the biases to a 0.25 grid | | used a multilevel B-spline interpolation of the biases to a 0.25 grid |
| to create the bias surface. This bias surface was then multiplied with | | to create the bias surface. This bias surface was then multiplied with |
| the mean annual precipitation sums to create the final | | the mean annual precipitation sums to create the final |
| climatologies.", | | climatologies.", |
| "doi": "doi:10.5061/dryad.kd1d4", | | "doi": "doi:10.5061/dryad.kd1d4", |
| "format": "geotiff", | | "format": "geotiff", |
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| "name": "CHELSA V1.2 (deprecated)", | | "name": "CHELSA V1.2 (deprecated)", |
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| "state": "active", | | "state": "active", |
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| "url_type": null | | "url_type": null |
| } | | } |
| ], | | ], |
| "spatial": | | "spatial": |
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| "spatial_info": "global", | | "spatial_info": "global", |
| "state": "active", | | "state": "active", |
| "subtitle": "", | | "subtitle": "", |
| "tags": [ | | "tags": [ |
| { | | { |
| "display_name": "BIOCLIM", | | "display_name": "BIOCLIM", |
| "id": "4b528997-0cc8-4f6a-853e-398f7c6c0597", | | "id": "4b528997-0cc8-4f6a-853e-398f7c6c0597", |
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| }, | | }, |
| { | | { |
| "display_name": "TEMPERATURE", | | "display_name": "TEMPERATURE", |
| "id": "bbdcfdc8-2700-4889-a062-c30bff494244", | | "id": "bbdcfdc8-2700-4889-a062-c30bff494244", |
| "name": "TEMPERATURE", | | "name": "TEMPERATURE", |
| "state": "active", | | "state": "active", |
| "vocabulary_id": null | | "vocabulary_id": null |
| } | | } |
| ], | | ], |
| "title": "Climatologies at high resolution for the earth\u2019s land | | "title": "Climatologies at high resolution for the earth\u2019s land |
| surface areas", | | surface areas", |
| "type": "dataset", | | "type": "dataset", |
| "url": null, | | "url": null, |
| t | "version": "1.2" | t | "version": "2.1" |
| } | | } |
Administrator: