Data

CLM GCM ranking

data.gov.au
Bioregional Assessment Program (Owned by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://data.gov.au/data/dataset/ea1534c2-88af-4a4c-a35c-cfb083fb7863&rft.title=CLM GCM ranking&rft.identifier=fc1071a3-5c30-4bf4-b288-428df83a1c32&rft.publisher=data.gov.au&rft.description=CLM GCM ranking - Data File## **Abstract** \n\nThe dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. \n\n\n\nA table showing GCM (Global climate models) rankings for the Clarence-Moreton bioregion.\n\n## **Purpose** \n\nThe ranking of GCM was created to select the model that produced median changes in precipitation. The selected model was used to predict future rainfall for the years 2013 to 2102.\n\n## **Dataset History** \n\nA total of 15 global climate model (GCM) was tested to identify the best GCM to predict future climate data for the Clarence-Moreton Bioregion. The objective in developing a future climate series was to choose the set of GCM's seasonal scaling factors that give the median change in mean annual precipitation. Four seasonal scaling factors for the seasons: summer (December-February), autumn (March-May), winter (June-August) and spring (September-November) were used to predict changes in precipitation in the Clarence-Moreton Bioregion under future climate. For each GCM the change in mean seasonal precipitation for 1 degree global warming was calculated. These seasonal changes were summed to get the change in mean annual precipitation. Based on resulting changes in mean annual precipitation for a 1 degree global warming the GCMs were ranked in ascending order. The GCM that produced median change in annual mean precipitation was selected to predict the future precipitation in the Clarence-Moreton Bioregion for the years 2013 to 2102.\n\n## **Dataset Citation** \n\nBioregional Assessment Programme (2016) CLM GCM ranking. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/fc1071a3-5c30-4bf4-b288-428df83a1c32.\n\n## **Dataset Ancestors** \n\n* **Derived From** [SEACI GCM scaling](https://data.gov.au/data/dataset/bde1e7b7-5bb3-483c-8616-c4176fde8818)\n\n&rft.creator=Bioregional Assessment Program&rft.date=2022&rft.coverage=POLYGON ((0 0, 0 0, 0 0, 0 0))&rft_rights=Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/, (c) Commonwealth of Australia (Bioregional Assessment Programme http://www.bioregionalassessments.gov.au)&rft_subject=Clarence-Moreton bioregion&rft_subject=climatologyMeteorologyAtmosphere&rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 4.0 International, Http://creativecommons.org/licenses/by/4.0/, (c) Commonwealth of Australia (bioregional Assessment Programme Http://www.bioregionalassessments.gov.au)

Creative Commons Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/, (c) Commonwealth of Australia (Bioregional Assessment Programme http://www.bioregionalassessments.gov.au)

Brief description

## **Abstract**

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.



A table showing GCM (Global climate models) rankings for the Clarence-Moreton bioregion.

## **Purpose**

The ranking of GCM was created to select the model that produced median changes in precipitation. The selected model was used to predict future rainfall for the years 2013 to 2102.

## **Dataset History**

A total of 15 global climate model (GCM) was tested to identify the best GCM to predict future climate data for the Clarence-Moreton Bioregion. The objective in developing a future climate series was to choose the set of GCM's seasonal scaling factors that give the median change in mean annual precipitation. Four seasonal scaling factors for the seasons: summer (December-February), autumn (March-May), winter (June-August) and spring (September-November) were used to predict changes in precipitation in the Clarence-Moreton Bioregion under future climate. For each GCM the change in mean seasonal precipitation for 1 degree global warming was calculated. These seasonal changes were summed to get the change in mean annual precipitation. Based on resulting changes in mean annual precipitation for a 1 degree global warming the GCMs were ranked in ascending order. The GCM that produced median change in annual mean precipitation was selected to predict the future precipitation in the Clarence-Moreton Bioregion for the years 2013 to 2102.

## **Dataset Citation**

Bioregional Assessment Programme (2016) CLM GCM ranking. Bioregional Assessment Derived Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/fc1071a3-5c30-4bf4-b288-428df83a1c32.

## **Dataset Ancestors**

* **Derived From** [SEACI GCM scaling](https://data.gov.au/data/dataset/bde1e7b7-5bb3-483c-8616-c4176fde8818)

Full description

CLM GCM ranking - Data File

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text: POLYGON ((0 0, 0 0, 0 0, 0 0))

Subjects
Clarence-Moreton bioregion | climatologyMeteorologyAtmosphere |

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Identifiers
ACN 633 798 857