Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe
Producing projections of future crop yields requires careful thought about the appropriate use of atmosphere-ocean global climate model (AOGCM) simulations. Here we describe and demonstrate multiple methods for ‘calibrating’ climate projections using an ensemble of AOGCM simulations in a ‘perfect si...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/52088 |
| _version_ | 1855521954896805888 |
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| author | Hawkins, E. Osborne, Tom M. Ho CK Challinor, Andrew J. |
| author_browse | Challinor, Andrew J. Hawkins, E. Ho CK Osborne, Tom M. |
| author_facet | Hawkins, E. Osborne, Tom M. Ho CK Challinor, Andrew J. |
| author_sort | Hawkins, E. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Producing projections of future crop yields requires careful thought about the appropriate use of atmosphere-ocean global climate model (AOGCM) simulations. Here we describe and demonstrate multiple methods for ‘calibrating’ climate projections using an ensemble of AOGCM simulations in a ‘perfect sibling’ framework. Crucially, this type of analysis assesses the ability of each calibration methodology to produce reliable estimates of future climate, which is not possible just using historical observations. This type of approach could be more widely adopted for assessing calibration methodologies for crop modelling. The calibration methods assessed include the commonly used ‘delta’ (change factor) and ‘nudging’ (bias correction) approaches. We focus on daily maximum temperature in summer over Europe for this idealised case study, but the methods can be generalised to other variables and other regions. The calibration methods, which are relatively easy to implement given appropriate observations, produce more robust projections of future daily maximum temperatures and heat stress than using raw model output. The choice over which calibration method to use will likely depend on the situation, but change factor approaches tend to perform best in our examples. Finally, we demonstrate that the uncertainty due to the choice of calibration methodology is a significant contributor to the total uncertainty in future climate projections for impact studies. We conclude that utilising a variety of calibration methods on output from a wide range of AOGCMs is essential to produce climate data that will ensure robust and reliable crop yield projections. |
| format | Journal Article |
| id | CGSpace52088 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace520882024-08-27T10:35:34Z Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe Hawkins, E. Osborne, Tom M. Ho CK Challinor, Andrew J. climate agriculture crop yield crop modelling crop forecasting Producing projections of future crop yields requires careful thought about the appropriate use of atmosphere-ocean global climate model (AOGCM) simulations. Here we describe and demonstrate multiple methods for ‘calibrating’ climate projections using an ensemble of AOGCM simulations in a ‘perfect sibling’ framework. Crucially, this type of analysis assesses the ability of each calibration methodology to produce reliable estimates of future climate, which is not possible just using historical observations. This type of approach could be more widely adopted for assessing calibration methodologies for crop modelling. The calibration methods assessed include the commonly used ‘delta’ (change factor) and ‘nudging’ (bias correction) approaches. We focus on daily maximum temperature in summer over Europe for this idealised case study, but the methods can be generalised to other variables and other regions. The calibration methods, which are relatively easy to implement given appropriate observations, produce more robust projections of future daily maximum temperatures and heat stress than using raw model output. The choice over which calibration method to use will likely depend on the situation, but change factor approaches tend to perform best in our examples. Finally, we demonstrate that the uncertainty due to the choice of calibration methodology is a significant contributor to the total uncertainty in future climate projections for impact studies. We conclude that utilising a variety of calibration methods on output from a wide range of AOGCMs is essential to produce climate data that will ensure robust and reliable crop yield projections. 2013-03 2014-12-16T06:37:31Z 2014-12-16T06:37:31Z Journal Article https://hdl.handle.net/10568/52088 en Limited Access Elsevier Hawkins E, Osborne TM, Ho CK, Challinor AJ. 2013. Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe. Agricultural and Forest Meteorology 170:19-31. |
| spellingShingle | climate agriculture crop yield crop modelling crop forecasting Hawkins, E. Osborne, Tom M. Ho CK Challinor, Andrew J. Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe |
| title | Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe |
| title_full | Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe |
| title_fullStr | Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe |
| title_full_unstemmed | Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe |
| title_short | Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe |
| title_sort | calibration and bias correction of climate projections for crop modelling an idealised case study over europe |
| topic | climate agriculture crop yield crop modelling crop forecasting |
| url | https://hdl.handle.net/10568/52088 |
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