Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study
Abstract Climate simulations often need to be adjusted before carrying out impact studies at a regional scale. Technically, bias adjustment methods are generally calibrated over the last few decades, in order to benefit from a more comprehensive and accurate observational network. At these timescale...
Published in: | Environmental Research: Climate |
---|---|
Main Authors: | , , , |
Other Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
IOP Publishing
2022
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1088/2752-5295/ac6adc https://iopscience.iop.org/article/10.1088/2752-5295/ac6adc https://iopscience.iop.org/article/10.1088/2752-5295/ac6adc/pdf |
id |
crioppubl:10.1088/2752-5295/ac6adc |
---|---|
record_format |
openpolar |
spelling |
crioppubl:10.1088/2752-5295/ac6adc 2024-06-02T08:11:29+00:00 Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study Bonnet, Rémy Boucher, Olivier Vrac, Mathieu Jin, Xia European Union CNRS, Sorbonne Université Ecole Polytechnique CNES ANR 2022 http://dx.doi.org/10.1088/2752-5295/ac6adc https://iopscience.iop.org/article/10.1088/2752-5295/ac6adc https://iopscience.iop.org/article/10.1088/2752-5295/ac6adc/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining Environmental Research: Climate volume 1, issue 1, page 011001 ISSN 2752-5295 journal-article 2022 crioppubl https://doi.org/10.1088/2752-5295/ac6adc 2024-05-07T13:56:27Z Abstract Climate simulations often need to be adjusted before carrying out impact studies at a regional scale. Technically, bias adjustment methods are generally calibrated over the last few decades, in order to benefit from a more comprehensive and accurate observational network. At these timescales, however, the climate state may be influenced by the low-frequency internal climate variability. There is therefore a risk of introducing a bias to the climate projections by bias-adjusting simulations with low-frequency variability in a different phase to that of the observations. In this study, we developed a new pseudo-reality framework using an ensemble of simulations from the IPSL-CM6A-LR climate model in order to assess the impact of the low-frequency internal climate variability of the North Atlantic sea surface temperatures on bias-adjusted projections of mean and extreme surface temperature over Europe. We show that using simulations in a similar phase of the Atlantic Multidecadal Variability reduces the pseudo-biases in temperature projections. Therefore, for models and regions where low frequency internal variability matters, it is recommended to sample relevant climate simulations to be bias adjusted in a model ensemble or alternatively to use a very long reference period when possible. Article in Journal/Newspaper North Atlantic IOP Publishing Environmental Research: Climate 1 1 011001 |
institution |
Open Polar |
collection |
IOP Publishing |
op_collection_id |
crioppubl |
language |
unknown |
description |
Abstract Climate simulations often need to be adjusted before carrying out impact studies at a regional scale. Technically, bias adjustment methods are generally calibrated over the last few decades, in order to benefit from a more comprehensive and accurate observational network. At these timescales, however, the climate state may be influenced by the low-frequency internal climate variability. There is therefore a risk of introducing a bias to the climate projections by bias-adjusting simulations with low-frequency variability in a different phase to that of the observations. In this study, we developed a new pseudo-reality framework using an ensemble of simulations from the IPSL-CM6A-LR climate model in order to assess the impact of the low-frequency internal climate variability of the North Atlantic sea surface temperatures on bias-adjusted projections of mean and extreme surface temperature over Europe. We show that using simulations in a similar phase of the Atlantic Multidecadal Variability reduces the pseudo-biases in temperature projections. Therefore, for models and regions where low frequency internal variability matters, it is recommended to sample relevant climate simulations to be bias adjusted in a model ensemble or alternatively to use a very long reference period when possible. |
author2 |
European Union CNRS, Sorbonne Université Ecole Polytechnique CNES ANR |
format |
Article in Journal/Newspaper |
author |
Bonnet, Rémy Boucher, Olivier Vrac, Mathieu Jin, Xia |
spellingShingle |
Bonnet, Rémy Boucher, Olivier Vrac, Mathieu Jin, Xia Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study |
author_facet |
Bonnet, Rémy Boucher, Olivier Vrac, Mathieu Jin, Xia |
author_sort |
Bonnet, Rémy |
title |
Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study |
title_short |
Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study |
title_full |
Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study |
title_fullStr |
Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study |
title_full_unstemmed |
Sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study |
title_sort |
sensitivity of bias adjustment methods to low-frequency internal climate variability over the reference period: an ideal model study |
publisher |
IOP Publishing |
publishDate |
2022 |
url |
http://dx.doi.org/10.1088/2752-5295/ac6adc https://iopscience.iop.org/article/10.1088/2752-5295/ac6adc https://iopscience.iop.org/article/10.1088/2752-5295/ac6adc/pdf |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Environmental Research: Climate volume 1, issue 1, page 011001 ISSN 2752-5295 |
op_rights |
http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/2752-5295/ac6adc |
container_title |
Environmental Research: Climate |
container_volume |
1 |
container_issue |
1 |
container_start_page |
011001 |
_version_ |
1800757637195759616 |