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 |
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Main Authors: | , , , |
Other Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
IOP Publishing
2022
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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 |
Summary: | 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. |
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