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Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting

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Standard

Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting. / Schmith, Torben; Thejll, Peter; Berg, Peter; Boberg, Fredrik; Christensen, Ole Bossing; Christiansen, Bo; Christensen, Jens Hesselbjerg; Madsen, Marianne Sloth; Steger, Christian.

I: Hydrology and Earth System Sciences, Bind 25, Nr. 1, 18.01.2021, s. 273-290.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Schmith, T, Thejll, P, Berg, P, Boberg, F, Christensen, OB, Christiansen, B, Christensen, JH, Madsen, MS & Steger, C 2021, 'Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting', Hydrology and Earth System Sciences, bind 25, nr. 1, s. 273-290. https://doi.org/10.5194/hess-25-273-2021

APA

Schmith, T., Thejll, P., Berg, P., Boberg, F., Christensen, O. B., Christiansen, B., Christensen, J. H., Madsen, M. S., & Steger, C. (2021). Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting. Hydrology and Earth System Sciences, 25(1), 273-290. https://doi.org/10.5194/hess-25-273-2021

Vancouver

Schmith T, Thejll P, Berg P, Boberg F, Christensen OB, Christiansen B o.a. Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting. Hydrology and Earth System Sciences. 2021 jan 18;25(1):273-290. https://doi.org/10.5194/hess-25-273-2021

Author

Schmith, Torben ; Thejll, Peter ; Berg, Peter ; Boberg, Fredrik ; Christensen, Ole Bossing ; Christiansen, Bo ; Christensen, Jens Hesselbjerg ; Madsen, Marianne Sloth ; Steger, Christian. / Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting. I: Hydrology and Earth System Sciences. 2021 ; Bind 25, Nr. 1. s. 273-290.

Bibtex

@article{8be7642449164f5fad791bb9039d8d62,
title = "Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting",
abstract = "Severe precipitation events occur rarely and are often localised in space and of short duration, but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of the occurrence of these rare events. These are often projected using data from regional climate model (RCM) simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterisations in the RCMs, the simulated present-day climate usually has biases relative to observations; these biases can be in the mean and/or in the higher moments. Therefore, the RCM results are adjusted to account for these deficiencies. However, this does not guarantee that the adjusted projected results will match the future reality better, since the bias may not be stationary in a changing climate. In the present work, we evaluate different adjustment techniques in a changing climate. This is done in an inter-model cross-validation setup in which each model simulation, in turn, performs pseudoobservations against which the remaining model simulations are adjusted and validated. The study uses hourly data from historical and RCP8.5 scenario runs from 19 model simulations from the EURO-CORDEX ensemble at a 0.11 degrees resolution. Fields of return levels for selected return periods are calculated for hourly and daily timescales based on 25-year-long time slices representing the present-day (1981-2005) and end-21st-century (2075-2099). The adjustment techniques applied to the return levels are based on extreme value analysis and include climate factor and quantilemapping approaches. Generally, we find that future return levels can be improved by adjustment, compared to obtaining them from raw scenario model data. The performance of the different methods depends on the timescale considered. On hourly timescales, the climate factor approach performs better than the quantile-mapping approaches. On daily timescales, the superior approach is to simply deduce future return levels from pseudo-observations, and the second-best choice is using the quantile-mapping approaches. These results are found in all European subregions considered. Applying the inter-model cross-validation against model ensemble medians instead of individual models does not change the overall conclusions much.",
keywords = "REGIONAL CLIMATE MODEL, TIME-INVARIANCE, PROJECTIONS, RESOLUTION, RAINFALL, DESIGN, SIMULATIONS, ASSUMPTIONS, PATTERN, OUTPUTS",
author = "Torben Schmith and Peter Thejll and Peter Berg and Fredrik Boberg and Christensen, {Ole Bossing} and Bo Christiansen and Christensen, {Jens Hesselbjerg} and Madsen, {Marianne Sloth} and Christian Steger",
year = "2021",
month = jan,
day = "18",
doi = "10.5194/hess-25-273-2021",
language = "English",
volume = "25",
pages = "273--290",
journal = "Hydrology and Earth System Sciences",
issn = "1027-5606",
publisher = "Copernicus GmbH",
number = "1",

}

RIS

TY - JOUR

T1 - Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting

AU - Schmith, Torben

AU - Thejll, Peter

AU - Berg, Peter

AU - Boberg, Fredrik

AU - Christensen, Ole Bossing

AU - Christiansen, Bo

AU - Christensen, Jens Hesselbjerg

AU - Madsen, Marianne Sloth

AU - Steger, Christian

PY - 2021/1/18

Y1 - 2021/1/18

N2 - Severe precipitation events occur rarely and are often localised in space and of short duration, but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of the occurrence of these rare events. These are often projected using data from regional climate model (RCM) simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterisations in the RCMs, the simulated present-day climate usually has biases relative to observations; these biases can be in the mean and/or in the higher moments. Therefore, the RCM results are adjusted to account for these deficiencies. However, this does not guarantee that the adjusted projected results will match the future reality better, since the bias may not be stationary in a changing climate. In the present work, we evaluate different adjustment techniques in a changing climate. This is done in an inter-model cross-validation setup in which each model simulation, in turn, performs pseudoobservations against which the remaining model simulations are adjusted and validated. The study uses hourly data from historical and RCP8.5 scenario runs from 19 model simulations from the EURO-CORDEX ensemble at a 0.11 degrees resolution. Fields of return levels for selected return periods are calculated for hourly and daily timescales based on 25-year-long time slices representing the present-day (1981-2005) and end-21st-century (2075-2099). The adjustment techniques applied to the return levels are based on extreme value analysis and include climate factor and quantilemapping approaches. Generally, we find that future return levels can be improved by adjustment, compared to obtaining them from raw scenario model data. The performance of the different methods depends on the timescale considered. On hourly timescales, the climate factor approach performs better than the quantile-mapping approaches. On daily timescales, the superior approach is to simply deduce future return levels from pseudo-observations, and the second-best choice is using the quantile-mapping approaches. These results are found in all European subregions considered. Applying the inter-model cross-validation against model ensemble medians instead of individual models does not change the overall conclusions much.

AB - Severe precipitation events occur rarely and are often localised in space and of short duration, but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of the occurrence of these rare events. These are often projected using data from regional climate model (RCM) simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterisations in the RCMs, the simulated present-day climate usually has biases relative to observations; these biases can be in the mean and/or in the higher moments. Therefore, the RCM results are adjusted to account for these deficiencies. However, this does not guarantee that the adjusted projected results will match the future reality better, since the bias may not be stationary in a changing climate. In the present work, we evaluate different adjustment techniques in a changing climate. This is done in an inter-model cross-validation setup in which each model simulation, in turn, performs pseudoobservations against which the remaining model simulations are adjusted and validated. The study uses hourly data from historical and RCP8.5 scenario runs from 19 model simulations from the EURO-CORDEX ensemble at a 0.11 degrees resolution. Fields of return levels for selected return periods are calculated for hourly and daily timescales based on 25-year-long time slices representing the present-day (1981-2005) and end-21st-century (2075-2099). The adjustment techniques applied to the return levels are based on extreme value analysis and include climate factor and quantilemapping approaches. Generally, we find that future return levels can be improved by adjustment, compared to obtaining them from raw scenario model data. The performance of the different methods depends on the timescale considered. On hourly timescales, the climate factor approach performs better than the quantile-mapping approaches. On daily timescales, the superior approach is to simply deduce future return levels from pseudo-observations, and the second-best choice is using the quantile-mapping approaches. These results are found in all European subregions considered. Applying the inter-model cross-validation against model ensemble medians instead of individual models does not change the overall conclusions much.

KW - REGIONAL CLIMATE MODEL

KW - TIME-INVARIANCE

KW - PROJECTIONS

KW - RESOLUTION

KW - RAINFALL

KW - DESIGN

KW - SIMULATIONS

KW - ASSUMPTIONS

KW - PATTERN

KW - OUTPUTS

U2 - 10.5194/hess-25-273-2021

DO - 10.5194/hess-25-273-2021

M3 - Journal article

VL - 25

SP - 273

EP - 290

JO - Hydrology and Earth System Sciences

JF - Hydrology and Earth System Sciences

SN - 1027-5606

IS - 1

ER -

ID: 260403501