EGU2020-11561
https://doi.org/10.5194/egusphere-egu2020-11561
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Exploring the role of observational uncertainty and resolution mismatch in the application of bias adjustment methods

Ana Casanueva1, Sixto Herrera1, Maialen Iturbide2, Stefan Lange3, Martin Jury4,5, Alessandro Dosio6, Douglas Maraun4, and José M. Gutiérrez2
Ana Casanueva et al.
  • 1Meteorology Group, University of Cantabria, Applied Mathematics and Computer Sciences, Santander, Spain (ana.casanueva@unican.es)
  • 2Meteorology Group. Instituto de Física de Cantabria, CSIC-Univ. of Cantabria, Spain
  • 3Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
  • 4Wegener Center for Climate and Global Change, University of Graz, Austria.
  • 5Department of Earth Sciences, Barcelona Supercomputing Center (BSC-CNS), Barcelona, Spain
  • 6European Commission Joint Research Centre (JRC), Italy

Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many bias adjustment methods, which merely correct for deficiencies in the distribution, have been developed. Despite adjusting the desired features under historical simulations, their application in a climate change context is subject to additional uncertainties and modifications of the change signals, especially for climate indices which have not been tackled by the methods. In this sense, some of the commonly-used bias adjustment methods allow changes of the signals, which appear by construction in case of intensity-dependent biases; some others ensure the trends in some statistics of the original, raw models. Two relevant sources of uncertainty, often overlooked, which bring further uncertainties are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect).

In the present work, we assess the impact of these factors on the climate change signal of a set of climate indices of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature -empirical or parametric-, fitted parameters and preservation of the signals) for a case study in the Iberian Peninsula. The quantile trend-preserving methods (namely quantile delta mapping -QDM-, scaled distribution mapping -SDM- and the method from the third phase of ISIMIP -ISIMIP3) preserve better the raw signals for the different indices and variables (not all preserved by construction). However they rely largely on the reference dataset used for calibration, thus present a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20km) and low (approximately 120km) spatial resolutions.

How to cite: Casanueva, A., Herrera, S., Iturbide, M., Lange, S., Jury, M., Dosio, A., Maraun, D., and Gutiérrez, J. M.: Exploring the role of observational uncertainty and resolution mismatch in the application of bias adjustment methods, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11561, https://doi.org/10.5194/egusphere-egu2020-11561, 2020

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Presentation version 1 – uploaded on 03 May 2020
  • CC1: Comment on EGU2020-11561, Jorn Van de Velde, 04 May 2020

    The results presented here are very interesting!

    However, how do you ensure that the uncertainty because of the reference dataset and spatial scale is, in a climate change context, not confounded with uncertainty because of climate change?

    I'm asking this as I found indications that bias non-stationarity caused by climate change can have a large influence on the adjustment, depending on the variable and the indices used. My poster on this is in the session 'Compound weather and climate events', as I focused on multivariate bias adjustment methods: https://meetingorganizer.copernicus.org/EGU2020/EGU2020-17387.html