bias correction of climate model data – the golden ... · bias correction of climate model data...

Bias correction of climate model data – the golden solution for impact models or cursed

black magic?

Stefan Hagemann, Jan O. HaerterMax Planck Institute for Meteorology, Hamburg

Claudio PianiICTP, Trieste

GCI, Bonn, Dec. 2010, Stefan Hagemann

Impact modelling chain

Climate model input: Precipitation, 2m Temperature, other ...

Interpolation to HM resolution

Bias correction required

Hydrology Model (HM)


Quality of observational datasets limits the quality of the bias correction.

It is assumed that the bias behaviour of the model does not change with time, i.e. the transfer function is time-independent and, thus, applicable in the future.

Limitation: Temporal errors of major circulation systems can not be corrected, e.g. onset of monsoon.

Bias correction – main assumptions


Delta change approach (e.g. Hay et al. 2000)

Multiple linear regression (e.g. Hay and Clark 2003; von Storch 1999)Analogue methods (von Storch and Navarra 1999; Moron et al. 2008)Local intensity scaling (Widmann et al. 2003; Schmidli et al. 2006)Quantile mapping (Panofsky and Brier 1968)

Themeßl et al. (2010): Quantile mapping performs best for RCM precipitation over the Alps

Bias correction methods


modeled

Statistical bias correction: Piani et al. (2010), J. Hydrol.

original

corrected


Summary of methodology for precipitation and temperature

In theory: bias correction adjusts all moments of distribution function for each day.In practice: For most regions, a 2-parameter fit to the transform function is used as a good approximation.Specific regions use 3 or 4 parameter transfer functions.Using larger number of parameters may not be adequate as correction needs to be time-independent on climatological time-scales (>10 years).Similar procedure was followed for temperature correction (always 2-parameter fit: linear transfer function).Monthly transfer functions are used, with smooth transitions for temperature.


Observations: Daily WATCH forcing data (Weedon et al. 2010)ERA40 data interpolated to 0.5° with elevation correction to CRU2m temperature: Correction with monthly means CRU data. Precipitation: Correction with monthly GPCC Vs.4 data, and a gauge-undercatch correction according to Jennifer Adam.

Bias correction factors are derived for period 1960-99 and applied to 1960-2100

Precipitation, (Snowfall fraction taken from GCM)Temperature: Tmean, Tmin, Tmax

Diurnal Range: ΔT = Tmax-TminSkewness: σ = (Tmean-Tmin) / ΔT

Data


ECHAM5

IPSL

CNRM

WFD

Bias CorrectedOriginal GCM

Standard deviation of precipitation (1960-99)


Climate Change: Amazon

GCM Precipitation change GCM Temperature change


...

Slope of transfer function ≠ 1 Climate change signal is changed


Annual mean slope of monthly temperature transfer functions

ECHAM5

IPSL

CNRM


Bias correction effectively improves both the mean and the variance of the precipitation and temperature fields in all but a few regions of the globe.Bias correction has an impact on the climate change signal for specific locations and months

Low precipitation amounts (or temperatures) are differently corrected as high amounts (due to different model biases slope of transfer function significantly differs from 1) If distribution between low and high amounts changes in a future climate, bias correction can lead to changes in the analysed signal.

For some regions, the impact of the bias correction on the climate change signal may be larger than the signal itself, thereby uncovering another level of uncertainty that is comparable in magnitude to the uncertainty related to the choice of the GCM or hydrology model. Note that the bias correction has only uncovered but not necessarily caused this extra level of uncertainty within the GCM – hydrology model (or any other impact model) modelling chain.

Summary


Difficult to judge whether the impact of the bias correction on the climate change signal leads to a more realistic signal or not. Giorgi and Coppola (2010) analysed 18 AR4 GCM projections

Projected regional precipitation changes are significantly correlated with the respective regional biases for about 30% of the seasonal/regional cases investigated. For temperature, only a negligible effect of the regional bias on the projected change was noticed.

This suggests at least for precipitation that an impact of the bias correction on the climate change signal may be reasonable. How to handle and possibly reduce the uncovered uncertainty will be subject to future investigations whose outcomes have to be communicated to the impact research communities.

Summary


Precipitation and temperature are correctly independently. Other GCM variables are not corrected.

Bias correction uses mathematical/statistical methods--> No black magicIt is improving but also impacting climate model results, so that it should also be taken with care.

Summary


Thank you for your attention!

Hagemann, S., C. Chen, J.O. Härter, J. Heinke, D. Gerten and C. Piani: Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology modelsJ. Hydrometeor., submitted


Nile

A = 6 largest Arctic Rivers = Mackenzie, N Dvina, Ob, Yenisey, Lena, Kolyma

Amazon

Congo

Mississippi Yangtze Kiang

Amur

Ganges/Brahmaputra

Danube

Baltic Sea

A AA

A A A

Parana Murray

Large catchments are considered


Spatial distribution of the choice of transfer function typeMostly linear TF used (yellow) additive TF is only option in very dry regions (orange)

Transfer functions


GCM Precipitation change: Ganges/Brahmaputra


Precipitation correction of CNRM: Ganges/Brahmaputra


Regions of an additive CNRM precipitation correction for April

Mapping example


Global modelling chain in WATCH

Climate model input from 3 GCMs: Precipitation, 2m Temperature, other ...

Interpolation to 0.5 degree

Statistical bias correction of P and T fields

Global Hydrology Model


Climate change: Danube

GCM Precipitation change GCM Temperature change


Global Hydrology Models

MPI-HM LPJmLDaily Input P, T P, T, LWn, SW Potential Evap. Thornthwaite Priestley-TaylorRunoff/Infiltration Saturation excess / Saturation excess

Beta functionSnowmelt Degree day Degree day

Refs. for MPI-HM: Hagemann and Dümenil Gates (2003), Hagemann and Dümenil (1998)

Refs. for LPJmL: Bondeau et al. (2007), Rost et al. (2008), Fader et al. (2010)

bias correction of climate model data – the golden ... · bias correction of climate model data...

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