How, why, what, when?

David RaynerUniversity of Gothenburg

Why?

'Global' is a place where nobody lives

John D. Cox

Ätran (Falkenberg)

Local impacts

Why?

There are no floods in the average climate.

What is a Global Climate Model?

https://www.e-education.psu.edu/earth103/node/524

6

Climate change impact modeling

Hydraulisk modell

Modified from SMHI

IPCC (AR4 WG 1 Chapter 1 page 113 Fig. 1.4).

Effe

ct o

f Res

olut

ion

Rummukainen, M., 2010. State-of-the-art with regional. doi:10.1002/wcc.008

Regional Climate Models

Source: Scale challenges in high resolution climate modelling

Regional Climate Models

11

Climate change impact modeling

Hydraulisk modell

Source – modifeid from SMHI

GCM outputs are “biased”

Observations GCM

ECHAM5 grid.

Climate model outputs are “biased”

K.M.A. Gabriel, W.R. Endlicher / Environmental Pollution 159 (2011) 2044e2050

Mortality rates for Berlin, 1994

Simulated flow in ÄtranTemperaturePrecipitation

Inflow: obs model

When to downscale?

When impact-assessment requiresrealistic climate time-series for the future.

"The most that can be expected from any model is that it can supply a useful approximation to reality: All models are wrong; some models are useful".

George Box

“Downscaling”.

Future time-series

Historical time-series

Global Climate Model outputs

Downscaling algorithms

• Bias Correction• Historical modification

What is the first-guess future climate?

Modelled historical

Observed historical

Modelled scenario

Bias Downscaled scenario

Bias Correction

Modelled historical

Observed historical

Climate Change

Downscaled scenario

Historical ModificationModelled scenario

Bias correction methods

Bias–correction:Distribution-based scaling

rådata

observationer

DBSdata

rådataobservationer

DBSdata

24

Climate change impact modeling

Hydraulisk modell

Source - SMHI

Bias-correction methods

• Advantages:– Better physical consistency.

• Disadvantages: – One time-series/climate model run.

• Example methods:– Bias-corrected RCM– Empirical-statistical downscaling.

Historical modification example.

Historical modification.• Advantages:

– Unmodelled parameters/time-periods.– Integrate information from models.

• Disadvantages:– Uncertain assumptions– Loose physical coherence

• Examples:– Weather-generators, delta-change– Analogue-resampling

One time-series containsClimate Change signal from 20 GCM runs!

Global Temp from CCSM3 SRESa1b run1.

Historical modification example.

When downscaling, think:

• What do you want to know?• How realistic must inputs be?• What data are available?