deliang chen regional climate group earth sciences centre gothenburg university sweden...

Deliang ChenRegional Climate GroupEarth Sciences CentreGothenburg UniversitySwedenwww.gvc.gu.se\ngeo\deliang\deliang.htm

Data for impact modelling in Sweden: Data for impact modelling in Sweden: Experiences with empirical downscaling Experiences with empirical downscaling

and use of weather generatorand use of weather generator

Acknowledgement: Christine Achberger, Cecilia HellströmYaoming Liao, Aristita Busuoic, Youmin Chen, Xiaodong Li

Tinghai Ou, Klaus Wyser, Lin Tang and SWECLIM colleagues

Outline

•Statistical versus dynamic downscaling •What we did and learnt?•Requirements from the impact community•Our answers to the requirements

Main downscaling approaches:

• Dynamical (higher resolution models)

• empirical/statistical downscaling processes

• statistical/dynamical downscaling processes

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• Dynamic downscaling builds on physically based models for both global and regional scales

• Statistical downscaling relies on GCM for large scale and statistical models for regional and/or local scales. Dynamic downscaling still has problems with today’s climate! Can deal with nonstandard or difficult (e.g. Sea ice) variables. Can handle a variety of different scales. Less problematic with bias (because of data-based). Fast ->large number of non-time slice scenarios However, more risky with extrapolations! Needs extensive data!

Dynamic versus statistical downscaling

What did we during last 5 years (SWECLIM time)?

A. On the dynamic side, a regional climate model (Rossby Center Model), together with two GCMs (HadCM3 and ECHAM4), has been used to get a number of regional (44*44 km) scenarios for Nordic countries;

B. Successful statistical models have been developed for monthly temperature and precipitation for Swedish stations. These model have been used to create MONTHLY scenarios for a number of GCM and emission scenarios.

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7January temperature in SW Sweden

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reconstruction

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Circulation is the dominating forcing of interannual and longer scale varabilitities

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120 obs SDE DDE ECHAM4

Improved seasonal cycles by downscalings (SD,DD)

Vänersborg, One station in southern Sweden

The maximum sea ice over the Baltic can be realistically predicted by a statistical model (Omstedt & Chen, 2002)

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observation numerical ice-ocean model statistical model

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Future changes based on the statistical downscaling model driven by 17 GCMs

from CMIP2 (Chen et al., 2003)

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Meeting needs of impact community

• Usually high spatial resolution (LRA,WG)

• Usually high temporal resolution (WG)

• Tailoring of information (WG)

• Capability for risk analysis and decisionmaking under uncertainty (WG)

• Transparency of scenarios

• Practical and useful tools (WG)

Our answer to the requirements of Impact community: LRA & WG

LRA=Lapse Rate ApproachWG=Weather Generator

LRA (local correction based on topography): observation or modelling based

• Observation based method uses observations at different sites in the area to determine the topography dependence

• Modelling based method uses a high resolution numerical model to simulate meteorological variables at different sites and the results are then used in determining the topography dependence

An Example: The temperature stations in Abisko area

Name St. no. Latitude (oN) Longitude (oE) Height(m) Nat_no

RITSEM 1 67.73 17.47 521 17792

AKTSE 2 67.15 18.30 530 17874

ALUOKTA 3 67.32 18.88 385 17879

TARFALA 4 67.90 18.62 1140 17897

ÅLLOLUOKTA 5 67.13 19.50 370 17974

NIKKALUOKTA 6 67.85 19.03 470 17995

GÄLLIVARE 7 67.13 20.67 365 18073

GÄLLIVARE FLYG. 8 67.15 20.83 312 18074

MALMBERGET 9 67.17 20.67 373 18075

KIRUNA FLYGPLATS 10 67.82 20.33 459 18094

ABISKO-AUT 11 68.35 18.82 388 18879

ABISKO 12 68.35 18.82 388 18880

KATTERJÄKK 13 68.42 18.17 500 18882

RIKSGRÄNSEN 14 68.43 18.13 508 18883

TORNETRÄSK 15 68.22 19.72 393 18976

KATTUVUOMA 16 68.28 19.90 355 18978

Lapse rate of temperature

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empe

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Height (m)

The precipitation stations in the area Name St. no. Latitude (oN) Longitude (oE) Height(m) Nat_no

RITSEM 1 67.73 17.47 521 17792

AKTSE 2 67.15 18.30 530 17874

ALUOKTA 3 67.32 18.88 385 17879

ÅLLOLUOKTA 5 67.13 19.50 370 17974

PUOLTSA 6 67.80 19,87 465 17994

NIKKALUOKTA 7 67.85 19.03 470 17995

KAITUM 8 67.53 20.12 490 18001

GÄLLIVARE 7 67.13 20.67 365 18073

MALMBERGET 9 67.17 20.67 373 18075

LADNIVAARA 10 67.27 20.27 460 18078

KILLINGI 11 67.52 20.28 485 18086

KIRUNA FLYGPLATS 12 67.82 20.33 459 18094

BJöRKLIDEN 13 68.38 18.68 360 18868

ABISKO 14 68.35 18.82 388 18880

KATTERJÄKK 15 68.42 18.17 500 18882

RIKSGRÄNSEN 16 68.43 18.13 508 18883

BERGFORS 17 68,15 19,80 480 18974

TORNETRÄSK 18 68.22 19.72 393 18976

KATTUVUOMA 19 68.28 19.90 355 18978

KUMMAVUOPIO 20 68.90 20.87 465 19097

Precipitation and height

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Y =4992.62015-21.70059 X+0.02585 X2

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Statistical Downscaling to Enhance Understanding at Local Scales

Source: A Study at the Abisko Laboratory of Net Primary Production under Changing Climate Conditions

Ongoing work on WGWG=Stochastic model:basic idea

given slow set of statistics (monthly means andstandard deviations, Y, from statistical or dynamical prediction),generate the high frequency variability of theweather (y) based on auto- and cross correlation:

=> y(t) = OT[Y, y(t-1)]

where OT is the time operator.

Weather GeneratorsPrecipitation Process

Occurrence Amount

Non-precipitation variables

Maximum temperatureMinimum temperature

Solar radiation

Model calibration (observation)

Synthetic data generation

Climate scenarios

GCM statistics

How a WG works?

Other meteorological variables

Condition the statistics of the daily variables (typically maximum/ minimum temperatures and solar radiation) on occurrence of precipitation.

In the classic WGEN model, multiple variables are modelled simultaneously with auto-regression:

( ) [ ] ( ) [ ] ( )tε+1-t=t BzAz

Where z(t) are normally distributed values for today’s nonprecipitation variables, z(t-1) are corresponding values for the previous day, and [A] and [B] are K K matrices of parameters, and (t) is white-noise forcing.

Other meteorological variables (cont.)

The z(t) are transformed to weather variables dependent on rainfall occurrence:

( )( ) ( )

( ) ( )tztσ+μ

tztσ+μ{=tT

kk,1k,1

kk,0k,0

k

if day t is dry

if day t is wet

where each Tk is any of the nonprecipitation variables, k,0 and k,0 are its mean and standard deviation for dry days, and k,1 and k,1 are its mean and standard deviation for wet days.

Seasonal dependence of the means and standard deviations is usually achieved through Fourier harmonics (i.e., sine and cosines).

Weather Generators

Area

Grid Box

Calibrate weather generator using area-average weather

Calibrate weather generator for each individual station within area

Station parameter set

Calculate changes in parameters from grid box data

Area parameter set Apply changes in parameters derived from difference between area and grid box parameter sets to individual station parameter files; generate synthetic data for scenario

Spatial Downscaling->high spatial resolution!

Weather GeneratorsTemporal Downscaling->high temporal resolution! – Use of monthly scenarios

Parameter file containing statistical characteristics of observed station data

Observed station data

WG

Monthly scenario information from GCM, RCM or SD

Generate daily weather data corresponding to the monthly scenario

Weather Generators

ADVANTAGES• the ability to generate time series of unlimited

length• opportunity to obtain representative weather

time series in regions of data sparsity, by interpolating observed parameter data

• ability to alter the WG’s parameters in accordance with scenarios of future climate change - changes in variability as well mean changes

Fundamental AssumptionThe statistical correlations between climatic variables derived from observed data are assumed to be valid

under a changed climate.

Weather Generators

Challenges

• seldom able to describe all aspects of climate accurately, especially persistent events, rare events and decadal- or century-scale variations

• designed for use, independently, at individual locations and few account for the spatial correlation of climate

A weather generator following Richardson (1981)

• P (W |D) = PWD

• P (D |D) =PDD= 1-PWD

• P (D |W) = PDW

• P (W |W) = PWW=1-PDW

0,,

/exp/ƒ

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Daily weather generation (Markov chain)

Source: Wilks and Wilby (1999)

Not yet!

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A 5 year simulation for Vännesborg

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7武汉站 月份逐日降水模拟

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Future

• Develop the WG further by including more variables and by testing new formulations such as higher order Markov chain, conditional probability on circulation.

• Continue cooperating with DNMI on development and application of the WG in Norway.