deliang chen regional climate group earth sciences centre gothenburg university sweden...
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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
R=0.84
N=122
observation
reconstruction
Tem
pera
ture
ano
mal
y (o
C)
Year
Circulation is the dominating forcing of interannual and longer scale varabilitities
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120 obs SDH DDH HadCM2
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itatio
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/mo
nth
)
Month
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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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Minimum
Mean
Extremelysevere
observation numerical ice-ocean model statistical model
Max
imum
ice
exte
nt (
103
km2 )
Year
Future changes based on the statistical downscaling model driven by 17 GCMs
from CMIP2 (Chen et al., 2003)
region1 region2 region3 region4 all stn
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ANN
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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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Y =15.0-0.0074 X
R2=0.79
JulyT
empe
ratu
re (
o C)
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
R2=0.26
AnnualP
reci
pita
tion
(mm
/yea
r)
Height (m)
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
1
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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水量
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Daily precipitation at a station
Date of a month
Precipitation (mm
)
Simulated versus observed monthly precipitation at a Swedish site
y = 0. 9991x
R2 = 0. 989
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实测值
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ulation (mm
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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.