folie 1 climate models, downscaling and uncertainties hans von storch, gkss research centre,...
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Climate models, downscaling and uncertainties
Hans von Storch, GKSS Research Centre, Geesthacht, and
KlimaCampus „clisap“, University of Hamburg Germany
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Who is this?
Hans von Storch (born 1949)
Diploma in mathematics,PhD in meteorology
Director of Institute for Coastal Research, GKSS Research Center, near Hamburg,
Professor at the Meteorological Institute, KlimaCampus, University of Hamburg
Works also with social and cultural scientists.
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Overview:1. Quasi-realistic climate models
(„surrogate reality“)
2. Free simulations and forced simulations for reconstruction of historical climate
3. Climate change simulations
4. Downscaling - Regional climate modelling
5. Regional scenarios
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Models as surrogate reality• dynamical, process-based models, • experimentation tool (test of hypotheses) • design of scenario • sensitivity analysis • dynamically consistent interpretation and extrapolation of observations in space and time (“data assimilation”) • forecast of detailed development (e.g. weather forecast)
characteristics: complexity quasi-realistic mathematical/mechanisticengineering approach
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Components of the climate system. (Hasselmann, 1995)
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Quasi-realistic climate models …
… are dynamical models, featuring discretized equations of the type
)(dt
dΨ, k
iki
k P with state variables Ψk and processes Pi,k.
The state variables are typically temperature of the air or the ocean, salinity and humidity, wind and current.
… because of the limited resolution, the equations are not closed but must be closed by “parameterizations”, which represent educated estimates of the expected effect of non-described processes on the resolved dynamics, conditioned by the resolved state.
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atmosphere
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Dynamical processes in the atmosphere
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Dynamical processes in a global atmospheric general circulation model
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Results of a survey among climate modellers in 1996, 2003 and 2008
Bra
y an
d vo
n S
torc
h, 2
010
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Modell
Beobachtet
Klimazonen
Klassifikation nach KoeppenErich Roeckner, pers. Mitteilung
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Observed Simulated Winter(DJF)
Erich Roeckner, pers. Mitteilung
Zyklogenese
Sturmbahn-dichten
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Precipitation in IPCC AR4 models
Erich Roeckner, pers. Mitteilung
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Free and forced simulations for reconstruction of historical climate
Free and forced simulations for reconstruction of historical climate
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.... tions"reconstrucdriven Data"
egetationor v
sheets) ice (e.g.,opography or t
ion)configurat orbital (incl.output solar or
ionsconcentrat aerosolor
ionsconcentrat gas greenhouse ith w
) ;F( :"Simulation Forced"
)F( :"Simulation Free"
t
tt1t
t1t
Different ways of running the model
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Free Simulation: 1000 yearsno solar variability, no changes in greenhouse gas concentrations, no orbital
forcing
Tem
pera
ture
(at
2m
) de
viat
ions
fr
om 1
000
year
ave
rage
[K
]
Zor
ita,
200
1
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1000-2000 simulation
Changing solar forcing and
time variable volcanic aerosol load;
greenhouse gases
1000-2000 simulation
Changing solar forcing and
time variable volcanic aerosol load;
greenhouse gases
Forced SimulationForced Simulation
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1675-1710vs. 1550-1800
Reconstruction from historical evidence, from Luterbacher et al.
Late Maunder Minimum
validation
Model-based reconstuction
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Global 1675-1710 temperature anomaly
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Climate change simulationsClimate change simulations
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Scenarios of what?
• Climate = the statistics of weather, usually described by probability density functions, in particular by- their moments (e.g., mean, std deviation, covariances), - percentiles and return values,- spatial characteristics (e.g., EOFs), - temporal characteristics (autocovariance function, spectra)
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Scenario building
• Construction of scenarios of emissions. • Construction of scenarios of concentrations of
radiatively active substances in the atmosphere.
• (Ok – not quite exact; aerosols …)• Simulation of climate as constrained by
presence of radiatively active substances in the atmosphere (“prediction” of conditional statistics).
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“SRES” ScenariosSRES = IPCC Special Report on
Emissions Scenarios
A world of rapid economic growth and rapidintroduction of new and more efficient technology.
A very heterogeneous world with an emphasis onfamily values and local traditions.
A world of “dematerialization” and introduction of clean technologies.
A world with an emphasis on local solutions toeconomic and environmental sustainability.
“ business as usual ” scenario (1992).
A1
A2
B1
B2
IS92a
IPC
C, 2
001
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Scenario building
1. Simulation with global models, which describe several compartments of the global earth system – relatively coarse spatial grid resolution (e.g., 200 km)
2. Simulation with regional models, often with only one or a few compartments (mostly atmosphere) – relatively high spatial grid resolution (e.g., 50 km)
3. Simulation with impact models – a large variety of different systems, e.g., storm surges or ocean waves.
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Scenario A2
Scenario B2
Danmarks Meteorologiske Institut
Annual temperature changes [°C]
(2071–2100) –(1961–1990)
Folie 28Agreement among 7 out of a total of 9 simulations
precipitation
IPCC (2001) „regional development“ scenarios A2 and B2.
IPCC (2001) „regional development“ scenarios A2 and B2.
Gio
rgi et
al., 20
01
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Typical “global” atmospheric model grid resolution with corresponding land mask.
T42 used in global models. (courtesy: Ole Bøssing-Christensen)
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global model
Well resolved
Insufficiently resolved
Spatial scales
vari
ance
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DownscalingDownscaling
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Globale development(NCEP)
Dynamical DownscalingCLM
Simulation with barotropicmodel of North Sea
Empirical Downscaling
Tide gauge St. Pauli
Cooperation with a variety of governmental agencies and with a number of private companies
Regional and local conditions – in the recent past and next
century
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Typical regional atmospheric
model grid resolutions with
corresponding land masks.
50 km grid used in regional
models (courtesy: Ole
Bøssing-Christensen)
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Well resolved
Insufficiently resolved
Spatial scales
vari
ance
regional model
Added value
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.operator suitable a
)(
:nintegratio Forward
modeln observatio
model dynamical
errorsn observatio and model , h wit
equation n Observatio
equation space State
1111
*1
*1
1
1
Kwith
)dK(dΨΨ
Gd
);ηF(ΨΨ
G
F
δ) G(Ψd
ε) ;ηF(ΨΨ
t*t
*t t
tt
tt*t
tt
ttt
tttt
Concept of Dynamical DownscalingRCM Physiographic detail
3-d vector of state
Known large scale state
projection of full state on large-scale scale
Large-scale (spectral) nudging
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Example Extreme Events (Wind & Waves)
2 24.38 25.17 25.96 24.05 25.21 26.37 7.12 7.49 7.86 6.41 6.77 7.135 25.86 27.28 28.70 25.75 27.64 29.53 7.84 8.44 9.04 6.93 7.54 8.15
25 28.44 31.33 34.22 28.09 32.77 37.45 8.99 10.35 11.71 7.52 9.21 10.902 22.50 23.16 23.82 23.16 24.03 24.90 5.89 6.15 6.41 5.52 5.84 6.165 23.76 24.82 25.88 24.33 25.94 27.55 6.34 6.83 7.32 5.89 6.46 7.03
25 25.67 28.00 30.33 26.43 29.75 33.07 6.90 8.20 9.50 5.99 7.88 9.772 23.29 24.15 25.01 23.11 24.03 24.95 6.78 7.06 7.34 5.60 5.84 6.085 24.89 26.32 27.75 24.15 25.94 27.73 7.37 7.79 8.21 5.97 6.46 6.95
25 26.68 30.70 34.72 26.42 29.75 33.08 8.04 9.03 10.02 6.34 7.88 9.42
EU
RS
ON
Yea
rs
Wind [m/s]
K13
Waves [m]Hipocas Observed Hipocas Observed
90rx 90
rx 90rx 90
rx 90rx 90
rx 90rxrx rx rx rx90
rx
2, 5, and 25-year return values with 90% confidence limits based on 10.000 Monte Carlo simulations each.
(Weisse and Günther. 2006)
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A set of model data of recent, ongoing and possible future coastal climate(hindcasts 1948-2008, reconstructions and scenarios for the future, e.g., 2070-2100)
Based on experiences and activities in a number of national andinternational projects (e.g. WASA, HIPOCAS, STOWASUS, PRUDENCE)
Presently contains atmospheric and oceanographic parameter(e.g. near-surface winds, pressure, temperature and humidity; upper air meteorological data such as geopotential height, cloud cover, temperature and humidity; oceanographic datasuch as sea states (wave heights, periods, directions, spectra) or water levels (tides and surges) and depth averaged currents, ocean temperatures)
Covers different geographical regions(presently mainly the North Sea and parts of the Northeast Atlantic; other areas such as the Baltic Sea, subarctic regions or E-Asia are to be included)
http://www.coastdat.de, contact: Ralf Weisse ([email protected])
What is coastDat?
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- Ship design- Navigational safety- Offshore wind- Oils spill risk- Interpretation of measurements- Chronic Oil Pollution- Ocean Energy
Wave Energy Flux [kW/m]
Currents Power [W/m2]
Some applications of
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Scenarios for Northern Germany
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RCAO HIRHAM
A2 - CTL: changes in 99 % - iles of wind speed (6 hourly, DJF): west wind sector selected (247.5 to 292.5 deg)
Sce
nari
os
for
207
0-2
10
0
Wo
t h,
pe
rso
na
l co
mm
un
ica
ti on
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North German Climate Office@GKSS
An institution set up to enable communication between science and stakeholders
• that is: making sure that science understands the questions and concerns of a variety of stakeholders
• that is: making sure that the stakeholders understand the scientific assessments and their limits.
Typical stakeholders: Coastal defense, agriculture, off-shore activities (energy), tourism, water management, fisheries, urban planning
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Online-Atlas „Klimawandel Norddeutschland“
Darstellung unterschiedlicher Größen zum Klimawandel in Norddeutschland für die Zeiträume 2011-2040, 2041-2070 und 2071-2100.
Darstellung der Differenz zu dem Kontrollzeitraum 1961-1990
Darstellung unterschiedlicher Treibhausgasszenarien (nach dem IPCC) gerechnet mit verschiedenen regionalen Modellen
http://www.norddeutscher-klimaatlas.de/
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Conclusions
• Global climate modeling allows the representation of global, continental and sub-continental scales. Global models fail on the regional and local scale.
• Global climates is varying because of both internal dynamics as well as external forcing.
• Scenarios of future climate change hinge on the validity of economic scenarios.
• Simulation of regional climate is a downscaling problem and not a boundary value problem.
• Marine weather (winds, waves) have been successfully reconstructed for the years 1958-97 with a 1-hourly resolution. (CoastDat@GKSS)
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Background information on this issue:
von Storch, H., S. Güss und M. Heimann, 1999: Das Klimasystem und seine Modellierung. Eine Einführung. Springer Verlag ISBN 3-540-65830-0, 255 pp
von Storch, H., and G. Flöser (Eds.), 2001: Models in Environmental Research. Proceedings of the Second GKSS School on Environmental Research, Springer Verlag ISBN 3-540-67862, 254 pp.
Müller, P., and H. von Storch, 2004: Computer Modelling in Atmospheric and Oceanic Sciences - Building Knowledge. Springer Verlag Berlin - Heidelberg - New York, 304pp, ISN 1437-028X
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http://coast.gkss.de/staff/[email protected]
Weblog KLIMAZWIEBELhttp://klimazwiebel.blogspot.com/