Modelling the Climate
“a modelling perspective on climate change”
Part 2
AE4-E40 Climate Change
7 oktober 2009
A. Pier Siebesma
KNMI & TU Delft
Multiscale Physics Department
The Netherlands
Contact: siebesma@knmi.nl
Delft
University of
Technology
Challenge the future
Previous Lecture
• Simple Energy Balance Models (0-dimensional models)
• Concept of Radiative Forcing
(1-dimensional models)
• How to “translate” this in a temperature change in a static climate
• Architecture of climate models (3-dimensional models)
Today
• Model Predictability
• Model Skill
• Model Sensitivity
• Future Climate Scenario’s (Global and Regional)
Climate modeling
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1.
Predictability
Climate modeling
3
Predictability for weather forecasting
Toy model for weather:
Lorenz-model
Ed Lorenz (1918-2008)
Founder of ”the chaos theory”
Climate modeling
4
Two time series for the x-component from nearly
identical initial conditions
Climate modeling
5
x
y
The butterfly effect is the sensitive dependence on initial conditions,
where a small difference in initial conditions in a deterministic nonlinear system
results large differences to a later state.
Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?
Lorenz (1972)
Climate modeling
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Ensemble prediction in the Lorenz Attractor
UK MetOffice
Climate modeling
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Ensemble prediction in a operational Weather
Forecast Model
Climate modeling
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Ensemble
Prediction
for the Bilt
from
WEdnesday
January 9
Climate modeling
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Ensemble
Prediction
for the Bilt
from
Thursday
January 10
Climate modeling
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Remarks
• Predictability horizon for “weather” is now between 5 and 15 days
(dependent on the initial state)
• Predictability horizon can be extended through
• More accurate estimate of the initial state (more observations)
• Improved model formulation (resolution and parameterizations)
• Error growth in non-linear systems is exponential. It becomes
therefore increasingly more difficult to extend the predictability
horizon.
Climate modeling
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Question
Can we make any reliable statements on changes in weather and
climate on time scales beyond 15 days? (seasonal, decadal, century
………)
Free after often received complaints at KNMI:
“ Why are those assholes at KNMI waisting our money on climate
predictions if they cannot even predict the weather of tomorrow”
Climate modeling
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Hint
Weather (atmospheric) prediction is essentially a initial value
problem:
timescale boundary conditions >> timescale prediction period (15 days)
e.g. Continents, Glaciers, Atmospheric Composition,
vegetation, solar constant, ocean temperatures can be
kept constant!
Atmosphere loses its “memory” after two weeks –
any predictability beyond two weeks residing in initial values
must arise from predictability from slowly varying boundary
conditions
Climate modeling
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Long lasting sea surface temperature (SST) anomalies: El Nino
On timescales
ofseasons to years:
Climate modeling
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….. and is influencing the
precipitation
Climate modeling
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El Niño Teleconnections
But only at certain areas in the world……..
Climate modeling
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Seasonal forecast – Nino SST, annual range
EUROSIP forecasts of SST anomalies over the NINO 3.4 region of the tropical
Pacific from July 2009, December 2009 and May 2010. Showing the individual
ensemble members (red); and the subsequent verification (blue)
TAC 42 Verification 2010
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Predictions at a seasonal scale
•
Extension beyond the 15 days predictability horizon is possible
through the thermal inertia of oceans, snow, soil
• Requires coupling of the atmosphere with the ocean (which is the
most important source of inertia)
• So far only “somewhat” successful in the tropics. Outside the
tropics the coupling between atmosphere and ocean is weak. In
Europe there is little skill on the seasonal scale*
• Note that the problem is slowly shifting from a initial value problem
(weather prediction) to a boundary condition (climate prediction)
problem
*therefore any seasonal numerical prediction of a horror winter in Europe does not have any skill .
Climate modeling
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Two types of predictions
• Edward N. Lorenz
2008)
(1917–
• Predictions of the 1st kind
• Initial-value problem
• Weather forecasting
• Lorenz: Weather forecasting
fundamentally limited to about 2
weeks
• Predictions of the 2nd kind
• Boundary-value problem
• IPCC climate projections
(century-timescale)
• No statements about individual
weather events
• Initial values considered
unimportant; not defined from
observed climate state
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Climate “Predictions”
• decadal (10yrs) to centennial is possible through
changes of the boundary conditions of the atmosphere:
•through the ocean (1 to 10 year),
•through change in greenhouse gases (10+ years)
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2.
Example : The Challenge Project
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Dutch Challenge Project
www.knmi.nl/research/CKO/Challenge
“Simulate with one global climate model the “Earth’s
Climate” a large number of times with small perturbations
in the initial conditions”
Stochastic perturbations in
temperature (<0.1%)
1900
1940
62 simulaties
2000
Historical concentrations of
Greenhouse gases, sulphate,
aerosols, solar variations and
vulcanic aerosols
2080
Greenhouse gases according to a
‘Business-as-usual’ (BAU) scenario
Climate modeling
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External Forcings
Variations in Solar Constant
Climate modeling
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External Forcings
Variations in Natural Aerosols: Vulcanic Eruptions
Santa Maria (1902), Guatemala
Novarupta (1912), Alaska
Agung (1963), Indonesië
Pinatubo (1991), Filipijnen
El Chichón (1982), Mexico
Climate modeling
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External Forcings
Variations in Greenhouse Gases
Climate modeling
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Start of the development of the
temperature in de Bilt
Atmosphere slowly “forgets”
its initial state
Limited predictability of weather
An ensemble of developments
of the climate sytem
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World Averaged Annual Temperature
observed
Model average
Climate modeling
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Winter temperatures in the Netherlands
•Larger variations on a smaller scale
•Cold winters will still happen in the 21st century but the
probability gets increasingly smaller
Climate modeling
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3.
Skill of Climate and Weather Models
Climate modeling
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Skill of Weather Prediction Models (ECMWF)
Predictive skill >60%
Improvement of weather predictions through:
• model (processes, resolution
• initialisations (satellites)
Climate modeling
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Leading to a larger predictability!
Climate modeling
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Significant increase in number of observations assimilated
Conventional and satellite data assimilated at ECMWF 1996-2010
DA/SAT Training Course, May 2010
32
ECMWF
But what is the skill of a Climate Model?
or
How well do climate models simulate
today’s climate?
Climate modeling
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No commonly accepted skill metrics for climate models yet
because:
• Unlike for weather prediction models a limited set of observables
(pressure fields) may not be sufficient.
• Opportunities to test climate model skills is limited
• Lack of reliable and consistent observations for present climate
A skill metrics would be desirable because:
• To objectively measure progress in climate model development
• To be able to set a standard for climate models that can participate in
future climate model scenario’s such as for IPCC
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A recent simple evaluation analysis
Reichler and Kim; Bull of the American Meteorological Society (2008)
•
One single performance index.
• Only evaluate climatological mean state for the period 1979-1999
•Take fields that that are available from models and observations
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Model output from 3 different climate model
intercomparison projects (CMIPS)
•CMIP1 : 18 different climate models (1995)
•CMIP2 : 17 different climate models (2003)
•CMIP3 : 22 different climate models (2007)
Method
Normalized error variance for each
variable v for model m:
Rescale e2 by the average error
found in the CMIP3 ensemble:
Take the mean over all climate
variables:
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Results of Performance index I
Best performing models have low I
Grey circles indicate the average I of a model group
Black circles indicate multimodel mean
Take home messages:
•Improvement of climate models over the years
•Multimodel mean outperforms any single model
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CMIP3 simulations using
anthropogenic and natural forcings
CMIP3 simulations using natural
forcings only!
Climate modeling
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Same picture for regional trends
Climate modeling
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4.
Climate Model Sensitivity:
Climate modeling
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Uncertainties in Future Climate model
Predictions with different climate models
IPCC 2007
1900
Past
Present
2.5-4.3°C
Future
Climate modeling
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Climate Model Sensitivity
 temperature
 radiative forcing
With feedbacks:
Water vapour
Snow albedo
clouds
Climate modeling
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2XCO2 Scenario for 12 Climate Models
Cloud feedback
Surface albedo feedback
Water vapor feedback
Radiative effects only
Dufresne & Bony, Journal of Climate 2008
Cloud effects “remain the largest source of uncertainty”
in model based estimates of climate sensitivity IPCC 2007
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Primarily due to marine low clouds
Stratocumulus
“Marine boundary layer clouds are at the heart of
tropical cloud feedback uncertainties in climate
models”
(duFresne&Bony 2005 GRL)
Shallow cumulus
Climate Modelling
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Climate Model Sensitivity
• Definition: temperature change resulting from a perturbation of 1 Wm-2
• Radiative forcing for 2XCO2 3.7 Wm-2 (R)
• Temperature response of climate models for 2XCO2 2~4.3 K
• Climate model sensitivity: 0.5-1.2 K per Wm-2
(T)
(T/R)
• The climate model sensitivity is not (very) dependent on the source of the
perturbation (radiative forcing)
• Main reason for this uncertainty are the representation of (low) clouds
• Reducing uncertainty of climate models can only be achieved through a
more realistic representation of cloud processes and is one of the major
challenges of climate modelling
Climate Modelling
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5.
Future Global Climate Scenario’s
Climate modeling
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EXPERIMENT TYPES
Emission scenarios from IPCC, includes also air pollution
giving aerosols
ppm
Climate modeling
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Projections of global temperature change
+2K
Source : IPCC
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IPCC 2007
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Projections for surface temperatures
Climate modeling
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Future seasonal mean Precipitation
Changes
“the wet get wetter and the dry get dryer”
Climate modeling
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Remarks
• Increase of precipitation at high latitudes
• Decrease of precipitation at the subtropical land regions
• Due to increased transport of water vapour from the lower
latitudes poleward.
• Note that Netherlands is on the borderline.
Climate modeling
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6.
Future Regional Climate Scenario’s
Climate modeling
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Global Climate Models have their
limitations
GCMs have a coarse resolution (150~300 km)
•
•
•
•
Land-sea mask
Topography
Convection, clouds, precipitation
Land atmosphere interaction
GCM
RCM
How can we increase the resolution ?
Climate modeling
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Dynamical downscaling with regional climate
models (RCMs)
•RCMs “are” GCMs, but:
• higher resolution (10km)
• limited domain
• RCM needs to be feeded at the
boundaries with data from a GCM
• Purpose: Better local representation
•But….. which GCM should be used
for downscaling????
•Acts like a looking glass.
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Change of Precipitation partly due to
change in large-Scale circulation patterns:
• which is dictated by the
GCM that is used for the
downcaling!!
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GCM2
GCM1
1 RCM with 2 GCM
(boundaries)
Climate modeling
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4 scenario’s for the Netherlands
gewijzigd
Luchtstromings
patronen
Gematigd+
Warm+
verandering
verandering
ongewijzigd
+ 1 °C
Gematigd
+ 2 °C
Warm
Wereld
Temperatuur
in 2050
t.o.v. 1990
Climate modeling
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KNMI 2007 Scenario’s
http://www.knmi.nl/klimaatscenarios/
Winter precip increases, also
extremes.
Summer precip decreases
(probably); increase extremes
Climate modeling
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Fractional Uncertainty for future global climate (%)
Internal Variability (Ocean Initialisation)
Scenario uncertainty
(Societal)
Model uncertainty
(e.g. clouds)
2000
Hawkins and Sutton (2009)
Time
2100
Verstoorde wolken in een opwarmend klimaat
60
The Road Ahead……..
• Better Observations (initialisation, monitoring, evaluation)
• Better Models ( Through process studies of relevant process
studies e.g. clouds)
• Emissions : Couple Carbon cycle with GCM’s but ultimately this
remains a societal and ethical problem (economics, politics)
Climate modeling
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Examples of Questions
1 a) Describe the greenhouse effect.
1b) Describe how the greenhouse effect is affected by increase of CO2
3) What are the main components that are needed
in a 3-dimensional climate model. Explain why they are necessary
4) What are parameterizations?
Why do they need to be included in climate models.
What would happen if you would run a climate model
without parameterizations of clouds.
5) Explain the concept of radiative forcing.
Which are the main contributors.
Which ones are the source of the largest uncertainties in the radiative forcing.
6) What defines the predictability of a numerical weather model.
Why is it possible that we can still make climate model predictions
on much longer timescales? Discuss the differences.
7) What is climate model sensitivity?
Which are the most important sources for uncertainty in climate model sensitivity?
Explain why.
8) How are regional climate models used for future climate scenario’s?
Describes the pro’s and con’s
Climate modeling
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