7 oktober 2009 challenge the future delft university of technology modelling the climate “a...
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7 oktober 2009
Challenge the future
DelftUniversity ofTechnology
Modelling the Climate“a modelling perspective on climate change”
Part 2 AE4-E40 Climate Change
A. Pier SiebesmaKNMI & TU DelftMultiscale Physics DepartmentThe NetherlandsContact: [email protected]
2Climate modeling
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)
4Climate modeling
Ed Lorenz (1918-2008)
Founder of ”the chaos theory”
Predictability for weather forecasting
Toy model for weather: Lorenz-model
6Climate modeling
Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas? Lorenz (1972)
x
yThe 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.
11Climate modeling
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.
12Climate modeling
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”
13Climate modeling
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
14Climate modeling
Long lasting sea surface temperature (SST) anomalies: El Nino
On timescales ofseasons to years:
17Climate modelingTAC 42 Verification 2010
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)
18Climate modeling
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.
19Climate modeling
Two types of predictions• Edward N. Lorenz (1917–
2008)
• 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
20Climate modeling
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)
22Climate modeling
1900 1940 2000 2080Historical concentrations of Greenhouse gases, sulphate, aerosols, solar variations and vulcanic aerosols
Greenhouse gases according to a‘Business-as-usual’ (BAU) scenario
62 simulatiesStochastic perturbations in temperature (<0.1%)
Dutch Challenge Project
“Simulate with one global climate model the “Earth’s Climate” a large number of times with small perturbations in the initial conditions”
www.knmi.nl/research/CKO/Challenge
24Climate modeling
Variations in Natural Aerosols: Vulcanic Eruptions
External Forcings
Pinatubo (1991), Filipijnen
El Chichón (1982), Mexico
Agung (1963), IndonesiëSanta Maria (1902), Guatemala
Novarupta (1912), Alaska
26Climate modeling
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
28Climate modeling
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
30Climate modeling
Skill of Weather Prediction Models (ECMWF)
Improvement of weather predictions through: • model (processes, resolution
• initialisations (satellites)
Predictive skill >60%
ECMWF DA/SAT Training Course, May 2010 32
Significant increase in number of observations assimilated
Conventional and satellite data assimilated at ECMWF 1996-2010
33Climate modeling
But what is the skill of a Climate Model?
or
How well do climate models simulate today’s climate?
34Climate modeling
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
35Climate modeling
A recent simple evaluation analysisReichler 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
36Climate modeling
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:
37Climate modeling
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
38Climate modeling
CMIP3 simulations using anthropogenic and natural forcings
CMIP3 simulations using natural forcings only!
41Climate modeling
Uncertainties in Future Climate model Predictions with different climate
models
2.5-4.3°CIPCC 2007
Past FuturePresent190
0
42Climate modeling
Climate Model Sensitivity
temperature radiative forcing
Water vapour
With feedbacks:
Snow albedo
clouds
43Climate modeling
Dufresne & Bony, Journal of Climate 2008
Radiative effects only
Water vapor feedback
Surface albedo feedback
Cloud feedback
Cloud effects “remain the largest source of uncertainty”in model based estimates of climate sensitivity IPCC 2007
2XCO2 Scenario for 12 Climate Models
44Climate Modelling
Primarily due to marine low clouds
“Marine boundary layer clouds are at the heart of tropical cloud feedback uncertainties in climate models”
(duFresne&Bony 2005 GRL)
Stratocumulus
Shallow cumulus
45Climate Modelling
• 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 (T)
• Climate model sensitivity: 0.5-1.2 K per Wm-2 (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 Model Sensitivity
47Climate modeling
Emission scenarios from IPCC, includes also air pollution giving aerosols
ppm
EXPERIMENT TYPES
51Climate modeling
Future seasonal mean Precipitation Changes
“the wet get wetter and the dry get dryer”
52Climate modeling
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.
54Climate modeling
Global Climate Models have their limitations
GCMs have a coarse resolution (150~300 km)
• Land-sea mask• Topography• Convection, clouds, precipitation• Land atmosphere interaction
RCM
GCM
How can we increase the resolution ?
55Climate modeling
Dynamical downscaling with regional climate
models (RCMs)
•RCMs “are” GCMs, but:• higher resolution (10km)• limited domain
• Purpose: Better local representation
• RCM needs to be feeded at the boundaries with data from a GCM
•Acts like a looking glass.
•But….. which GCM should be used for downscaling????
56Climate modeling
Change of Precipitation partly due to change in large-Scale circulation patterns:
• which is dictated by the GCM that is used for the downcaling!!
58Climate modeling
4 scenario’s for the Netherlands
Gematigd+verandering
+ 1 °C + 2 °C
Luchtstromingspatronen
WereldTemperatuur
Warm+verandering
Gematigd Warm
gew
ijzig
do
ng
ewijz
igd
in 2050 t.o.v. 1990
59Climate modeling
KNMI 2007 Scenario’s
http://www.knmi.nl/klimaatscenarios/
Winter precip increases, also extremes.
Summer precip decreases (probably); increase extremes
60Verstoorde wolken in een opwarmend klimaat
Fractional Uncertainty for future global climate (%)
2000 2100Time
Model uncertainty (e.g. clouds)
Scenario uncertainty (Societal)
Internal Variability (Ocean Initialisation)
Hawkins and Sutton (2009)
61Climate modeling
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)
62Climate modeling
Examples of Questions1 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