7 oktober 2009 Challenge the future Delft University of Technology Modelling the Climate “a modelling perspective on climate change” Part 2 AE4-E40 Climate Change A. Pier Siebesma KNMI & TU Delft Multiscale Physics Department The Netherlands Contact:

2 Climate 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)

3 Climate modeling 1. Predictability

4 Climate modeling Ed Lorenz ( ) Founder of ”the chaos theory” Predictability for weather forecasting Toy model for weather: Lorenz-model

5 Climate modeling Two time series for the x-component from nearly identical initial conditions

6 Climate modeling Does the flap of a butterfly ’ s wings in Brazil set off a tornado in Texas? Lorenz (1972) x y The butterfly effect is the sensitive dependence on initial conditions, where a small difference in initial conditions in a deterministic nonlinear systemnonlinear system results large differences to a later state.

7 Climate modeling Ensemble prediction in the Lorenz Attractor UK MetOffice

8 Climate modeling Ensemble prediction in a operational Weather Forecast Model

9 Climate modeling Ensemble Prediction for the Bilt from WEdnesday January 9

10 Climate modeling Ensemble Prediction for the Bilt from Thursday January 10

11 Climate 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.

12 Climate 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 ”

13 Climate 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

14 Climate modeling Long lasting sea surface temperature (SST) anomalies: El Nino On timescales ofseasons to years:

15 Climate modeling ….. and is influencing the precipitation

16 Climate modeling El Niño Teleconnections But only at certain areas in the world……..

17 Climate modeling TAC 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 Showing the individual ensemble members (red); and the subsequent verification (blue)

18 Climate 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.

19 Climate modeling Two types of predictions Edward N. Lorenz (1917– 2008 ) Predictions of the 1 st kind Initial-value problem Weather forecasting Lorenz: Weather forecasting fundamentally limited to about 2 weeks Predictions of the 2 nd 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

20 Climate 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)

21 Climate modeling 2. Example : The Challenge Project

22 Climate modeling Historical concentrations of Greenhouse gases, sulphate, aerosols, solar variations and vulcanic aerosols Greenhouse gases according to a ‘Business-as-usual’ (BAU) scenario 62 simulaties Stochastic 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”

23 Climate modeling Variations in Solar Constant External Forcings

24 Climate 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

25 Climate modeling Variations in Greenhouse Gases External Forcings

26 Climate 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

27 Climate modeling World Averaged Annual Temperature observed Model average

28 Climate modeling Winter temperatures in the Netherlands Larger variations on a smaller scale Cold winters will still happen in the 21 st century but the probability gets increasingly smaller

29 Climate modeling 3. Skill of Climate and Weather Models

30 Climate modeling Skill of Weather Prediction Models (ECMWF) Improvement of weather predictions through: model (processes, resolution initialisations (satellites) Predictive skill >60%

31 Climate modeling Leading to a larger predictability !

ECMWF DA/SAT Training Course, May Significant increase in number of observations assimilated Conventional and satellite data assimilated at ECMWF

33 Climate modeling But what is the skill of a Climate Model? or How well do climate models simulate today’s climate?

34 Climate 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

35 Climate modeling 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 Take fields that that are available from models and observations

36 Climate 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 e 2 by the average error found in the CMIP3 ensemble: Take the mean over all climate variables:

37 Climate 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

38 Climate modeling CMIP3 simulations using anthropogenic and natural forcings CMIP3 simulations using natural forcings only!

39 Climate modeling Same picture for regional trends

40 Climate modeling 4. Climate Model Sensitivity:

41 Climate modeling Uncertainties in Future Climate model Predictions with different climate models °C IPCC 2007 PastFuture Present 1900

42 Climate modeling Climate Model Sensitivity  temperature  radiative forcing Water vapour With feedbacks: Snow albedo clouds

43 Climate 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 XCO 2 Scenario for 12 Climate Models

44 Climate 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

45 Climate 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 : 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

46 Climate modeling 5. Future Global Climate Scenario’s

47 Climate modeling Emission scenarios from IPCC, includes also air pollution giving aerosols ppm EXPERIMENT TYPES

48 Climate modeling Projections of global temperature change Source : IPCC +2K

49 Climate modeling IPCC 2007

50 Climate modeling Projections for surface temperatures

51 Climate modeling Future seasonal mean Precipitation Changes “the wet get wetter and the dry get dryer”

52 Climate 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.

53 Climate modeling 6. Future Regional Climate Scenario’s

54 Climate 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 ?

55 Climate 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????

56 Climate 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!!

57 Climate modeling 1 RCM with 2 GCM (boundaries) GCM1 GCM2

58 Climate modeling 4 scenario’s for the Netherlands in 2050 t.o.v. 1990

59 Climate modeling KNMI 2007 Scenario’s Winter precip increases, also extremes. Summer precip decreases (probably); increase extremes

60 Verstoorde wolken in een opwarmend klimaat Fractional Uncertainty for future global climate (%) Time Model uncertainty (e.g. clouds) Scenario uncertainty (Societal) Internal Variability (Ocean Initialisation) Hawkins and Sutton (2009)

61 Climate 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)

62 Climate modeling 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