What is a climate model? If we cannot predict weather, how can we predict climate?

1. CLIM 101: Weather, Climate and Global Society What is a climate model? If we cannot predict weather, how can we predict climate? Jagadish Shukla Lecture 11: Oct 6, 2009

2. Reading for Week 6Lecture 11What is a climate model? • GW Chapter 5 • IPCC WG1 Chapter 8, FAQ 8.1 How Reliable Are the Models Used to Make Projections of Future Climate Change?

3. Intergovernmental Panel on Climate Change (IPCC) IPCC has been established by WMO and UNEP to assess scientific, technical and socio- economic information relevant for the understanding of climate change, its potential impacts and options for adaptation and mitigation. Working Group I: The Physical Science Basis Working Group II:Impacts, Adaptation and Vulnerability Working Group III:Mitigation of Climate Change • Largest number of U.S. scientists: nominated by the U.S. Govt. • Highest skepticism : “U.S. Govt.”

4. What is a Model? • Quantitative and/or qualitative representation of natural processes (may be physical or mathematical) • Based on theory • Suitable for testing “What if…?” hypotheses • Capable of making predictions

5. What is a Model? Input Data Model Output Data What input data might we consider for a typical climate model? What output data might we consider for a typical climate model? Tunable Parameters What are the tunable parameters of interest?

6. Climate System Modeling Atmospheric General Circulation Model Physical processes Basic Equations Dynamics

7. S, , a, g, Ω O3 H2O CO2 Ω CLIMATE DYNAMICS OF THE PLANET EARTH g a  (albedo) Gases: H2O, CO2, O3 S T4 h*: mountains, oceans (SST) w*: forest, desert (soil wetness) CLIMATE . stationary waves (Q, h*), monsoons WEATHER hydrodynamic instabilities of shear flows; stratification & rotation; moist thermodynamics day-to-day weather fluctuations; wavelike motions: wavelength, period, amplitude

8. Newton’s law Energy conservation Mass conservation (approximation)  = p / ps

9. What is a Climate Model? • Equations of motions and laws of thermodynamics to predict rate of change of: • T, P, V, q, etc. (A, O, L, CO2, etc.) • 10 Million Equations: • 100,000 Points × 100 Levels × 10 Variables • With Time Steps of:~ 10 Minutes • Use Supercomputers

10. Discretization Atmosphere and ocean are continuous fluids … but computers can only represent discrete objects

11. Discretization Atmosphere and ocean are continuous fluids … but computers can only represent discrete objects

12. ENIAC Columbia NASA John von Neumann IBM 360 Seymour Cray & Cray-1 Cray-2

13. Weather Prediction Future pressure = current pressure + (rate of change of pressure) x t Future temperature = current temp. + (rate of change of temp.) x t Current pressure & temperature: use global observations For rate of change: use mathematical equations For producing forecast: use supercomputers

14. Sea Level Pressure (mb) & Precipitation Rate (mm/12Hr) 00Z Tue 10 Nov 1998

15. Sea Level Pressure (mb) & Precipitation Rate (mm/12Hr) 12Z Tue 10 Nov 1998

16. Sea Level Pressure (mb) & Precipitation Rate (mm/12Hr) 00Z Wed 11 Nov 1998

17. Numerical Weather Prediction • Determine (continuous) equations to be solved • Equation of state or Ideal Gas Law (Boyle’s Law relates P  V, Charles’ Law relates V  T, Gay-Lussac’s Law relates T  P) • Conservation of mass (dry air, water) • Conservation of energy • Conservation of angular momentum • Result: set of coupled, nonlinear, partial differential equations • Discretize the equations for numerical solution (typically requires computer) • Measure current state of global atmosphere to obtain initial conditions • Solve the initial value problem to produce a forecast • Take into account uncertainty in measured atmospheric state by repeating step 4 over an ensemble of slightly different initial conditions

25. Science 23 October 1998, Volume 282, pp. 728-731 Predictability in the Midst of Chaos: A Scientific Basis for Climate Forecasting J. Shukla Soil Wetness SST Anomalies (oC)

26. El Nino/Southern Oscillation 1998 JFM SST [oC] JFM SST Climatology [oC] 1998 JFM SST Anomaly [oC]

27. El Nino/Southern Oscillation

28. Rainfall Anomalies

29. Model Simulation of ENSO Effects 500 hPa Height Anomalies (ACC = 0.98) Vintage 2000 AGCM

30. European Heat Wave 2003

31. JJA 2003 SST Anomaly

32. JJA obs OBS.SST-CLIM.SST exp. result significant at more than 90% sig.lev.

33. 2060s observations HadCM3 Medium-High (SRES A2) 2040s 2003 Temperature anomaly (wrt 1961-90) °C GEC is more acute than ever • 2003 • Heat wave hits • Europe • 30,000 people die • in Western Europe

36. Courtesy of P. Houser (GMU)

38. 200 km

40. Computing Capability & Global Model Grid Size (km) * Core counts above O(104) are unprecedented for weather or climate codes, so the last 3 columns require getting 3 orders of magnitude in scalable parallelization (scalar processors assumed; vector processors would have lower processor counts) Range: Assumed efficiency of 10-40% 0 - Atmospheric General Circulation Model (AGCM; 100 levels) 1 - Coupled Ocean-Atmosphere-Land Model (CGCM; ~ 2X AGCM computation with 100-level OGCM) 2 - Earth System Model (ESM; ~ 2X CGCM computation) Thanks to Jim Abeles (IBM)

41. Geographic resolution characteristic of the generations of climatemodels used in the IPCC Assessment Reports: FAR (IPCC, 1990), SAR (IPCC, 1996),TAR (IPCC, 2001a), and AR4 (2007). • The figures above show how successive generationsof these global models increasingly resolved northern Europe. These illustrationsare representative of the most detailedhorizontal resolution used for short-termclimate simulations. • The century-long simulations cited in IPCC Assessment Reportsafter the FAR were typically run with the previous generation’s resolution. Verticalresolution in both atmosphere and ocean models is not shown, but it has increasedcomparably with the horizontal resolution, beginning typically with a single-layer slabocean and ten atmospheric layers in the FAR and progressing to about thirty levels inboth atmosphere and ocean.

49. Center of Ocean-Land-Atmosphere studies Projection of Global Warming Mean of 15 Models Surface Air Temperature Difference (Sresa1b YR 71-100) minus (20c3m 1969-98), Global Average = 2.61

50. Increase in Surface Temperature Observations Predictions with Anthropogenic/Natural forcings Predictions with Natrual forcings 1.0º C IPCC 2007