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Inez Fung University of California, Berkeley

Global Warming: What do we really know?. Inez Fung University of California, Berkeley MSRI Climate Change Summer School July 14 2008. 1. Power Source: Blackbody Radiation. 620 K. 380 K. Planck’s Law:

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Inez Fung University of California, Berkeley

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  1. Global Warming: What do we really know? Inez Fung University of California, Berkeley MSRI Climate Change Summer School July 14 2008

  2. 1. Power Source: Blackbody Radiation 620 K 380 K Planck’s Law: The amount and spectrum of radiation emitted by a blackbody is uniquely determined by its temperature Max Planck (1858 – 1947) Nobel Prize 1918 Emission from warm bodies peak at short wavelengths Sun: ~6000K :0.5m (shortwave) Earth: ~300K :10m (longwave) wavelength

  3. What is a greenhouse gas? n1symmetric O O C C n2 bending 15 mm O O n2asymmetric 4.3 mm O C O Greenhouse effect: Radiation at specific wavelengths excite CO2 into higher energy states: energy is “absorbed” by the CO2 molecules

  4. Earth’s Energy Balance: without GHG 100 20absorbed by atm N2 • Latent heat Sensible heat 50 absorbed by sfc 20 Shortwave Longwave 70 30 70

  5. Earth’s Energy Balance: with GHG Shortwave Longwave 100 70 30 20absorbed by atm 23 7 114 95 50 absorbed by sfc CO2, H2O, GHG

  6. Earth Spectrum Incoming from Sun: High energy, short wavelength 0.5 mm Outgoing from Earth Low energy Long wavelength 20 mm 10 mm

  7. What do we really know? • Climate Forcing • Climate Feedback • Climate Response • Equilibrium (?5000 years) • Transient (<500 years) • Climate Projections

  8. Changing Composition of Earth’s Atmosphere Ancient air bubbles trapped in ice contains info about past atm composition

  9. The Last 500,000 yearsand the last 200 years

  10. Climate Forcing: expressed as a change in radiative heating (W/m2) at surface for a given change in trace gas composition or other change external to the climate system Cumulative climate forcing since 1800 Hansen PNAS 2001

  11. Ship Tracks:- more cloud condensation nuclei- smaller drops- more drops- more reflective- D energy balance

  12. What do we really know? • Climate Forcing • Climate Feedback • Climate Response • Equilibrium (?5000 years) • Transient (<500 years) • Climate Projections

  13. Climate Feedback Given a climate forcing (e.g. CO2 increase)  initial warming • Amplifying loops (positive feedback) magnify the warming • Diminishing loops (negative feedback)

  14. Climate Feedbacks Evaporation from ocean, Increase water vapor in atm Enhance greenhouse effect Increase cloud cover; Decrease absorption of solar energy Warming Decrease snow cover; Decrease reflectivity of surface Increase absorption of solar energy

  15. What do we really know? • Climate Forcing • Climate Feedback • Climate Response • Equilibrium (?5000 years) • Transient (<500 years) • Climate Projections

  16. At equilibrium (thousands of years):High CO2 --> warm; Low CO2 --> cold J. Hansen

  17. Warmest 7: 1998, 2002, 2003, 2004, 2005, 2006, 2007 Warming greatest at high latitudes Amplification of warming due to decrease of albedo (melting of snow and ice)

  18. Melting glaciers on Greenland--> feedback --> accelerating warming

  19. Oceans: Bottleneck to warminglong memory of climate system • 4000 meters of water, heated from above • Stably stratified • Very slow diffusion of chemicals and heat to deep ocean • Fossil fuel CO2: • 200 years emission, • penetrated to upper 500-1000 m • Slow warming of oceans --> continue evaporation, continue warming

  20. What do we really know? • Climate Forcing • Climate Feedback • Climate Response • Equilibrium (?5000 years) • Transient (<500 years) • Climate Projections

  21. Weather Prediction by Numerical ProcessLewis Fry Richardson 1922

  22. Weather Prediction by Numerical ProcessLewis Fry Richardson 1922 • Grid over domain • Predict pressure, temperature, wind • Temperature • -->density • Pressure Pressure gradient • Wind • temperature

  23. Weather Prediction by Numerical ProcessLewis Fry Richardson 1922 • Predicted: • 145 mb/ 6 hrs • Observed: • -1.0 mb / 6 hs

  24. First Successful Numerical Weather Forecast: March 1950 • Grid over US • 24 hour, 48 hour forecast • 33 days to debug code and do the forecast • Led by J. Charney (far left) who figured out the quasi-geostrophic equations

  25. ENIAC: <10 words of read/write memory Function tables (read memory)

  26. 16 operations in each time step Platzman, Bull. Am Meteorol. Soc. 1979

  27. Reasons for success in 1950 • More & better observationsafter WWII--> initial conditions + assessment • Faster computers & correct computational math(24 hour forecast in 24 hours) • Improved physics - • Atm flow is quasi 2-D (Ro<<1) • quasi-geostrophic vorticity equations • filtered out gravity waves • Initial C: pressure (no need for u,v) • t ~30 minutes (instead of 5-10 minutes)

  28. Continued Success Since 1950 • More & better observations • Faster computers and advanced computational mathematics • Improved physics

  29. Atmosphere momentum mass energy water vapor convective mixing

  30. Modern climate models • Forcing: solar irradiance, volanic aerosols, greenhouse gases, … • Predict:T, p, wind, clouds, water vapor, soil moisture, ocean current, salinity, sea ice, … • Very high spatial resolution: • <1 deg lat/lon resolution • ~50 atm layers • ~30 ocn layers • ~10 soil layers • ==> 6.5 million grid boxes • Very small time steps (~minutes) • Ensemble runs (multiple experiments) • Model experiments (e.g. 1800-2100) take weeks to months on supercomputers

  31. Processes in Climate Models • Radiative transfer: solar & terrestrial • phase transition of water • Convective mixing • cloud microphysics • Evapotranspirat’n • Movement of heat and water in soils

  32. Climate Dial: Three phases of water liquid Saturation Vapor Pressure (mb) B 275 280 285 290 295 300 A vapor Temperature (K) A  B + water vapor + greenhouse Warming C C A  C + water vapor + cloud cover + greenhouse - absorption of sunlight 100% relative humidity Ice  Liquid + absorption of sunlight

  33. Attribution Observations • are observed changes consistent with • expected responses to forcings • inconsistent with alternative explanations Climate model: All forcing Climate model: Solar+volcanic only IPCC AR4

  34. 21stC warming depends on rate of CO2 increase 21thC “Business as usual”: CO2 increasing 380 to 680 ppmv 20thC stabilizn: CO2 constant at 380 ppmv for the 21stC Meehl et al. (Science 2005)

  35. Projections of Climate Change 2020-2029 2090-2099 greatest over land & at most high N latitudes and least over the South. Ocean & parts of the N Atlantic Ocean IPCC AR4

  36. Multi-model Projections of Climate Change 9oF 7oF 3oF Inter-model range IPCC AR4 Uncertainties in global projections: 2020: concurrence 2050: depend on CO2 increase 2100: depend on CO2 increase and ocean response time

  37. Stern Review 2006

  38. 2000 2020 Stern Review 2006

  39. PROBLEM: The Elusive Carbon Sink • Only half of the CO2 produced by human activities is remaining in the atmosphere • Where are the sinks that are absorbing over 40% of the CO2 that we emit? • Land or ocean? • Eurasia/North America? • Why does CO2 buildup vary dramatically with nearly uniform emissions? • How will CO2 sinks respond to climate change?

  40. Cumulative Ocean Carbon Sink of FF CO2 • Thermocline: barrier to transport of perturbations to depth • Thermohaline circulation: lateral transport of perturbation (Cumulative) Sabine et al 2004

  41. 21st Century Correlations & Regressions: FF= SRES A2 ;  = Coupled minus Uncoupled {dT, dSoil Moisture Index} Warm-wet Warm-dry Regression of dNPP vs dT NPP decreases with carbon-climate coupling Fung et al. Evolution of carbon sinks in a changing climate. PNAS 2005

  42. Changing Carbon Sink Capacity Airborne fraction =atm increase / Fossil fuel emission • With SRES A2 (fast FF emission): as CO2 increases • Capacity of land and ocean to store carbon decreases (slowing of photosyn; reduce soil C turnover time; slower thermocline mixing …) • Airborne fraction increases --> accelerate global warming Fung et al. Evolution of carbon sinks in a changing climate. PNAS 2005

  43. Ocean momentum mass energy salinity

  44. Numerical Weather Prediction ( ~ days) Initial Conditions t = 0 hr Prediction t = 6 hr 12 18 24 • Predict evolution of state of atmosphere (t) • Error grows w time --> limit to weather prediction

  45. Seasonal Climate Prediction ( El – Nino Southern Oscillation ) {Prediction} t = 1 month 2 3 { Initial Conditions} Atm + Ocn t = 0 • Coupled atmosphere-ocean instability • Require obs of initial states of both atm & ocean, • esp. Equatorial Pacific • {Ensemble} of forecasts • Forecast statistics (mean & variance) – probability • Now – experimental forecasts (model testing in ~months)

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