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The Science B ehind Climate Change

The Science B ehind Climate Change. Daniel Rothenberg Based on presentations by Dan Chavas , Marty Singh, Tim Cronin, Morgan O’Neill MIT Energy Club: Energy 101 series November 13, 2012. http://rsd.gsfc.nasa.gov/rsd/images/goes8_lg.jpg

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The Science B ehind Climate Change

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  1. The Science Behind Climate Change Daniel Rothenberg Based on presentations by Dan Chavas, Marty Singh, Tim Cronin, Morgan O’Neill MIT Energy Club: Energy 101 series November 13, 2012 http://rsd.gsfc.nasa.gov/rsd/images/goes8_lg.jpg http://images.sciencedaily.com/2007/11/071114163448-large.jpg

  2. Goal • I hope you take home… • The basic scientific logic for why we care about increasing greenhouse gas concentrations • Insight into what is well-understood about climate change • Answers to your questions

  3. Outline Why greenhouse gas emissions matter • Energy balance models: why GHGs  warming • Radiative-convective model: simplest tool to quantify warming (at the surface where we live) • General Circulation Model: exploring our future climate

  4. Outline Why greenhouse gas emissions matter • Energy balance models: why GHGs  warming • Radiative-convective model: simplest tool to quantify warming at the surface (where we live) • General Circulation Model: exploring our future climate

  5. Physics review: Blackbody radiation 1) Stefan-Boltzmann’s Law: Fout =  T4 2) Wien’s Law: λmax~ 1/T http://upload.wikimedia.org/wikipedia/commons/f/ff/BlackbodySpectrum_loglog_150dpi_en.png

  6. Physics review: Blackbody radiation 1) Stefan-Boltzmann’s Law: Fout =  T4 2) Wien’s Law: λmax~ 1/T http://upload.wikimedia.org/wikipedia/commons/f/ff/BlackbodySpectrum_loglog_150dpi_en.png

  7. Physics review: Blackbody radiation 1) Stefan-Boltzmann’s Law: Fout =  T4 2) Wien’s Law: λmax~ 1/T http://upload.wikimedia.org/wikipedia/commons/f/ff/BlackbodySpectrum_loglog_150dpi_en.png

  8. Physics review: Blackbody radiation 1) Stefan-Boltzmann’s Law: Fout =  T4 2) Wien’s Law: λmax~ 1/T http://upload.wikimedia.org/wikipedia/commons/f/ff/BlackbodySpectrum_loglog_150dpi_en.png

  9. Physics review: Blackbody radiation 1) Stefan-Boltzmann’s Law: Fout =  T4 2) Wien’s Law: λmax~ 1/T http://upload.wikimedia.org/wikipedia/commons/f/ff/BlackbodySpectrum_loglog_150dpi_en.png

  10. Physics review: Blackbody radiation 1) Stefan-Boltzmann’s Law: Fout =  T4 2) Wien’s Law: λmax~ 1/T http://upload.wikimedia.org/wikipedia/commons/f/ff/BlackbodySpectrum_loglog_150dpi_en.png

  11. Energy BalanceEquilibrium Energy in = Energy out T = constant

  12. Energy BalanceEquilibrium Energy in = Energy out T = constant

  13. Energy BalanceEquilibrium Energy in = Energy out T = constant

  14. Energy Balance models for climate • “Rock-star” model

  15. Energy Balance models for climate • “Rock-star” model

  16. Energy Balance: The “rock-star” modelEquilibrium Energy in = Energy out T = constant Archer “Global Warming: Understanding the Forecast”

  17. Energy Balance: The “rock-star” modelEquilibrium Tsun = 5800 K Tearth Fout = T4 Energy in = Energy out T = constant

  18. Energy Balance: The “rock-star” modelEquilibrium Tsun = 5800 K 1.5*108 km Tearth Rearth = 6*103 km Fout = T4 Energy in = Energy out T = constant

  19. Energy Balance: The “rock-star” modelEquilibrium S0 ≈ 1367 W/m2 Energy in = Energy out T = constant

  20. Energy Balance: The “rock-star” modelEquilibrium S0 ≈ 1367 W/m2 Energy in = Energy out T = constant

  21. Energy Balance: The “rock-star” modelEquilibrium SA = 4πR2 S0 ≈ 1367 W/m2 A = πR2 Energy in = Energy out T = constant

  22. Energy Balance: The “rock-star” modelEquilibrium S0/4 S0 ≈ 1367 W/m2 Stop = S0/4 Energy in = Energy out T = constant

  23. Energy Balance: The “rock-star” modelAlbedo Albedo () = fraction of incoming solar radiation reflected Global mean albedo:  ≈ 0.3 NASA MODIS Energy in = absorbed solar radiation = (1 -  )S0/4

  24. Energy Balance: The “rock-star” modelEquilibrium S0/4 S0/4 S0 ≈ 1367 W/m2 Stop = S0/4 Fin = (1 -  )S0/4 Energy in = Energy out T = constant

  25. Energy Balance: The “rock-star” modelEquilibrium Fout S0/4 S0/4 S0 ≈ 1367 W/m2 Stop = S0/4 Fin = (1 -  )S0/4 Fout = Te4 Energy in = Energy out T = constant

  26. Energy Balance: The “rock-star” modelEquilibrium Fin (Solar) = Fout(Terrestrial) (1 -  )S0/4 = Te4 Te= 255 K = 0 F

  27. Energy Balance: The “rock-star” modelEquilibrium Fin (Solar) = Fout(Terrestrial) (1 -  )S0/4 = Te4 Te= 255 K = 0 F Actual Global Surface Temperature Tsurface = 288 K  60°F We need an atmosphere!

  28. Energy Balance models for climate • “Rock-star”: too cold • 1-layer Total Greenhouse

  29. Energy Balance: The 1-layer “total greenhouse” modelEquilibrium Fout S0/4 Assume a single layer atmosphere that absorbs all the terrestrial rays, and none of the solar rays. S0/4 Tatmos Tsurface

  30. Energy Balance: The 1-layer “total greenhouse” modelEquilibrium Fout S0/4 Assume a single layer atmosphere that absorbs all the terrestrial rays, and none of the solar rays. S0/4 Tatmos Energy out has decreased because of the absorbing gases. Now more energy is coming in than going out and so the system will warm up! Tsurface

  31. Energy Balance: The 1-layer “total greenhouse” modelEquilibrium Fout S0/4 Assume a single layer atmosphere that absorbs all the terrestrial rays, and none of the solar rays. S0/4 Tatmos Tsurface Tsurface= 303 K = 85 F But this is way too hot…

  32. Energy Balance models for climate • “Rock-star”: too cold • 1-layer Total Greenhouse: too hot • 1-layer Leaky Greenhouse

  33. The greenhouse is “leaky”… • N2and O2, which comprise 99% of the atmosphere are diatomic and thus do not interact with outgoing terrestrial radiation • However, some trace gases do absorb outgoing radiationover certain wavelength bands within the terrestrial spectrum • Greenhouse gases in order of importance: • Water vapor (H2O) • Carbon dioxide (CO2) • Ozone (O3) • Methane (CH4) • Nitrous Oxide (N2O) NASA

  34. Energy Balance: The 1-layer “leaky greenhouse” modelEquilibrium The atmosphere now absorbs a fractionof the terrestrial radiation emitted from the surface. Fout S0/4 Simple conceptual understanding of the Earth’s greenhouse effect S0/4 Tatmos • But: • The real atmosphere is not a single ‘layer’ • The real atmosphere absorbs differently at different wavelengths Tsurface

  35. Energy Balance models for climate • “Rock-star”: too cold • 1-layer Total Greenhouse: too hot • 1-layer Leaky Greenhouse: good, but not realistic • Multi-layer radiative

  36. Energy Balance: The multi-layer radiative modelEquilibrium Manabe & Strickler (1964) • Many atmospheric layers • More realistic radiative transfer Tsurface = 332 K = 138 F Ouch but warm air tends to rise…

  37. Convection (vertical) • Surface is much hotter than the air just above it: cannot be maintained • Heat transfer from surface to air above it via conduction • Air that is warmer than its surroundings will rise • Atmosphere will efficiently transfer heat upwards from surface via convection

  38. Energy Balance models for climate • “Rock-star”: too cold • 1-layer Total Greenhouse: too hot • 1-layer Leaky Greenhouse: good, but not realistic • Multi-layer radiative: realistic but not dynamically stable • Multi-layer radiative-convective

  39. Outline Why greenhouse gas emissions matter • Energy balance models: why GHGs  warming • Radiative-convective model: simplest tool to quantify warming at the surface (where we live) • General Circulation Model: exploring our future climate

  40. Multi-layer radiative-convective modelEquilibrium Manabe & Strickler (1964) • Many atmospheric layers • More realistic radiativetransfer • Convective upward heat transport Tsurface = 300 K = 80F YAY

  41. Multi-layer radiative-convective modelEquilibrium Manabe & Strickler (1964) • Many atmospheric layers • More realistic radiativetransfer • Convective upward heat transport • Now have a model where we can change – • Chemistry of the atmosphere • Incoming solar radiation • - and measure how it changes the surface temperature Tsurface = 300 K = 80F YAY This is the simplest model of the mean global atmosphere that is dynamically and thermodynamically stable. Thus, we can use it as tool to analyze the impact of increasing GHGs on surface temperatures

  42. Multi-layer radiative-convective modelEquilibrium Manabe & Strickler (1964) • Many atmospheric layers • More realistic radiativetransfer • Convective upward heat transport Doubling CO2 (keeping everything else constant) + 2.3°F Tsurface = 300 K = 80F YAY This is the simplest model of the mean global atmosphere that is dynamically and thermodynamically stable. Thus, we can use it as tool to analyze the impact of increasing GHGs on surface temperatures

  43. Climate Feedbacks Clouds Water vapor Ice-albedo NASA Earth Observatory

  44. Water vapor feedback Positive feedback: Clausius-Clapeyron relation atmospheric water vapor increases exponentially with temperature.

  45. Water vapor feedback Positive feedback: Clausius-Clapeyron relation atmospheric water vapor increases exponentially with temperature. Doubling CO2 + water vapor + 4.1°F

  46. The 2xCO2 Baseline: + 4 oF • This result is based on straightforward physics – it is the baseline warming caused by 2xCO2 • The real climate system has many other internal processeswithin that may amplify/suppress this warming • However these other processes are less well understood and more complicated to account for Other processes: • Horizontal variations in climate • Movement of atmosphere, including clouds and aerosols • Ocean • Land interactions: melting of ice, vegetation • Biogeochemical cycles (e.g. carbon cycle)

  47. Outline Why greenhouse gas emissions matter • Energy balance models: why GHGs  warming • Radiative-convective model: simplest tool to quantify warming at the surface (where we live) • General Circulation Model: exploring our future climate

  48. General Circulation Model • Must integrate the governing (Navier-Stokes) equations forward in time, keeping track of: • Heat • Moisture • Momentum • Other tracers NOAA GFDL

  49. Putting all the components together

  50. Sub-grid-scale processes Many processes occur at much smaller scales than the grid-box size (e.g. clouds, rainfall, vegetation, etc.) Must represent small-scale processes with parameterization: NOAA

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