1. Aerosol and climate Stefan Kinne MPI - Meteorology

2. climate forecasts in 100 years … “when using advancements in technology to fight anthropogenic change”  surface temperature are expected to increases  fewer but more violent storms are expected  storm tracks are expected to shift  changes to regional precipitation patterns Dec - Feb TT MORE LESS

3. climate forecasts precipitation change (%) for August

4. can we trust climate models? models perform  long-term control runs to test ‘stability’  standard tests with now responses (today, past)  comparisons to other models (global or regional) but REMEMBER models are as good as  the DATA used as model input  the parameterizations for sub-grid and complex processes  the DATA used for model evaluations

5. what we know from DATA. … since 1880 temperature at the Earth’s surface has increased by 0.75K … to exceed expectations for natural variability so … do we understand what factors cause (d) climate change ? courtesy of NASA-GISS

6. 2 major factors human ‘footprints’ from  industralization  urbanization  changes in farming  enhanced (greenhouse) gas concentrations CO2, CH4, O3, CFCs, …  increased (aerosol) particle concentrations direct emission new particles(soot, …) indirect emission gas  particles (sulfate, secondary organics …)

7. quantifying climate impact ToA radiative energy GAIN: -solar radiation ( +340 W/m2) radiative energy LOSS: -refl. solar radiation -loss of ‘heat’ ( - 340 W/m2) examine changes to energy balance (gain=loss) at the ToA ToA - top of the atmosphere WARMING: GAIN > LOSS COOLING: GAIN < LOSS

8. global annual ToA impacts  anthrop. greenhouse gases warm +2.8 W/m2 confident estimate  anthrop. aerosol cools by presence - 0.2 W/m2  poorly understood by feedback - 1.? W/m2 very poorly understood …for feedback impact help from global modeling is needed ( good DATA needed ) aerosols do not just cool ! strong regional differences anthropogenic aerosol impact

9. why aerosol (‘small’ atmos. particles) ?  aerosol is at the juncture of many processes in the Earth-Atmosphere- System  aerosol is highly variable many sources short lifetimes diff. order of magnitudes in size changing properties over time  cloud changes due to anthropogenic activity are likely associated with aerosol (e.g. more CCN + …) ocean desert industry cities volcano forest aerosol  clouds aerosol  chemistry aerosol  biosphere aerosol  aerosol naturalpartly natural / partly anthropogenic

10. aerosol data to relate to  stratification by size size class radius-range accumulation (ac) 0.05  m-0.5  mcarbon, sulfate coarse (co) 0.5  m-10  mdust, sea-salt  stratification by type composition absorption* H2O uptake sizes sulfateNOstrongac black carbonstrongdelayedac organic carbon weakweakac dustweakin mixed stateco sea-saltNOvery strongco * at visible wavelengths

11. aerosol – optical prop. of interest  aerosol is defined by amount “number concentration” size “size-distribution” absorption “refractive index” MIE–calculations  opt. properties  measurement substitutes are (vis) aerosol optical thickness ( AOD ) (amount) AOD-spectral dep [ Angstrom param] (size) s ingle s cattering a lbedo (  0 ) (absorption)

12. getting data on a global scale?  GOBAL MODELING can do it  emission  (transport, processing)  mass  mass  (size choice, mixing)  opt. properties Q: can emission input be trusted ? Hardly! Q: how accurate are assumptions or processes … … if there are few integral properties to ‘tune’ to?  but simulated data fields are complete !  SATELLITE REMOTE SENSING is handicapped  amount and maybe size-info can be retrieved (with assumptions to other properties)  accurate ?

13. AOD annual average maps  Aer –AERONET  MIS – MISR  Mc5 – MODIS coll 5  Mc4 – MODIS coll 4  AVn – AVHRR NOAA  AVg – AVHRR GISS  POL – POLDER  TOo – TOMS, old  TOn – TOMS,new of multi-annual (satellite) remote sensing retreivals

14. AOD seasonal distributions of a remote sensing retrieval composite a nice picture, but probably still too uncertain for many modeling aspects

15. aerosol remote issues Satellite  AOD estimate from reflection data requires information on absorption  the background is poorly defined (surface albedo at high accuracy required)  only a limited number of aerosol prop. Accessible  (almost) global coverage  cloud free conditions Ground-based  AOD estimate from transmission is straight forward (light attenuation)  the background is well defined (sun or sky)  all aerosol properties can be determined with sky radiance data  local measurements  cloud-free conditions

16. AERO NET the ‘reference’  ground-based sun-/sky- photometer roboters are inter-connected (and -calibrated) to establish a land-based worldwide monitoring network  sun-mode (looking at the sun)  AOD and AOD spectral dep (amount, size estimate)  at.38,.44,.50,.67,.87, 1.02  m  sky-mode (looking at the sun and the sky)  AOD, ssa, size-distr (amount, size, absorption, shape)  at.44,.50,.67,.87  m

17. idea of solar spectral choices  when measuring attenuation we like to  avoid spectral regions with trace-gas absorption  minimize the impact from Rayleigh scatter (blue sky)  maximize the solar signal (strongest for visible light)  attenuation provide data on AOD  attenuation at diff wavelengths give info on size large particles: AOD (green) = AOD (red) small particles: AOD (green) > AOD (red)  small size sensitivity for 0.1 to 2.0  m range

18. MIRCOTOPS II  supplement AERONET land data statistics with handheld data on AOD and AOD spectral dep.  AOD and AOD spectral dep (amount, size estimate)  at.44,.50,.67,.87  m  method  GPS (location) UTC (time)  expected sun-intensity I o  measured sun-intensity I = I o *exp (-AOD/  0)  0 is the cosine of the solar zenith angle  sample length: 12 seconds  if you can keep the instrument focused on the sun  if you stay out of (cloud-contamination) trouble during that time … then we have a good sample.

19. first results:  there is no clear indication that aerosol does increase in size as scans near cloud boundaries  but AOD values between clouds are usually larger than AOD values far away from clouds  increased wind-speed sharply increased AOD amounts and also increase aerosol size  the particles of the smoke stack are much smaller than sizes of background aerosol  aerosol size seems to be repsonsive to column water amounts

20. AERONET tied global distributions annual averageMay average AOD.55  m  0.55  m AnP.55  m.87  m May Atlantic

21. AOD Angstrom datelat /lon 4/30-15 /-31.045 0.45 4/29-19/ -32.060 0.52 4/28-22 /-34.050 0.64 4/27-27 /-37.090 0.73 4/26-31 /-39.098 0.21 4/25-33 /-42.115 1.51 4/24-37 /-46.110 0.73 4/23 -39 /-50.068 0.55 4/22-42 /-54.090 0.46

22. outlook  … let us see, if our data match the climatology in other words let’s get sun-burned

23. extras

24. from the recent IPCC report  fossil fuel use, agricultural and land use have been the dominant cause for so far unprecident increases in greenhouse gases (CO2, CH4, N2O)  although anthropogenic aerosol partially slow that warming, the overall impact is a warming  global mean land surface temperatures and also ocean temperatures continue to rise  ice amounts (e.g. glaciers) are decreasing … yet greenhouse gas concentrations continue to increase !

25. Overview  Modeling – what are we doing at MPI-Met ?  IPCC – what is it about ?  IPCC – what was simulated ?  IPCC – what do results tell us ?  IPCC – what do results mean for us ? MPI-Met: Max-Planck-Institute for Meteorology IPCC: Intergovernmental Panel on Climate Change

26. What is a model ?  idealized representation to make an object more accessible to studies to demonstrate the most relevant and selected aspects of the object  examples http://www.solarviews.com/cap/earth/

27. purpose of a (climate) model  reduce the complexity  avoid irrelevant details  obtain a manageable system  climate models use quantitative methods to simulate the interactions of ‘Earth System’ ATMOSPHERE OCEAN LAND surface ICE (cyrosphere) components of the Earth System

28. Earth System Model Overview Society Economics Land use Atmosphere Dynamics Physics Chemistry Aerosols Ocean Dynamics Physics Biogeochem. Land Hydrology Vegetation

29. the Earth System Model  covers as many processes and interactions as possible on a global scale dynamics in atmosphere and ocean is characterized in general circulation models (GCM) GCMs discretize the equations for fluid motion and integrate these forward in time global modeling involves parameterizations to approximate complex and / or sub-grid processes  limited (at least) by computing power !

30. Discretization  Time 15min (atmosphere), 1day (ocean)  Space 1.9deg (31 atm.layers up to 30km altitude) T63/L31 1.5deg (see below, every 5 th shown) Alps region need to be captured by ‘10 points’

31. other modeling issues  need to parameterize unresolved dynamics (e.g. turbulence, gravity waves) unresolved small-scale processes (e.g. clouds, precip)  complexity nonetheless …also to optimize computer performance MPI model for IPCC: 1700 printed pages of code!  errors cannot be avoided large errors are found, smaller go undetected  substantial effort 80TB model output from the IPCC runs at MPI-Met

32. IPCC - what is it about? (~ 30) modeling groups were asked to perform  three future scenarios (A1B, B1, A2)  multiple runs for each scenario  sampling of uncertainties to test  scenario assumptions  natural variability  model formulations (20 institutes with their own models participated)

33. specific IPCC experiments experimentsyears pre-industrial control 530 20 th century 1860 - 2000 21 st century stabilization2001 - 2100 (2000 forcing) scenarios A1B, B1, A2 2001 - 2100 A1B, B1 stabilization 2101 - 2200 (2100 forcing ) A1B stabilization ext.2201 - 2300 (2100 forcing)

34. IPCC simulations + scenarios TIME   A2 – business as usual  A1B – some reductions  B1 – lots of new techn.  500+ years  year 1860 pre-industrial year 2004 current year 2100 future

35. projected atm. CO2 concentr. A2 business as usual A1Ba few measures (most likely) B1active efforts with new technologies

36. MPI model configurations  carbon-cycle  aerosol  regional  IPCC Momentum Energy H2O Sun/Space const. Irrad. Energy Atmosphere ECHAM5 T63 L31 Ocean MPIOM 1.5°L40 Land ECHAM5/HD IPCC A1B, B1, A2 GHG conc. SO4 conc. PRISM IPCC model configuration

37. MPI, IPCC - is it reliable ? Momentum Heat, Water ECHAM5 + river runoff Coupling interface (PRISM) ECHAM5 T63L31 MPI-OM 1.5°L40 Concentrations (GHG, SO 4 ) Atmosphere Land Surface Ocean Sea ice

38. MPI, IPCC standard tests 1% per year CO 2 increase 70 (until 2xCO 2 ) + 150 years 1% per year CO 2 increase 150 (until 4xCO 2 ) + 250 years ECHAM5 (with observed SST) 1978 – 1999 ECHAM5/MLO control 100 years ECHAM5/MLO CO 2 doubling 100 years

39. stability test of pre-ind. run (1)  global annual mean 2m temperature 100200 300 400 13.5 14.0 14.5 [°C] 0.026 °C / century years

40. stability test of pre-ind. run (2) 100 200 300400 years 5 10 15 5 10 15 20 NH SH 0 sea-ice area (10 6 km2) observed range multi-annual average

41. MPI, IPCC - changes to surf. T? Momentum Heat, Water ECHAM5 + river runoff Coupling interface (PRISM) ECHAM5 T63L31 MPI-OM 1.5°L40 Concentrations (GHG, SO 4 ) Atmosphere Land Surface Ocean Sea ice

42. simulated surface temperature

43. consistency and observations 20C_1 20C_2 20C_3 OBS A1B_1 A1B_2 A1B_3 Global annual mean surface air temperature (deviation from 1961-1990) [°C] year

44.  Ts [° C] annual mean temp. change B1 A1B snow-regions

45. MPI, IPCC – change to land use ? Momentum Heat, Water ECHAM5 + river runoff Coupling interface (PRISM) ECHAM5 T63L31 MPI-OM 1.5°L40 Concentrations (GHG, SO 4 ) Atmosphere Land Surface Ocean Sea ice

46. Koeppen classification  Tropical Af - no dry season, > 60 mm of rainfall in dry month Am - monsoon type, short dry season but wet ground Aw - distinct dry season. 1 month with precip < 60 mm  Arid BS - steppe climate BW - desert  Temperate / Continental Cw / Dw - winter dry season. (10* times more precip in sum) Cs - summer dry season (3* times more precip in winter) Cf / Df - > 30 mm precipitation in the driest month  Polar ET - polar tundra, soil is permanently frozen to 100 meter+ EF - polar ice caps covered with snow and ice based on monthly Temperature and Rain

47. mod obs land classifications (Koeppen) simulated (top) vs observed (bottom)

48. A1B 2071- 2100 now 1961-90 land classifications (Koeppen) simulated (top) vs observed (bottom)

49. MPI, IPCC - change in storms Momentum Heat, Water ECHAM5 + river runoff Coupling interface (PRISM) ECHAM5 T63L31 MPI-OM 1.5°L40 Concentrations (GHG, SO 4 ) Atmosphere Land Surface Ocean Sea ice

50. storm track density (DJF 1961-90) observedsimulated

51. changes in tropical storms May - October 1961-1990 2071-2100 expect in future fewer weak storms but more violent storms

52. NH storm intensity PDF 1961-1990 2071-2100 ‘tail’ of the distribution

53. EU storm intensity PDF East Atlantic Mediterranean 2071-2100 1961-1990 expect in future fewer storms in the Mediterranean

54. changes in EU storm intensity Dec - Feb fewer storms more storms

55. August precipitation change (%) sugg. changes in precipitation

56. IPCC – what to expect...by the end of the century  amplification of warming at high northern latitudes  enhanced land-sea contrast (stronger warming over land)  enhanced precipitation at high latitudes and tropics  poleward expansion of the arid climate zones  no overall increase in the intensity of winter storms at northern mid-latitudes (e.g. Europe)......but regional shifts are expected for instance... enhanced activity in eastern Atlantic (GB, Norway, east-EU) reduced activity in the Mediterranean area

57. What to do?  reduction of greenhouse gas emission won’t be easy – neither maintaining current emissions with growing population and expected 3 rd world contributions  reduction of a single culprit often will not have the desired impact (through feedbacks)  how much can our Earth-Atmosphere System take before a ‘tipping point’ of no return is reached?

58. What happens if? constant GHG but no AER (su) after yr 2000 OBS constant GHG constant AER after yr 2000 Roeckner / Brasseur

60. radiative forcing – climate impact quantify energy balance changes (solar:, IR: ) at the Top of the Atmosphere (ToA) ToA EARTH ATMOSPHERE SYSTEM (E-A-S) reference (pre-industry) modified (current) year 1750year 2000

61. radiative forcing – climate impact quantify energy balance changes at ToA  enhanced loss to space: COOLING of E-A-S (neg)  reduced loss to space: WARMING of E-A-S (pos) top of atmosphere (ToA) EARTH ATMOSPHERE SYSTEM (E-A-S) reference (pre-industry) modified (current) AER more  solar reflection back to space: EAS cooling GHG less  thermal rad. lost to space: EAS warming

62. anthropogenic change - 0.5 ( -0.9 to -0.1) Is the annual global aerosol direct forcing THAT uncertain? taken from IPCC 2007

63. anthropogenic aerosol forcing global annual avg - 0.36 W/m 2

64. not all forcings are equal !  anthrop  - 1.00 - 0.87 - 1.36  total (n+a)  - 2.30 - 1.57 - 4.27  - 1.75 - 2.33 - 1.71  anthrop  - 0.46 - 0.43 - 0.53 solar ToA -sea -land sol+ir ToA -sea -land atm -sea -land surf -sea -land  total (n+a)  - 4.41 - 4.95 - 2.89  - 3.59 - 4.27 - 1.76 note : there is good agreement on what can be ‘measured’ : solar total clear-sky forcing over oceans at (-5.4 to -4.6W/m2)  - 2.24 - 2.71 - 0.92  - 1.84 - 1.42 - 4.62

65. we are changing our climate … since 1880 temperature at the Earth’s surface has increased by 0.75K … to exceed expectations for natural variability … but do we understand what drives climate change and what factors modulate climate change ? courtesy of NASA-GISS

66. aerosol – ‘small’ atmos. particles  aerosol – more than an atmospheric size class many sources short lifetime diff. magnitudes in size changing over time  aerosol  clouds  aerosol  chemistry  aerosol  biosphere  aerosol  aerosol ocean desert industry cities volcano forest

67. aerosol – small atmos. particles  aerosol – more than an atmospheric size class many sources short lifetime diff. magnitudes in size changing over time  aerosol  clouds  aerosol  chemistry  aerosol  biosphere  aerosol  aerosol desert highly variable in space and time ! rapid atmospheric ‘cycling’ wind, convection fall-out, rainout

68. aerosol – optical prop. of interest  aerosol is defined by amount “number concentration” size “size-distribution” absorption “refractive index” MIE–calculations  opt. properties  measurement substitutes are (vis) aerosol optical thickness ( aot ) (amount) aot-spectral dep [ Angstrom param] (size) s ingle s cattering a lbedo (  0 ) (absorption)