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Aerosol and climate

Aerosol and climate. Stefan Kinne MPI - Meteorology. 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

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Aerosol and climate

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  1. Aerosoland 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 DT MORE LESS Dec - Feb

  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 courtesy of NASA-GISS so … do we understand what factors cause (d) climate change ?

  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 a particles (sulfate, secondary organics …)

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

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

  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 + …) • aerosol  clouds • aerosol  chemistry • aerosol  biosphere • aerosol  aerosol natural partly natural / partly anthropogenic ocean industry cities forest desert volcano

  10. aerosol data to relate to • stratification by size size class radius-range • accumulation (ac) 0.05mm-0.5mm carbon, sulfate • coarse (co) 0.5mm-10mm dust, sea-salt • stratification by type composition absorption* H2O uptake sizes • sulfate NO strong ac • black carbon strong delayed ac • organic carbon weak weak ac • dust weak in mixed state co • sea-salt NO very strong co * 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 [Angstromparam] (size) • single scattering albedo (w0) (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 of multi-annual (satellite) remote sensing retreivals • 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

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

  15. 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 aerosol remote issues

  16. AERONETthe‘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 mm • sky-mode (looking at the sun and the sky) • AOD, ssa, size-distr (amount, size, absorption, shape) • at .44, .50, .67, .87 mm

  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 2.0mm 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, .87mm • method • GPS (location) UTC (time)  expected sun-intensity Io • measured sun-intensity I = Io *exp (-AOD/m0) • m0 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 average May average May Atlantic AOD .55mm w0 .55mm AnP .55mm .87mm

  21. AOD Angstrom date lat /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 Atmosphere DynamicsPhysicsChemistryAerosols Ocean DynamicsPhysicsBiogeochem. Land HydrologyVegetation Society Economics Land use

  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 5th shown) Alps region need to be captured by ‘10 points’

  31. 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)

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

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

  34. projected atm. CO2 concentr. A2 business as usual A1B a few measures (most likely) B1 active efforts with new technologies

  35. MPI model configurations • carbon-cycle • aerosol • regional • IPCC Sun/Spaceconst. Irrad. IPCCA1B, B1, A2 AtmosphereECHAM5 T63 L31 Energy MomentumEnergyH2O LandECHAM5/HD GHG conc.SO4 conc. IPCC model configuration PRISM OceanMPIOM 1.5°L40

  36. MPI, IPCC - is it reliable ? Concentrations (GHG, SO4) ECHAM5 T63L31 Atmosphere Land Surface ECHAM5 + river runoff MomentumHeat, Water Coupling interface (PRISM) Ocean Sea ice MPI-OM 1.5°L40

  37. MPI,IPCC standard tests 1% per year CO2 increase 70 (until 2xCO2) + 150 years 1% per year CO2 increase 150 (until 4xCO2) + 250 years ECHAM5 (with observed SST) 1978 – 1999 ECHAM5/MLO control 100 years ECHAM5/MLO CO2 doubling 100 years

  38. stability test of pre-ind. run (1) • global annual mean 2m temperature [°C] 14.5 14.0 0.026 °C / century 13.5 100 200 300 400 years

  39. stability test of pre-ind. run (2) 15 NH 10 multi-annual average 5 years 100 200 300 400 observed range 20 15 SH 10 sea-ice area (106 km2) 5 0

  40. MPI, IPCC - changes to surf. T? Concentrations (GHG, SO4) ECHAM5 T63L31 Atmosphere Land Surface ECHAM5 + river runoff MomentumHeat, Water Coupling interface (PRISM) Ocean Sea ice MPI-OM 1.5°L40

  41. simulated surface temperature

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

  43. D Ts annual mean temp. change A1B snow-regions B1 [° C]

  44. MPI, IPCC – change to land use ? Concentrations (GHG, SO4) ECHAM5 T63L31 Atmosphere Land Surface ECHAM5 + river runoff MomentumHeat, Water Coupling interface (PRISM) Ocean Sea ice MPI-OM 1.5°L40

  45. Koeppen classification based on monthly Temperature and Rain • 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

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

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

  48. MPI, IPCC - change in storms Concentrations (GHG, SO4) ECHAM5 T63L31 Atmosphere Land Surface ECHAM5 + river runoff MomentumHeat, Water Coupling interface (PRISM) Ocean Sea ice MPI-OM 1.5°L40

  49. storm track density (DJF 1961-90) observed simulated

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

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