Vulkaner och öknar: vad berättar modeller och satellitdata?

Transkript

1 Fjärranalysdagarna 2013 Vulkaner och öknar: vad berättar modeller och satellitdata? Lennart Robertson och Michael Kahnert, SMHI

2 Fjärranalysdagarna 2013 Innehåll MACC Monitoring the Atmosphere Composition and Climate Vulkaner: är det något på gång? Vad är kemiska transportberäkningar? Eyafjallajökull: modell och satellitdata Sahara stoft: modell och MODIS AOD observationer Data assimilation av Sahara stoft Vägen framåt Ber om ursäkt för att presentationen är en blandning av svenska och engelska. 2

3 Fjärranalysdagarna 2013 Monitoring Atmospheric Composition and Climate MACC is a pre-operational atmospheric service of the European GMES programme. MACC combines state-of-the-art atmospheric modelling with Earth observation data to provide information services covering Coordinated by ECMWF. Participants from 45 institutes and about 40 counties. European Air Quality Global Atmospheric Composition Climate UV, Solar Energy, Stratospheric Ozone 3

4 Fjärranalysdagarna 2013 MACC: from data to products 4

5 Fjärranalysdagarna 2013 Vulkaner: är det något på gång? Eyjafjallajökull: 2010, 14 april till 24 maj (VEI 4). Grimsvotn: 2011, maj. Hekla: den 25 mars :31 Dear all, the Icelandic Meteorological Office has changed the aviation colour-code of Hekla volcano (63 59'30.16" '23.8" 1440m a.s.l.) from green to yellow, signifying elevated unrest above a known background level. Since 10 March 2013, at least seven microearthquakes, ranging in size from magnitude 0.4 to 1, have been detected over a small area ~4.5 km to the north-east of the volcano's summit. Sourced mainly at 11 to 12 km depth, these earthquakes have a high-frequency character suggestive of brittle fracturing rather than magma movements. At Hekla, such a clustering of earthquakes in time and space is unusual in between eruptions. Katla? 5

6 Fjärranalysdagarna 2013 VEI Volcanic Explosivity Index 6

7 Exempel på sidhuvud - ÅÅÅÅ MM DD (Välj Visa, Sidhuvud sidfot för att ändra) Katla? VEI1? VEI4 or VEI5; about 0.7 cubic km ejected material VEI VEI VEI5; 1.5 cubic km ejected material. Flood discharge 200, ,000 m³/s VEI VEI VEI VEI VEI VEI5 or VEI6, 5 cubic km of tephra and 18 cubic km of lava Wikipedia.org 7

8 Fjärranalysdagarna 2013 Kemisk transportmodellering Modellberäkningar i ett antal boxar Boxarna fylls med: Meteorologi: vindar, temperatur, fuktighet, nederbörd Spårämnen: stoft, ozone, SO 2 Transportekvationer, kemiska samband Emissioner Vulkaner, uppvirvling av stoft, seaspray Industri, uppvärmning, trafik Markanvändning, andel öppen mark, skog 8

9 Eyjafjallajökull 16 April 2010

10 Eyjafjallajökull 17 April 2010

11 Emission PM10 (ton/s) Eyjafjallajökull eruption started 14 April 2010 Dormant since 24 May Emission PM10 (ton/s) Days after start of eruption Diagram shows SMHI estimate of fine volcanic ash emission (PM10) as function of time Total (all PM) estimated ash emission ~ million tons Assumption: 25 % of emitted volcanic ash is PM10 The eruption started under a glacier As a comparison: During indicated max intensity same amount of PM10 was emitted in 3 min as the total annual Swedish anthropogenic PM10-emission (Ref: Mastin et al (2009) A multidisciplinary effort to assign realistic source parameters.during eruptions. J. Volcanology and Geothermal Research 186, 10-21, 2009)

12 Model and satellite comparison Fri 16th April 0130 UTC Two days after the first eruption Meteosat Dust product

13 MODIS data three days after eruption start NASA's Aqua and Terra satellites captured visible images of the ash plume (brown) from the Eyjafjallajökull volcano from April 17 to April 19 (left to right). Credit: NASA's MODIS Rapid Response Team

14 Different satellite and lidar data LIDAR data (quick look) from Linköping, Sweden NOAA AVHRR Satellite :57 UTC Meteosat :45

15 Fjärranalysdagarna 2013 Stoft mot moln eller is? Våglängdsberoende refraktionsindex. Refraktionsindex ökar med ökad våglängd för moln och is. Refraktionsindex minskar med ökad våglängd för stoft. Tydlig separering av förekomst av moln/is och stoft är nödvändigt. Stoft kan inte separeras från moln/is inne i eller under moln. 15

16 Meteosat Edge of visible plume on satellite retrievals Mon 10th May 1200 UTC 25 days after the firsdt eruption Model and satellite comparison

17 Measurements by a Lansen aeroplane On the 17th of May 2010 the Swedish Air Force aircraft Lansen 32 sampled areas associated to zone 3 (little or no contamination of particles) and zone FOI Umeå: Lennart Thaning Airforce base at Såtenäs Officer: Christian Christensen

18 Cross section along one of the legs

19 Measurements by a Lansen aeroplane On the 17th of May 2010 the Swedish Air Force aircraft Lansen 32 sampled areas associated to zone 3 (little or no contamination of particles) and zone Sample Height Lat/Lon Time CET Estimated total conc. of particles (calculated from Si-conc.) no m deg; min pm µg/m N / 1400E 5608N / 1352E N / 1209E 5720N / 1134E N / 1134E 5808N / 1050E < N / 1050E 5752N / 0900E N / 0900E 5817N / 1114E blank - - <10

20 4 th International Workshop on Air Quality Forecasting Research (IWAQFR) Model for windblown dust A model for prediction of desert dust cycle in the atmosphere: S. Nickovic, G. Kallos, A Papdopoulos, O Kakaliagou J Geo Res Vol 106, No D16, pp 18,113-18,129, 2001 Threshold friction velocity: u *t ~ (2gr) 1/2 Emission if u * > u *t Dust emission Q ~ u * 3 (1 - (u *t /u * ) 2 ) Soil moisture dependent Fraction of clay and sand dependent Size distributed 20

21 4 th International Workshop on Air Quality Forecasting Research (IWAQFR) No physiography data no model Most data needed are available through ECMWF MARS archive. Information on deserts (1x1 degree) added from E. Matthews, 1984, Sigma Data Services, prepared for NCAR. 21

22 4 th International Workshop on Air Quality Forecasting Research (IWAQFR) ECMWF Vegetation classes Index Vegetation type H/L 1 Crops, mixed farming L 2 Short grass L 3 Evergreen needleleaf trees H 4 Deciduous needleleaf trees H 5 Evergreen broadleaf trees H 6 Deciduous broadleaf trees H 7 Tall grass L 8 Desert 9 Tundra L 10 Irrigated crops L 11 Semidesert L 12 Ice caps and glaciers 13 Bogs and marshes L 14 Inland water 15 Ocean 16 Evergreen shrubs L 17 Deciduous shrubs L 18 Mixed forest/woodland H 19 Interrupted forest H 20 Water and land mixtures L 22

23 4 th International Workshop on Air Quality Forecasting Research (IWAQFR) Sand and clay Sand Clay 23

24 4 th International Workshop on Air Quality Forecasting Research (IWAQFR) Hemispheric dust simulation (only deserts dust) Apparently are parts of northern Canada given desert class 24

25 4 th International Workshop on Air Quality Forecasting Research (IWAQFR) Comparison with MODIS AOD 25

26 4 th International Workshop on Air Quality Forecasting Research (IWAQFR) Assimilated PM10 from MODIS AOD 26

27 Fjärranalysdagarna 2013 Data assimilation Data assimilation är att kombinera modell och observationer. Kan ses som att generalisera observationer med hjälp av modeller. Viktigt del för att hålla modeller i närheten av vekligheten. 27

28 Source: Carefully selected satellite sensor channels (or wavelength ranges) make it possible to retrieve information on aerosol and trace gas properties.

29 NASA s A-Train convoy of of satellites NASA s A-Train consists of series of satellites following each other within few minutes on the same track and each carrying a suite of sensors. Many of these sensors are specifically designed for air quality applications. The simultaneous observations in time and space of air quality along with other meteorological and surface data is proving to be very useful. Especially, few of these sensors have unprecedented vertical resolution.

30 NASA s Example A-Train of satellite convoy data for of chemistry satellites Product Total Ozone Total Nitrogen Dioxide Ozone Profile Tropospheric Nitrogen Dioxide Total Ozone Ozone profile Total Ozone Total Nitrogen Dioxide Total Sulfur Dioxide Aerosol optical depth Total Nitrogen Dioxide Available from EUMETSAT GOME-2 EUMETSAT GOME-2 EUMETSAT GOME-2 EUMETSAT GOME-2 ENVISAT SCIAMACHY ENVISAT SCIAMACHY ENVISAT SCIAMACHY ENVISAT SCIAMACHY ENVISAT SCIAMACHY Aqua MODIS/Aura OMI Aura OMI

31 4 th International Workshop on Air Quality Forecasting Research (IWAQFR) Example: In-situ and AIRS ozone data 31

32 Fjärranalysdagarna 2013 Vägen framåt Vulkaner: Översätta satellitinformation till softlast (dust load) och koppla tillbaka till utsläppspunkten. Invers modellering genom modell Backa modellen med observationer i hand tillbaka till vulkanen. Skatta mängd, höjd och tidpunkt för delar av vulkanutbrottet. Satellitdata för andra komponenter: Inom ramen för MACC förädla satellitdata genom data-assimilation för dagliga prognoser och årliga återanalyser av atmosfärens kemiska tillstånd. 32

33 Fjärranalysdagarna 2013 TACK! 33