PredictabilityofSevereWeather in theamazonbasin

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1 PredictabilityofSevereWeather in theamazonbasin Maria Assunção F. Silva Dias Department of Atmospheric Sciences Universidade de São Paulo 3rd WMO/WWRP International Symposiun on Nowcasting and Very Short Range Forecasting Rio de Janeiro, 6 to 10 August 2012

2 Outline Do we have severe weather in the Amazon Basin? Environment of mesoscale convective systems in the Amazon Effects of aerosol from biomass burning on cloud microphysics

3 DO WE HAVE SEVERE WEATHER IN THE AMAZON BASIN?

4 GOES April 2007

5 Dec-Jan-Feb Jun-Jul-Aug Sep-Oct-Nov Petersen & Rutledge, 2001

6 Tornado reports in Brazil Silva Dias 2011 Weather, Climate, and Society N NE CW SE S

7 Large blowdowns in the rainforest Nelson et al (Ecology,1994, 75,3,pp

8 Geographic distribution of large blowdowns isolines from 5 to 85 km2 per LANDSAT scene (Nelson et al 1994)

9 Convective cloud downdrafts as the cause of large blowdowns in the Amazon rainforest. Garstang et al 1998 Meteorology and Atmospheric Physics Outflow velocities due to density driven downdrafts in the convective clouds are shown from observations to reach 15 m/s and correspond to the propagation velocities of long lived Amazon squall lines. Maintenance over 48 h of these squall lines depends upon the correspondence between the outflow velocity and the propagation velocity. The storms propagate within a basic current moving at 5 m/s, increasing the outflow velocities to 21 m/s. The development of a storm-generated pressure field must be called upon to increase the magnitude of the density and basic currents to more than 30 m/s.

10 2010July UTC 2010July UTC 2010July UTC Long Lived Amazon Squall Lines Alcantara et al 2010 Atmos. Res. 2010July UTC 2010July UTC

11 Rickenbach 2004: Mon. Wea. Rev.: Many of nocturnal convective events in SW Amazon are traced to largescale squall lines, which propagate westward thousands of kilometers from their point of origin along the northeast coast of Brazil.

12 number of cases LONG LIVED SQUALL LINES SHORT LIVED SQUALL LINES COASTAL CONVECTIVE LINES Amazonian Squall Lines Cohen et al J F M A M J J A S O N D month

13 ENVIRONMENT OF MESOSCALE CONVECTIVE SYSTEMS IN THE AMAZON

14 Cohen et al 1995 MWR Average hodograph for Belem at 1200 UTC during GTE/ABLE No Squall lines Coastal Convective Line Squall lines 7 consecutive days with Squall lines Easterly Low Level Jet ~ mb Westerly Upper Level Jet ~ 150 mb

15 Alcantara et al 2010 Low Level Jet averaged over each convective line category for period (ERA40 reanalysis and 30 min GOES 10 images) Long Lived Squall Line Coastal Line of Convection Zonal wind speed (m/s)

16 TRMM/LBA Southwest Amazon Dec1998 February1999

17 Herdies et al. 2002, JGR (shaded) Monsoon Break Mean moisture flux and divergence in active and non active phases of the South Atlantic Convergence Zone - SACZ

18 Easterlies and Westerlies are two modes of the continental scale circulation and are part of the intraseasonal oscillations Wet Seasons Jones and Carvalho, 2002

19 E E More lightning during the easterlies = deep convective systems with large Ice Water Content Petersen & Rutledge, 2000 Blakeslee,2000 E E Blakeslee, 2000

20 Westerly c 24Feb99

21 Pereira et al, 2002 Multi-sensor analysis of a squall line in the Amazon Region. Journal of Geophysical Research Easterly case Net 1-26 Jan 1999 rainfall (mm) local time

22 NCAR S-pol Westerly cases Petersen et al, 2000 Easterly cases

23 Halverson et al, 2002: Environmental Characteristics of Convective Systems during TRMM-LBA. Mon. Wea. Rev.

25 Regime-averaged soundings at ABRACOS Hill for (a) mean westerly conditions and (b) mean easterly conditions

27 EFFECTS OF AEROSOL FROM BIOMASS BURNING

28 Williams et al 2002Contrasting convective regimes over the Amazon: Implications for cloud electrification JGR Wet Season Easterlies Wet Season Westerlies Dry Season Williams et al, 2002

29 > 5000 fires GOES-8 WF_ABBA Local smoke plume (deforestation fires) (picture from A. Andreae) smoke smoke smoke Regional smoke plume ~5 millions km 2 (Prins et al. 1998)

30 Freitas et al, 2005, 2007 Transport of biomass burning products (CATT- BRAMS) meio_ambiente

31 Aerosol and cloud microphysics interactions

32 Aeronetmeasurements in Alta Floresta and Rondonia Artaxoet al Alta Floresta AERONET Aerosol Optical Thickness Jan 99 - Nov M a r M a y Ju l A u g O ct D e c Ja n M a r M a y Ju n A u g O ct N o v Ja n F e b A p r Ju n Ju l S e p N o v D e c F e b A p r M a y Ju l S e p O ct D e c -0 2 Aerosol Optical Thickness Aeronet Abracos Hill Rondonia 500 nm May-99 Jul-99 Sep-99 Nov-99 Jan-00 Mar-00 May-00 Jul-00 Sep-00 Nov-00 Jan-01 Mar-01 May-01 Jul-01 Sep-01 Nov-01 Jan-02 Mar-02 May-02 Jul-02 Sep-02 Nov F e b Mar-99 A O T n m N o v D e c A erosol optical thickness 500 nm Jan-99

33 Conceptual model: HAIL Graphics by Robert Simmon, NASA

precipitating system when first detected by the radar.

34 Albrecht et al 2011 Journal of Geophysical Research Mean total aerosol concentration for each type of precipitating system when first detected by the radar. For LBA campaigns TRMM/LBA and SMOCC (SW Amazon) polluted clean

35 Reutter et al 2009, Atmos. Chem. Phys. An updraft-limited regime that is characterized by low w/n CN ratios (< 10 4 m s 1 cm 3 ), low maximum values of water vapour supersaturation (S max < 0.2%), low activated fractions of aerosol particles (N CD /N CN < 20%). In this regime N CD is directly proportional to w and practically independent of N CN. No sensitivity to number concentration of aerosol. An aerosol-limited regime that is characterized by high w/n CN ratios (> 10 3 m s 1 cm 3 ), high maximum values of water vapour supersaturation (S max > 0.5%), and high activated fractions of aerosol particles (N CN /N CN > 90%). In this regime N CD is directly proportional to N CN and practically independent of w. As the number concentration of aerosol increases there is a shift to ice phase microphysics. HIGH AEROSOL LOAD DRY SEASON AMAZON CLEAN WET SEASON AMAZON

36 Reutter et al 2009 INDOEX Amazon Dry season Amazon Wet season

37 Expected Aerosol Effects Longo et al, 2003 Radiation Colder surface, warmer lower troposphere =stabilizing effect less clouds less rainfall? Cloud microphysics Inhibit rainfall in warm clouds Deep clouds? Rainfall accumulation Warm clouds Aerosol Concentration Scheme of aerosol effects on precipitation Accumulated rain Maritime & moderate (wet) continental clouds (like GATE and PRESTORM) Khain et al, 2003 Dry unstable situation (like Texas clouds) Aerosol concentration Cold clouds

38 Flight region: SW Amazon Microphysics measurements with aircraft: CCN spectrum, cloud and rain drop spectra, cloud liquid water content, temperature, dew point temperature, pressure and GPS positioning. Flights from Sept 21 to Oct 13, 2002 Day a 30b 01a 01b 04a 04b Time (LT) 13:24 15:30 14:07 16:16 14:43 17:04 14:02 16:18 14:27 16:03 13:37 15:38 11:14 12:17 13:49 16:40 10:16 12:25 16:47 17:20 Day 04c 05a 05b 06a 06b a 11b Time (LT) 16:48 18:36 12:13 14:18 15:24 17:01 11:12 13:15 14:19 16:45 14:53 16:30 13:32 15:27 10:43 12:10 13:21 14:46 11:45 13:38 11:06 12:25 13:20 15:50 13:54 15:49

39 Andreaeet al 2004 Smoking rain clouds over the Amazon - Science

40 liquid +ice Lin et al 2006 JGR TRMM Precipitation Radar MODIS AOT CWF ~ CAPE

42 Martins et al, 2009 Cloud condensation nuclei from biomass burning during the Amazonian dry-to-wet transition season In Meteorology and Atmospheric Physics., v.103, /s00703 Martins et 2009.Impact of biomass burning aerosols on precipitation in the Amazon: A modeling case study In Journal of Geophysical Research., v.114, D02207 Martins, J A, Silva Dias, M A F The impact of smoke from forest fires on the spectral dispersion of cloud droplet size distributions in the Amazonian region In Environmental Research Letters., v.4,

43 Shape parameter in cloud droplet diameter distribution n( D) = N t Γ( ν ) D D n ν 1 1 D n exp D D n dn/dd (1,5 m.cm 3 ) m DIÂMETRO (µm)

44 Frequency of shape parameters for all flights 50 FREQ QUÊNCIA (%) CLEAN LIMPO POLUÍDO POLLUTED PARÂMETRO DE FORMA

45 Grid specifications for the numerical experiments with BRAMS (Brazilian modifications to RAMS - BRAMS Freitas et al 2009 Atmos.Chemistry and Physics) Grid 1 Grid 2 Grid 3 Grid 4 Starting and end time 00Z, 23 September Z, 24 September 2002 Number of grid points (x, y, z) (62,62,43) (62,62,43) (122,122,43) (42,42,43) Horizontal grid spacing (x, y) (64, 64 km) (16, 16 km) (4, 4 km) (1, 1 km) Vertical grid spacing 43 levels with variable stretching factor Time step ,5 1,875 Grid center (10,92 ºS 62,41ºW)

46 Observed shape parameter x CCN concentration numerical experiments PARÂMETRO Shape pa arameter DE FORMA R = Cloud CONCENTRAÇÃO droplet concentration DE GOTÍCULAS (cm -3 ) (cm -3 )

48 Total Condensate CCN300

49 ccn300 ccn450 ccn600 ccn900

50 Vertical structure of cloud and ice water mixing rate observed at the time of maximum liquid water path CCN300 CCN450 CCN600 CCN900

51 Partitioning of the precipitation according to the intensity of averaged rain rate change in PDF RAIN RATE STA (mm/h) PREC. > 5 mm/h 1 mm/h < PREC. < 5 mm/h PREC. < 1 mm/h 0 CCN300 CCN450 CCN600 CCN900 CCN900R SCENARIO

52 Conclusions Environmental conditions of thunderstorms in the Amazon Basin are mild when compared to the mid-latitude cases There is sensitivity of convection to aerosol number concentration in the wet season More aerosol -> deeper convection, more ice, more ligthning In clean conditions convection has a more maritime behavior In the dry season and transition seasons convection is updraft limited and shows no sensitivity to aerosol concentration Deep convection is limited by the available thermodynamic instability and dynamic lifting mechanisms Thunderstorms are more continental

Interactions radiation-cloud-aerosols

Interactions radiation-cloud-aerosols Maria Assunção Faus da Silva Dias Departamento de Ciências Atmosféricas Universidade de São Paulo Summary Aerosol cloud microphysics Aerosol - radiation Aerosol layer