Variability of the mesoscale organization of shallow convection over - - PowerPoint PPT Presentation

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Variability of the mesoscale organization of shallow convection over - - PowerPoint PPT Presentation

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Variability of the mesoscale organization of shallow convection over the tropical Atlantic Sandrine Bony 1 , Bjorn Stevens 2 , Tristan LEcuyer 3 , Alyson Douglas 3 , Addisu Semie 1 & the EUREC 4 A ISSI science team 4 1 : LMD/IPSL, CNRS,

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Variability of the mesoscale organization of shallow convection over the tropical Atlantic

CFMIP, October 2018, NCAR,Boulder

Sandrine Bony1, Bjorn Stevens2, Tristan L’Ecuyer3, Alyson Douglas3, Addisu Semie1 & the EUREC4A ISSI science team4

1: LMD/IPSL, CNRS, Sorbonne University, Paris, France 2: Max Planck Institute for Meteorology, Hamburg, Germany 3: University of Wisconsin-Madison, Madison, WI, USA 4: ISSI: International Space Science Institute, Bern, Switzerland

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Mesoscale organization of trade-wind shallow clouds during winter

MODIS Aqua 10 Feb 2017 (NASA Worldview)

Barbados

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Mesoscale organization of trade-wind shallow clouds during winter

MODIS Aqua 10 Feb 2017 (NASA Worldview)

Barbados

  • 1. How variable is it?
  • 2. How does it relate to large-scale conditions?
  • 3. Does it matter?
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10 deg x 10 deg

A group of 10 scientists (B Stevens, S Bony, H Brogniez, L Hentgen, C Hohenegger, C Kiemle,

T L’Ecuyer , AK Naumann, C Schär , P Siebesma, J Vial, D Winker and P Zuidema) looked at MODIS

imagery and classifjed visually the type of mesoscale organization present in the area (each day of DJF for 10 years, i.e. 900 images, each being analyzed by 5 difgerent persons).

Mesoscale organization of trade-wind shallow clouds during winter

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Visual classifjcation of MODIS imagery by a group of scientists

→ 4 main patterns of mesoscale organization

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Visual classifjcation of MODIS imagery by a group of scientists

→ 4 main patterns of mesoscale organization

“Cold pools” (53 %)

NASA MODIS imagery

200 km

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Visual classifjcation of MODIS imagery by a group of scientists

→ 4 main patterns of mesoscale organization

“Flowers” (16 %) “Cold pools” (53 %)

NASA MODIS imagery

200 km

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Visual classifjcation of MODIS imagery by a group of scientists

→ 4 main patterns of mesoscale organization

“Fish” (17 %) “Flowers” (16 %) “Cold pools” (53 %)

NASA MODIS imagery

200 km

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Visual classifjcation of MODIS imagery by a group of scientists

→ 4 main patterns of mesoscale organization

“Fish” (17 %) “Flowers” (16 %) “Cold pools” (53 %) “Sugar” (14 %)

NASA MODIS imagery

200 km

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Interannual variability of mesoscale organization patterns

Based on the “visual classifjcation”

  • f MODIS satellite imagery

Cold pools Fish Sugar Flowers

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Characterization of the mesoscale organization of shallow clouds

Use geostationary data:

  • GridSat-B1 data (gridded, 0.07° resolution)
  • Dec 2000 to Feb 2017, DJF

, 3-hourly Use IR brightness temperature to:

  • select situations with a prominence of shallow convection
  • identify shallow clouds and cloud clusters

Characterize organization through:

  • number of clusters
  • total area covered by shallow clouds
  • mean cluster size
  • spatial distribution of cloud clusters (Iorg)

(comparison to a theoretical random distribution of the CDF

  • f nearest neighbor distances, Tompkins and Semie 2017):
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Large clusters Small clusters

Random

  • r regular

spatial distribution

cluster size Iorg

Characterization of the main convective organization patterns

Fish Flowers Cold pools Sugar

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Interannual variability of mesoscale organization patterns

Based on the “visual classifjcation”

  • f MODIS satellite imagery

Based on the analysis of geostationary data using a simple classifjcation scheme

Cold pools Fish Sugar Flowers

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Correlations with LTS, EIS, Tair-Ts, ω700, etc much less signifjcant

  • Clustering favored in warm, weak wind regimes
  • Random or regular organizations favored in cold, windy regimes

R2=0.6 R2=0.7

Sea Surface Temperature [Reynolds, 2000-2017] Surface wind speed [ERA interim, 2000-2017)

Correlation with large-scale meteorology (interannual time scale)

1 degC 2 m/s

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Correlation with large-scale meteorology (daily timescale, 2000-2017)

Surface wind speed [ERA interim] Zonal wind shear (700mb-Sfc) [ERA interim] SST [Reynolds] RH (400-600hPa layer) [Megha-Tropiques] Inversion strength (EIS) [ERA interim]

Clustered Random

  • r regular

SUGAR COLD POOLS FLOWERS FISH

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How contrasted are cloud properties?

7 Jan 2008

Cloud top height stratifjed by Iorg (CloudSat and CALIPSO) 2007-2011

Cloud mask along A-Train orbits [CloudSat and CALIPSO]

9 Feb 2007 20 Feb 2007 7 Feb 2007

Lowest Iorg (FL+CP) Highest Iorg (FI+SU) Low-cloud top height [MODIS & GEO] Low-cloud area [MODIS & GEO]

Clustered Random

  • r regular

SUGAR COLD POOLS FLOWERS FISH

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Does it matter for TOA radiation? (Daily timescale, 2001-2017)

SW CRE [CERES] NET CRE [CERES] LW CRE [CERES] 10 W/m2 Low-cloud top height [MODIS & GEO] Low-cloud area [MODIS & GEO]

Clustered Random

  • r regular

SUGAR COLD POOLS FLOWERS FISH

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Conclusions

  • Shallow clouds exhibit a large diversity of mesoscale organizations in the winter trades,

both at daily and interannual time scales.

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Conclusions

  • Shallow clouds exhibit a large diversity of mesoscale organizations in the winter trades,

both at daily and interannual time scales.

  • The 4 main patterns of mesoscale organization are associated with contrasted large-scale conditions:

Regular or random organizations (cold pools/fmowers): → low SST, high wind speed, small wind shear Clustered organizations (sugar/fjsh): → high SST, low wind speed, strong wind shear Large cloud clusters (fmowers/fjsh): → dry free troposphere, strong trade inversion → deeper, larger cloud fraction Small cloud clusters (cold pools/sugar): → moist free troposphere, weak trade inversion → shallower, smaller cloud fraction

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Conclusions

  • Shallow clouds exhibit a large diversity of mesoscale organizations in the winter trades,

both at daily and interannual time scales.

  • The 4 main patterns of mesoscale organization are associated with contrasted large-scale conditions:

Regular or random organizations (cold pools/fmowers): → low SST, high wind speed, small wind shear Clustered organizations (sugar/fjsh): → high SST, low wind speed, strong wind shear

  • Mesoscale organizations are associated with difgerent cloud fractions and thus difgerent CRE,

‘regular/random’ organizations cooling more than ‘clustered’ organizations (by ~ 10 W/m2)

Large cloud clusters (fmowers/fjsh): → dry free troposphere, strong trade inversion → deeper, larger cloud fraction Small cloud clusters (cold pools/sugar): → moist free troposphere, weak trade inversion → shallower, smaller cloud fraction

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Conclusions

  • Shallow clouds exhibit a large diversity of mesoscale organizations in the winter trades,

both at daily and interannual time scales.

  • The 4 main patterns of mesoscale organization are associated with contrasted large-scale conditions:

Regular or random organizations (cold pools/fmowers): → low SST, high wind speed, small wind shear Clustered organizations (sugar/fjsh): → high SST, low wind speed, strong wind shear

  • Mesoscale organizations are associated with difgerent cloud fractions and thus difgerent CRE,

‘regular/random’ organizations cooling more than ‘clustered’ organizations (by ~ 10 W/m2)

  • In climate change: higher SSTs and weaker trade winds might favour ‘clustered’ organizations at the

expense of ‘regular/random’ organization, thus producing a positive “organization cloud feedback”.

Large cloud clusters (fmowers/fjsh): → dry free troposphere, strong trade inversion → deeper, larger cloud fraction Small cloud clusters (cold pools/sugar): → moist free troposphere, weak trade inversion → shallower, smaller cloud fraction

?

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Conclusions

  • Shallow clouds exhibit a large diversity of mesoscale organizations in the winter trades,

both at daily and interannual time scales.

  • The 4 main patterns of mesoscale organization are associated with contrasted large-scale conditions:

Regular or random organizations (cold pools/fmowers): → low SST, high wind speed, small wind shear Clustered organizations (sugar/fjsh): → high SST, low wind speed, strong wind shear

  • Mesoscale organizations are associated with difgerent cloud fractions and thus difgerent CRE,

‘regular/random’ organizations cooling more than ‘clustered’ organizations (by ~ 10 W/m2)

  • In climate change: higher SSTs and weaker trade winds might favour ‘clustered’ organizations at the

expense of ‘regular/random’ organization, thus producing a positive “organization cloud feedback”.

  • Physical mechanisms underlying these difgerent organization patterns?

→ EUREC4A fjeld campaign (Jan-Feb 2020)

Large cloud clusters (fmowers/fjsh): → dry free troposphere, strong trade inversion → deeper, larger cloud fraction Small cloud clusters (cold pools/sugar): → moist free troposphere, weak trade inversion → shallower, smaller cloud fraction

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Thank You