Kyle Nelson's Master's Thesis presentation on The Role of Optically Thin Liquid Clouds in the 2012 Greenland Ice Sheet Surface Melt Event. Presented August 6, 2014 at the University of Wisconsin-Madison's Department of Atmospheric and Oceanic Sciences.

1. 1
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
The Role of Optically Thin
Liquid Clouds in the 2012
Greenland Ice Sheet
Surface Melt Event
Kyle Nelson
Department of Atmospheric and Oceanic Sciences
Cooperative Institute for Meteorological Satellite Studies
University of Wisconsin-Madison
M.S. Thesis Presentation
August 6, 2014

2. 2
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Introduction
• July 2012: new record in surface melt
extent over Greenland Ice Sheet (GIS)
• Observed surface melt over the entire GIS
• Bennartz et al. (2012): Thin, low-level,
liquid clouds occurred very frequently over
Summit, Greenland when melt occurred
– One factor that warmed surface above
freezing

3. 3
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Background
Bennartz et al. (2012)

4. 4
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Surface Melt Extent
From Nghiem et al. (2012)

5. 5
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Research Questions
• What range of cloud optical depth (and LWP)
yields a positive surface radiative forcing?
• What is the sensitivity of cloud base height to
surface radiative forcing?
• What is the frequency of occurrence of thin
liquid clouds over the GIS in 2012?
• Did thin, liquid clouds contribute to the July
2012 surface melt event over the entire GIS?

6. 6
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Methods
• Radiative transfer modeling
• Satellite remote sensing
– MYD06 (Col. 5 & 6) Standard Cloud Product
– CALIPSO: CALIOP and IIR
– PATMOS-x MOD02 1.6μm
– Cloud Phase Determination
• ECMWF ERA Interim Reanalysis

7. 7
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Cloud Surface Radiative Forcing
ΔFnet = FSW
↓
− FSW
↑
+ FLW
↓
− FLW
↑
FSW,net = FSW
↓
− FSW
↑
FLW,net = FLW
↓
− FLW
↑
CSW =
∂FSW,net
∂a0
Ac
∫ da
CLW =
∂FLW,net
∂a0
Ac
∫ da
Cnet = CSW +CLW
!
• CSW, CLW & CNET are the
shortwave, longwave,
and net cloud forcing
for the surface
• FSW,net & FLW,net are the
net shortwave and
longwave fluxes at the
surface
• Ac is the total cloud
amount
• a is the cloud fraction

8. 8
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Solar Zenith Angle vs SRF
Total
Longwave
Shortwave

9. 9
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Cloud Base Height vs SRF
2 4 6
−250
−200
−150
−100
−50
0
50
100
Cld Base Ht: 0.5km; SZA = 65
Cld Tau
SurfaceRadiativeForcing
2 4 6
−250
−200
−150
−100
−50
0
50
100
Cld Base Ht: 1km; SZA = 65
Cld Tau
SurfaceRadiativeForcing
2 4 6
−250
−200
−150
−100
−50
0
50
100
Cld Base Ht: 1.5km; SZA = 65
Cld Tau
SurfaceRadiativeForcing
2 4 6
−250
−200
−150
−100
−50
0
50
100
Cld Base Ht: 2km; SZA = 65
Cld Tau
SurfaceRadiativeForcing
2 4 6
−250
−200
−150
−100
−50
0
50
100
Cld Base Ht: 2.5km; SZA = 65
Cld Tau
SurfaceRadiativeForcing
2 4 6
−250
−200
−150
−100
−50
0
50
100
Cld Base Ht: 3km; SZA = 65
Cld Tau
SurfaceRadiativeForcing
Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7Fresh Snow 4 of 7
Total
Longwave
Shortwave

10. 10
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Summary of Modeled Cloud Forcing
• Determined range of cloud optical depth
and LWP that contribute to surface
warming
– Optical Depth: 1.5-6.5
– Liquid Water Path: 10-40 g/m2
• Assuming 10μm particle effective radius
• Maximum increases with solar zenith angle

11. 11
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
MODIS: Moderate Resolution
Imaging Spectroradiometer
• Passive sensor,
measuring radiances
at 36 wavelengths
• Spatial resolutions of
250m to 1km
• Using Aqua-MODIS,
part of NASA’s A-Train
– 1:30pm local equator
crossing time

12. 12
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
MYD06 Standard Cloud Product
• Visible and infrared techniques
– Cloud-particle phase (ice vs. water, clouds vs. snow)
– Effective cloud-particle radius
– Cloud optical thickness
• Infrared only technique
– Cloud-top temperature
– Cloud-top height
– Effective emissivity
– Cloud phase (ice vs. water, opaque vs. non-opaque)
– Cloud fraction
• Visible only technique
– Cirrus reflectance

13. 13
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Liquid Clouds vs All Clouds (MODIS), July 2012

14. 14
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Thin Liquid Clouds vs All Clouds (MODIS), July 2012

15. 15
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
MODIS Optical Depth Distribution

16. 16
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Liquid Water Path Distribution - MWR

17. 17
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Matchups: MODIS/CALIOP/IIR
• Cross-instrument comparison of cloud
optical depth
• Determine which sensor or combination of
data from different sensors produces a
LWP distribution using ground based
observations from the microwave
radiometer at Summit, Greenland

18. 18
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
CALIPSO: Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observations
• Launched April 28, 2006
• Part of NASA’s A-Train
• CALIPSO combines an active LIDAR
instrument with passive infrared and
visible imagers to diagnose the vertical
structure and properties of thin clouds and
aerosols over the globe.

19. 19
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
CALIOP: Cloud-Aerosol Lidar with
Orthogonal Polarization
• CALIOP is a two-wavelength polarization-
sensitive LIDAR that focuses on the vertical
distributions of clouds and aerosols and their
properties
• Vertical resolutions of 30m
• 335m ground-spot spacing
• Level 2 products include
– Aerosol and cloud feature masks
– Aerosol subtype
– Extinction
– Optical depth

20. 20
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
IIR: Imaging Infrared Radiometer
• 3 channel imaging radiometer in the thermal
infrared
• Channels at 8.65μm, 10.6μm and 12.05μm
• IIR measurements are combined with the
LIDAR information enabling the retrieval of
the size of ice particles in semi-transparent
clouds.
• The pairing of 10.6μm and 12.05μm channels
is sensitive to small particles, while the
8.65μm and 12.05μm channels are more
sensitive to large particles

21. 21
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
CALIOP vs MODIS MYD06

22. 22
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
IIR vs MODIS MYD06

23. 23
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
IIR vs CALIOP

24. 24
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
IIR: Thin Liquid Clouds

25. 25
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Alternate Methods Needed
• MODIS Optical Depth
– Standard 2-channel visible retrieval
– Or 1.6μm & 2.1μm
• MODIS Liquid Water Path
– MODIS effective radius & optical depth
• All based on visible or infrared channels
– Challenging over highly reflective and cold
surfaces
• Can we use 1.6μm or 2.1μm channels?

26. 26
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Issues: MODIS 2-channel Retrievals

27. 27
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Solution: Single Channel Retrieval

28. 28
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Solution: PATMOS-x & Look Up Tables
• Pathfinder Atmospheres – Extended
• PATMOS-x MOD02 (Terra)
– Level-1B Calibrated Geolocated Radiances
– Raw 1.6 micron reflectance
– Mapped to a .1°x.1° grid
– One measurement per gridbox per day
• Measurement closest to NADIR
– Used in conjunction with a look-up table
• Work backward from reflectance to optical depth
• Provided by A. Walther

29. 29
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
PATMOS-x Gridboxes

30. 30
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Sensitivity to Choice of Re - Liquid

31. 31
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Was there a significant difference
between July 2011 and July 2012?
• No melting of the GIS interior in July 2011
• Melting over the entire GIS in July 2012

32. 32
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Number of Cloudy Days: July 2011 vs 2012

33. 33
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Percentage Cloudy – 2°x2° box over Summit
July
2011
July
2012
Calendar Day
1
31
PercentCloudy
100
0
100
0

34. 34
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
July
2011
July
2012
Calendar Day
1
31
FractionofThinCloudsvsAllClouds
100
100
0
Fraction of Thin Clouds – 2°x2° at Summit0

35. 35
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Cloud Phase Determination
• PATMOS-x MOD02 3.7μm reflectance
• Similar method used by Key & Intrieri
(2000) and Pavolonis & Key (2003) with
AVHRR
• Threshold between ice and liquid cloud
– 3.7μm reflectance >0.07 and
– Cloud top temperature >243K

36. 36
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Sensitivity to Choice of Re - Liq

37. 37
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Sensitivity to Choice of Re - Ice

38. 38
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
July
2011
July
2012
Calendar Day
1
31
FractionofThinLiquidCloudsvsAllClouds
100
100
0
Fraction of Thin Liquid Clouds – 2°x2° at Summit0

39. 39
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Frequency of Liquid vs All Cloud – July 2011 vs 2012

40. 40
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Frequency of Thin Liq vs All Cloud – July 2011 vs 2012

41. 41
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
To Review:
• ICECAPS surface and PATMOS-x satellite
data show a high presence of optically
thin, liquid clouds over the GIS during the
record melt event in July 2012
• In July 2011 and 2012, the frequency of
occurrence and spatial coverage of thin,
liquid clouds was similar

42. 42
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Another factor in addition to cloud
cover must have influenced the
surface temperature over the GIS to
push the temperature above freezing

43. 43
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
ECMWF ERA Interim
• Qualitatively assess the general
atmospheric circulation and temperature
advection in the proximity of the GIS
• Monthly Means at select pressure levels
– Geopotential Height
– Temperature
– U- and V-wind

44. 44
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison

45. 45
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison

46. 46
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison

47. 47
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Conclusions
• Optically thin liquid clouds, regardless of cloud
base height, played a key role in the 2012 and
2011 melt events by increasing near-surface
temperatures across the GIS
• The work of Bennartz et al. (2013) is extended here
by expanding the study domain from a point
observation at Summit to the entirety of
Greenland by leveraging satellite data products
• Radiative transfer modeling showed that these
optically thin clouds warm the surface during the
day regardless of cloud base height

48. 48
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Conclusions (cont.)
• Standard cloud products from the MODIS,
CALIOP and IIR sensors did not reliably detect
optically thin, liquid clouds over ice and snow
• Frequency of occurrence and geospatial location
of optically thin liquid clouds over the GIS was
found to be nearly identical in July 2011 and 2012
• With observed melting over almost the entire GIS
in July 2012 (Nghiem 2012), warm air advection is
likely the dominant contributor
– This agrees with the findings of Bennartz et al. (2013)
and Neff et al. (2014)

49. 49
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Future Research
• Frequency of thin, liquid clouds in other years
– Significant surface melt
– No surface melt
• Repeat study over the Arctic Ocean to
determine the role of thin, liquid clouds on
the surface energy budget of sea ice
• Expand the time domain to the entire MODIS
satellite record to establish a 15+ year
climatology of location and frequency of
occurrence of optically thin liquid clouds in
the Arctic

50. 50
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Acknowledgements
• Jeff Key – Research Advisor
• Steve Ackerman – Academic Advisor
• Ralf Bennartz
• Andy Heidinger
• Andi Walther
• Denis Botambekov
• SSEC PEATE Group
• PATMOS-x Team
• Tristan L’Ecuyer – MS Committee
• Grant Petty – MS Committee
• Mark Kulie
• Erik Gould
This work was supported
by the NOAA Climate
Data Records Program

51. 51
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Questions?
Meteorologist | Education & Outreach Specialist
University of Wisconsin-Madison
kyle.nelson@ssec.wisc.edu
@wxkylenelson

52. 52
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Look Up Table Calculations

53. 53
[
]
Dept. AOS & CIMSS
Kyle Nelson - UW-Madison
Selected References
• Ackerman, S., R. Holz, R. Frey, E. Eloranta, B. Maddux, and M. McGill (2008): Cloud
detection with MODIS. Part II: Validation, J. Atmos. Oceanic Technol., 25, 1073–1086, doi:
10.1175/2007JTECHA1053.1.
• Bennartz, R. et al. (2013): July 2012 Greenland melt extent enhanced by low-level liquid
clouds. Nature Vol. 496, 83-86, doi: 10.1038/nature12002.
• Curry, J., J. Schramm, W. Rossow and D. Randall, 1996: Overview of Arctic Cloud and
Radiation Characteristics. J. Climate, 9, 1731–1764. doi:
10.1175/1520-0442(1996)009<1731:OOACAR>2.0.CO;2
• Heidinger, A. et al. (2013): The Pathfinder Atmospheres Extended (PATMOS-X) AVHRR
Climate Data Set. BAMS, doi: 10.1175/BAMS-D-12-00246.1 (in press).
• Key, J. and A. Schweiger (1998): Tools for atmospheric radiative transfer: Streamer and
FluxNet. Computers and Geosciences, 24(5), 443-451.
• Key, J. and J. Intrieri (2000): Cloud particle phase determination with the AVHRR. J. Appl.
Meteorol., 39(10), 1797-1805.
• Neff, W. D. et al. (2013): Continental heat anomalies and the extreme melting of the
Greenland ice surface in 2012 and 1889. Journal of Geophys. Research: Atmospheres. doi:
10.1002/2014JD021470 (in press).
• Nghiem, S. V. et al. (2012): The extreme melt across the Greenland Ice Sheet in 2012.
Geophys. Res. Lett. 39, L20502, doi: 10.1029/2012GL053611.
• Shupe, M. D. and J. Intrieri (2004): Cloud radiative forcing of the Arctic surface: the
influence of cloud properties, surface albedo, and solar zenith angle. J. Clim. 17, 616–628.