Expressions of ENSO and the SAM in Modeled Antarctic
Surface Temperatures
Expressions of ENSO and the SAM in Modeled Antarctic
Surface Temperatures
The Transantarctic Mountains, A. Huerta, 2003
David B. ReuschEMS Earth and Environmental Systems Institute and Department of GeosciencesThe Pennsylvania State University
David B. ReuschEMS Earth and Environmental Systems Institute and Department of GeosciencesThe Pennsylvania State University
Funded by the Office of Polar Programs,
National Science Foundation
Special thanks to David Bromwich and Andy Monaghan for sharing their
data
IntroductionIntroduction Questions
Are there robust spatial relationships between surface temperatures and ENSO/SAM?
Does SAM modulate the ENSO fingerprint? Methods
Extract patterns of variability in temperature and circulation data (SOMs)
Stratify data by SAM/ENSO states and hope there’s something to interpret in the patterns
Questions Are there robust spatial relationships between
surface temperatures and ENSO/SAM? Does SAM modulate the ENSO fingerprint?
Methods Extract patterns of variability in temperature
and circulation data (SOMs) Stratify data by SAM/ENSO states and hope
there’s something to interpret in the patterns
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Polar MM5 initialized by ERA-40 Jan 1979 - Aug 2002 60 km grid, 6-hourly
Daily averages, DJF (Jul) T-2m (Z850, Z700, Z500)
See Monaghan et al, 2006
Polar MM5 initialized by ERA-40 Jan 1979 - Aug 2002 60 km grid, 6-hourly
Daily averages, DJF (Jul) T-2m (Z850, Z700, Z500)
See Monaghan et al, 2006
The Model DatasetThe Model Dataset
Grid Domain
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Self-organizing Maps (SOMs)Self-organizing Maps (SOMs)
Patterns arranged in a grid Patterns arranged in a grid by their relative similarityby their relative similarity
1) Generalized pattern extractor1) Generalized pattern extractor
Holocene ice core chemistryHolocene ice core chemistry
Each input record matches Each input record matches one pattern most closelyone pattern most closely
Concise summary of data Concise summary of data variability expressed as a variability expressed as a user-defined number of user-defined number of generalized patternsgeneralized patterns
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Subset input by time period, Subset input by time period, external index, etc.external index, etc.
Self-organizing Maps (SOMs)Self-organizing Maps (SOMs)
Group input records that Group input records that match the same pattern; use match the same pattern; use pattern insteadpattern instead
2) Data classifier by pattern matching2) Data classifier by pattern matching
Basis for frequency, transition Basis for frequency, transition and trajectory mapsand trajectory maps
Sample Frequency MapSample Frequency Map
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
125 m
200 m
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Seasonal Land-Only Temperatures
Seasonal Land-Only Temperatures
Broadening study period Switch to land-only
Focus on ice sheet Ocean temperatures of less interest
Still daily resolution
Broadening study period Switch to land-only
Focus on ice sheet Ocean temperatures of less interest
Still daily resolution
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Land-only Surface Temperature Climatology
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
SAM and TemperatureSAM and Temperature
Create data subsets using high/low values of the SAM index
Create frequency maps for each year in each subset
Identify which pattern frequencies are statistically unusual
Count how often each pattern is unusual
Create data subsets using high/low values of the SAM index
Create frequency maps for each year in each subset
Identify which pattern frequencies are statistically unusual
Count how often each pattern is unusual
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
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65 °S
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
80%
20%
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
T = f(ENSO) o g(SAM)?T = f(ENSO) o g(SAM)?
r2 ~ 0
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
Background ● ENSO ● Land-only T-2m ● SAM ● T <= SAM,ENSO
December 1982Low SAM/El Niño
December 1994High SAM/El Niño
Potential Next StepsPotential Next Steps
Work with READER data Not high spatial density like a model grid… But the records are longer Then again, it’s monthly not daily…
Other fields that express the SAM Compare notes with those using other
methodologies!
Work with READER data Not high spatial density like a model grid… But the records are longer Then again, it’s monthly not daily…
Other fields that express the SAM Compare notes with those using other
methodologies!
SummarySummary
Intriguing signatures of both El Niño and the SAM are in the data
Untangling joint effects SAM/ENSO remains a challenging task
Intriguing signatures of both El Niño and the SAM are in the data
Untangling joint effects SAM/ENSO remains a challenging task
[email protected]@psu.eduNSF ANT 06-36618NSF ANT 06-36618
J-M05 Manifestation of anthropogenic forcing and natural variability in the Arctic and Antarctic climate systems
June 28 – July 11, Melbourne, AUS
www.iugg2011.com
Call for Abstracts still TBA
J-M05 Manifestation of anthropogenic forcing and natural variability in the Arctic and Antarctic climate systems
June 28 – July 11, Melbourne, AUS
www.iugg2011.com
Call for Abstracts still TBA
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