SST Observations of large scale ocean...
Transcript of SST Observations of large scale ocean...
ESA Oceans2006, Hamburg © I. S. Robinson (2006)
SST : Large scale ocean processes 1
Ian RobinsonIan Robinson
2525--29 Sep, Hamburg29 Sep, Hamburg
ESA Training Course: Ocean2006 ESA Training Course: Ocean2006
National Oceanography Centre Southampton
Professor of Oceanography from Space, University of Southampton.Co-head of the Ocean Observations and Climate Research Group, NOCS
SST Observations of large scale ocean processes
SST Observations of large scale ocean processes
SST: Large scale processes 225-29 Sep, HamburgESA Training Course: Ocean2006
Outline of lectureOutline of lectureOutline of lecture
Why use satellites to observe large scale ocean processes?
How global sea surface temperature (SST) distribution varies through the year
Processing global data to reveal new features
Some large scale ocean phenomena that can be measured from satellites
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Satellite data provide the best means to observe large scale ocean processes
Satellite data provide the best means to Satellite data provide the best means to observe large scale ocean processesobserve large scale ocean processes
Applications to large scale oceanography exploit the unique benefits of satellite data
Global coverage with good spatial detailRepeated coverage over many yearsA consistent view from the same sensorOpportunity to combine data from different sensor types
Special data analysis methods for time series of large scale images
Creating climatologiesProducing anomaliesHovmöller plots
A new look at large scale ocean phenomena
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Large scale ocean processes from SpaceLarge scale ocean processes from SpaceLarge scale ocean processes from Space
Climatology of the oceansObserve the whole world ocean at onceIsolate the seasonal cycleIdentify long term fluctuations or trendsLook for characteristic patterns of ocean behaviour
Large scale propagating featuresRossby wavesTropical instability waves
El Niño Detection from multiple data typesPossibility of prediction
Monsoons etc
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1996 : 011996 : 021996 : 031996 : 041996 : 051996 : 061996 : 071996 : 081996 : 091996 : 101996 : 111996 : 12
Time series of monthly SST composites; 1996
Time series of monthly SST Time series of monthly SST composites; 1996composites; 1996
Annual Mean 1996
Monthly mean SST data from the NASA “Pathfinder” analysis for 1996. The spatial resolution is 9 km.
-2 2 6 10 14 18 22 28 30 34 C
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Monthly mean Satellite SST: 1997Monthly mean Satellite SST: 1997Monthly mean Satellite SST: 1997
Monthly mean SST data from the NASA “Pathfinder” analysis for 1997. The spatial resolution is 9 km.
-2 2 6 10 14 18 22 28 30 34 C
1997 : 011997 : 021997 : 031997 : 041997 : 051997 : 061997 : 071997 : 081997 : 091997 : 101997 : 111997 : 12Annual Mean 1997
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month B
month B
Year N, month B
Average over N years for month B
Seasonal climatologies from image time seriesSeasonal climatologies from image time seriesSeasonal climatologies from image time series
Jan
Dec
Jan
Dec
Jan
Dec
Average over N years for month A
Instead of using monthly averages, we can use any interval, e.g. 3 days, 8 days, as long as the same calendar interval is used from each year
NOTE – Ideally the climatology should also map the standard deviation
Year 1
Year n
Year N
month A
month A
month A
yr2, A
yr3, A
yr n+1, A
SST: Large scale processes 825-29 Sep, HamburgESA Training Course: Ocean2006
Generating Anomaly Datasets for SST or other variables
Generating Anomaly Datasets for SST or Generating Anomaly Datasets for SST or other variablesother variables
Day 001
Day 365
Composite SST map for a specific period e.g. days 172-176 in 2005 Select the data for days 172-176
from the N-year climatology(Note that the period must match the way the climatology is constructed.).
Anomaly for days 172-176 in 2005
Subtract relevant clim-atology from SST map.
Climatology
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SST Anomaly map from AVHRRSST Anomaly map from AVHRRSST Anomaly map from AVHRR
0 1 2 3 4 5-1-2-3-4-5Degrees C relative to climatological mean
Data processed by Al Strong and provided by the Products System Branch of NOAA/NESDIS
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Anomaly differences over 3-4 daysAnomaly differences over 3Anomaly differences over 3--4 days4 days
5th April – 19 April, 2005 From NOAA website5th April – 19 April, 2005 From NOAA website
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Anomaly differences over a yearAnomaly differences over a yearAnomaly differences over a year
20 April, 2004 – 19 April, 2005 From NOAA website
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Analysing time sequences of global datasetsAnalysing time sequences of global datasetsAnalysing time sequences of global datasets
Time series of 2-D mapped data stack up as “cubes” of an ocean variableThis is height, but could be SSTSlice the cube in different planes
Latitu
deLongitude
Time
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Analysing a 3-dimensional dataset Analysing a 3Analysing a 3--dimensional dataset dimensional dataset The challenge of extracting the information distributed in latitude, longitude and time
Longitude
Tim
eLa
titude
Tim
eLongitude
Hovmüller Diagram
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Applications of space-time section plots Applications of spaceApplications of space--time section plots time section plots To emphasise a process in which a property changes its distribution quite slowly with time
Section across the Arctic to show the ice edge advance / retreat.A North-South (Meridional) section to show the seasonal migration of temperature contours at a particular longitude.Compare response of different variables, and at different longitudes
Any wavelike motionWaves on the Antarctic Circumpolar CurrentRossby WavesTropical Instability Waves
Compare SST, altimetry and wind sections to reveal atmosphere-ocean interactions.
El Niño
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Rossby waves are revealed in Hovmöller plotsRossby waves are revealed in Hovmöller plotsRossby waves are revealed in Hovmöller plots
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Estimating the speed of Rossby WavesEstimating the speed of Rossby WavesEstimating the speed of Rossby Waves
Proportional to slope of linesMust be measured objectively for consistencyNeeds to be automated for global coverageUse Radon Transform AnalysisCan detect several waves of different speeds if present
(From Cipollini, Cromwell, Quartly and Challenor, 2000)
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Comparison with theoretical Rossby wave models
Comparison with theoretical Rossby wave Comparison with theoretical Rossby wave modelsmodels
Altimetry gave the first chance to test the theoretical models
Discrepancies discoveredLed to refinement of theory (Killworth & Blundell)
Improved understanding
Importance of Rossby wave measurements
Slow east-west information feedbackPotential role in climate processes
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Westward propagating waves detected in the SST anomaly field
Westward propagating waves detected Westward propagating waves detected in the SST anomaly fieldin the SST anomaly field
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Rossby Wave speedsRossby Wave speedsRossby Wave speedsDetected independently by height, SST and colour signature
and compared with theoretical models
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Rossby Wave speed v. latitudeRossby Wave speed v. latitudeRossby Wave speed v. latitude
from Hill, Robinson & Cipollini, JGR, 2000
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Tropical Instability WavesTropical Instability WavesTropical Instability WavesTMI (microwave)-derived SST, 17-19 Sep, 2001
From Caltabiano PhD thesis, NOCS, 2005
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TMI (microwave)-derived SST, (3-day maps ~ Sep)
From Caltabiano PhD thesis, SOC, 2005
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Hovmöller Plots from TMI From Caltabiano PhD thesis, SOC, 2005
Tim
e →
Deg C
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Filtered Hovmöller Plots from TMI
TropicalInstability
Waves (TIW)
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El NiñoEl NiñoEl NiñoA change in the ocean-atmosphere pattern over the Pacific ocean – recurs after 3 – 7 yearsClear example of atmosphere-ocean feedback
Interesting scientificallyIt is not in itself an effect of global warming - though it may be affected by it
Has large, global human and economic consequences
1982-3 event had estimated insurance costs in $ billions due to changes in global weather patternsLeads to excessive rainfall or droughts (and drought-related wildfires) in parts of globe with consequent human impactsAffects marine living resources
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The El Niño process (1) Normal tropical Pacific conditions
The El Niño process The El Niño process (1) Normal tropical Pacific conditions(1) Normal tropical Pacific conditions
Westward blowing winds (easterlies) along equator generate a current that results in warmer water at the western side (near Indonesia; so-called “warm pool”)
Temperature gradient east-west of ~6-7˚ (~23˚→ ~29˚)Sea surface higher in west than eastThermocline deeper to west, shallower to east → upwelling in east → supply of nutrients → growth of plankton → fish“warm pool” leads to increased atmospheric convection and precipitation in the west (Note: precipitation and evaporation affect sea surface salinity)
West East
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The El Niño process (2) El Niño conditionsThe El Niño process (2) El Niño conditionsThe El Niño process (2) El Niño conditionsWestward blowing winds weaken along equator allowing warm water to propagate from west to east (equatorial Kelvin wave) → a positive feed back effect
Temperature gradient east-west is almost non-existentSea surface higher in east than westThermocline shallows in west, deepens in east → suppresses upwelling in east → less nutrients → decreased plankton → less fish“warm pool” moves east leading to increased atmospheric convection and precipitation in the mid-tropical Pacific
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The El Niño process (3) La Niña conditionsThe El Niño process (3) La Niña conditionsThe El Niño process (3) La Niña conditions
Westward blowing winds intensify along equator pushing warm water to back to west from eastTemperature gradient east-west re-established, but cool “tongue” extends further into central tropical PacificSea surface now lower across central tropical PacificThermocline deepens in west,shallows in east →increased upwelling in east → more nutrients →increased plankton → more fish“warm pool” moves west leading to increased atmospheric convection and precipitation beyond Indonesia (overshoot)
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ATSR SSTsEvidence of El
Niño in Bi-monthly ATSR SST record
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Evidence of El Niño in SST AnomaliesEvidence of El Niño in SST AnomaliesEvidence of El Niño in SST Anomalies
1997-011997-021997-031997-041997-051997-061997-071997-081997-091997-101997-111997-12 1998-011998-021998-031998-041998-051998-061998-071998-081998-091998-101998-111998-12
0 1 2 3 4 5-1-2-3-4-5Degrees C relative to climatological mean
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ConclusionConclusionConclusionGlobal maps of SST reveal a variety of large scale ocean features
Sometimes special processing is needed to make the phenomena stand out clearly
El Niño has a strong SSTanomaly signature
The big challenge is to use the information available from satellites to constrain short-term climate forecasting models
There is plenty of scope for more research
Question: How should we set about detecting global warming in the global SST data ?