Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et...

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APN Training: Satellite Altimetry Lecture #5 Research Professor Robert R. Leben Colorado Center for Astrodynamics Research University of Colorado at Boulder Sea Level Reconstruction Using Tide Gauges and Satellite Altimetry Jason-1

Transcript of Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et...

Page 1: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Sea Level Reconstruction Using Tide Gauges and Satellite Altimetry

Jason-1

Page 2: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Estimating Rate of Sea Level Rise

• Rates of sea level rise over the past several decades using either fully empirical or semi-empirical techniques have been published recently.• Church et al. (2004), Jevrejeva et al. (2006),Church et al.

(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009)

• Each technique uses satellite altimetry data, tide gauge data or some combination of the two.

• Over the period from 1950 to 2010, estimates of global mean sea level (GMSL) range from around 1.5 mm/yr to 2.1 mm/yr.

• Such studies often form the basis for projecting GMSL into the future.

Page 3: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Reconstructing Sea Level with CSEOFs

A procedure for reconstructing sea level has been implemented using CSEOFs in place of the more conventional EOFs.

1. Compute CSEOF loading vectors (LVs) from 16-year satellite altimeter record, 1993-2008, and truncate to 10 LVs to avoid overfitting of the tide gauge data.

2. High-pass filter tide gauge data using a 3-year window to retain signals well represented by the CSEOF LVs and to remove tide gauge datum artifacts.

3. Calculate a weighted least-squares fit of the LVs to compute the reconstructed PCs and time series.

Page 4: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

AVISO Sea Level Trends: 1993-2008

Page 5: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Reconstructed Sea Level Trends: 1950-2009

Page 6: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Global Mean Sea Level (GMSL)

Rate = 2.5 mm/year from 1950-2010

Page 7: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

• The CSEOF reconstructed trend and trend computed from the AVISO altimeter record show good agreement over the period from 1993 to 2010. • The GMSL trend computed from the CSEOF reconstruction over the

period from 1950 to 2010 is 2.5 mm/yr, higher than previous estimates over the same period.

• Caution must be used when interpreting GMSL trends computed from sea level reconstructions.• The GMSL obtained from reconstructions depends heavily on the tide

gauge editing and on the selected method of weighting for the reconstruction procedure.

• Ideally, regional clustering of tide gauges should be avoided by using equal area weighting (Merrifield et al. 2009).

• The effects of tide gauge editing and weighting on the results of reconstructions still needs to be better understood.

Page 8: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Empirical Orthogonal Functions (EOFs)

Page 9: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Signals and Noise

“The large amounts of data that are usually studied in climate exhibit a complex mixture of signal and noise. The purpose of statistical analysis is to disentangle this mixture to find the needle (the signal) in the haystack (the noise). The allegory with the needle in the haystack has two sides.

First, it is difficult to find the needle in the haystack. Second, after it has been found, it should be easily recognizable as a needle simply by looking at it. To identify a climatic signal, advanced techniques may be

required, but after its identification, the signal usually may be described by means of simple techniques such as composites, correlations, etc.”

- Von Storch & Frankinoul (1998)

Page 10: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

EOFs

A fundamental characteristic of ocean data is the high dimension of the variables representing the state of the system at any given time. Often it is advantageous to perform a subspace decomposition to split the full phase space into two subspaces, i.e. the signal subspace and the noise subspace.One such statistical method is Empirical Orthogonal

Functions. The roots of this method arose in the early 1900’s as

objective tests of intelligence were being developed. Although, later on several other fields independently developed the statistical techniques.

Page 11: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Scatterplot - a 2-point analysis

σuu µ uv µ vu σvv

Page 12: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Consider the Data Matrix - F

Page 13: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Computing EOFs with SVD

Basic Algorithm1. Put the data into a matrix, F, so that each row is a time series of the

data at a point and the columns are variables or spatial data at specific times.

2. Remove the mean from each row so that there is no time mean signal in the data set.

3. Use SVD to decompose the data into three matrices:F = U*S*VT

where:U = the left eigenvectorsVT = the right eigenvectorsS = singular valuesIn this example:U = the EOFs (spatial patterns)S*VT = the EOF time series or Principal Components (PCs).

Page 14: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Some Nomenclature

There is no consistent terminology for EOF and PC analyses. Here are some terms commonly used.EOFs may be called the loading vectors.principal

component loading patterns, principal vectors, or principal loadings.The EOF time series may be called eigenvector time

series, expansion coefficient time series, expansion coefficients, time coefficients, time components, principal component time series, principal component scores, principal component amplitudes, principal components, or scores.

We will call the EOFs the loading vectors (LVs) and the EOF time series the principal components (PCs).

Note that switching the association of time and space indices in the data matrix, F, does not make a difference in the SVD decomposition. The EOFs are now associated with VT, and

Page 15: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Percent of Variance Explained

The eigenvalues of the expansion are equal to the square of the singular values. To compute the percent of variance explained by a given EOF use the following formula:

% variance of component i = Si2 / trace(SST)

or% variance of all components = diag(S*ST)/ trace(SST)

SST is sometimes referred to as the communalities matrix.

Page 16: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Global SSH EOFs and PCs - Modes 1 & 2

Page 17: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Global SSH EOFs and PCs - Modes 3 & 4

Page 18: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Spatial Map of Percent Variance EOF 1

Page 19: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Cyclostationary EOFs

Page 20: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Non-Stationary Periodic Signals

The spatial patterns of many geophysical processes change in time at longer time-scales than their well-defined periods. For example, most annual cycles are not stationary and change amplitude over time.

In many studies, the signal associated with the annual cycle is removed prior to analyzing the data. The annual cycle is often not of scientific interest and certain types of

analysis can be hindered by the presence of this signal. Techniques commonly applied for removing the annual cycle do not

handle the time-dependent spatial patterns well. Simply removing the time-invariant annual cycle is often not sufficient.

Page 21: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Cyclostationary EOFs

To account for time-varying spatial patterns, Kim et al. (1996) introduced Cyclostationary EOFs (CSEOFs).In traditional EOF analysis, space-time data are

represented in terms of loading vectors (LVs) and principal components (PCs).LVs represent patterns or spatial variability in the dataset.PCs represent the temporal variability of the LVs.

The significant difference between CSEOF and EOF analysis is the time-dependence of the LVs. The temporal evolution of the LVs is constrained to be periodic with a

certain “nested period”.For the analysis of the annual cycle, this nested period is equal to one

year.

Page 22: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Computing CSEOFs

The method for computing CSEOFs is based on the theory of harmonizable functions.

CSEOFs are defined as products of Bloch functions and Fourier functions.

Several papers by K.-Y. Kim establish the theoretical basis of CSEOFs in addition to providing methods for efficiently computing CSEOFs. Kim et al. (1996); Kim and North (1997); Kim and Wu (1999); Kim and Chung (2001).

Page 23: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

CSEOFs of SSHA

CSEOFs are computed from the multi-mission AVISO altimeter dataset spanning the 16-year record from 1993-2008.

CSEOF #1 accounts for 35% of the variability in the dataset and reflects the annual SSH variability.

CSEOF #2 accounts for 17% of the variability in the dataset, which is associated with the El Nino/Southern Oscillation (ENSO) signal.

In total, just 17 CSEOFs account for 99% of the SSHA variability in the AVISO dataset.

Page 24: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

CSEOF #1 or “Annual” Mode

Page 25: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Animation of the “Annual” CSEOF

Page 26: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

January

Page 27: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

February

Page 28: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

March

Page 29: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

April

Page 30: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

May

Page 31: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

June

Page 32: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

July

Page 33: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

August

Page 34: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

September

Page 35: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

October

Page 36: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

November

Page 37: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

December

Page 38: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Annual signal contribution to GMSL

Page 39: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

CSEOF #2 or “ENSO” Mode

Page 40: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

MEI vs. CSEOF Mode 2 PC Amplitude

The Multivariate ENSO Index (MEI) is computed from a cluster analysis of sea-level pressure, zonal and meridional surface winds, SST, surface air temperature, and total cloudiness fraction of the sky from the COADs database.Correlation of the MEI with the CSEOF “ENSO” mode

amplitude is 0.92 over the 60-year time period 1950-2010.

Page 41: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

August 9, 1978 “Annual” Signal

Page 42: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

August 9, 1978 “ENSO” Signal

Page 43: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

August 9, 1978 Sum of Modes 3-10

Page 44: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Reconstructed Sea Level vs. Seasat Altimetry

Page 45: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Revisiting an Early Seasat Study

Haines (1991) – “Evaluation of Seasat and Geosat orbit and altimetry with application to long-term sea level changes in the North Pacific”, Ph.D. Thesis, Univ. of Colo, Boulder.

The thesis result was a single snapshot of the SSH changes between August 1978 and August 1987.

Geosat/Seasat crossover height differenced from Jul 27 – Aug 22 were used for the analysis…

… hence the mid-point date August 9 used for our reconstructions.

Page 46: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

August 9, 1978

Page 47: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Oceanographic Conditions: August 1978

August 1978 was nominally a weak La Niña year. This was confirmed by the 60 July/August MEI

values from the 1950-2009 record.In the 60-year MEI record the July/August 1978

MEI value ranks as the 20th “highest” of the 60 monthly values.

Page 48: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

August 9, 1987

Page 49: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Oceanographic Conditions: August 1987

August 1987 was an exceptionally strong El Niño month. This was confirmed by the 60 July/August MEI

values from the 1950-2009 record.In the 60-year MEI record the July/August 1989

MEI value ranks as 59th “highest” of the 60 monthly values.Only the July/August 1998 MEI value ranks

higher. That value was during the 1997/98 El Niño one of the strongest events in the 20th

century.

Page 50: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

SSH Difference August 87 minus August 78

Page 51: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

SSH Difference August 87 minus August 78

Page 52: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

SSH Differences August 1987- August 1978Haines (1991) Figure 4.3 (c) CSEOF Reconstruction

Representation of the large-scale SSH changes over the 9-year time period in the two completely independentdatasets show remarkable agreement.

Page 53: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Conclusions

Our brief revisit to Seasat has provided an initial assessment of the fidelity of global SSHA maps reconstructed by fitting patterns of satellite-derived sea level variability to tide gauge measurements. Although these comparisons are qualitative and

preliminary at this time, the overall agreement between the 1978 Seasat SSHA fields and, especially, the Geosat/Seasat 1987/1978 SSH differences in comparison to the corresponding reconstructed fields are very encouraging.

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APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Indonesia Region Sea Level Trends

Page 55: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

AVISO Sea Level Trends: 1993-2008

Page 56: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Reconstructed Sea Level Trends: 1950-2009

Page 57: Sea Level Slides - apnindonesia.files.wordpress.com file(2006),Rahmstorf et al. (2007),Grinsted et al. (2009),Merrifield et al. (2009) • Each technique uses satellite altimetry data,

APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Future Studies

Future studies are planned to use Seasat and Geosat altimeter data to assess the quantitative accuracy of the reconstructed times series.We also plan to exploit the reconstructed fields to

improve filtering of residual Seasat and Geosat orbit and pathlength errors.A validated dataset will allow us to study in detail the

long-term climate signals described by the sea level record.Please use our reconstructed dataset in your research.

We have given you a preliminary version of the dataset. Check with us for updates and for literature citation if you intend to publish a paper. [email protected]

mailto:[email protected]?subject=CCAR%20CSEOF%20Reconstructed%20Sea%20Level�
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APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder

Dedication/Acknowledgements

This paper is dedicated to George H. Born in honor of his 25 years of service as Director of CCAR. We thank him for his dedicated effort to advance satellite altimetry from the pioneering Seasat and TOPEX/Poseidon programs through to the present, ongoing, and planned operation satellite altimeter missions that are essential for monitoring the Earth’s climate system. Not too bad for a farm boy from Edna, Texas. This work was supported by NASA OSTM Science Team

grants NNX08AR60G and NNX08AR48G. BDH and RRL are deeply indebted to K.-Y. Kim for the great amount of assistance he has provided in the development of the CSEOF sea level reconstruction algorithms.

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APN Training: Satellite AltimetryLecture #5

Research Professor Robert R. LebenColorado Center for Astrodynamics Research University of Colorado at Boulder


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