A. Bonaduce, N. Pinardi Mediterranean Sea level reconstruction during the last century A. Bonaduce (1), N. Pinardi (2) (1) Centro Euro-Mediterraneo per.

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A. Bonaduce, N. Pinardi Mediterranean Sea level reconstruction during the last century A. Bonaduce (1), N. Pinardi (2) (1) Centro Euro-Mediterraneo per i Cambiamenti Climatici (Bologna, Italy) (2) University of Bologna, Environmental Science (Ravenna, Italy)

Reconstruct the sea-level signal in the Meditteranean sea during the last century using satellite altimetry and tide-gauge data Validate reconstruction results with altrimetry (basin scale) and tide-gauge data (locally) Estimate sea-level trends over a centennial time period Investigate the sea-level trend spatial and temporal variability in the Med. Sea during last century Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Objectives

Mediterranean Sea level reconstruction A. Bonaduce, N. Pinardi Sea Level (SL) from observations Data: Permanent Service for Mean Sea Level (PSMSL) in-situ Tide-gauge (1885 – ongoing; 93 stations) PSMSL Revised Local Reference (RLR) dataset (Woodworth & Player, 2003) RLR: tide-gauge datum history and a common denominator of 7000 mm below the average sea level (Klein & Lichter, 2009) 45 stations in Med during the period ongoing (satellite era)

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Sea Level (SL) from observations Data: remote-sensing Satellite Altimetry (Oct 1992 – ongoing) AVISO Delayed Time (M)Sea Level Anomaly (MSLA) (Pujol & Larnicol, 2005) merged product: 6 satellites Topex/Poseidon, Jason-1, Jason-2, ERS-1, ERS-2, Envisat ± 1 week; long wave correction, inverse barometer correction applied (and others..) satellite tracks cutted at 25 km from the coast to avoid reflection (AVSIO, 2011) Spatially homogeneus: regular grid 1/8° (-5 W E; 30 N - 46 N) (Ducet et al., 2000) *ECMWF : European Centre for Medium-Range Weather Forecast

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Sea Level (SL) from observations Methods: remote-sensing and in-situ SL signal decomposition = steric component (Mellor and Ezer, 1995) Satellite Altimetry signal Tide Gauge signal Inverse barometer effect (Dorandeu and Le Traon, 1999) (ECMWF analysis) Altimetry What we compare Tide gauge ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 )

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Sea Level (SL) from observations Methods: Satellite steric signal GRACE: Satellite Gravimetry -----> Sea Level Equivalent (Don Chumbers, 2006) (2002 – ongoing; 1° horiz. Resolution !! ) Steric Signal: Satellite Altimetry - GRACE (Garcia, 2006, Garcia, 2010) SATELLITE - GRACE [Garcia, 2010 time window]

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Sea Level (SL) from observations Results: low frequency SL variability 1993 – 2010 monthly data [cm] West Med: Marseille Central Med: Valletta Adriatic Sea: Trieste East Med: Hadera

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Sea Level (SL) Reconstruction Methods 1 st step: SVD of satellite altimetry data detrended (take the spatial info. from lead EOFs) GIA correction ICE-5G (Stocchi and Spada, 2009) 2 nd step: optimal interpolation (Kaplan, 2000) of tide gauge data over the satellite grid (take the temporal info. from tide gauge data) pre-processed as described before; GIA corrected ICE-5G model (Spada and Galassi, 2012, accept.) Result : SL with satellite spatial resolution during the tide gauge time window (> 100 yr) In Med 4 stations with > 100 year of data: Marseille, Trieste, Venezia, Genova

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Sea Level (SL) Reconstruction Methods : 1 st step “feature extraction” Z = data matrix n x m; where n = data point, and m = time steps U = left singular vectors (eigenvectors of cov. Matrix, EOFs ) L = singular values; V = right singular vectors SVD on altimetry data Considering only EOFs leading modes M (space reduction): Space dependentTime dependent EOFs(M) temporal amplitudes We want estimate instead Estimated from tide-gauge data TARGET

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Sea Level (SL) Reconstruction Methods : 2 nd step optimal interpolation Z0 = tide-gauge data; H = transfer operator (0 and 1); Λ = eigenvalues R = error covariance matrix (consider sampling error and cov. of truncated modes) Minimizing the cost function: P = estimate for error covariance in the solution Using this solution in the previous expression (TARGET) we can reconstruct sea-level field with altimetry spatial domain over tide-gauge time window

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi EOFs selection (Calafat, 2011) Methods Training period: computing EOFs Validation period: reconstruction period where EOFs are not computed X-axis = n° EOFs; Y-axes = RMSE [cm] Several reconstructions considering altimetry data in the tide-gauge positions and increasing n° EOFs considered. Variance (%)

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Tide-gauge stations used ( ) 8 sub-periods (15 years/each) choosing the most Complete time series Up to 6 months gaps: splines > 6 months gaps: linear fitting > 12 months gaps: discarded Red dots = discarded Green dots = used

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Results Reconstruction validation during the satellite era: Cor. patt. and RMSE Correlation Map RMSE [cm]

Sea level climate variability in the Mediterranean seaA. Bonaduce, Tutor: N. Pinardi Sea Level (SL) from observations Results: Satellite altimetry trend spatial variability SLA Trend 1993 – 2010 [mm yr-1] : 2.1 +/- 0.7 (seasonal signal removed; GIA correction applied )

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Results Reconstruction validation during the satellite era: Trend spatial variability Reconstructed sea-level trend 1993 – /- 0.5 mm yr-1 Trend difference Altimetry – Reconstr. (difference < 2 mm/yea are not shown )

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Results Reconstruction validation

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Results Sea-level Reconstruction 1993 – 2010 (monthly) 1885 – 2010 (annual)

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Results Sea-level Reconstruction – 2010 (annual) DJFM NAO index (Hurrel, 2009)

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Sea level reconstruction Results: Trend spatial variability over a centennial time period ( )

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Sea level reconstruction Results: Trend variations as function of period considered (Leibman et al., 2010) Overturning (25 years); Stable Positive (> 90 years) No signigicant sea-level trend (0.1 +/- 0.1 mm yr-1) x n° years Change in Annual Sea level

Mediterranean Sea level reconstruction A. Bonaduce, N. Pinardi Conclusions Sea-level reconstructions are a powerfull technique to merge the sea-level signal recorded by different instruments Comparing with satellite altimetry: reconstruction skill result very high along the coast and in the shallow water areas, while low correlation patterns have been found in deep water areas and where the mesoscale activity is high (i.e. Iera-Petra gyre) or present particular dynamics (i.e. Ionian reversal). Low number of EOFs considered affect reconstruction skill Locally reconstruction results are comparable with tide-gauge data during the past During the satellite era altimetry and reconstructed sea-level trends, return leading patterns The basin mean reconsrt. sea-level trend is 2.5 +/- 0.5 mm yr-1 Over a centennial time period is not possible to identify a significant trend (0.1 +/- 0.1 mm yr-1). Over a decadal time scale it is possible to observe an overturning of sea-level trend phases (positive and negative), while a stable positive trend is observable in the Med. Sea considering more than 90 years of data.

Mediterranean Sea level reconstructionA. Bonaduce, N. Pinardi Future work Unormalized and unrotated EOFs have been used to reconstruct the sea-level signal. Looking at the differences between altimetry and reconstruction, further analysis will be carried out using rotated EOFs (Richman, 1985) in order to account for the domain shape dependency and sub-domain instability. Other data “merging” methods will be analysed (i.e. OceanVar; Dobricic, 2008) in order to be able to consider an higher number of leading EOFs to reconstruct the Sea-level signal. Perform sea-level reconstruction using both satellite altimetry and Mediterranean Sea ocean re-analysis data (Adani et al., 2010)