Summer 2011: Observing the Diurnal SST cycle

over the Mediterranean Sea

Salvatore Marullo

ENEA: Technical Unit Development of Applications of Radiations, Satellite Oceanography Laboratory, Frascati, Italia

Rosalia Santoleri, Daniele Ciani

Gruppo di Oceanografia da Satellite, CNR-Istituto di Scienze dell'Atmosfera e del Clima, Roma, Italia

Pierre Le Borgne, Sonia Pere

Meteo-France/DP/CMS, Lannion, France

Nadia Pinardi

Department of Environmental Sciences, University of Bologna, Ravenna, Italia

Marina Tonani

Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italia

THE 44th INTERNATIONAL LIEGE COLLOQUIUM ON OCEAN DYNAMICS, Liège, University Campus, 7 to 11 May 2012

1. To Reconstruct the hourly Sea Surface Temperature diurnal cycle over the Mediterranean Sea using numerical model analysis and geostationary satellite measurements.

2. To Evaluate Errors and investigate their

causes

3. To better Understand the Physics of

diurnal warming events

Objectives:

Reconstructing the hourly SST field:

The Mediterranean Optimal Interpolation (OI) Schema

Input: SEVIRI SSTs (O&SI SAF Product) every 1 hour at 0.05 deg resolution

SEVIRI SST anomalies: Subtract the MyOcean Mediterranean model hourly SST to SEVIRI SST

Time Window: Select SEVIRi SST anomalies in a window of ± 24 hours

Run Optimal Interpolation

Add the Model SST of the Central interpolation Time

The first guess: MyOcean Mediterranean Model

The Mediterranean Forecasting System running at INGV is producing every day a new ten days of physical forecast for the Mediterranean Sea. The analyses for the previous fifteen days are produced once a week. The model resolution is 1/16x1/16 degree and 71 unevenly spaced vertical levels. It is the operational nominal product for the Mediterranean (Tonani et al 2010)

Model: NEMO (Nucleus for European Modelling of the Ocean-Ocean PArallelise) version 3.2 off-line coupled with WAM (Wave Analysis Model) Resolution:1/16 deg. x 1/16 deg. horizontal resolution and 71 unevenly spaced vertical levels (Oddo et al., 2009) Assimilation: sea level anomaly, sea surface temperature, in situ temperature profiles by VOS XBTs, in situ temperature and salinity profiles by ARGO floats, and in situ temperature and salinity profiles from CTD (Dobricic et al. 2008). Satellite OA-SST data are used for the correction of surface heat fluxes

A Diurnal Warming Event on July 7th 2011 M

yO

cean M

odel

Hourly O

ISS

T

SE

VIR

I H

ou

rly

°C

Evaluating Errors

Our first attempt was to validate the optimally interpolated diurnal cycle using moored buoy data

provided by MyOcean in situ TAC.

Most of the buoys are very close to

the coast!

As expected, RMS and Bias are larger near the coast.

The Interpolation does not produces significant variation of the basic statistical

parameters of the difference between in situ (buoys) and remotely sensed data.

Bias RMSE R N MyOcean Model -0.35 0.58 0.965 2052

SEVIRI Valid Pixels 0.14 0.56 0.954 992

SEVIRI Interpolated -0.01 0.45 0.984 1060

Buoy # 61002, Depth = ?, Lon=4.7 °E, Lat=42.1 °N, Distance from the coast = 28 Grid points ≈ 85 Nm

Distances are in nm

MTM spectrum of the detrended sea surface temperature

measured by buoy (a), SEVIRI (b) and the MyOcean

Mediterranean Model (c). The associated 90%, 95% and 99%

significance levels are shown by the three smooth curves In

blue, green and red respectively. The band-width parameter is

p=2, and K=3 tapers were used. Periods that pass the 99%

confidence limit are also indicated.

A second attempt….. Using drifters

Summarizing: The Model tends to overestimate drifters SSTs by 1-2 tents of degrees. This bias is due to a night/afternoon/morning bias. SEVIRI tends to underestimate drifters SSTs by 1-2 tents of degrees. The OI interpolation do not significantly change the basic statistics parameters of the differences.

SEVIRI Model

Bias -0.16 0.15

STD 0.49 0.58

R 0.9707 0.9591

N 3336 3336

SEVIRI Model

Bias -0.06 0.14

STD 0.57 0.60

R 0.9567 0.9531

N 485 485

Valid

Pix

els

In

terp

ola

ted P

ixels

The mean Diurnal Cycle over the matchups points

Comparing Model and Satellite SSTs

The Mean SST diurnal cycle as seen by MyOcean Model and the SEVIRI data

SST

An

om

aly

1. The amplitude of the model cycle is less intense than the corresponding SEVIRI, OI and drifters amplitudes due to the different thickness of the surface ocean layer they represent.

2. The model SST seem delayed respect to the satellite SST by 1-2 hours for the same reason.

Only valid Pixels Only Interpolated Pixels

1. The time shift can be partially be ascribed to the different packaging of the SST data in to the hourly files: MyOcean model hourly SSTs are given as the average of data between 00:00 and 00:01, 01:00 and 02:00 ……. While SEVIRI SSTs are given as the best SST measure within time intervals of one hour centered over each single hour of the day (01:30 and 02:30, 02:30 and 03:30 ……)

Model 00:00-01:00 01:00-02:00 02:00-03:00 03:00-04:00

SEVIRI-SAF 23:30-00:30 00:30-1:30 01:30-02:30 02:30-03:30

Evaluating the capability of model and satellite SSTs to catch Diurnal Warming (DW) events over the Mediterranean Sea

We arbitrary defined DW those event where the difference between the

actual, daytime, SST and the mean SST of the previous night is greater than

0.5 °C.

The mean of the previous night SST was defined as the mean of all the valid

(eventually interpolated) SST measurements between midnight and the time

when the solar zenith angle becomes less that 90°.

DT = Actual hourly SST - Mean SST of the previous night

The operational Model cannot reproduce the extreme

diurnal warming events that occurred in the Mediterranean

Sea during Summer 2011.

MyOcean Model

SEVIRI Data

Understanding the physical mechanism that

modulates amplitude of the diurnal cycle

South Tyrrhenian Sea: GOTM Simulation from

July 1st to 10th

1. MyOcean Mediterranean Model (NEMO) used as Initial conditions for

temperature and salinity profiles

2. Heat and Momentum Fluxes from:

ECMWF (as used by the MyOcean Mediterranean Model)

Turbulent fluxes as GOTM but Bignami (1995) for LW and Reed

(1977) for SW

Turbulent fluxes as GOTM but SAF products for LW and for SW

ECMWF Meteorological data digested by GOTM

1. Several turbulence closure

2. Grid zooming applied at the surface

WIN

D S

TR

ES

S

HE

AT

LO

SS

S

HO

RT

WA

VE

Momentum (1 case) and

Heat fluxes (4 cases) Turb.

Method TKE

equation Model dissip. scale

Stability function

second-order model (TKE_3_2_8_3)

dynamic equation (k-epsilon style)

dynamic dissipation rate equation

Schumann and Gerz [1995]

turbulence Model calculating TKE and length scale TKE_2_2_8_1

dynamic equation (k-epsilon style)

dynamic dissipation rate equation

constant stability functions

turbulence Model calculating TKE and length scale TKE_2_2_3_1

dynamic equation (k-epsilon style)

Xing and Davies [1995]

constant stability functions

turbulence Model calculating TKE and length scale MY_2_3_9_1

dynamic equation (Mellor-Yamada style)

dynamic Mellor-Yamada q^2l-equation

constant stability functions

Turbulence Models Investigated

Simulation using Bignami and Reed for

radiative fluxes

Simulation using SAF products for radiative

fluxes

Simulation using fluxes computed by GOTM

Simulation using fluxes used by MyOcean

Mediterranean Model

SEVIRI OISST

Detrended SSTs From GOTM and SEVIRI OISST

GO

TM

SS

T+

SE

VIR

I Gotmturb.nml configuration (2,2,3,1)

Turb method: turbulence Model calculating TKE and length scale

Type of equation for TKE: dynamic equation (k-epsilon style)

Type of model for dissipative length scale: Xing and Davies [1995]

Type of stability function: constant stability functions

Meteorological data (U10m, V10m, T2m,

D2m, TCC, MSLP) from ERA interim, Daily

Fields every 6 hours http://data-portal.ecmwf.int/data/d/interim_daily

SST From SAF (every 1 h)

Fluxes using Bignami and Red

Fluxes using SAF SWR and LWR

Fluxes computed by GOTM

Fluxes used by MyOcean Mediterranean Model

WIN

D S

TR

ES

S

HE

AT

LO

SS

S

HO

RT

WA

VE

MED-MFC FLUX

ECMWF+SAF ECMWF+Bignami+Red

Meteo data

TKE_3_2_8_3 0.490 0.597 0.732 0.747

TKE_2_2_8_1 0.369 0.430 0.515 0.555

TKE_2_2_3_1 0.363 0.416 0.499 0.528

MY_2_3_9_1 0.424 0.547 0.620 0.798

RMS between GOTM simulated SSTs (detrended) obtained using

several configuration and air-sea fluxes and SEVIRI SST (K)

Zooming

over

the first 2 meters (TKE_2_2_3_1)

Jul 3

rd

Jul 7

th

Big

na

mi+

Re

d

SA

F L

W &

SW

R

GO

TM

Flu

xe

s

MyO

ce

an

Flu

xe

s

July 3rd 2011 (4 am to 3 pm) July 7th 2011(4 am to 3 pm)

Drifters depth

Mooring depth

-0.0

-0.4

-0.8

-0.0

-0.4

-0.8

0.0

0

0.0

5

0.1

0

0.1

5

0.0

0

0.0

5

0.1

0

0.1

5

0.0

0

0.0

5

0.1

0

0.1

5

0.0

0

0.0

5

0.1

0

0.1

5

0.0

0

0.0

5

0.1

0

0.1

5

0.0

0

0.0

5

0.1

0

0.1

5

0.0

0

0.0

5

0.1

0

0.1

5

0.0

0

0.0

5

0.1

0

0.1

5

T(z)-T(z=0) Time (UTC)

GOTM Temperature difference between 0.025 m and

0.25 m of depth

Conclusions ①The optimal interpolation schema is able to correctly

reconstruct the diurnal SST cycle including sub-diurnal

components and diurnal warming events.

②The amplitude of the diurnal cycle produced by the MyOcean

Mediterranean Model is less intense than the corresponding

SEVIRI, OI and drifters amplitudes due to the different thickness

of the surface ocean layer they represent.

③1-D models represent an useful tool to investigate the upper

ocean response to air-sea interactions at daily and sub-daily

frequencies.

④ A difference between satellite a buoys (drifters and moored)

SST must be expected as consequence of air-sea exchanges.

⑤ This imply that the “zero bias” goal is not the correct

approach to follow.

⑥ The contribution of dedicated in situ experiments will be very

important.