Satellite SST in Coupled Data Assimilation
Chris Old and Chris Merchant
School of GeoSciences, The University of Edinburgh
ESA Data Assimilation Projects, Working Meeting
19th Februaty 2013, The University of Reading
Satellite SST in coupled data assimilation
Task 3: Evaluating observation strategies
The key objective of this task is to review the correlated variability of surface oceanic
and atmospheric datasets, both within the coupled model systems and with the
observations themselves. These results will provide input to the development of a set
of test experiments (Task 4), by identifying suitable test periods and data sets.
Deliverable:
KO+ 9 months D6 (initial): A current reanalysis assessment report identifying case
study periods and showing areas where coupled model improvements may be
expected
Introduction
1
Satellite SST in coupled data assimilation
WP3.1 Consistency of current surface ocean and atmospheric analysis products
Use satellite measurements as a baseline to examine any inconsistencies within the
products, e.g. regions where there are discrepancies in SSTs between atmosphere and
ocean products, such as regions where strong (or weak) SST diurnal cycles are
detected but where the analysed surface wind speeds seem too high (or low).
The space-time sampling of satellite data will be assessed against spatial and
temporal variability of the ocean and atmospheric reanalysis fields. This sampling
uncertainty will be used in combination with uncertainty components from other
sources developed in the ARC SST and SST CCI projects.
Introduction
2
Relevant Science Questions
What are the processes that are expected to be improved through CDA?
Are these processes significantly affected by the way in which SSTs are used?
Does the SST remain a prescribed boundary condition in CDA, i.e. does the skin
represent an internal boundary to the coupled model?
Will SSTs actively evolve in the CDA system?
Are there oceanic processes that will have a greater impact in a fully coupled system?
Answers to these question will help define what constitutes suitable test case
senarios.
Introduction
3
Expected regions of inconsistency
Diurnal variability in SST.
Thin surface layer effect, generally sub-daily (can persist overnight).
Hurricane driven rapid overturning of mixed layer.
Deep effect, anomalies persist up to 30 days.
MJO related regions of air-sea interactions.
Short time scales needed to get seasonal process right. Periodicity 30-70 days.
Geographically interesting regions:
1. Upwelling regions
2. Western boundary current extensions (Kuroshio, Gulf Stream)
3. Agulhas current retroflection
4. Californian Current
5. Leeuwin Current
6. Meddies
Introduction
4
Diurnal SST
Instantaneous air-sea heat flux
modified by up to 50 Wm-2.
Amplitude of cycle observed up
to ~6K.
dSST excursions spatially and
temporally coherent.
dSST event magnitude and
horizontal length scale anticorrelated.
Extremes where low wind
speed sustained from early
morning to mid-afternoon.
Observed dSST between 9AM and 2PM on 2nd June 2006. Data
from the SEVIRI instrument on MSG2.
Observations
(Gentemann et al., 2008;
Merchant et al., 2008)
5
Hurricane SST Modification
SST modification by passage of hurricane Katrine, August 2005. (Time series from AMSR-E SST product)
SST can drop by up to 6⁰C in hurricane wake (Nelson, 1997)
Hurricane wake can be > 200km wide (Nelson, 1997)
Rapid cooling within 6 hours of hurricane passage (D’Asaro et al., 2007)
Restratification has e-folding time of 6 to 30 days (Price et el., 2008)
Observations
6
Madden-Julian Ocsillation (Boisseson et al., 2012)
Major mode of intraseasonal variability in tropical atmosphere.
Periodicity of 30-70 days.
Influences monsoons, evolution of El Nino, weather regimes of NA Europe in winter.
One of main benchmarks for skill of extended-range forecast systems.
There is a strong SST-MJO feedback through latent and sensible heat fluxes.
Studies show simulation of MJO needs accurate representation of air-sea interactions
through good representation of intraseasonal variability and of diurnal SST cycle.
Reduced forecast skill occurs when have incorrect phase relationship between SST
and MJO convection.
Coupled models shown to give improved phase relationship for MJO.
Key indicator is phase relationship between SST and outgoing longwave radiation.
Observations
7
ECMWF ERA Interim (Dee et al., 2011)
Primary requirement of reanalysis is that it represents the available observations.
Multivariate reanalysis must exhibit physical coherence, i.e. estimated parameters
must be consistent with the laws of physics as well as with the observations.
Data assimilation is 12 hourly using 4D-Var.
System is evolved using the ECMWF IFS with a 30 minute time step , 60 model layers
(TOA at 0.1 hPa), and a spectral T255 horizontal resolution (~79km on reduced
Gaussian grid).
The skill and accuracy of the forecast model determines how well the assimilated
information can be retained.
Archive contains 6-hourly gridded estimates of 3-D meteorological variables and 3hourly estimates of many surface parameters and other 2-D fields.
Reanalysis Fields
8
SST in ERA Interim Reanalysis
SST are required as a boundary condition for atmospheric forecast model.(IFS does
not incorporate its own analysis of SST fields).
Data assimilation process is constrained by covariance of errors in observations (R).
R accounts for measurement errors and the inability of the model to represent smallscale information contained in some observations.
This means spatial and temporal variability that the finite model cannot resolve are
considered to be observational errors rather than model limitations (i.e. model
uncertainty).
The prescribed SST fields are generated from in situ and satellite observations.
The temporal resolution of the underlying SST data varies over the reanalysis period
from monthly to weekly to daily.
Monthly and weekly data are linearly interpolated to give daily mean values.
Reanalysis Fields
9
Daily SSTs For Early Reanalysis Period
The early part of the ERA Interim Reanalysis was forced using monthly and weekly
SST records linearly interpolated to daily mean SST values.
ARC/CCI SSTs provides a high spatial and temporal resolution climate quality SST
record back to the early 1990s.
This record could be compared with the prescribed SST used to set the boundary
condition for the reanalysis to identify any areas where there are significant
differences in the daily SSTs.
Given the relationship between SST and convection over the ocean, incorrect SSTs
could account for reanalysis producing less cloud over the ocean than observed.
In the coupled system this will lead to increased solar heating of the oceans surface
layer.
Other Possibilities
10
Observation Error Covariance
Comment from Coupled Data Assimilation Workshop:
An understanding and quantification of satellite SST error covariance length scales in
time and space can be used to constrain the coupled assimilation of surface fields.
Little work has been done to date to quantify satellite based SST error covariance
length scales.
Propose that preliminary work be started on SST error covariance as part of this work
package.
A methodology was proposed at the December 2012 progress meeting.
Other Possibilities
11
Where Do We Place Effort?
Consider with respect to the CDA systems being developed within this project.
1. Diurnal SST variability
What is the relevance to CDA system?
Is the impact on MJO relevant in the context of this project?
2. Air-sea interactions
Will CDA system be sensitive to hurricane wakes?
Will CDA system respond to upwelling/wind driven processes?
3. Small scale SST patterns
Are small scale patterns relevant to a 1-D CDA system?
Will CDA system take advantage of high res SST gradients for air-sea
interaction?
4. SST error covariance
Will spatial/temporal SST error covariance affect the CDA?
We need to agree on what is most relevant to this project in terms of developing case
studies to test the CDA systems.
CDA Queries
12
13
Observed - Modelled dSST – 2nd June 2006
Case 1: Wind speed too large
Observed – Modelled dSST difference ( K )
Case 2: Wind speed too low
Case3: Wind field offset
Extra Info
14
Simulated Temporal Correlation scales
r ~ ( 1/e time scale ) / days
Extra Info
15
Simulated Spatial Correlation scales
Zonal
Meridional
Approximation to r ~ ( 1/e length scales ) / km
Extra Info
16