Satellite SST in Coupled Data Assimilation

1. Satellite SST in Coupled Data Assimilation Chris Old and Chris Merchant School of GeoSciences, The University of Edinburgh ESA Data Assimilation Projects, Working Meeting 19thFebruaty 2013, The University of Reading

2. 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

3. 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

4. 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

5. 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: • Upwelling regions • Western boundary current extensions (Kuroshio, Gulf Stream) • Agulhas current retroflection • Californian Current • Leeuwin Current • Meddies Introduction

6. 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 anti-correlated. Extremes where low wind speed sustained from early morning to mid-afternoon. (Gentemannet al., 2008; Merchant et al., 2008) Observed dSST between 9AM and 2PM on 2nd June 2006. Data from the SEVIRI instrument on MSG2. Observations

7. 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

8. 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

9. 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 3-hourly estimates of many surface parameters and other 2-D fields. Reanalysis Fields

10. 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 small-scale 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

11. 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

12. 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

13. 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

15. 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

16. Simulated Temporal Correlation scales r ~ ( 1/e time scale ) / days Extra Info

17. Simulated Spatial Correlation scales Zonal Meridional Approximation to r ~ ( 1/e length scales ) / km Extra Info