Time mean temperature increments from ocean data assimilation systems

Transcription

1 Time mean temperature increments from ocean data assimilation systems Mike Bell, Matt Martin, Drew Peterson (Met Office), Magdalena Balmaseda (ECMWF), Maria Valdivieso (University of Reading) April 2016

2 Content Motivation and aims Data and (potential for) methods Preliminary results Discussion and conclusions

3 Motivation (1) 2005 Argo has provided almost global coverage of T, S profiles to 2000 m depth every 10 days Satellites have also provided high resolution surface temperature and surface height data Atmospheric analyses in this period are also relatively reliable So we would expect ocean data assimilation systems to provide accurate re-analyses of T and S during this period

4 Motivation (2) We can use these analyses to assess ocean model systems: start model integrations from the analyses subtract final model state from the analyses We can then explore development of system errors in the first few months of the integrations before feedbacks complicate the assessment and examine seasonal and geographical variations Seasonal forecast systems naturally produce suitable integrations for study

5 Motivation (3) - Assimilation increments T(t) Assimilation window assimilation increments re-analysis observations t If the assimilation scheme is working well the time-mean assimilation increments should be equal and opposite to the model system s mean errors

6 Aims Diagnose ocean model system biases work out how to reduce them! Diagnose performance of ocean assimilation systems work out how to improve them! Assess suitability of ocean assimilation systems for seasonal predictions simulations of ocean heat uptake

7 Data GloSea5 forecast biases and assimilation increments ORCA025, N216 (MacLachlan et al 2014) DJF season only: for period forecasts started 1 Nov; analyses use ERAi forcing DJF mean of forecasts minus analyses GloSea5 assimilation increments (Waters et al 2014) All seasonal means: for period for vertical means ( equivalent to surface fluxes) as a function of depth ECMWF ORAS4 (1 o : Balmaseda et al. 2013) and ORAP5 (1/4 o : Zuo et al. 2015) for 2009 only

8 Potential for methods Compare results from different assimilation systems (e.g. CMCC, ECMWF, Mercator, Met Office, NCEP GODAS) Compare results from ocean models of different resolutions 1 o, 1/4 o, 1/12 o global or regional systems Perform sensitivity experiments, e.g. withhold some types of observational data run without surface wind stresses or heat fluxes

9 GloSea5 DJF forecast biases and assimilation increments (K/day) K/day 0.05 K/day

10 GloSea5 DJF forecast biases and assimilation increments (K/day) 50 S 50 N

11 GloSea5 DJF forecast biases and assimilation increments (K/day) K/day 0.05 K/day

12 GloSea5 assimilation increments ( ) (K/day) at 300 m depth DJF MAM JJA SON -0.1 K/day 0.1 K/day

13 GloSea5 assimilation increments vertically integrated: DJF MAM JJA SON -500 Wm Wm -2

14 ECMWF assimilation increments (2009 only) vertically integrated: Frontal patterns regions from ORAP5 similar to GloSea5 Magnitudes are somewhat smaller (assimilation windows are 10 days for ORAS4, 5 days for ORAP5, 1 day for GloSea5) ORAS4 (1 o ) ORAP5 (1/4 o ) -200 Wm Wm -2

15 Vertical means of advective heating by timemean and time-varying flow (Griffies et al. 2015) CM2.5 and CM2.6 (years ) 1 o 1/4 o 1/10 o Heating by advection of time-mean u. T -200 Wm Wm -2 Heating by advection of time-varying components u'. T'

16 Discussion Fronts seem to slump / weaken initially in many regions would this be true in higher resolution models? Assimilation increments are cold near the surface in all seasons globally need to confirm this is due to SST assimilation Net vertically integrated heat inputs from data assimilation are large compared with standard estimates of surface heat fluxes

17 Conclusions The Argo period is already long enough to reveal major biases in 1/4 o global ocean forecast systems Comparison of different assimilation systems and different model resolutions would be worthwhile The method for assimilation of SST data needs to be explored further