Skin SST assimilation using GEOS Atmospheric Data Assimilation System

Transcription

1 Skin SST assimilation using GEOS Atmospheric Data Assimilation System Santha Akella Collaboration with: Ricardo Todling and Max Suarez Global Modeling & Assimilation Office NASA CDAW 2016 (October, 18, 2016) 1 gmao.gsfc.nasa.gov

2 GMAO COUPLED DATA ASSIMILATION PLAN: INTEGRATED EARTH SYSTEM ANALYSIS (IESA) GMAO IESA Next Target MERRA-2 (2015) Atmosphere, Ozone, Aerosols, Ocean (& sea-ice), Land, Chemistry Atmosphere, Ozone, Aerosols Continental Shelves, Ice Shelves,... Merra2Ocean, Merra2Chem Coupling: Crosscomponent observational feedback MERRA (2009) Atmosphere, Ozone Coupled Components 2 MerraAero, MerraOcean, MerraLand, MerraChem Uncoupled Components in analysis/ forecast gmao.gsfc.nasa.gov

3 ATMOSPHERE-OCEAN CDA Background fields AOGCM Weakly coupled analysis: separate atmosphere & ocean analyses (for e.g., separate B & H operators) Strongly coupled analysis: single analysis, with B & H that are horizontally and vertically, fully-correlated an ob at ocean bottom can produce an increment in stratosphere! Why opt for weak coupling? Latency of ocean obs Slower time-scale of (deep) ocean Direct assimilation of satellite radiance observations requires surface-ocean; not so much the deep ocean 3 gmao.gsfc.nasa.gov

4 GMAO WEAKLY COUPLED AO-CDA (PLAN) Coupling AGCM A-ANA OGCM O-ANA Prescribed No analysis for No diurnal cycle Prescribed atmosphere None ocean-surface skin SST Exclude diurnal obs Analysis for Semi- Prognostic ocean-surface as above as above skin SST Prognostic ocean-surface Resolve diurnal cycle Analyzed atmosphere Weak as above above analyzed ocean Include run-off Include diurnal obs 4 gmao.gsfc.nasa.gov

5 BACKGROUND: SST VARIABILITY (THERMAL STRATIFICATION) I, I I I I Near-surface Temperature is actually T(z); T(z~0) Skin SST changes with z depends on the atmospheric conditions Infrared Z ob ~0 Microwave Z ob ~O(mm) E8 6-5 g-10- n A. A: 9 profiles ~7m/s during wind 7-m/s wind speed (shifted -0.5%) B: 10 profiles during 2.5-m/s wind speed B. ~2.5 m/s C: 5 profiles during calm weather C. calm in situ Z ob ~O(cm)- O(m) 18- A B C A I3 C Soloviev & Lukas (1997) Deep Sea Res. Vol 1 I I I I I I Temperature ( C)

6 BACKGROUND: SST VARIABILITY (DIURNAL) 2-4 o K Night Time/ Strong Wind Day Time, light Wind Diurnal Warming aliases on SST -Climate 6 Implication

7 SKIN SST (T S ) IN GEOS ADAS Current Status Ts set based on SSTfnd (OSTIA SST) Net Heat Flux at surface: diagnostic Development Sub-system Implements Provides Relevance Prognostic Ts = SSTfnd + Diurnal Diurnal variability & Similar to the AGCM Model for Ts Warming - Cool Skin thermal stratification ECMWF- IFS Atmos Analysis of IR radiances Shared with NCEP, Ts Analysis Fit to observations Analysis (AVHRR addition) using CRTM Coupling of near- *Direct Radiance Ts Analysis AGCM Analysis Feedback surface ocean Assimilation Increment 7 temperature to atmosphere for SST

8 T S MODEL T (z) =SST fnd + T w (z) T c (z) OSTIA SST SkinSST T (z = 0) T w (z) = T max w [1 ( z d ) µ s ] Cool Skin Prognostic model T c (z) = T max c [1 ( z )] Diagnostic Diurnal T(z) Warming Diurnal warming: apple z apple d Cool skin layer: 0 apple z apple 8 gmao.gsfc.nasa.gov

9 T S T w = a (µ s,d,q # surf ) b (µ s,d,u surf,q # surf ) T w T c = f(u surf ) g(q # surf ) Cool Skin T w (z) = T max w [1 ( z d ) µ s ] Diurnal T(z) Warming T c (z) = T max c [1 ( z )] Diurnal warming: apple z apple d Cool skin layer: 0 apple z apple 9 gmao.gsfc.nasa.gov

10 T S ANALYSIS 1. Temperature profile: T(z) is used by the analysis to compute fit to the observations 2. Brightness temperature (T b ) and Jacobian (dt b /dt z ) are computed using CRTM with Z ob : A. Z IR = 15 microns approximate: dt z /dt s = 1 B. Z MW = 1 mm 3. Added AVHRR (infrared) observations (N-18, MetOp-A) channels 4 Items 1 & 2 impact analysis of all satellite radiance observations Observations: AVHRR is in addition to the existing set In situ SST (from buoys) are currently withheld - used for validation Ts is analyzed with the entire (upper-air) atmospheric analysis 10

11 T S INCREMENT AGCM has been enabled to apply the increment in Ts = (T ana s T bkg s )/(6 hours) at every time step handled like all other analysis increments it is applied to Ts in the air-sea interface layer 11 gmao.gsfc.nasa.gov

12 National Aeronautics and Space Administration RESULTS: from data assimilation cycled experiments 12 gmao.gsfc.nasa.gov

13 DIURNAL WARMING & COOL-SKIN SkinSST = OSTIA SST + T w T c SkinSST OSTIA SST T w T c + Apr, 2012 monthly mean Feb, 2015 monthly mean Skin - OSTIA SST ( o K) 13

14 BASIN AVERAGED DIURNAL VARIABILITY OSTIA 14

15 BASIN AVERAGED DIURNAL VARIABILITY- TROPICS OSTIA SST 15

16 BASIN AVERAGED DIURNAL VARIABILITY- TROPICS OSTIA 16

17 NEAR-SURFACE THERMAL STRATIFICATION Cool-Skin DiurnalWarming Air-Sea Interface T(z) Bottom of Interface Layer OSTIA SST 17 gmao.gsfc.nasa.gov

18 ANALYSIS OF AVHRR OBSERVATIONS Ch3 (night time) MetOp-A Monthly Mean O-B (before bias correction) ctl Exp: Less Bias Correction exp 18

19 ANALYSIS OF AVHRR OBSERVATIONS Ch3 (night time) MetOp-A Monthly Mean O-B (before bias correction) ctl Cool-skin is beneficial! Exp: Less Bias Correction exp 19

20 FIT TO IN SITU BUOYS (WITHHELD DATA) Monthly Mean Fit to Hourly measurement of Temperature at Zob ~ 0.2 m Drifting buoys SST (Feb, 2015) OBS-OSTIA SST OBS - EXP T(z) exp OSTIA OSTIA (%) Mean (K) Std Dev (K) T-WPAC (61) T-INDN (37) T-ATLN (24) T-EPAC (118) T-WPAC(61) T-INDN(37) T-ATLN(24) T-EPAC(118)

21 IMPACT ON IR (HYPER-SPECTRAL) OBS Similar positive benefit for IASI on MetOp-A 21

22 SUMMARY- SKIN SST IN GEOS ADAS Near-surface sea surface: thermally stratification due to diurnal warming a thin cool-skin layer Modeled Infrared Observations (AVHRR) measure Skin SST Assimilated GEOS Atmospheric Data Assimilation System provides: near-sea-surface temperature that is tightly coupled to the atmosphere better near-surface meteorology (improved O-B fit, forecast skill) 22

23 National Aeronautics and Space Administration CURRENT WORK (PLAN) 23 gmao.gsfc.nasa.gov

24 GMAO WEAKLY COUPLED AO-CDA (PLAN) Coupling AGCM A-ANA OGCM O-ANA Prescribed No analysis for No diurnal cycle Prescribed atmosphere None ocean-surface skin SST Exclude diurnal obs Analysis for Semi- Prognostic ocean-surface as above as above skin SST Prognostic ocean-surface Resolve diurnal cycle Analyzed atmosphere Weak as above above analyzed ocean Include run-off Include diurnal obs 24 gmao.gsfc.nasa.gov

25 COUPLED ASSIMILATION Weakly Couple Atmosphere & Ocean Data Assimilation Systems Using the coupled AOGCM Hourly T(z=1 m) T(z), Ts: Atmospheric DAS T(z), SSTfnd: Ocean DAS T(z>2 m) 25 gmao.gsfc.nasa.gov

26 COUPLED ASSIMILATION Isotherms ( o C) [10S, 10W] Weakly Couple Atmosphere & 10S 10W Ocean Data Assimilation Systems Coupling feedback- Impact on: Upper ocean, Mixed Layer Air-sea fluxes Predictability > 3 days Temperature ( o C) 1 m 5 m 10 m 20 m 26 26

27 Questions, Feedback, Suggestions Thank You! 27

28 EXTRA SLIDES 28

29 bers continue to overdevelop Katrina throughou EXTENSION TO HYBRID GEOS ADAS Current GMAO ADAS is a Hybrid analysis system: Deterministic Probabilistic (central) + (ensembles) 29

30 HYBRID ANALYSIS FOR T S Analyze for Ts using : Deterministic (central): Without the Ts prognostic model: persistent, large-scale For all ensemble members, Ts OSTIA SST errors Ensemble generated covariance Be(Ts) 0! Probabilistic (ensembles): First step: a realistic Be(Ts) flow dependent, smallscale errors 30 gmao.gsfc.nasa.gov

31 HYBRID ANALYSIS FOR T S Analyze for Ts using : Ensemble Mean & Std Dev Ts ( o C) Feb 28, 2015 Deterministic (central): persistent, large-scale errors Probabilistic (ensembles): flow dependent, smallscale errors Mean (contour), Std Dev (shaded) 31 gmao.gsfc.nasa.gov

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33 CHANGE NSW ET (ctl) SURFACE HEAT FLUX SW = SW IN (exp) 0 Qnet = SWnet net Hs Hl + LWnet ; LWnet = LWsurf net net Ts4 2 2 Diurnal Warming: Hs 2 3W/m ; Hl 8 10W/m 2 2 Cool-Skin: Hs 4! 2W/m ; Hl 10! 8W/m Similar change in LWnet. 9 / 16 33