Surface Fluxes from In Situ Observations

Transcription

1 Surface Fluxes from In Situ Observations Outline: Background / Sampling Problem Use of In Situ Observations to Produce Flux Datasets Use of In Situ Observations For Flux Dataset Evaluations Evaluation Pitfalls Some Outstanding Issues Surface Flux Reference Site example: the Southern Ocean Flux Station CLIVAR Energy Flow Workshop, 3 Sep 215, Simon Josey

2 Heat Flux Components: Drivers and Global Means Latent : wind speed x (sea-air humidity difference) Sensible : wind speed x (sea-air temperature difference) Longwave (-53) Latent (-77,-96) Shortwave (165,173) Shortwave: cloud cover dependent Longwave: cloud cover, atmospheric humidity and temperature, SST Sensible (-13,-14) Brackets show as an example the range of values from original and adjusted COREv2 dataset (Large and Yeager, 29)

3 In Situ Observations The Sampling Problem Flux datasets require knowledge of SST, near surface wind speed, air temperature and humidity (and cloud amount for radiative terms). January 2 July 2 All available ship reports with sufficient information (wind speed, SST, air temp and humidity) to calculate latent heat flux for 2 sample months. Major issue for reanalysis and blended flux datasets, in addition to ship based products. Not possible to reliably correct global fields using ship observations as a basis approach taken for example in CORE2 for humidity.

4 The Sampling Problem Meets High Resolution Models Ship meteorological variable observations 1/12o NEMO Surface Flux Anomaly Field (W/m2) In situ observations are much too sparse to define a 1/12o (or even 1/4o) model surface forcing field consequently flux anomalies likely to be set by model SST not atmospheric variability.

5 Use of In Situ Observations for Flux Dataset Production Ship based flux datasets: NOC1.1, NOC2, UWM/COADS (da Silva). Atmospheric reanalyses: ERA4, NCEP, ERA-Interim, JRA, MERRA, NCEP CFSR, 2CR. Satellite only fields: Various products, now span order 2 years. Satellite + reanalysis fields synthesis: OAFLUX. Hybrid products: CORE. OAFlux Blended DFS Reading Residual COREv2 IFREMER Reanalysis SEAFLUX J-OFURO GSSTF Remote Sensing HOAPS-3 ISCCP CFSR 2CR ERA-15 NCEP-1 NCEP-2 ERA-4 JRA ERA- MERRA Interim ERA2C JRA-55 In Situ UWM/COADS NOC1.1 2 NOC1.1a NOC Updated from: Josey, S. A., S. Gulev and L. Yu, 213: Exchanges through the ocean surface, in G. Siedler, J. Church, W. J. Gould and S. Griffies (eds): Ocean Circulation and Climate, Academic Press, International Geophysics Series, Volume 13, p

6 Use of In Situ Observations for Flux Dataset Production Ship based flux datasets: NOC1.1, NOC2, UWM/COADS (da Silva). Atmospheric reanalyses: ERA4, NCEP, ERA-Interim, JRA, MERRA, NCEP CFSR, 2CR. Satellite only fields: Various products, now span order 2 years. Satellite + reanalysis fields synthesis: OAFLUX. Hybrid products: CORE. Influenced by Reanalyses and/or In Situ Obs. Blended OAFlux DFS Reading Residual COREv2 IFREMER SEAFLUX J-OFURO GSSTF Remote Sensing HOAPS-3 ISCCP In Situ Obs. Assimilated Reanalysis CFSR 2CR ERA-15 NCEP-1 NCEP-2 ERA-4 JRA ERA- MERRA Interim ERA2C JRA-55 In Situ UWM/COADS NOC1.1 2 NOC1.1a NOC Updated from: Josey, S. A., S. Gulev and L. Yu, 213: Exchanges through the ocean surface, in G. Siedler, J. Church, W. J. Gould and S. Griffies (eds): Ocean Circulation and Climate, Academic Press, International Geophysics Series, Volume 13, p

7 Use of In Situ Observations for Flux Dataset Production Ship based flux datasets: NOC1.1, NOC2, UWM/COADS (da Silva). Atmospheric reanalyses: ERA4, NCEP, ERA-Interim, JRA, MERRA, NCEP CFSR, 2CR. Satellite only fields: Various products, now span order 2 years. Satellite + reanalysis fields synthesis: OAFLUX. Hybrid products: CORE. Influenced by Reanalyses and/or In Situ Obs. Blended Reading Residual SEAFLUX J-OFURO GSSTF IFREMER DFS COREv2 Independent of Reanalyses and/or In Situ Obs.??? Remote Sensing OAFlux HOAPS-3 ISCCP In Situ Obs. Assimilated Reanalysis CFSR 2CR ERA-15 NCEP-1 NCEP-2 ERA-4 JRA ERA- MERRA Interim ERA2C JRA-55 In Situ UWM/COADS NOC1.1 2 NOC1.1a NOC Updated from: Josey, S. A., S. Gulev and L. Yu, 213: Exchanges through the ocean surface, in G. Siedler, J. Church, W. J. Gould and S. Griffies (eds): Ocean Circulation and Climate, Academic Press, International Geophysics Series, Volume 13, p

8 Zonal Mean Net Heat Flux 7 6 Global zonal mean net air sea heat flux from: NCEP-1(red dashed), ERA-4 (red solid), NCEP CFSR (blue solid), Trenberth residual (black dashed), NOC1.1a (green solid), NOC1.1 (gray solid), NOC2 (gray dash-dot), UWM/COADS (green dashed), OAFlux/ISCCP (purple solid), and COREv2 (purple dashed). Large spread at a given latitude (3-5 Wm-2) reflects failure of many datasets to achieve closure. Leads to strong divergence in implied ocean heat transport Latitude Qnet (Wm-2) 5 1 From: Josey, S. A., S. Gulev and L. Yu, 213: Exchanges through the ocean surface, in G. Siedler, J. Church, W. J. Gould and S. Griffies (eds): Ocean Circulation and Climate, Academic Press, International Geophysics Series, Volume 13, p

9 Use of Heat Transport Obs. To Constrain Surface Fluxes Has a long history. Linear inverse analysis developed by Isemer, Willebrand and Hasse (1989) and employed by da Silva et al. (1994) using 2-3 constraints. Extended by Grist and Josey (23, GJ3) to constrain the NOC1.1 (formerly SOC) surface heat flux climatology using about 1 heat transport constraints from WOCE. Each of 4 heat flux components adjusted within reasonable uncertainty range. Global adjustments preferred to regional adjustments as discontinuities arise with regional adjustment. Problems adjustments led to reduced agreement with flux buoy data in key locations. Grist, J. P. and S. A. Josey, 23: Inverse Analysis Adjustment of the SOC Air Sea Flux Climatology Using Ocean Heat Transport Constraints. J. Climate, 16, doi:

10 Use of Heat Transport Obs. To Constrain Surface Fluxes Has a long history. Linear inverse analysis developed by Isemer, Willebrand and Hasse (1989) and employed by da Silva et al. (1994) using 2-3 constraints. Extended by Grist and Josey (23, GJ3) to constrain the NOC1.1 (formerly SOC) surface heat flux climatology using about 1 heat transport constraints from WOCE. Each of 4 heat flux components adjusted within reasonable uncertainty range. Global adjustments preferred to regional adjustments as discontinuities arise with regional adjustment. Problems adjustments led to reduced agreement with flux buoy data in key locations. Grist, J. P. and S. A. Josey, 23: Inverse Analysis Adjustment of the SOC Air Sea Flux Climatology Using Ocean Heat Transport Constraints. J. Climate, 16, doi:

11 Use of Heat Transport Obs. To Constrain Surface Fluxes Has a long history. Linear inverse analysis developed by Isemer, Willebrand and Hasse (1989) and employed by da Silva et al. (1994) using 2-3 constraints. Extended by Grist and Josey (23, GJ3) to constrain the NOC1.1 (formerly SOC) surface heat flux climatology using about 1 heat transport constraints from WOCE. Each of 4 heat flux components adjusted within reasonable uncertainty range. Global adjustments preferred to regional adjustments as discontinuities arise with regional adjustment. Problems adjustments led to reduced agreement with flux buoy data in key locations. Level of agreement with flux buoy data. Black = unadjusted net heat flux, grey = adjusted net heat flux (Solution 3). Grist, J. P. and S. A. Josey, 23: Inverse Analysis Adjustment of the SOC Air Sea Flux Climatology Using Ocean Heat Transport Constraints. J. Climate, 16, doi:

12 Surface Fluxes Reference Site Array Surface flux reference site measurements provide a key resource for reanalysis evaluation and, if used well, correction. Underused at present? Symbols indicate surface flux moorings Moorings restricted to Tropics but coverage reasonably good there. Should be possible to develop a best flux dataset for the Tropics but caution required (reanalysis assimilation impacts - Josey et al., 14). Extratropics has very few observations, more moorings needed, correction not currently possible.

13 Use of In Situ Observations to Characterise Air-Sea Flux Regimes 1 (a) SOFS surface flux mooring observations have provided first characterisation of airsea interaction in the Southern Ocean. 1 (a).5 Hs.5 1 (b) 3 (c) lwhs swn W Wm m 2 n Hl (b) 3 2 (c) (d) 2 m m 2 lwhnnet sww W n Hl Hs sw n Lat. & Sen. SW & LW 2 G m 2 HE WJm net E G J m 2.35 SOFS mooring location (black triangle). QuickSCAT ( ) mean wind vectors (arrows) and speed (color). Mean maximum winter ice (grey). lwn swn lwn Hl (d) 1 2 (e) Apr 3 May Jun Jul Range of extreme heat loss events. Net heat flux deployment mean is -1 Wm-2 i.e. the Southern Ocean at this location loses heat to the atmosphere. 1 Net Heat T ok Hs Hl W m 2 Tau N m 2 Tau N m 2 Aug 21 Sep Oct Nov Dec Jan Feb Mar Apr 1 Figure 2 Mean daily (lines) and monthly (circles) fluxes derived from SOFS 1-minute observations: (a) Wind stress,.35 (b) turbulent sensible (black) and latent (red) heat, (c) net long- (red) and short-wave (black) 1 radiation, and.7(d) net heat flux. Periods when the turbulent heat flux (sum of latent and sensible) exceeds 22 Wm are indicated by grey shading (in panel b). Heat fluxes are positive for ocean heat gain from the 3 (e) atmosphere. Panel (e) shows the integrated energy gain (E) by the ocean from the atmosphere (black line, T ok Schulz, E., S. A. Josey and R. Verein, 212: First Air-Sea Flux Mooring Measurements in the Southern Ocean, Geophys. Res. Lett., 39, L1666, doi:1.129/212gl5229.

14 Use of In Situ Data to provide Ocean Model Boundary Conditions In situ data assimilated by atmospheric reanalyses (NCEP, ERA-Interim). Reanalysis surface fields then modified (COREv2 from NCEP, DFS from ERA-Interim) and used as a boundary condition for model forcing (both ocean hindcasts and state estimates). Example Southern Ocean State Estimate (SOSE, Tamsitt et al., 215, submitted.) Net heat flux (Wm-2) from Southern Ocean State Estimate 25-1 Mean. Positive = ocean heat gain.

15 Use of In Situ Observations for Evaluations Observations from surface moorings are being increasingly used for flux product evaluations. Example: recent study of Jin et al. (215) combines satellite qa and Ta with OAFlux (includes atmospheric reanalyses). Gives better agreement with buoy measurements. Satellite Buoy OAFlux Buoy Jin, X., L. Yu, D. Jackson, and G. Wick, 215: An Improved Near-Surface Specific Humidity and Air Temperature Climatology for the SSM/I Satellite Period.J. Atmos. Oceanic Technol. doi:1.1175/jtech-d

16 Using In Situ Observations for Flux Dataset Evaluation: Potential Problems How do we validate evaluate flux datasets? Flux moorings. Observations of high value, but caution needed as they may already have been assimilated in reanalyses. Example TAO mooring array impacts on ERAInterim reanalysis (Josey et al., 214). ERA-Interim and derived products (TropFlux, Drakkar Forcing Set) exhibit a strong TAO mooring related pattern in 2m specific humidity field. Examples shown for 1994 annual mean (panel a) and anomaly (panel b, relative to ). Crosses indicate mooring locations. Equatorial section (panel c) reveals clear colocation with mooring longitude (veritical lines). Josey, et al. 214: Unexpected Impacts of the Tropical Pacific Array on Reanalysis Surface Meteorology and Heat Fluxes, Geophys. Res. Lett., 41, doi:1.12/214gl6132 OAFlux NCEP/NCAR ERA-Interim TropFlux ERA4

17 Using In Situ Observations for Flux Dataset Evaluation: Potential Problems How do we validate evaluate flux datasets? Flux moorings. Observations of high value, but caution needed as they may already have been assimilated in reanalyses. Example TAO mooring array impacts on ERAInterim reanalysis (Josey et al., 214). ERA-Interim and derived products (TropFlux, Drakkar Forcing Set) exhibit a strong TAO mooring related pattern in 2m specific humidity field. Examples shown for 1994 annual mean (panel a) and anomaly (panel b, relative to ). Crosses indicate mooring locations. Equatorial section (panel c) reveals clear colocation with mooring longitude (veritical lines). Josey, et al. 214: Unexpected Impacts of the Tropical Pacific Array on Reanalysis Surface Meteorology and Heat Fluxes, Geophys. Res. Lett., 41, doi:1.12/214gl6132 Research Ship High Res. Flux Estimates / Direct Flux Measurements. Not assimilated but routine met observations (6 hourly air temp, wind speed) are. Is this an issue for evaluation? Is reanalysis accuracy likely to be enhanced at research ship locations due to assimilation of routine meteorological observations? OAFlux NCEP/NCAR ERA-Interim TropFlux ERA4 Jiang et al., 212: Spatial Variation in Turbulent Heat Fluxes in Drake Passage. J. Climate, 25,

18 In situ data assimilation biases - Implications ECMWF (and consequently DFS, TropFlux) humidity, wind and net heat flux fields show TAO mooring artefacts in Tropical Pacific. NCEP and OAFlux do not exhibit this problem. Latitude Consequences: Caution required in use of ECMWF (ERA-Interim) and derived products (TropFlux, DFS) for Tropical Pacific ocean heat uptake studies. a.) NCEP/NCAR Net Heat Flux Difference, (W m-2) Longitude b.) TropFlux Net Heat Flux Difference, (W m-2) How do we make best use of buoy data for flux dataset development and evaluation? More problematic than previously realised. Latitude Longitude c.) 1/12 NEMO Net Heat Flux Difference, (W m-2) Details see: Josey, S. A., L. Yu, S. Gulev, X. Jin, N. Tilinina, B. Barnier and L. Brodeau, 214: Unexpected Impacts of the Tropical Pacific Array on Reanalysis Surface Meteorology and Heat Fluxes, Geophys. Res. Lett., 41, doi:1.12/214gl6132 Latitude Longitude Change in surface heat uptake : , warm colors mean more heat going into ocean.

19 What Are Flux Datasets Useful For? Need to recognise what surface flux datasets can and cannot be used for. Very difficult to determine interannual variability / trends in global mean net heat flux. However, interannnual variability at regional scales can be explored.e.g. winter (Grist et al., 215; Josey et al., 215). Winter experienced extremely strong net heat loss over mid-latitude North Atlantic. Driven by anomalous northerly airflow. Re-emergent SST anomaly in winter Grist, J. P., S. A. Josey, Z. L. Jacobs, R. Marsh, B. Sinha and E. van Sebille, 215: Extreme air-sea interaction over the North Atlantic subpolar gyre during the winter of and its sub-surface legacy, Climate Dynamics, DOI 1.17/ s Josey, S. A., J. P. Grist, D. Kieke, I. Yashayaev and L. Yu, 215: Extraordinary Ocean Cooling and New Dense Water Formation in the North Atlantic, in State of the Climate in 214, Bull. Amer. Meteor. Soc., 96 (7), S67 S68.

20 Some Outstanding Issues Poor Recognition of Fundamental Surface Flux Issues Lack of accurate surface flux measurements over a significant portion of the global ocean hinders the development of flux datasets. A reliable (or reference) net surface heat flux dataset accurate at a regional level in the annual mean to within 5-1 Wm -2 does not exist. Large differences between available products up to 2-3 Wm -2. Evaluation Methodology Danger of lack of independence in flux dataset evaluations needs to be recognised e.g. TAO mooring impacts on reanalyses. Very difficult to do a clean evaluation / comparison. Information on what data is assimilated in reanalyses (and with what weight) is typically not easily accessible. CONCEPT-HEAT could make progress on this issue.. Not clear at present what mooring evaluations tell us. Ideally withhold mooring data from atmospheric reanalyses Other Routes Forward Upper ocean is well-sampled, globally over the past decade (25-onward) due to Argo. Better constrained than the atmosphere over oceans. Will ocean reanalyses and/or coupled ocean atmosphere reanalyses ultimately be needed to reduce the uncertainty in annual net heat flux fields to less than 1 Wm -2?

21 Summary Surface Flux Datasets: Available from a a wide range of sources : reanalysis, satellite, ship observations, blended / hybrid products. Order 15-2 current datasets. Global ocean heat budget closure. Still a long way from obtaining wellbased closure of the budget, unconstrained products tend to be biased warm by 1-25 Wm -2. Bias is probably due to multiple sources of error at the 2-5 Wm -2 level. Potential for progress. The maturing air-sea flux reference site array provides an increasing data resource. Significant effort needed to fully utilise these measurements and resolve evaluation issues. Recent flux overview: Josey, S. A., S. Gulev and L. Yu, 213: Exchanges through the ocean surface, in Siedler, G., Griffies, S., Gould, J. and Church, J. (Eds.): Ocean Circulation and Climate 2nd Ed. A 21st century perspective, Academic Press.