Preparing Ocean Observations for Reanalysis

Transcription

1 Preparing Ocean Observations for Reanalysis Nick Rayner, Met Office Hadley Centre 5 th International Conference on Reanalyses, Rome, 13 th -17 th November 2017 Material provided by John Kennedy, Clive Wilkinson and Holly Titchner Crown Copyright 2017, Met Office

2 SST from ships, buoys and satellites Sea ice concentration from charts and satellites Evolution of the use of ocean measurements in reanalysis Statistical analysis: globallycomplete forcing data Atmosphere-only reanalysis Temperature and salinity profiles from MBTs, XBTs, bottles, CTDs and Argo Assimilation Ocean-only reanalysis Crown Copyright 2017, Met Office

3 SST from ships, buoys and satellites Sea ice concentration from charts and satellites Temperature and salinity profiles from MBTs, XBTs, bottles, CTDs and Argo Evolution of the use of ocean measurements in reanalysis Statistical analysis: globallycomplete forcing data Assimilation Weakly-coupled reanalysis Crown Copyright 2017, Met Office

4 SST from ships, buoys and satellites Sea ice concentration from charts and satellites Temperature and salinity profiles from MBTs, XBTs, bottles, CTDs and Argo Evolution of the use of ocean measurements in reanalysis Assimilation Fully-coupled reanalysis In all cases, whatever the length of the reanalysis, to achieve a climate-quality result, the same preparation is needed Crown Copyright 2017, Met Office

5 Categories of work needed to prepare observations for reanalysis Data assembly (see e.g. Peter Thorne s presentation) Data Rescue Quality Control Achieving consistency: bias correction or homogenisation Uncertainty quantification Feedback analysis Crown Copyright 2017, Met Office

6 Quality control Crown Copyright 2017, Met Office

7 Not-so-subtle errors in SST data 60N FAIL Latitude 0N PASS 60S 0C SST April C Crown Copyright 2017, Met Office

8 The subtle dangers of quality control Background field Crown Copyright 2017, Met Office

9 Effective use of backgrounds to trap errors buoy run aground noisy sensor buoy picked up by a ship Atkinson et al, 2013: JGR, doi: /jgrc Crown Copyright 2017, Met Office

10 Achieving internal consistency: bias correction / homogenisation Crown Copyright 2017, Met Office

11 Aspects of internal consistency Time ships with evolving SST measurement methods satellites buoys surface bottles CTDs MBTs XBTs Argo Depth Climate quality reanalysis requires observations that are consistent: in time between different components of the observing system for one variable Where observations for different variables are brought together need to remove relative biases between observing system components addressing different variables surface and subsurface Crown Copyright 2017, Met Office

12 April 1905 April 1935 Ocean data for coupled reanalysis: HadIOD April 1965 April 1985 Contains platform ID, position, time, depth, Maximum platform & instrument depth of type, observed temperature & salinity, provenance information and data in any location April a unique 1995 April 2010 ID, together with quality flags, bias corrections and through uncertainty time estimates Atkinson, et al 2014: JGR, doi: /2014jc >4000m Crown Copyright 2017, Met Office

13 Ways of achieving consistency Compare everything and develop empirical corrections, relative to a chosen reference Risks picking the wrong reference and biasing the whole system Understand each data source physically and correct according to its own biases A Call for New Approaches to Quantifying Biases in Observations of Sea Surface Temperature. Kent et al. (2017) BAMS Crown Copyright 2017, Met Office

14 Ways of achieving consistency Compare everything and develop empirical corrections, relative to a chosen reference Risks picking the wrong reference and biasing the whole system Understand each data source physically and correct according to its own biases Then compare to everything else and check consistency Crown Copyright 2017, Met Office

15 Ways of achieving consistency Compare everything and develop empirical corrections, relative to a chosen reference Risks picking the wrong reference and biasing the whole system Understand each data source physically and correct according to its own biases Then compare to everything else and check consistency But this requires good metadata, which is often lacking However, this allows potential propagation of error structure Let the reanalysis handle it still requires good understanding and metadata Crown Copyright 2017, Met Office

16 Corrections to ship SST in HadIOD Ensemble of estimated Engine Room Intake measurement biases Ensemble of estimated biases in SST measurements made using buckets to sample water Crown Copyright 2017, Met Office

17 Use of HadIOD in historical ocean reanalyses ( ) See poster by Chunxue Yang 1, Simona Masina 2,3 and Andrea Storto 2 1 ISAC-CNR, Italy; 2 CMCC, Bologna, Italy; 3 INGV, Bologna Crown Copyright 2017, Met Office

18 Uncertainty quantification Crown Copyright 2017, Met Office

19 This is what we have December This is what we want Crown copyright Met Office

20 Crown copyright Met Office Available observations do not uniquely define the past

21 The Ensemble Generator First, generate a range of plausible bias adjustments to the data Bias adjustment Crown copyright Met Office

22 Reject in situ ensemble members that disagree with ARC ATSR ARC ATSR Constrained 20-member HadSST3 ensemble 1000 member ensemble Reduced to ~10 members Crown copyright Met Office

23 Blending satellites - daily AVHRR ATSR BLEND Crown copyright Met Office

24 Blending satellite and in situ pentad SATELLITE Crown copyright Met Office IN SITU BLEND

25 From one realisation of the in situ bias adjustments, produce 10 interchangeable realisations of the broad-scale reconstruction Crown copyright Met Office Bias adjustment Broad-scale reconstruction

26 GUESS EOFS project on to OBSERVATIONS AT EACH TIME STEP Crown copyright Met Office EOF1 EOF2 EOF3 EOF4 BROAD-SCALE RECONSTRUCTION & time series of weights of EOFs Bayesian PCA

27 EOF1 EOF2 EOF3 EOF4 Weights of EOFS project on to OBS AT EACH LOCATION NEW EOFs Crown copyright Met Office Bayesian PCA

28 NEW EOFS project on to OBSERVATIONS AT EACH TIME STEP BROAD-SCALE Crown copyright Met Office EOF1 EOF2 EOF3 EOF4 RECONSTRUCTION & time series of weights of EOFs Bayesian PCA

29 Then, from each of the 10 realisations of the broad-scale reconstruction, we can create an ensemble of interchangeable local OIs of the residuals from that reconstruction Crown copyright Met Office Bias adjustment Broad-scale reconstruction Local OI of residuals

30 Crown copyright Met Office Drawing samples from Local OI

31 One random selection from the analyses of the residuals gives us one of our realisations of HadISST2 Crown copyright Met Office Bias adjustment Broad-scale reconstruction Local OI of residuals

32 Pick 10 such random paths to span the total uncertainty in the analysis and provide an ensemble of interchangeable versions of HadISST2 Crown copyright Met Office Bias adjustment Broad-scale reconstruction Local OI of residuals

33 HadISST2 ensemble is underdispersive

34 Uncertainties in method Alternatives to climatology Alternate methods AARI ice charts Small changes to current method Concentration infilling (when only extents are known) Input data sources Alternatives to near neighbour infilling Filling of spatial/temporal gaps Structural uncertainty Changes to filling of spatial gaps between Atlas Climatologies/NIC charts in SH Bias adjustment Reference data set Alternate methods Changes to current method Uncertainties in current method Future: generate sea ice ensemble SIBT1850 instead of Walsh and Chapman Compilation NIC ice charts instead of passive microwave during overlap period Others? Other passive microwave products Others? AMSR-E passive microwave Passive microwave

35 Data rescue Crown Copyright 2017, Met Office

36 Example Documents: Christian Salvesen Archive, Edinburgh

37 Example Document: Logbook Norvegia - 4 December 1928 Vestfold Archive, Sandefjord 4-Hourly Wind direction Wind force Weather Sea state Pressure Temperature

38 Whaling Records Archive of Sea Mammal Research Unit, University of St. Andrews 1. 3/2/1931 No ice 68⁰S, 177⁰E wind west, fresh 2. 28/2/1930 Icepack 66.25⁰S, ⁰E wind south, fresh Note: a separate form for each whale captured.

39 Imaging of historical Southern Ocean observations Antarctic & Southern Ocean Research vessels, whaling vessels, commercial shipping (sail and steam) National Maritime Museum Valparaíso, Chile 22,000 UK National Meteorological Archive 21,570 Sea Mammal Research Unit, Scotland 14,890 Christian Salvesen Archive, Scotland 2,730 Maritime Museum, Mariehamn Finland 20,300 Vestfold Archive, Sandefjord, Norway 30,500 Total 111,990 When added to previous work total 137K images and estimated up to 7M observations Clive Wilkinson

40 Summary Data assembly Data Rescue Quality Control Achieving consistency: bias correction or homogenisation Uncertainty quantification Feedback analysis Crown Copyright 2017, Met Office