Preparing Ocean Observations for Reanalysis

Nick Rayner, Met Office Hadley Centre 5th International Conference on Reanalyses, Rome, 13th-17th November 2017 Material provided by John Kennedy, Clive Wilkinson and Holly Titchner

www.metoffice.gov.uk © Crown Copyright 2017, Met Office Evolution of the use of ocean measurements in reanalysis SST from ships, buoys and satellites Statistical analysis: globally- Atmosphere-only Sea ice complete forcing reanalysis concentration data from charts and satellites

Temperature and salinity profiles from Ocean-only Assimilation MBTs, XBTs, reanalysis bottles, CTDs and Argo

www.metoffice.gov.uk © Crown Copyright 2017, Met Office Evolution of the use of ocean measurements in reanalysis SST from ships, buoys and satellites Statistical analysis: globally- Weakly-coupled Sea ice complete forcing reanalysis concentration data from charts and satellites

Temperature and salinity profiles from MBTs, XBTs, Assimilation bottles, CTDs and Argo

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

• Data assembly (see e.g. Peter Thorne’s presentation) • Data Rescue • Quality Control • Achieving consistency: bias correction or homogenisation • Uncertainty quantification • Feedback analysis

60N FAIL

PASS Latitude 0N

Background field

noisy sensor buoy run aground

Time

ships with evolving SST measurement methods satellites buoys surface

bottles CTDs MBTs XBTs Argo

Climate quality reanalysis requires observations that are consistent: surface Depth • in time and sub- surface • 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 www.metoffice.gov.uk © Crown Copyright 2017, Met Office April 1905 April 1935

Ocean data for coupled reanalysis: April 1965 April 1985 HadIOD Contains platform ID, position, time, depth, Maximumplatform & instrument depth of type, observed temperature data& salinity, in any provenance location information andApril a unique 1995 April 2010 ID, together with quality flags, bias corrections and throughuncertainty time estimates

• 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 https://doi.org/10.1175/BAMS-D-15-00251.1

• 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

• 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

Ensemble of estimated Engine Room 0.2 Intake measurement biases

Ensemble of estimated biases in SST measurements made using buckets to sample water -0.2

-0.4

1900 1950 2000

See poster by Chunxue Yang1, Simona Masina2,3 and Andrea Storto2 1ISAC-CNR, Italy; 2CMCC, Bologna, Italy; 3INGV, Bologna

December 27-31 1961

This is what we want

First, generate a range of plausible bias adjustments to the data

Bias adjustment

ARC ATSR

Constrained 20-member HadSST3 ensemble

1000 member ensemble Reduced to ~10 members

AVHRR ATSR BLEND

Bias adjustment Broad-scale reconstruction © Crown copyright Met Office GUESS EOFS

project on to OBSERVATIONS

AT EACH TIME STEP

BROAD-SCALE RECONSTRUCTION

EOF1 EOF2 & EOF3 time series of EOF4 weights of EOFs

OBS

AT EACH LOCATION

NEW EOFs

project on to OBSERVATIONS

AT EACH TIME STEP

BROAD-SCALE RECONSTRUCTION EOF1 & EOF2 EOF3 time series of weights of EOFs EOF4

© Crown copyright Met Office Bayesian PCA 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

Bias adjustment Broad-scale Local OI of reconstruction residuals © Crown copyright Met Office Drawing samples from Local OI

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

Bias adjustment Broad-scale Local OI of reconstruction residuals © Crown copyright Met Office HadISST2 ensemble is under- dispersive Uncertainties in Alternatives to method climatology

Alternatives to Changes to filling of Alternate near neighbour spatial gaps between methods infilling Atlas Climatologies/NIC charts in SH Small changes to current Filling of method spatial/temporal Changes to Alternate gaps current method methods Bias Concentration infilling adjustment (when only extents are known) Uncertainties in Structural current method uncertainty AARI ice charts Reference Input data data set sources

Others? SIBT1850 instead Others? Passive of Walsh and microwave Chapman Compilation

Future: Other passive NIC ice charts instead of microwave passive microwave products AMSR-E generate sea during overlap period passive ice ensemble microwave Data rescue

4-Hourly Wind direction Wind force Weather Sea state Pressure Temperature 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, 179.30⁰E – wind south, fresh Note: a separate form for each whale captured. Imaging of historical Southern Ocean observations

Antarctic & Southern Ocean Research vessels, whaling vessels, commercial shipping (sail and steam) 1900-1950 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 Summary

• Data assembly • Data Rescue • Quality Control • Achieving consistency: bias correction or homogenisation • Uncertainty quantification • Feedback analysis