The Future of Earth System Reanalyses: An ocean perspective

Transcription

1 The Future of Earth System Reanalyses: An ocean perspective Magdalena Alonso Balmaseda on behalf of many colleagues CLIVAR-GSOP, GODAE-OceanView, C3S, CMEMS, EOS-COST, Era-Clim2,ECMWF

2 Oceans 2 - Ocean Weather The Grand Reservoir A Temperamental Thermostat A Rich Ecosystem Ocean Reanalyses Climate monitoring Seamless prediction from days to decades. Detection and attribution of high impact weather related to climate variability and change, Energy-Water-Carbon Cycles

3 This image cannot currently be displayed. Ocean versus Atmosphere: a reminder Atmospheric wind speed (12h) 2 - Ocean Weather Ocean velocity 5-day mean Spatial scales Continental boundaries Temporal Scales, Memory and observations o o o o Long memory -thermal and dynamical inertia. Number of observations in atmosphere ~ times more than in the ocean Observation impact potentially lasts longer than in the atmosphere. Ocean (re-) analyses take long time to spin up Variability and stability o o o o Ocean large scale variability is mostly forced by atmosphere. The ocean internal chaotic component is associated with eddy scale There are unstable ocean-atmospheric coupled modes at planetary scale. ENSO, AMOC, PDO key for prediction and ocean heat uptake Ocean - atmosphere interaction also occurs at small eddy and frontal scale

4 A Brief History of Ocean Reanalyses ICR2~ 2001 Infancy Initialization of ENSO. Atmospheric Reanalysis available ICR3~ 2006 Awareness Established for operational Seasonal Forecasting Awareness of existence of Ocean Reanalyses ICR5~ 2012 Maturity Benchmarking. First insights into the ocean climate Seasonal and Decadal forecasting ICR5: 2017 Consolidation Reanalyses for climate High resolution ocean and sea ice ICR6: 2022 New Horizons Coupled DA Century-long reanalyses Higher resolution ICR7: 2027 The Future Orchestrating Earth System Reanalysis:

5 Ocean reanalyses: Continuous evolution as components improve Ensemble generation Ocean and Sea-Ice models driven by fluxes from atmospheric reanalyses + Ocean Observations collected and quality controlled Data Assimilation Model Bias Corrections Time evolution of the Ocean Observing System XBT s 60 s Satellite Moorings/Altimeter ARGO SST Elephant seals Sea Level Sea Ice Concentration

6 Changing Observing System + Model error = Spurious Variability Number of Temperature Observations Depth= meters EQATL Depth of the 20 degrees isotherm -70 ega8 omona.assim_an0 edp1 omona.assim_an Dealing with model error 1) Improving models 2) How to extrapolate observation information into the past? Nudging to climatology Additive bias correction: iterative and adaptive methods Time Pirata Moorings Assim No Assim Assim +BC Adjoint methods

7

8

9 Consolidation Phase: Exploitation and Learning from previous reanalyses vintage Acknowledgement of value of Ocean Reanalyses for Climate Concerted evaluation efforts: ORAIPv1 Data Bases Decadal forecasting Capability Building: Produce a new vintage of ORAS New reprocessed data sets: HadISStv2, EN4, ESA-CCI, MyOcean OSTIA SST and SeaIce, XBT corrections High resolution ocean models - Sea Ice Reanalyses -Ensembles (My Ocean-CMEMS) 12

10 ORA-IP v1 Production Vintage 2010-Evaluated during Objectives: To quantify signal/noise from ensemble of ORAs To gain insight into ocean variability and trends To identify current system deficiencies To measure progress To exploit existing multi-ora ensemble For real-time ocean monitoring For climate indicators For model validation For initialization of coupled models ORAIPv1 Special Issue in Climate Dynamics Create-IP Concept-heat EOS-Polar Intercomparison CMEMS ORAIP v1 data repository with version control Real time Multi-ORA Monitoring

11 ORA-IP v1 21 ORAs : 13 Low res, 8 High res 4 Coupled DA 6 starting around 1950 s 6 Observation only products OHC Interannual variability from ensemble Variable Ocean Heat Content Paper Palmer et al Steric Height Heat Fluxes MLD Salinity Sea Ice AMOC Storto et al Valdivieso et al Toyoda et al a,b Shi et al Chevallier et al Karspec et al Special Issue in Climate Dynamics Summary Paper: Balmaseda et al, J.Op.Oceanogr Palmer et al 2015, Clim Dyn

12 ORA-IP v1: Lessons Learnt Robust Signals and Uncertainty Sources Well constrained Temperature variability upper 300m Sea Level Mixed Layer Depth Total Steric Height Sea Ice Edge Poorly constrained Deep ocean (below 700m): Steric Height Partition (Halo-Thermo). Steric Height depth range contribution Atlantic Meridional Overturning Circulation Cross Equatorial transports Salinity Surface Heat Fluxes: Sea Ice Thickness Sources of uncertainty and errors Lack of Observations: deep ocean, S prior to Argo, SSS, Sea Ice thickness Modelling: air-sea interaction and vertical mixing Data Assimilation method. Especially altimeter assimilation

13 ORAIP v1: Data bases ORAIP in ICDC (Integrated Climate Data Center) University of Hamburg 1) ORA-IP repository with Version control Benchmark to measure progress Resource for climate studies CREATE-ORA repository NASA Climate Data Services: web visualization Lead: Tony Lee 2) NASA CREATE-ORA Follows obs4mip strategy Standard CMIP format and common 1 o x1 o, 33-level grid Monthly means (T,S,U,V). Time range Products: CFSR (NOAA NCEP) GECCO2 (Univ. Hamburg) GDFL CDA (NOAA GFDL) GODAS (NOAA NCEP) MRI (JMA) ORAS4 (ECMWF) ORAP5 (ECMWF) CMCC

14 Real time Multi-ORA Ocean Monitoring International Multi-ORA real-time monitoring of climate: Temperature (NCEP) and Salinity (BoM) European MULTI-ORA by CMEMS (GREP) Towards ORAIP-v2 Polar ORA-IP NA-ORAIP (Laura Jackson s poster) Multi-ORA for initialization of coupled forecasts? Temperature and ENSO monitoring, Yan Xue, NOAA

15 IQuOD in a nutshell To maximize the quality, consistency and completeness of the long-term global subsurface ocean temperature database (Essential Ocean Variable & Essential Climate Variable) subsurface profiles (intelligent) metadata uncertainty GSOP WG 148 SG-IQuOD Courtesy of Catia Domingues

16 New Vintage of high resolution Ocean-SeaIce Reanalyses ORAS5: 1979 to present 5 ensembles GLOBAL Ocean Heat Content High resolution ocean: 0.25 deg, 1m upper ocean Assimilation of Sea Ice concentration Updated Model and DA versions Latest reprocessed Observations SST: HadISSTv2 + OSTIA SIC: OSTIA SL: AVISO v5 Insitu: EN4 XBT bias correction? Era-Interim instead of Era-40? Zuo et al, in preparation European Centre for Medium-Range Weather Forecasts 20

17 Atlantic Meridional Overturning Circulation High res ocean improves the AMOC estimate during RAPID period. AMOC poorly constrained before AMOC Sensitivities AMOC very sensitive to: SST product Sea Ice product Ocean Resolution Zuo et al, In preparation

18 CMEMS GREP: Global ReAnalysis Ensemble Product Four European ORAs 1993-present (behind RT) GLORYS-C-GLORS-GLOSEA5-ORAS5 Cunningham et al 2007 Bryden et al 2005 RAPID estimate Courtesy of Laura Jackson 22 NA-ORAIP

19 De Boisseson et al 2017 To be used for Initialization of Seasonal and Decadal Forecasts Courtesy P. Laloyaux

20 A glimpse of the history of ENSO during the XX-Century Multivariate ENSO INDEX; SST and SLP Consistency between estimates Upper 300m Ocean Heat Content Eq Pac After Wolter and Timlin 2011 ORA-20C ORAS4 NoAssim Unusually warm El Nino with strong heat discharge during period Decadal modulation of ENSO? Changes in Atmosphere Observing System? SST?

21 Courtesy P. Laloyaux

22 Air-sea coupling surface fluxes assimilation increments In uncoupled DA the surface fluxes not consistent with ocean observations From Laloyaux et al, in preparation

23 What about the ensemble spread in coupled data assimilation? Compare ensemble spread of CERA-20C with ORA-20C (uncoupled) Uncoupled: Forcing and SST perturbations. By design, only capture only seasonal dependence Coupled: Spread generated by coupling. SST from HadISST. same observations, same data assimilation, same observation perturbations We diagnose the flow dependence of the spread: Decadal, interannual, intraseasonal Work in progress

24 Decadal.. ORA-20C Solar radiation CERA-20C 1900s 1940s 1970s 2000s

25 Zoom on : Onset of El Nino Equatorial daily time series of actual reanalysis fields SST Thermocline depth Zonal Wind Stress Fresh Water Flux Coherent behaviour among variables SST-Precipation-Wind and thermocline response Seasonal cycle, intraseasonal variability and onset of El Nino can be appreciated

26 Zoom on : Onset of El Nino Equatorial daily time series of UNCOUPLED ensemble spread SST Thermocline depth Zonal Wind Stress Fresh Water Flux Coherent spread between ocean and atmopheric variables only at seasonal time scales (by design) Ocean variables -SST and Thermocline depth- spread show intraseasonal TIWs- and interannual modulation

27 Zoom on : Onset of El Nino Equatorial daily time series of COUPLED ensemble spread SST Thermocline depth Zonal Wind Stress Fresh Water Flux Artefact: Monthly modulation of HadISST spread Artefact: Spread collapses at TAO mooring locations Coherent behaviour among variables SST-Precipation-Wind and thermocline at seasonal-intraseasonal-interannual time scales

28 Next few years: Challenges and Opportunities Towards consistent earth system reanalyses as far back as possible, with increasing accuracy and reliable uncertainty estimates DA methods Multi-scale methods, model bias correction, smoothers Flow dependent error Attention to the Achilles Heels: Sea-ice, AMOC, equatorial transports and altimeter data Preparedness to assimilate new observation types: Deep Argo Sea level from Tide gauges Coral proxies New Satellite observations: Sea Ice thickness, SSS Multiple window weak constrained 4dvar Tremolet Continue Data rescue activities SST and SeaIce reconstructions QC collection of insitu Include Bio-Chemistry and Carbon

29 New Observations: Deep Argo Improvements to DA methods needed to exploit these observations Spatial decorrelation scales Long temporal scales Bias: extrapolation of information to the past

30 Assimilating Sea Level from tide gauges? From Chepurin et al 2014 Records going back more than 100 years exit. They have been used for validation of ORAs Need for careful reprocessing to eliminate gravity and tide effects. Need special background covariances or observation operators. Empirical EOFs is a possibility

31 Non conventional observations: Observation operators for corals? Narhuti et al 2011, Cobb et al 2003

32 A grand Challenge for Coupled DA : SST errors on WBC Typical SST error from 0.25 o ocean analysis at 0.25 Current ocean DA unable to constrain the position of Gulf Stream. What is the nature of air-sea interaction over sharp SST fronts? -ve or +ve feedback? If -ve feedback, coupled DA should solve the problem If +ve feedback, errors will amplify in coupled DA. Challenge for forward ocean modelling Challenge for DA: Bias correction methods.

33 Orchestrating research and production of Earth System Reanalyses Computer capabilities and data bases Refining Uncertainty Refining Resolution Community Research and Development Earth System Reanalyses Sectorial Applications Regional aspects Evaluation conductor consistent earth system reanalyses as far back as possible, with increasing accuracy and reliable uncertainty estimates 37

34 Thank you for your attention!