Evaluation of Tropical Pacific Observing Systems Using NCEP and GFDL Ocean Data Assimilation Systems

Similar documents
The Oceanic Component of CFSR

GFDL, NCEP, & SODA Upper Ocean Assimilation Systems

EVALUATION OF THE GLOBAL OCEAN DATA ASSIMILATION SYSTEM AT NCEP: THE PACIFIC OCEAN

Global Ocean Monitoring: A Synthesis of Atmospheric and Oceanic Analysis

GSOP Activities related to Ocean Reanalysis Intercomparison and Observing System Assessments

ENSO prediction using Multi ocean Analysis Ensembles (MAE) with NCEP CFSv2: Deterministic skill and reliability

Assimilation of Satellite Sea-surface Salinity Fields: Validating Ocean Analyses and Identifying Errors in Surface Buoyancy Fluxes

OSSE OSE activities with Multivariate Ocean VariationalEstimation (MOVE)System. II:Impacts ofsalinity and TAO/TRITON.

HYBRID GODAS STEVE PENNY, DAVE BEHRINGER, JIM CARTON, EUGENIA KALNAY, YAN XUE

Evaluation of the Tropical Pacific Observing System from the ocean data assimilation perspective

Errors in Ocean Syntheses: Estimation and Impact

Data assimilation for ocean climate studies

The ECMWF prototype for coupled reanalysis. Patrick Laloyaux

Multiple Ocean Analysis Initialization for Ensemble ENSO Prediction using NCEP CFSv2

(Revised title and authors)

3.3 THE GLOBAL OCEAN DATA ASSIMILATION SYSTEM (GODAS) AT NCEP. David W. Behringer* NOAA/NCEP/Environmental Modeling Center. 2.

Overview of data assimilation in oceanography or how best to initialize the ocean?

STRONGLY COUPLED ENKF DATA ASSIMILATION

The GMAO s Ensemble Kalman Filter Ocean Data Assimilation System

The ECMWF coupled data assimilation system

Decadal Hindcasts and Forecasts at GFDL

Data Assimilation of Argo Profiles in Northwest Pacific Yun LI National Marine Environmental Forecasting Center, Beijing

Downscaling Global Warming with a Regional Ocean- Atmosphere Model over the Tropical Atlantic

How Much of Monthly Subsurface Temperature Variability in the Equatorial Pacific Can Be Recovered by the Specification of Sea Surface Temperatures?

Sea-Ice Prediction in the GFDL model framework

Ocean Data Assimilation for Seasonal Forecasting

Understanding Oceans Sustaining Future. Shaoqing Zhang

Salinity variability associated with changes in the hydrological cycle variables

Skillful climate forecasts using model-analogs

Impact of Argo, SST, and altimeter data on an eddy-resolving ocean reanalysis

Regional eddy-permitting state estimation of the circulation in the Northern Philippine Sea

Initial Results of Altimetry Assimilation in POP2 Ocean Model

Ocean data assimilation systems in JMA and their representation of SST and sea ice fields

NOAA In Situ Satellite Blended Analysis of Surface Salinity: Preliminary Results for

Global Ocean Monitoring: Recent Evolution, Current Status, and Predictions

Strongly coupled data assimilation experiments with a full OGCM and an atmospheric boundary layer model: preliminary results

On the relative importance of Argo, SST and altimetry for an ocean reanalysis

Assessing the Quality of Regional Ocean Reanalysis Data from ENSO Signals

E-AIMS. Global ocean analysis and forecasting: OSE/OSSEs results and recommandations

Intraseasonal and seasonal variations in in the eastern equatorial Indian Ocean. Y. Masumoto, H. Hase, Y. Kuroda, and H. Sasaki

Ocean Observations for an End-to-End Seasonal Forecasting System

The CONCEPTS Global Ice-Ocean Prediction System Establishing an Environmental Prediction Capability in Canada

Preliminary study of multi-year ocean salinity trends with merged SMOS and Aquarius data.

The Maritime Continent as a Prediction Barrier

Improved analyses and forecasts with AIRS retrievals using the Local Ensemble Transform Kalman Filter

The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model

Ocean data assimilation for reanalysis

How DBCP Data Contributes to Ocean Forecasting at the UK Met Office

Pacific HYCOM. E. Joseph Metzger, Harley E. Hurlburt, Alan J. Wallcraft, Luis Zamudio and Patrick J. Hogan

Ensemble-variational assimilation with NEMOVAR Part 2: experiments with the ECMWF system

Recent Results with the GFDL High- Resolution Coupled Modeling Systems

MERSEA IP WP 5 Integrated System Design and Assessment. Internal Metrics for the MERSEA Global Ocean: Specifications for Implementation

Norwegian Climate Prediction Model (NorCPM) getting ready for CMIP6 DCPP

Global Ocean Monitoring: Recent Evolution, Current Status, and Predictions

Ensemble-variational assimilation with NEMOVAR Part 2: experiments with the ECMWF system

CERA: The Coupled ECMWF ReAnalysis System. Coupled data assimilation

The Bureau of Meteorology Coupled Data Assimilation System for ACCESS-S

Sea level variability in the North Indian Ocean

Recent Developments at NOAA/GFDL

SEA SURFACE SALINITY VARIABILITY OF THE 1999 CLIMATE SHIFT IN THE TROPICAL PACIFIC

The Future of Earth System Reanalyses: An ocean perspective

WP2.5 Strengths/weaknesses in exis6ng coupled DA, coupled error covariances and model drib/bias correc6on

MJO modeling and Prediction

On the role of the GRACE mission in the joint assimilation of altimetric and TAO data in a tropical Pacific Ocean model

Project of Strategic Interest NEXTDATA. Deliverables D1.3.B and 1.3.C. Final Report on the quality of Reconstruction/Reanalysis products

Developments to the assimilation of sea surface temperature

Surface Fluxes from In Situ Observations

Early Successes El Nino Southern Oscillation and seasonal forecasting. David Anderson, With thanks to Magdalena Balmaseda, Tim Stockdale.

O.M Smedstad 1, E.J. Metzger 2, R.A. Allard 2, R. Broome 1, D.S. Franklin 1 and A.J. Wallcraft 2. QinetiQ North America 2. Naval Research Laboratory

Coupled data assimilation for climate reanalysis

Application and improvement of Ensemble Optimal Interpolation on Regional Ocean Modeling System (ROMS)

Moist static energy budget diagnostics for. monsoon research. H. Annamalai

Applications of an ensemble Kalman Filter to regional ocean modeling associated with the western boundary currents variations

T2.2: Development of assimilation techniques for improved use of surface observations

Quality control methods for KOOS operational sea surface temperature products

Aspects of the practical application of ensemble-based Kalman filters

Decadal variability in the Kuroshio and Oyashio Extension frontal regions in an eddy-resolving OGCM

Near-surface observations for coupled atmosphere-ocean reanalysis

The ECMWF System 3 ocean analysis system

SUPPLEMENTARY INFORMATION

Forecasting the MJO with the CFS: Factors affecting forecast skill of the MJO. Jon Gottschalck. Augustin Vintzileos

Observations of seasurface. in situ: evolution, uncertainties and considerations on their use

Theoretical and Modeling Issues Related to ISO/MJO

The Climate Service Based on Climate Observation in China

ENSO Outlook by JMA. Hiroyuki Sugimoto. El Niño Monitoring and Prediction Group Climate Prediction Division Japan Meteorological Agency

The 2009 Hurricane Season Overview

Air sea satellite flux datasets and what they do (and don't) tell us about the air sea interface in the Southern Ocean

NOAA Climate Test Bed Jin Huang CTB Director

Evaluation of the IPSL climate model in a weather-forecast mode

Incorporating state-dependent Temperature Salinity constraints in the background error covariance of variational ocean data assimilation

Vertical Moist Thermodynamic Structure of the MJO in AIRS Observations: An Update and A Comparison to ECMWF Interim Reanalysis

Impact of frontal eddy dynamics on the Loop Current variability during free and data assimilative HYCOM simulations

Data assimilation for the coupled ocean-atmosphere

Practical Considerations of Ocean State Estimation.

Application of EnOI Assimilation in BCC_CSM1.1: Twin. Experiments for Assimilating Sea Surface Data and T/S Profiles

The NCEP Climate Forecast System. Submitted to the J. Climate

From El Nino to Atlantic Nino: pathways as seen in the QuikScat winds

ECMWF: Weather and Climate Dynamical Forecasts

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBER

A Dressed Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific

Transcription:

Evaluation of Tropical Pacific Observing Systems Using NCEP and GFDL Ocean Data Assimilation Systems Y. Xue 1, C. Wen 1, X. Yang 2, D. Behringer 1, A. Kumar 1, G. Vecchi 2, A. Rosati 2, R. Gudgel 2 1 NCEP/NOAA, USA 2 GFDL/NOAA, USA US CLIVAR Summit, August 4-6, 2015, Tucson, AZ

What is Ocean Reanalysis? 1979 2015 Reanalysis Surface Fluxes Ocean Model Data AssimilaBon Scheme (opbmally combine model solubons and ocean observabons) OSE is an ocean reanalysis experiment in which observations from one particular observing platform is withheld. Ocean ObservaBons SST Argo XBT AlBmeter Moorings

Is the Quality of Ocean Reanalysis Sensitive to Data Distribution? 1980-1993 1994-2003 2004-2009 Xue et al. 2012 3

Tropical Pacific Observing Systems Black: All data Red: TAO/TRITON Blue: XBT Green: Argo 8S- 8N 2004-2011 4

Coordinated Observing System Experiments at NCEP and GFDL (2004-2011) CTL ALL nomoor noargo In situ data included no profiles all profiles all except moorings all except Argo XBT TAO Argo What are the mean biases and RMSE (monthly anomalies) in the control simulations when no in situ observations are assimilated? How well are mean biases and RMSE constrained by assimilation of all in situ observations? What are influences of withholding mooring or Argo data on mean biases and RMSE? 5

NCEP s Global Ocean Data Assimilation System MOM4p1, 0.5 o resolution, 1/4 o in 10 o S-10 o N, 40 levels Daily fluxes from NCEP Reanalysis 2 3D-VAR, Univariate in temperature and salinity (Behringer 2007) Horizonal covariance with scale elongated in zonal direction, vertical covariance as function of local vertical temperature gradient Temperature profiles and OISST assimilated Synthetic salinity derived with climatology T/S relationship and temperature profiles from XBT and moorings, observed salinity from Argo assimilated Altimetry SSH not assimilated Temperature (salinity) at 5m is relaxed to daily OISST (Levitus salinity climatology) with 10 (30) day relaxation time scales.

GFDL s Ensemble Coupled Data Assimilation System MOM4, 1 o resolution, 1/3 o in 10 o S-10 o N, 50 levels Coupled model CM2.1 Ensemble Kalman Filter with 12 ensembles (Zhang et al 2007) Flow-dependent ensemble covariance of temperature and salinity 3-D air temperature and wind from NCEP renalyses assimilated Observed temperature and salinity profiles and observed SST assimilated Altimetry SSH not assimilated

Validation data Evaluation of OSEs TAO temperature and current Altimetry SSH from AVISO Surface current from OSCAR EN4 temperature and salinity analysis (objective analysis based on in situ data only) is used as reference but not the truth Evaluation Methods Mean and annual cycle Standard deviation (STD) Root-mean-square error (RMSE) and anomaly correlation coefficient (ACC) Integrated RMSE in upper 300m

Mean Upper 300m Heat Content (HC300) NCEP GFDL All in situ obs. included Impacts of withholding TAO Impacts of withholding Argo Impacts of withholding both TAO and Argo

RMSE of HC300 Anomaly NCEP GFDL All in situ obs. included Impacts of withholding TAO Impacts of withholding Argo Impacts of withholding both TAO and Argo

RMSE of Salinity at Equator NCEP GFDL All in situ obs. included TRITON Impacts of withholding TAO Impacts of withholding Argo Impacts of withholding both TAO and Argo

RMSE of Salinity in Upper 300m NCEP GFDL All in situ obs. included Impacts of withholding TAO Impacts of withholding Argo Impacts of withholding both TAO and Argo

RMSE of Surface Zonal Current NCEP GFDL All in situ obs. included Impacts of withholding TAO Impacts of withholding Argo Impacts of withholding both TAO and Argo

Summary of NOAA OSE Project Without assimilabon of any in situ data, both GODAS and ECDA had large mean biases, STD biases and RMSE. AssimilaBon of in situ data significantly reduced mean biases, STD biases and RMSE in all variables except zonal current at equator. For constraining temperature analysis, the TAO/TRITON and Argo is complimentary in the equatorial Pacific, and the Argo is cribcal in off- equatorial regions. For constraining salinity, sea surface height and surface current analysis, the influence of Argo data is more important. The influence of the TRITON salinity data is cribcal. GODAS was more sensibve to withholding Argo data in off- equatorial regions than ECDA. The results suggest that mulbple ocean data assimilabon systems should be used to assess sensibvity of ocean analyses to changes in the distribubon of ocean observabons. Xue et al. 2015: EvaluaBon of Tropical Pacific Observing Systems Using NCEP and GFDL Ocean Data AssimilaBon Systems. Climate Dynamics, Online

Thanks! 15

RecommendaBons for Ocean Observing Systems from CPC s PerspecBve Both mooring and Argo data are needed to constrain large model biases. The impacts of TAO/TRITON are largest in the far western and eastern equatorial Pacific based on GFDL and NCEP data assimilabon systems. So the moorings in those regions are recommended to be given a high priority. Paired temperature and salinity profiles such as those from Argo are easier than temperature- alone profiles to be assimilated by data assimilabon systems. So more paired T/S profiles are recommended. Salinity observabons are very sparse before 2004 and conbnuous salinity profiles should be maintained so that a long record of high quality salinity analysis can be made. Current observabons at the TAO/TRITON moorings should be conbnued since they are cribcal in validabon of ocean current analysis. Surface fluxes in NCEP reanalysis products have large uncertainbes. The TAO/ TRITION flux products should be conbnued since they are cribcal in validabon of NCEP fluxes (SW, LW, LH, SH, humidity, surface winds). QuanBBes to beger define mixed layer depth and ocean velocity including entrainment velocity, and to help improve ocean model parameterizabons. Yan Xue Climate Predic5on Center 16