in association with GOV ST F. Hernandez M. Balmaseda, Y. Fujii, K. - - PowerPoint PPT Presentation

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in association with GOV ST F. Hernandez M. Balmaseda, Y. Fujii, K. - - PowerPoint PPT Presentation

Apr 12, 2023 253 likes •447 views

CLIVAR-GSOP report in association with GOV ST F. Hernandez M. Balmaseda, Y. Fujii, K. Haines, T. Lee, Y. Xue Outcomes from the ongoing ORA-IP project New real time ORA intercomparison GOVST 5 Meeting, Beijing, 13-17 October 2014

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SLIDE 1

GOVST 5 Meeting, Beijing, 13-17 October 2014

CLIVAR-GSOP report in association with GOV ST

  • F. Hernandez
  • M. Balmaseda, Y. Fujii, K. Haines, T. Lee, Y. Xue
  • Outcomes from the ongoing ORA-IP project
  • New real time ORA intercomparison
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SLIDE 2

GOVST 5 Meeting, Beijing, 13-17 October 2014

CLIVAR-GSOP/GODAE OceanView Ocean Reanalysis Intercomparison (ORA-IP, 2012-2014)

  • Reanalysis production is an on-going activity, following the

feedbacks and outcomes of GSOP 2006-2009

  • New vintages are produced approximately every 5 years
  • Improved quality controlled observations (XBT corrections, Argo

corrections and black lists)

  • Improved and extended forcing fluxes
  • Improved models and methods
  • We need to assess uncertainties among ocean reanalyses

(through intercomparison and validation with independent data) due to model errors and bias, and observing system reliability over time

  • benefits of the ensemble approach both to improve the

estimation of the signals and to provide uncertainty ranges

  • We need to facilitate the use of ocean reanalyses by other

communities

  • We need to prepare for quasi-real time monitoring of the
  • cean

Courtesy of M. Balmaseda

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SLIDE 3

GOVST 5 Meeting, Beijing, 13-17 October 2014 See a summary at http://www.clivar.org/sit es/default/files/Exchange s/Exchanges_64.pdf

More than 20 participating ORA’s and observed products:

  • some coupled
  • from 1° to ¼° resolution
  • different models, forcing, DA

Balmaseda et al, The Ocean Reanalyses Intercomparison Project (ORA-IP) JOO, accepted 2014

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SLIDE 4

Ocean Heat Content

by Matt Palmer

0-300m 0-700m 0-1500m 0-4000m

Less dispersion near the surface, in particular after 2002 (Argo) DA altimetry since 1993

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SLIDE 5

Steric Sea Level (SSL)

by Andrea Storto

Contours indicate 95% confidence level

  • The Intercomparison has helped to

identify errors in some reanalyses products.

  • The Intercomparison has also helped to

identify errors in some GRACE products.

  • Ensemble of reanalyses (REAENS)
  • utperforms obs-only products (OAENS)

(although comparison is not fair, since OAENS has less ensemble members).

  • Partition btw haline/thermal component

less clear among ORAs, as well as contribution at depth of the SSL trend

Correlation REAENS vs alti-GRACE Cx REAENS – Cx OAENS

ORA water mass representation impact !

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SLIDE 6

Sea Level

by Fabrice Hernandez SL index (0-12°N, 84-108°W)

SL index validation against SL-CCI Correlation of ORA-EM (detrended and no seas. Cycle) Against Tide Gauges

  • GSLR not assessed
  • Good performance of

ORA assimilating satellite altimetry

  • ORA-EM smooth out

noisy signals

  • Valuable for regional sea

level indices

  • 97-98 Niño,

Kelvin Waves

  • 3-4 mm/y

negative trend (strengthening Trade Winds)

  • Consistent

pattern, small spread

What can we learn from ORA when altimetry derived products look more reliable ?

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SLIDE 7

Mixed Layer Depth (MDL)

by Takahiro Toyoda

Validation of MLDs from syntheses without model (EN3v2a, ARMOR3D) and ensemble mean of 17 reanalyses (ENSMEAN) Differences from MILA-GPV deduced from individual TS profiles

  • f Argo data

(x)Month-(y)latitude diagram for temporal (2005-2011) and zonal mean values

  • Issues on the way MLD is computed, monthly time scales and vertical averaging: Smaller

negative biases due to higher vertical resolution in the reanalyses

  • Negative biases in syntheses without model due to vertical, horizontal (within a grid) and

temporal averaging of profiles (c.f., de Boyer Montegut, 2004)

  • Model biases in ensemble members largely canceled out in ENSMEAN

Strongly dependent on forcing errors and vertical mixing, technical issues

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SLIDE 8

Surface Fluxes and Transports

by Maria Valdivieso Most ocean model products have positive bias into the ocean (mean net surface heat flux into the ocean). The bias is often smaller than observational products, e.g., ISCCP/OAFlux and NOC2.0 The bias is comparable than atmospheric reanalyses in some cases. Interannual variations are usually few Wm-2 ,smaller than the bias.

Time mean Global net surface Heat Flux and increment corrections

Interannual Std

Negative contribution of assimilation increments (removing heat from the ocean on global average) Total neat heat flux still positive 2 W/m2, consistent with net ocean warming

ORA integrated assessment

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SLIDE 9

Mean March Sea Ice Thickness: 2007

Too thin

ICESa t

GFDL UR025. 4 GLOSEA5 GMAO MOVEG2 MOVECOR E CMCC ERAN G2V3

Too thick

Sea Ice

by Greg Smith

Mean March Sea Ice Thickness: 2007

(Predictor for seasonal sea ice extent) LIM too thick CICE too thin Large variability central Arctic/Siberia

Large discrepancies among ORAs

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SLIDE 10

Summary and Outlook

1. Open Assessment of products

  • Ensemble mean appears to be a robust estimation
  • Ensemble spread useful to estimate structural uncertainty
  • Some relevant indices have been defined

NEXT step: Dissemination of results in scientific literature. GODAE Special Issue in JOO. Summary paper Special Issue in Clim Dyn. Individual contributions 2. We need to facilitate data access and usage:

  • Data repository of data entering the intercomparison (unified grid

and format)

  • Data repository with ensemble mean and spread. With a version

number to assess progress in the future. ORAIP v1 3. Monitoring of relevant indices still pending. 4. Balance between “Ensemble of All System” versus “Best Systems” needs to be addressed. Courtesy of M. Balmaseda

  • 1. Intercomparison brought to assess the reliability of multi

model ensemble approach JOO paper accepted, Clim Dyn. Contribution ongoing

  • 2. Under discussion: CMIP like repository (only ENS or all

ORA?)

  • 3. Highly expected from some GOV participant… ongoing

acions

  • 4. Multi-model ensemble approach versus ensemble from

individual systems: common issues with Native Class 1 consensus forecasting approach

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SLIDE 11
  • Extend CLIVAR-GSOP/GODAE OceanView Ocean

Reanalyses Intercomparison Project (ORA-IP) into real time

  • Assess uncertainties in temperature analysis of

tropical Pacific in support of ENSO monitoring and prediction

  • Explore any connections between gaps in ocean
  • bservations and spreads among ensemble ORAs
  • Articulate needs for sustained ocean observing

systems in support of TPOS2020

  • Monitor signal-to-noise ratio in the global ocean

temperature, 300m heat content, depth of 20C isotherm

Yan Xue Climate Prediction Center 11

Real-Time Ocean Reanalyses Intercomparison

  • Y. Xue, Y. Fujii, M. Balmaseda proposal
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SLIDE 12

http://www.cpc.ncep.noaa.gov/products/GODAS/multiora_body.html 6 OOS, joining FOAM (UK-Met) and PSY3 (Mercator) Duplicating with 1992- 2013 climatology

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SLIDE 13
  • The ensemble mean (ensemble

spread) can be used to measure signal (noise).

  • The signal-to-noise (SN) ratio is

relatively low in the western (central- eastern) Pacific where negative (positive) anomalies presented.

  • The low signal-to-noise ratio may

be partially attributed to the sparse

  • bservations in those regions.

GODAS

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SLIDE 14

Signal, Noise and Signal-to-Noise Ratio (1985-2013)

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SLIDE 15

Influences of ocean

  • bservations on spread

among ocean reanalyses

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SLIDE 16

Warm Water Volume Index Derived From Ensemble Mean of Ocean Reanalyses

MJ 82 MJ 97 MJ 14 Jun 2014 Jun 1997 (DJF NINO3.4=+2.2) Jun 1982 (DJF NINO3.4=+2.2) Jun 1991 (DJF NINO3.4=+1.6) Jun 2009 (DJF NINO3.4=+1.6) Jun 2006 (DJF NINO3.4=+0.7) Jun 2002 (DJF NINO3.4=+1.1) MJ 02 MJ 91 MJ 06 MJ 09

  • Warm Water Volume averaged in May-June 2014 is similar to

that in May-June of 2009, 2006 and 1991. However, the pattern

  • f subsurface temperature anomaly averaged in 5S-5N in Jun

2014 is mostly similar to Jun 1991.

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SLIDE 17

82/83 91/92 97/98 02/03 06/07 09/10 Upper 300m Heat Content Anomaly Averaged in 5S-5N 12/13 14/15

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SLIDE 18

GOVST 5 Meeting, Beijing, 13-17 October 2014

CLIVAR-GSOP together with GOV

  • The GOV OSEval-TT workshop is hosting CLIVAR-GSOP discussions

next december

  • The ORA-IP is a success, we are learning a lot from it (obs, model,

forcing, DA limits and errors), and it will continue

  • The starting real time ORA intercomparison is:

– Proposed to be endorsed and supported by GOV IV-TT – Participants are operational centres involved in GOV – The climate monitoring and ocean state reporting activity corresponds to what GOV OOC wanted to implement by participating to the CLIVAR-GSOP ORA- IP project

  • Near Real Time Ocean climate monitoring:

– Could it be a GOV showcase, in association with OOPC? – There is an obvious link with seasonal prediction (at least for ENSO), which was not addressed specifically inside GOV – Status of the ocean observing system to be linked with OSEval-TT