CLIVAR WGSIP. Strategy for Development of Seasonal Prediction

Transcription

1 CLIVAR WGSIP Strategy for Development of Seasonal Prediction Adam Scaife and Ben Kirtman CLIVAR Working Group on Seasonal to Interannual Prediction April 2011

2 CLIVAR WGSIP Working Group on Seasonal to Interannual Prediction A panel of 13 international members. Co-chairs: Ben Kirtman and Adam Scaife develop a programme of numerical experimentation for seasonal-to-interannual variability and predictability, paying special attention to assessing and improving predictions WCRP Workshop on Seasonal Prediction, 2007: Launched the Climate Historical Forecast Project Identified 3 major areas for improvement of seasonal forecast skill: sea-ice, stratosphere, land surface Another major activity involves CMIP5: CMIP5 protocol for decadal predictions jointly developed between WGSIP and the WGCM

3 Climate-system Historical Forecast Project (CHFP) Forecasts for past seasons being made available by WGSIP: Seasonal hindcasts (reforecasts) with actual O-A initial conditions and forcings such as GHGs but no cheating i.e. no future information 4 seasons (1 st November, 1 st February, 1 st May and 1 st August start dates) At least 6 members per start date, for years since 1979 depending on forecast centre Data is being made available from a dedicated server and most major seasonal forecast groups worldwide are participating: AIMS Provide a baseline assessment of our seasonal prediction capabilities using the best available models of the climate system and data for initialisation. Provide a framework for assessing of current and planned observing systems, and a test bed for integrating process studies and field campaigns into model improvements Provide an experimental framework for focused research on how various components of the climate system interact and affect one another Provide a test bed for evaluating IPCC class models in seasonal prediction mode.

4 Three major topics and (now) three experiments: Land Surface: the GLACE experiment: Soil moisture experiments in seasonal mode Led by R Koster Stratosphere: Stratospheric Historical Forecast Project High Top Low Top hindcasts Led by A Scaife Sea Ice: Ice Historical Forecast Project Case studies with/without initial sea-ice data (2007/1996) Led by D Peterson

5 Stratospheric hindcast participants: Institute Met Office HC Meteo France ECMWF CCCMA NCEP CPTEC Model HadGEM Arpege OPA IFS CMAM CFS v1 CPTEC Resolution N96L85 N96L38 L91 L31 L91 L62 T63L71 T63L41 L64 Model top 85km 40km 0.01hPa 10hPa 0.01hPa 5hPa ~100km ~31km Reference Martin et al 2006, J. Clim., 19, Gueremy et al, 2005, Tellus, 57A, p Scinocca et al 2008, ACP, 8, Saha et al, J.Clim., vol.19, no.15, p Contact Adam.scaife@metoffice.gov.uk Michel.deque@meteo.fr jean.philippe.piedelievre@meteo.fr t.stockdale@ecmwf.int John.Scinocca@ac.gc.ca George.Boer@ec.gc.ca Hualu.Pan@noaa.gov Judith.perlwitz@noaa.gov.uk pnobre@cptec.inpe.br IFM- GEOMAR ECHAM5 T63L31 T63L47 10hPa 0.01hPa Roeckner et al 2003, MPI report No. 349, 127pp Manzini et al 2006, J. Clim., 19, nkeenlyside@ifm-geomar.de

6 SHFP Current Status Several groups are doing or have done hindcasts with vertically extended models: Meteo France COMPLETE ECMWF underway UKMO Hadley Centre COMPLETE CCCMA underway CPTEC tbc IfM-GEOMAR tbc DATA repository: CIMA (Carolina Vera) kindly agreed to host SHFP data llel to CHFP First files to be uploaded after CHFP data are done

7 SPARC interaction Attended the SPARC SSG in Oct 2009 on behalf of WGSIP Presented material on strat-trop coupling in S2D prediction Presented WGSIP CHFP and plan for Strat HFP SPARC DynVar group has agreed to analyse the hindcasts General skill analysis Using standard probabilistic verification measures for different regions - WGSIP Predictions of annular modes Sudden warmings and intraseasonal predictability Particularly in early part of forecasts ENSO teleconnections Strong evidence of an effect on Europe, perhaps SH too Interannual predictability from the QBO Not all models will simulate a QBO but all will initialise it Blocking frequency Do the high-top models exhibit better blocking stats Are they linked to SSWs

8 Our real aim is to improve predictions of the AO/NAO! Forecast NAO from forecasts around 1 st Nov Observed NAO (EMSLP gridded, Ansell and Allan 2006) Observed NAO (station data, Jones et al 1997)

9 Summary WGSIP members have carried out baseline hindcasts: the Climate Historical Forecast Project (CHFP). CHFP data are now being uploaded and are appearing in the online archive at CIMA (c.f. CMIP5) To address three areas identified as likely candidates for increased skill we have three active experiments: land surface stratosphere sea-ice Few Points: What role for WGSIP in CMIP5 decadal analysis Any other groups interested in helping to analyse WGSIP data Next meeting is at ICTP in Italy in September