Related work¶

US CLIVAR Decadal Prediction Working Group (http://www.usclivar.org/working-groups/decadal-predictability)¶

Their scientific objectives are to:

• Define a framework to distinguish natural decadal variability from anthropogenically-forced variability and to quantify their relative magnitude.
• Develop a framework for understanding decadal variability through metrics that can be used as a strategy to assess and validate decadal climate predictions simulations.

Results from the verification assessment of a few of the decadal hindcast experiments: http://clivar-dpwg.iri.columbia.edu/

L. Goddard, A. Kumar, A. Solomon, D. Smith, G. Boer, P. Gonzalez, V. Kharin, W. Merryfield, C. Deser, S. J. Mason, B. P. Kirtman, R. Msadek, R. Sutton, E. Hawkins, T. Fricker, G. Hegerl, C. a. T. Ferro, D. B. Stephenson, G. A. Meehl, T. Stockdale, R. Burgman, A. M. Greene, Y. Kushnir, M. Newman, J. Carton, I. Fukumori, und T. Delworth, A verification framework for interannual-to-decadal predictions experiments, Clim Dyn, S. 1–28.

WCRP on Decadal Predictions (http://www.wcrp-climate.org/decadal/)¶

Decadal prediction is a "meeting ground" for the weather and climate modeling communities. The climate-change community is typically focused on the problem of estimating anthropogenically-induced climate change on centennial timescales. For this community, the provision of accurate initial conditions is not a major concern, since the level of predictability of the first kind is believed to be small on century timescales. By contrast, the numerical weather prediction and seasonal forecast community have well-developed data assimilation schemes to determine initial conditions, however the models do not incorporate many of the cryospheric and biogeochemical processes believed to be important on timescales of centuries. A focus on decadal prediction by the two groups may help expedite the development of data assimilation schemes in Earth system models, and the use of Earth system models for shorter-range prediction, e.g., seasonal. For example, as has been discussed elsewhere (Palmer et al 2008), seasonal predictions can be used to calibrate probabilistic climate-change projections, in a seamless prediction system. Hence there is common ground over which to base a cooperation of the two communities in order to develop seamless prediction systems.

WMO STANDARDIZED VERIFICATION SYSTEM (SVS) FOR LONG-RANGE FORECASTS (LRF) (http://www.bom.gov.au/wmo/lrfvs/)¶

Summary taken from http://www.wmo.int/pages/prog/www/DPS/LRF/ATTACHII-8SVSfrom%20WMO_485_Vol_I.pdf

1. Diagnostics Information required incorporates derived diagnostic measures and contingency tables. Estimates of the statistical significance of the scores achieved are also required. Additional diagnostic measures are suggested but are not incorporated into the core SVS as yet. Use of the additional diagnostics is optional.
2. Parameters Key variables and regions are proposed. However producers are not limited to these key parameters, and can thus all contribute regardless of the structure of individual forecast systems. The parameters to be verified are defined on three levels:
   • Level 1: Diagnostic measures aggregated over regions and for indices,
   • Level 2: Diagnostic measures evaluated at individual grid points,
   • Level 3: Contingency tables provided for individual grid points.
     The SVS makes provision for staggered implementation of the three levels of information and the inclusion of estimates of
     skill significance over a two-year period.
3. Verification data sets Key data sets of observations against which forecasts may be verified are proposed.
4. System details Details of forecast systems employed.
5. Exchange of verification information The SVSLRF verification results are made available through a website maintained by the Lead Centre. The functions of the Lead Centre for SVSLRF include creating and maintaining coordinated websites for the LRF verification information so that potential users would benefit from a consistent presentation of the results. The website address is http://www.bom.gov.au/wmo/lrfvs/.

For more information refer to the previously mentioned document or the web page especially:

• User guide: http://www.bom.gov.au/wmo/lrfvs/users.shtml
• Documentation: http://www.bom.gov.au/wmo/lrfvs/documentation.shtml

ECMWF Demeter (old) (http://www.ecmwf.int/research/demeter/general/index.html)¶

Verification: http://www.ecmwf.int/research/demeter/d/charts/verification
Plots: http://www.ecmwf.int/research/demeter/d/overview/verification/

Decadal Prediction with GEOS-5 (for CMIP5) (http://gmao.gsfc.nasa.gov/research/climate/decadal_pred_GEOS-5/)¶

Reported results: http://downloads.usgcrp.gov/downloads/igim/07_Rienecker.pdf

Other interesting and related projects¶

• AMOC
  • US AMOC - http://www.usclivar.org/amoc
  • UK RAPID - http://www.rapid.ac.uk/rw/

Other References¶

• K. Haines, L. Hermanson, C. Liu, D. Putt, R. Sutton, A. Iwi, und D. Smith, Decadal climate prediction , Phil. Trans. R. Soc. A, Bd. 367, Nr. 1890, S. 925–937, März 2009.
• R. G. Sargent, Verification And Validation Of Simulation Models , 1998, S. 55–64.
• V. Pope und T. Davies, Testing and evaluating atmospheric climate models , Computing in Science Engineering, Bd. 4, Nr. 5, S. 64 – 69, Okt. 2002.
• J. P. C. Kleijnen, Sensitivity analysis and optimization in simulation: design of experiments and case studies , in Proceedings of the 27th conference on Winter simulation, Washington, DC, USA, 1995, S. 133–140.
• G. Rabadi, S. Bowling, C. Keating, und R. Unal, Sensitivity analysis framework for large and complex simulation models , in Proceedings of the Summer Computer Simulation Conference, Vista, CA, 2009, S. 291–298.