The complete physical climate system includes contributions from the atmosphere, oceans, land surface and cryosphere, in producing regional and sub-seasonal-to-decadal climate anomalies. This experimental framework is based on advances in climate research during the past decade, which have lead to the understanding that models and predictions of a given climate anomaly over any region are incomplete without a proper treatment of the effects of sea surface temperature, sea ice, snow cover, soil wetness, vegetation, stratospheric processes, and atmospheric composition (carbon dioxide, ozone, etc.).

The observed current climate changes are a combination of anthropogenic influences and natural variability. In addition to possible anthropogenic influence on climate due to changing the atmospheric composition, land use in the tropics is undergoing extensive changes. This can lead to significant changes in the biophysical properties of the land surface, in turn impacting atmospheric variability on sub-seasonal-to-decadal timescales. It is therefore essential that the research work of two separate science communities (climate change and seasonal prediction) be merged into a focused effort to understand the predictability of the complete climate system.

The results of these experiments provide a framework for future experiments. Specifically these results will:

  • 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; and
  • provide a test bed for evaluating IPCC class models in seasonal prediction mode.

 Certain elements of the proposed experiment are already part of various WCRP activities. The intent is to leverage these ongoing activities and to coordinate and synthesize these activities into a focused seasonal prediction experiment that incorporates all elements of the climate system. These experiments are the first necessary steps in developing seamless weekly-to-decadal prediction of the complete climate system.

CHFP Experimental Protocol

The core experiment is an 'Interactive Atmosphere-Ocean-Land-Ice Prediction Experiment' emphasizing the use of comprehensive coupled general circulation models, which includes realistic interactions among the component models. The experiment is to perform seven-month lead ensemble (10-members) predictions of the total climate system. If possible longer leads and larger ensembles are encouraged. The initialization strategy is to use the best available observations of all the components of the climate system.

While the emphasis is on comprehensive coupled general circulation models, uncoupled component, intermediate, simplified and statistical models are encouraged to participate where appropriate. The fundamental experimental design is to mimic real prediction in the sense that no “future” information can be used after the forecast is initialized. For example, the PROVOST or DSP experiments would be excluded because they use observed SST as the simulation evolves, whereas the SMIP/HFP experiment could be included as a subset since no future information is used as the forecast evolves.

The component models should be interactive, but this is left open to encourage wider participation, e.g. for groups without sea ice or vegetation models. The only requirement is that no “future” information is used once the prediction is initialized. This requirement necessarily includes any tuning or training of the component models or the development of statistical prediction schemes.

Thus, the component models are:
Ocean - Open but interactive (e.g., slab mixed layer or GCM)
Atmosphere - Open but interactive (most likely a GCM)
Land - Open but interactive (e.g. SSiB, Mosaic, BATS, CLM, Bucket)
Ice - Open but interactive (e.g., thermodynamic or dynamic)

Protocol

Coupled models and resolution are left to the individual participants, but it is desirable that the models have a realistic simulation of the atmosphere, ocean, land and ice and the interactions among these components. Simplified component models (e.g., slab mixed layer or statistically predicted ice) are acceptable as long as no future information is used in developing the simplified model.

Atmospheric initial states are taken from NCEP (or ECMWF), with reanalysis in February, May, August and November of each year from 1979-present. Forecasts should be initialized at 00Z and 12Z on the last five days of each preceding month forming a 10-member ensemble. Other strategies for generating the ensemble members are acceptable as long as the basic principle of 'no future information' is not violated. Each ensemble member should be run for at least six months. Additional ensemble members and longer leads are encouraged.
a. Additional retrospective forecasts using each month of each year from 1979-present are encouraged.
b. Additional retrospective forecasts using initial conditions from each February, May, August, and November 1960-1978 are encouraged.

  • Oceanic initial states: (if appropriate) to be taken from most appropriate ocean data assimilation system
  • Sea Ice initial states: (if appropriate) to be taken from best available observational data
  • Land initial states: (if appropriate) to be taken from most appropriate land data assimilation system or consistent offline analyses driven by observed meteorology (i.e., GSWP; GLACE2)

CHFP Project timeline

Data requirements

It is noted that some forecast providers may not be able to provide the complete list noted here. Participation is strongly encouraged even if all the data requirements cannot be met.

- Guide for data producers (see here for minimum monthly output requirements)

Atmospheric output

a. Every 24 hours at 00 GMT-
i. Pressure levels (instantaneous): Geopotential Height, Temperature, Velocity and specific humidity for 850, 500, 200, (if available 100, 50, 10; these higher pressure levels are used for interactions with APARC) hPa.
ii. Surface (instantaneous): 2m Tmax - daily, 2m Tmin - daily, Total soil moisture, Snow depth, Sea surface temperature and surface radiative temperature over land (if available), Mean sea level pressure, soil heat flux over land (if available).
iii. Surface (accumulated): Total precipitation, Downward surface solar radiation, Downward surface longwave radiation, Surface net solar radiation, Surface net longwave radiation, Top net solar radiation, Top net longwave radiation, Surface momentum flux, latent and sensible heat flux.

b. Every 6 hours at 00, 06, 12, 18 GMT-
i. Surface (instantaneous): Total cloud cover, 10m wind, 2m Temperature, 2m Dew Point, 2 m specific humidity.

Oceanic output

a. Every Month-
i. Accumulated temperature, salinity and currents in the (at least) upper 400 m, surface fluxes of heat, momentum and fresh water, sea level height, mixed layer depth.

b. Every 24 hours at 00 GMT-
i. Temperature, salinity and currents in the (at least upper 400 m) at the equator, 2N and 2S (5N and 5S optional).
c. Every 6 hours at 00, 06, 12 18 GMT-
i. Surface fluxes of heat, momentum, and freshwater. Sea surface temperature and mixed layer depth

Sea ice output

a. Every 24 hours at GMT -
i. Surface fluxes of heat and momentum. Snow cover, Sea ice concentration, thickness and temperature.

Land surface output

Soil wetness and vegetation predicted.
Snow cover and depth predicted.

Chemical composition

Prescribed and varying. This explicitly includes the transient changes in the chemical composition from 1979-present.

CHFP Data Archive

On the CHFP Data Archive page, you will find guidance on preparing, serving and using data from the Climate-system Historical Forecast Project. Please read the relevant guide(s) before starting work, and please provide feedback on these web pages to wcrp@wmo.int. Note that these pages are still in an early stage of development, and the data conventions described are still subject to possible change.

CHFP Data Server at CIMA

The CHFP data set is being hosted by the Centro de Investigaciones del Mar y la Atmósfera (CIMA), Argentina. The CHFP dataset is open and free for non-commercial purposes. After registering anyone can obtain the model output.

Please use the following acknowledgment when using CHFP data:

"We acknowledge the WCRP/CLIVAR Working Group on Seasonal to Interannual Prediction (WGSIP) for establishing the Climate-system Historical Forecast Project (CHFP, see Kirtman and Pirani 2009) and the Centro de Investigaciones del Mar y la Atmosfera (CIMA) for providing the model output http://chfps.cima.fcen.uba.ar/. We also thank the data providers for making the model output available through CHFP."

Support documents to archive and retrieve CHFP data

CHFP netCDF specification:

Data is provided in netCDF via THREDDS or OpenDAP servers. In most cases aggregation servers are used, and data retrievals from a single data host will typically contain output from multiple models.

It is recommended that participants send CIMA a test file to address any technical issues before transferring the full data set.

CHFP experiments and diagnostic sub-projects

Experiments associated with the CHFP

The fundamental experimental design is to mimic real prediction, in the sense that no “future” information can be used after the forecast is initialized. For example, the PROVOST or DSP experiments would be excluded because they use observed SST as the simulation evolves, whereas the SMIP experiment could be included as subset since no future information is used as the forecast evolves.

smip Seasonal Prediction Intercomparison Project  
glace Global Land-Atmosphere Coupling Experiment pdf
glace2 Global Land-Atmosphere Coupling Experiment 2 pdf
stratHFP Stratosphere-resolving Historical Forecast Project pdf
iceHFP Sea Ice Historical Forecast Project pdf
Potential subjects for diagnostic sub-projects

The following is an abbreviated list of potential sub-projects. It is anticipated that a large number of addition sub-projects will be implemented as the experimental results become available.

  • Limit of Predictability Estimates: One potential estimate for the limit of predictability is to determine when a particular forecast probability density function (pdf) is indistinguishable from climatological pdf of the forecasts
  • ENSO mechanism diagnostic: Recharge oscillator versus delayed oscillator, role of stochastic forcing, westerly wind events
  • impact of the AO on seasonal predictability
  • regional predictability
  • local land surface predictability
  • extreme events
  • monsoon predictability
  • diurnal cycle in ocean
  • diurnal cycle in the atmosphere
  • coupled feedbacks
  • intra-seasonal oscillations
Submitted sub-projects
Principle investigator Title Proposal Publications
Eric Guilyardi Atmosphere feedbacks and ENSO predictability pdf -

The diagnostic sub-projects will include extensive interactions with the applications community and the regional panels within CLIVAR, as well as the GEWEX, SPARC and CliC WCRP Projects. These interactions and collaborations are viewed as critical elements of the implementation plan and are strongly encouraged.

Participants are invited to email a brief description of their diagnostic sub-project, not to exceed 1-2 pages in length to wcrp@wmo.int. WGSIP will review proposed sub-projects with a view to maximize collaboration and avoid duplication of effort. See below for a list of potential sub-projects.

CHFP Publications

Srivastava, A., S. Rao, N. Pradhan, P. Pillai, and V. Prasad, 2021: Gain of one-month lead time in seasonal prediction of Indian summer monsoon prediction: comparison of initialization strategies. Theor. Appl. Climatol., 143, 1083-1096, https://doi.org/10.1007/s00704-2020-03470-3.

Hu, Z., A. Kumar, and J. Zhu, 2021: Dominant modes of ensemble mean signal and noise in seasonal forecasts of SST. Clim. Dyn., 56, 1251-1264, https://doi.org/10.1007/s00382-020-05531-9.

Pillai, P., S. Rao, A. Srivastava, D. Ramu, M. Pradhan, and R. Das, 2021: Impact of the Tropical Pacific SST biases on the simulation and prediction of Indian summer monsoon rainfall in CFSv2, ECMWF-System4, and NMME models. Clim. Dyn., 56, 1699-1715, https://doi.org/10.1007/s00382-020-05555-1.

Meehl, G., J. Richter, H. Teng, A. Capotondi, K. Cobb, F. Doblas-Reyes, M. Donat, M. England, J. Fyfe, W. Han, H. Kim, B. Kirtman, Y. Kushnir, N. Lovenduski, M. Mann, W. Merryfield, V. Nieves, K. Pegion, N. Rosenbloom, S. Sanchez, A. Scaife, D. Smith, A. Subramanian, L. Sun, D. Thompson, C. Ummenhofer, and S. Xie, 2021: Initialized Earth System prediction from subseasonal to decadal timescales. Nat. Rev. Earth Env., 2, 340-357, https://doi.org/10.1038/s43017-021-00155-x.

Saurral, R.I, W. Merryfield, M. Tolstykh, W.-S. Lee, F. Doblas-Reyes, J. García-Serrano, F. Massonnet, G. Meehl, and H. Teng, 2021: A data set for intercomparing the transient behavior of dynamical model-based subseasonal to decadal climate predictions. J. Adv. Mod. Earth Sys., 13, e2021MS002570, https://doi.org/10.1029/2021MS002570.

Sannikov, S., N. Sannikova, I. Petrova, and O. Cherepanova, 2021: The forecast of fire impact on Pinus sylvestris renewal in southwestern Siberia. J. Forest. Res., 32, 1911-1919, https://doi.org/10.1007/s11676-020-01260-1.

Pradhan, M., S. Rao, T. Doi, P. Pillai, A. Srivastava, and S. Behera, 2021: Comparison of MMCFS and SINTEX-F2 for seasonal prediction of Indian summer monsoon rainfall. Int. J. Climatol., 41, 6084-6108, https://doi.org/10.1002/joc.7169.

Jain, S., A. A. Scaife, N. Dunstone, D. Smith and S. K. Mishra, 2020: Risk Estimation of Unprecedented Monsoon Rainfall over India using Dynamical Ensemble Simulations. Environ. Res. Lett.,https://doi.org/10.1088/1748-9326/ab7b98.

Tanessong, R.S., T. C. Fotso-Nguemo, A. J. K. Mbienda, G. M. Guenang, A. Tchakoutio Sandjon, S. Kaissassou and D. A. Vondou, 2020: Assessing Climate-system Historical Forecast Project (CHFP) seasonal forecast skill over Central Africa. Theor. Appl. Climatol., https://doi.org/10.1007/s00704-020-03176-6.

Osman, M. and C. S. Vera, 2020: Predictability of extratropical upper-tropospheric circulation in the Southern Hemisphere by its main modes of variability. J. Clim., 33, 1405–1421, https://doi.org/10.1175/JCLI-D-19-0122.1.

Jain, S., A. A. Scaife, and A. K. Mitra, 2019: Skill of Indian summer monsoon rainfall prediction in multiple seasonal prediction systems. Climate Dyn., 52, 5291–5301, https://doi.org/10.1007/s00382-018-4449-z.

Scaife, A. A., L. Ferranti, O. Alves, P. Athanasiadis, J, Baehr, M, Déqué, T. Dippe, N. Dunstone, D. Fereday, R. G. Gudgel, R. J. Greatbatch, L. Hermanson, Y. Imada, S. Jain, A. Kumar, C. MacLachlan, W. Merryfield, W. A. Müller, H. L. Ren, D. M. Smith, Y. Takaya, G. Vecchi and X. Yang, 2019: Tropical rainfall predictions from multiple seasonal forecast systems. Int. J. Climatol., 39, 974-988, https://doi.org/10.1002/joc.5855.

Battisti, D. S., D. J. Vimont and B. P. Kirtman, 2019: 100 years of progress in understanding the dynamics of atmosphere–ocean variability. A Century of Progress in Atmospheric and Related Sciences: Celebrating the American Meteorological Society Centennial, Meteor. Monogr., No. 59, Amer. Meteor. Soc.,  https://journals.ametsoc.org/doi/10.1175/AMSMONOGRAPHS-D-18-0025.1

Merryfield, W.J., F. J. Doblas‐Reyes, L. Ferranti, J.-H. Jeong, Y. J. Orsolini, R. I. Saurral, A. A. Scaife, M. A. Tolstykh and M. Rixen, 2017: Advancing climate forecasting. Eos, 98, 17–21, https://doi.org/10.1029/2017EO086891.

Gleixner S., N. S. Keenlyside, T. D. Demissie, F. Counillon,Y. Wang and E. Viste E, 2017: Seasonal predictability of Kiremt rainfall in coupled general circulation models. Environ. Res. Lett., 12,114016, https://doi.org/10.1088/1748-9326/aa8cfa.

Tompkins, A. M., M. I. O. D. Zárate, R. I. Saurral, C. Vera, C. Saulo, W. J. Merryfield, M. Sigmond, W.-S. Lee, J. Baehr, A. Braun, A. Butler, M. Déqué, F. J. Doblas‐Reyes, M. Gordon, A. A. Scaife, Y. Imada, M. Ishii, T. Ose, B. Kirtman, A. Kumar, W. A. Müller, A. Pirani, T. Stockdale, M. Rixen, and T. Yasuda, 2017: The Climate-System Historical Forecast Project: Providing open access to seasonal forecast ensembles from centers around the globe. Bull. Amer. Meteor. Soc., 98, 2293–2301, https://doi.org/10.1175/BAMS-D-16-0209.1.

Osman, M., and C. S. Vera, 2017: Climate predictability and prediction skill on seasonal time scales over South America from CHFP models. Climate Dyn., 49, 2365–2383, https://doi.org/10.1007/s00382-016-3444-5.

Butler, A. H., A. Arribas, M. Athanassiadou, J. Baehr, N. Calvo, A. Charlton‐Perez, M. Déqué, D. I. V. Domeisen, K. Fröhlich, H. Hendon, Y. Imada, M. Ishii, M. Iza, A. Y. Karpechko, A. Kumar, C. MacLachlan, W. J. Merryfield, W. A. Müller, A. O'Neill, A. A. Scaife, J. Scinocca, M. Sigmond, T. N. Stockdale, and T. Yasuda, 2016: The climate-system historical forecast project: Do stratosphere-resolving models make better seasonal climate predictions in boreal winter? Quart. J. Roy. Meteor. Soc., 142, 1413–1427, https://doi.org/10.1002/qj.2743.

Osman, M., C. S. Vera, and F. J. Doblas-Reyes, 2016: Predictability of the tropospheric circulation in the Southern Hemisphere from CHFP models. Climate Dyn., 46, 2423–2434, https://doi.org/10.1007/s00382-015-2710-2.

Kirtman, B. and A. Pirani, 2009: The State of the Art of Seasonal Prediction Outcomes and Recommendations from the First World Climate Research Program (WCRP) Workshop on Seasonal Prediction, Bull. Amer. Meteor. Soc., 90, https://doi.org/10.1175/2008BAMS2707.1