Model-Analogs (MA) and Linear Inverse Model (LIM) forecasts for Months 1-24

(Experimental NOAA/PSL and U. of Colorado/CIRES Forecast)

Notes: NEW: added 20th century hindcasts (see description below). Watch this space for exciting new addtions.

Experimental forecasts of numerous tropical fields, including precipitation, outgoing longwave radiation (OLR), sea surface temperature (SST), and sea surface height (SSH); other variables may become available at a later date. Anomalies represent monthly averages and are relative to a 1991-2010 monthly climatology, except for the 20th century hindcasts which are relative to a "fair-sliding" 30-yr climatology covering the period immediately prior to the hindcasts.

Current Month 6 MA precipitation forecast and Niño3.4 Months 1-24 forecast from all models:



All gridded hindcast data are available in netCDF format for download here.
All hindcast time series are available for download (ASCII format) here.
The most current forecast output is available for download here.

Details of the techniques

Model-analogs (MA):
Seasonal forecasts are made by starting a climate model from an initial estimate of the latest global three‐dimensional ocean, atmosphere, and land conditions and then using supercomputers to run the model's equations forward in time. These extensive calculations are only feasible at a few national operational centers and large research institutions. However, many similar models are also used for long simulations of the Earth's preindustrial climate, which are made freely available for climate change studies.

The easy availability of such climate simulations allows us to employ an alternative forecasting approach: We draw seasonal forecasts from the information already existing within these simulations, instead of by making new model computations. Within each simulation (typically about 1000 years long), we determine the best matches, or “model‐analogs,” to current observed tropical Indo‐Pacific ocean surface anomalies (sea surface temperatrures (SSTs) and sea surface heights (SSHs)). We contruct an "ensemble" of these analogs, by finding 20 different model states that each most closely match the observed initial ocean surface anomaly. How the model-analog ensemble evolves over the next 24 months within each long simulation is then the seasonal forecast ensemble. This produces forecasts not just of SST and SSH, but of any other variable (such as precipitation) that is also in the model simulation. This analysis is carried out for each of four models, NCAR's CCSM4 and CESM1 and GFDL's CM2.1 and FLOR.

For full, more technical descriptions, see:

  • Ding, H., M. Newman, M. A. Alexander, and A. T. Wittenberg, 2019: Diagnosing secular variations in retrospective ENSO seasonal forecast skill using CMIP5 model-analogs. Geophys. Res. Lett., 46, 1721-1730, doi: 10.1029/2018GRL080598.
  • Ding, H., M. Newman, M. A. Alexander, and A. T. Wittenberg, 2018: Skillful climate forecasts of the tropical Indo-Pacific Ocean using model-analogs. J. Climate, 31, 5437-5459, doi: 10.1175/JCLI-D-17-0661.1.

    We have additionally developed model-analogs using nine different CMIP6 models, all based on their pre-industrial simulations. These are run in the same manner as the four "NMME" models described above, with hindcasts covering the same period and also run in a real-time manner. Additionally, these models were "initialized" with the CERA-20C reanalysis, which starts in 1900, to make a "20th century hindcast" dataset extending from 1900-2009. See the "20th century hindcasts" button above for these results, which currently are only verified for two tropical Pacific indices: the Niño3.4 SST index and the eqSOI mean sea level pressure index.

    For full, more technical descriptions of the 20th century hindcasts, see:

  • Lou, J., M. Newman, and A. Hoell, 2023: Multi-decadal variation of ENSO forecast skill since the late 1800s. npj Climate and Atmos. Sci., 6, 89, doi: \ 10.1038/s41612-023-00417-z.

    Linear Inverse Model (LIM):
    For full, more technical descriptions, see:

  • Newman, M. and P. D. Sardeshmukh, 2017: Are we near the predictability limit of tropical sea surface temperatures? Geophys. Res. Lett., 44, doi: 10.1002/2017GL074088.
  • Penland, C., and P. D. Sardeshmukh, 1995: The Optimal Growth of Tropical Sea Surface Temperature Anomalies. J. Climate, 8, 1999-2024, doi: 10.1175/1520-0442(1995)008<1999:togots>;2.

    Related Forecasts

    Official CPC/IRI ENSO forecast

    Page credits

    Web page design: Don Hooper
    Realtime forecast code development: Yan Wang and Matt Newman
    20th century hindcasts generated by: Jiale Lou

    Standard disclaimer: these forecasts are experimental. NOAA/PSL and CIRES/University of Colorado are not responsible for any loss occasioned by the use of these forecasts.