The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the ...

/pdf/the-ncep-climate-forecast-system-version-2-3tkemm81z9.pdf

The NCEP climate forecast system version 2

Variations in the El Nino/Southern Oscillation (ENSO) are associated with a wide array of regional climate extremes and ecosystem impacts1. Robust, long-lead forecasts would therefore be valuable for managing policy responses. But despite decades of effort, forecasting ENSO events at lead times of more than one year remains problematic2. Here we show that a statistical forecast model employing a deep-learning approach produces skilful ENSO forecasts for lead times of up to one and a half years. To circumvent the limited amount of observation data, we use transfer learning to train a convolutional neural network (CNN) first on historical simulations3 and subsequently on reanalysis from 1871 to 1973. During the validation period from 1984 to 2017, the all-season correlation skill of the Nino3.4 index of the CNN model is much higher than those of current state-of-the-art dynamical forecast systems. The CNN model is also better at predicting the detailed zonal distribution of sea surface temperatures, overcoming a weakness of dynamical forecast models. A heat map analysis indicates that the CNN model predicts ENSO events using physically reasonable precursors. The CNN model is thus a powerful tool for both the prediction of ENSO events and for the analysis of their associated complex mechanisms.

Deep learning for multi-year ENSO forecasts

El Nino–Southern Oscillation (ENSO) is a naturally occurring mode of tropical Pacific variability, with global impacts on society and natural ecosystems. While it has long been known that El Nino events display a diverse range of amplitudes, triggers, spatial patterns, and life cycles, the realization that ENSO’s impacts can be highly sensitive to this event-to-event diversity is driving a renewed interest in the subject. This paper surveys our current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions.

/pdf/understanding-enso-diversity-4u58u69lqw.pdf

Understanding ENSO Diversity

The Arctic has warmed more than twice as fast as the global average since the late twentieth century, a phenomenon known as Arctic amplification (AA). Recently, there have been considerable advances in understanding the physical contributions to AA, and progress has been made in understanding the mechanisms that link it to midlatitude weather variability. Observational studies overwhelmingly support that AA is contributing to winter continental cooling. Although some model experiments support the observational evidence, most modelling results show little connection between AA and severe midlatitude weather or suggest the export of excess heating from the Arctic to lower latitudes. Divergent conclusions between model and observational studies, and even intramodel studies, continue to obfuscate a clear understanding of how AA is influencing midlatitude weather.

/pdf/divergent-consensuses-on-arctic-amplification-influence-on-5g4bkm06qg.pdf

Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2016RG000549

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2...

/pdf/the-north-american-multimodel-ensemble-phase-1-seasonal-to-5aes907jsr.pdf

The North American multimodel ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction

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The North American multimodel ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction