The committee was requested to assess how researchers and forecasters have addressed these challenges and to recommend avenues for further progress. Specifically, the committee was tasked to review the current understanding of ISI predictability, describe how past improvements in forecast systems have occurred, identify gaps in our current understanding of ISI predictability, assess the performance of current ISI forecast systems, and recommend strategies and best practices for future improvements to ISI forecasts and our overall understanding of ISI predictability.
The committee begins from the premise that the ability to predict the climate accurately at ISI timescales stems from our knowledge of “sources of predictability,” the variables or processes operating within and among the atmosphere, ocean, and land that affect the state of the climate on ISI timescales. The sources of predictability are measured, represented, and simulated by ISI forecast systems through an assemblage of “building blocks,” namely observational systems, statistical and dynamical models, and data assimilation schemes. This report illustrates the relationship between the sources of predictability and the building blocks of ISI forecast systems. In addition, this report discusses techniques and protocols for the verification and dissemination of ISI forecasts by operational forecasting centers, highlighting the impact that these practices can have on forecast quality and opportunities for improvement. This report concludes with recommendations for improving ISI forecast systems, targeting both operational forecasting centers and the broader research community.
This report explores three interrelated categories of predictability sources that exist within the climate system. The first of these sources of predictability is related to particular variables that exhibit inertia or memory, such as ocean heat content, in which anomalous conditions can take relatively long periods of time (days to years) to decay. The second type of source of predictability is related to patterns of variability or feedbacks. Coupling among processes in the climate system can give rise to characteristic patterns that explain some portion of the spatial and temporal variance exhibited by key climate variables, such as temperature or precipitation. An example is the El Niño-Southern Oscillation (ENSO), where anomalous conditions in the tropical Pacific Ocean influence seasonal climate in the mid-latitudes around the globe. The third source of predictability is due to external forcing. Volcanic eruptions, changes in solar activity, and the accumulation of greenhouse gases in the atmosphere are all examples of external forcing. These events or processes can affect the climate on ISI timescales in predictable ways that can be exploited for making climate predictions.
It is important to note that the processes that affect the climate on ISI timescales can themselves operate on a variety of timescales. This is depicted in Figure S.1, which provides many examples of processes that affect the climate at ISI timescales and can serve as sources of predictability. These sources can be related to phenomena that occur in, on, or among the ocean, atmosphere, and land surface components of the climate system.
The ability of ISI forecast systems to represent these sources of predictability accurately partially determines the quality of the predictions. Past improvements in prediction quality have accompanied increased understanding of the sources of predictability and incorporation of this understanding into forecast systems. Future advances in the quality of ISI predictions are closely