BOX 3.1


Observation—measurement of a climate variable (e.g., temperature, wind speed). Observations are made in situ or remotely. Many remote observations are made from satellite-based instruments.

Statistical model—a model that has been mathematically fitted to observations of the climate system using random variables and their probability density functions.

Dynamical or Numerical model—a model that is based, primarily, on physical equations of motion, energy conservation, and equation(s) of state. Such models start from some initial state and evolve in time by updating the system according to physical equations.

Data assimilation—the process of combining predictions of the system with observations to obtain a best estimate of the state of the system. This state, known as an “analysis”, is used as initial conditions in the next numerical prediction of the system.

Operational forecasting—the process of issuing forecasts in real time, prior to the target period, on a fixed, regular schedule by a national meteorological and/or hydrological service.

Initial conditions/Initialization—Initial conditions are estimations of the state (usually based on observational estimates and/or data assimilation systems) that are used to start or initialize a forecast system. Initialization can include additional modification of the initial conditions to best suit the particular forecast system.

Progress since the 1960s can be discussed in terms of advances in forecasting approaches (including their evaluation) and improved understanding and treatment of underlying mechanisms. One major direction of advancement in forecasting has been that of dynamical modeling (see “Dynamical Models”section in this chapter). Generally the dynamical models continued to improve according to advancements in computational resources and a growing knowledge of the key processes to be modeled. However, official forecasts in the United States depended on subjective interpretation of these objective products. In addition, various statistical (empirical) modeling approaches were developed and improved to remain as capable as the dynamical approaches in their validation. Other countries have been developing similar capabilities for seasonal prediction since the 1980s, largely depending on numerical modeling.

Recognition of the role of tropical SST anomalies, especially those associated with ENSO, in driving remote climate anomalies has led to much work in predicting tropical SST. Some of the key advancements in estimating these SSTs developed during the TOGA international study in conjunction with the deployment of the Tropical Atmosphere Ocean (TAO) array in the 1980s and 1990s (NRC, 1996; see “Ocean Observations” section in this chapter and “ENSO” section in Chapter 4).

Further expansion of the efforts in ISI forecasting have been undertaken by CLIVAR (Climate Variability and Predictability), a research program administered by the World Climate

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