Longrange outlooks

The atmosphere-ocean system is a non-linear (chaotic) system making exact long-term prediction of individual weather events impossible. Small errors in the initial conditions used to start a model simulation invariably to grow in magnitude and spatial scale and the entire globe will generally be affected by a small observational error at a single point before long. Therefore, long-term weather prediction and climate prediction do not try to predict individual weather events for these would certainly be in error. Instead they generally try to represent the statistics of the climate rather than the weather itself and are often associated with probabilities based on statistical relationships.

Like numerical forecasting at shorter timescales, long-range (monthly and seasonal) outlooks use a combination of dynamical and statistical approaches in order to assess the probability of certain weather situations. Long-range forecasts rely on the idea that some types of weather, despite being unpredictable in their details, may, under certain circumstances, be more likely than in others. One major recent advance in long-range forecasts is the realization that El Niño-Southem Oscillation has documented statistical effects in many parts of the globe. For any particular El Niño or La Niña it is generally not realistic to forecast increased/decreased precipitation at most points in the globe but many regions show a statistical tendency towards more or less precipitation or higher/lower temperatures depending on the phase of ENSO. Long-range forecasts make use of these statistical relationships. ENSO has a fairly regular periodicity allowing for some skill in predicting changes in phase just from climatology. Several dynamical models also try to predict the future phase of ENSO, though these have not been dramatically more sucessful than a knowledge of the climatology. The phase of ENSO is the single most important factor going into long range forecasts today.

The United States NCEP is again typical of the methodology used globally. It currently issues thirty-day and three-month seasonal forecasts up to one year into the future. The primary information used in these outlooks is the phase of ENSO, recent and extended climate history, the pattern of soil moisture which can affect temperature and precipitation far into the future, and an ensemble of twenty GCM model runs driven with predicted SSTs from an AOGCM simulation over the period. This information is used to produce a variety of indices which predict the probability of three equally likely categories of temperature (near normal, above/below normal) and precipitation (near average,

Figure 8.7 Forecast of North American weather for December 1985 made one month ahead (A) Predicted 700-mb contours (gp dam). Solid arrows indicate main tracks of cyclones, open arrows of anticyclones, at sea-level. The forecasting of such tracks has recently been discontinued. (B) and (C) Forecast average temperature (B) and average precipitation (C) probabilities. There are three classes of temperature, above normal, normal and below normal, and similarly heavy, near normal and light for precipitation. Each of these classes is defined to occur 30 per cent of the time in the long run; near-normal temperature or moderate precipitation occur 30 per cent of the time in the long run; near-normal temperature or moderate precipitation occur 40 per cent of the time. The 30 per cent heavy lines indicate indifference (for any departure from average), but near-normal values are most likely in unshaded areas.

Source: From Monthly and Seasonal Weather Outlook, 39(23) (28 November 1985), Climate Analysis Center, NOAA, Washington, DC.

above/below the median) (see Figures 8.7 and 8.8), together with tables for many cities. Figure 8.8A illustrates the observed height field corresponding to Figure 8.7A for December 1985, showing that the pattern is well represented on the forecast chart. Figure 8.8B and C show that in this case, as is usual, the temperature forecasts are more reliable than those for precipitation.

A statistical techniqe called a canonical correlation analysis uses all the above information to produce longrange outlooks. Simulated 700-mb heights, global SST patterns, US surface temperature and precipitation for the past year are all used to infer possible preferred patterns. Temperature and precipitation history give information about persistence and trends over the year. ENSO is emphasized in this analysis but other natural modes of variability such as the North Atlantic Oscillation are also accounted for.

Secondary analyses which use single predictor variables are also available and become more or less useful than the correlation analysis under differing circumstances. The composite analysis estimates ENSO effects by defining whether a La Niña, El Niño, or neutral conditions are forecast for the period of interest and then taking into account whether there is confidence that this one phase of ENSO will exist. Another index predicts future temperature and precipitation based on persistence in the past ten or fifteen years. This measure emphasizes trends and long-term regimes. A third secondary index is a constructed analogue forecast from soil moisture patterns.

Figure 8.8 Actual North American weather for December 1985 (cf. Figure 8.7). Observed 700-mb contours (5 gp dam) corresponding to Figure 8.7A, B and C. Observed temperature (B) and precipitation (C), corresponding to Figure 8.7B and C.

Source: From Monthly and Seasonal Weather Outlook, 40(1) (1986), Climate Analysis Center, NOAA, Washington, DC.

Figure 8.8 Actual North American weather for December 1985 (cf. Figure 8.7). Observed 700-mb contours (5 gp dam) corresponding to Figure 8.7A, B and C. Observed temperature (B) and precipitation (C), corresponding to Figure 8.7B and C.

Source: From Monthly and Seasonal Weather Outlook, 40(1) (1986), Climate Analysis Center, NOAA, Washington, DC.

Forecast skill for long-range outlooks is mixed. For all measures skill in temperature is higher than for precipitation. Precipitation forecasts generally show little skill unless there is a strong El Niño or La Niña. Temperature outlooks show the highest skill in late winter and late summer.

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