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In SEAS5, anomalies are evaluated relative to 1993-2016 model climate (shorter S-M-climate), both for consistency with Copernicus C3S and because anomalies relative to a more recent “past” are likely to be more relevant to most users.  However, the re-forecasts are also produced from 1981-2016 (longer S-M-climate). This period is the basis of the verification charts provided online, and also allows users to explore the choice of different reference and calibration periods.

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For each forecast parameter, forecast lead-time, calendar start date (the 1st of each month) and location, the 600 re-forecasts of the shorter S-M-climate are analysed to determine the median and terciles of the model climate distribution.  The terciles are:

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Using the forecast we can calculate the fraction of ensemble members that predict values to be above the upper tercile or below the lower tercile of the model climate distribution, or indeed lie in between.  The predicted "probabilities" can be very different from 1/3 within each tercile category.  These situations are of particular interest because they indicate a departure from the distribution of results in the re-forecasts making up the shorter S-M-climate.

Quintiles

For each forecast parameter, forecast lead-time, calendar start date (the 1st of each month) and location, the 600 re-forecasts of the shorter S-M-climate are analysed to determine the median and quintiles of the model climate distribution. The quintiles are:

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Using the forecast we can calculate the fraction of ensemble members that predict values to be above the upper quintile or below the lower quintile of the model climate distribution, or indeed lie in between.  The predicted "probabilities" can be very different from 1/5 within each tercile category.  These situations are of particular interest because they indicate a large departure from the distribution of results in the re-forecasts making up the shorter S-M-climate.

Probabilities (tercile category) charts

These show the proportion of ensemble members lying within each tercile category (i.e. below the lower tercile, between the lower and upper tercile, or above the upper tercile) of the shorter S-M-climate. Contour intervals are chosen to show both where there is an unusually high chance of a particular category occurring and also where there is an unusually low chance of a particular category occurring.    

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Fig8.3.1-1: The chart shows the probability that precipitation will lie in the upper tercile category of the shorter S-M-climate over the three months Oct-Dec 2023, DT September 2023 run.

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Fig8.3.1-2: The chart shows the probability that precipitation will lie in the lower tercile category of the shorter S-M-climate over the three months Oct-Dec 2023, DT September 2023 run.

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Fig8.3.1-3: The chart shows the probability that precipitation will lie in the upper quintile category of the shorter S-M-climate over the three months Oct-Dec 2023, DT September 2023 run.

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Fig8.3.1-4: The chart shows the probability that precipitation will lie in the lower tercile category of the shorter S-M-climate over the three months Oct-Dec 2023, DT September 2023 run.

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These charts show the probability (proportion of ensemble members) being above the upper tercile (shades of green, wetter) or below the lower tercile (shades of brown, drier) of the shorter S-M-climate.  This plot gives a convenient, simple overview of a seasonal forecast.  Darker colours imply greater confidence in anomalously high precipitation (green) or low precipitation (brown).

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Fig8.3.1-5: The chart shows a summary of the probabilities (proportion of ensemble members) that precipitation will lie in the higher or lower tercile category of the shorter S-M-climate over the three months Oct-Dec 2023, DT September 2023 run.

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These charts show the probability (proportion of ensemble members) being greater than the median of the shorter S-M-climate over the three months Oct-Dec 2023, DT September 2023 run.   The probabilities are shaded symmetrically above 60% and below 40%.  Contours are used to show where the S-M-climate and the forecast are significantly different at the 1% level, based on a Wilcoxon rank-sum test which is efficient at detecting shifts in the distribution.  See the implications of using mean values of the S-M-Climate below.

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In regions with very skewed S-M-Climate distributions, it is possible for the extreme but rare observations to cause the mean value of the whole distribution to be shifted from a value that would describe the climate more realistically.  The problem predominantly affects evaluation of anomalies of precipitation, but on rare occasions can theoretically also affect temperature.

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Inspection of the distribution of probabilities within the S-M-Climate for the region will allow assessment of the probability that dry conditions will occur.  The median value (at 50%) often gives a much better indication of ‘usual’ conditions.

Anomaly charts of rainfall or temperature are based upon anomalies between the forecast seasonal ensemble mean values and the S-M-Climate mean values.  Users should beware misrepresentation of anomalies on the charts in arid regions.  In these cases it is wise to compare the forecast mean precipitation with the median of the S-M-Climate.  See Fig8.3.1-8 for an illustration.

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