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To complement the EFI and SOT for two parameters in the extended range (introduced with IFS cycle 46r1 in June 2019) a facility to view extended-range Cumulative Distribution Functions (CDFs) for those parameters was also introduced (in February 2020). Unlike  Unlike ECMWF's pre-existing CDF-viewing tools, used for 24h periods at shorter ranges, which show absolute values, these CDFs depict anomalies (relative to the ER-M-Climate distribution). They  They cover the following:

Parameters:

27-metre weekly day mean 2m temperature anomaly
7-day total precipitation anomaly.

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Cumulative Distribution Function (CDF)s for ensemble temperature and rainfall forecasts may be constructed from ensemble extended range forecasts.   It is important to note that here it is anomalies from the "norm" that are considered rather than absolute temperature or rainfall values.  The anomalies for the extended range climate (ER-M-climate) (black line) are the frequencies of departures from the mean (here defined as the "norm") of the ER-M-climate for the date in question (i.e.the light green lines on the diagram indicate the value at 50% probability and marked as 0°C anomaly).   Some anomalies are positive, some in the tails of the plot are extremely positive; some are negative, some some in the tails of the plot extremely negative.   The CDF for the ENS ensemble values is constructed from the anomaly of the temperature forecast by each ensemble member (red line) as a departure from the mean or "norm" of the ER-M-climate.

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Fig8.2.6.6A: The large positive EFI shows the ENS ensemble temperature anomaly distribution is for warmer anomalies much above the ER-M-climate anomaly distribution.  The positive upper tail SOT (quantile 90) indicates there are several ENS ensemble members predicting extreme warm temperature anomalies (above the 99th ER-M-climate percentile shown by the dashed green line).  This suggests a warm temperature anomaly may be confidently forecast (large positive EFI), probably exceptional but not necessarily extreme (SOT +0.4).  Confidence in extreme temperatures rises as SOT values increase - users should focus on SOT values >0.5.

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Fig8.2.6.6B: The large positive EFI shows the ENS ensemble temperature anomaly distribution is for warmer anomalies much above the ER-M-climate anomaly distribution.  The negative upper tail SOT (quantile 90) indicates generally ENS ensemble members are not predicting an extreme temperature anomaly (above the 99th ER-M-climate percentile shown by the dashed green line) - however, note one ENS ensemble member (extreme top end of red curve) is predicting an extreme temperature anomaly (above the 99th ER-M-climate percentile) though less extreme than the extreme of ER-M-climate anomaly.   This suggests a warm temperature anomaly is confidently forecast (fairly large positive EFI), but will be unexceptional (SOT –0.7) compared with ER-M-climate - but nevertheless one member does suggest a near exceptional warm anomaly is possible.

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Fig8.2.6.6C: The small positive EFI suggests the frequencies of ENS ensemble temperature anomaly distribution is near or a little above the ER-M-climate anomaly distribution.  The negative lower tail SOT (quantile 10) indicates generally ENS ensemble members are not predicting an extreme temperature anomaly (below the 1st ER-M-climate percentile shown by the dashed green line) - however note one ENS ensemble member (extreme bottom end of red curve) is predicting an extreme temperature anomaly (below the 1st M-climate percentile) though less extreme than the ER-M-climate anomaly.   This suggests the temperature anomalies are similar to the ER-M-climate anomaly distribution (small positive/negative EFI), and a cold anomaly, if it occurs, will  be unexceptional compared with ER-M-climate (SOT –1.7) - but nevertheless one member does suggest an exceptional cold anomaly is possible.

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Fig8.2.6.6D: The large negative EFI shows the frequencies of ENS ensemble temperature anomaly distribution is for colder anomalies well below the ER-M-climate anomaly distribution.  The positive lower tail SOT (quantile 10)  indicates there are several ENS ensemble members predicting extreme cold temperature anomalies (below the 1st ER-M-climate percentile shown by the dashed green line).   This suggests a cold temperature anomaly may be confidently forecast (large negative EFI), exceptional and probably extreme (SOT +0.95).  Confidence in extreme temperatures rises as SOT values increase - users should focus on SOT values >0.5.

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Fig8.2.6.7A: An example CDF for snowfall - snowfall is just considered as equivalent rainfall.   Moderately large positive EFI shows the equivalent rainfall anomaly distribution is above the ER-M-climate anomaly distribution.  The positive upper tail SOT (quantile 90) indicates there are several ENS ensemble members predicting extreme equivalent rainfall anomalies (above the 99th ER-M-climate percentile shown by the dashed green line).  This suggests uncertainty that a significant equivalent rainfall anomaly is forecast (moderate EFI.  Note: 50% of ENS ensemble members forecast less than about 1mm precipitation (the lower part of the ER-M-climate only just above 0mm), but equally 25% of ENS ensemble members forecast more than about 2mm precipitation (significantly above ER-M-climate where about 97% of precipitation less than 2mm). If a significant rainfall occurs it could be an exceptional rainfall equivalent (SOT 0.8).  Confidence in extreme rainfall rises as SOT values increase - users should focus on SOT values >0.5.

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Fig8.2.6.7B: The moderate positive EFI suggests the ENS ensemble rainfall anomaly distribution is slightly above the ERM-climate anomaly distribution.  The negative upper tail SOT (quantile 90) indicates there are very few if any ENS ensemble members predicting extreme equivalent rainfall anomalies (above the 99th ER-M-climate percentile shown by the dashed green line).  This suggests uncertainty that larger than normal rainfall anomaly may be forecast (moderate EFI - but note 70% of ENS ensemble members forecast less than about 1mm precipitation, equally 15% of ENS ensemble members forecast more than about 2mm precipitation), but it is unlikely there will be an exceptional rainfall event (SOT -0.6).

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Fig8.2.6.7C: An example of a rainfall CDF most frequently encountered where very few ENS ensemble members forecast any rain at all.  The small negative EFI shows the ENS ensemble rainfall anomaly distribution is lower than the ER-M-climate anomaly distribution.  The negative upper tail SOT (quantile 90) indicates there are very few if any ENS ensemble members (and in this case none of them) predict extreme equivalent rainfall anomalies (above the 99th ER-M-climate percentile shown by the dashed green line).  This suggests confidence that larger than normal rainfall anomaly will not occur (small EFI) and an exceptional rainfall event will not occur(SOT -1.2).

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