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The ER-M-climate is used in association with Extended Range ENS forecasts, primarily to highlight significant forecast anomalies in weekly averages of 2m temperature, soil temperature and extended range

• 2m temperature.
• soil temperature.
• sea-surface temperature

...

• .
• mean sea level pressure.
• precipitation. 

Creation of ER-M-Climate

The ER-M-Climate is derived from a set of extended range re-forecasts evaluated over several years for data times either side of the extended ENS run itself.  The re-forecast runs are at the same resolution as the extended range run itself (currently ~18km up to day15 and ~36km thereafter) and run over the 46-day extended range ENS period.   

Selection of re-forecasts

The set of re-forecasts is based on the three consecutive dates surrounding the day and month of the extended ENS run in question.  Also further re-forecasts are , and precipitation from the norm for a given location and time of year.To evaluate the ER-M-climate, 3 consecutive re-forecast sets are used, based on Mondays and Thursdays, the middle one of which corresponds to the same date (i.e. day-of-month and month) as the data time of the extended ENS run itself.  These 3 re-forecast sets are each created using the same calendar start dates for each of the last 20 years.    

Each re-forecast consists So each ER-M-climate dataset consists of 3 sets of an 11-member ensemble (1 control and 10 perturbed members) run over the 46-day Extended Range ENS period.  .  Therefore altogether 20 years x 3 runs x 11 ENS members = 660 re-forecast values are available to define the ER-M-climate for a given location and forecast lead-time.

based on Mondays and Thursdays (is it still this with daily extended range forecasts?)

   Note also that the ER-M-climate re-forecast runs are at the same resolution as the extended range runs themselves (currently ~18km up to day15 and ~36km thereafter).  In fact it is essentially the same re-forecast runs that are utilised to build the M-Climate and the ER-M-Climate; the key difference is that those runs are grouped together in different ways.

ER-M-climate is updated twice a week, every Monday and Thursday, and based on 00UTC runs only (there are no 12UTC re-forecast sets). 

The new files start to be used from the 00UTC run that day.  So if one compares, for the same lead-time, the ER-M-climate quantile plots (e.g. for a Thursday run, and a run the following Monday, they will be slightly different as the ER-M-climate changes).  The impact of twice-weekly updates to the ER-M-climate is similar to that for M-climate and can be significant, particularly in spring and autumn when mean temperatures are changing most rapidly day by day.  Note also that 33% of the re-forecast members making up the ER-M-Climate change each time that the ER-M-climate is updated.  For the shorter range M-Climate the equivalent change is 1/9 = 11%.  So the ER-M-climate is a little more prone to jumpiness related to sampling.

Different reference periods for M-Climate and ER-M-Climate

One might ask why ECMWF uses different length reference periods to define the M-Climate and the ER-M-Climate.  The reason is that they are used in different ways.  In the shorter ranges, where extremes are a priority, we want to define the climatological distribution tails as well as we can, and it has been shown that using 1980 realisations (spanning 4 weeks) achieves this much better than using 660 (spanning 1 week).  Conversely for longer ranges the priority is correct representation of seasonal cycles, which can be better achieved by spanning 1 week rather than 4.  And because we use week-long averaging in the extended ranges the tails should not be so prone to having a reduced sample size anyway.