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Seasonal spread charts are computed for the beginning of each ERA5 decade and for the latest period too (they are available by request from the ERA5 team). These charts give an idea about the level of uncertainties for different seasons, regions, periods, levels and variables. For instance, for the summer season of 1980:
For 200 hPa zonal wind the largest uncertainties are in the tropical regions. | For 850 hPa temperature, the uncertainties are generally larger in the Southern Hemisphere (this corresponds well with the fact that we have fewer observations in the Southern Hemisphere). | For MSLP the Antarctic region has the largest spread/uncertainty. |
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For all variables it is clear that the uncertainties are decreasing with time, i.e. the spread values are smaller for recent periods than for older ones.
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One of the most important aspects that determines the ERA5 uncertainties is the amount and quality of available observations. The Global Observing System (GOS) has been evolving during the ERA5 period, which means that the observation amounts are generally increasing with time and as a result, uncertainties are decreasing. However there are some short periods, where there are fewer observations available. Typically, in the 1980s when the number of satellite observations was still quite low, there are some short periods, when missing observations cause an increase in the uncertainty i.e. an increase of the ensemble spread. The evolution of the mean spread for vorticity and temperature, for 3 different model levels, demonstrates this:
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It can be seen that generally the spread (uncertainty) is steadily decreasing over time except for some jumps in the early periods. These jumps correspond to the blips in observation amounts. For instance, at the end of 1979 there were some shorter periods when the MSU and SSU instruments onboard the TIROS-N and NOAA-9 satellites were providing significantly fewer observations than normal. It is noted here that in the vast observing system of the present day, there is a degree of resilience which means that the assimilation system is much less sensitive to the failure of one instrument or satellite.
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Indeed, the instantaneous spread fields might be noisy at particular locations especially in the early reanalysis periods. For instance on 00 UTC 19800301 the MSLP spread is noisy over the Antarctic, the 850 hPa temperature spread is particularly noisy in the Southern Hemisphere and the 200 hPa zonal wind spread is noisy in the tropical region:
MSLP spread | 850 hPa temperature spread | 200 hPa zonal wind spread |
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The main reason for this is the limited ensemble size of 10 members that introduces considerable sampling noise. On the other hand, if we consider the mean seasonal (JJA 1980 in this case) spread for the three variables the fields are much smoother and easier to interpret. For seasonal mean fields, this sampling noise is averaged out and as a result will provide smoother spread fields:
| MSLP (mean seasonal (JJA) | 850 hPa temperature (mean seasonal (JJA) | 200 hPa zonal wind (mean seasonal (JJA) |
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When I look at active systems such as extra-tropical cyclones or tropical cyclones I expect a larger uncertainty, yet I do not see that clearly in the ensemble spread
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