ERA5

ERA5 is the fifth-generation global climate and weather reanalysis produced by ECMWF.  It is an improved and more comprehensive ECMWF climate reanalysis and is the fundamental initialising analysis for re-forecasts.

ERA5 products are normally updated once per month and within three months of real-time.  Quality assurance processing is applied to ensure consistency by removal of biases in models and observations.   Preliminary daily updates of the dataset can be available to users within seven days of real time.   ERA5 is available for dates from 1979 and is being extended forward in near real time.

ERA5 provides (compared to ERA-Interim):

  • a much improved representation of the troposphere - higher spacial resolution (137 height levels, horizontal resolution 31km; 62km for EDA).
  • an improved representation of tropical cyclones.
  • better global balance of precipitation and evaporation.
  • better precipitation over land in the deep tropics.
  • better soil moisture.
  • more consistent sea surface temperatures and sea ice.
  • uncertainty estimates.
  • many output parameters.
  • uses historical data (back to 1950).
  • output frequency of analysis fields (hourly, 3-hourly for EDA).

There is a potential problem related to inconsistencies between the model climate (initialised with ERA5) and current real time forecast (introduced in Cy48r1).

The current real-time forecast uses new glacier fields and multi-layer snow scheme which are not included in ERA5.  This inconsistency was addressed in 49r1.

ERA6

ERA6 is the sixth-generation global climate and weather reanalysis being developed by ECMWF.  It is a further improved and more comprehensive ECMWF climate reanalysis.   It is expected to be introduced by early 2027 using data from the period 2006-2016 and will be gradually extended to use data from the period 1946-date by 2029.  ERA6 is based upon IFS Cy49r2 and will benefit from 75 years of reanalysis and includes satellite reprocessing and data rescue.  It will be maintained close to real time once completed

It will become the fundamental initialising analysis for re-forecasts and will be available daily.

ERA6 provides:

  • a higher horizontal resolution of 14km (TCo799) for all components (ERA5: 31km for atmosphere, 40km for waves).
  • an uncertainty estimate at 28km (TCo399) from 11-member ensemble (63km for ERA5).
  • the ocean model (NEMO4) fully coupled in DA trajectories

  • methods dealing with stratospheric biases.

  • better forcing by large lakes.

  • better initialization of soil moisture.

  • and resolves several other ERA5 known issues.

Products include:

  • 3D+2D ocean parameters.
  • Height levels for the lowest part of the atmosphere.
  • Hourly products for an extended list of new parameters (e.g., 2m humidity, clear-air turbulence (CAT)). 

Tentative first impressions are:

  • 2 m dew point: The oceans in both hemispheres are generally more humid in ERA6. Australia shows particularly large differences. Over land, especially in tropical regions, ERA6 is slightly drier than ERA5.
  • Skin temperature: ERA6 is generally warmer over the oceans. North Africa and the mountainous regions of East Asia show substantially warmer skin temperatures. Elsewhere, ERA6 is slightly colder, particularly over the polar regions, where differences in the representation of snow over ice appear to play a role.
  • Total column water vapour: ERA5 contains more atmospheric water vapour over most of the globe. The main exceptions are tropical regions, including parts of Africa and Brazil, where ERA6 is more moist.
  • 10 m wind speed: ERA6 generally produces faster surface winds than ERA5.
  • Sea surface temperature: ERA6 has warmer SSTs across the tropics and slightly cooler SSTs in the extratropics. These differences are likely related to the new ocean model and the removal of partial coupling. The differences in the daily maximum SST are even more pronounced, most likely reflecting changes in the treatment of the diurnal cycle in ERA6.
  • Total soil moisture: ERA6 appears to have lower total soil moisture over most land areas. The reason for this behaviour is not clear.
  • Snow water equivalent: ERA6 generally has lower SWE across the Northern Hemisphere extratropics, while the polar regions exhibit higher SWE than ERA5.


Reanalysis 

Reanalysis combines observations made in the past with the current IFS model to provide a complete, consistent and model-compatible numerical representation of past weather and climate.  The analysis is a physically consistent blend of (past) observations with a short-range forecast based on the previous analysis.  The quality and availability of the past observational data is improved and careful quality control is carried out as reanalyses are produced.  Reanalyses are produced at lower resolution than current weather forecasts, but they use the same modern data assimilation system and forecasting model throughout the reanalysis period.   

Reanalyses contain estimates of atmospheric parameters:

  • atmospheric parameters (e.g. air temperature, pressure, and wind at different altitudes).
  • surface parameters (e.g. rainfall, soil moisture content, and sea-surface temperature).

The estimates are produced as grid-box averages for all locations on earth. The reanalyses can made from data several decades old even when data was less available than currently (e.g. from paper-based records).  

A reanalysis is necessary for initiation of each re-forecast.   Spacial resolution are generally different between reanalyses and the model version used to create the re-forecasts.



Re-forecasts

The system uses historical re-forecast runs on dates in past years relating to the date (i.e. month and day) of the current ensemble run.   Re-forecasts are based on an ensemble of forecast members ideally using the same model techniques and physics as the current model.   The re-forecast ensemble uses the appropriate reanalysis field for initialisation.   Perturbations are applied to all but the control.  This is similar to the operational ensemble, but does not involve any data assimilation.

The perturbations derive from singular vectors (SVs) plus geographical averages of ensemble of data assimilations (EDA).  The EDAs are perturbations that have been computed operationally over the most recent 12 months.  This approach means that the flow-dependence inherent in operational EDA perturbations is missing in the re-forecasts.  Stochastic physics are also used during the re-forecast runs, as in operational ensemble runs.  

The set of re-forecast ensembles is based on previous dates which can stretch back several decades.  They differ in number and detail according to the IFS model configuration and are the basis for deriving the corresponding model climates.  These are described in the relevant section for medium range M-climate, sub-seasonal range SUBS-M-climate, and seasonal S-M-climate.

The procedures adopted for using re-forecasts allow for seasonal variations and model changes to be taken into account.  But note the model climates (M-climate, SUBS-M-climate, or S-M-climate) can nevertheless be different from the observed climate.

Some limitations of re-forecasts

Impact of differences between reanalysis and re-forecast systems

The model climate is generally compatible with model forecast output but there are still some local inconsistencies.  In particular:

  • the land surface scheme used by ERA-Interim differs from the current IFS model version.  An equivalent "offline land surface reanalysis" that is compatible with the current model version may be used instead. 
  • reanalysis is not performed over open water surfaces and climatological reference data is used instead.       
  • real-time IFS contains a lake model (FLake) while ERA-Interim did not.   Representation of temperature in the model climate may diverge in a systematic way from the actual changes within the forecast period.  These effects are particularly important over and around large lakes or inland waters (e.g. the Great Lakes - notably Lake Superior, Aral Sea, Caspian Sea and some others). 

Forecasters should consider possible deficiencies in model climates when considering extreme forecast index EFI data.  For an example of the effect, see Fig5.3.4-1 and Fig5.3.4-2.  Such effects have also appeared in the extended range ensemble and seasonal forecasts.

 

Fig5.3.4-1: Extreme Forecast Index (EFI) for 2m temperature for Days10-15, ensemble forecast run DT 00UTC 20 June 2017.

 

Fig5.3.4-2: Cumulative Distribution Function (CDF) for 2m temperature for Days10-15 in the middle of Lake Superior (red), with M-climate (black).  The initialisation techniques are different for real-time forecasts (using lake surface temperature observed by satellites), and for the re-forecasts (for which this information is not available).  This can lead to the model climate developing anomalously warm or cold lake surfaces and corresponding 2 m CDF temperature curve (black).   This affects subsequent extreme forecast index (EFI) and shift of tails (SOT) fields.   Here the realistic real-time forecast of 2m temperature CDF (red) over Lake Superior is thus incorrectly flagged as having a strongly negative EFI value in Fig5.3.4-1(left).  


More information is given in Documentation and a full description of ERA-5.

More information regarding differences between ERA-Interim and ERA-5.


Additional sources of information

(Note: In older material there may be references to issues that have subsequently been addressed)



(FUG associated with Cy50r1)