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Introduction
Here we document the ERA5-Land dataset that, which, eventually, will cover the same period as ERA5, January 1950 to near real time (NRT). ERA5-Land data released so far covers the period from 1981 to in its consolidated version, covers the period from January 1950 to 2-3 months before the present. The back-extension from 1950 started production in early 2020 with the release planned by Q3-2020. In In addition, the facility to deliver timely updates is being implemented and it will be made available shortly ERA5-Land-T version delivers non-checked close to Near-Real-Time (NRT) daily updates. ERA5-Land timely updates (hereafter called ERA5-LandT) will be -T is synchronized with the timely updates of the ERA5 close to NRT daily updates provided by the ERA5 climate reanalysis (ERA5T).
ERA5-Land is a replay of the land component of the ERA5 climate reanalysis, but with a series of forced by meteorological fields from ERA5. Note that ERA5-Land always uses forcing fields based on the final release of ERA5 (i.e., expver=0001). ERA5-Land comes with a series of improvements making it more accurate for all types of land applications. In particular, ERA5-Land runs at enhanced resolution (9 km vs 31 km in ERA5). The temporal frequency of the output is hourly and the fields are masked for all oceans, making them lighter to handle. Click this link here for comparison of the ERA5-Land features against other ECMWF reanalyses.
ERA5-Land is produced under a single simulation, without coupling to the atmospheric module of the ECMWF's Integrated Forecasting System (IFS) or to the ocean wave model of the IFS. It runs without data assimilation, making it computationally affordable for relatively quick updates. For example, if significant improvements of the land surface model are implemented, the whole or part of the dataset can be reprocessed in a relatively short period. Also, updates are possible in case improved auxiliary datasets are used as input for the production.
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Observations indirectly influence the simulation through the atmospheric forcing of ERA5. This forcing drives the ERA5-Land single simulation and it has been obtained by assimilating observations through a 4D-VAR data assimilation system and a Simplified Extended Kalman Filter. |
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The The core of ERA5-Land is the Tiled ECMWF Scheme for Surface Exchanges over Land incorporating land surface hydrology (H-TESSEL). It uses version CY45R1 of the IFS.
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Currently, the data can only be downloaded on a regular latitude/longitude grid of 0.1°x0.1° via the CDS catalogue. In the near future the data will also be made available in ECMWF member states with access to the ECMWF Meteorological Archival and Retrieval System (MARS) can also retrieve the data in the native grid.
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NOTE: Please, note that since 1st Jan 2020 the new ECMWF Meteorological Interpolation and Regridding interpolation package (MIR) has been used to interpolate the atmospheric forcing of ERA5 into the ERA5-Land grid. While this change will be unnoticeable for the overwhelming majority of users, locally and under very limited conditions (some areas with high orography, some coastal points) some fields may suffer of a very small discontinuity this day. |
Land Surface Model
H-TESSEL is the land surface model that is the basis of ERA5-Land. The H-TESSEL version used in the production of ERA5-Land corresponds to that of the IFS model documentation CY45R1.
Data organisation and access
The The full ERA5-Land and ERA5-Land-T data are archived in the ECMWF data archive (MARS) and the data have been copied to the Climate Data Store (CDS). The ERA5-Land (or recent ERA5-Land-T) data should be downloaded using the using the CDS catalogue or the CDS API, which can obtain data from the CDS copy or from MARS (Member State users can access the data using MARS directly, in the usual manner). Documentation on how to use the CDS API to download ERA5-Land data can be found found here. The installation and downloading steps are similar to those of ERA5.
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Convention used in MARS: the date and time of the data is specified with three MARS keywords, 'date', 'time' and 'step'. For parameters labelled as analyses (see list of parameters), step=0 hours so that date and time specify the analysis time. All forecasts start at 00UTC (time=00 hours), and for parameters labelled as forecasts (see list of parameters), date specifies the forecast start day and step specifies the number of hours since the start of the forecast, with a maximum of step=24 hours. The combination of date, time and forecast step defines the validity date/time. For analyses, the validity date/time is equal to the analysis date/time.
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For sub-daily data for the HRES (stream=oper) the parameters labelled as analyses (type=an) are available hourly. The once daily short forecasts, run from 00 UTC, also provide data hourly, with steps from 01 to 24. The uncertainty is currently provided by ERA5 EDA fields, which are available every 3 hours for the surface fields.
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Please, note that the convention for accumulations used in ERA5-Land differs with that for ERA5. The accumulations in the short forecasts of ERA5-Land (with hourly steps from 01 to 24) are treated the same as those in ERA-Interim or ERA-Interim/Land, i.e., they are accumulated from the beginning of the forecast to the end of the forecast step. For example, runoff at day=D, step=12 will provide runoff accumulated from day=D, time=0 to day=D, time=12. The maximum accumulation is over 24 hours, i.e., from day=D, time=0 to day=D+1,time=0 (step=24).
- HRES: accumulations are from 00 UTC to the hour ending at the forecast step
- For the CDS time, or validity time, of 00 UTC, the accumulations are over the 24 hours ending at 00 UTC i.e. the accumulation is during the previous day
- Synoptic monthly means (stream=mnth): accumulations have units of "variable_units per forecast_step hours"
- Monthly means of daily means (stream=moda): accumulations have units that include "per day", see section Monthly means
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In addition to the sub-daily data, all ERA5-Land parameters are also available as monthly means. Monthly means are available in two forms:
- Synoptic monthly means, for each particular time and forecast step (stream=mnth) - in the CDS, referred to as "monthly averaged by hour of day".
- Monthly means of daily means, for the month as a whole (stream=moda) - in the CDS, referred to as "monthly averaged". These monthly means are created from the hourly data for the HRES.
Monthly means for:
- parameters labelled as analyses or instantaneous forecasts are created from data with a valid time in the month, between 00 and 23 UTC on each day of the month.
- accumulations are created from data with a forecast period falling within the month. Monthly means of daily means for accumulations are created from the last forecast step (24) of the forecasts for each day of the month.
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Data update frequency
Initial release data, i.e. data with just a few days behind real time, is called ERA5-Land-T. Both for the CDS and MARS, daily updates for ERA5-Land-T are available about 5 days behind real time and monthly mean updates are available about 5 days after the end of the month.
The daily updates for ERA5-Land-T data on the CDS occur at no fixed time during the day.
For the CDS, ERA5-Land-T data for a month is overwritten by the final ERA5-Land data about two months after the month in question.
For MARS, the final ERA5-Land data are available about two months after the month in question. In addition, the last few months of data are kept online and can be accessed much quicker than older data on tape.
In the event that serious flaws are detected in ERA5-Land-T, the latter could be different to the final consolidated ERA5-Land data. Based on experience with the production of ERA5-Land so far, our expectation is that such an event would occur only when a flaw in the atmospheric forcing (from ERA5) is detected, and the expectation is that the latter occur only on rare occasions.
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Please, note that the convention for accumulations used in ERA5-Land differs with that for ERA5. The accumulations in the short forecasts of ERA5-Land (with hourly steps from 01 to 24) are treated the same as those in ERA-Interim or ERA-Interim/Land, i.e., they are accumulated from the beginning of the forecast to the end of the forecast step. For example, runoff at day=D, step=12 will provide runoff accumulated from day=D, time=0 to day=D, time=12. The maximum accumulation is over 24 hours, i.e., from day=D, time=0 to day=D+1,time=0 (step=24).
- HRES: accumulations are from 00 UTC to the hour ending at the forecast step
- For the CDS time, or validity time, of 00 UTC, the accumulations are over the 24 hours ending at 00 UTC i.e. the accumulation is during the previous day
- Synoptic monthly means (stream=mnth): accumulations have units of "variable_units per forecast_step hours"
- Monthly means of daily means (stream=moda): accumulations have units that include "per day"
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- , see section Monthly means
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In addition to the sub-daily data, all ERA5-Land parameters are also available as monthly means. Monthly means are available in two forms:
- Synoptic monthly means, for each particular time and forecast step (stream=mnth) - in the CDS, referred to as "monthly averaged by hour of day".
- Monthly means of daily means, for the month as a whole (stream=moda) - in the CDS, referred to as "monthly averaged". These monthly means are created from the hourly data for the HRES.
Monthly means for:
- parameters labelled as analyses or instantaneous forecasts are created from data with a valid time in the month, between 00 and 23 UTC on each day of the month.
- accumulations are created from data with a forecast period falling within the month. Monthly means of daily means for accumulations are created from the last forecast step (24) of the forecasts for each day of the month.
The accumulations in monthly means of daily means (stream=moda) have units that include "per day". So for accumulations in this stream:
- The hydrological parameters are in units of "m of water equivalent per day" and so they should be multiplied by 1000 to convert to kgm-2day-1 or mmday-1
- The hydrological parameters are in units of "m of water equivalent per day" and so they should be multiplied by 1000 to convert to kgm-2day-1 or mmday-1.
- The energy (turbulent and radiative) and momentum fluxes should be divided by 86400 seconds (24 hours) to convert to the commonly used units of Wm-2 and Nm-2, respectively.
The accumulations in synoptic monthly means (stream=mnth) have units that include "variable_units per forecast_step hours". So for accumulations in this stream:
- The hydrological parameters are in units of "m of water equivalent per forecast_step hours" and so they should be multiplied by 1000 to convert to kgm-2 per forecast_step hours or mm per forecast_step hours.
- The energy (turbulent and radiative) and momentum fluxes should be divided by 60 x 60 x fc_step 86400 seconds (24 hours) to convert to the commonly used units of Wm-2 and Nm-2, respectively.
Data format
Surface fields in ERA5-Land are encoded either in GRIB1 or GRIB2 format. Tables 1 and 2 indicate the format for all parameters in ERA5-Land. Note that the retrieval of the data in NetCDF format is still possible via the CDS.
The article "What are GRIB files and how can I read them" might be helpful.
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Tables 1 and 2 below describe the surface parameters available in ERA5-Land (levtype=sfc). Information on all ECMWF parameters is available from the ECMWF parameter database.
For the sake of completeness, most of the forcing fields used to run ERA5-Land are included in the catalogue. Note, however, that these fields have purely been interpolated to the ERA5-Land grid and they are not obtained by running the land surface model. They are included in Table 1 and Table 2 below under the column "Used as forcing field".
To download auxiliary land invariant parameters, please use ERA5 (see table1 of ERA5 documentation). The land-sea mask and orography (through the geopotential) fields used to produce the ERA5-Land dataset are also attached here below, already interpolated to a regular lat/lon grid of 0.1°x0.1°:
...
name
...
units
...
shortName
...
paramId
...
land-sea mask (grib version 2)
land-sea mask (netCDF4)
...
x
...
geopotential (grib version 1)
geopotential (grib version 2)
geopotential (netCDF4)
...
x
Parameters described as "instantaneous" below, are valid at the specified time.
Note that in the tables below, "an" and "fc" is just a label used for convention to archive the data in MARS.
Table 1: stream=oper/mnth/moda, levtype=sfc: surface parameters: instantaneous
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
...
K
...
lmlt
...
228008
...
x
...
...
m
...
lmld
...
228009
...
x
...
...
K
...
lblt
...
228010
...
x
...
...
K
...
ltlt
...
228011
...
x
...
...
dimensionless
...
lshf
...
228012
...
x
...
...
K
...
lict
...
228013
...
x
...
...
m
...
licd
...
228014
...
x
...
...
(0 - 1)
...
asn
...
32
...
x
...
...
kg m**-3
...
rsn
...
33
...
x
...
Volumetric soil water layer 11
...
m**3 m**-3
...
swvl1
...
39
...
x
...
Volumetric soil water layer 21
...
m**3 m**-3
...
swvl2
...
40
...
x
...
Volumetric soil water layer 31
...
m**3 m**-3
...
swvl3
...
41
...
x
...
Volumetric soil water layer 41
...
m**3 m**-3
...
swvl4
...
42
...
x
...
Leaf area index, low vegetation2
...
m**2 m**-2
...
lai_lv
...
66
...
x
...
Leaf area index, high vegetation2
...
m**2 m**-2
...
lai_hv
...
67
...
x
...
...
Pa
...
surface_pressure...
sp
...
134
...
x
...
...
K
...
stl1
...
139
...
x
...
...
m of water equivalent
...
sd
...
141
...
x
...
...
m s**-1
...
u10
...
165
...
x
...
...
m s**-1
...
v10
...
166
...
x
...
...
K
...
2t
...
167
...
x
...
...
K
...
2d
...
168
...
x
...
...
K
...
stl2
...
170
...
x
...
...
K
...
stl3
...
183
...
x
...
...
m of water equivalent
...
skin_reservoir_content...
src
...
198
...
x
...
...
K
...
skt
...
235
...
x
...
...
K
...
stl4
...
236
...
x
...
...
K
...
tsn
...
238
...
x
...
...
(0 - 1)
...
fal
...
243
...
x
...
1
...
| title | Soil levels |
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2 Leaf Area Index (LAI) parameters are based on a monthly climatology as explained IFS model documentation CY45R1. So the user will only see monthly variability, but not inter-annual variability.
Table 2: stream=oper/mnth/moda, levtype=sfc: surface parameters: accumulations
...
name
...
units
...
shortName
...
paramId
...
an
...
fc
...
...
m
...
sro
...
8
...
x
...
...
m
...
ssro
...
9
...
x
...
...
m of water equivalent
...
smlt
...
45
...
x
...
...
m of water equivalent
...
sf
...
144
...
x
...
...
m of water equivalent
...
e
...
182
...
x
...
...
m
...
ro
...
205
...
x
...
...
m
...
tp
...
228
...
x
...
...
m
...
pev
...
228251
...
x
...
Accumulations are described in section ERA5-Land: data documentation#accumulations. The accumulations in monthly means (stream=moda/mnth) are described in section monthly means
How to use lake-related fields
Independently whether a model grid point is over a lake or not, the IFS computes lake variables all over the globe, at each grid-box. This is to ease output field aggregation at diverse model resolutions and to have a warm start of the model with shorter spin-up time if lake cover is upgraded, i.e., it is still a decent lake initial condition if lake location are updated or a new lake is added operationally. Lake depths (input parameter for our lake parametrization) are specified for each grid-box either with in-situ values or with a default 25 m value; over ocean we use ocean bathymetry. Worth to mention that the later default values will be changed soon (extra information in this HESS reference). The computed lake variable values are not taken into account in the total grid-box flux calculations if lake is not present in the grid-box.
The lake fields provided in ERA5-Land can be used in combination with the lake location. The latter in the model is determined by lake cover field (parameter name CL, in fraction: 0 - grid-box has no lakes, 1 - grid-box is fully covered with lake/s). Lake depths are presented in the field DL (in meters).
The ECMWF model also contains an ice module, a snow module and a bottom sediments module. The present setup of the IFS is running with no bottom sediment and snow modules (snow accumulation over ice is not allowed and snow parameters are used only for albedo purposes). In the implementation in the IFS lake ice can be fractional within a grid-box with inland water (10 cm of ice means 100 % of a grid-box or tile is covered with ice; 0 cm of ice means 100 % of the grid-box is covered by water; in between a linear interpolation is applied) (Manrique-Sunen et al., 2013). At present, the water balance equation is not included for lakes and the lake depth and surface area are kept constant in time (IFS Documentation, 2017, chapter 8 and 11 ). Lake parametrization also requires the lake fraction CL, lake depth DL (preferably bathymetry), and lake initial conditions. DL is the most important external parameter that uses the lake parametrization.
Known issues
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As it was done for ERA5, the original plan for ERA5-Land was to provide an estimate of the uncertainty fields based on a dedicated 10-member ensemble run. The latter generated an ensemble of forcing fields that should, in principle, reproduce the space of uncertainty for the land surface fields. The first experiments demonstrated that the spread of the ensemble was clearly under dispersive, i.e. the uncertainty was unrealistically low. A reason for this is the low spread shown by the ensemble of ERA5 forcing fields. As a result of these experiments we took the decision of not providing the uncertainty fields of ERA5-Land. The opposite would have assigned, for instance, unrealistically high confidence to ERA5-land fields in a data assimilation experiment. Our recommendation is, for the time being, to use the uncertainty estimate of the corresponding ERA5 field, which should provide a second order approximation to the estimate of the real uncertainty. Future experiments will also perturbe, among other, key land surface model parameters, therefore providing a more realistic spread of the ERA5-Land ensemble surface fields. |
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Three components of the total evapotranspiration have values swapped as follows:
- variable "Evaporation from bare soil" (mars parameter code 228101 (evabs)) has the values corresponding to the "Evaporation from vegetation transpiration" (mars parameter 228103 (evavt)),
- variable "Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the "Evaporation from bare soil" (mars parameter code 228101 (evabs)),
- variable "Evaporation from vegetation transpiration" (mars parameter code 228103 (evavt)) has the values corresponding to the "Evaporation from open water surfaces excluding oceans" (mars parameter code 228102 (evaow)).
The accumulations in synoptic monthly means (stream=mnth) have units that include "variable_units per forecast_step hours". So for accumulations in this stream:
- The hydrological parameters are in units of "m of water equivalent per forecast_step hours" and so they should be multiplied by 1000 to convert to kgm-2 per forecast_step hours or mm per forecast_step hours.
- The energy (turbulent and radiative) and momentum fluxes should be divided by 60 x 60 x fc_step to convert to the units of Wm-2 and Nm-2, respectively.
Data format
Surface fields in ERA5-Land are encoded either in GRIB1 or GRIB2 format. Tables 1 and 2 indicate the format for all parameters in ERA5-Land. Note that the retrieval of the data in NetCDF format is still an option available via the CDS.
The article "What are GRIB files and how can I read them" might be helpful.
For GRIB format, ERA5-Land-T data can be identified by the key expver=0005 in the GRIB header. Consolidated ERA5-Land data is identified by the key expver=0001.
For netCDF data requests which return just ERA5-Land or just ERA5-Land-T data, there is no means of differentiating between ERA5-Land and ERA5-Land-T data in the resulting netCDF files.
For netCDF data requests which return a mixture of ERA5-Land and ERA5-Land-T data, the origin of the variables (1 or 5) will be identifiable in the resulting netCDF files. See this link for more details applied to ERA5 data.
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Tables 1 and 2 below describe the surface parameters available in ERA5-Land (levtype=sfc). Information on all ECMWF parameters is available from the ECMWF parameter database.
For the sake of completeness, most of the forcing fields used to run ERA5-Land are included in the catalogue. Note, however, that these fields have purely been interpolated to the ERA5-Land grid and they are not obtained by running the land surface model (i.e., a purely linear interpolation based on a triangular mesh). They are included in Table 1 and Table 2 below under the column "Used as forcing field".
Auxiliary land invariant parameters are attached here below, already interpolated to a regular lat/lon grid of 0.1°x0.1°. Relevant information about how to use/interpret these fields are in chapter 8 of https://www.ecmwf.int/en/elibrary/18714-ifs-documentation-cy45r1-part-iv-physical-processes, and plots of these fields are in chapter 11.
Parameters described as "instantaneous" in Table 2 are valid at the specified time.
Note that in the tables below, "an" and "fc" is just a label used for convention to archive the data in MARS.
Table 1: surface parameters: invariants (in time)
name | download link | units | shortName | paramId | GRIB1 | GRIB2 | netCDF4 |
|---|---|---|---|---|---|---|---|
Geopotential (GRIB version 1) Geopotential(GRIB version 2) Geopotential (netCDF4) | m**2 s**-2 | z | 129 | x | x | x | |
Lake cover (GRIB version 2) Lake cover (netCDF4) | (0-1) | cl | 26 | x | x | ||
| m | dl | 228007 | x | x | |||
Land-sea-mask (GRIB version 2) Land-sea mask (netCDF4) | (0-1) | lsm | 172 | x | x | ||
Low vegetation cover (GRIB version 2) Low vegetation cover (netCDF4) | (0 - 1) | cvl | 27 | x | x | ||
High vegetation cover (GRIB version 2) High vegetation cover (netCDF4) | (0 - 1) | cvh | 28 | x | x | ||
Glacier mask (GRIB version 2) Glacier mask (netCDF4) | Proportion | glm | 260294 | x | x | ||
Surface roughness (GRIB version 1) Surface roughness (netCDF4) | m | sr | 173 | x | x | ||
Soil type (GRIB version 2) Soil type (netCDF4) | ~ | slt | 43 | x | x | ||
Type of low vegetation (GRIB version 1) Type of low vegetation (netCDF4) | ~ | tvl | 29 | x | x | ||
Type of high vegetation (GRIB version 1) Type of high vegetation (netCDF4) | ~ | tvh | 30 | x | x |
Table 2: stream=oper/mnth/moda, levtype=sfc: surface parameters: instantaneous
name | units | Variable name in CDS | shortName | paramId | an | fc | GRIB1 | GRIB2 | Used as forcing field | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | K | lake_mix_layer_temperature | lmlt | 228008 | x | x | ||||
| 2 | m | lake_mix_layer_depth | lmld | 228009 | x | x | ||||
| 3 | K | lake_bottom_temperature | lblt | 228010 | x | x | ||||
| 4 | K | lake_total_layer_temperature | ltlt | 228011 | x | x | ||||
| 5 | dimensionless | lake_shape_factor | lshf | 228012 | x | x | ||||
| 6 | K | lake_ice_temperature | lict | 228013 | x | x | ||||
| 7 | m | lake_ice_depth | licd | 228014 | x | x | ||||
| 8 | Snow cover | % | snow_cover | snowc | 260038 | x | x | |||
| 9 | Snow depth | m | snow_depth | sde | 3066 | x | x | |||
| 10 | (0 - 1) | snow_albedo | asn | 32 | x | x | ||||
| 11 | kg m**-3 | snow_density | rsn | 33 | x | x | ||||
| 12 | m**3 m**-3 | volumetric_soil_water_layer_1 | swvl1 | 39 | x | x | ||||
| 13 | m**3 m**-3 | volumetric_soil_water_layer_2 | swvl2 | 40 | x | x | ||||
| 14 | m**3 m**-3 | volumetric_soil_water_layer_3 | swvl3 | 41 | x | x | ||||
| 15 | m**3 m**-3 | volumetric_soil_water_layer_4 | swvl4 | 42 | x | x | ||||
| 16 | m**2 m**-2 | leaf_area_index_low_vegetation | lai_lv | 66 | x | x | ||||
| 17 | m**2 m**-2 | leaf_area_index_high_vegetation | lai_hv | 67 | x | x | ||||
| 18 | Pa | surface_pressure | sp | 134 | x | x | x | |||
| 19 | K | soil_temperature_level_1 | stl1 | 139 | x | x | ||||
| 20 | m of water equivalent | snow_depth_water_equivalent | sd | 141 | x | x | ||||
| 21 | m s**-1 | 10m_u_component_of_wind | u10 | 165 | x | x | x | |||
| 22 | m s**-1 | 10m_v_component_of_wind | v10 | 166 | x | x | x | |||
| 23 | K | 2m_temperature | 2t | 167 | x | x | ||||
| 24 | K | 2m_dewpoint_temperature | 2d | 168 | x | x | ||||
| 25 | K | soil_temperature_level_2 | stl2 | 170 | x | x | ||||
| 26 | K | soil_temperature_level_3 | stl3 | 183 | x | x | ||||
| 27 | m of water equivalent | skin_reservoir_content | src | 198 | x | |||||
| 28 | K | skin_temperature | skt | 235 | x | x | ||||
| 29 | K | soil_temperature_level_4 | stl4 | 236 | x | x | ||||
| 30 | K | temperature_of_snow_layer | tsn | 238 | x | x | ||||
| 31 | (0 - 1) | forecast_albedo | fal | 243 | x | x |
1
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2 Leaf Area Index (LAI) parameters are based on a monthly climatology as explained IFS model documentation CY45R1. So the users will only see monthly variability, but not inter-annual variability.
Table 3: stream=oper/mnth/moda, levtype=sfc: surface parameters: accumulations
name | units | Variable name in CDS | shortName | paramId | an | fc | GRIB1 | GRIB2 | Used as forcing field | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | m | surface_runoff | sro | 8 | x | x | ||||
| 2 | m | sub_surface_runoff | ssro | 9 | x | x | ||||
| 3 | m of water equivalent | snow_evaporation | es | 44 | x | x | ||||
| 4 | m of water equivalent | snowmelt | smlt | 45 | x | x | ||||
| 5 | m of water equivalent | snowfall | sf | 144 | x | x | x | |||
| 6 | Surface sensible heat flux | J m**-2 | surface_sensible_heat_flux | sshf | 146 | x | x | |||
| 7 | Surface latent heat flux | J m**-2 | surface_latent_heat_flux | slhf | 147 | x | x | |||
| 8 | Surface solar radiation downwards | J m**-2 | surface_solar_radiation_downwards | ssrd | 169 | x | x | x | ||
| 9 | Surface thermal radiation downwards | J m**-2 | surface_thermal_radiation_downwards | strd | 175 | x | x | x | ||
| 10 | Surface net solar radiation | J m**-2 | surface_net_solar_radiation | ssr | 176 | x | x | x | ||
| 11 | Surface net thermal radiation | J m**-2 | surface_net_thermal_radiation | str | 177 | x | x | x | ||
| 12 | m of water equivalent | total_evaporation | e | 182 | x | x | ||||
| 13 | m | runoff | ro | 205 | x | x | ||||
| 14 | m | total_precipitation | tp | 228 | x | x | x | |||
| 15 | Evaporation from the top of canopy | m of water equivalent | evaporation_from_the_top_of_canopy | evatc | 228100 | x | x | |||
| 16 | Evaporation from bare soil | m of water equivalent | evaporation_from_bare_soil | evabs | 228101 | x | x | |||
| 17 | Evaporation from open water surfaces excluding oceans | m of water equivalent | evaporation_from_open_water_surfaces_excluding_oceans | evaow | 228102 | x | x | |||
| 18 | Evaporation from vegetation transpiration | m of water equivalent | evaporation_from_vegetation_transpiration | evavt | 228103 | x | x | |||
| 19 | m | potential_evaporation | pev | 228251 | x | x |
Accumulations are described in section ERA5-Land: data documentation#accumulations. The accumulations in monthly means (stream=moda/mnth) are described in section monthly means
Guidelines
Expand title Actual and potential evapotranspiration Actual evapotranspiration in the ERA5-Land datasets is called "Total Evaporation" (param ID 182) and is the sum of the following four evaporation components:
- Evaporation from bare soil
- Evaporation from open water surfaces excluding oceans
- Evaporation from the top of canopy
- Evaporation from vegetation transpiration
For the ERA5-Land datasets, actual evapotranspirationand it's four components can be downloaded from the C3S Climate Data Store (CDS) under the category heading "Evaporation and Runoff".
For details about the computation of actual evapotranspiration, please see Chapter 8 of Part IV : Physical processes, of the IFS documentation:
The potential evapotranspiration in the ERA5-Land CDS dataset is given by the parameter potential evaporation (pev).
Pev data can be downloaded from the CDS under the category heading "Evaporation and Runoff", in the "Download data" tab for the ERA5-Land datasets.
Note The definitions of potential and reference evapotranspiration may vary according to the scientific application and can have the same definition in some cases. Users should therefore ensure that the definition of this parameter is suitable for their application.
Please note that to give more flexibilty to users, PEV in ERA5 and ERA5Land are not computed in the same way:
- ERA5: the definition of PEV in ERA5 is computed for agricultural land as if it is well watered (no soil moisture stress) and assuming that the atmosphere is not affected by this artificial surface condition.
- ERA5Land: the definition of PEV in ERA5Land is computed as an open water evaporation (Pan evaporation) and assuming that the atmosphere is not affected by this artificial surface condition.
Note Please note that based on ERA5-Land atmospheric forcing, other independent (offline) methods such us "Priesley-Taylor1 (1972) , Schmidt2 (1915) or de Bruin3 (2000)" can also be used to estimate Potential evapotranspiration.
1PRIESTLEY, C. H. B., & TAYLOR, R. J. (1972). On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Monthly Weather Review, 100(2), 81-92. Retrieved Aug 27, 2021, from https://journals.ametsoc.org/view/journals/mwre/100/2/1520-0493_1972_100_0081_otaosh_2_3_co_2.xml
2Schmidt, W., 1915: Strahlung und Verdunstung an freien Wasserflächen; ein Beitrag zum Wärmehaushalt des Weltmeers und zum Wasserhaushalt der Erde (Radiation and evaporation over open water surfaces; a contribution to the heat budget of the world ocean and to the water budget of the earth). Ann. Hydro. Maritimen Meteor., 43, 111–124, 169–178.
3de Bruin, H. A. R., , and Stricker J. N. M. , 2000: Evaporation of grass under non-restricted soil moisture conditions. Hydrol. Sci. J., 45, 391–406, doi:10.1080/02626660009492337.
Expand title How to use lake-related fields Independently whether a model grid point is over a lake or not, the IFS computes lake variables all over the globe, at each grid-box. This is to ease output field aggregation at diverse model resolutions and to have a warm start of the model with shorter spin-up time if lake cover is upgraded, i.e., it is still a decent lake initial condition if lake location are updated or a new lake is added operationally. Lake depths (input parameter for our lake parametrization) are specified for each grid-box either with in-situ values or with a default 25 m value; over ocean we use ocean bathymetry. Worth to mention that the later default values will be changed soon (extra information in this HESS reference). The computed lake variable values are not taken into account in the total grid-box flux calculations if lake is not present in the grid-box.
The lake fields provided in ERA5-Land can be used in combination with the lake location. The latter in the model is determined by lake cover field (parameter name CL, in fraction: 0 - grid-box has no lakes, 1 - grid-box is fully covered with lake/s). Lake depths are presented in the field DL (in meters).
The ECMWF model also contains an ice module, a snow module and a bottom sediments module. The present setup of the IFS is running with no bottom sediment and snow modules (snow accumulation over ice is not allowed and snow parameters are used only for albedo purposes). In the implementation in the IFS lake ice can be fractional within a grid-box with inland water (10 cm of ice means 100 % of a grid-box or tile is covered with ice; 0 cm of ice means 100 % of the grid-box is covered by water; in between a linear interpolation is applied) (Manrique-Sunen et al., 2013). At present, the water balance equation is not included for lakes and the lake depth and surface area are kept constant in time (IFS Documentation, 2017, chapter 8 and 11 ). Lake parametrization also requires the lake fraction CL, lake depth DL (preferably bathymetry), and lake initial conditions. DL is the most important external parameter that uses the lake parametrization.
Known issues
Expand title Uncertainty fields As it was done for ERA5, the original plan for ERA5-Land was to provide an estimate of the uncertainty fields based on a dedicated 10-member ensemble run. The latter generated an ensemble of forcing fields that should, in principle, reproduce the space of uncertainty for the land surface fields. The first experiments demonstrated that the spread of the ensemble was clearly under dispersive, i.e. the uncertainty was unrealistically low. A reason for this is the low spread shown by the ensemble of ERA5 forcing fields.
As a result of these experiments we took the decision of not providing the uncertainty fields of ERA5-Land. The opposite would have assigned, for instance, unrealistically high confidence to ERA5-land fields in a data assimilation experiment.
Our recommendation is, for the time being, to use the uncertainty estimate of the corresponding ERA5 field, which should provide a second order approximation to the estimate of the real uncertainty. Future experiments will also perturbe, among other, key land surface model parameters, therefore providing a more realistic spread of the ERA5-Land ensemble surface fields.
Expand title Swapped value of the components of the total evapotranspiration Three components of the total evapotranspiration have values swapped as follows:
- variable "Evaporation from bare soil" (mars parameter code 228101 (evabs)) has the values corresponding to the "Evaporation from vegetation transpiration" (mars parameter 228103 (evavt)),
- variable "Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the "Evaporation from bare soil" (mars parameter code 228101 (evabs)),
- variable "Evaporation from vegetation transpiration" (mars parameter code 228103 (evavt)) has the values corresponding to the "Evaporation from open water surfaces excluding oceans" (mars parameter code 228102 (evaow)).
Expand title Low values of snow cover and snow depth on the eastern side of the Antarctic ice sheet Low values of snow cover and snow depth were found on the eastern side of the Antarctic ice sheet, as shown in Fig. 1. The issue is due to the application of an old glacier mask to the Antarctica, which excludes the patch shown in the figure as glacier. Inaccuracies in the glacier mask are due to errors in satellite measurements datasets. While, due to the lower horizontal resolution, in ERA5 this ice sheet part is a sea point, in ERA5-Land the area is categorised as land without an initial ice mass. Since the initialization doesn't consider a glacier there (estimated at a constant 10 m of snow water equivalent), the low amount of precipitation along with potential excess of sublimation makes them to obtain unrealistic low numbers there.
Fig 1: ERA-Land Snow depth (m of water equivalent) on 01-01-2015 eastern side of the Antarctic ice sheet.
Expand title Limited impact from sub-optimal tropical cyclones in the forcing from the ERA5 preliminary dataset for 1950-1978. From 1950-1978 ERA5-Land was forced by the preliminary ERA5 back extension which has a sub-optimal representation for a number of tropical cyclones.
The over-estimation of a number of tropical cyclones for this period affects some products over the oceans in the vicinity of tropical cyclone tracks. Over land much smaller impact is expected, and therefore, the effect on the ERA5-Land product from 1950-1978 is more limited.
This is supported by the figure below that plots, for each location, the minimum pressure from the ERA5 forcing (top panels) and the maximum daily accumulated total precipitation for ERA5-Land (lower panels) for the (preliminary) back extension (left panels) and for the period from the late 1970s to 2010 inclusive (right panels). Note that these show the most extreme situations, i.e., the absolute extremes in the about 30-year periods that were considered in each plot. Less extreme statistics, like 99, 95 (etc.) percentiles or mean distributions will show a much smaller impact of tropical cyclones.
From these panels it is seen that for the forcing from ERA5 (top panels), in general, local minimum pressure is similar between 1950-1978 and 1979-2010. There are of course sampling differences between the two, each about 30-year, periods. Large differences that are likely related to anomalously strong tropical cyclones are very localized, such as for some areas over North Australia, East Madagascar, Philippines and Northeast China. Note again that these affect a few cases only in the 29-year dataset.
The effect on the ERA5-Land precipitation is shown in the lower panels. Even for these extremes it is difficult to pin-point locations that could be affected by anomalous tropical cyclones.
Caption: locally minimum of 6-hourly pressure for ERA5 forcing data (top panels) and maximum daily total precipitation (lower panels) over the indicated period of time for the back extension (left panels) and for later periods (right panels). Numbers represent the averages for the locally extreme values over the indicated areas.
Expand title Definition of Potential Evaporation (PEV) modified Note that on 18-11-2021 we modified the definition of Potential Evaporation (PEV) provided in the CDS catalogue entry for both, the hourly and the monthly fields. The reason is that until this date the definition of PEV was similar to that of the same field provided by ERA5, whereas in reality they are computed differently:
- PEV in ERA5 is computed for agricultural land as if it is well watered (no soil moisture stress) and assuming that the atmosphere is not affected by this artificial surface condition.
- PEV in ERA5-Land is computed as an open water evaporation (Pan evaporation) and assuming that the atmosphere is not affected by this artificial surface condition.
Expand title Incorrect values of monthly averaged reanalysis of accumulated fields from September 2022 to February 2024 An issue with the ERA5-Land monthly averaged data from 1950 to present dataset was identified in January 2024 on both CDS discs and MARS tapes. It affects the following part of the dataset:
Product: Monthly averaged reanalysis
Time period: from September 2022 to February 2024
Area: Global
Variables: all accumulated variables (please refer to Table 3 in ERA5-Land documentation)
Incorrect data for period September 2022 to February 2024 is no longer available to download using the interactive CDS Download data form over the web, but remains accessible for the time being via CDS API or direct MARS access.
Incorrect data from September 2022 to February 2024 will not be replaced on CDS discs nor MARS tapes. Instead users can recover the same (and correct) data using the product monthly averaged reanalysis by hour of day at 00:00.
A fix was implemented for ERA5-Land monthly averaged data stored on CDS discs in time for the release of March 2024 monthly averaged data (i.e. from March 2024 onwards).
How to cite the ERA5-Land dataset
In addition to the terms and conditions of the license(s), users must:
- cite the CDS catalogue entry;
- provide clear and visible attribution to the Copernicus programme and attribute each data product used;
Step 1: Check the licence to use Copernicus Products for attribution/reference clause.
Step 2: Cite the CDS catalogue entry (as traceable source of data).
Step 3: Provide clear and visible attribution to the Copernicus programme and attribute each data product used (to accredit the creators of the data). Throughout the content of your publication, the dataset used is referred to as Author (YYYY).
The 3-steps procedure above is illustrated with this example:
Use Case 1: ERA5-Land hourly data from 1950 to present
For complete details, please refer to How to acknowledge and cite a Climate Data Store (CDS) catalogue entry and the data published as part of it.
Reference articles
J. Muñoz-Sabater,
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Low values of snow cover and snow depth were found on the eastern side of the Antarctic ice sheet, as shown in Fig. 1. The issue is due to the application of an old glacier mask to the Antarctica, which excludes the patch shown in the figure as glacier. Inaccuracies in the glacier mask are due to errors in satellite measurements datasets. While, due to the lower horizontal resolution, in ERA5 this ice sheet part is a sea point, in ERA5-Land the area is categorised as land without an initial ice mass. Since the initialization doesn't consider a glacier there (estimated at a constant 10 m of snow water equivalent), the low amount of precipitation along with potential excess of sublimation makes them to obtain unrealistic low numbers there.
Fig 1: ERA-Land Snow depth (m of water equivalent) on 01-01-2015 eastern side of the Antarctic ice sheet. |
How to cite ERA5-Land
(1) Please acknowledge the use of ERA5-Land as stated in the Copernicus C3S/CAMS License agreement:
"5.1.2 Where the Licensee communicates or distributes Copernicus Products to the public, the Licensee shall inform the recipients of the source by using the following or any similar notice: 'Generated using Copernicus Climate Change Service Information [Year]'.
5.1.3 Where the Licensee makes or contributes to a publication or distribution containing adapted or modified Copernicus Products, the Licensee shall provide the following or any similar notice: 'Contains modified Copernicus Climate Change Service Information [Year]';
Any such publication or distribution covered by clauses 5.1.1 and 5.1.2 shall state that neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains."
(2) cite the ERA5-Land dataset (as part of the bibliography) as follows:
- For "ERA5-Land hourly data from 1981 to present" downloaded from the Climate Data Store:
Muñoz Sabater, J., (2019): ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < DD-MMM-YYYY >), 10.24381/cds.e2161bac
- For "ERA5-Land monthly averaged data from 1981 to present" downloaded from the Climate Data Store:
Muñoz Sabater, J., (2019): ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on < DD-MMM-YYYY >), 10.24381/cds.68d2bb3
References
Further ERA5-Land references and related information are available from the ECMWF e-Library.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: A state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci Sci. Data Discuss. [preprint], ,13, 4349–4383, 2021. https://doi.org/10.5194/essd-13-4349-2021-82, in review, 2021.
Further ERA5-Land references and related information are available from the ECMWF website.
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This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreementAgreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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