...
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 | ||
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 |
...
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
...
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.
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: Acknowledge according to Check the licence to use Copernicus Productsfor attribution/reference clause.
Step 2: Provide clear and visible attribution to the Copernicus program using Cite the CDS web catalogue entry citation (see link to "Citation" under References on the Overview page of the dataset entry and use the citation under "Citing the web catalogue entry").(as traceable source of data).
Step 3: Cite each dataset used as indicated on the relevant CDS entries (see link to "Citation" under References on the Overview page of the dataset entry and use the citation under "Citing the data").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, 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. Data,13, 4349–4383, 2021. https://doi.org/10.5194/essd-13-4349-2021.
...