Abstract
Abstract. Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly (Muñoz-Sabater, 2019a) and monthly (Muñoz-Sabater, 2019b) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.
Highlights
The land surface state plays a crucial role in the coupled Earth system, especially on seasonal to interseasonal predictability and climate projections (Koster et al, 2004)
Examples of existing global offline datasets are the Global Offline Land-surface Data-set (GOLD) (Dirmeyer and Tan, 2001), MERRA-Land (Reichle et al, 2011), and ERA-Interim/Land (Balsamo et al, 2015). The latter was motivated by important updates to the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface scheme introduced in the operational forecasting model in 2006, when the production of ERA-Interim started
The reason is twofold: (1) it allows the production of parallel streams, accelerating the production and the public availability of the data; (2) the atmospheric forcing necessary to produce ERA5-Land is derived from ERA5, and the production needs the corresponding segment of ERA5 completed for the same time period
Summary
The land surface state plays a crucial role in the coupled Earth system, especially on seasonal to interseasonal predictability and climate projections (Koster et al, 2004). Land data assimilation systems (LDASs) provide an important component of reanalyses, which can mitigate model errors and enhance the representation of the land surface state in regions and periods with available observations (Albergel et al, 2017) This can result in temporal and spatial inconsistencies (e.g. due to changing observations’ availability) as well as limitations in the closure of the surface water budget (Zsoter et al, 2019). Examples of existing global offline datasets are the Global Offline Land-surface Data-set (GOLD) (Dirmeyer and Tan, 2001), MERRA-Land (Reichle et al, 2011), and ERA-Interim/Land (Balsamo et al, 2015) The latter was motivated by important updates to the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface scheme introduced in the operational forecasting model in 2006, when the production of ERA-Interim started. These changes embedded in ERAInterim/Land provided seasonal forecasting with more accurate and consistent land initial conditions
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