D8.2: Report on the implemented revised land-surface information in IFS
Creators: name: Dutra, Emanuel
name: Pedruzo-Bagazgoitia, Xabier
Publisher: name: Zenodo
Year: 2024

description: About this document

This report describes the revisions to the land-surface information in the IFS and provides a first estimate on the importance of the representation of detailed surface heterogeneity in stormresolving simulations.

Work package in charge

WP8

Executive summary

The land surface description in the ECMWF IFS up to cycle 2 was based on datasets produced in the early 2000’s. These input datasets do not fully benefit from the developments of remote sensing land cover and vegetation data sets during the past 20 years. The latest generation of remote sensing products of land cover and vegetation have higher spatial resolution, in the order of 1 km and finer, and improved classification algorithms. Therefore, to take full advantage of the recent advances in earth observations and demonstrate the capacity of storm-resolving models in capturing landscape variability globally, the IFS land surface information was revised. This revision aimed at improving the representation of land-surface heterogeneities at stormresolving resolutions, enabling a first assessment of the importance of resolving the full scales of surface heterogeneity for global climate statistics.

The IFS land cover and vegetation information was revised for the cycle 3 simulations using the latest ESA-CCI land cover and Copernicus Global Land Service Leaf Area Index datasets. In addition to the high resolution land information (300m resolution, while the previous dataset used had 1 km resolution) the key differences include (i) the reduction of high vegetation cover, which is compensated by an increase of low vegetation; (ii) introduction of a realistic disaggregation of LAI between low and high vegetation types while conserving the original product seasonality and (iii) representation of urban conditions.

Land Surface Temperature (LST) is the primary focus in this report as it carries the blueprint of the surface energy budget, reflecting land conditions: (i) LST is the radiative surface temperature controlling the surface longwave radiation emission, (ii) the surface sensible heat flux depends on the gradient between near surface air temperature and LST; (iii) the surface latent heat flux depends on the gradient between near surface air humidity and saturated humidity assumed at the surface temperature; and (iv) the ground heat flux depends on the gradient between LST and the underlying soil temperature. We use the LST product from the Satellite Application Facility on Land Surface Analysis (LSA SAF) which provides clear-sky estimates of LST over the geostationary Meteosat Second Generation (MSG) region every 15 minutes since 2004 with a resolution of 3 km at the equator. Two examples illustrate that the high-resolution surface information as well as an urban scheme had a positive impact in cycle 3 IFS simulations, when compared with cycle 2 when evaluated against satellite LST, improving both the spatial and diurnal LST variability. A methodology for estimating the resolved sub-grid scale variability of LST was developed and showed consistent results between the cycle 3 simulations and the satellite data, particularly in (i) showing the added value of IFS 4.4km versus IFS 9km in representing the sub-grid scale LST variability and (ii) differences between daily maximum and minimum LST variability and their relation with the underlying orography variability, highlighting the role of high resolution land cover surface heterogeneities in controlling LST variability. The inclusion of ICON cycle 3 simulations in the report also showed some limitations in representing LST, particularly with the underestimation of maximum temperatures. These are associated with the current model approach to close the surface energy balance within the uppermost soil layer with finite thickness and heat capacity. This report demonstrates the capacity of storm-resolving simulations in representing land-surface heterogeneities, verified against satellite observations. These developments are necessary in the context of the increasing need for local climate information, and for local projections of direct socio-economic relevance.