Upgraded global mapping information for earth system modelling: an application to surface water depth at the ECMWF

Abstract

Abstract. Water bodies influence local weather and climate, especially in lake-rich areas. The FLake (Fresh-water Lake model) parameterisation is employed in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) model which is used operationally to produce global weather predictions. Lake depth and lake fraction are the main driving parameters in the FLake parameterisation. The lake parameter fields for the IFS should be global and realistic, because FLake runs over all the grid boxes, and then only lake-related results are used further. In this study new datasets and methods for generating lake fraction and lake depth fields for the IFS are proposed. The data include the new version of the Global Lake Database (GLDBv3) which contains depth estimates for unstudied lakes based on a geological approach, the General Bathymetric Chart of the Oceans and the Global Surface Water Explorer dataset which contains information on the spatial and temporal variability of surface water. The first new method suggested is a two-step lake fraction calculation; the first step is at 1 km grid resolution and the second is at the resolution of other grids in the IFS system. The second new method involves the use of a novel algorithm for ocean and inland water separation. This new algorithm may be used by anyone in the environmental modelling community. To assess the impact of using these innovations, in situ measurements of lake depth, lake water surface temperature and ice formation/disappearance dates for 27 lakes collected by the Finnish Environment Institute were used. A set of offline experiments driven by atmospheric forcing from the ECMWF ERA5 Reanalysis were carried out using the IFS HTESSEL land surface model. In terms of lake depth, the new dataset shows a much lower mean absolute error, bias and error standard deviation compared to the reference set-up. In terms of lake water surface temperature, the mean absolute error is reduced by 13.4 %, the bias by 12.5 % and the error standard deviation by 20.3 %. Seasonal verification of the mixed layer depth temperature and ice formation/disappearance dates revealed a cold bias in the meteorological forcing from ERA5. Spring, summer and autumn verification scores confirm an overall reduction in the surface water temperature errors. For winter, no statistically significant change in the ice formation/disappearance date errors was detected.