Upgraded global mapping information for earth system modelling: anapplication to surface water depth at ECMWF

Abstract. Water bodies influence local weather and climate, especially in lake-rich areas (e.g. lake regions in Canada and northern Russia). In 2015 a parametrization to represent inland water bodies was included in the Integrated Forecasting System (IFS) used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) to produce global weather predictions. The parameterization is based on the Fresh-water Lake model (FLake). In the IFS, FLake runs on any inland surface grid-point. It thus depends on global, realistic and complete lake depth and lake cover data as input. Operationally used lake depths make use of the Global Lake Data Base (GLDBv1) over lakes, a default value of 25 m where lake depth information is missing, and over the ocean bathymetry from ETOPO1. In this study we present the upgraded GLDBv3 dataset, which makes use of mean depth estimates based on geological origin over land, and the GEBCO ocean bathymetry. To assess the impact of using GLDBv3 instead of GLDBv1 in-situ measurements of lake water surface temperatures and information on ice formation/disappearance dates for 27 lakes collected by the Finnish Environment Institute were used for verification. The dataset includes daily temperature measurements and ice formation/disappearance dates recorded by observers. A set of surface experiments were carried out using the IFS and atmospheric forcing from the ERA5 reanalysis to test the operational and new lake depths. These simulations were done at a grid spacing of about 9 km and covered the 5-year period 2010–2014. When verified against in-situ lake depth measurements, the new lake depths in GLDBv3 have a much lower mean absolute error, bias and standard deviation error compared to the current operationally used GLDBv1 depths. Indirect verification was used to compare measured and modelled lake water surface temperatures and ice formation/disappearance dates. On average the mean absolute error of lake surface temperatures is reduced by 13.4 %, biases are reduced by 12.5 % and the standard deviation error by 20.3 % when GLDBv3 is used instead of GLDBv1. Seasonal verification of mixed layer depth temperature and ice formation/disappearance dates (using lake mixing seasons) revealed a cold bias in meteorological forcing from ERA5. However, for spring, summer and autumn verification confirms an overall reduction in the errors in surface water temperatures. For winter verification based on ice formation/disappearance dates shows no statistically significant change in ice disappearance date errors.