Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set
Abstract. Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Thus, the Global Climate Observing System (GCOS) lists LWT as an Essential Climate Variable (ECV). Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years finally offering a unique opportunity for satellite-based climate studies. Herein we present a satellite-based lake surface water temperature (LSWT) data set for European (pre-alpine) water bodies based on the extensive AVHRR 1 km data record (1989–2013) of the Remote Sensing Research Group at the University of Bern. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and Metop-A) data. The high accuracy needed for climate related studies requires careful pre-processing and consideration of the atmospheric state. Especially data from NOAA-16 and prior satellites were prone to noise, e.g., due to transmission errors or fluctuations in the instrument's thermal state. This has resulted in partly corrupted thermal calibration data and may cause errors of up to several Kelvin in the final resulting LSWT. Thus, a multi-stage correction scheme has been applied to the data to minimize these artefacts. The LSWT retrieval is based on a simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with operational analysis and reanalysis data from the European Centre for Medium Range Weather Forecasts. The resulting LSWTs were extensively validated using in situ measurements from lakes with various sizes between 14 and 580 km2 and the resulting biases and RMSEs were found to be within the range of −0.4–0.6 K and 1.0–1.9 K, respectively. The upper limits of the reported errors could be rather attributed to uncertainties in the data comparison between in situ and satellite observations than inaccuracies of the satellite retrieval. The cross-platform consistency of the retrieval was found to be within ~0.2 K. A comparison with LSWT derived through global sea surface temperature (SST) algorithms shows lower RMSEs and biases for the simulation-based approach. A running project will apply the developed method to retrieve LSWT from the northern part of Finland to southern Italy to derive the climate signal of the last 30 years. The data are available at doi:10.1594/PANGAEA.831007.