Reply on EC1

Abstract

Numerical weather prediction models (NWP) are known to possess a distinct moist bias in the mid-latitude lower stratosphere which is expected to affect the ability to accurately predict weather and climate. This paper investigates the vertical structure of this bias in the European Centre for Medium-Range Weather Forecast’s (ECMWF) latest global reanalysis ERA5 using a unique multi-campaign data set of highly-resolved water vapor profiles observed with a differential absorption lidar (DIAL) onboard the High Altitude and LOng Range Research Aircraft (HALO). In total, 41 flights in the midlatitudes provide more than 31000 humidity profiles varying by four orders of magnitude. The data set covers different synoptic situations and seasons and thus is suitable to characterize the strong vertical gradients in the upper troposphere and lower stratosphere (UTLS). The comparison to ERA5 indicates high positive and negative deviations in the UT which on average lead to a slightly positive bias (+20 %). In the LS, the bias rapidly increases up to a maximum of +55 % at 1.3 km altitude above the thermal tropopause (tTP), and decreases again to 15–20 % at 4 km altitude. This vertical structure is reproduced in all flights. The depth of the layer of increased bias is smaller at high tropopause altitudes and larger when the tropopause is located low. Our results also suggest a seasonality of the bias, with the maximum in summer exceeding fall by up to a factor of 3. During one field campaign, co-located ozone and water vapor profile observations enable a classification of the observations into tropospheric, stratospheric and mixed air using H2O-O3 correlations. It is shown that the bias is higher in the mixed air while being small in tropospheric and stratospheric air which highlights that excessive transport of moisture into the LS plays a decisive role for the formation of the bias. Future climatological studies should consider the analysed lower-stratospheric moist bias in ERA5. Our results show that a better representation of mixing processes in NWP models could lead to a reduced LS moist bias that, in turn, may have a positive impact on weather and climate forecasts. The moist bias should be borne in mind for climatological studies using reanalysis data.