Vertical structure of the lower-stratospheric moist bias in the ERA5 reanalysis and its connection to mixing processes

Abstract

Numerical weather prediction (NWP) models 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 the moist bias in the European Centre for Medium-Range Weather Forecasts (ECMWF) latest global reanalysis ERA5 using a unique multi-campaign data set of highly resolved water vapour profiles observed with a differential absorption lidar (DIAL) on board the High Altitude and LOng range research aircraft (HALO). In total, 41 flights in the mid-latitudes from six field campaigns provide roughly 33 000 profiles with humidity varying by 4 orders of magnitude. The observations cover different synoptic situations and seasons and thus are suitable to characterize the strong vertical gradients of moisture 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 (15 %–20 %). In the LS, the moist 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. Such a vertical structure is frequently observed, although the magnitude varies from flight to flight. The layer depth of increased moist bias is smaller at high tropopause altitudes and larger when the tropopause is low. Our results also suggest a seasonality of the moist bias, with the maximum in summer exceeding autumn by up to a factor of 3. During one field campaign, collocated ozone and water vapour profile observations enable a classification of tropospheric, stratospheric, and mixed air using water vapour–ozone correlations. It is revealed that the moist bias is high 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 moist bias. Our results suggest that a better representation of mixing processes in NWP models could lead to a reduced LS moist bias that, in turn, may lead to more accurate weather and climate forecasts. The lower-stratospheric moist bias should be borne in mind for climatological studies using reanalysis data.