How adequately are elevated moist layers represented in reanalysis and satellite observations?

Abstract

Abstract. We assess the representation of Elevated Moist Layers (EMLs) in ERA5 reanalysis, the IASI L2 retrieval Climate Data Record (CDR) and the AIRS-based CLIMCAPS-Aqua L2 retrieval. EMLs are free tropospheric moisture anomalies that typically occur in the vicinity of deep convection in the tropics. EMLs significantly effect the spatial structure of radiative heating, which is considered a key driver for meso-scale dynamics, in particular convective aggregation. To our knowledge, the representation of EMLs in the mentioned data products have not been explicitly studied, a gap we address in this work. We assess the different datasets' capability of capturing EMLs by collocating them with 2146 radiosondes launched from Manus Island within the Western Pacific warmpool, a region where EMLs occur particularly often. We identify and characterise moisture anomalies in the collocated datasets in terms of moisture anomaly strength, vertical thickness and altitude. By comparing the distributions of these characteristics, we deduce what specific EML characteristics the datasets are capturing well and what they are missing. Distributions of ERA5 moisture anomaly characteristics match those of the radiosonde dataset quite well and remaining biases can be removed by applying a 1 km moving average to the radiosonde profiles. We conclude that ERA5 is a suitable reference dataset for investigating EMLs. We find that the IASI L2 CDR is subject to stronger smoothing than ERA5 with moisture anomalies being on average 13 % weaker and 28 % thicker than collocated ERA5 anomalies. The CLIMCAPS L2 product shows significant biases in its mean vertical humidity structure compared to the three other investigated datasets. These biases manifest as an underestimation of mean moist layer height of about 1.3 km compared to the three other datasets, a general mid-tropospheric moist bias and an upper tropospheric dry bias. Biases found in the all-sky scenes do not change significantly when limiting the analysis to clear-sky scenes. We calculate radiatively driven vertical velocities derived from longwave heating rates to estimate the dynamical effect of the moist layers. Moist-layer-associated vertical velocity values derived from GRUAN soundings range between 2 to 3 hPa hour-1 while mean meso-scale pressure velocities from the EUREC4A field campaign range between 1 to 2 hPa hour-1, highlighting the dynamical significance of EMLs. Subtle differences in the representation of moisture and temperature structures in ERA5 and the satellite datasets create large relative errors in ωrad on the order of 40–80 % with reference to GRUAN, indicating limited usefulness of these datasets to assess the dynamical impact of EMLs.

How Moisture Shapes Low‐Level Radiative Cooling in Subsidence Regimes

“…Strong emphasis is made on the role of elevated layers of moist air, called moist intrusions. These intrusions are occasionally transported from lower latitudes and sporadically reduce or cancel low-level cooling, but they can be missed by satellite retrieval algorithms (Prange et al, 2021(Prange et al, , 2022 despite being major components of the large-scale circulation (Sokol & Hartmann, 2022). After detailed analysis in Section 4, we conclude that intrusion mass and altitude are important degrees of freedom in the reduction of low-level cooling by moist intrusions.…”

“…This height-dependent "spectral shading" motivates future theoretical work to capture the radiative effects of elevated moist intrusions within an effective spectral emission window 𝐴𝐴 Δ ν𝜈 to be used in Equations 13 and 14. It also calls for an exploration of elevated moist layers with novel detection techniques (Prange et al, 2022), using upcoming remote sensing instruments with high vertical resolution (e.g., Krebs, 2022).…”

“…Remote-sensing observations, in turn, do not resolve the detailed structure of radiative cooling in the lower troposphere sufficiently well (Stevens et al, 2017), which complicates the direct comparison with observations. Similarly in the middle troposphere, idealized simulations also point to the emergence of elevated moist layers at mid-levels and their association with aggregation of deep convective clouds (Sokol & Hartmann, 2022;Stevens et al, 2017), but these moist layers are also often undetected by satellite retrieval algorithms (Lerner et al, 2002;Prange et al, 2021Prange et al, , 2022. Thus, the present work aims at exploring the relationship between the vertical structure of humidity and low-level radiative cooling in subsiding regimes, as a means to provide simple necessary conditions for self-aggregation in the observable atmosphere, with a special focus on shallow cloud patterns.…”

How adequately are elevated moist layers represented in reanalysis and satellite observations?

“…Data availability. The collocation datasets are publicly available on Zenodo (Prange et al, 2022). These include only the data that are used to deduce our results, i.e.…”