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

How adequately are elevated moist layers represented in reanalysis and satellite observations? Marc Prange1,2 , Stefan A. Buehler1 , and Manfred Brath1 1 Meteorologisches 2 International Institut, Universität Hamburg, Bundesstraße 55, 20146 Hamburg, Germany Max Planck Research School on Earth System Modelling (IMPRS-ESM), Bundesstraße 53, 20146 Hamburg, Germany Correspondence: Marc Prange () Received: 4 August 2022 – Discussion started: 25 August 2022 Revised: 13 November 2022 – Accepted: 16 November 2022 – Published: 17 January 2023 Abstract. We assess the representation of elevated moist layers (EMLs) in ERA5 reanalysis, the Infrared At- mospheric Sounding Interferometer (IASI) L2 retrieval Climate Data Record (CDR) and the Atmospheric Infrared Sounder (AIRS)-based Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS)-Aqua L2 retrieval. EMLs are free-tropospheric moisture anomalies that typically occur in the vicinity of deep convection in the tropics. EMLs significantly affect 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 has not been explicitly studied – a gap we start to 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 warm pool, 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 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 that yields a general mid-tropospheric moist bias and an upper-tropospheric dry bias. Aside from these biases, the CLIMCAPS L2 product shows a similar, if not better, capability of capturing EMLs compared to the IASI L2 CDR. More nuanced evaluations of CLIMCAPS’ capabilities may be possible once the underlying cause for the identified biases has been found and fixed. 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 ωrad derived from longwave heating rates to estimate the dynamical effect of the moist layers. Moist-layer-associated ωrad values derived from Global Climate Observing System Reference Upper-Air Network (GRUAN) soundings range between 2 and 3 hPa h−1 , while mean meso-scale pressure velocities from the EUREC4 A (Elucidating the Role of Clouds-Circulation Coupling in Climate) field campaign range between 1 and 2 hPa h−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 % to 80 % with reference to GRUAN, indicating limited usefulness of these datasets to assess the dynamical impact of EMLs. Published by Copernicus Publications on behalf of the European Geosciences Union. Research article Atmos. Chem. Phys., 23, 725–741, 2023 https://doi.org/10.5194/acp-23-725-2023 © Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License. 726 1 Introduction The vertical structure of water vapour in the troposphere is a key driver for meso-scale processes, such as the development and maintenance of convective systems. In particular, it determines the vertical structure of radiative heating due to water vapour’s strong ability to absorb and emit infrared (IR) radiation. The spatial structure of radiative heating in the vicinity of convection is capable of driving circulations that contribute to the maintenance of the convection (Muller and Bony, 2015; Wing et al., 2017; Schulz and Stevens, 2018; Muller et al., 2022). Hence, understanding the vertical structure of water vapour is key for our understanding of convective aggregation, which remains a large contributor of uncertainty to climate projections (Bony et al., 2015). A common meso-scale phenomenon affecting the vertical humidity structure in the tropics is elevated moist layers (EMLs) in the lower troposphere to mid-troposphere, which frequently occur either in the vicinity of deep convection or in association with extratropical dry air intrusions (Villiger et al., 2022). EMLs can extend horizontally over several hundred kilometres and have lifetimes of about a day (Stevens et al., 2017; Johnson et al., 1996). In the convection-dominated regions near the Intertropical Convergence Zone (ITCZ), especially over the western Pacific warm pool, EMLs are particularly common and manifest as a secondary maximum of relative humidity (RH) in the climatological profile near the melting level at around 5 km altitude (Romps, 2014). It is important to capture EMLs in observational and reanalysis datasets, which serve as reference for modelling studies (Lang et al., 2021; Eyring et al., 2016; Teixeira et al., 2014; Ferraro et al., 2015; Brands et al., 2013; Jiang et al., 2012). In particular, Lang et al. (2021) highlight the importance of reducing uncertainties in clear-sky mid-tropospheric humidity in global storm-resolving models that yield significant differences in the models’ radiation budgets. Hence, having suitable global and long-term satellite and reanalysis datasets to assess such model differences is of great value. In a case study, Stevens et al. (2017) found strong limitations of passive satellite-based humidity retrievals to resolve an EML, suggesting a somewhat fundamental EML blind spot for such observations. This is particularly surprising for the advanced hyperspectral IR instruments such as AIRS (Atmospheric Infrared Sounder) or IASI (Infrared Atmospheric Sounding Interferometer), which offer rich vertical information content about temperature and water vapour. In our recent study (Prange et al., 2021), we found a physical explanation for the apparent EML blind spot, suggesting that the limited temperature information available with the particular retrieval setup deployed by Stevens et al (...truncated)