The vertical structure and spatial variability of lower-tropospheric water vapor and clouds in the trades

Abstract

Abstract. Horizontal and vertical variability of water vapor is omnipresent in the tropics, but its interaction with cloudiness poses challenges for weather and climate models. In this study we compare airborne lidar measurements from a summer and a winter field campaign in the tropical Atlantic with high-resolution simulations to analyze the water vapor distributions in the trade wind regime, its covariation with cloudiness, and their representation in simulations. Across model grid spacing from 300 m to 2.5 km, the simulations show good skill in reproducing the water vapor distribution in the trades as measured by the lidar. An exception to this is a pronounced moist model bias at the top of the shallow cumulus layer in the dry winter season which is accompanied by a humidity gradient that is too weak at the inversion near the cloud top. The model's underestimation of water vapor variability in the cloud and subcloud layer occurs in both seasons but is less pronounced than the moist model bias at the inversion. Despite the model's insensitivity to resolution from hecto- to kilometer scale for the distribution of water vapor, cloud fraction decreases strongly with increasing model resolution and is not converged at hectometer grid spacing. The observed cloud deepening with increasing water vapor path is captured well across model resolution, but the concurrent transition from cloud-free to low cloud fraction is better represented at hectometer resolution. In particular, in the wet summer season the simulations with kilometer-scale resolution overestimate the observed cloud fraction near the inversion but lack condensate near the observed cloud base. This illustrates how a model's ability to properly capture the water vapor distribution does not necessarily translate into an adequate representation of shallow cumulus clouds that live at the tail of the water vapor distribution.

Tropical Free‐Tropospheric Humidity Differences and Their Effect on the Clear‐Sky Radiation Budget in Global Storm‐Resolving Models

“…To distinguish between different dynamic regimes of the tropics, namely subsidence and deep convective regimes, which are not necessarily co‐located in different models, we compare RH statistics in moisture space (Bretherton et al., 2005; Naumann & Kiemle, 2020; Schulz & Stevens, 2018). To span the moisture space, the randomly selected atmospheric profiles (Section 2.2) are ranked by their vertically IWV.…”

“…(2005), who used it to study the energy balance of convective self‐aggregation in radiative‐convective equilibrium simulations. Later, the depiction in moisture space has also proven useful for analyzing observational data (Schulz & Stevens, 2018) and to bypass the issue of co‐location when comparing observations and model simulations (Naumann & Kiemle, 2020).…”

“…The representation of the atmosphere in moisture space is inspired by Bretherton et al (2005), who used it to study the energy balance of convective self-aggregation in radiative-convective equilibrium simulations. Later, the depiction in moisture space has also proven useful for analyzing observational data (Schulz & Stevens, 2018) and to bypass the issue of co-location when comparing observations and model simulations (Naumann & Kiemle, 2020). This paper is organized as follows: In Section 2 we introduce the DYAMOND simulations and describe our post-processing of the model output.…”

Two Decades of Temperature and Specific Humidity Variance Scaling With the Atmospheric Infrared Sounder

“…In situ observations suggest that V6IR 𝜎 q may be too large at 925 hPa. Using a differential absorption lidar (DIAL) in the subtropical north Atlantic Ocean, Naumann and Kiemle (2020) show that 𝜎 q peaks around 1.5 km (∼850 hPa) in the summer and 2 km (∼800 hPa) in the winter. Kiemle et al (2017) also used DIAL observations to show that 𝜎 q peaks between 1 and 3 km (∼900-700 hPa) depending on the time period of observation.…”

Tropical free-tropospheric humidity differences and their effect on the clear-sky radiation budget in global strom-resolving models

“…To distinguish between different dynamic regimes of the tropics, namely subsidence and deep convective regimes, which are not necessarily co-located in different models, we compare RH statistics in moisture space (Bretherton et al, 2005;Naumann & Kiemle, 2020;Schulz & Stevens, 2018). To span the moisture space, the randomly selected atmospheric profiles (Section 2.2) are ranked by their vertically IWV.…”