Abstract

Abstract. Upper tropospheric (UT) cloud systems constructed from Atmospheric Infrared Sounder (AIRS) cloud data provide a horizontal emissivity structure, allowing the convective core to be linked to anvil properties. By using machine learning techniques, we composed a horizontally complete picture of the radiative heating rates deduced from CALIPSO lidar and CloudSat radar measurements, which are only available along narrow nadir tracks. To train the artificial neural networks, we combined the simultaneous AIRS, CALIPSO and CloudSat data with ERA-Interim meteorological reanalysis data in the tropics over a period of 4 years. The resulting non-linear regression models estimate the radiative heating rates as a function of about 40 cloud, atmospheric and surface properties, with a column-integrated mean absolute error (MAE) of 0.8 K d−1 (0.5 K d−1) for cloudy scenes and 0.4 K d−1 (0.3 K d−1) for clear sky in the longwave (shortwave) spectral domain. Developing separate models for (i) high opaque clouds, (ii) cirrus, (iii) mid- and low-level clouds and (iv) clear sky, independently over ocean and over land, leads to a small improvement, when considering the profiles. These models were applied to the whole AIRS cloud dataset, combined with ERA-Interim, to build 3D radiative heating rate fields. Over the deep tropics, UT clouds have a net radiative heating effect of about 0.3 K d−1 throughout the troposphere from 250 hPa downward. This radiative heating enhances the column-integrated latent heating by about 22±3 %. While in warmer regions the net radiative heating profile is nearly completely driven by deep convective cloud systems, it is also influenced by low-level clouds in the cooler regions. The heating rates of the convective systems in both regions also differ: in the warm regions the net radiative heating by the thicker cirrus anvils is vertically more extended, and their surrounding thin cirrus heat the entire troposphere by about 0.5 K d−1. The 15-year time series reveal a slight increase of the vertical heating in the upper and middle troposphere by convective systems with tropical surface temperature warming, which can be linked to deeper systems. In addition, the layer near the tropopause is slightly more heated by increased thin cirrus during periods of surface warming. While the relative coverage of convective systems is relatively stable with surface warming, their depth increases, measured by a decrease of their near-top temperature of -3.4±0.2 K K−1. Finally, the data reveal a connection of the mesoscale convective system (MCS) heating in the upper and middle troposphere and the (low-level) cloud cooling in the lower atmosphere in the cool regions, with a correlation coefficient equal to 0.72, which consolidates the hypothesis of an energetic connection between the convective regions and the subsidence regions.

Highlights

  • Upper tropospheric (UT) clouds play a vital role in the climate system by modulating the Earth’s energy budget and the UT heat transport

  • In order to achieve our goal of creating complete 3D heating rate fields, we developed nonlinear regression models based on artificial neural networks (ANNs) which use as input the combined Atmospheric Infrared Sounder (AIRS) and ERA-Interim data described in Sect. 2.1 and 2.2

  • As the mean absolute error (MAE) only provides an average estimation of the quality of the prediction, we considered the difference between the predicted radiative heating rates (HRs) and those determined from CALIPSO–CloudSat measurements over tropical ocean, separately for Cb, cirrus anvil (Ci), thin cirrus (thin Ci) and mid-level and low-level clouds

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Summary

Introduction

Upper tropospheric (UT) clouds play a vital role in the climate system by modulating the Earth’s energy budget and the UT heat transport. Cloud properties retrieved from measurements of the cross-track scanning Atmospheric Infrared Sounder (AIRS) aboard the polar orbiting Aqua satellite have a large instantaneous horizontal coverage (Stubenrauch et al, 2017) They have been used by Protopapadaki et al (2017) to reconstruct UT cloud systems. The space-borne active lidar and radar measurements of the CALIPSO and CloudSat missions (Stephens et al, 2018a) supply the cloud vertical structure, in particular the radiative heating rates (Henderson et al, 2013) As this information is only available along successive narrow nadir tracks, separated by about 2500 km, we employed machine learning techniques on cloud, atmospheric and surface properties to build a 3D description of these cloud systems.

Data and methods
AIRS cloud data and cloud system data
Atmospheric and surface data
CALIPSO–CloudSat vertical structure and collocation with AIRS
Artificial neural network construction
Sensitivity studies and evaluation
Ci and thin Ci
Sensitivity to input variables
Scenes used for the training
Construction of tropical heating rate fields
The impact of tropical UT cloud systems
Tropics-wide cloud radiative heating
Relation between regional surface temperature and MCSs
Findings
Conclusions and outlook
Full Text
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