Abstract

Abstract. The coupling between soil, vegetation and atmosphere is thought to be crucial in the development and intensification of weather extremes, especially meteorological droughts, heat waves and severe storms. Therefore, understanding the evolution of the atmospheric boundary layer (ABL) and the role of land–atmosphere feedbacks is necessary for earlier warnings, better climate projection and timely societal adaptation. However, this understanding is hampered by the difficulties of attributing cause–effect relationships from complex coupled models and the irregular space–time distribution of in situ observations of the land–atmosphere system. As such, there is a need for simple deterministic appraisals that systematically discriminate land–atmosphere interactions from observed weather phenomena over large domains and climatological time spans. Here, we present a new interactive data platform to study the behavior of the ABL and land–atmosphere interactions based on worldwide weather balloon soundings and an ABL model. This software tool – referred to as CLASS4GL (http://class4gl.eu, last access: 27 May 2018) – is developed with the objectives of (a) mining appropriate global observational data from ∼15 million weather balloon soundings since 1981 and combining them with satellite and reanalysis data and (b) constraining and initializing a numerical model of the daytime evolution of the ABL that serves as a tool to interpret these observations mechanistically and deterministically. As a result, it fully automizes extensive global model experiments to assess the effects of land and atmospheric conditions on the ABL evolution as observed in different climate regions around the world. The suitability of the set of observations, model formulations and global parameters employed by CLASS4GL is extensively validated. In most cases, the framework is able to realistically reproduce the observed daytime response of the mixed-layer height, potential temperature and specific humidity from the balloon soundings. In this extensive global validation exercise, a bias of 10.1 m h−1, −0.036 K h−1 and 0.06 g kg−1 h−1 is found for the morning-to-afternoon evolution of the mixed-layer height, potential temperature and specific humidity. The virtual tool is in continuous development and aims to foster a better process understanding of the drivers of the ABL evolution and their global distribution, particularly during the onset and amplification of weather extremes. Finally, it can also be used to scrutinize the representation of land–atmosphere feedbacks and ABL dynamics in Earth system models, numerical weather prediction models, atmospheric reanalysis and satellite retrievals, with the ultimate goal of improving local climate projections, providing earlier warning of extreme weather and fostering a more effective development of climate adaptation strategies. The tool can be easily downloaded via http://class4gl.eu (last access: 27 May 2018) and is open source.

Highlights

  • Climate and weather phenomena are largely influenced by land surface processes and the characteristics of the landscape

  • CLASS4GL is provided as an open-source Python library, it is conveyed under the GNU General Public License version 3 (GPLv3), and it can be downloaded via http://class4gl.eu

  • Global data of weather balloon soundings are taken from the Integrated Global Radiosonde Archive (IGRA; Durre et al, 2006) which is maintained under the auspices of the National Oceanic and Atmospheric Administration (NOAA)

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Summary

Introduction

Climate and weather phenomena are largely influenced by land surface processes and the characteristics of the landscape. The advantage of using these ABL bulk models is twofold: (a) unlike climate models, they can be routinely initialized and constrained by observations and are interpretable in terms of the interaction between variables; (b) unlike merely statistical analysis of observational data, they provide an unambiguous understanding of the deterministic links among the variables in the system. These mechanistic models require detailed observations describing the entire state or evolution of the soil, vegetation and atmosphere. A perspective is provided in which the potential of this framework to contribute to a better understanding of land–atmosphere interactions over different climates is discussed (Sect. 4)

Data and methods
ABL model
Automized balloon data mining
Gridded ancillary data
Results and discussion
Conclusion and perspective
Full Text
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