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Dillon J. Amaya, Michael A. Alexander, Antonietta Capotondi, Clara Deser, Kristopher B. Karnauskas, Arthur J. Miller, and Nathan J. Mantua
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Shanshan Wang, Jianping Huang, and Xing Yuan
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Jizeng Du, Kaicun Wang, and Baoshan Cui
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Jonghun Kam, Seung-Ki Min, Piotr Wolski, and Jong-Seong Kug
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N. C. Privé
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Bianca Adler, Alexander Gohm, Norbert Kalthoff, Nevio Babić, Ulrich Corsmeier, Manuela Lehner, Mathias W. Rotach, Maren Haid, Piet Markmann, Eckhard Gast, George Tsaknakis, and George Georgoussis

Abstract

While the exchange of mass, momentum, moisture, and energy over horizontally homogeneous, flat terrain is mostly driven by vertical turbulent mixing, thermally and dynamically driven mesoscale flows substantially contribute to the Earth–atmosphere exchange in the atmospheric boundary layer over mountainous terrain (MoBL). The interaction of these processes acting on multiple scales leads to a large spatial variability in the MoBL, whose observational detection requires comprehensive instrumentation and a sophisticated measurement strategy. We designed a field campaign that targets the three-dimensional flow structure and its impact on the MoBL in a major Alpine valley. Taking advantage of an existing network of surface flux towers and remote sensing instrumentation in the Inn Valley, Austria, we added a set of ground-based remote sensing instruments, consisting of Doppler lidars, a ceilometer, a Raman lidar, and a microwave radiometer, and performed radio soundings and aircraft measurements. The objective of the Cross-Valley Flow in the Inn Valley Investigated by Dual-Doppler Lidar Measurements (CROSSINN) experiment is to determine the mean and turbulent characteristics of the flow in the MoBL under different synoptic conditions and to provide an intensive dataset for the future validation of mesoscale and large-eddy simulations. A particular challenge is capturing the two-dimensional kinematic flow in a vertical plane across the whole valley using coplanar synchronized Doppler lidar scans, which allows the detection of cross-valley circulation cells. This article outlines the scientific objectives, instrument setup, measurement strategy, and available data; summarizes the synoptic conditions during the measurement period of 2.5 months; and presents first results.

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Stephanie C. Herring, Nikolaos Christidis, Andrew Hoell, Martin P. Hoerling, and Peter A. Stott

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Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2019 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

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Nikolaos Christidis and Peter A. Stott
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Seung-Ki Min, Min-Gyu Seong, Dong-Hyun Cha, Minkyu Lee, Fraser C. Lott, Andrew Ciavarella, Peter A. Stott, Maeng-Ki Kim, Kyung-On Boo, and Young-Hwa Byun
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Blair Trewin, Anny Cazenave, Stephen Howell, Matthias Huss, Kirsten Isensee, Matthew D. Palmer, Oksana Tarasova, and Alex Vermeulen

Abstract

The World Meteorological Organization has developed a set of headline indicators for global climate monitoring. These seven indicators are a subset of the existing set of essential climate variables (ECVs) established by the Global Climate Observing System and are intended to provide the most essential parameters representing the state of the climate system. These indicators include global mean surface temperature, global ocean heat content, state of ocean acidification, glacier mass balance, Arctic and Antarctic sea ice extent, global CO2 mole fraction, and global mean sea level. This paper describes how well each of these indicators are currently monitored, including the number and quality of the underlying datasets; the health of those datasets; observation systems used to estimate each indicator; the timeliness of information; and how well recent values can be linked to preindustrial conditions. These aspects vary widely between indicators. While global mean surface temperature is available in close to real time and changes from preindustrial levels can be determined with relatively low uncertainty, this is not the case for many other indicators. Some indicators (e.g., sea ice extent) are largely dependent on satellite data only available in the last 40 years, while some (e.g., ocean acidification) have limited underlying observational bases, and others (e.g., glacial mass balance) with data only available a year or more in arrears.

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