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Ariane Frassoni
,
Carolyn Reynolds
,
Nils Wedi
,
Zied Ben Bouallègue
,
Antonio Caetano Vaz Caltabiano
,
Barbara Casati
,
Jonathan A. Christophersen
,
Caio A. S. Coelho
,
Chiara De Falco
,
James D. Doyle
,
Laís G. Fernandes
,
Richard Forbes
,
Matthew A. Janiga
,
Daniel Klocke
,
Linus Magnusson
,
Ron McTaggart-Cowan
,
Morteza Pakdaman
,
Stephanie S. Rushley
,
Anne Verhoef
,
Fanglin Yang
, and
Günther Zängl
Open access
Markus Gross
,
Hui Wan
,
Philip J. Rasch
,
Peter M. Caldwell
,
David L. Williamson
,
Daniel Klocke
,
Christiane Jablonowski
,
Diana R. Thatcher
,
Nigel Wood
,
Mike Cullen
,
Bob Beare
,
Martin Willett
,
Florian Lemarié
,
Eric Blayo
,
Sylvie Malardel
,
Piet Termonia
,
Almut Gassmann
,
Peter H. Lauritzen
,
Hans Johansen
,
Colin M. Zarzycki
,
Koichi Sakaguchi
, and
Ruby Leung

Abstract

Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently. Once development is mature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model. As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherent model are here termed physics–dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problem with references to examples in the literature, to show how the problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/or models use variable resolutions.

Open access
Cathy Hohenegger
,
Felix Ament
,
Frank Beyrich
,
Ulrich Löhnert
,
Henning Rust
,
Jens Bange
,
Tobias Böck
,
Christopher Böttcher
,
Jakob Boventer
,
Finn Burgemeister
,
Marco Clemens
,
Carola Detring
,
Igor Detring
,
Noviana Dewani
,
Ivan Bastak Duran
,
Stephanie Fiedler
,
Martin Göber
,
Chiel van Heerwaarden
,
Bert Heusinkveld
,
Bastian Kirsch
,
Daniel Klocke
,
Christine Knist
,
Ingo Lange
,
Felix Lauermann
,
Volker Lehmann
,
Jonas Lehmke
,
Ronny Leinweber
,
Kristina Lundgren
,
Matthieu Masbou
,
Matthias Mauder
,
Wouter Mol
,
Hannes Nevermann
,
Tatiana Nomokonova
,
Eileen Päschke
,
Andreas Platis
,
Jens Reichardt
,
Luc Rochette
,
Mirjana Sakradzija
,
Linda Schlemmer
,
Jürg Schmidli
,
Nima Shokri
,
Vincent Sobottke
,
Johannes Speidel
,
Julian Steinheuer
,
David D. Turner
,
Hannes Vogelmann
,
Christian Wedemeyer
,
Eduardo Weide-Luiz
,
Sarah Wiesner
,
Norman Wildmann
,
Kevin Wolz
, and
Tamino Wetz

Abstract

Numerical weather prediction models operate on grid spacings of a few kilometers, where deep convection begins to become resolvable. Around this scale, the emergence of coherent structures in the planetary boundary layer, often hypothesized to be caused by cold pools, forces the transition from shallow to deep convection. Yet, the kilometer-scale range is typically not resolved by standard surface operational measurement networks. The measurement campaign Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) aimed at addressing this gap by observing atmospheric variability at the hectometer-to-kilometer scale, with a particular emphasis on cold pools, wind gusts, and coherent patterns in the planetary boundary layer during summer. A unique feature was the distribution of 150 self-developed and low-cost instruments. More specifically, FESSTVaL included dense networks of 80 autonomous cold pool loggers, 19 weather stations, and 83 soil sensor systems, all installed in a rural region of 15-km radius in eastern Germany, as well as self-developed weather stations handed out to citizens. Boundary layer and upper-air observations were provided by eight Doppler lidars and four microwave radiometers distributed at three supersites; water vapor and temperature were also measured by advanced lidar systems and an infrared spectrometer; and rain was observed by a X-band radar. An uncrewed aircraft, multicopters, and a small radiometer network carried out additional measurements during a 4-week period. In this paper, we present FESSTVaL’s measurement strategy and show first observational results including unprecedented highly resolved spatiotemporal cold-pool structures, both in the horizontal as well as in the vertical dimension, associated with overpassing convective systems.

Open access
Yongkang Xue
,
Ismaila Diallo
,
Aaron A. Boone
,
Tandong Yao
,
Yang Zhang
,
Xubin Zeng
,
J. David Neelin
,
William K. M. Lau
,
Yan Pan
,
Ye Liu
,
Xiaoduo Pan
,
Qi Tang
,
Peter J. van Oevelen
,
Tomonori Sato
,
Myung-Seo Koo
,
Stefano Materia
,
Chunxiang Shi
,
Jing Yang
,
Constantin Ardilouze
,
Zhaohui Lin
,
Xin Qi
,
Tetsu Nakamura
,
Subodh K. Saha
,
Retish Senan
,
Yuhei Takaya
,
Hailan Wang
,
Hongliang Zhang
,
Mei Zhao
,
Hara Prasad Nayak
,
Qiuyu Chen
,
Jinming Feng
,
Michael A. Brunke
,
Tianyi Fan
,
Songyou Hong
,
Paulo Nobre
,
Daniele Peano
,
Yi Qin
,
Frederic Vitart
,
Shaocheng Xie
,
Yanling Zhan
,
Daniel Klocke
,
Ruby Leung
,
Xin Li
,
Michael Ek
,
Weidong Guo
,
Gianpaolo Balsamo
,
Qing Bao
,
Sin Chan Chou
,
Patricia de Rosnay
,
Yanluan Lin
,
Yuejian Zhu
,
Yun Qian
,
Ping Zhao
,
Jianping Tang
,
Xin-Zhong Liang
,
Jinkyu Hong
,
Duoying Ji
,
Zhenming Ji
,
Yuan Qiu
,
Shiori Sugimoto
,
Weicai Wang
,
Kun Yang
, and
Miao Yu

Abstract

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Free access