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Wiebke Schubotz
,
Daniel Klocke
,
Ulrich Löhnert
,
Andreas Macke
,
Bjorn Stevens
, and
Allison Wing
Free access
Frédéric Hourdin
,
Thorsten Mauritsen
,
Andrew Gettelman
,
Jean-Christophe Golaz
,
Venkatramani Balaji
,
Qingyun Duan
,
Doris Folini
,
Duoying Ji
,
Daniel Klocke
,
Yun Qian
,
Florian Rauser
,
Catherine Rio
,
Lorenzo Tomassini
,
Masahiro Watanabe
, and
Daniel Williamson

Abstract

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. We discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.

Full access
Xubin Zeng
,
Daniel Klocke
,
Ben J. Shipway
,
Martin S. Singh
,
Irina Sandu
,
Walter Hannah
,
Peter Bogenschutz
,
Yunyan Zhang
,
Hugh Morrison
,
Michael Pritchard
, and
Catherine Rio
Full access
Mark J Rodwell
,
Linus Magnusson
,
Peter Bauer
,
Peter Bechtold
,
Massimo Bonavita
,
Carla Cardinali
,
Michail Diamantakis
,
Paul Earnshaw
,
Antonio Garcia-Mendez
,
Lars Isaksen
,
Erland Källén
,
Daniel Klocke
,
Philippe Lopez
,
Tony McNally
,
Anders Persson
,
Fernando Prates
, and
Nils Wedi

Medium-range weather prediction has become more skillful over recent decades, but forecast centers still suffer from occasional very poor forecasts, which are often referred to as “dropouts” or “busts.” This study focuses on European Centre for Medium-Range Weather Forecasts (ECMWF) day-6 forecasts for Europe. Although busts are defined by gross scores, bust composites reveal a coherent “Rex type” blocking situation, with a high over northern Europe and a low over the Mediterranean. Initial conditions for these busts also reveal a coherent flow, but this is located over North America and involves a trough over the Rockies, with high convective available potential energy (CAPE) to its east. This flow type occurs in spring and is often associated with a Rossby wave train that has crossed the Pacific. A composite on this initial flow type displays enhanced day-6 random forecast errors and some-what enhanced ensemble forecast spread, indicating reduced inherent predictability.

Mesoscale convective systems, associated with the high levels of CAPE, act to slow the motion of the trough. Hence, convection errors play an active role in the busts. The subgrid-scale nature of convection highlights the importance of the representation of model uncertainty in probabilistic forecasts. The cloud and extreme conditions associated with mesoscale convective systems also reduce the availability and utility of observations provided to the data assimilation.

A question of relevance to the wider community is, do we have observations with sufficient accuracy to better constrain the important error structures in the initial conditions? Meanwhile, improvements to ensemble prediction systems should help us better predict the increase in forecast uncertainty.

Full access
Graeme Stephens
,
Jan Polcher
,
Xubin Zeng
,
Peter van Oevelen
,
Germán Poveda
,
Michael Bosilovich
,
Myoung-Hwan Ahn
,
Gianpaolo Balsamo
,
Qingyun Duan
,
Gabriele Hegerl
,
Christian Jakob
,
Benjamin Lamptey
,
Ruby Leung
,
Maria Piles
,
Zhongbo Su
,
Paul Dirmeyer
,
Kirsten L. Findell
,
Anne Verhoef
,
Michael Ek
,
Tristan L’Ecuyer
,
Rémy Roca
,
Ali Nazemi
,
Francina Dominguez
,
Daniel Klocke
, and
Sandrine Bony

Abstract

The Global Energy and Water Cycle Exchanges (GEWEX) project was created more than 30 years ago within the framework of the World Climate Research Programme (WCRP). The aim of this initiative was to address major gaps in our understanding of Earth’s energy and water cycles given a lack of information about the basic fluxes and associated reservoirs of these cycles. GEWEX sought to acquire and set standards for climatological data on variables essential for quantifying water and energy fluxes and for closing budgets at the regional and global scales. In so doing, GEWEX activities led to a greatly improved understanding of processes and our ability to predict them. Such understanding was viewed then, as it remains today, essential for advancing weather and climate prediction from global to regional scales. GEWEX has also demonstrated over time the importance of a wider engagement of different communities and the necessity of international collaboration for making progress on understanding and on the monitoring of the changes in the energy and water cycles under ever increasing human pressures. This paper reflects on the first 30 years of evolution and progress that has occurred within GEWEX. This evolution is presented in terms of three main phases of activity. Progress toward the main goals of GEWEX is highlighted by calling out a few achievements from each phase. A vision of the path forward for the coming decade, including the goals of GEWEX for the future, are also described.

Open access
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
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