Search

Previous studies agree on an impact of the Atlantic Multidecadal Variability (AMV) on total seasonal rainfall amounts over the Sahel. However, whether and how AMV affects the distribution of rainfall or the timing of the West African Monsoon is not well known. Here we analyze daily rainfall outputs from atmosphere-ocean coupled models. Models show dry biases over the Sahel, where the mean intensity is consistently smaller than observations, and wet biases over the Guinea Coast, where they simulate too many rainy days. In addition, most models underestimate the average length of the rainy season over the Sahel, some due to a too late monsoon onset and others due to a too early cessation. In response to a persistent positive AMV pattern imposed in the Atlantic, following a protocol largely consistent with the one proposed by the Component C of the Decadal Climate Prediction Project (DCPP-C), models show an enhancement in total summer rainfall over West African land mass, including the Sahel. Both the number of wet days and the intensity of daily rainfall events are enhanced over the Sahel. The former explains most of the changes in seasonal rainfall in the northern fringe, while the latter is more relevant in the southern region, where higher rainfall anomalies occur. This dominance is connected to the changes in the number of days per type of event: the frequency of both moderate and heavy events increases over the Sahel’s northern fringe. Conversely, over the southern limit, it is mostly the frequency of heavy events which is enhanced, affecting the mean rainfall intensity there. Extreme rainfall events are also enhanced over the whole Sahel in response to a positive phase of the AMV. Models with stronger negative biases in rainfall amounts tend to show weaker changes in response to AMV, suggesting systematic biases could affect the simulated responses. The monsoon onset over the Sahel shows no clear response to AMV, while the demise tends to be delayed and the overall length of the monsoon season enhanced between 2 and 5 days with the positive AMV pattern. The effect of AMV on the seasonality of the monsoon is more consistent to the West of 10º W, with all models showing a statistically significant earlier onset, later demise and enhanced monsoon season with the positive phase of the AMV. Our results suggest a potential for the decadal prediction of changes in the intraseasonal characteristics of rainfall over the Sahel, including the occurrence of extreme events.

Climate change is expected to have far reaching impacts on human and natural systems during the 21stcentury. To guide policy decisions on climate change adaptation and mitigation, there is a need for developing new types of climate information systems that can provide timely information on regional and local impacts of climate change. The European Commission’s Destination Earth (DestinE) programme aims towards this by developing high precision digital twins (DT) of the Earth. We present here the overview of one of the two first priority DTs, Climate Change Adaptation DT. The Climate DT will encompass a pre-exascale climate information system that can support climate change adaptation efforts.The Climate DT harnesses two different kilometer-scale Earth-system models (ESMs), ICON and IFS-FESOM/NEMO. The models will be adapted to two EuroHPC pre-exascale computing systems: LUMI that is currently in operation in Kajaani, Finland, and Mare Nostrum 5 that will be available during 2023 in Barcelona, Spain.The Climate DT introduces the idea of a generic state vector (GSV), which is evolved by the ESMs and streamed to applications. This enables the ESMs to work at an unprecedented scale (multi-decadal simulations on 5km or finer global meshes) and thus improves the fidelity of the information the models provide as well as its relevance for the users. This approach also creates the basis for an information system that can scale across an unlimited number of applications and enable interactivity during the future phases of DestinE.Use cases from different impact sectors are implemented within Climate DT as applications that operate on the streamed GSV. The Climate DT will explore five use cases which will provide information on (1) wind energy supply and demand, (2) wildfire risk and emissions, (3) river flows, (4) hydrometeorological extreme events, and (5) heat stress in urban environments. Additional applications operating on the GSV include a quality assessment and uncertainty quantification framework, used for monitoring and evaluation of the GSV. This framework will utilize observational operators to evaluate the GSV against observational data, and in so doing enable the use of tools from numerical weather prediction for quality assessment and observation-based model tuning.In this presentation, we will give an overview of the Climate DT, describing the objectives for the first phase, technical design of the DT, and the progress made so far. The use cases and the new possibilities provided by the streaming of the GSV are discussed in more detail by Doblas-Reyes et al., and the development of the digital infrastructure for Climate DT by Narayanappa et al.

Societies use climate services as part of their mitigation and adaptation strategies to a changing climate (e.g., Cortekar et al., 2016; Vaughan and Dessai, 2014; Scott, Lemieux and Malon, 2011), and have been defined and framed around particular single applications or sectors, either agriculture, health, energy, water management or disaster risk management (WMO, 2009, Council, 2001)- just to mention a few. Whilst this can bring potential benefits such as high specialization and adaptation (WMO, 2019; Lemos, 2015), co-benefits of articulated climate services among different sectors have not been fully assessed in the broader societal system, where these are developed and implemented. Understanding and valuing the nexus between the sectors during the design, development and implementation of climate services might help project optimization, and eventually benefit the community, country, entire region or society.Climate services ecosystems are defined -slightly modifying the business-perspective definition of Vargo and Akaka (2012)- as relatively self-contained, self-adjusting systems of resource-integrating actors connected by shared institutional goals, and mutual-value creation through exchange of climate services (Goddard, Gonzalez Romero, et al., 2020). In other words, a climate-services ecosystem involves interactions between different sectors sharing the same or similar climate services, which enhances resilience, and lends efficiency and value, by optimally orchestrating the available solutions. These ecosystems tend to be more robust to climate impacts than a collection of climate services focused on certain applications or just one sector, because shocks to one part of the ecosystem are redistributed and dampened through the entire network. Since by definition these ecosystems take advantage of existing climate services in different society-relevant sectors, the overall benefit is directly dependent on the ecosystem configuration itself. The ability to scale high-quality climate services, not just to other locations but to other sectors, and the ability for these climate-service networks to organize into ecosystems is hypothesized to be a crucial ingredient to resilience in the face of climate variability and change, given that resources are finite.The analysis of the ecosystems though Dynamical Casual Network Theory allows us to understand, characterize and forsee potential behaviour and changes in relationships between the elements of the networks, supporting the decision-making processes within. In combination with projects like Climateurope2, this concept of climate services ecosystems can help with the standardization of climate services.

Stakeholders in all socio-economic sectors require reliable forecasts at multiple timescales as part of their decision-making processes. Although basing decisions mostly on a particular timescale (e.g., weather, subseasonal, seasonal) is the present status quo, this approach tends to lead to missing opportunities for more comprehensive risk-management systems (Goddard et al. 2014). While today a variety of forecasts are produced targeting distinct timescales in a routine way, these products are generally presented to the users in different websites and bulletins, often without an assessment of how consistent the predictions are across timescales. Since different models and strategies are used at different timescales by both national and international seasonal and subseasonal forecasting centers (Kirtman et al. 2014, Kirtman et al. 2017, Vitart et al. 2017), and skill is different at those timescales, it is key to guarantee that a physically consistent “bridging” between the forecasts exists, and that the cross-timescale predictions are overall skilful and actionable, so decision makers can conduct their work. Here, we propose and explore a new methodology –that we call the Xit (“cross-it”) operator– based on the Liang-Kleeman information flow (e.g., Tawia Hagan et al. 2019) and wavelet spectra and entropy (e.g., Zunino et al. 2007), to “bridge” forecasts at different timescales in a smooth and physically-consistent manner. In summary, the Xit operator (1) conducts a wavelet spectral analysis (e.g., Ng and Chan 2013, Zunino et al. 2007) and (2) a non-stationary time-frequency causality analysis (e.g., Tawia Hagan et al. 2019, Liang 2015) on forecasts at different timescales to assess cross-timescale coherence and physical consistency in terms of various sources of predictability. In principle, the approach permits to identify which “intrinsic” periods/scales (i) in the timescale continuum (t) are more suitable for the bridging to occur, and/or which ones can produce more skillful forecasts, by pointing to particular target times—i.e., potential windows of opportunity (Mariotti et al. 2020)—in the forecast period where wavelet entropy (uncertainty) is lower. While the first component of the Xit operator, i.e., the wavelet spectral and entropy analysis (Zunino et al. 2007), is designed to identify the optimal time-frequency bands for cross-timescale bridging, the fact that two forecast systems (e.g., a subseasonal and a seasonal) exhibit significant wavelet coherence does not imply that bridging those systems will provide physically-consistent predictions. The second component of the Xit operator, i.e., the non-stationary causality analysis (Tawia Hagan et al. 2019), is thus designed to assess physical consistency of the bridging by analyzing the causal link between different climate drivers (acting at different timescales) and the forecast variable of interest.

The Destination Earth Climate Adaptation Digital Twin (Climate DT) will design and implement a climate information system running on pre-exascale high-performance computing platforms to support climate adaptation efforts. An overview of the overall Climate DT will be given by Kontkanen et al. (this session).The Climate DT will provide global climate data for both the historical period and the near-term future with unprecedented spatial and temporal resolution. The downstream applications will access the full model state vector (MSV) at runtime. This will lead to an interactive system where applications harnessing the MSV can be added, removed, or modified as required by the user. The climate MSV, which contains both the prognostic and a large number of diagnosed variables, will be continuously streamed (understood as all the user-requested variables being available in a federated and curated repository for a limited period of time before being erased) at both high frequency and native resolution. The applications will be able to consume this data at runtime as it is streamed. This is equivalent to applications using all the model data they require in a similar manner as one observes a physical system with all the necessary detail to satisfy specific requirements. Additional functionalities will be provided to help the data consumers access relevant statistics, to speed up the data processing and facilitate the data reduction (e.g., on-the-fly bias adjustment). This approach reduces the entry-level requirements for applications to participate in this completely new approach to access climate information data sources. The applications have the possibility of not only interacting with the model to extract the required climate data and indicators on real time, but also iteratively contribute to the design of the experimental set up and request additional variables and indicators.To illustrate the broadest possible applicability of the Climate DT concept, five different pilot use cases were selected for the co-design, implementation, user feedback and evaluation of the Climate DT. The selected use cases focus on wildfires, urban climate, river discharge, wind energy, and hydrometeorological applications. Another consumer of the MSV will be the climate model evaluation. Furthermore, the use cases will present technical recipes for users to access the data and link their applications or impact models to the digital twin.Each use case has identified specific key users. A close exchange with these key users is foreseen to meet the user requirements. To ensure transferability of the work to other users, an exchange with a wider circle of users is foreseen at a more advanced phase at dedicated stakeholder meetings.An overview of the use cases, the technical concepts and the ongoing user engagement and co-design activities will be given to demonstrate the novelty, potential and advantages the digital twin offers. The use cases will illustrate the progress beyond current practices that is possible with these new climate simulation workflow compared to the traditional way of delivering climate simulation.

The Mediterranean region is often considered a climate change hotspot. State of the art climate projections display a large uncertainty due to factors such as the modeling approach or variability phasing, in particular for projections of the next few decades. With the great amount of climate information from the last Coupled Model Intercomparison Project (CMIP6), it is desirable to constrain the multi-model multi-member ensemble for the near-term future to obtain more robust and better performing projections. We explore different subsetting methods that select those members that better capture the variability at the starting date of the projection’s study period. To find the best information we explore variations of different parameters in the selection method based on relevant climate drivers in the Mediterranean region. Such parameters are the reference against which the best members are selected, and comprise: the time period used, the constraining regions considered and the variable or metric that drive the constraint. We find that these subsetting methods can improve the accuracy of near-term climate projections for the next 20 years compared to the unconstrained projections. This evaluation of the results allows us to make informed and robust decisions about the near-term future projections based on quality estimates borrowed from climate prediction practices.

Climate change will have far reaching impacts on human and natural systems during the 21st century. To increase the understanding of the present and future climate impacts and build resilience, improved Earth system modelling is required. The European Commission Destination Earth (DestinE) initiative aims to contribute to this by developing high precision digital twins (DTs) of the Earth. We present our solution to a climate-change adaptation DT, which is one of the two DTs developed during the first phase of DestinE. The objective of the climate change adaptation DT is to improve the assessment of the impacts of climate change and different adaptation actions at regional and national levels over multi-decadal timescales. This will be achieved by using two storm- and eddy-resolving global climate models, ICON (Icosahedral Nonhydrostatic Weather and Climate Model) and IFS (Integrated Forecasting System). The models will be run at a resolution of a few km on pre-exascale LUMI and MareNostrum5 supercomputers, which are flagship systems of the European High Performance Computing Joint Undertaking (EuroHPC JU) network. Following a radically different approach, climate simulations will be combined with a set of impact models, which enables assessing impacts on different sectors and topics, such as forestry, hydrology, cryosphere, energy, and urban areas. The end goal is to create a new type of climate simulations, in which user requirements are an integral part of the workflow, and thus adaptation solutions can be effectively deployed.

In initialized seasonal to decadal (S2D) predictions, model hindcasts rapidly drift away from the initial observed state and converge toward a preferred state characterized by systematic error, or bias. Bias and drift are among the greatest challenges facing initialized prediction today. Differences in trends between initial states and drifted states, combined with bias and drift, introduce complexities in calculating anomalies to assess skill of initialized predictions. We examine several methods of calculating anomalies using the Decadal Prediction Large Ensemble (DPLE) using the Community Earth System Model (CESM) initialized hindcasts and focus on Pacific and Atlantic SSTs to illustrate issues with anomaly calculations. Three methods of computing anomalies, one as differences from a long term model climatology, another as bias-adjusted differences from the previous 15 year average from observations, and a third as differences from the previous 15 year average from the model, are contrasted and each is shown to have limitations. For the first, trends in bias and drift introduce higher skill estimates earlier and later in the hindcast period due to the trends that contribute to skill. For the second, higher skill can be introduced in situations where low frequency variability in the observations is large compared to the hindcasts on timescales greater than 15 years, while lower skill can result if the predicted signal is small and the bias-correction itself produces a transition of SST anomalies to the opposite sign of those that are observed. The third method has somewhat lower skill compared to each of the others, but has less difficulties with not only the long term trends in the model climatology, but also with the unrealistic situational skill from using observations as a reference. However, the first 15 years of the hindcast period cannot be evaluated due to having to wait to accumulate the previous 15 year model climatology before the method can be applied. The IPO transition in the 2014–2016 time frame from negative to positive (predicted by Meehl et al. in in Nat Commun, 10.1038/NCOMMS11718, 2016) did indeed verify using all three methods, though each provides somewhat different skill values as a result of the respective limitations. There is no clear best method, as all are roughly comparable, and each has its own set of limitations and caveats. However, all three methods show generally higher overall skill in the AMO region compared to the IPO region. Portions of this study were supported by the Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program of the U.S.Department of Energy's Office of Biological & Environmental Research (BER) via National Science Foundation IA 1947282, and under Award Number DE-SC0022070. This work also was supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (NSF) under Cooperative Agreement No.1852977

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration. This work was supported by the European Union Seventh Framework Programme (FP7/2007-13) under Grant 308378 (SPECS Project; http://specs-fp7.eu/). The ECMWF experiments were supported by the EU-FP7 ImagineS project (http://fp7-imagines. eu/) in support to the Copernicus Global land. Further support was provided to this work by the European Union’s Horizon 2020 research and innovation programme under grant agreement N. 641816 (CRESCENDO project; http://crescendoproject.eu/) and under grant agreement N. 704585 (PROCEED project). Acknowledgement is made for the use of ECMWF’s computing and archive facilities in this research (special project SPITALES).

Las predicciones a largo plazo proporcionan un abanico de posibles cambios en las condiciones atmosféricas de un cierto mes, estación o año venidero, con respecto a los denominados valores normales. Debido a la naturaleza caótica de la atmósfera, es imposible predecir las variaciones diarias del tiempo en una localidad específica con meses de antelación. Incluso es imposible predecir con exactitud el tiempo promedio en zonas de cierta amplitud. En este capítulo se examinan estos conceptos punto por punto y se describe qué pueden aportar estas predicciones a largo plazo a la sociedad.

The final publication is available at Springer via http://dx.doi.org/10.1007/s00382-015-2879-4 Land surfaces and soil conditions are key sources of climate predictability at the seasonal time scale. In order to estimate how the initialization of the land surface affects the predictability at seasonal time scale, we run two sets of seasonal hindcasts with the general circulation model EC-Earth2.3. The initialization of those hindcasts is done either with climatological or realistic land initialization in May using the ERA-Land re-analysis. Results show significant improvements in the initialized run occurring up to the last forecast month. The prediction of near-surface summer temperatures and precipitation at the global scale and over Europe are improved, as well as the warm extremes prediction. As an illustration, we show that the 2010 Russian heat wave is only predicted when soil moisture is initialized. No significant improvement is found for the retrospective prediction of the 2003 European heat wave, suggesting this event to be mainly large-scale driven. Thus, we confirm that late-spring soil moisture conditions can be decisive in triggering high-impact events in the following summer in Europe. Accordingly, accurate land-surface initial conditions are essential for seasonal predictions. The research leading to these results has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under grant agreements 308378 (SPECS), 282378 (DEN-FREE) and 607085 (EUCLEIA), and from the Spanish Ministerio de Economía y Competitividad (MINECO) under the project CGL2013-41055-R. We acknowledge the s2dverification R-based package (http://cran.r-project.org/web/packages/s2dverification/index.html). We also thank ECMWF for providing the ERA-Land initial conditions and computing resources through the SPICCF Special Project.

In this work we present the EC-Earth coupled climate model, which is a seamless Earth System Model (ESM) used to carry out climate research in 24 academic institutions and meteorological services from 11 countries in Europe. This model couples several components and it is continuously under development. As a coupled model, EC-Earth consists of several different sub models, each of them presenting different degrees of performance because of their different features and complexities. This represents an important disadvantage at the time of consuming computing time in supercomputing environments, which usually is granted by the corresponding host institution provided that the efficient usage of the computing resources is demonstrated. For this reason, a study to determine the best distribution of computing processors between components was carried out to assess empirically the different performance of the model depending on the amount of computing processors allocated to each component. This experimentation allowed us to optimize the speedup/efficiency balance of the model. Moreover, the obtained results highlight the important drawback caused by the low scalability of one of the EC-Earth components.

This chapter assesses the scientific literature describing expectations for near-term climate (present through mid-century). Unless otherwise stated, 'near-term' change and the projected changes below are for the period 2016-2035 relative to the reference period 1986-2005. Atmos- pheric composition (apart from CO2; see Chapter 12) and air quality projections through to 2100 are also assessed.

In absence of other mechanisms, the main input of CO2into the Venusian atmosphere is via volcanic out gassing. Since Venus can be regarded as a planet-wide large igneous province, we can expect large quantities of CO2 being transferred into its atmosphere via volcanic out gassing. We have quantified the maximum possible amount of CO2 that can be out gassed via a single massive episode of resurfacing of the planet. This figure (5.6 × 1019 kg of CO2) is about 8 times smaller than the total CO2 present in the Venusian atmosphere (4.55 × 1020 kg CO2). The lack of planet-wide, efficient mechanisms for the recycling of CO2 on Venus indicates that CO2 has progressively accumulated in the atmosphere. Based on these considerations we suggest that the “equivalent” to eight global resurfacing episodes would be required to account for the present values of CO2 atmosphere.

The impact of using realistic varying instead of fixed greenhouse gas concentrations is investigated in an ensemble of 6-month integrations of a coupled general circulation model (CGCM) from 1958 to 2001. Results suggest that an increase of greenhouse gas concentration is conducive to a decrease of tropical storm activity over all basins except the western North Atlantic and the eastern North Pacific. This result seems consistent with the impact of an increase of greenhouse gas concentration on the thermodynamical and dynamical variables that have an impact on the tropical cyclone activity. The interdecadal variability of tropical storm frequency is more realistic when using realistic varying GHG concentration than when using constant GHG. The improvement seems to be due to a more realistic trend of tropical storm frequency than to an improvement of the interdecadal variability around this trend. Therefore, it is important for seasonal forecasting systems to take account of the variation of GHG in the hindcast period used to calibrate the system.

As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future. LH and AS were supported by the EUCP project funded by the European Commission’s Horizon 2020 programme, Grant Agreement 776613 and supported by the U.K.–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. AS, DS, and MS were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. The EC-Earth simulations at SMHI were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC and NSC and have been performed as part of the NordForsk-funded Nordic Centre of Excellence project (Award 76654) Arctic Climate Predictions: Pathways to Resilient, Sustainable Societies (ARCPATH). SY and TT were supported by the ARCPATH (NordForsk Award 76654) and the Danish state-funded National Centre for Climate Research [Nationalt Center for Klimaforskning (NCFK)]. SY was also partly supported by the EUCP project (Horizon 2020 Grant Agreement 776613). The EC-Earth simulations at BSC were performed using resources from the Red Española de Supercomputación. HP and WM received funding from the German Federal Ministry of Education and Research (BMBF) project MiKlip (FKZ 01LP1519A). Takahito Kataoka and Hiroaki Tatebe were supported by the Integrated Research Program for Advancing Climate Models (TOUGOU) Grant JPMXD0717935457 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. MIROC simulations were performed on the Earth Simulator at JAMSTEC. NK, YW, FC, and IB were supported by the Trond Mohn Foundation (Grant BFS2018TMT01; Bjerknes Climate Prediction Unit). Peer Reviewed "Article signat per 45 autors/es: Leon Hermanson, Doug Smith, Melissa Seabrook, Roberto Bilbao, Francisco Doblas-Reyes, Etienne Tourigny, Vladimir Lapin, Viatcheslav V. Kharin, William J. Merryfield, Reinel Sospedra-Alfonso, Panos Athanasiadis, Dario Nicoli, Silvio Gualdi, Nick Dunstone, Rosie Eade, Adam Scaife, Mark Collier, Terence O’Kane, Vassili Kitsios, Paul Sandery, Klaus Pankatz, Barbara Früh, Holger Pohlmann, Wolfgang Müller, Takahito Kataoka, Hiroaki Tatebe, Masayoshi Ishii, Yukiko Imada, Tim Kruschke, Torben Koenigk, Mehdi Pasha Karami, Shuting Yang, Tian Tian, Liping Zhang, Tom Delworth, Xiaosong Yang, Fanrong Zeng, Yiguo Wang, François Counillon, Noel Keenlyside, Ingo Bethke, Judith Lean, Jürg Luterbacher, Rupa Kumar Kolli, and Arun Kumar"