The mission of the Climate, Weather and Water Forum (CWWF) is to facilitate an annual dialog among scientists, engineers, students, public and private enterprises and government entities on pressing issues related to climate change, weather extremes, water availability, and sustainability. We aim to:
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The World Weather Research Programme (WWRP)/World Climate Research Programme (WCRP) Subseasonal to Seasonal Prediction (S2S) project was launched in 2013 with the primary goals of improving forecast skill and understanding sources of predictability on the subseasonal timescale (from 2 weeks to a season) around the globe. Particular emphasis was placed on high-impact weather events, on developing coordination among operational centers, and on promoting the use of subseasonal forecasts by the applications communities. This 10-year project ended in December 2023. A key accomplishment was the establishment of a database of subseasonal forecasts, called the S2S database. This database enhanced our understanding of S2S sources of predictability and windows of opportunity that contributed to improvements in forecast skill. A major legacy of the S2S project was the establishment and designation of the World Meteorological organization (WMO) Global Producing Centres and Lead Centre for Sub-Seasonal Predictions Multi-Model Ensemble, which will provide real-time sub-seasonal multi-model products to national and regional meteorological services.
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Climate extremes pose an ever-increasing threat to human societies. Storms, Heat Waves, Droughts, Floods, Tornadoes etc constitute the dominant natural hazard on average. Exposure to these events, and their derivative events such as fires is growing, in part due to climate change and in part due to increasing human populations and their occupancy of vulnerable areas. The costs of developing infrastructure, financial relief (insurance), and other coping programs appear prohibitive at the global scale, and many of these instruments lead to an increase in the potential for other (e.g., environmental) adverse outcomes. While a warming planet due to anthropogenic forcing of the atmosphere is the focal point of much of the climate discourse, the events in question are largely determined by the dominant modes of atmospheric circulation and heat transport. The underlying equations driving these phenomena are expected to hold even in the future. They are typically nonlinear and chaotic, leading to varying and limited predictability. In this talk, we plan to explore whether this setting is ripe for thinking about strategic approaches to weather modification by small perturbations that could allow us to limit or dramatically reduce exposure to the extreme climate events by nudging: adaptive chaos control. The technical and social implications of such an approach vs the current and traditional discourse on this topic are open for discussion.
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Hong Kong has a sub-tropical climate and wide variety of weather. Different extreme weather events, including tropical cyclones, rainstorms, extreme temperatures, can affect Hong Kong and bring significant impacts to the society. Looking into the future, against the background of climate change, Hong Kong will expect even warmer climate, more variable rainfall, more intense typhoons, and a sea level that keeps rising in the coming centuries. This may affect the frequency and severity of various extreme weather and increase the climate risk. Over the years, the Hong Kong Observatory (HKO) has been monitoring climate change and providing various climate services in support of climate adaptation and resilience in Hong Kong. This presentation will provide an overview of relevant services of HKO with examples illustrating the utilization of climate data and climate predictions/projections as well as expert advice in climate risk assessments, infrastructure design, water resources management, climate partnership and climate change related public education activities.
Climate services are scientifically based information and products that enhance users’ knowledge and understanding about the impacts of climate on their decisions and actions. This rapidly growing field requires work at the interface between scientific research and user demand for relevant climate information to create effective tools. Studies have found climate services are most effective when they are co-developed and co-produced with potential users. Understanding the needs of users and ensuring that the climate information provided is both useful and accessible is a challenge even when the users and providers are in the same country, but this challenge is more pronounced when users are in a different country from the providers. This talk will explore the collaborative efforts and user engagement strategies employed in developing climate services for the agricultural sector in the UK and China through the Climate Science for Service Partnership (CSSP China).
Under the background of global warming and rapid reduction of the Arctic sea ice, polar sea ice forecasting is playing an increasingly important role. Improving the ability of polar sea ice forecasting is an important guarantee for polar ship navigation, polar energy development and protection. In recent years, some countries have developed and established polar sea ice forecasting systems, and the operational forecasting of polar sea ice has advanced substantially. This talk introduces operational sea ice forecasting services for the polar regions in the NMEFC of China. NMEFC provides sea ice forecasts for the polar regions since 2010, current sea ice forecasting products include seasonal sea ice prediction for the Arctic ocean at leading time of 3 months, synoptic sea ice forecasts for the polar regions at leading time of 7 days, and high-resolution sea ice forecasts for key regions of the Arctic Northeast Passage with a horizontal resolution of 1 km. In the future, with development of advanced data assimilation scheme, more accurate and timely sea ice forecasting products will benefit for Chinese scientific and navigation activities in the polar regions.
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Extreme weather events have become more frequent and intense under a warming climate, and they have severe impacts on the stable operation of the power grid and the assurance of power supply capacity. At the same time, as the large-scale wind and solar power generation gradually connects to the grid, the randomness, volatility, and intermittency of renewable energy represented by the wind and solar power pose serious challenges to the balance and scheduling of the power grid. Accurate forecasting of these resources such as wind, solar radiation, and precipitation at different time scales is the foundation for efficient grid integration and consumption. The high-precision power meteorological numerical forecasting provides multi-temporal and spatial scale forecasting information for power dispatch, planning, and design, reducing the difficulty of grid integration and consumption and the risk of insufficient power supply. This helps the new power system adapt to weather and climate risks and enhances its ability to ensure safe power supply. This presentation introduces the applications and challenges of the seamless numerical forecasting at the time scale ranging from hours to days and weeks in the power meteorology from the perspectives of power forecasting, meteorological disaster risk management, energy security risks brought by climate change, and new technologies.
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The changing properties of ENSO and their impacts on regional monsoon rainfall may present a fundamental challenge to climate forecasting, as observed in recent decades. My talk will first clarify the relationship between the global monsoon and ENSO from 1979 to 2014 and the Asian Precipitation (AP) and ENSO over the past 120 years. I will show that the NH land monsoon and Asian precipitation exhibit a stable relationship with the ENSO intensity. However, the changing ENSO property impacts monsoon precipitation on the regional scale. Predicting the Asian summer monsoon requires an understanding of SST anomalies during the developing and decaying phases of ENSO events. Therefore, the current classification of El Niño diversity, based on boreal winter SST patterns, is ineffective. We have reclassified 33 El Niño events from 1901 to 2017 into three types: super, moderate Eastern Pacific (MEP), and central Pacific (CP) events, each exhibiting distinct development mechanisms and varying climate impacts on regional monsoons. Regional monsoons respond differently to ENSO diversity and phases. Since the 1970s, the onset of El Niño has shifted from the eastern to the western Pacific, resulting in a more frequent occurrence of Super and Central Pacific (CP) El Niño events, as well as multiyear La Niña (ML) conditions. We hypothesize that this historical regime change is rooted in background warming in the western Pacific, which leads to increases in zonal and vertical temperature gradients in the equatorial central Pacific. The warming in the western Pacific enhances zonal advective feedback, leading to more frequent Super and Central Pacific (CP) events and increasing the likelihood of multiyear La Niña. The projections from the CMIP5 models and the large-ensemble experiments of the CMIP6 CESM2 model indicate that both the frequency and intensity of severe El Niño events and multiyear La Niña will significantly increase if the projected central Pacific zonal SST gradients are enhanced. More extreme ENSO events, such as super El Niño and multiyear La Niña, will exacerbate adverse socioeconomic impacts if the western Pacific continues to warm relative to the central Pacific. This conclusion drawn from the historical warming period has vital implications for projecting future changes in ENSO behavior.
Irrigation is playing an increasingly vital role in agriculture and is essential for meeting the growing global demand for food. Numerous studies have examined the effects of irrigation on local meteorology, consistently showing a significant influence on near-surface conditions—though the magnitude of this impact varies widely by location. Additionally, theoretical work suggests that irrigation can generate breeze-like circulations within the atmospheric boundary layer. However, direct observational evidence supporting this phenomenon remains limited. This study explores the influence of irrigation on surface conditions and the atmospheric boundary layer in the Ebro Basin, a heavily irrigated region with a semi-arid climate in northeastern Spain. Model simulations were conducted for several days in July 2021 and compared against data collected during an intensive field campaign. The results provide the first clear observational evidence of an irrigation-induced breeze flowing from irrigated zones into adjacent semi-arid areas. These findings underscore the need to account for irrigation in numerical models used for weather forecasting, climate projections, and sustainable agricultural planning.
The El Niño-Southern Oscillation (ENSO) is a leading mode of interannual climate variability with far-reaching global impacts. Understanding how ENSO-driven changes evolve in a warming climate is essential to project future climate variability. Here, we show that climate models robustly project an amplification of ENSO’s influence on global sea surface temperature (SST) under greenhouse warming. This amplification is primarily driven by two factors: changes in El Niño-induced surface wind speed and alterations in the climatological air-sea humidity difference. The former is linked to enhanced atmospheric teleconnections associated with ENSO, while the latter stems from an overall increase in global SST. Our findings suggest that future El Niño events may exert stronger regional climate impacts, not only through intensified atmospheric teleconnections but also by reinforcing local air-sea interactions.
Enormous progress has been achieved in the last 30 years in numerical weather and seasonal predictions through improved models (physics and dynamics), data asismilation and the massive use of new data, essentially satellite data. I will quickly scroll through this in relation to convection developments. However, some persistent tropical errors remain and it was claimed that current and future km-scale models will "naturally" solve the problems of convection organization, equatorial convergence, easterly wind bias etc. So far, we have not seen this, this applies not only to the ECMWF IFS model. It is shown that these errors relate to the angular momentum budget during the first 2 days of the forecasts, and that machine learning models trained on the ERA5 reanalysis data seem to suffer much less from this problem. Other model errors in northern hemispheric predictions likely originate from the polar regions. Recently developed hybrid models using a combination of the physical models and the machine learning forecasts through relaxation methods can provide attractive progress, but possible improvements and understanding of the physical models remain our focus.
Monsoon onset signifies the commencement of the rainy season and the reversal of wind circulation over the Asian monsoon area. The factors of the monsoon onset include the thermal condition and arrival of disturbances (e.g. tropical cyclones, Intraseasonal variability). While the prediction of the monsoon onset timing remains a challenging issue, the Cloud system resolving global climate model (CRGCM), which has the advantage of reproducing the tropical disturbance, shows the potential to extend the predictability of the onset timing. Here, we analyze the historical experiment of the Global climate model and CRGCM with prescribed observed SST, especially focusing on the monsoon onset. The results show a significant negative interannual correlation between seasonal mean Indian summer monsoon (ISM) strength and the ISM onset timing (summer monsoon tends to be stronger following the early onset) in the climate models while the observational data does not show such significant interannual relation. The ISM system in the model might be mainly driven by the thermal condition in longer persistency. We will discuss the combinational effects of different time scale variations such as thermal conditions and disturbances.
Wind downscaling is crucial for refining coarse‐scale wind estimates, improving local‐scale predictions, and supporting various applications like risk assessment and planning. Dynamic downscaling models demand extensive computational resources and time, leading to a shift toward more efficient statistical downscaling, whereas it often overlooks inter‐variable and inter‐station spatial correlations. Addressing this, we propose TerraWind, a deep learning‐based downscaling method for complex terrain regions. TerraWind enhances accuracy by incorporating topographic factors and inter‐station linkages, capturing wind field interactions with terrain at multiple scales. Experimental results in Eastern China demonstrate that TerraWind reduces wind speed Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by an average of 42.6% and 33.3%, respectively, compared to three interpolation methods (bicubic, bilinear, and Inverse Distance Weighting). Furthermore, TerraWind achieves an average reduction of 35.3% in wind speed MAE and 25.6% in wind speed RMSE compared to four deep learning models (Wind‐Topo, DeepCAMS, RCM‐emulator, and Uformer). The TerraWind framework is then combined with a physics-based parametric wind model for tropical cyclone (TC), namely TerraWind-TC, to address its weakness of underestimating strong winds in TCs. Experiments on 46 TC cases demonstrate that TerraWind-TC can effectively reduce the underestimation of strong winds, attaining an 82.3% reduction in MBE (from -13.44m/s to -2.37m/s) for winds exceeding 17.2m/s. TerraWind-TC is a successful example of improving weather simulation through the combination of physical and ML-based models.
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Skillful seasonal climate prediction is critical for food and water security over the world’s heavily populated regions, such as in continental East Asia. Current models, however, face significant difficulties in predicting the summer mean rainfall anomaly over continental East Asia, and forecasting rainfall spatio-temporal evolution presents an even greater challenge. Here, we benefit from integrating the spatiotemporal evolution of rainfall to identify the most crucial patterns intrinsic to continental East-Asian rainfall anomalies. A physical-statistical prediction model is developed to capture the predictability offered by these patterns through a detection of precursor signals that describe slowly varying lower boundary conditions. The presented model demonstrates a prediction skill of 0.51, at least twice as high as that of the best dynamical models available (0.26), indicating improved prediction for both the spatio-temporal evolution and summer mean of rainfall anomalies. This advance marks a crucial step toward delivering skillful seasonal predictions to populations in need of new tools for managing risks of both near-term climate disasters, such as floods and droughts, and long-term climate change.
Atmospheric reanalysis products are a critical source of process-based diagnostics for studies of the atmospheric water and energy cycles; however, the influence of data assimilation means that budgets based on atmospheric reanalyses are not readily closed. Moreover, different reanalysis products may give very different results for the same diagnostics, while standard budget decompositions do not distinguish the effects of data assimilation from the effects of high-frequency "eddy" terms. In this talk, I will describe a new dataset prepared for the APARC Reanalysis Intercomparison Project (A-RIP) that explicitly separates the influences of advection, parameterized physics, and data assimilation in budgets for atmospheric moisture, thermodynamic energy, and momentum, with high-frequency eddy terms as the residual. I will describe the framework and its motivation, provide example applications based on three state-of-the-art atmospheric reanalysis products, and briefly outline some important recommendations and resources for users of reanalysis products arising from A-RIP studies so far.
Subseasonal precipitation whiplashes, marked by sudden shifts between dry and wet extremes, can disrupt ecosystems and human well-being. Predicting these events two to six weeks in advance is crucial for disaster management. Here, we show that the propagation diversity of the Madden-Julian Oscillation (MJO)––a key source of subseasonal predictability––will alter under anthropogenic warming. This is evidenced by a 40% increase in fast-propagating events by the late 21st century. Fast-propagating MJOs may rise in a period as early as 2028–2063, increasing the global risk of precipitation whiplashes through teleconnections. We propose a heuristic framework diagnosing that MJO’s acceleration is primarily driven by enhanced atmospheric stabilization and El Niño-like sea surface warming. The expected rise in fast-propagating MJOs could improve the predictability of subseasonal weather whiplashes, offering critical lead time for disaster preparedness. Understanding these impending shifts is essential for enhancing subseasonal prediction capabilities.
Water is essential for life on Earth. Modern human societies are massively using water to answer to their vital needs but also to develop complex urban environments associated industrial activities that always request more resources. Competition among uses is rising and requests a holistic approach able to ensure a sustainable development combining an efficient use of water and a protection of natural hydro environments. At the same time, a large population is still lacking access to safe drinking water and basic sanitation facilities due to lack of proper policy, insufficient investments and too limited innovation deployment. AI solutions represent a possibility to speed up efficiency in water sector management by improving monitoring and optimizing processes among various competing uses. The massive deployment of sensors initiated over the last decade is now able to produce the needed data for AI implementation. To be fully beneficial to the water resources management, the new solutions must be transformative and push to revisit many of the business processes currently implemented within the water sector. If AI looks promising, its computational cost is also a new challenge for environment as datacenters are requesting massive water quantities for cooling and are in direct competitions with other essential water uses and environmental flows. The presentation will address these global challenges and will review the roadmap for AI deployment in the water sector.
Cloud and precipitation have significant impacts on both weather and climate. However, due to limitations in physical understanding and parameterization capabilities, current weather and climate models exhibit considerable uncertainty in simulating cloud- and precipitation-related physical processes, making them one of the largest sources of uncertainty in studies on climate change and climate modeling. Machine learning algorithms can leverage big data to construct numerical models without relying on a deep understanding of physical processes, thus offering a new approach to improving the understanding and simulation of cloud and precipitation. This report will briefly introduce the application scenarios of machine learning in this field, with a focus on its effectiveness in cloud fraction parameterization, precipitation prediction, and model bias diagnosis.
In this talk, we explore the capabilities of Large Language Models (LLMs) in atmospheric science through an in-depth case study and introduce a specialized benchmark designed to systematically evaluate their reasoning abilities. The case study illustrates practical applications of LLMs across various atmospheric tasks, including data processing, physical diagnostics, climate forecasting, and strategies for climate adaptation. The proposed benchmark assesses model performance comprehensively across critical atmospheric domains, such as atmospheric dynamics, physics, hydrology, geophysics, and oceanography, to provide detailed insights into the reasoning strengths of state-of-the-art LLMs. Our findings demonstrate that reasoning-oriented models consistently outperform other model categories, particularly when subjected to variations in numerical precision and symbolic perturbations, highlighting the essential role of advanced reasoning capabilities in effectively addressing complex atmospheric science challenges.
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Atmospheric rivers (ARs) are key agents in distributing extratropical precipitation and transporting moisture poleward. Climate models forced by historical anthropogenic forcing suggest an increase in AR activity in the extratropics over the past four decades. However, reanalyses indicate a ~6° to 10° poleward shift of ARs during boreal winter in both hemispheres, featuring a rise along 50°N and 50°S and a decrease along 30°N and 30°S. Our analysis demonstrates that low-frequency sea surface temperature variability in the tropical eastern Pacific exhibits a cooling tendency since 2000 that plays a key role in driving this global AR shift, mostly over extratropical oceans, through a tropical-driven eddy-mean flow feedback. This mechanism also operates on interannual timescales, controlled by the El Niño–Southern Oscillation, and is less pronounced over the Southern Ocean due to weaker eddy activity during austral summer. These highlight the sensitivity of ARs to large-scale circulation changes driven by both internal variability and external forcing in current and upcoming decades.
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The study focuses on understanding the significant influence of the Tibetan Plateau (TP) as a potential signal on Atmospheric River (AR) activity in the North Pacific. The research is structured into three main parts. Firstly, we obtain an optimal AR datasets based on deep learning method and we identify the highly sensitive heating region on the TP by establishing a relationship with AR frequency, using a correlative analysis. It clearly indicates that the southern TP plays a pivotal role as a strong signal positively correlated with AR activity in the North Pacific. Furthermore, we attribute this anomalous heating to latent heat release with an ample moisture supply. Secondly, we investigate the causes of anomalous latent heating in the southern TP. Our findings highlight the remote moisture contribution rather than the local evaporation contributes more to the heating positive abnormal by applying the Water Accounting Model-2Layers. The anomalous heating is primarily attributed to the synergy of more moisture from the Indian Ocean, the Arabian Sea, and the Bay of Bengal, and the western extension of the Northwest Pacific Subtropical High (NWPSH). The second reason is the additional moisture from Eurasian with Sea Surface Temperature abnormal. Finally, the triggered diabatic Rossby wave can transport to the east of Japan inducing enhanced westerlies with upper-level divergence field developing a cyclonic and anticyclonic vortex structures attracting abundant moisture to the North Pacific and then feeding more AR. Moreover, southern TP heating and eastern-propagation anticyclone form a positive feedback interaction mechanism by the western extend NWPSH. The results underscore the southern TP heating can be considered a valuable forecasting for AR activity in the North Pacific.
(Listed in alphabetical order of the last name; To be updated)
IAP-CAS, China
Principal Scientist, ECMWF, Europe
CNRM - Université de Toulouse, Météo-France, France
Fudan Univeristy, China
UC Santa Barbara, United States
University of Illinois Urbana-Champaign, United States
President of IAHR / Université Côte d'Azur, France
Aviation Meteorological Center, China
Provost of HKUST, Hong Kong
Univeristy of Hawaii, United States
RIKEN Center for Computational Science, Japan
Seoul National University, South Korea
Arizona State University, United States
Pusan National University, South Korea
Senior Scientific Officer, HKO, Hong Kong
Yunnan University of Finance and Economics, China
Shanghai Meteorological Bureau, China
National Marine Environmental Forecasting Center, China
Chinese Academy of Meteorological Sciences, China
Sun Yat-Sen Univeristy, China
Yale Univeristy, United States
Climate Scientist, Met Office, United Kingdom
China Electric Power Research Institute of State Grid, China
Chinese Academy of Meteorological Sciences (CMA), China
Nanjing Univeristy, China
Principal Scientist, ECMWF, Europe
University of Hawaii, United States
Univeristy of Chinese Academy of Sciences, China
Tsinghua University, China
China Re, China
Director, Shanghai Typhoon Institute, China
HKUST, Hong Kong
National Climate Center, China
Ocean Univeristy of China, China
Forum Chair / HKUST
Forum Co-Chair / BNU
Forum Co-Chair / NOAA
Event Coordinator / HKUST
Registration / HKUST
Transportation / HKUST
Scheduling / HKUST
Budget / HKUST
Secretariat / HKUST
Promotion / BNU
Logistics and Materials / HKUST
Engagements and Activities / BNU
Human Resources / HKUST
Onsite Assistants / HKUST
Onsite Assistants / HKUST
