The main goals of the session is (i) to identify gaps in current climate prediction methods and (ii) to report and evaluate the latest progress in climate forecasting on subseasonal-to-decadal and longer timescales. This will include presentations and discussions of developments in the predictions for the different time horizons from dynamical ensemble and statistical/empirical forecast systems, as well as the aspects required for their application: forecast quality assessment, multi-model combination, bias adjustment, downscaling, exploration of artificial-intelligence methods, etc.
Following the new WCRP strategic plan for 2019-2029, prediction enhancements are solicited from contributions embracing climate forecasting from an Earth system science perspective. This includes the study of coupled processes between atmosphere, land, ocean, and sea-ice components, as well as the impacts of coupling and feedbacks in physical, hydrological, chemical, biological, and human dimensions. Contributions are also sought on initialization methods that optimally use observations from different Earth system components, on assessing and mitigating the impacts of model errors on skill, and on ensemble methods.
We also encourage contributions on the use of climate predictions for climate impact assessment, demonstrations of end-user value for climate risk applications and climate-change adaptation and the development of early warning systems.
A special focus will be put on the use of operational climate predictions (C3S, NMME, S2S), results from the CMIP5-CMIP6 decadal prediction experiments, and climate-prediction research and application projects.
An increasingly important aspect for climate forecast's applications is the use of most appropriate downscaling methods, based on dynamical, statistical, artificial-intelligence approaches or their combination, that are needed to generate time series and fields with an appropriate spatial or temporal resolution. This is extensively considered in the session, which therefore brings together scientists from all geoscientific disciplines working on the prediction and application problems.
Subseasonal to decadal predictions provide essential information that bridges the gap in timescales between weather forecasts and long-term climate projections. The science and practice of making such predictions using global climate models initialized with observational data has advanced considerably in recent years, and as a result operational subseasonal, seasonal and decadal prediction services are now a reality. Nonetheless, important remaining challenges must be overcome if these predictions are to more fully realize their potential value for society. This talk highlights five key challenges recommended as targets for focused international research; these are set against a backdrop of wider challenges encompassing climate modelling and services across time scales.
How to cite: Muñoz, Á. G., Merryfield, W. J., and Hudson, D.: Progresses and Challenges for Subseasonal to Interdecadal Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19889, https://doi.org/10.5194/egusphere-egu25-19889, 2025.
Drought warnings are vital to sectors like agriculture and water management, especially at the seasonal time scale. Identifying the sources of drought predictability in regions where a prediction system demonstrated potential for useful applications of the forecasts, represents an important step toward building confidence in the predictions and refining the seasonal predictions. To better identify higher forecast skill in this context, one possible approach is to focus on specific “windows of opportunity”. The approach aims to identify periods when persistent anomalies occurring in the ocean, the atmosphere or the land surface may positively precondition the predictive ability of the seasonal forecast. In the case of SPI3, a high potential for preconditioned predictive skill is identified in the Middle East region, as suggested by a robust relationship with large-scale climate modes. Building on these results, this study explores the contributions of individual years to the skill for the region during the autumn season and in the hindcast period 1993-2016. We used a Multi Model Ensemble (MME) of eight seasonal prediction systems (SPSs) provided by the Copernicus Climate Data Store (CDS) and observations from the Climate Research Unit (CRU) to calculate the SPI3 time series and the values of the Pacific and Indian teleconnection indices, the Oceanic Nino Index (ONI) and the Dipole Mode Index (DMI), respectively. A novel methodology is implemented to cluster the year-by-year MME contributions to the Pearson correlation coefficient (PCC) that are preconditioned by the large-scale teleconnections.
Results indicate that years with extreme high or low values of ONI and DMI are the main contributors to the forecasting skill of the MME drought predictions over the Middle East. In particular, a window of opportunity is identified in four (out of 24) years that show significantly high contribution to overall skill. These years are robustly preconditioned by El Niño or La Niña events. Among the years with higher contributions, 1994 stands out as being more influenced by the DMI, thus driven primarily by SST anomalies in the Indian Ocean rather than the Pacific Ocean. The methodological approach developed in this study successfully highlighted the potential windows of opportunity for seasonal prediction in the Middle East region, and could be applied extensively to develop early warnings for the coming seasons to serve agriculture and water management operations.
How to cite: Dal Monte, T., Alessandri, A., Cherchi, A., Donat, M., and Gaetani, M.: Windows of Opportunity for Seasonal Prediction of droughts: the case of the Middle East, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11600, https://doi.org/10.5194/egusphere-egu25-11600, 2025.
AI deep learning for climate science has attracted increasing attentions in recent years with rapidly expanded applications to many areas. In this talk, I will briefly present our recent progresses on using various deep learning methods for seasonal-to-multi-seasonal predictions of ENSO, the Indian Ocean Dipole (IOD), summer precipitation in China and East Africa, Arctic sea ice cover, ocean waves, as well as the bias correction and downscaling of dynamical model’s forecasts. The results suggest that many popular deep learning methods, such as convolutional neural networks, residual neural network, long-short term memory, ConvLSTM, multi-task learning, cycle-consistent generative adversarial networks and vision transformer, can be well applied to improve our understanding and predictions of climate. In addition, a brief introduction of AI large models for ensemble weather-subseasonal-seasonal-decadal forecasts, together with the perspective on the future development of AI methods, will also be presented.
How to cite: Luo, J.-J.: AI deep learning for climate forecasts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1847, https://doi.org/10.5194/egusphere-egu25-1847, 2025.
Seamless climate predictions combine information across different timescales to deliver information potentially useful for sectors like agriculture, energy, and public health. Seamless operational forecasts for periods spanning from sub-annual to multi-annual timescales are currently not available throughout the year. We show that this gap can be closed by using a well-established climate model analog method. The method consists in sampling model states from the CMIP6 transient simulation catalog based on their similarity with the observed sea surface temperature as a means of model initialization.
Here we present the methodology and basic skill evaluation of the analog-based temperature and standardized precipitation index retrospective predictions with forecast times ranging from 3 months up to 4 years. We additionally compare these predictions with the non-initialized CMIP6 ensemble and with two operational benchmarks produced with state-of-the-art dynamical forecasts systems: one on seasonal timescales and the other on annual to multi-annual timescales.
The analog method provides skillful climate predictions across the different timescales, from seasons to several years, offering temperature and precipitation forecasts comparable to those from state-of-the-art initialized climate prediction systems, particularly at the annual to multi-annual timescales. However, unlike operational decadal prediction systems that provide only one or two initializations per year, the analog-based system can generate seamless predictions with monthly initializations, offering year-round climate information. Additionally, analog predictions are computationally inexpensive once the multi-model transient climate simulations have been completed. We argue that these predictions are a valuable complement to existing operational prediction systems and may improve regional climate adaptation and mitigation strategies.
How to cite: Acosta Navarro, J. C., Aranyossy, A., De Luca, P., Donat, M. G., Hrast Essenfelder, A., Mahmood, R., Toreti, A., and Volpi, D.: Seamless seasonal to multi-annual climate predictions by constraining transient (CMIP6) climate model simulations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11821, https://doi.org/10.5194/egusphere-egu25-11821, 2025.
The El Niño/Southern Oscillation (ENSO) is the primary internal climatic driver shaping extreme events worldwide1,2,3. Its intensity and frequency in response to greenhouse gas (GHG) warming has puzzled scientists for years, despite consensus among models about changes in average conditions4-16. Recent research has shed light on changes not only in ENSO variability5,7,8,10,13, but also in the occurrence of extreme5,6,11,12,13,14 and multi-year El Niño4,15, and La Niña9,11,16 events under GHG warming. Here, we investigate potential changes in ENSO predictability associated with changes in ENSO dynamics in the future by using long-range deep-learning forecasts trained on extensive large ensemble simulations of Earth System Models under historical forcings and the future high GHG emissions scenario. Our results show a remarkable increase in the predictability of ENSO events, ranging from 35% to 65% under the high GHG emissions scenario due to reduced ENSO irregularity, supported by a broad consensus among multi-models. Under GHG warming, an El Nino-like warming flattens the thermocline depth with upper ocean stratification. This flattening of the thermocline depth leads to an increased transition frequency between El Niño and La Niña events, driven by strengthened recharge-discharge oscillation with enhanced thermocline feedback and SST responses to zonal wind stress. As a result, ENSO complexity would reduce with increased regularity and reduced skewness, increasing ENSO predictability. These results imply that the future social and economic impacts of ENSO events may be more manageable, despite an expected increase in the frequency of extreme ENSO events.
How to cite: Yang, Y.-M., Park, J.-H., Lee, J.-Y., An, S.-I., Yeh, S.-W., Kug, J.-S., and Ham, Y.-G.: Increased multi-year ENSO predictability under greenhouse gas warming accounted by large ensemble simulations and deep learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3974, https://doi.org/10.5194/egusphere-egu25-3974, 2025.
The climate fluctuates on various timescales and in various patterns, giving rise to extreme events over the globe. Skillful predictions of such climate variations would therefore benefit society, and there have been substantial efforts. For the CMIP6 Decadal Climate Prediction Project (DCPP), we performed decadal predictions with ten ensemble members using the Model for Interdisciplinary Research on Climate version 6 (MIROC6). However, since models tend to underestimate signal-to-noise ratio in some sectors, such as the Atlantic, a large ensemble size appears to be required for skillful predictions of those variations. To better understand the predictability on timescales out to a season to a decade, we have prepared a set of initialized predictions using MIROC6 that consists of 10-year-long hindcasts starting every November between 1960-2021, with 50 ensemble members. Compared to the original 10-member ensemble hindcast, both seasonal and decadal prediction skills are broadly improved (e.g., SAT and SLP over southeast China and Scandinavia for the first winter, North and South Pacific SSTs for decadal prediction). Regarding the decadal prediction skill, the impact of initialization is seen up to lead year 7-10 for the North and eastern tropical Pacific Oceans.
Also, building on our experience with decadal climate predictions, we have been working on decadal carbon predictions in recent years. Our efforts on earth system predictions will be introduced as well.
How to cite: Kataoka, T., Tatebe, H., Koyama, H., and Mori, M.: A large ensemble of decadal predictions using MIROC6, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21425, https://doi.org/10.5194/egusphere-egu25-21425, 2025.
Drought persistence is a critical factor in assessing water availability and its impacts on agriculture, ecosystems, and society. In this respect, poorly constrained soil properties in climate models such as field capacity – i.e. the maximum water a soil can retain after drainage of excess moisture – may strongly affect severity and persistence of simulated soil drought conditions. This study examines for the first time the regulating role of soil properties, particularly of field capacity, in shaping drought memory and its broader impacts. Using the CMIP6 multi-model ensemble and observations, we analyze drought dynamics across various phases of the hydrological cycle applying non-parametric standardized indices: Standardized Precipitation Index (precipitation deficits), Standardized Precipitation-Evapotranspiration Index (precipitation-evapotranspiration balance), Standardized Soil Moisture Index (soil moisture deficits), and Standardized Runoff Index (reduced runoff). Our analysis investigates the persistence between hydrological drought indicators, showing that soils with greater field capacity sustain drought conditions longer, emphasizing the importance of accurately modeling soil properties to capture drought persistence effectively. The historical CMIP6 simulations are compared with observational datasets, including GLEAM and CRU, to assess the deviation between model outputs and observed climate conditions. The future scenarios (SSP126, SSP245, SSP370, SSP585) are also examined, revealing significant regional differences in projected drought behavior depending on the degree of radiative-forcing increase during 21st century. High-emission scenarios project prolonged drought conditions due to increased temperatures and evapotranspiration feedback, while low-emission pathways are effective in preserving more stable hydrological dynamics. Our results show that, in water limited and transition areas such as the Euro-Mediterranean region, the persistence of droughts and its projected change considerably depend on the modeled field capacity. This study highlights the essential role of field capacity and other soil characteristics in regulating the variability and the persistence of drought events. By bridging historical validation with future projections, it provides a comprehensive understanding of drought dynamics and trends, also identifying observational constraints for the Earth System Models. These findings are crucial for refining predictions of agricultural and hydrological drought impacts and for guiding adaptation strategies in water-limited regions that are vulnerable to drought exacerbation under climate change.
How to cite: Possega, M., Di Carlo, E., Senigalliesi, V., and Alessandri, A.: Understanding Soil Modulation of Drought Persistence in CMIP6 Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1040, https://doi.org/10.5194/egusphere-egu25-1040, 2025.
Climate services are essential to support climate-sensitive decision making, enabling adaptation to climate change and variability, and mitigate the sources of anthropogenic climate change, while considering the values and contexts of those involved. The unregulated nature of climate services can lead to low market performance and lack of quality assurance. Best practices, guidance, and standards serve as a form of governance, ensuring quality, legitimacy, and relevance of climate services. The Climateurope2 project (www.climateurope2.eu) addresses this gap by engaging and supporting an equitable and diverse community of climate services to provide recommendations for their standardisation. Four components of climate services are identified (the decision context, the ecosystem of actors and co-production processes, the multiple knowledge systems involved, and the delivery and evaluation of these services) to facilitate analysis. This has resulted in the identification of nine key messages summarising the susceptibility for the climate services standardisation. The recommendations are shared with relevant standardisation bodies and actors as well as with climate services stakeholders and providers.
How to cite: Doblas-Reyes, F., Lera St Clair, A., Baldissera Pacchetti, M., Checchia, P., Cortekar, J., Klostermann, J. E. M., Krauß, W., Muñoz, A., Mysiak, J., Paz, J., Terrado, M., Villwock, A., Volarev, M., and Zorita, S.: Standardisation of equitable climate services by supporting a community of practice, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5233, https://doi.org/10.5194/egusphere-egu25-5233, 2025.
The concept of hydroclimate services is predominantly recognised as web portals dedicated to the dissemination of data to potential users. However, the scope of climate services extends beyond the sole provision of data. This communication presents a comprehensive ecosystem of tools and resources associated with the development of an updated national hydrological projection dataset in France. The ecosystem was brought to life through a close collaboration between scientists and water managers in two joint projects: Explore2 and LIFE Eau&Climat. Tools and resources were thus developped with and for water resource managers, and designed to enhance the comprehension of both the conceptual framework and the data itself, facilitating utilisation in accordance with best practices for climate change adaptation.
The project websites serve as gateways to the ecosystem and the tools: the Explore2 website contains interviews with the scientific contributors, and the LIFE Eau&Climat website is hosted by the national website dedicated to water managers. A summary of the joint final public event accompanies the replay of the one-day conference and debates on a dedicated website. A compendium of antecedent research projects on climate change impacts on hydrology has been collated to summarise the state of the art prior to the two projects. A MOOC has been developed in conjunction with scientists to facilitate the comprehension of the Explore2 project, its design, and its application in adaptation studies.
Moreover, the Explore2 dataverse (https://entrepot.recherche.data.gouv.fr/dataverse/explore2) brings together a variety of products in an organised and searchable way, including thematic scientific reports, GIS layers, and other key metadata. It also contains three types of station datasheets aimed at locally contextualising outputs: hydrological model performance datasheets, projection results datasheets, and uncertainty quantification datasheets. The MEANDRE interactive data visualisation tool (https://meandre.explore2.inrae.fr/) offers a guided tour of the salient take-home messages and a comprehensive exploration of the Explore2 hydrological projection dataset. This multi-model dataset (GCMs/RCMs/bias correction methods/hydrological models) is made available through the DRIAS-Eau portal (https://drias-eau.fr/), which functions as a water mirror of the established DRIAS-Climat portal. The utilisation of this dataset for local climate change impact studies is facilitated by a methodological guide written as an adventure gamebook (https://livreec.inrae.fr/) and based on real-life studies carried out by water managers during the LIFE Eau&Climat project. Furthermore, experiments of sonification of hydrological projections offer a novel approach to apprehending future changes (https://explore2enmusique.github.io/).
This ecosystem has been met with great anticipation and acclaim by local to national-scale water managers, paving the way for ongoing local prospective studies. These will be able to confront future resources with the ecological needs of aquatic environments and human water usage.
This work is funded by the EU LIFE Eau&Climat project (LIFE19 GIC/FR/001259).
How to cite: Vidal, J.-P., Sauquet, E., Héraut, L., Siauve, S., Evin, G., Soubeyroux, J.-M., Tocquer, F., Bornançin-Plantier, A., Magand, C., and Berel, M.: Hydroclimate services are more than just providing data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9933, https://doi.org/10.5194/egusphere-egu25-9933, 2025.
Timely and fast dissemination are some of the key factors for the effective climate information services in order to support effective climate action. Various mean of communication channel has been used by an authoritative agency to disseminate their climate information, including social media. Currently, social media become one of the most effective chanel to disseminate of weather and climate information. Social media is not only a powerfull tools to ensure the timely, fast, massive dissemination of weather and climate information, but it is also easy to use and access by the general public. Social media also can be optimized public outreach and public education in order to raising awareness and mobilizing an effective climate action. Through it’s real time response tools, social media also can be used to strengthen the engagement between meteorological and hydrological services with their users. Our research will describe the effectiveness of social media to disseminate weather and climate information in order to support climate action in Indonesia.
How to cite: Achmad Fachri, R. and Ezra Reynara, A.: The Use of Social Media on Weather and Climate Information Dissemination To Support Effective Climate Action, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15694, https://doi.org/10.5194/egusphere-egu25-15694, 2025.
Afforestation and greening initiatives are increasingly considered viable strategies for mitigating climate change, particularly in arid regions. In this study, we assess the climate impacts of large-scale afforestation in the Arabian Peninsula (AP). The afforestation is represented by replacing sandy bare soil with woody savanna vegetation, assumed to be naturally sustained by rainfall, in the absence of overgrazing. Using a 30-year regional climate model simulation, we prescribe afforestation within a circular area of 4.5° radius (approximately 71.9 million hectares) centered at 24.2°N, 44.3°E. The afforestation modifies surface characteristics, including darker albedo (0.25 vs. 0.38 for bare soil), a green fraction of 0.3, and a leaf area index (LAI) of 0.1.
Our results show that the afforestation slows down near-surface winds and due to darker surface, increases sensible heat flux, leading to enhanced warming of the atmosphere over vegetated areas. Despite these warming effects, the additional vegetation promotes higher rainfall due to increased moisture availability and reduction of subsidence. This study underscores the dual role of afforestation in modulating regional climate, serving as both a climate mitigation measure and a potential warming source, depending on regional conditions. These findings highlight the importance of considering water availability and local climate factors when designing greening policies for arid regions.
How to cite: Luong, T. M., Zampieri, M., and Hoteit, I.: The Role of Afforestation in Modulating Arid Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19049, https://doi.org/10.5194/egusphere-egu25-19049, 2025.
Tropical Instability Waves (TIWs) play a crucial role in modulating Sea Surface Temperature (SST) variability in tropical oceans, yet their representation in current forecast systems remains challenging. This study investigates the relationship between TIWs and sub-seasonal SST predictability while evaluating the performance limitations of the Licoms Forecast System. Through comprehensive analysis of observational data and model outputs, we demonstrate that TIWs provide significant potential for enhancing sub-seasonal SST forecast skill through their regular wave patterns and predictable evolution characteristics. However, our findings reveal that the current Licoms forecast systems systematically underestimate both TIW intensity and wavelength. Critical examination of error sources indicates that these deficiencies primarily originate from initialization fields rather than model physics or dynamics.
How to cite: Tianyan, L., Yongqiang, Y., and Weipeng, Z.: The role of Pacific Tropical Instability Wave in Sub-Seasonal SST predictability , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2210, https://doi.org/10.5194/egusphere-egu25-2210, 2025.
The Arctic region has been warming at a rate significantly faster than the global average, leading to an accelerated decline in sea ice. This trend is expected to continue, potentially resulting in a "low-ice regime," which could make sea ice conditions more unpredictable. Anticipating changes in Arctic sea ice and climate states is therefore crucial for guiding various human activities, from natural resource management to risk assessment decisions. While global climate and Earth system models project continuous sea ice decline over decadal time scales, achieving reliable seasonal forecasts remains challenging. To address this, we apply dynamical downscaling with the state-of-the-art Regional Arctic System Model (RASM), which enables us to forecast Arctic sea ice on time scales ranging from weeks to six months. RASM is a fully coupled regional climate model that integrates components for the atmosphere, ocean, sea ice, and land, interconnected through the flux coupler of the Community Earth System Model. In our study, we simulate RASM at a horizontal resolution of 1/12 degree (approximately 9 km) for both the ocean and sea ice, with 45 vertical levels in the ocean and five thickness categories for sea ice. The atmosphere is configured on a 50-km grid with 40 vertical levels, dynamically downscaled from the NOAA/NCEP Climate Forecasting System version 2 (CFSv2) at 72-hour intervals for the upper half of the atmosphere. Monthly ensemble forecasts extending up to six months are generated using initial conditions derived from a fully-coupled RASM hindcast simulation without bias correction and assimilation. This presentation highlights results for September sea ice predictions initialized on April 1, May 1, June 1, July 1, August 1, and September 1, covering pan-Arctic and regional sea ice spatio-temporal conditions from 2012 to 2021. Specifically, we examine how lead time and initial conditions affect the quantitative skill of seasonal predictability for Arctic sea ice and demonstrate skillful predictions of September sea ice up to six months in advance. Overall, our study underscores that enhancing model physics and obtaining more realistic initial conditions are crucial for achieving skillful sub-seasonal to seasonal predictions.
How to cite: Lee, Y., Maslowski, W., Craig, A., Clement Kinney, J., and Osinski, R.: Sub-seasonal to Seasonal Arctic Summer Sea Ice Forecasts Using Dynamical Downscaling with the Regional Arctic System Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1545, https://doi.org/10.5194/egusphere-egu25-1545, 2025.
Orography plays a fundamental role in shaping the atmospheric circulation and affects key atmospheric processes. Therefore, weather and climate models must adequately represent its effects to obtain accurate predictions. Since all orographic scales are found to influence the atmospheric flow, the parameterization of unresolved orographic drag has been recognized as crucial to simulate a realistic mid-latitude circulation. Moreover, in the last few years, it has become clear that orographic gravity wave drag (OGWD) and turbulent orographic form drag (TOFD) parameterization schemes play a crucial role in reducing some of the long-standing circulation biases affecting climate models. However, they are still considered a potential source of errors, due to the uncertainties which affect some poorly constrained physical parameters. Furthermore, these schemes need boundary conditions suitable to characterize the physical features of sub-grid orography. The strategies for the generation of such boundary conditions can vary a lot between different modelling centres, and it has been shown to be an important source of uncertainty.
GLOBO is a global atmospheric general circulation model developed at the Institute for Atmospheric Science and Climate of the Italian National Research Council (ISAC-CNR). It is currently in use within many operational frameworks, including a global monthly probabilistic forecast system that contributes to the Subseasonal-to-seasonal (S2S) project database. In an effort to improve and modernize the model, we implemented a novel orographic drag parameterization package, based on state-of-the-art OGWD and TOFD schemes. Simultaneously with the development of the orographic drag parameterizations, we developed a novel software package, OROGLOBO (OROGraphic ancillary files generator for GLOBal atmospheric mOdels) designed for the generation of the orographic boundary conditions. This unique open-source tool is designed to exploit a state-of-the-art, high resolution global Digital Elevation Model to generate boundary conditions for OGWD and TOFD schemes, gathering the main algorithms and techniques available in the literature in a single software.
Here, we present the results of this model update. A new set of retrospective forecasts was performed, consisting of an 8-members ensemble, initialized every 5 days and integrated for 35 days, during the period 2001-2020, including the developments in orographic physical parameterization and boundary conditions. This set of simulations is compared to the corresponding hindcasts set performed with the standard model configuration and used to calibrate the operational ensemble of global sub-seasonal probabilistic forecasts. We evaluate the impact of the improved representation of unresolved orographic drag on the simulation and prediction of the Northern Hemisphere mid-latitudes circulation. We assess the change in prediction skill for atmospheric blocking events and associated extreme temperature and wind conditions.
How to cite: Davoli, G., Mastrangelo, D., Cherchi, A., and Alessandri, A.: A new orographic drag parameterization package for the GLOBO model: implementation and evaluation , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-872, https://doi.org/10.5194/egusphere-egu25-872, 2025.
In recent years, alternating drought and extreme precipitation events have highlighted the need for subseasonal to seasonal forecasts of the terrestrial water cycle. In particular, predictions of the impacts of dry and wet extremes on subsurface water resources are crucial to provide stakeholders in agriculture, forestry, the water sector, and other fields with information supporting the sustainable use of these resources.
In this context, we release an experimental Water Resources Bulletin (https://adapter-projekt.de/bulletin/index.html) four times per year, offering probabilistic forecasts of the total subsurface water storage (TSS) anomaly at a 0.6 km resolution, from the surface down to 60 m depth, for the upcoming seven months across Germany. These seasonal forecasts are generated using the integrated, physics-based hydrological model ParFlow/CLM, forced by 50 ensemble members of the SEAS5 seasonal forecast from the European Centre for Medium-Range Weather Forecasts (ECMWF).
To evaluate our forecasts, we evaluated six 7-months probabilistic forecasts covering the vegetation period (March to September) for the years 2018 to 2023 with a reference long-term historical time series based on the same ParFlow/CLM setup. The forecast skill was assessed by comparing these seasonal forecasts to a climatology-based 10-member pseudo-forecast over the 2013–2023 period (using the leave-one-out method), extracted from the reference time series.
The monthly Continuous Ranked Probability Skill Score (CRPSS), which evaluates the ensemble distribution based on daily TSS data, indicates that the probabilistic forecast outperforms the climatology-based pseudo-forecast in most regions, except in 2018 and, to a lesser extent, in 2020 and 2022. This can be attributed to an under-representation of extremely dry members in the ensemble, combined with the memory effect of the initial conditions at increasing soil depths. For example, while March 2018 started with a slightly above-average TSS and experienced a strong meteorological drought leading to an agricultural drought, the initial TSS anomaly in March 2019 was already negative, with a less pronounced precipitation deficit during the vegetation period. This resulted in a much higher forecast skill, because of the memory effect accurately simulated with the physics-based model. Notably, the forecast skill only slightly decreases with increasing lead time, both for precipitation and TSS.
The analysis of the Relative Operating Characteristic Skill Score (ROCSS) for the lower quintile of the TSS distribution assesses whether negative TSS anomalies (i.e., droughts) are adequately represented within the probabilistic forecast ensemble. The results are consistent with those of the CRPSS, showing lower skill in 2018. Nevertheless, the ROCSS analysis overall indicates moderate to high skill for the probabilistic forecast, while the climatology-based pseudo-forecast demonstrates no skill. This confirms that the dry conditions experienced in central Europe in recent years were captured within the probabilistic forecast, underlining the added value of these forecasts and their usefulness in the experimental Water Resources Bulletin.
How to cite: Belleflamme, A., Hammoudeh, S., Goergen, K., and Kollet, S.: Assessment of the skill of seasonal probabilistic hydrological forecasts with ParFlow/CLM over central Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15251, https://doi.org/10.5194/egusphere-egu25-15251, 2025.
Vegetation plays a crucial role in the land surface water and energy balance modulating the interactions and feedback with climate at the regional to global scale. The availability of unprecedented Earth observation products covering recent decades (and extended up to real-time) are therefore of paramount importance to better represent the vegetation and its time evolution in the land surface models (LSMs) used for offline analysis/initialization and for the seasonal-to-decadal predictions.
Here, we integrate realistic vegetation Leaf Area Index (LAI) variability from latest generation satellite campaigns, available through Copernicus Land Monitoring Service (CLMS), in three different LSMs that conducted the same coordinated set of offline land-only simulations forced by hourly atmospheric fields derived from the ERA5 atmospheric reanalysis. The experiment implementing realistic interannually-varying LAI (SENS) is compared with simulations utilizing a climatological LAI (CTRL) to quantify the vegetation feedback and the effects on the simulation of near-surface soil moisture.
The results show that the inter-annually varying LAI considerably affects the simulation of near-surface soil moisture anomalies in all three models and over the same water-limited regions, but surprisingly the effects diverge among models: compared with ESA-CCI observations, the near-surface soil moisture anomalies significantly improve in one of the three LSMs (HTESSEL-LPJGuess) while the other two (ECLand and ISBA-CTRIP) display opposite effects with significant worsening of the anomaly correlation coefficients. It is found that the enhanced simulation of near-surface soil moisture is enabled by the positive feedback that is activated by the effective vegetation cover (EVC) parameterization, implemented only in HTESSEL-LPJGuess. The EVC parameterization works such that the effective fraction of the bare soil being covered by vegetation does vary with LAI following an exponential function constrained by available satellite observations. The increased (reduced) soil-moisture limitation during dry (wet) periods produces negative (positive) LAI and therefore EVC anomalies, which in turn generate a dominating positive feedback on the near-surface soil moisture of HTESSEL-LPJGuess by exposing more (less) bare soil to direct evaporation from the sub-surface layer. On the other hand, in the EC-Land and ISBA-CTRIP models, EVC is fixed in time as it cannot vary with LAI and so the positive feedback described cannot be activated. The only feedback on near-surface soil moisture anomalies that operates in these two models is negative and comes from the reduced (increased) transpiration related to the negative (positive) LAI anomalies.
Simply prescribing observed vegetation data into LSMs does not guarantee the introduction of the correct coupling and feedback on climate. In this respect, this multi-model comparison experiment demonstrates the fundamental role of the inclusion of the underlying vegetation processes in LSMs. Ignoring the proper representation of the vegetation processes could lead to unrealistic (and even the opposite effects compared with observations) behaviour in reanalysis and climate predictions.
How to cite: Alessandri, A., Possega, M., Di Carlo, E., Cherchi, A., Boussetta, S., Balsamo, G., Ardilouze, C., Dayon, G., and van Oorschot, F.: Representation of Temporal Variations of Vegetation in Reanalysis and Climate Predictions: Diverging Soil-Moisture Response in Land Surface Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13385, https://doi.org/10.5194/egusphere-egu25-13385, 2025.
Flooding is the most frequent and damaging hydrometeorological disaster in Indonesia, with Java being especially vulnerable due to its dense population and rapid urbanization. This study aims to refine the Impact-Based Forecasting (IBF) model to improve flood hazard predictions and mitigation efforts. Using Global Precipitation Measurement (GPM-IMERG) rainfall data as the hazard component combined with vulnerability and capacity datasets from InaRISK, this research focuses on enhancing the precision and reliability of impact assessments.
Initial analyses highlight the potential of impact-based rainfall thresholds and assessment probabilistic impacts to refine the IBF model and reduce subjectivity in impact assessments. By linking calculated impact values and disaster magnitudes for the 2014 – 2023 period, this study shows a promising skill for significant and severe flood events, although improvements are needed for minor and minimal disaster classifications.
This research lays the groundwork for a more robust and scalable IBF model tailored to Java’s unique challenges. The findings aim to support BMKG’s operational needs, enabling the delivery of more actionable early warnings and targeted disaster preparedness measures. By addressing critical gaps in existing IBF systems, this study contributes to bridging the divide between hazard-impact forecasts and societal resilience, ultimately mitigating the impacts of floods in Indonesia.
How to cite: Purnama, D. R., Tett, S. F. B., Doherty, R., and Pramuwardani, I.: Impact-Based Forecasting Model for Flood Hazard Mitigation in Java, Indonesia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7271, https://doi.org/10.5194/egusphere-egu25-7271, 2025.
Women face disproportionate impacts from climate change due to significant barriers to accessing education and protection. In Indonesia, women often lack access to essential resources and opportunities, particularly in urban informal settlements. However, women also hold a pivotal position in the community in advancing climate literacy. Despite progressive regulations supporting women’s rights, gaps in implementation persist, highlighting the need for targeted initiatives to enhance women’s understanding of climate issues and their capacity to lead resilience efforts. Indonesia has established strong policies for gender equality and climate action, such as Presidential Regulation No. 59/2017 and the National Action Plan for Climate Change Adaptation (RAN-API), which emphasize gender-responsive strategies. However, translating these policies into real-world actions remains a challenge, highlighting the need to better connect scientific research and community insights to effective governance and implementation. This study identifies a critical gap in urban climate literacy and proposes empowering women as a solution. By leveraging women’s social network in Indonesia, the project disseminates climate knowledge and fosters collective action. Key initiatives include training women in climate literacy, introducing sustainable practices such as urban gardening, and developing accessible educational tools like songs, games, and visual materials. These programs are designed to position women as trusted leaders within their communities. Structured monitoring and evaluation methods, including annual surveys and peer-led literacy programs, ensure continuous improvement and scalability. Preliminary findings demonstrate that women-led climate literacy initiatives significantly enhance community resilience and resource allocation. Empowered women influence their families and peers, creating a ripple effect that strengthens societal adaptability. This scalable model integrates women-centered initiatives into governance frameworks, building pathways for sustainable, inclusive development. By empowering women, we transform vulnerability into strength, paving the way for a resilient future.
How to cite: Rachmawati, A. and Dewita, A.: From Policy to Action: Empowering Women to Lead Climate Resilience in Indonesia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15484, https://doi.org/10.5194/egusphere-egu25-15484, 2025.
Despite growing awareness of climate change, many Indonesians remain climate-silent, posing a significant challenge to the country's efforts to mitigate its impacts. This study aims to analyze the factors contributing to climate silence in Indonesia, using psychological theories related to climate science denial. A rapid systematic review was conducted to gather evidence, revealing five key drivers of climate denial: limited cognitive abilities, ideological beliefs, sunk costs, perceived risks, and discredence. These barriers are further shaped by factors such as government policies, economic conditions, religious influences, and insufficient environmental education.
This skepticism towards climate change undermines adaptation and mitigation efforts by disrupting community engagement and participation. The findings highlight the importance of government support in addressing the root causes of climate skepticism. Employing the concept of inoculation through a misconception-based learning approach—integrated into religion and education—can help reshape mindsets. Enhancing public understanding of climate change is essential to fostering community involvement and support for effective climate mitigation initiatives.
Keywords: climate silence, climate denial, psychological drivers, Indonesia.
How to cite: Dewita, A. and Inayah, B.: Psychological Drivers of Climate Silence: A Challenge to Indonesia's Climate Action, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14900, https://doi.org/10.5194/egusphere-egu25-14900, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 5
EGU25-18989 | ECS | Posters virtual | VPS5
Enhancing Hydrological Processes in Earth System Models: Implementing Groundwater Dynamics for Improved Climate RepresentationsMon, 28 Apr, 14:00–15:45 (CEST) | vP5.2
In the context of climate change, a global, widespread shift to increased water limitation is expected over approximately 73% of terrestrial ecosystems, with important implications for food and water security, CO2 uptake, and evaporative cooling. Water-limited regions, exposed to climate-change-related increasing droughts and intense anthropogenic water use, are extremely vulnerable to transitions towards drier eco-hydro-climatological regimes. In the longer term, the ongoing drought conditions may intensify the decline of groundwater levels, threatening groundwater-dependent ecosystems and exacerbating the risk of desertification, thereby amplifying a positive feedback on regional climate change. In some Mediterranean climate-type regions, such as SouthWestern Australia, a dry and warm transition has already been observed. Recent findings are a clear warning that also over the Euro-Mediterranean sector groundwater level may have a negative trend resulting from a decrease in precipitation and/or increasing withdrawal.
Soil water storage and groundwater dynamics represent important hydrological processes related to these transitions but they are greatly simplified in state-of-the-art Earth System Models (ESMs). Therefore, it is essential to improve the representation of hydrological processes and their coupling with the atmosphere and the land surface in ESMs. In this respect, the land surface model included in EC-Earth (ECLAND) still lacks a representation of groundwater and instead implements a free drainage condition at the bottom of the unsaturated soil column.
In this work, we intend to implement a more realistic groundwater representation in EC-Earth by including a global-scale water table to replace the free drainage bottom boundary condition. As a preliminary measure, the impact of groundwater on the shallow, unsaturated zone is evaluated by constraining the vertical water fluxes with a static water table depth (WTD) derived from a global estimate simulation based on observations. We evaluated the effects of this implementation on water and energy fluxes against a network of stations in land-only simulations from 1979 to the present, with boundary forcing taken from ERA5 reanalysis. First findings suggest that including a WTD has an impact on water exchanges between saturated and unsaturated soil in water-limited regions, particularly in semi-arid and transitional climates, which can not be neglected in Earth system models.
How to cite: Senigalliesi, V., Alessandri, A., Kollet, S., Gelsinari, S., Cherchi, A., and Di Carlo, E.: Enhancing Hydrological Processes in Earth System Models: Implementing Groundwater Dynamics for Improved Climate Representations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18989, https://doi.org/10.5194/egusphere-egu25-18989, 2025.
EGU25-21341 | Posters virtual | VPS5
Skillful Seasonal Prediction of Indian Wind Energy Resources during Summer Monsoon SeasonMon, 28 Apr, 14:00–15:45 (CEST) | vP5.3
EGU25-3766 | Posters virtual | VPS5
Seasonal Predictability of Late-Spring Precipitation in the Southern Great PlainsMon, 28 Apr, 14:00–15:45 (CEST) | vP5.4
The Southern Great Plains are subject to fluctuating precipitation extremes that pose significant challenges to agriculture and water management. Despite advancements in forecasting, the mechanisms driving these climatic variations remain incompletely understood. This study investigates the relative contributions of the tropical Pacific and Atlantic Oceans to April-May-June precipitation variability in this region. Using partial ocean assimilation experiments within the Community Earth System Model, we identify a substantial influence of inter-basin interactions, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these variations. Our statistical analysis suggests that these tropical inter-basin contrasts offer a more reliable indicator for late-spring precipitation anomalies than the El Niño-Southern Oscillation. This finding is corroborated by analyses from seven climate forecasting systems in the North American Multi-Model Ensemble, providing a promising outlook for improving real-time forecasting in the Southern Plains. However, the predictive skill of these inter-basin contrasts is currently limited by the lower predictability of the tropical Atlantic, underscoring the need for future research to enhance climate prediction models.
How to cite: Chikamoto, Y., Wang, S., Chang, H.-I., and Castro, C.: Seasonal Predictability of Late-Spring Precipitation in the Southern Great Plains , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3766, https://doi.org/10.5194/egusphere-egu25-3766, 2025.
EGU25-7421 | ECS | Posters virtual | VPS5
Integrating Climate Projections and Geospatial Analysis to Identify Rainwater Harvesting Suitability in Lombok Island, IndonesiaMon, 28 Apr, 14:00–15:45 (CEST) | vP5.5
Water scarcity is a growing challenge exacerbated by climate change, particularly in regions like Lombok Island, Indonesia, where water resources are crucial for sustainable development. This research aims to identify suitable locations for Rainwater Harvesting (RWH) by integrating geospatial analysis, the Analytic Hierarchy Process (AHP), and climate projections using CMIP6 data. The study utilizes multiple parameters, including rainfall, land use/land cover (LULC), slope, drainage density, soil texture, and runoff depth, to develop a comprehensive suitability map for RWH.
Historical rainfall data from CHIRPS (1981–2010) and future rainfall projections for mid-century (2031–2060) and end-century (2071–2100) under SSP2-4.5 and SSP5-8.5 scenarios were analyzed to account for climatic variations. Each parameter was processed using geospatial tools, with weights assigned through AHP based on expert input, ensuring a robust multi-criteria decision-making framework. Suitability maps were generated for each temporal scenario, highlighting areas with high to very high potential for RWH, particularly in North and East Lombok.
The results reveal dynamic shifts in RWH site suitability over time, with increasing precipitation under SSP5-8.5 scenarios expanding high-suitability areas. These findings highlight the potential for RWH to manage water resources adaptively in response to projected climate variability. By aligning the outputs with existing water management infrastructure, such as dams, the study provides actionable insights for regional planners and policymakers.
How to cite: Ulfah, A., Renwick, J., and Patria Megantara, R.: Integrating Climate Projections and Geospatial Analysis to Identify Rainwater Harvesting Suitability in Lombok Island, Indonesia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7421, https://doi.org/10.5194/egusphere-egu25-7421, 2025.
