CL5.1.1

Impact studies, devoted to the energy demand in buildings or to the watershed hydrology, often need climate variable time series at the hourly time scale. However, climate projection outputs are mostly available at the daily time step, except for a few variables provided at the 3- or 6-hourly time step in some cases. In this paper, two ways of computing a diurnal cycle developed and used in impact studies at EDF/R&D are discussed.

The first approach has been defined to provide consistent hourly projections for four variables used to estimate the energy demand for heating/cooling of buildings at the 2050 horizon: temperature, wind speed, radiation and relative humidity at different geographical locations in France. The main idea is to use the distribution of the daily and geographical mean temperature over the whole French territory to identify an analogue day in the ERA5 reanalysis database. Then, for each variable and each location, the differences (for temperature) or ratios (for the other variables) between the daily mean and each hourly value are added to / multiplied by the daily mean value to assess local diurnal cycles. The approach is illustrated for 3 locations in France and its validation in terms of spatial and intervariable consistency is discussed, together with a highlight of the limitations and possible improvements.

The second approach uses a statistical bias correction method, namely the CDF-t (“Cumulative Distribution Function-transform”) approach (Michelangeli et al., 2009). The CDF-t was initially developed to spatially downscale and bias correct climate model projections by defining a correction function regarding the cumulative distribution functions of observed and modelled data over the reference and future time periods. In this work, it was adapted to temporally downscale climatic projections of surface thermal radiation downward from the daily to the 3-hourly time step for seven locations. It was based on daily series for the 1976-2065 time period and 3-hourly time series for 33 years scattered in the total time period. The CDF-t was applied as follow: 3-hourly time series are considered as “observations”. The 33 years displaying those data constitute the calibration time period, the remaining 57 years from the 1976-2065 time period are considered as the projection time period. Daily data are first roughly downscaled to the 3-hour time step by allocating the same daily values to all the 3-hour time steps across the 1976-2065 time period. Then the CDF-t method was applied to the 3-hour series considering the calibration and projection time periods. Results show satisfactory performances in terms on inter-annual and seasonal variability and cumulative distribution function. Mean annual bias is below 5% across the seven locations.

Reference:

Michelangeli, P.-A., Vrac, M., and Loukos, H. Probabilistic downscaling approaches: Application to wind cumulative distribution functions, Geophys. Res. Lett., 2009, 36, L11708, doi:10.1029/2009GL038401.

How to cite: Parey, S., Collet, L., Gailhard, J., Oueslati, B., and Michelangeli, P.-A.: Retrieving diurnal cycles from daily projections for impact studies, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5525, https://doi.org/10.5194/egusphere-egu22-5525, 2022.

Demonstrating the potential economic value of seasonal forecasts is a fundamental signal to mainstream their application in real life and make them actionable instruments to cope with future climate change and to promote adaptation measures and sustainable management of natural resources.

This presentation aims to explore the potential economic value of monthly seasonal forecasts of water flows in a reservoir located in Colombia (i.e. Betania) delivering hydropower production during the period 1993-2016. To assess the economic value a maximizing simulation is applied using two alternative forecasts samples, namely the climatological mean, and the forecasts produced by the climate service SCHT (www.https://gecosistema.com/climate-tools/scht-smart-climate-hydropower-tool/). Then, the simulation has been fed with effective realizations to produce a benchmark. Finally, we get the maximum potential value, when effective realization is considered, and the potential achievable values of the alternative forecasts and their deviation with respect to the benchmark. The simulation is set as a revenue maximizing problem for a representative producer according to a series of technical constraints, such as the reservoir capacity and its volume.

Results demonstrate that SCHT forecasts have a positive value compared to the climatological mean forecasts in normal conditions. For this reason, the analysis is enriched with a sensitivity test on the technical constraints. Therefore, we produce a set of alternative scenarios considering different capacity and volume levels. For volume, we assume either 100% or 50% (net of natural discharges), and for capacity, we assume either 900 m³/s (the maximum) or 600 m³/s. The final objective is to understand whether the technical constraints affect the results and which constraints has a higher impact.

How to cite: Delpiazzo, E., Gianni, C., Mazzoli, P., Dalla Valle, F., and Bagli, S.: Potential economic value of seasonal forecasts of water flows for hydropower production., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7917, https://doi.org/10.5194/egusphere-egu22-7917, 2022.

In the agriculture sector, climate-related economic losses have reached an average of €73 bn a year worldwide. It is, hence, of utmost importance to act now to build a climate-resilient agricultural system by forecasting the future climate risk and implementing adaptation actions. Agriculture insurance plays a crucial role in promoting the resilience of the agricultural sector to external shocks, as it covers the production and financial risks of farmers, and related shortfall risks of interconnected stakeholders throughout the food chain. For a climate-proof agriculture, the insurance industry needs to evolve. Offering traditional coverage is becoming insufficient, with climate change unfolding its impact on the severity and frequency of extreme events.

We present a climate service for monitoring and forecasting the climate risk on crop and livestock production tailored to the needs of insurers providing agricultural coverage. The climate service has been developed and validated within the ESA-funded project TERRA - climaTe sERvices for a Resilient Agriculture, thanks to the engagement of key stakeholders in the whole value chain. The service is a risk assessment and monitoring tool based on a climate-enhanced vulnerability index. Such an index seamlessly integrates satellite data, reanalysis products and seasonal forecasts. In particular, it combines the level-2 variables from sentinel 1, 2 and 3, available through the Copernicus Global Land Service, in a unified vulnerability index, that is integrated with the reanalysis products and seasonal forecasts available through the Climate Data Store of the Copernicus Climate Change Service. The climate-enhanced vulnerability index is applied to the specific case of forage production in Italy.

How to cite: Dal Gesso, S., Venturini, M., and Petitta, M.: Synergizing earth observations and seasonal forecasts within an innovative climate index: the case of forage production in Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12180, https://doi.org/10.5194/egusphere-egu22-12180, 2022.

Producing and providing useful information for climate services requires vast volumes of data to come together which requires technical standards. Especially in the case of extreme climate events, where scientific methods for appropriate assessments, detection or even attribution are facing high complexity for the data processing workflows, therefore the production of climate information services requires optimal technical systems to underpinn climate services with science. These climate resilience information systems like the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S) can be enhanced when scientific workflows for extreme event detection are optimized as information production service, accordingly deployed to be usable by extreme event experts to facilitate their work through a frontend. Deployment into federated data processing systems like CDS requires that scientific methods and their algorithms be wrapped up as technical services following standards of application programming interfaces (API) and, as good practice, even FAIR principles. FAIR principles means to be Findable within federated data distribution architectures, including public catalogues of well documented scientific analytical processes. Remote storage and computation resources should be operationally Accessible to all, including low bandwidth regions and closing digital gaps to ‘Leave No One Behind’. including Data inputs, outputs, and processing API standards are the necessary conditions to ensure the system is Interoperable. And they should be built from Reusable building blocks that can be realized by modular architectures with swappable components, data provenance systems, and rich metadata.
Here we present challenges and preliminary prototypes for service which are based on OGC API standards for processing (https://ogcapi.ogc.org/processes/) open geospatial consortium (OGC). We are presenting blueprints on how AI-based scientific workflows can be ingested into climate resilience information systems to enhance climate services related to extreme weather and impact events. The importance of API standards will be pointed out to ensure reliable data processing in federated spatial data infrastructures. Examples will be taken from the EU H2020 Climate Intelligence (CLINT; https://climateintelligence.eu/) project, where extreme events components will be developed for C3S. Within this project, appropriate technical services will be developed as building blocks ready to deploy into digital data infrastructures like C3S but also European Science Cloud, or the DIAS. This deployment flexibility results out of the standard compliance and FAIR principles. In particular, a service employing state-of-the-art deep learning based inpainting technology to reconstruct missing climate information of global temperature patterns will be developed. This OGC-standard based web processing service (WPS) will be used as a prototype and extended in the future to other climate variables. Developments focus on heatwaves and warm nights, extreme droughts, tropical cyclones and compound and concurrent events, including their impacts, whilst the concepts are targeting generalised opportunities to transfer any kind of scientific workflow to a technical service underpinning scientific climate service. The blueprints are taking into account how to chain the data processing from data search and fetch, event index definition and detection as well as identifying the drivers responsible for the intensity of the extreme event to construct storylines guiding to the event.

How to cite: Hempelmann, N., Alvarez-Castro, C., Kadow, C., Kindermann, S., Ehbrecht, C., Plésiat, É., and Pechlivanidis, I.: Deployment of scientific climate services for extreme events investigations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6197, https://doi.org/10.5194/egusphere-egu22-6197, 2022.

Cloud-based big earth data workflow architectures for operational decision making across communities need to follow FAIR (Findable, Accessible, Interoperable, Reusable) principles in order to be effective. This presentation highlights mature implementations of OGC standards-based building blocks for climate data processing and service provision that are deployed in leading climate services information server systems such as the COPERNICUS Climate Change Service C3S. OGC Web Processing Services (WPS) form the bases of component operations in these implementations, from simple polygon subsetting to climate indices calculation and complex hydrological modelling. Interoperable building blocks also handle security functions such as user registration, client-site utilities, and data quality compliance. 

A particular focus will be the ROOCS (Remote Operations on Climate Simulations) project, a set of tools and services to provide "data-aware" processing of ESGF  (Earth System Grid Federation) and other standards-compliant climate datasets from modelling initiatives such as CMIP6 and CORDEX. One example is the WPS service ‘Rook’, that enables remote operations, such as spatio-temporal subsetting, on climate model data. It exposes all  the operations available in the ‘daops’ library based on Xarray. Finch is a WPS-based service for remote climate index calculations, also used for the analytics of ClimateData.ca, that dynamically wraps Xclim, a Python-based high-performance distributed climate index library. Finch automatically builds catalogues of available climate indicators, fetches data using “lazy”-loading, and manages asynchronous requests with Gunicorn and Dask. Raven-WPS provides parallel web access to a dynamically-configurable ‘RAVEN’ hydrological modelling framework with numerous pre-configured hydrological models (GR4J-CN, HBV-EC, HMETS, MOHYSE) and terrain-based analyses. Coupling GeoServer-housed terrain datasets with climate datasets, RAVEN can perform analyses such as hydrological forecasting without requirements of local access to data, installation of binaries, or local computation.

The EO Exploitation Platform Common Architecture (EOEPCA) describes an app-to-the-data paradigm where users select, deploy and run application workflows on remote platforms where the data resides. Following OGC Best Practices for EO Application Packages, Weaver executes workflows that chain together various applications and WPS inputs/outputs. It can also deploy near-to-data applications using Common Workflow Language (CWL) application definitions. Weaver was developed especially with climate services use cases in mind.

The architectural patterns illustrated by these examples will be exercised and tested in the upcoming OGC Climate Services Pilot initiative, whose  outputs will be also  incorporated into disaster risk indicators developed in the upcoming OGC Disaster Pilot 2022.

Further reading:

https://docs.google.com/document/d/1IrwlEiR-yRLcoI9fGh2B1leH4KU0v0SUMWQqiaxc1BM/edit

How to cite: Lieberman, J., Hempelmann, N., Stephens, A., Ehbrecht, C., Smith, T., Landry, T., Wilson, C., and Pechorro, E.: FAIR building blocks for climate resilience information systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6593, https://doi.org/10.5194/egusphere-egu22-6593, 2022.

To strengthen the seasonal forecast adoption on the vine management, this work introduces an observation-forecast blending approach to wine-sector indicators over the Iberian Peninsula. Five bioclimatic indicators (temperature and precipitation based) were considered as highly important from the perspective of wine-sector users, and the predictions were prepared for each month of the growing season by combining with previous observations as the indicator period progressed. The performance of this approach was then assessed, for each initialization date throughout the entire growing season, with Pearson’s correlation coefficient and Fair Ranked Probability Skill Score. The results show a marked increase in the skill metrics during the growing season from the early combinations for all the indicators. This progressive improvement of the forecasting skill provides the users with an opportunity to ponder anticipation and confidence when finding the best moment to make a specific decision and, thus, to improve their management. Meanwhile, the environmental impact could be reduced with the thoughtful application derived from the customised knowledge of climate information

How to cite: Chou, C., Marcos-Matamoros, R., Palma Garcia, L., Pérez-Zanón, N., Teixeira, M., Silva, S., Fontes, N., Graça, A., Dell'Aquila, A., Calmanti, S., and González-Reviriego, N.: Advanced seasonal predictions for vine management based on bioclimatic indicators tailored to the wine sector, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13357, https://doi.org/10.5194/egusphere-egu22-13357, 2022.

We've reached an inflection point in our relationship with the climate. Accelerating climate volatility is threatening the assets we rely on. To adapt, we need to be smarter, more prescient, more decisive, and more collaborative than ever before. We need new instruments and new insights. We need what we call Climate Intelligence.

Cervest creates Climate Intelligence for every person, asset, and decision. Climate Intelligence transforms how we build, manage, and de-risk our most valuable assets. Climate Intelligence enables us to adapt and decarbonize at scale to build an equitable and resilient future for our planet.

Cervest is at a turning point: we have concluded the first round of development whereby the initial offerings our scientists developed have been explored by major enterprises worldwide and incorporated in their decisions and reporting. A co-creation phase has been part of the process whereby we elicited users’ feedbacks. Learnings from this phase both confirmed some of our expectations and revealed needs we had not fully appreciated, many of which are leading to the deepening of our market education efforts as well as scientific-product developments. 

We find that to proactively adapt and decarbonize, it is essential to establish fruitful collaborations among different actors, and more specifically between Cervest and publicly funded institutions. It is within such collaborations that a swift exchange of information, data and expertise clarifies roles and responsibilities, ultimately accelerating our common drive to support adaptation and mitigation. 

We will share the lessons we have learned and, following the conveners’ call, our view of what is desirable from the public sector, ultimately engaging with academics.

How to cite: Zamboni, L. and White, J.: Cervest as the provider of Climate Intelligence: learnings from our users, collaborations with academics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9940, https://doi.org/10.5194/egusphere-egu22-9940, 2022.

To support the development of climate-resilient pathways, several developing countries are urged to strengthen their scientific base to generate climate and weather information, products and services. This is usually achieved by developing the capacity of national hydro-meteorological services (NMHS) and warning services, to support in turn adaptation planning for intermediary and final users.

In West Africa, several countries, have adopted National Framework of Climate Services (NFCS), with the support of the Global Framework of Climate Services (GFCS) secretariat. They operationalize these NFCS by implementing national or regional climate and weather information-based programs, supported by different international agencies. The ultimate goal of these programs is to enhance the service delivery and warnings to communities, by strengthening the “last mile” connectivity.

Burkina Faso, Chad, Cote d'Ivoire, Liberia, Mali, Niger, Senegal, as well as the Economic Community of West African Sates (ECOWAS) organization itself benefit from the support of international agencies, to tackle the food insecurity and recurrent storm flooding issues they face. By enhancing the institutional capacities of their NHMS and other relevant institutions, including their disaster management authorities and early warning system for food security management agencies, the projects intend to solve the climate related food security crises, improve the short range forecasts for high impact weather and, generally speaking boost the uptake of climate information by the different stakeholders from different socio-economic sectors.

The underlying theory of change (ToC) for these programs is based on a systematic causal chain assuming that the improvement and modernization of the hydro meteorological systems and services of the countries, will result in the provision to communities and national users with adapted, accurate and timely weather, climate and hydrological information; taking advantage of these improvements, the national/regional enhanced institutions will then efficiently consider the demands of stakeholders at all levels of the country and adapt their offer accordingly.

This research is assessing the implementation of some NFCSs and "hydromet programs" running in West Africa. The preliminary results show that the causal chain of the ToC is not so straightforward. Even if opportunities exist in the region, challenges are still big. They range from lack of knowledge of the spectrum and diversity of stakeholders, their specific needs and demands that would inform their action, the broader sensitization to the use of the climate information and collection of their feedback on the services. These challenges suggest in particular that, the allocation of resources to weather in the public sector is unlikely to become more effective until the so called "weather prediction enterprise", takes an integrated perspective on weather forecasts, impacts and policy that provides decision makers with reliable information on the costs and benefits of alternative courses and make it easier for “outsiders” to penetrate that community, due to the required expertise.

How to cite: Kane, C.: National Frameworks for Climate Services in West Africa: Are we on the right pathway?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12229, https://doi.org/10.5194/egusphere-egu22-12229, 2022.

In Ghana, most of the farmers are engaged in small-scale rainfed farming where the success is influenced by how the farmers are able to march their decisions to the prevailing weather conditions. Climate information services (CIS), which includes weather forecasts, can help farmers to reduce their vulnerability to climate extremes and allow them to maximize agricultural productivity. Current services in Ghana and elsewhere in the world, however, only provides information on the recent and forecasted meteorological variables, primarily precipitation and temperature. Having access to other practical knowledge, such as soil moisture content would help farmers further in the decision-making process. Soil moisture is a key component for better farm management practices, because the plant establishment and growth are directly impacted by the soil moisture content. Therefore, this study aims to assess the importance of soil moisture information in farmers’ agricultural decision-making and to understand how this information is being perceived, assessed, and applied. An exploratory research, combined with field visits, farmer interviews, including questionnaires, and focus group discussions was carried out in three local farming communities i.e. Gbulung, Napakzoo, and Yapalsi in the outskirts of Tamale, northern Ghana. Results show that farmers clearly understand the importance of soil moisture for agriculture decision-making in every farming stages. Many farmers expressed that soil moisture information is highly important for fertilizer application and sowing. This information, however, is not well received by the farmers, causing farmers to rely on their indigenous knowledge to monitor the soil moisture conditions. Soil moisture forecast is ranked as the second critical information for farmers after precipitation. Capacity building and frequent interactions at farmer field schools and trainings could increase the farmer’s understanding and awareness of the role of soil moisture in agricultural decision-making. Moreover, farmers show an interest in a CIS embedded with a soil moisture forecast advisory module (CIS-SM) that could help them to increase the water-use efficiency and in the end, reduce the pressure on available water resources for agriculture.

How to cite: Paparrizos, S., Sutanto, S., Jamaldeen, B. M., Issahaku, A. K., Kranjac-Berisavljevic, G., Gandaa, B. Z., Nauta, L., Supit, I., and van Slobbe, E.: The role of soil moisture information in developing robust climate services for smallholder farmers: evidence from Ghana, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8383, https://doi.org/10.5194/egusphere-egu22-8383, 2022.