Scholarship code CU1.294

Seasonal forecasts for climate impact assessment

  • Reference person
  • Host University/Institute
    IUSS Pavia
  • Internship
  • Research Keywords
    Climate variability
    Climate modelling
    Climate predictions
  • Reference ERCs
  • Reference SDGs
    GOAL 2: Zero Hunger
    GOAL 7: Affordable and Clean Energy
    GOAL 12: Responsible Consumption and Production


Effective policies for the adaptation to weather and climate related impacts rely on the availability of skilful predictions at diverse timescales. Weather forecasts and climate predictions are nowadays widely used in impact studies, and an increasing focus is being placed on forecasts at seasonal (i.E. Out to several months) forecasts. The seasonal time scale is in fact crucial in the activity planning of several key socio-economic sectors, such as energy, agriculture, and health, among others. Over the last decades the seasonal prediction skill has considerably improved, and it is now considered useful for societal applications. However, seasonal forecasts need to be better exploited/improved further and their economic value need to be quantified properly (depending on the region, season and/or sectoral application).The objective of this research programme is to determine the usability and value of seasonal forecasts for climate impact assessment in specific socio-economic sectors. Available forecast products, possibly combined and optimized from multi-model ensembles, will be analysed to compute the skill for essential climate variables like precipitation and temperature, circulation patterns and related metrics in targeted regions and seasons. The analysis and evaluation of the seasonal forecast skill need proper observational datasets and high-resolution reanalysis products for comparison. For applications as in agriculture or renewable energy sectors the analysis of climate variables and related metrics will be extended to the related climate impact indicators (e.G. Renewable energy potentials and drought indices).

Suggested skills:

The ideal candidate should have a strong background in data analysis and statistics (analysis of probability distribution functions, uncertainties, etc.) and be familiar with the management of large datasets. He/she should have basic knowledge of climate dynamics, climate change and the associated impacts. Some knowledge and preliminary understanding of numerical modelling can be an added value.

Research team and environment

The activities will be carried out in the CARISMA group at IUSS Pavia, in close collaboration with the CNR-ISAC in Bologna.The CARISMA team is composed by STEM and Social scientists working in the prism of climate change on: data analysis and modelling of Earth system and economic system processes; impact assessment of extreme natural events and anthropogenic activities on human and natural environments; risk management of natural and anthropogenic hazards; formulation and proposal of new economic, political and legal models of sustainable development.The Institute of Atmospheric Science and Climate of the National Research Council of Italy (CNR-ISAC) aims at the understanding of the atmosphere, climate and Earth system sciences in a multidisciplinary approach. One of its main research areas, CAMEO (climate and meteorology, modelling and earth observations) combines theoretical, experimental and numerical applications for climate variability and predictability/predictions, from sub-seasonal to multi-annual timescale, on diverse spatial scales (from global large-scale to individual meteorological events).IUSS and ISAC-CNR are actively committed towards internationalisation, inclusion and diversity.