This video gives you a brief overview of the module. For the full roadmap, see Curriculum → Roadmap
Overview:
This module, designed to be completed in around 30 minutes, explores the role of climate data and models in society and research. Participants will learn to distinguish between different types of climate data—observations, reanalysis, and projections—and understand their respective contributions to climate science. The module also examines IPCC scenarios, such as Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs), which outline potential future pathways based on varying emissions and societal trajectories. Finally, the concept of bias correction is introduced as a crucial tool to address systematic errors in model outputs, enhancing their usability for research and decision-making. The final quiz testes the knowledge and comprehension of the roadmap text.
Curriculum
- 2 Sections
- 2 Lessons
- 30 Minutes
References:
- Cannon, A. J. (2008). Probabilistic multisite precipitation downscaling by an expanded Bernoulli–Gamma density network. Journal of Hydrometeorology, 9(6), 1284-1300.
- Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015). Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate, 28(17), 6938-6959.
- Cannon, A. J., Jeffery, S., & Hiebert, J. (2018). Climate Data Bias Correction. In: Canadian Climate Data and Scenarios (CCDS). Canadian Centre for Climate Modelling and Analysis.
- Casanueva, A., Bedia, J., Herrera, S., Fernández, J., & Gutiérrez, J. M. (2020). Direct and component-wise bias correction of multi-variate climate indices: the percentile adjustment function diagnostic tool. Climate Dynamics, 54(11), 4501-4523.
- Chen, H., Xu, C. Y., Guo, S., & Singh, V. P. (2010). A monthly water balance-based drought severity index and its application in drought monitoring. Hydrological Processes, 24(8), 1044-1056.
- Diaz-Nieto, J., & Wilby, R. L. (2005). A comparison of statistical downscaling and climate change factor methods: Impacts on low flows in the River Thames, United Kingdom. Climatic Change, 69, 245-268.
- Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012). Technical note: Downscaling RCM precipitation to the station scale using quantile mapping–a comparison of methods. Hydrology and Earth System Sciences, 16(9), 3383-3390.
- Hay, L. E., Wilby, R. L., & Leavesley, G. H. (2000). A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States. Journal of the American Water Resources Association, 36(2), 387-397.
- Koenker, R., & Hallock, K. F. (2001). Quantile regression. Journal of Economic Perspectives, 15(4), 143-156.
- Lafon, T., Dadson, S., Buys, G., & Prudhomme, C. (2013). Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods. International Journal of Climatology, 33(6), 1367-1381.
- Lenderink, G., Buishand, T. A., & Van Deursen, W. P. (2007). Estimates of future discharges of the river Rhine using two scenario methodologies: Direct versus delta approach. Hydrology and Earth System Sciences, 11(3), 1145-1159.
- Maraun, D. (2016). Bias correcting climate change simulations–a critical review. Current Climate Change Reports, 2(4), 211-220.
- Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204.
- Switanek, M. B., Troch, P. A., Castro, C. L., Leuprecht, A., Chang, H. I., Mukherjee, R., & Demaria, E. M. (2017). Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes. Hydrology and Earth System Sciences, 21(6), 2649-2666.
- Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456, 12-29.
- Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., & Ganguly, A. R. (2017). Deepsd: Generating high resolution climate change projections through single image super-resolution. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1663-1672).
- Vrac, M., Stein, M., & Hayhoe, K. (2007). Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing. Climate Research, 34(3), 169-184.
- Wilks, D. S., & Wilby, R. L. (1999). The weather generation game: A review of stochastic weather models. Progress in Physical Geography, 23(3), 329-357.
Giusy Fedele
Giusy is a Junior Scientist at CMCC’s Regional Models and Geo-Hydrological Impacts (REMHI) division in Caserta, where she works on several national and international projects. A key focus of her work lies in the development of both dynamical and statistical downscaling techniques, with applications spanning from weather to climate scales.
Massimo Milelli
Massimo is a meteorologist with extensive experience at the Regional Weather Centre of Arpa Piemonte (Turin, Italy). He is Head of the Meteo & Climate Department at the CIMA Foundation (Savona, Italy). Since 2012, he has led a Working Group within the COSMO Consortium and serves on its Scientific Management Committee. His work has always been closely linked to numerical weather prediction (NWP) models, particularly COSMO and ICON. His main research interests include urban meteorology, NWP verification, and post-processing techniques, with R as his preferred analytical tool. He is also actively involved in several H2020 projects.
Riccardo Biondi
Riccardo Biondi is a researcher at the Meteo & Climate Department at the CIMA Foundation (Savona, Italy). He’s the founder and organizer of the international training school on Convective and Volcanic Clouds (CVC) detection, monitoring and modeling and the (Geo)Science Communication School.
