Training

Practical introduction to the Sea level gridded data: plotting data during an El Niño event and over a strong current

Surface Albedo product. Three case studies: data retrieval, visualisation, time-series creation, set up Python environment

LAI and fAPAR retrieved with satellite remote sensing. Computation of inverse variance weights

An introduction to the different sources of climate data and how to find them.

Learn about the various data sources, and strategies to find climate data: processing, choosing projections, scenarios, ensembles and variables.

Learn about Essential Climate Variables, the different types of climate data resources, and their respective pros and cons.

Explore the different types of measurements, the types of observing systems and their measurement uncertainty.

This lesson teaches users the basics of climate reanalysis. The lesson explains how reanalyses are made, an overview of global reanalyses datasets, and their strengths and limitations.

Uncover how climate models work and how they can be evaluated. Differences between climate projections, predictions and scenarios are explained.

This lesson teaches about downscaling and bias correction methods. An exercise for bias correction is included.

This lesson teaches how to use climate models in the development of national climate scenarios. Examples are provided for The Netherlands, Switzerland and the U.K.

This lesson teaches about sources of uncertainty in climate projections, what robust signals are, and when we can be confident in a change.