Datasets

This dataset provides agroclimatic indicators used to characterise plant-climate interactions for global agriculture. Agroclimatic indicators are useful in conveying climate variability and change in the terms that are meaningful to the agricultural sector. The objective of this dataset is to provide these indicators at a global scale in an easily accessible and usable format for further downstream analysis and the forcing of agricultural impact models.

The dataset contains model projections of changes in marine physics and biogeochemistry and the lower trophic levels of the marine food web across the Northwest European Shelf and Mediterranean Sea out to the year 2100.

The dataset presents wave climate indicators based upon ocean surface wave parameters computed for a European-wide domain. This dataset provides an understanding of the European wave climate under the impact of climate change. It provides added value for various coastal sectors and studies such as port, shipping, and coastal management.

The dataset presents time series of the coastal wave climate based upon ocean surface wave parameters computed for a European-wide domain. This dataset provides an understanding of the wave climate under the impact of climate change for the Northwest European Shelf and Mediterranean Sea. It provides added value for various coastal sectors and studies such as port, shipping, and coastal management.

This dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modelling.

This catalogue entry provides the time series of the originally gridded Agrometeorological indicators from 1979 to present derived from reanalysis, also known as AgERA5. The main difference with the related entry is that the dataset is organised here to make time the primary dimension for analysis and extraction. For any user-defined location (point) or small area, the dataset returns the temporal evolution of the selected variables, enabling efficient time-series studies without requiring users to download and manage full global gridded files.