Overview of Climate Data

Types of Climate Datasets

Generally, climate data can be sourced from historical observations of climate using instrumentation or from climate model simulations. In the context of electricity system planning and design, both observed historical climate as well as future climatic conditions are of interest. Products of both groups may need to be employed in the development of adequate climate-related information for an electricity sector resilient to changing climate conditions.

TipClimate Data are Public Domain

Note that all datasets discussed in this guide are public domain and available free of charge.

Observed data is provided as point information at station locations. To provide continuous fields of climate information, station data can be interpolated spatially to produce gridded historical data. They provide a portrait of climate beyond the stations point locations but are impacted by potential interpolation errors, particularly in data sparse regions, or in regions of complex topography (e.g., mountainous areas).

The approach that attempts to overcome these drawbacks is the reproduction of historical climate through the combination of a maximum of available climate information with a physical climate simulation approach known as climate reanalysis. Reanalysis products have substantially improved over recent years and are often used as surrogates for observational data in climate science and studies.

Observational data and reanalysis data provide a representation of the actual evolution and sequence of events as they occurred and provide robust estimates of climate when averaged over climatic periods (usually 30 years).

Simulated data from climate models for electricity system planning and design may be sourced from internationally coordinated ensembles of climate model simulations or products derived thereof. These simulations typically cover multiple decades and are available for historical and future periods. They are fully consistent in time and space and are typically provided on regular grids. The grid resolution depends on the model or the data product.

Like observational data, bias-adjusted historical climate simulations may be used to derive climate estimates of historical climate. These estimates will not be identical to climate estimates from observed data yet will generally fall into the range of natural variability of the observed climate. Since climate model simulations extend into the future, estimates for future climate may also be derived. It is important to note that the sequence of events produced by a historical climate simulation is distinct from the sequence of historically observed events, although their respective climate estimate is the robust characteristic they have in common.

See the pages for each of the three main categories of climate data:

Observational Data

Observational data are direct physical measurements from weather stations. These provide the most accurate and high-resolution climate records, but their coverage is limited to specific locations, leading to gaps in remote areas. Instrument or human failure may cause gaps in climate station records. Gridded observational datasets address the spatial limitation of station data by interpolating their data across a defined grid, providing more comprehensive spatial coverage. These datasets, offer a balance between accuracy and spatial representation, although uncertainties arise in data-sparse regions and temporal data gaps will persist in a gridded data product.

Observational data are presented in detail in the Observational Data section.

Reanalysis Data

Reanalysis datasets combine historical observations with atmospheric process models to create consistent, long-term reconstructions of the atmosphere and the surface. They offer global coverage and high temporal resolution, making them valuable for analyzing past climate conditions and trends. However, reanalysis products rely on model-based data assimilation, which may introduce biases, especially in areas with limited observations.

Reanalysis data are presented in detail in the Reanalysis Data section.

Climate Projection Data

Climate models simulate atmospheric and earth system processes of the planet (Global Climate Model - GCM) or a region of it (Regional Climate Model - RCM). They are operated for known historical boundary conditions of the earth and under different future greenhouse gas scenarios. The resulting simulated climate data are essential for understanding past and future climate patterns and assessing potential changes and impacts. Uncertainties and biases of climate models are addressed by employing climate model simulation ensembles and applying bias adjustment procedures to raw model outputs.

Climate model data are presented in detail in the Climate Projection Data section.

Together, these datasets enable a comprehensive view of both historical and future climate conditions, supporting a wide range of applications in the electricity sector. To avoid some of the challenges of using climate data and to support good practice and understanding the reader may consult the Climate Data Fundamentals page.