Hazard datasets (WIP)

Hazard datasets (WIP)#

Caution

Work in progress page!

Climate-related hazards have traditionally been prominently featured in the conceptualization of climate risk, often dominating the discourse. In this context, the IPCC Assessment Reports (ARs) have played a key role in shaping the role of hazard in climate risk conceptualization, as it is essential for understanding the potential intensity, frequency, and spatial distribution of climate risk.

This page collects references to open-access datasets that characterize climate hazards for the past, present and future. The collected entries are broadly categorized into general datasets that characterize the state of the earth system and datasets created to quantify specific hazards. The collection is not complete and favours datasets with global or European coverage.

Tip

We highly recommend to consider alternative and complementary local datasets for a risk analysis in addition to the options listed here. Contact to your local meteorological service and search for other providers of data tailored to your area of interest. Look out especially for regional climate model projections and statistically downscaled and bias-corrected datasets that take into account local conditions. Some information may only be accessible in the local language.

General climate datasets#

These are datasets of quantities that characterize the Earth system in general. For use a meaningful hazard indicator, they usually require further processing. Available variables include atmospheric temperature, wind speed and precipitation but also information about the state of other Earth system components, e.g., soil properties.

Observations#

Observations comprise information from surface weather stations and other platforms that monitor the state of the Earth system like aircraft and satellites. Consistent and quality-controlled timeseries of observations can be a source of reliable and accurate local information. Some stations have long historical records suitable for analysis of the local climate of the past. However, differences in measurement techniques and equipment can complicate comparisons between observational records in time and space. The spatial and temporal coverage of station data is often incomplete and highly local events, e.g., precipitation extremes, may be missed by the network. Satellites cover large areas in general but only pass infrequently over a specific area if placed in a polar orbit. Gridded observation datasets aim to fill gaps in the coverage by combining observations from different sources with interpolation and statistical techniques.

Reanalysis#

Reanalysis products are recreations of the state of the Earth system by a computer model under the consideration of available observations. Reanalysis datasets provide complete and consistent gridded information in time and space and can be considered a best model estimate for the state of the atmosphere and related components. The approach presents a generally better way to consolidate the information of a diverse and incomplete set of observations when compared to interpolation techniques but limitations of both the model that produces the reanalysis and the coverage of the observational record apply. Notably, reanalysis data is not bound to reproduce the observations and values on the grid usually represent the average conditions in the associated grid boxes. Modern reanalysis products offer explicit uncertainty estimates from an associated ensemble system.

../../_images/hazard_image.png — Fig. 13 A global reanalysis provides the boundary conditions for a regional reanalysis, which in turn provides the background for a specialized surface reanalysis. As the modelling of local conditions improves with increasingly specialized models, more observations can be considered in a given area. Source: **UERRA data user guide**.#

Climate model projections#

These datasets are produced by simulations of the Earth system with climate models (“model runs”). As for reanalysis (Fig. 13), there are global climate models (GCMs) and regional climate models (RCMs), with the latter driven by boundary conditions from the former. Climate model runs are usually started in the past and provide a consistent dataset of the historical and future climate. This allows for the correction of model bias with respect to other historical data records and for the assessment of change signals without the introduction of model bias. Projections of climate models depend on assumptions about drivers of the Earth system, which are formalized in emissions scenarios and socioeconomic pathways.

Climate models represent physical processes in varying degrees of simplification. This reduces computational costs but limits how well these processes and their effects are (re)produced in simulations. E.g., many clouds and orographic effects occur at spatial and temporal scales not explicitly resolved by current climate models. Because each model has its specific representation of the Earth system, projections differ between models for the same scenarios. Chaos and stochasticity in the Earth system additionally mean that multiple runs with the same model and scenario do not produce identical projections. Single- and multi-model ensembles of projections should therefore be considered to account for the inherent uncertainties of climate modelling in quantitative CRA.

Hazard-specific datasets#

They usually built on top of general climate datasets and have additional processing and filtering applied to quantify specific hazards more directly. This ranges from the detection of hazardous events and basic statistical summaries of climate variables in an easy-to-access format to the application of dedicated hazard models to produce hazard indicators.

Hazard datasets (WIP)

Contents

Hazard datasets (WIP)#

General climate datasets#

Observations#

Reanalysis#

Climate model projections#

Hazard-specific datasets#

Floods#

Heatwaves#

Fire#

Wind#