Glossary of terms

Bias correction

Bias-correction is the process of mathematically scaling climate model outputs to account for their systematic errors, in order to improve their fitting to observations. Bias correction methods can be mathematically simple, e.g. subtraction, or more complex, e.g. fitting statistical curves. No climate model or data processing technique can entirely eliminate uncertainty, but bias-correction can help reduce mismatches between modeled gridded data and measured historical data for point locations. Bias correction is applied to most modeled climate data.

Downscaling

Downscaling is a collection of methods which can transform lower-resolution (coarser, more pixelated) datasets into higher-resolution products. This is useful for providing increased detail on local conditions. Downscaling techniques can be divided into two broad categories: dynamical and statistical.

Dynamical downscaling

Dynamical downscaling involves setting up boundary conditions for a region using a relatively coarse Global Climate Model (GCM), then creating a higher resolution regional model based on physical principles. This method is computationally intensive.

Statistical downscaling

Statistical downscaling involves defining a mathematical relationship between historic observed climate data and the output of GCMs or other climate models for the same time period, then using this relationship to create future projections.

Emissions scenario

Emission scenarios reflect plausible future human greenhouse gas and other aerosol emissions. In the past, the Intergovernmental Panel on Climate Change (IPCC) referenced emission scenarios when describing possible pathways for climate change. Later, Representative Concentration Pathways (RCPs) were designed to help climate modelers connect emissions trajectories with Radiative Forcing values. The RCP4.5 scenario represented a future with fewer increases to emssions, and the RCP8.5 scenario represented a future with greater global emissions. RCP6.0 was a middle-of-the-road scenario. Today, the scenarios used by the IPCC are known as Shared Socio-economic Pathways, or SSPs. SSPs include information about demographics and policy, which underlie each scenario.

Exceedance probability

Exceedance probability is the statistically determined probability that a certain value will be exceeded in a specified future time period. In hydrology, the exceedance probability is the inverse of the annual recurrence interval, return interval, or return period.

Global climate model (GCM)

A global climate model (GCM) is a mathematical representation of the interactions and energy balance among Earth’s atmosphere, land surface, ocean, and sea ice. Climate models divide the globe into a three-dimensional grid of cells that interact with one another as a coupled system. Outputs from GCMs provide long-term climate projections.

Gridded dataset

A gridded dataset is a continuous grid-based representation of a variable (e.g. temperature) in two dimensions (e.g latitude and longitude). In contrast, point datasets or line (vector) datasets offer values for only a subset of possible locations (e.g. cities, rivers, or roads). Gridded datasets may be low-resolution (large grid cells) or high-resolution, but do not have data gaps or discontinuities. If gridded datasets are created from point or vector datasets, mathematical interpolation and modeling is required.

Reanalysis

Climate reanalyses combine past weather observations with models to generate time series of climate variables. ERA5 is the latest climate reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It provides hourly data on multiple parameters, with estimates of uncertainty.