Weather Hindcasting - Using models to look backwardsACRE and its partners undertake the recovery and digitisation of historical surface terrestrial and marine weather data from around the globe. The data they rescue is stored in international databankswhich are used by a host of climate reanalysis systemsto estimate past weather and its variations. Generally speaking, climate reanalysis systems take the concepts of weather forecasting models and use them to reconstruct the past weather instead of forecasting tomorrow’s weather. This weather ‘hindcasting’ recreates what past weather probably looked like. A more nuanced description of the difference between climate models and weather forecasting models can be found here. Products of Reanalysis SystemsACRE data are described as “observational” as it originates from human or machine-recorded observations of weather instruments such as thermometers, barometers, anemometers, etc. Using limited sets of these observational data as a starting point, climate reanalysis systems simulate the full array of the Earth’s weather patterns. They do this over several decades or longer, and can cover the entire globe from the Earth’s surface to well above the stratosphere. By calculating the overall historical state of the climate, they generate estimates of many more weather variables beyond the original limited set of observational data started with. These interpolated variables, or reanalysis “products”, can cover 50 or more weather conditions including atmospheric temperature, pressure, wind and humidity at different altitudes, surface rainfall and soil moisture content, and even ocean-surface temperature and salinity. They also create an array of derived variables, such as fluxes. Thus, limited historical observations are used to create a much richer perspective of our climate heritage.With this perspective, climate scientists use reanalysis products to assess climate variability over a long period of time, so gaining an insight into how it may be changing. Specific outputs from the systems are also useful in other fields of study including ecology (climate impact on species), commercial risk analysis (insuring for storm/flood damage) and the social sciences (human reactions to climate extremes). A more comprehensive review of how reanalyses products are used can be found in the Applications section of this website.Differences Between SystemsReanalysis systems can be distinguished by the geographical span and resolution, temporal coverage and individual characteristics of their output products. Most systems have a global reach, they cover at least the last 30-50 years of weather. Some systems produce 50+ estimates to gauge uncertainty in the ground, air and water weather products. An important feature is the level of accuracy in each of the products, considering that they combine uncertain observations with an uncertain forecast. For estimates of the weather 100 years ago, reanalysis fields in the northern hemisphere can be expected to have an accuracy similar to current three to four day weather forecasts.Those systems focused on reanalyses after the 1940s generally use large amounts of automatically recorded data by radiosondes, ocean buoys, automatic ground weather stations, and eventually satellites. These late 20th Century measurement devices have automated data-logging capabilities that generate a very rich set of observational weather data. However, before the 1940s, weather observations are generally only available from physical recording media like paper. Some of the observations were made by machines onto paper, while others were recorded by humans, resulting in far less data being available for reanalysis. What data does exist (and has been discovered) has to be digitised before it can be assimilated by the reanalysis systems. This is done either by humans or by character recognition software. Both are laborious processes resulting in a further reduction in available data. Addressing this deficiency is one of ACRE’s core activities..
“Climate scientists use reanalysis products to assess climate variability…”