climate Improved

Improved climate resilience through Improved seasonal Predictions

Lack of water, floods, or crop losses: As a result of climate change, pronounced periods of drought and rainfall are occurring more frequently and more intensively all around the world, causing human suffering and major economic damage. The more precise seasonal forecasts for the coming months are, the more effectively these consequences can be mitigated.…

Lack of water, floods, or crop losses: As a consequence of climate change, pronounced periods of drought and rain are occurring more often and more intensively all around the Earth, causing human suffering and significant financial damage. The more precise seasonal forecasts for the coming months are, the more efficiently these impacts can be mitigated. A study team from Karlsruhe Institute of Technology (KIT) has been in a position to improve global forecasts using statistical methods so that they can be used in the regional level. The investigators describe the new strategy and the financial benefits of seasonal forecasts in the journals Earth System Science Data and Scientific Reports.

One of the consequences of global warming relates to more frequent and more extreme periods of drought or precipitation that are causing significant issues globally – such as in the supply of food, energy, or drinking water. Improved seasonal meteorological forecasts can be extremely useful here:”If we are able to predict rainfall amounts and temperatures more accurately for the weeks and months to come, local decision makers can, e.g., more proactively plan and manage reservoirs or seed selection for the planting season. In this way, they can reduce damage and losses,” says Professor Harald Kunstmann who works in the Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), KIT Campus Alpin, in Garmisch-Partenkirchen and in the University of Augsburg. Using statistical methods, he and his team have now been able to derive local predictions from global climate models which are more precise than the seasonal forecasts accessible to date. The researchers developed this method inside the framework of an global project known as”Seasonal Water Resource Management in Arid Regions” (SaWaM for short), that was financed by the German Federal Ministry of Education and Research (BMBF) and has been completed.

Regionalized Global Forecasts with Local Relevance

Until today, only international climate models are available in most cases in regards to send regional forecasts over a normal period of weeks or months. “For high-resolution seasonal forecasts, however, these models in their basic form are actually not suitable at all,” explains Dr. Christof Lorenz in the Campus Alpin of KIT, who’s a co-developer of their new method. The explanations for this are, amongst others, inconsistencies between predictions which use different start times and deviations from climatological reference data due to model errors. “Thanks to the statistical correction and regionalization procedures we developed, we can now derive seasonal forecasts that are many times more accurate,” says Lorenz. In the regions studied, such as Sudan, Ethiopia, Iran, northeastern Brazil, Ecuador, Peru, and West Africa, the new method enabled the investigators to predict abnormal heat and drought periods up to seven weeks in progress – with greater outcomes than ever before.

Thanks to their intense precision for preparing seasonal predictions, the new methods can now be put to practical use. “In particular, by providing early warning of wet or dry periods with an above-average extent, the improved forecast allows to initiate local measures to minimize damage in due time,” explains Tanja Portele, a participating climate researcher who works at the Campus Alpin of KIT and at the University of Augsburg. The scientists could demonstrate the economic value of their strategy with climate data from several years. “We’ve shown that seasonal drought forecasts when used in clinic can save up to 70 percent of the prices, which wou

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