EGU 2023 General Assembly

Water management received increasing attention in the last decades since it is a key to coping with climate change and global warming. Within this framework, water scarcity will be one of the main issues to be addressed by humans, mainly because of its subsequent effects on, but not limited to, the agricultural sector. To tackle this challenge, the Drought Observatory (DO) of CNR-IBE developed an operational climate service to provide seasonal forecasting, of the Standardized Precipitation Index (SPI) to support drought risk management over the Mediterranean area.

The forecast tool stands on the most recent and evolute version of the ECMWF numerical seasonal forecast system, named SEAS, (5 and 5.1). Each month, from 2017 onwards, SEAS5 provides an ensemble of 51 members of daily simulations, lasting seven months each; these simulations are freely accessible from the Copernicus Data Store (CDS). In addition, from 1981 to 2016, CDS provided a hindcast of 25 members simulation runs (named System 4). SEAS daily precipitation seasonal forecasts, with a horizontal resolution of 1°x1°, are then bias adjusted using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset (version 2.8). MSWEP is a global precipitation product with an original 3-hourly, 0.1° resolution available from 1979 to the present; it merges gauges, satellite, and reanalysis data to obtain high-quality precipitation estimates at every location. The bias adjustment is computed using the CSTools R Package (CSTools: Assessing Skill of Climate Forecasts on Seasonal-to-Decadal Timescales) applying a quantile-quantile mapping algorithm. This algorithm adjusts/corrects the quantiles of the modeled distribution (the raw SEAS5 daily precipitation distribution) by using an observed distribution set as a reference (the MSWEP daily precipitation distribution). Thus each SEAS5 grid-points of each ensemble member is 1) reprojected onto the highest resolution MSWEP dataset, and then 2) the resulting high-resolution daily time-series precipitation distribution is adjusted using a quantile transformation. A 1981 – 2016 period is selected to adjust and train the quantile mapping algorithm. From the resulting high resolution and bias-adjusted daily rainfall forecast dataset, we then compute the SPI index for a series of timescales: 1, 3, and 6 months, for the period 1981 onwards.

From the verification analysis seasonal forecast skills vary on time and geographical areas. It is thus possible to identify windows of opportunity for specific tasks in cooperation with users. Within this framework, bias-corrected seasonal forecasts are valuable supporting information for water resources management and decision-making processes. During the drought that occurred in the summer of 2022, the DO was widely used by national and international media to deliver accurate information on the drought trend. This fact underlines the need for timely and science-based data to inform also the wider public.

These new bias-adjusted forecasts, along with the empirical seasonal forecasts and other monitoring drought and vegetation indices, will be freely accessible through the Drought Observatory Climate Service (https://droughtcentral.it).