|The BINGO project has received funding from the European Union's Horizon 2020 Research and Innovation programme, under the Grant Agreement number 641739.|
To study short- and long-term changes in climate, BINGO used climate models to simulate the present climate, as well as ten-year predictions and future projections.
BINGO used pre-existing climate data – ERA-Interim reanalysis (1979-2015) and MiKlip decadal ensemble predictions (2015-2024) – as a basis for dynamical downscaling with a regional climate model, covering all research sites. This produced the higher spatial (12 km) and temporal (up to hourly) resolutions of the hydrological variables required by each research site for their own hydrological modelling activities.
The climate data produced in BINGO were disseminated via an online “Data Extraction and COnversion” tool ( DECO ) at the Freie Universität Berlin’s Freva portal. The DECO tool, developed within BINGO, converts standardized climate data into formats specific for use with a selected hydrological model and also offers bias correction of the data.
BINGO focused particularly on the representation of extreme events, for which higher spatial resolution data offer considerable added value. In subsequent steps, O(1 km) data were thus produced from the 12 km simulations by (i) further dynamically downscaling episodes with a higher chance of extreme precipitation, and (ii) developing a stochastic weather generator to inexpensively generate ensembles for each site.
The production of climate data within BINGO can be summarized in two main phases:
Pre-existing EURO-CORDEX climate projections were also integrated into the project and spatial maps of intensity-duration-frequency curves were produced.
Dynamical downscaling in BINGO was performed with the COSMO-CLM regional climate model. Using ERA-Interim reanalysis as boundary forcing, a regionalization of the past and present climate (1979-2015) over Europe was produced at 12 km resolution, with a temporal resolution of 1- or 3-hours depending on the variable ; the years 1989-2008 had already been simulated as part of the EURO-CORDEX project. At the same resolution, a ten-member ensemble of MiKlip decadal predictions (2015-2024) was produced over sub-regions of Europe encompassing the research sites.
The 12 km simulations were bias corrected using the CDF-transform method and the best available reference data (deliverablesD2.1 and D2.2) . Users then had the option to receive data via DECO either with or without bias correction.
The long record for the past and a decade ahead produced by BINGO served to determine average conditions as a baseline to define floods and droughts and to provide data for successive use in the integrated analysis phase.
The largest added value in very-high-resolution O(1 km) climate data is found in the representation of precipitation extremes. Such data are, however, computationally expensive to produce via dynamical downscaling. BINGO thus adopted a two-pronged approach to producing O(1 km) climate data at affordable computational expense.
In the first approach, large-scale weather patterns with an elevated risk of extreme precipitation were identified from observations and reanalysis via a clustering algorithm, for each research site. Local-scale meteorological predictors of intense precipitation are also identified for each site. These factors were then combined in a classification algorithm (Meredith et al., 2018), which tests each day in the 12 km simulations for an enhanced risk of intense precipitation. Identified “potential extreme days” are then dynamically downscaled to 2.2 km resolution at hourly frequency. The resulting catalogue of O(1 km) extremes at each site is ideal for stress-testing of hydraulic infrastructure, design situations and process-orientated case studies.
In the second approach, a stochastic weather generator was produced. This uses the 12 km simulations as boundary conditions to inexpensively produce large ensembles of BINGO-relevant hydrological variables at 1 km and hourly resolutions, which are consistent with the large-scale situation in the forcing data. Such large ensembles of continuous data would not be computationally feasible via dynamical downscaling.
While BINGO’s main focus is decadal predictions, the EURO-CORDEX archive was also exploited to study climate projections up to 2100 under different climate scenarios (RCP2.6, RCP4.5 and RCP8.5). For inter-comparability, 12 km simulations which used the same global and regional models as in the decadal predictions were added to the DECO platform at daily resolution. Bias correction of these data was also provided.
Spatial maps of return-levels for extreme precipitation events for the research sites Badalona, Bergen, Tagus and Wupper – for various return-periods and event durations – were also computed. The return-levels were obtained based on a duration-dependent Generalized Extreme Value distribution with spatial covariates.
* This project deliverable (in particular, values for periods 2009-2015 and 1979-1989) can be made available upon request. If you’re interested in having access to the data, get in contact with us .