Data-driven discovery of mechanisms underlying present and near-future precipitation changes and variability in Brazil
Abstract. Untangling the complex network of physical processes driving regional precipitation regimes in the present (1979–2014) and near-future climates (2020–2050) is fundamental to support a more robust scientific basis for decision making in the water-energy-food nexus. We propose a data-driven mechanistic approach to: (Goal 1) identify changes and variability of the regional precipitation mechanisms and (Goal 2) reduce the ensemble spread of future projections by weighting and filtering models that satisfactorily represent these drivers in present climate. Goal 1 is achieved by applying the Partial Least Squares (PLS) technique, a two-sided variant of principal component analysis (PCA), on a reanalysis dataset and 30 simulations of the future climate submitted to CMIP6 to discover the links between global sea-surface temperature (SST) and precipitation in Brazil. Goal 2 is achieved by selecting and weighting the future climate simulations from climate models that better represent the dominant modes discovered by the PLS in the present climate; with this subset of climate simulation, we produce precipitation change maps following IPCC’s WG1 methodology. The main mechanistic link discovered by the technique is that the generalised warming of the oceans promotes a suppression of precipitation in Northeast and Southeast Brazil, possibly mediated by the intensification of the Hadley circulation. We show that this pattern of precipitation suppression is stronger in the near-future precipitation change maps produced using our methodology. This demonstrates that a reduction of epistemic uncertainty is achieved after we select models that skillfully represent these mechanisms in the present climate. Therefore, the approach is capable of supporting both a quantitative analysis of regional changes as well as the construction of storylines supported by mechanistic evidence.
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