Near term climate change in Emilia-Romagna (Italy) using CMIP6 decadal climate predictions

Abstract

<p>Ongoing climate change makes both short- and long-term adaptation and mitigation strategies urgently needed. While many long-term climate models have been developed and investigated in recent years, little attention has been paid to short-term simulations. The first attempts to perform multi-model initialized decadal forecasts were presented in the fifth Coupled Model Intercomparison Project 5 (CMIP5). Near-term climate prediction models are new socially relevant tools to support the decision makers delivering climate adaptation solutions on an annual or decadal scale. Recent improvements in decadal models were coordinated in CMIP6 and the World Climate Research Program (WCRP) Grand Challenge on Near Term Climate Prediction, as part of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6, IPCC). The Decadal Climate Prediction Project (DCPP) provides decadal climate forecasts based on advanced techniques for the reanalysis of climate data, initialization methods, ensemble generation and data analysis. The initialization allows to consider the predictability of the internal climate variability reducing the prediction errors compared to those of the long-term projections, whose simulations do not take into account the phasing between the internal variability of the model and the observations. The aim of this work is to assess the near-future climate change in the Emilia-Romagna region in northern Italy until 2031. The hydrological variables analyzed are the daily precipitation and maximum and minimum temperature. An ensemble of models, with the highest resolution available, is used to handle the uncertainty in the predictions. Initially, to assess the reliability of the selected climate models, the hindcast data of the DCPP are checked against observations. Then, the DCPP predictions are used to investigate the variability of precipitation and temperature in the near future over the investigated area. Some climate features that are referenced to have an important impact on human health and activities are evaluated, such as drought indices and heat waves.</p>

A Deep Learning-Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non-linearity and Non-Gaussianity Challenges

“…As for the latter, researchers have proposed to re-parameterize non-Gaussian variables to conform to Gaussian distributions through techniques like normal-score transformation (Li et al, 2018), Gaussian anamorphosis (Schöniger et al, 2012;Todaro et al, 2023), principal component analysis (Vo & Durlofsky, 2014), discrete cosine transformation (Jung et al, 2017), level set (Chang et al, 2010), or DL (Canchumuni et al, 2017;Z. Han et al, 2022).…”