Large scale climate and rainfall seasonality in a Mediterranean Area: Insights from a non‐homogeneous Markov model applied to the Agro‐Pontino plain

Abstract

AbstractIn the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and longer climate projections from coupled ocean–atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non‐Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro‐Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56‐years record (1951–2004) of daily rainfall measurements from 7 stations in Agro‐Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter‐annual and seasonal variability, as well as average and extreme rainfall patterns.Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed.