Peano Daniele

Institute for Earth System Predictions
Earth System Modelling and Data Assimilation Division

via C. Berti Pichat 6/2 - 40127 Bologna, Italy

+39 051 0301600

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Dr. Daniele Peano (male) is a Scientist at CMCC in the “Earth System Modelling and Data Assimilation” (ESYDA) Division.

He has experience in ice sheet, land surface and climate modelling.

He has contributed to the development of the land component of the CMCC global coupled model with a specific focus on the land biogeochemical cycle (CMCC-CM2, CMCC-ESM2), high-resolution configurations (CMCC-CM2-HR4, CMCC-CM2-VHR4), and he is working on the development of the new CMCC coupled model (CMCC-CM3).

He has contributed to various International projects (i.e. CRESCENDO, PRIMAVERA) the sixth phase of the Coupled Model Intercomparison Projects (CMIP6), and some of CMIP6 specific MIPs (LUMIP, LS3MIP, C4MIP, HighResMIP).

He is nowadays involved in various international projects (e.g. GoNEXUS as CMCC PI, CONCERTO as WP leader), and CMCC activities for the new CMIP phase (CMIP7).

ULTIME PUBBLICAZIONI
Soil-moisture-atmosphere coupling hotspots and their representation in seasonal forecasts of boreal summer
TN0301 - The new CMCC Seasonal Prediction System SPS4
Biogeochemical versus biogeophysical temperature effects of historical land-use change in CMIP6
Methodological Framework for the Evaluation of Climate Change Impacts on Rural Basins Using the GR2M Model
Air temperature and precipitation constraining the modelled wetland methane emissions in a boreal region in Northern Europe
Adaptation measures to global change in the Serpis River Basin (Spain): An evaluation considering agricultural benefits, environmental flows, and invasive fishes
Near-global summer circulation response to the spring surface temperature anomaly in Tibetan Plateau –– the GEWEX/LS4P first phase experiment
The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system
End-to-End Workflows for Climate Science: Integrating HPC Simulations, Big Data Processing, and Machine Learning
Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation – Initialization and Improved Prediction
Vedi tutti gli articoli & papers di Peano Daniele