大约在20世纪90年代中期，作者建立了一个基础广泛的研究计划，目的是更好地了解意大利气候在过去100/150年的演变。该计划是在欧洲（UE IMPROVE和ALPCLIM项目）和国家项目（国家研究委员会（CNR）项目“重建地中海地区过去的气候”）内制定的。目前，该项目正在意大利农业和林业政治部的一个项目"Progetto Finalizzato CLIMAGRI"的范围内进行。此外，在今后两年中，将在"与全球气候变化现象有关的当地气候变异性"研究方案范围内开展进一步的活动，该方案由意大利"Minisaper l'Istruzione，l'Universita 'e la Ricerca"和热那亚、米兰、的里雅斯特、都灵和乌迪内大学资助。迄今为止进行的研究提高了意大利数据的可获得性和质量，并提供了关于过去100/150年温度、降水和其他一些参数演变的令人感兴趣的信息。本文总结了研究计划中取得的主要成果。
英文摘要：
Around the mid 1990s, the authors set up a broad-based research program with the aim of better understanding the evolution of Italian climate in the last 100/150 years. The program was developed both within European (UE IMPROVE and ALPCLIM projects) and National projects (National Research Council (CNR) project "Reconstruction of the Past Climate in the Mediterranean area"). At present it is in progress within the "Progetto Finalizzato CLIMAGRI", a project of the Italian "Ministero per le Politiche Agricole e Forestali". Moreover, in the next two years, further activities will be performed within the research program "Local climate variability in relation to global climatic change phenomena" funded by the Italian "Ministero per l'Istruzione, l'Universita' e la Ricerca" and by Genoa, Milan, Trieste, Turin and Udine Universities. The studies so far carried out have improved the availability and the quality of Italian data and have produced interesting information on the evolution of temperature, precipitation and some other parameters in the last 100/150 years. The paper summarises the main results obtained within the research program.
During the night between 9 and 10 September 2017, multiple flash floods associated to a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours, associated with a return period higher than 200 years, caused all the largest streams of the Livorno municipality to flood several areas of the town. We used the limited-area Weather Research and Forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging…
Precipitation extremes intensify in most regions in climate-model projections. Changes in vertical velocities contribute to the changes in intensity of precipitation extremes but remain poorly understood. Here, we find that mid-tropospheric vertical velocities in extratropical precipitation extremes strengthen overall in simulations of 21st-century climate change. For each extreme event, we solve the quasi-geostrophic omega equation to decompose this strengthening into different physical contributions. We first consider a dry decomposition in which latent heating is treated as an external forc…
We investigate the potential added value of running three limited-area ensemble systems (with the WRF, Meso-NH and MOLOCH models and a grid spacing of approximately 2.5 km) for two heavy-precipitation events in Italy. Such high-resolution ensembles include an explicit treatment of convective processes and dynamically downscale the ECMWF global data, which have a grid spacing of approximately 18 km. The predictions are verified against rain-gauge data and their accuracy is evaluated over that of the driving coarser-resolution ensemble system. Furthermore, we compare the simulation speed (define…
Climate projection uncertainty can be partitioned into model uncertainty, scenario uncertainty and internal variability. Here, we investigate the different sources of uncertainty in the projected frequencies of daily maximum temperature and precipitation extremes, which are defined as events that exceed the 99.97th percentile. This is done globally using initial-condition large ensembles. For maximum temperature extremes, internal variability dominates in the next two decades. Around the middle of the 21st century model and scenario uncertainty become the dominant contribution in the tropics b…
Seasonal cycle of Precipitation over Major River Basins in South and Southeast Asia: A Review of the CMIP5 climate models data for present climate and future climate projections
Monthly streamflow records from a set of gauging stations, selected to form a reference hydrologic network, are analyzed together with precipitation and temperature data to establish whether the streamflows in the Guadalquivir River Basin have experienced changes during the last half of the XXth century that can be attributed to hydrological forcing. The observed streamflows in the reference network have undergone generalized and significant decreases both at seasonal and annual scales during the study period. Annual rainfall, though, did not experienced statistically significant changes. The …
An intercomparison of generative machine learning methods for downscaling precipitation at fine spatial scales
Earth Sciences New Zealand（新西兰地球科学）；School of Computer Science, University of Auckland（奥克兰大学计算机科学学院）；NVIDIA AI Technology Centre, NVIDIA Corporation（英伟达人工智能技术中心，英伟达公司）；ARC Centre of Excellence for Weather of the 21st Century & Climate Change Research Centre, University of New South Wales（新南威尔士大学21世纪天气卓越研究中心及气候变化研究中心）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（联邦科学与工业研究组织（CSIRO））；Laboratory for Turbulence Research in Aerospace and Combustion, Department of Mechanical and Aerospace Engineering, Monash University（莫纳什大学机械与航空航天工程系航空与燃烧湍流研究实验室）
Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951-2014) and projected (2015-2100) climate for the four scenarios (SSP126, SSP245, SS…
This paper investigates the impacts of climate change on groundwater droughts making use of regional projections and standardized indices: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Groundwater Index (SGI). The method adopted, using historical precipitation and temperature data and water levels collected in monitoring wells, first investigates the possible correlations between meteorological and groundwater indices at each well. Then, if there is a correlation, a linear regression analysis is used to model the…
