6 results on '"Palazzi, Elisa"'
Search Results
2. Snowpack Changes in the Hindu Kush–Karakoram–Himalaya from CMIP5 Global Climate Models
- Author
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Terzago, Silvia, von Hardenberg, Jost, Palazzi, Elisa, and Provenzale, Antonello
- Published
- 2014
3. Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments.
- Author
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Terzago, Silvia, Andreoli, Valentina, Arduini, Gabriele, Balsamo, Gianpaolo, Campo, Lorenzo, Cassardo, Claudio, Cremonese, Edoardo, Dolia, Daniele, Gabellani, Simone, von Hardenberg, Jost, Morra di Cella, Umberto, Palazzi, Elisa, Piazzi, Gaia, Pogliotti, Paolo, and Provenzale, Antonello
- Subjects
ATMOSPHERIC models ,SNOW ,METEOROLOGICAL stations ,SNOW accumulation ,MOUNTAINS ,TIME series analysis - Abstract
Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- and 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
4. Current Status and Future Projections of the Snow Depth in the Third Pole from CMIP5 Global Climate Models
- Author
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Palazzi Elisa, Provenzale Antonello, Terzago Silvia, and Von Hardenberg Jost
- Subjects
Plateau ,geography.geographical_feature_category ,Third pole ,CMIP5 global climate models ,Elevation ,Climate change ,Snow depth ,Snowpack ,Snow ,Hindu-Kush Karakorum Himalaya ,Current (stream) ,Geography ,General Circulation Model ,Climatology ,China - Abstract
The Tibetan plateau and the Hindu-Kush Karakoram Himalaya mountains, with mean elevation above 4,000 m a.s.l., are the world’s largest snow and ice reservoir outside the polar regions and they are often referred to as the “Third Pole”. These mountains provide water to about 1.5 billion people in Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal and Pakistan, and changes in snow dynamics would impact on water availability for downstream populations. Despite its importance, the knowledge on the snow dynamics in the Third Pole region is still incomplete, due to difficult and sporadic surface observations. In this work we investigate how CMIP5 Global Climate Model (GCM) simulations represent the snowpack in the Third Pole environment and we compare the results to the ERA-Interim/Land reanalysis. Then we discuss the historical snow depth trends and the projections for the XXI century under RCP8.5 scenario.
- Published
- 2015
5. The spatiotemporal variability of precipitation over the Himalaya: evaluation of one-year WRF model simulation.
- Author
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Norris, Jesse, Carvalho, Leila, Jones, Charles, Cannon, Forest, Bookhagen, Bodo, Palazzi, Elisa, and Tahir, Adnan
- Subjects
METEOROLOGICAL precipitation ,METEOROLOGICAL research ,RAINFALL ,CYCLONES ,SNOW - Abstract
The Weather Research and Forecasting (WRF) model is used to simulate the spatiotemporal distribution of precipitation over central Asia over the year April 2005 through March 2006. Experiments are performed at 6.7 km horizontal grid spacing, with an emphasis on winter and summer precipitation over the Himalaya. The model and the Tropical Rainfall Measuring Mission show a similar inter-seasonal cycle of precipitation, from extratropical cyclones to monsoon precipitation, with agreement also in the diurnal cycle of monsoon precipitation. In winter months, WRF compares better in timeseries of daily precipitation to stations below than above 3-km elevation, likely due to inferior measurement of snow than rain by the stations, highlighting the need for reliable snowfall measurements at high elevations in winter. In summer months, the nocturnal precipitation cycle in the foothills and valleys of the Himalaya is captured by this 6.7-km WRF simulation, while coarser simulations with convective parameterization show near zero nocturnal precipitation. In winter months, higher resolution is less important, serving only to slightly increase precipitation magnitudes due to steeper slopes. However, even in the 6.7-km simulation, afternoon precipitation is overestimated at high elevations, which can be reduced by even higher-resolution (2.2-km) simulations. These results indicate that WRF provides skillful simulations of precipitation relevant for studies of water resources over the complex terrain in the Himalaya. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
6. Sensitivity of snow models to the accuracy of the meteorological forcing in a mountain environment.
- Author
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Terzago, Silvia, Pogliotti, Paolo, Cremonese, Edoardo, Cella, Umberto Morra di, Gabellani, Simone, Piazzi, Gaia, Dolia, Daniele, Cassardo, Claudio, Andreoli, Valentina, Hardenberg, Jost von, Palazzi, Elisa, and Provenzale, Antonello
- Subjects
- *
SNOW accumulation , *ATMOSPHERIC models , *SNOWPACK augmentation , *SNOW cover , *SNOW , *MOUNTAINS , *TIME series analysis - Abstract
A wide range of snow models with different degrees of complexity is currently available, and typically the higher is the model sophistication, the heavier are input data requirements and computational costs. The choice of an appropriate degree of complexity depends on the specific purpose. Multi-layer, physically-based snow models are typically used to reconstruct the vertical structure of the snowpack with a high level of detail and high accuracy, for example useful for avalanche risk forecasting, while simpler physical or empirical snow models are employed as one component of a modelling chain, or when a coarse estimate of snow cover characteristics is sufficient. Among the major challenges for cryospheric modelling research are i) the quantification of the snow model complexity which is needed to achieve accurate estimates of snow mass in different modelling frameworks and ii) the assessment of how the accuracy of the meteorological variables used to force snow models affects the quality of snow simulations. The latter aspect is particularly crucial in high mountain environments, where the variability of meteorological parameters is high both in space and time. In the present work five snow models of different complexity – the multi-layer models SNOWPACK and GeoTOP, the intermediate complexity models UTOPIA and SMASH, and the relatively simpler model S3M – are compared to assess their ability in reproducing the temporal evolution of snow water equivalent, snow density and snow depth, at the experimental site of Torgnon, 2160 m a.s.l. in the Western Italian Alps. High-quality meteorological forcing and detailed characterization of snowpack properties are available for this measurement site. First, we evaluate the models forced by the accurate Torgnon station measurements at 30 minute temporal resolution, to measure the model skills in case of "optimal" forcing. Second, we force the models with input data with gradually lowered frequency and/or accuracy, which are obtained by spatial interpolation of neighboring station measurements and from three global reanalyses (ERA-Interim, ERA5 and GLDAS) by extracting the meteorological time series at the gridpoint closest to the Torgnon station.This study provides information on how sensitive the snow models are to the accuracy of forcing data, exploring the feasibility of driving these models with coarser spatial and temporal resolution datasets, including interpolation of surface station measurements and reanalyses, typically the only data available in remote mountain areas. Guidelines on the trade-offs between model complexity and model performances are also provided, with the perspective of employing the best performing models to simulate past and future snowpack conditions at fine spatial scales. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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