Conference Agenda

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Session Overview
Session
CLIMA: Clima, cambiamenti climatici
Time:
Friday, 18/Feb/2022:
2:30pm - 4:00pm

Session Chair: Alessandro Ceppi
Session Chair: Veronica Manara

External Resource:
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Presentations
2:30pm - 2:45pm

Extreme precipitation events over northern Italy: Decadal trends

F. Grazzini1,3, G. Fragkoulidis2

1LMU Meteorologisches Institut München, Germany; 2JGU Institute for Atmospheric Physics Mainz, Germany; 3ARPAE-SIMC Bologna, Italy

Heavy precipitation events are a characteristic component of the Mediterranean hydrological cycle, accounting for a significant amount of total annual precipitation. Extreme rainfall can be generated by distinct meteorological phenomena. Therefore, it can be revealing to take into account their different dynamic evolution when studying trends. In this work, we analyze the observed trends of the three categories of extreme precipitation events identified by the authors in a previous classification: Cat1 - frontal and orographic precipitation, Cat2 - frontal with embedded convection, Cat3 - mostly convective cases. While a general increase in precipitation volumes is evident during all extremes events, a contrasting trend in frequency is emerging in the three categories. Positive trends of PV anomalies over northern-central Italy and water vapour transport over the North Atlantic are identified as likely contributing factors in the observed increase in Cat2 frequency over the examined period 1979-2020.



2:45pm - 3:00pm

A convection-permitting and limited-area model hindcast driven by ERA5 data

V. Capecchi1, F. Pasi1,2, C. Brandini1,2, B. Gozzini1

1LaMMA - Laboratorio di Monitoraggio e Modellistica Ambientale per lo sviluppo sostenibile, Italy; 2IBE/CNR - Istituto per la BioEconomia, Italy

We describe a weather hindcast obtained by dynamically downscaling the ERA5 data. The models used to perform the hindcast are BOLAM (with a grid spacing of 7 km over the Mediterranean domain) and MOLOCH (with a grid spacing of 2.5 km over Italy). BOLAM is used to provide initial and boundary conditions to the inner grid of the MOLOCH model, which is set in a convection-allowing configuration. The period simulated is 1979-2020. The performances of such limited-area, high-resolution and long-term hindcast are evaluated comparing modelled data against observations for two near-surface variables: precipitation and 2-metre temperature. Any potential added-value of the BOLAM/MOLOCH hindcast is assessed with respect to the ERA5-Land dataset, which is used as a benchmark. Results demonstrate that the BOLAM/MOLOCH hindcast perform better than ERA5-Land data as regards the mean annual accumulated precipitation with a root mean square error of approximately 300 mm vs 315 mm. On the other hand, ERA5-Land data provide more accurate information as regards 2-metre temperature, although the bias indicates an overestimation of observed temperatures. Moreover, by evaluating the outputs for selected historical precipitation events, we show that the use of the convection-permitting MOLOCH model is beneficial to reduce the positioning and intensity errors of the precipitation forecasts. We finally discuss how the demonstrated reliability of the BOLAM and MOLOCH models associated to the relatively low computational cost, promote their use as a valuable tool for downscaling not only reanalyses but also climate projections.



3:00pm - 3:15pm

Il ruolo della morfologia sulla distribuzione spaziale degli estremi di pioggia sub-giornalieri italiani

P. Mazzoglio1, I. Butera1, M. Alvioli2, P. Claps1

1Dipartimento di Ingegneria dell'Ambiente, del Territorio e delle Infrastrutture, Politecnico di Torino, Torino, Italia; 2Istituto di Ricerca per la Protezione Idrogeologica, Consiglio Nazionale delle Ricerche, Perugia, Italia

L’influenza della morfologia e della quota sulla distribuzione delle piogge estreme, sebbene ampiamente documentata in letteratura, non è ancora stata analizzata approfonditamente sull’intero territorio italiano. In questo studio proponiamo alcune analisi delle relazioni fra morfologia e valori medi degli estremi annui di pioggia di durata sub-giornaliera (da 1 a 24 ore), usando un nuovo dataset di misure pluviometriche (Improved Italian – Rainfall Extreme Dataset, I2-RED). I2-RED contiene osservazioni di più di 5000 pluviometri dal 1916 al 2019, ottenute dall’unione di molte banche dati indipendenti.
La dipendenza degli estremi di pioggia dalla quota e da altre variabili geomorfologiche è stata analizzata complessivamente a scala nazionale mediante relazioni di regressione multipla. Tale analisi ha dimostrato che la quota del terreno non è la sola variabile che influenza la variabilità degli estremi: longitudine, latitudine, distanza dalla costa, ostruzioni morfologiche e pioggia media annua influiscono infatti significativamente, con un contributo che varia a seconda della durata degli eventi (da 1 a 24 ore).
Significative distorsioni locali in aree con morfologia complessa hanno fatto poi emergere l’utilità di scorporare l’analisi delle relazioni usando una scala di maggiore dettaglio e criteri morfologici di segmentazione del territorio nazionale. Sono state usate classificazioni geomorfologiche di letteratura e, nelle aree omogenee da esse definite, si è indagata la variabilità della pioggia estrema con la sola quota. I risultati ottenuti hanno dimostrato che l’uso di relazioni locali fra pioggia e quota produce livelli locali di distorsione molto bassi e ottima efficienza di stima, risultando quindi più rappresentative delle relazioni multivariate ottenute a scala nazionale.



3:15pm - 3:30pm

New perspectives on climate services for energy

M. Petitta1,2, M. Callegari2, F. Catalano1, I. Cionni1, A. Crespi2, M. Palma1

1ENEA,SSPT-MET-CLIM, Roma, Italy; 2EURAC Research, Institute for Earth Observation, Bolzano/Bozen, Italy

In the last 10 years, climate services have attracted the interest of local stakeholders, political administrators, and industrial actors. This interest increases pressure on the need to present climate data and predictions in a trustworthy manner and as certified and credible information that can be understood and managed not only by the scientific community but also by a wider audience such as institutional actors and private companies.

Energy is the sector where the application of climate services is already more mature. Several public and private institutions are now beginning to incorporate the results of climate services research into their decision-making processes.

Here we present the activities carried out in the framework of the European H2020 project SECLI-FIRM (http://www.secli-firm.eu/). The aim of SECLI-FIRM was to develop tailored and innovative seasonal climate prediction products which can reduce operational costs and risks for European industries that are sensitive to climate variability. The activities were performed in collaboration with a wide range of industry partners in a number of case studies in Europe. In particular, we have developed innovative methodologies to exploit seasonal forecasts of key climate variables in order to provide relevant information for the energy sector and assess the added value of the integration of forecasts out to several months in energy production and management.

We will present the developed methods and main results in tailoring seasonal climate predictions for energy-related applications in Europe. In particular, we analyse the capabilities and accuracy of the current ECMWF SEAS5 operational seasonal forecasting system in detecting extreme events, we present improvements in the multi-model approach and the ability of downscaling and bias-adjustment techniques to improve the quality of derived climate services and their integration in case-study applications.



3:30pm - 3:45pm

Snow Cover Variability in the Greater Alpine Region in the MODIS Era (2000–2019)

D. Fugazza, V. Manara, A. Senese, G. Diolaiuti, M. Maugeri

Department of Environmental Science and Policy, Università degli studi di Milano, Italy

Snow cover is particularly important in the Alps for tourism and the production of hydroelectric energy. In this study, we investigate the spatiotemporal variability in three snow cover metrics, i.e., the length of season (LOS), start of season (SOS) and end of season (EOS), obtained by gap-filling of MOD10A1 and MYD10A1, daily snow cover products of MODIS. We analyze the period 2000–2019, evaluate snow cover patterns in the greater Alpine region (GAR) as a whole and subdivide it into four subregions based on geographical and climate divides to investigate the drivers of local variability. We found differences in space and time, with the northeastern region having generally the highest LOS (74 ± 4 days), compared to the southern regions, which exhibit a shorter snow duration (48/49 ± 2 days). Spatially, the variability in LOS and other metrics is clearly related to elevation (r2 = 0.85 for the LOS), while other topographic (slope, aspect and shading) and geographic variables (latitude and longitude) play a less important role at the MODIS scale. A high interannual variability was also observed from 2000 to 2019, as the average LOS in the GAR ranged between 41 and 85 days. No significant trends in snow cover metrics were seen over the GAR when considering all grid cells. Considering 500-m elevation bands and subregions, as well as individual grid points, we observed significant negative trends above 3000 m a.s.l., with an average of −17 days per decade. While some trends appeared to be caused by glacierized areas, removing grid cells covered by glaciers leads to an even higher frequency of grid cells with significant trends above 3000 m a.s.l., reaching 100% at 4000 m a.s.l. Trends are however to be considered with caution because of the limited length of the observation period.



3:45pm - 4:00pm

Quantifying drought risk through multiple large-ensembles: the case of Cape Town 2015-17 and Central America 2015-19 multiyear droughts.

S. Pascale1, S. Kapnick2, T. Delworth2, W. Cooke2, H. Hidalgo3

1Università di Bologna, Italy; 2Geophysical Fluid Dyanamics Laboratory, USA; 3University of Costa Rica, San Jose, COsta Rica

In regions with highly seasonal rainfall regimes (e.g., Mediterranean and monsoonal climates), multiyear meteorological droughts are extreme events which have large impacts on people and the economy as they put enormous stress on water resources. Two well-known, recent examples of these kind of extremes are the Cape Town “Day Zero” winter drought (2015-2017) and the Central America 2015-2019 summer drought. However, it is not a trivial task to determine if anthropogenic climate change played any role in these events, given the shortness of rainfall observational records. In this talk, using different Large Ensembles (LEs), some of which at relatively high horizontal resolutions, I will discuss to what extent the prolonged rainfall deficits associated with these two events were affected by human-caused climate change.



 
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