Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Global Mean Climate and Main Patterns of Variability in the CMCC‐CM2 Coupled Model
2018324 citationsPier Giuseppe Fogli, Tomas Lovato et al.profile →
CMIP6 Simulations With the CMCC Earth System Model (CMCC‐ESM2)
2022183 citationsTomas Lovato, Daniele Peano et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Silvio Gualdi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Silvio Gualdi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Silvio Gualdi more than expected).
This network shows the impact of papers produced by Silvio Gualdi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Silvio Gualdi. The network helps show where Silvio Gualdi may publish in the future.
Co-authorship network of co-authors of Silvio Gualdi
This figure shows the co-authorship network connecting the top 25 collaborators of Silvio Gualdi.
A scholar is included among the top collaborators of Silvio Gualdi based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Silvio Gualdi. Silvio Gualdi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Athanasiadis, Panos, Marie‐Estelle Demory, Alex R. Baker, et al.. (2018). The representation of North Atlantic eddy-driven jet and the associated E-vectors in PRIMAVERA historical simulations and the effect of model resolution.. EGUGA. 14084.1 indexed citations
Sanna, Antonella, Andrea Borrelli, Stefano Materia, et al.. (2015). The new CMCC - Seasonal Prediction System..1 indexed citations
13.
Materia, Stefano, Andrea Borrelli, Alessio Bellucci, et al.. (2014). Impact of atmosphere and land surface initial conditions on seasonal forecast of global surface temperature. EGUGA. 16409.3 indexed citations
Scoccimarro, Enrico, Silvio Gualdi, Antonella Sanna, Edoardo Bucchignani, & Myriam Montesarchio. (2011). Extreme events in high resolution CMCC regional and global climate models.1 indexed citations
16.
Gualdi, Silvio, Enrico Scoccimarro, Alessio Bellucci, et al.. (2010). Climate variability and change in the Euro-Mediterranean Region: results from a global AOGCM coupled with an interactive high-resolution model of the Mediterranean Sea. EGU General Assembly Conference Abstracts. 9100.1 indexed citations
17.
Williams, Paul D., Éric Guilyardi, Gurvan Madec, Silvio Gualdi, & Enrico Scoccimarro. (2009). The role of mean ocean salinity in climate. HAL (Le Centre pour la Communication Scientifique Directe). 285.1 indexed citations
18.
Sperber, Kenneth R., et al.. (2004). The Madden-Julian Oscillation in ECHAM4 Coupled and Uncoupled GCMs. Climate Dynamics. 25.12 indexed citations
19.
Gualdi, Silvio, et al.. (2002). Interannual Variability In The Tropical Indian Ocean As Simulated By A Cgcm. CentAUR (University of Reading). 6056.1 indexed citations
20.
Fischer, A. S., P. Délécluse, Silvio Gualdi, Éric Guilyardi, & Pascal Terray. (2002). Triggers for Tropical Indian Ocean Variability and Links to ENSO in a Constrained Coupled Climate Model. AGUFM. 2002.3 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.