Lorenzo Zampieri

@cmcc.it

Ocean modeling and Data Assimilation Section
Euro-Mediterranean Center on Climate Chnage

Lorenzo Zampieri


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https://researchid.co/lore-zamp

RESEARCH, TEACHING, or OTHER INTERESTS

Geophysics, Modeling and Simulation
20

Scopus Publications

642

Scholar Citations

13

Scholar h-index

17

Scholar i10-index

Scopus Publications

  • Drivers of Arctic and Antarctic sea ice variability
    Patricia DeRepentigny, Lorenzo Zampieri
    Comprehensive Cryospheric Science and Environmental Change, 2026
  • Numerical Weather Prediction Model Coupling—Strategies, Challenges, and Outlook
    Josh Kousal, Charles Pelletier, Jasper M. C. Denissen, Luise Schulte, Sarah Keeley, et al.
    Bulletin of the American Meteorological Society, 2026
  • Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
    Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, et al.
    Geoscientific Model Development, 2025
    We report on the first multi-year kilometre-scale global coupled simulations using ECMWF's Integrated Forecasting System (IFS) coupled to both the NEMO and FESOM ocean–sea ice models, as part of the H2020 Next Generation Earth Modelling Systems (nextGEMS) project. We focus mainly on an unprecedented IFS-FESOM coupled setup, with an atmospheric resolution of 4.4 km and a spatially varying ocean resolution that reaches locally below 5 km grid spacing. A shorter coupled IFS-FESOM simulation with an atmospheric resolution of 2.8 km has also been performed. A number of shortcomings in the original numerical weather prediction (NWP)-focused model configurations were identified and mitigated over several cycles collaboratively by the modelling centres, academia, and the wider nextGEMS community. The main improvements are (i) better conservation properties of the coupled model system in terms of water and energy budgets, which also benefit ECMWF's operational 9 km IFS-NEMO model; (ii) a realistic top-of-the-atmosphere (TOA) radiation balance throughout the year; (iii) improved intense precipitation characteristics; and (iv) eddy-resolving features in large parts of the mid- and high-latitude oceans (finer than 5 km grid spacing) to resolve mesoscale eddies and sea ice leads. New developments at ECMWF for a better representation of snow and land use, including a dedicated scheme for urban areas, were also tested on multi-year timescales. We provide first examples of significant advances in the realism and thus opportunities of these kilometre-scale simulations, such as a clear imprint of resolved Arctic sea ice leads on atmospheric temperature, impacts of kilometre-scale urban areas on the diurnal temperature cycle in cities, and better propagation and symmetry characteristics of the Madden–Julian Oscillation.
  • An Assessment of Subseasonal Prediction Skill of the Antarctic Sea Ice Edge
    Yuchun Gao, Yongwu Xiu, Yafei Nie, Hao Luo, Qinghua Yang, et al.
    Journal of Geophysical Research Oceans, 2024
    In this study, the subseasonal Antarctic sea ice edge prediction skill of the Copernicus Climate Change Service (C3S) and Subseasonal to Seasonal (S2S) projects was evaluated by a probabilistic metric, the spatial probability score (SPS). Both projects provide subseasonal to seasonal scale forecasts of multiple coupled dynamical systems. We found that predictions by individual dynamical systems remain skillful for up to 38 days (i.e., the ECMWF system). Regionally, dynamical systems are better at predicting the sea ice edge in the West Antarctic than in the East Antarctic. However, the seasonal variations of the prediction skill are partly system‐dependent as some systems have a freezing‐season bias, some had a melting‐season bias, and some had a season‐independent bias. Further analysis reveals that the model initialization is the crucial prerequisite for skillful subseasonal sea ice prediction. For those systems with the most realistic initialization, the model physics dictates the propagation of initialization errors and, consequently, the temporal length of predictive skill. Additionally, we found that the SPS‐characterized prediction skill could be improved by increasing the ensemble size to gain a more realistic ensemble spread. Based on the C3S systems, we constructed a multi‐model forecast from the above principles. This forecast consistently demonstrated a superior prediction skill compared to individual dynamical systems or statistical observation‐based benchmarks. In summary, our results elucidate the most important factors (i.e., the model initialization and the model physics) affecting the currently available subseasonal Antarctic sea ice prediction systems and highlighting the opportunities to improve them significantly.
  • Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses
    Francesco Cocetta, Lorenzo Zampieri, Julia Selivanova, Doroteaciro Iovino
    Cryosphere, 2024
    The recent development of data-assimilating reanalyses of the global ocean and sea ice enables a better understanding of the polar region dynamics and provides gridded descriptions of sea ice variables without temporal and spatial gaps. Here, we study the spatiotemporal variability of the Arctic sea ice area and thickness using the Global ocean Reanalysis Ensemble Product (GREP) produced and disseminated by the Copernicus Marine Service (CMS). GREP is compared and validated against the state-of-the-art regional reanalyses PIOMAS and TOPAZ, as well as observational datasets of sea ice concentration and thickness for the period 1993–2020. Our analysis presents pan-Arctic metrics but also emphasizes the different responses of ice classes, the marginal ice zone (MIZ), and pack ice to climate changes. This aspect is of primary importance since the MIZ accounts for an increasing percentage of the summer sea ice as a consequence of the Arctic warming and sea ice extent retreat, among other processes. Our results show that GREP provides reliable estimates of present-day and recent-past Arctic sea ice states and that the seasonal to interannual variability and linear trends in the MIZ area are properly reproduced, with the ensemble spread often being as broad as the uncertainty of the observational dataset. The analysis is complemented by an assessment of the average MIZ latitude and its northward migration in recent years, a further indicator of the Arctic sea ice decline. There is substantial agreement between GREP and reference datasets in the summer. Overall, GREP is an adequate tool for gaining an improved understanding of the Arctic sea ice, also in light of the expected warming and the Arctic transition to ice-free summers.
  • Development of a total variation diminishing (TVD) sea ice transport scheme and its application in an ocean (SCHISM v5.11) and sea ice (Icepack v1.3.4) coupled model on unstructured grids
    Qian Wang, Yang Zhang, Fei Chai, Y. Joseph Zhang, Lorenzo Zampieri
    Geoscientific Model Development, 2024
    As the demand for increased resolution and complexity in unstructured sea ice models is growing, higher demands are also placed on the sea ice transport scheme. In this study, we couple the Semi-implicit Cross-scale Hydro-science Integrated System Model (SCHISM, v5.11) with Icepack (v1.3.4), the column physics package of the Los Alamos sea ice model (CICE); a key step is to implement a total variation diminishing (TVD) transport scheme for the multi-class sea ice module in the coupled model. Compared with the second-order upwind scheme and the finite-element flux-corrected transport (FEM-FCT) scheme, the TVD transport scheme is overall superior when evaluated based on conservation, accuracy, efficiency (even with very high resolution), and strict monotonicity. Although it is slightly weaker than FEM-FCT in terms of accuracy alone, the TVD scheme still outperforms the other two schemes in comprehensive performance. The new coupled model outperforms the existing single-class ice model of SCHISM in the case of Lake Superior. For the Arctic Ocean case, it successfully reproduces the long-term changes in the sea ice extent, sea ice boundary, concentration observations from satellites, and thickness from in situ measurement.
  • Antarctic sea ice prediction with A convolutional long short-term memory network
    Xiaoran Dong, Qinghua Yang, Yafei Nie, Lorenzo Zampieri, Jiuke Wang, et al.
    Ocean Modelling, 2024
  • Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC
    Lorenzo Zampieri, David Clemens‐Sewall, Anne Sledd, Nils Hutter, Marika Holland
    Geophysical Research Letters, 2024
    Models struggle to accurately simulate observed sea ice thickness changes, which could be partially due to inadequate representation of thermodynamic processes. We analyzed co‐located winter observations of the Arctic sea ice from the Multidisciplinary Drifting Observatory for the Study of the Arctic Climate for evaluating and improving thermodynamic processes in sea ice models, aiming to enable more accurate predictions of the warming climate system. We model the sea ice and snow heat conduction for observed transects forced by realistic boundary conditions to understand the impact of the non‐resolved meter‐scale snow and sea ice thickness heterogeneity on horizontal heat conduction. Neglecting horizontal processes causes underestimating the conductive heat flux of 10% or more. Furthermore, comparing model results to independent temperature observations reveals a ∼5 K surface temperature overestimation over ice thinner than 1 m, attributed to shortcomings in parameterizing surface turbulent and radiative fluxes rather than the conduction. Assessing the model deficiencies and parameterizing these unresolved processes is required for improved sea ice representation.
  • Advances in Machine Learning Techniques Can Assist Across a Variety of Stages in Sea Ice Applications
    Clare Eayrs, Won Sang Lee, Emilia Jin, Jean-François Lemieux, François Massonnet, et al.
    Bulletin of the American Meteorological Society, 2024
  • A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses
    Lorenzo Zampieri, Gabriele Arduini, Marika Holland, Sarah P. E. Keeley, Kristian Mogensen, et al.
    Monthly Weather Review, 2023
    Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions. Significance Statement This study illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea ice–covered regions of the Arctic under clear-sky conditions. The correction applied to the temperature field is consistent with the local weather and the sea ice and snow conditions, meaning that it responds to seasonal changes in sea ice cover as well as to its long-term decline due to global warming. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.
  • Sea-Ice Forecasts With an Upgraded AWI Coupled Prediction System
    Longjiang Mu, Lars Nerger, Jan Streffing, Qi Tang, Bimochan Niraula, et al.
    Journal of Advances in Modeling Earth Systems, 2022
  • AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model
    Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, et al.
    Geoscientific Model Development, 2022
  • On the Importance of Representing Snow Over Sea-Ice for Simulating the Arctic Boundary Layer
    Gabriele Arduini, Sarah Keeley, Jonathan J. Day, Irina Sandu, Lorenzo Zampieri, et al.
    Journal of Advances in Modeling Earth Systems, 2022
  • Response of northern hemisphere weather and climate to arctic sea ice decline: Resolution independence in polar amplification model intercomparison project (pamip) simulations
    Jan Streffing, Tido Semmler, Lorenzo Zampieri, Thomas Jung
    Journal of Climate, 2021
  • Impact of Sea-Ice Model Complexity on the Performance of an Unstructured-Mesh Sea-Ice/Ocean Model under Different Atmospheric Forcings
    Lorenzo Zampieri, Frank Kauker, Jörg Fröhle, Hiroshi Sumata, Elizabeth C. Hunke, et al.
    Journal of Advances in Modeling Earth Systems, 2021
  • Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model
    Longjiang Mu, Lars Nerger, Qi Tang, Svetlana N. Loza, Dmitry Sidorenko, et al.
    Journal of Advances in Modeling Earth Systems, 2020
  • Sea Ice Targeted Geoengineering Can Delay Arctic Sea Ice Decline but not Global Warming
    Lorenzo Zampieri, Helge F. Goessling
    Earth S Future, 2019
  • Predictability of Antarctic Sea Ice Edge on Subseasonal Time Scales
    Lorenzo Zampieri, Helge F. Goessling, Thomas Jung
    Geophysical Research Letters, 2019
  • Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm
    Nils Hutter, Lorenzo Zampieri, Martin Losch
    Cryosphere, 2019
  • Bright Prospects for Arctic Sea Ice Prediction on Subseasonal Time Scales
    Lorenzo Zampieri, Helge F. Goessling, Thomas Jung
    Geophysical Research Letters, 2018

RECENT SCHOLAR PUBLICATIONS

  • Representing the Surface Ocean in ECMWF's data-driven forecasting system AIFS
    S Hahner, L Zampieri, JR Bidlot, P Browne, M Chantry, MCA Clare, ...
    arXiv preprint arXiv:2604.25559 , 2026
    2026
  • Numerical Weather Prediction Model Coupling—Strategies, Challenges, and Outlook
    J Kousal, C Pelletier, JMC Denissen, L Schulte, S Keeley, P Dueben, ...
    Bulletin of the American Meteorological Society 107 (1), E183-E189 , 2026
    2026
    Citations: 2
  • Learning Coupled Earth System Dynamics with GraphDOP
    E Boucher, M Alexe, P Lean, E Pinnington, S Lang, P Laloyaux, ...
    arXiv preprint arXiv:2510.20416 , 2025
    2025
    Citations: 1
  • Sea Ice Thickness, Drift, and Deformation Estimates from Airborne Laser Scanning Data and Machine Learning: Unveiling New Process Understanding
    N Hutter, L Zampieri, C Bitz, C Haas
    2025
  • Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2. 5 and NEMOv3. 4
    T Rackow, X Pedruzo-Bagazgoitia, T Becker, S Milinski, I Sandu, ...
    Geoscientific Model Development 18 (1), 33-69 , 2025
    2025
    Citations: 36
  • Drivers of Arctic and Antarctic sea ice variability
    P DeRepentigny, L Zampieri
    Elsevier , 2025
    2025
  • Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2. 5 and NEMOv3. 4, Geosci. Model Dev., 18, 33–69
    T Rackow, X Pedruzo-Bagazgoitia, T Becker, S Milinski, I Sandu, ...
    2025
    Citations: 33
  • Learning machine learning with lorenz-96
    D Balwada, R Abernathey, S Acharya, A Adcroft, J Brener, V Balaji, ...
    Journal of Open Source Education 7 (82), 241 , 2024
    2024
    Citations: 4
  • An assessment of subseasonal prediction skill of the Antarctic sea ice edge
    Y Gao, Y Xiu, Y Nie, H Luo, Q Yang, L Zampieri, X Lv, P Uotila
    Journal of Geophysical Research: Oceans 129 (11), e2024JC021499 , 2024
    2024
    Citations: 4
  • Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses
    F Cocetta, L Zampieri, J Selivanova, D Iovino
    The Cryosphere 18 (10), 4687-4702 , 2024
    2024
    Citations: 12
  • Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses
    F Cocetta, L Zampieri, J Selivanova, D Iovino
    The Cryosphere 18 (10), 4687-4702 , 2024
    2024
    Citations: 83
  • Development of a total variation diminishing (TVD) sea ice transport scheme and its application in an ocean (SCHISM v5. 11) and sea ice (Icepack v1. 3.4) coupled model on …
    Q Wang, Y Zhang, F Chai, YJ Zhang, L Zampieri
    Geoscientific Model Development 17 (18), 7067-7081 , 2024
    2024
    Citations: 1
  • Antarctic sea ice prediction with A convolutional long short-term memory network
    X Dong, Q Yang, Y Nie, L Zampieri, J Wang, J Liu, D Chen
    Ocean Modelling 190, 102386 , 2024
    2024
    Citations: 22
  • Modeling the winter heat conduction through the sea ice system during MOSAiC
    L Zampieri, D Clemens‐Sewall, A Sledd, N Hutter, M Holland
    Geophysical Research Letters 51 (8), e2023GL106760 , 2024
    2024
    Citations: 10
  • Advances in machine learning techniques can assist across a variety of stages in Sea Ice applications
    C Eayrs, WS Lee, E Jin, JF Lemieux, F Massonnet, M Vancoppenolle, ...
    Bulletin of the American Meteorological Society 105 (3), E527-E531 , 2024
    2024
    Citations: 9
  • GREP reanalysis captures the evolution of the Arctic Marginal Ice Zone across timescales
    F Cocetta, L Zampieri, J Selivanova, D Iovino
    EGUsphere 2024, 1-22 , 2024
    2024
    Citations: 1
  • A machine learning correction model of the winter clear-sky temperature bias over the Arctic sea ice in atmospheric reanalyses
    L Zampieri, G Arduini, M Holland, SPE Keeley, K Mogensen, MD Shupe, ...
    Monthly Weather Review 151 (6), 1443-1458 , 2023
    2023
    Citations: 31
  • lzampier/zampieri_2023_paper: Postprocessing for" Modelling the winter heat conduction through the sea ice system during MOSAiC"
    L Zampieri
    Zenodo , 2023
    2023
    Citations: 1
  • Sea‐ice forecasts with an upgraded AWI coupled prediction system
    L Mu, L Nerger, J Streffing, Q Tang, B Niraula, L Zampieri, SN Loza, ...
    Journal of Advances in Modeling Earth Systems 14 (12), e2022MS003176 , 2022
    2022
    Citations: 13
  • AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model
    J Streffing, D Sidorenko, T Semmler, L Zampieri, P Scholz, ...
    Geoscientific Model Development 15 (16), 6399-6427 , 2022
    2022
    Citations: 33

MOST CITED SCHOLAR PUBLICATIONS

  • Bright prospects for Arctic sea ice prediction on subseasonal time scales
    L Zampieri, HF Goessling, T Jung
    Geophysical Research Letters 45 , 2018
    2018
    Citations: 112
  • Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses
    F Cocetta, L Zampieri, J Selivanova, D Iovino
    The Cryosphere 18 (10), 4687-4702 , 2024
    2024
    Citations: 83
  • Predictability of Antarctic sea ice edge on subseasonal time scales
    L Zampieri, HF Goessling, T Jung
    Geophysical Research Letters 46 (16), 9719-9727 , 2019
    2019
    Citations: 51
  • Toward a data assimilation system for seamless sea ice prediction based on the AWI climate model
    L Mu, L Nerger, Q Tang, SN Loza, D Sidorenko, Q Wang, T Semmler, ...
    Journal of Advances in Modeling Earth Systems 12 (4), e2019MS001937 , 2020
    2020
    Citations: 43
  • Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2. 5 and NEMOv3. 4
    T Rackow, X Pedruzo-Bagazgoitia, T Becker, S Milinski, I Sandu, ...
    Geoscientific Model Development 18 (1), 33-69 , 2025
    2025
    Citations: 36
  • Sea ice targeted geoengineering can delay Arctic sea ice decline but not global warming
    L Zampieri, HF Goessling
    Earth's Future 7 (12), 1296-1306 , 2019
    2019
    Citations: 35
  • Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2. 5 and NEMOv3. 4, Geosci. Model Dev., 18, 33–69
    T Rackow, X Pedruzo-Bagazgoitia, T Becker, S Milinski, I Sandu, ...
    2025
    Citations: 33
  • AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model
    J Streffing, D Sidorenko, T Semmler, L Zampieri, P Scholz, ...
    Geoscientific Model Development 15 (16), 6399-6427 , 2022
    2022
    Citations: 33
  • A machine learning correction model of the winter clear-sky temperature bias over the Arctic sea ice in atmospheric reanalyses
    L Zampieri, G Arduini, M Holland, SPE Keeley, K Mogensen, MD Shupe, ...
    Monthly Weather Review 151 (6), 1443-1458 , 2023
    2023
    Citations: 31
  • Impact of sea‐ice model complexity on the performance of an unstructured‐mesh sea‐ice/ocean model under different atmospheric forcings
    L Zampieri, F Kauker, J Fröhle, H Sumata, EC Hunke, HF Goessling
    Journal of Advances in Modeling Earth Systems 13 (5), e2020MS002438 , 2021
    2021
    Citations: 30
  • On the importance of representing snow over sea‐ice for simulating the Arctic boundary layer
    G Arduini, S Keeley, JJ Day, I Sandu, L Zampieri, G Balsamo
    Journal of Advances in Modeling Earth Systems 14 (7), e2021MS002777 , 2022
    2022
    Citations: 28
  • Response of Northern Hemisphere weather and climate to Arctic sea ice decline: Resolution independence in Polar Amplification Model Intercomparison Project (PAMIP) simulations
    J Streffing, T Semmler, L Zampieri, T Jung
    Journal of Climate 34 (20), 8445-8457 , 2021
    2021
    Citations: 26
  • Antarctic sea ice prediction with A convolutional long short-term memory network
    X Dong, Q Yang, Y Nie, L Zampieri, J Wang, J Liu, D Chen
    Ocean Modelling 190, 102386 , 2024
    2024
    Citations: 22
  • Sea‐ice forecasts with an upgraded AWI coupled prediction system
    L Mu, L Nerger, J Streffing, Q Tang, B Niraula, L Zampieri, SN Loza, ...
    Journal of Advances in Modeling Earth Systems 14 (12), e2022MS003176 , 2022
    2022
    Citations: 13
  • Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses
    F Cocetta, L Zampieri, J Selivanova, D Iovino
    The Cryosphere 18 (10), 4687-4702 , 2024
    2024
    Citations: 12
  • Modeling the winter heat conduction through the sea ice system during MOSAiC
    L Zampieri, D Clemens‐Sewall, A Sledd, N Hutter, M Holland
    Geophysical Research Letters 51 (8), e2023GL106760 , 2024
    2024
    Citations: 10
  • AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model, Geosci. Model Dev., 15, 6399–6427, 10.5194
    J Streffing, D Sidorenko, T Semmler, L Zampieri, P Scholz, ...
    gmd-15-6399-2022 , 2022
    2022
    Citations: 10
  • Advances in machine learning techniques can assist across a variety of stages in Sea Ice applications
    C Eayrs, WS Lee, E Jin, JF Lemieux, F Massonnet, M Vancoppenolle, ...
    Bulletin of the American Meteorological Society 105 (3), E527-E531 , 2024
    2024
    Citations: 9
  • Linear Kinematic Features (leads & pressure ridges) detected and tracked in RADARSAT Geophysical Processor System (RGPS) sea-ice deformation data from 1997 to 2008
    N Hutter, L Zampieri, M Losch
    2019
    Citations: 7
  • Learning machine learning with lorenz-96
    D Balwada, R Abernathey, S Acharya, A Adcroft, J Brener, V Balaji, ...
    Journal of Open Source Education 7 (82), 241 , 2024
    2024
    Citations: 4