Arctic sea ice dynamics forecasting through interpretable machine learning

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors. Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution. Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers. Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term. The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region

Global Variability of Simulated and Observed Vegetation Growing Season

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On the role of Eurasian autumn snow cover in dynamical seasonal predictions

Seasonal predictions leverage on predictable or persistent components of the Earth system that can modify the state of the atmosphere. The land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of the snow in recent dynamical seasonal forecasts is therefore unclear. Here we assess the role of Autumn Eurasian snow cover in a set of five operational seasonal forecasts with large ensemble size and high resolution and with the help of targeted idealised simulations. Forecast systems reproduce realistically regional changes of the surface energy balance. Retrospective forecasts and idealised sensitivity experiments identify a coherent change of the circulation in the Northern Hemisphere. The main features of the atmospheric response are a wave-train downstream over the Pacific and North America and a signal in the Arctic. The latter does not emerge in reanalysis data but is compatible with a lagged but weak and fast feedback from the snow to the Arctic Oscillation

El Niño teleconnection to the Euro-Mediterranean late-winter: the role of extratropical Pacific modulation

El Niño Southern Oscillation (ENSO) represents the major driver of interannual climate variability at global scale. Observational and model-based studies have fostered a long-standing debate on the shape and intensity of the ENSO influence over the Euro-Mediterranean sector. Indeed, the detection of this signal is strongly affected by the large internal variability that characterizes the atmospheric circulation in the North Atlantic–European (NAE) region. This study explores if and how the low-frequency variability of North Pacific sea surface temperature (SST) may impact the El Niño-NAE teleconnection in late winter, which consists of a dipolar pattern between middle and high latitudes. A set of idealized atmosphere-only experiments, prescribing different phases of the anomalous SST linked to the Pacific Decadal Oscillation (PDO) superimposed onto an El Niño-like forcing in the tropical Pacific, has been performed in a multi-model framework, in order to assess the potential modulation of the positive ENSO signal. The modelling results suggest, in agreement with observational estimates, that the PDO negative phase (PDO−) may enhance the amplitude of the El Niño-NAE teleconnection, while the dynamics involved appear to be unaltered. On the other hand, the modulating role of the PDO positive phase (PDO+) is not reliable across models. This finding is consistent with the atmospheric response to the PDO itself, which is robust and statistically significant only for PDO−. Its modulation seems to rely on the enhanced meridional SST gradient and the related turbulent heat-flux released along the Kuroshio–Oyashio extension. PDO− weakens the North Pacific jet, whereby favoring more poleward propagation of wave activity, strengthening the El Niño-forced Rossby wave-train. These results imply that there might be conditional predictability for the interannual Euro-Mediterranean climate variability depending on the background state

Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model

Euro-Mediterranean Centre on Climate Change coupled climate model (CMCC-CM2) represents the new family of the global coupled climate models developed and used at CMCC. It is based on the atmospheric, land and sea ice components from the Community Earth System Model coupled with the global ocean model Nucleus for European Modeling of the Ocean. This study documents the model components, the coupling strategy, particularly for the oceanic, atmospheric, and sea ice components, and the overall model ability in reproducing the observed mean climate and main patterns of interannual variability. As a first step toward a more comprehensive, process-oriented, validation of the model, this work analyzes a 200-year simulation performed under constant forcing corresponding to present-day climate conditions. In terms of mean climate, the model is able to realistically reproduce the main patterns of temperature, precipitation, and winds. Specifically, we report improvements in the representation of the sea surface temperature with respect to the previous version of the model. In terms of mean atmospheric circulation features, we notice a realistic simulation of upper tropospheric winds and midtroposphere geopotential eddies. The oceanic heat transport and the Atlantic meridional overturning circulation satisfactorily compare with present-day observations and estimates from global ocean reanalyses. The sea ice patterns and associated seasonal variations are realistically reproduced in both hemispheres, with a better skill in winter. Main weaknesses of the simulated climate are related with the precipitation patterns, specifically in the tropical regions with large dry biases over the Amazon basin. Similarly, the seasonal precipitation associated with the monsoons, mostly over Asia, is weaker than observed. The main patterns of interannual variability in terms of dominant empirical orthogonal functions are faithfully reproduced, mostly in the Northern Hemisphere winter. In the tropics the main teleconnection patterns associated with El Nino-Southern Oscillation and with the Indian Ocean Dipole are also in good agreement with observations

Farmers in the transition toward sustainability: what is the role of their entrepreneurial identity?

This is the final version. Available on open access from Frontiers Media via the DOI in this recordData availability statement: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Introduction: The European Union has recently prompted a shift toward Ecological Intensification (EI) practices, aiming to harmonize agricultural productivity and environmental conservation. Despite the benefits of EI, its implementation has been limited, as farmers face challenges in business reorganization and supply chain adaptation. This paper investigates the role of contract farming (CF) in promoting the adoption of sustainable practices among Italian wheat producers. Specifically, it analyzes the influence of farmers’ entrepreneurial identity on their engagement in such initiatives. Methods: Using the case study of Barilla Group’s Carta del Mulino initiative, an innovative contract farming scheme incentivizing sustainable EI practices, the study explores the relationship between entrepreneurial identity and participation in CF schemes supporting EI. Data from a sample of 314 soft wheat farmers in four regions of Northern Italy were collected to examine the role of entrepreneurial identity in the adoption of sustainable practices and participation in CF schemes. To evaluate the research hypotheses, two distinct econometric models were developed. Results and discussion: The findings reveal that farmers with a more developed entrepreneurial identity are more likely to adopt more sustainable agricultural practices and engage in contractual schemes involving EI practices. The study highlights the importance of fostering and supporting farmers’ entrepreneurial identity while increasing their knowledge of alternative agricultural techniques to address the challenges of the agricultural sector. This integration of individual perspectives (entrepreneurial identity) with a systems view (contract farming schemes) offers valuable insights for future research, policy, and practice in agri-food systems sustainability.European Union Horizon 202

Goal frames and sustainability transitions: how cognitive lock-ins can impede crop diversification

This is the final version. Available on open access from Springer via the DOI in this record. Transitions towards more sustainable agricultural systems are often characterised by ‘lock-ins’, understood as self-reinforcing mechanisms that reproduce the status quo and impede change. While socioeconomic, technological and institutional lock-ins have been widely used to understand processes of sustainable transitions in agri-food systems, the role of so-called cognitive lock-ins is still under-investigated. In this study, we focus on how institutional settings create cognitive lock-ins in farmers’ decision-making related to the adoption of sustainable agricultural practices. We apply goal framing for environmental behaviour and transition theory in explaining how socio-technical conditions may shape farmer’s decision-making. Empirically, we focus on the example of diversifying crop rotations with legumes as an established strategy to increase biodiversity and soil health, and reduce agrochemical use, emissions and pollution, which still remains rare in European agriculture. We use two cases in the Atlantic pedo-climatic region, Cornwall, UK, and Gelderland, Netherlands. Using in-depth interview data with farmers and extensive supplementary secondary data, we explore how context-specific socio-technical settings interact with farmers’ normative, gain-oriented and hedonic goal frames to shape the (un-)desirability of crop diversification with legumes. This creates conditions recognisable as cognitive lock-ins: the context of farmers’ decision-making creates cognitive processes that drastically reduce the perceived viability of alternative agricultural practices. Our findings in this case suggest the framework developed for this study may help to identify regionally specific, as well as common, barriers and solutions to crop diversification and comparable practices that are relevant to transitions towards sustainability in agri-food systems.European Union Horizon 202

Esterase Cleavable 2D Assemblies of Magnetic Iron Oxide Nanocubes: Exploiting Enzymatic Polymer Disassembling to Improve Magnetic Hyperthermia Heat Losses

Here, we report a nanoplatform based on iron oxide nanocubes (IONCs) coated with a bioresorbable polymer that, upon exposure to lytic enzymes can be disassembled increasing the heat performances in comparison with the initial clusters. We have developed bi-dimensional (2D) clusters by exploiting benchmark iron oxide nanocubes as heat mediators for magnetic hyperthermia and a polyhydroxyalkanoate (PHA) copolymer, a biodegradable polymer produced by bacteria that can be digested by intracellular esterase enzymes. The comparison of magnetic heat performance of the 2D assemblies with 3D centro-symmetrical assemblies or single iron oxide nanocubes emphasize the benefit of the 2D assembly. On one hand, the heat losses of 2D assemblies dispersed in water are better than the 3D assemblies, but worse than for single nanocubes. On the other hand, when the bi-dimensional magnetic beads (2D-MNBs) are incubated with the esterase enzyme at a physiological temperature, their magnetic heat performances began to progressively increase. After 2 hours of incubation, specific absorption rate values of the 2D assembly double the ones of individually coated nanocubes. Such an increase can be mainly correlated to the splitting of the 2D-MNBs into smaller size clusters with a chain- like configuration containing few nanocubes. Moreover, 2D-MNBs exhibited non-variable-heat performances even after intentionally inducing their aggregation. Magnetophoresis measurements indicate a comparable response of 3D and 2D clusters to external magnets (0.3T) that is by far faster than that of single nanocubes. This feature is crucial for a physical accumulation of magnetic materials in the presence of magnetic field gradients. This system is the first example of a nanoplatform that, upon exposure to lytic enzymes, such as those present in a tumor environment, can be disassembled from the initial 2D-MNB organization to chain-like assemblies with clear improvement of the heat magnetic losses resulting in better heat dissipation performances. The potential application of 2D nano-assemblies based on the cleavable PHAs for preserving their magnetic losses inside cells will benefit hyperthermia therapies mediated by magnetic nanoparticles under alternating magnetic fields

Education for innovation and entrepreneurship in the food system: the Erasmus+ BoostEdu approach and results

Innovation and entrepreneurship are key factors to provide added value for food systems. Based on the findings of the Erasmus+ Strategic Partnership BoostEdu, the objective of this paper is to provide answers to three knowledge gaps: 1) identify the needs for innovation and entrepreneurship (I&E) in the food sector; 2) understand the best way to organize learning; 3) provide flexibility in turbulent times. BoostEdu aimed to provide a platform for continuing education within I&E for food professionals and was carried out through co-creation workshops and the development of an e-learning course. The results of the project in particular during the Covid-19 pandemics, highlighted the need for flexible access to modules that are complementary to other sources and based on a mix of theoretical concepts and practical experiences. The main lessons learned concern the need of co-creation and co-learning processes to identify suitable practices for the use of innovative digital technologies

Esterase-Cleavable 2D Assemblies of Magnetic Iron Oxide Nanocubes: Exploiting Enzymatic Polymer Disassembling to Improve Magnetic Hyperthermia Heat Losses

Here, we report a nanoplatform based on iron oxide nanocubes (IONCs) coated with a bioresorbable polymer that, upon exposure to lytic enzymes, can be disassembled increasing the heat performances in comparison with the initial clusters. We have developed two-dimensional (2D) clusters by exploiting benchmark IONCs as heat mediators for magnetic hyperthermia and a polyhydroxyalkanoate (PHA) copolymer, a biodegradable polymer produced by bacteria that can be digested by intracellular esterase enzymes. The comparison of magnetic heat performance of the 2D assemblies with 3D centrosymmetrical assemblies or single IONCs emphasizes the benefit of the 2D assembly. Moreover, the heat losses of 2D assemblies dispersed in water are better than the 3D assemblies but worse than for single nanocubes. On the other hand, when the 2D magnetic beads (2D-MNBs) are incubated with the esterase enzyme at a physiological temperature, their magnetic heat performances began to progressively increase. After 2 h of incubation, specific absorption rate values of the 2D assembly double the ones of individually coated nanocubes. Such an increase can be mainly correlated to the splitting of the 2D-MNBs into smaller size clusters with a chain-like configuration containing few nanocubes. Moreover, 2D-MNBs exhibited nonvariable heat performances even after intentionally inducing their aggregation. Magnetophoresis measurements indicate a comparable response of 3D and 2D clusters to external magnets (0.3 T) that is by far faster than that of single nanocubes. This feature is crucial for a physical accumulation of magnetic materials in the presence of magnetic field gradients. This system is the first example of a nanoplatform that, upon exposure to lytic enzymes, such as those present in a tumor environment, can be disassembled from the initial 2D-MNB organization to chain-like assemblies with clear improvement of the heat magnetic losses resulting in better heat dissipation performances. The potential application of 2D nanoassemblies based on the cleavable PHAs for preserving their magnetic losses inside cells will benefit hyperthermia therapies mediated by magnetic nanoparticles under alternating magnetic fields