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Accurate simulation of sea ice is critical for predictions of future Arctic sea ice loss, looming climate change impacts, and more. A key feature in Arctic sea ice is the formation of melt ponds. Each year melt ponds develop on the surface of the ice and primarily via affecting the albedo, they have an enormous effect on the energy budget and climate of the Arctic. As melt ponds are subgrid scale and their evolution occurs due to a number of competing, poorly understood factors, their representation in models is parametrised. Sobol sensitivity analysis, a form of variance based global sensitivity analysis is performed on an advanced melt pond parametrisation (MPP), in Icepack, a state-of-the-art thermodynamic column sea ice model. Results show that the model is very sensitive to changing its uncertain MPP parameter values, and that these have varying influences over model predictions both spatially and temporally. Such extreme sensitivity to parameters makes MPPs a potential source of prediction error in sea-ice models, given that the (often many) parameters in MPPs are usually poorly known. Machine learning (ML) techniques have shown great potential in learning and replacing subgrid scale processes in models. Given the complexity of melt pond physics and the need for accurate parameter values in MPPs, we propose an alternative data-driven MPPs that would prioritise the accuracy of albedo predictions. In particular, we constructed MPPs based either on linear regression or on nonlinear neural networks, and investigate if they could substitute the original physics-based MPP in Icepack. Our results shown that linear regression are insufficient as emulators, whilst neural networks can learn and emulate the MPP in Icepack very reliably. Icepack with the MPPs based on neural networks only slightly deviates from the original Icepack and overall offers the same long term model behaviour. We then searched for the smallest possible emulator that achieves good performance by performing features selection based on mutual information. Results indicates that a smaller model, based only on a portion of the full set of input variables needed by the physical MPP, is also sufficient to approximate and replace the physical MPP. This smaller emulators are not only computationally faster but also easier to interpret on a physical ground. Several and diverse challenges still exist, yet this study is an encouraging first step, prior to using real data, towards the adoption of data-driven MPPs in sea-ice models. [ABSTRACT FROM AUTHOR]

The effects of micropropagation conditions on avocado (Persea americanaMill.) have been measured in leaves and plants cultured in vitro. The consequences of the type and concentration of sugar in the medium and of carbon dioxide concentration in the atmosphere on the rates of photosynthesis and amounts of ribulose 1,5-biphosphate carboxylase-oxygenase (EC 4.1.1.39; Rubisco) and total soluble protein (TSP) were measured. At the highest sucrose supply (87.6 mM), Rubisco content was substantially decreased in leaves, and even more when elevated CO2(1 000 μmol·mol−1) was supplied. Maximum photosynthetic rate (Pmax) was significantly decreased when plants developed in high sucrose and elevated CO2. However, Rubisco concentration was significantly greater when glucose was supplied at the same molar concentration or when the concentration of sucrose was small (14.6 mM), and no differences were observed due to the CO2concentration in the air in these treatments. The ratio of Rubisco to total soluble protein (Rubisco/TSP) was dramatically decreased in plants grown in the highest concentration of sucrose and with elevated CO2. Leaf area and ratio of leaf fresh weight/(stem + root) fresh weight, were greater in plants grown with CO2enriched air. However, upon transplanting, survival was poorer in plants grown on low sucrose/high CO2compared to those grown on high sucrose/high CO2.