The Arctic climate is currently experiencing a notable rise in air temperatures and an altered precipitation dynamic. These changes are not just regional phenomena; they have far-reaching implications for the global climate. Temperature records from the past few decades unequivocally show that the Arctic is warming at a rate of more than four times the global average, a phenomenon known as Arctic amplification. This warming trend has led to a significant reduction in sea-ice extent and thickness, fundamentally altering the region’s albedo effect, its ability to reflect solar radiation back into space. As sea-ice diminishes, dark ocean waters absorb more sunlight, further warming the region and accelerating ice melt. This dissertation delves into these transformations, focusing on the characteristics of stable water isotopes in the Arctic water cycle and their utility in understanding the multifaceted connections between atmosphere, ocean, and sea-ice. Central to this dissertation is the utilization of stable water isotopes as a diagnostic proxy to unravel the dynamics of the Arctic hydrosphere and cryosphere. Through the comprehensive analysis of over 2200 samples of seawater, snow, and sea-ice collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, this work provides an unprecedented view of the isotopic signatures of the components shaping the central Arctic water cycle. The study highlights how the isotopic composition of sea-ice varies, revealing insights into its formation processes. First-year ice (FYI) is generally more enriched in δ18O than second-year ice (SYI), due to its initial freezing from the seawater with enriched δ18O, whereas SYI is influenced by contributions of snow melt. A distinct isotopic threshold between FYI and SYI, helped characterizing an insulated FYI (iFYI) layer underneath the SYI. Hence, different sources and formation mechanisms of sea ice could be differentiated. Furthermore, the isotopic signatures of surface seawater offer insights into spatio-temporal patterns of relative contributions of different freshwater sources from Siberian rivers to Greenland Ice Sheet melt as well as summer sea-ice and snow melt to the Arctic Ocean’s water budget. A ground-breaking discovery in this dissertation using the MOSAiC expedition data is that a significant portion, approximately 20 %, of the Arctic’s winter snow cover originates not from traditional meteoric sources, but directly from vapour diffusion from the sea-ice through sublimation. This process occurs under the intense temperature gradients characteristic of Arctic winters, contributing to the formation of a unique snow-like structure atop the sea-ice. This “oceanic” source of snow introduces new perspectives on understanding vapour fluxes across the snowpack, the biogeochemistry of gas exchanges, and the implications for sea salt aerosol formation. The implications extend further, affecting mass-balance calculations and physical properties of snow on Arctic sea-ice. Over a year-long period, the investigation of Arctic snow highlights significant seasonal changes closely tied to local or regional atmospheric conditions, based on 911 snow isotope measurements. Snow, with its highly variable and depleted δ18O values, plays a crucial role in the Arctic water cycle. During autumn, an inherited signal in snow isotopes from earlier precipitation events led to distinct differences between δ18O values of snow and water vapour. During the winter months, when the RV Polarstern transitioned from the Siberian to the Atlantic sector of the Arctic Ocean, significant differences in δ18O and d-excess values in snow and vapour were noted. These differences suggest kinetic fractionation, likely primarily driven by sublimation, during the severe cold and dry conditions. This observation is associated with a generally low statistical correlation between the δ18O in snow and air temperature. This highlights the greater influence of post-depositional processes on the snow isotopes, compared to the those during deposition. Wind-driven snow re-distribution, occurring consistently throughout the winter, led to a mixed but depleted δ18O signal in surface snow across the sea-ice by spreading meteoric snow of lower δ18O. This effect is more evident in ridge situations, contrasting sharply with flat ice samples in both, snow profile heights and isotopic compositions. Summer months show isotopic alignment between surface snow and vapour under warmer conditions, suggesting equilibrium fractionation between them. In general, this dissertation contributes to a better understanding of the Arctic water cycle and their stable isotopic signatures, emphasizing their importance in deciphering the feedback mechanisms driving current changes in the Arctic climate. By offering detailed insights into the interactions between snow, sea-ice, ocean and atmosphere, this work advances our knowledge of the Arctic system.

Genome-scale metabolic models are mathematical representations of all known reactions occurring in a cell. Combined with constraints based on physiological measurements, these models have been used to accurately predict metabolic fluxes and effects of perturbations (e.g. knock-outs) and to inform metabolic engineering strategies. Recently, protein-constrained models have been shown to increase predictive potential (especially in overflow metabolism), while alleviating the need for measurement of nutrient uptake rates. The resulting modelling frameworks quantify the upkeep cost of a certain metabolic flux as the minimum amount of enzyme required for catalysis. These improvements are based on the use of in vitro turnover numbers or in vivo apparent catalytic rates of enzymes for model parameterization. In this thesis several tools for the estimation and refinement of these parameters based on in vivo proteomics data of Escherichia coli, Saccharomyces cerevisiae, and Chlamydomonas reinhardtii have been developed and applied. The difference between in vitro and in vivo catalytic rate measures for the three microorganisms was systematically analyzed. The results for the facultatively heterotrophic microalga C. reinhardtii considerably expanded the apparent catalytic rate estimates for photosynthetic organisms. Our general finding pointed at a global reduction of enzyme efficiency in heterotrophy compared to other growth scenarios. Independent of the modelled organism, in vivo estimates were shown to improve accuracy of predictions of protein abundances compared to in vitro values for turnover numbers. To further improve the protein abundance predictions, machine learning models were trained that integrate features derived from protein-constrained modelling and codon usage. Combining the two types of features outperformed single feature models and yielded good prediction results without relying on experimental transcriptomic data. The presented work reports valuable advances in the prediction of enzyme allocation in unseen scenarios using protein constrained metabolic models. It marks the first successful application of this modelling framework in the biotechnological important taxon of green microalgae, substantially increasing our knowledge of the enzyme catalytic landscape of phototrophic microorganisms.