Mixed-layer salinity budget in the tropical Indian Ocean: seasonal cycle based only on observations

The mixed-layer salinity (MLS) budget in the tropical Indian Ocean is estimated from a combination of satellite products and in situ observations over the 2004-2012 period, to investigate the mechanisms controlling the seasonal MLS variability. In contrast with previous studies in the tropical Indian Ocean, our results reveal that the coverage, resolution, and quality of available observations are now sufficient to approach a closed monthly climatology seasonal salt budget. In the South-central Arabian Sea and South-western Tropical Indian Ocean (SCAS and STIO, respectively), where seasonal variability of the MLS is pronounced, the monthly MLS tendency terms are well captured by the diagnostic. In the SCAS region, in agreement with previous results, the seasonal cycle of the MLS is mainly due to meridional advection driven by the monsoon winds. In the STIO, contrasting previous results indicating the control of the meridional advection over the seasonal MLS budget, our results reveal the leading role of the freshwater flux due to precipitation.

Iron isotope systematics in Arctic rivers

The input of iron to the Arctic Ocean plays a critical role in the productivity of aquatic ecosystems and is potentially impacted by climate change. We examine Fe isotope systematics of dissolved and colloidal Fe from several Arctic and sub-Arctic rivers in northern Eurasia and Alaska. We demonstrate that the Fe isotopic (δ56Fe) composition of large rivers, such as the Ob’ and Lena, has a restricted range of δ56Fe values ca.–0.11 ± 0.13‰, with minimal seasonal variability, in stark contrast to smaller organic-rich rivers with an overall δ56Fe range from–1.7 to + 1.6‰. The preferential enrichment with heavy Fe isotopes observed in low molecular weight colloidal fraction and during the high-flow period is consistent with the role of organic complexation of Fe. The light Fe isotope signatures of smaller rivers and meltwater reflect active redox cycling. Data synthesis reveals that small organic-rich rivers and meltwater in Arctic environments may contribute disproportionately to the input of labile Fe in the Arctic Ocean, while bearing contrasting Fe isotope compositions compared to larger rivers.

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/(Mentaschi et al., 2016).