Research on global ice-volume changes during Pleistocene glacial cycles is hindered by a lack of detailed sea-level records for time intervals older than the last interglacial. Here we present the first robustly dated, continuous and highly resolved records of Red Sea sea level and rates of sea-level change over the last 500,000 years, based on tight synchronization to an Asian monsoon record. We observe maximum ‘natural’ (pre-anthropogenic forcing) sea-level rise rates below 2 m per century following periods with up to twice present-day ice volumes, and substantially higher rise rates for greater ice volumes. We also find that maximum sea-level rise rates were attained within 2 kyr of the onset of deglaciations, for 85% of such events. Finally, multivariate regressions of orbital parameters, sea-level and monsoon records suggest that major meltwater pulses account for millennial-scale variability and insolation-lagged responses in Asian monsoon records.

Grain size distribution (GSD) data are widely used in Earth sciences and although large data sets are regularly generated, detailed numerical analyses are not routine. Unmixing GSDs into components can help understand sediment provenance and depositional regimes/processes. End‐member analysis (EMA), which fits one set of end‐members to a given data set, is a powerful way to unmix GSDs into geologically meaningful parts. EMA estimates end‐members based on covariability within a data set and can be considered as a nonparametric approach. Available EMA algorithms, however, either produce suboptimal solutions or are time consuming. We introduce unmixing algorithms inspired by hyperspectral image analysis that can be applied to GSD data and which provide an improvement over current techniques. Nonparametric EMA is often unable to identify unimodal grain size subpopulations that correspond to single sediment sources. An alternative approach is single‐specimen unmixing (SSU), which unmixes individual GSDs into unimodal parametric distributions (e.g., lognormal). We demonstrate that the inherent nonuniqueness of SSU solutions renders this approach unviable for estimating underlying mixing processes. To overcome this, we develop a new algorithm to perform parametric EMA, whereby an entire data set can be unmixed into unimodal parametric end‐members (e.g., Weibull distributions). This makes it easier to identify individual grain size subpopulations in highly mixed data sets. To aid investigators in applying these methods, all of the new algorithms are available in AnalySize, which is GUI software for processing and unmixing grain size data.

First-order reversal curve (FORC) diagrams are constructed from a class of partial magnetic hysteresis loops known as first-order reversal curves and are used to understand magnetization processes in fine magnetic particle systems. A wide-ranging literature that is pertinent to interpretation of FORC diagrams has been published in the geophysical and solid-state physics literature over the past 15 years and is summarized in this review. We discuss practicalities related to optimization of FORC measurements and important issues relating to the calculation, presentation, statistical significance, and interpretation of FORC diagrams. We also outline a framework for interpreting the magnetic behavior of magnetostatically noninteracting and interacting single domain, superparamagnetic, multidomain, single vortex, and pseudosingle domain particle systems. These types of magnetic behavior are illustrated mainly with geological examples relevant to paleomagnetism, rock magnetism, and environmental magnetism. These technical, experimental, and interpretational considerations are relevant to applications that range from improving particulate media for magnetic recording in materials science, to providing a foundation for understanding geomagnetic recording by rocks in geophysics, to interpreting depositional, microbiological, and environmental processes in sediments.