On the success (or not) of climate models: Atlantic Meridional Overturning Circulation (AMOC) transports huge amounts of heat from low to high latitudes and has a major influence on climate. Climate models have predicted that global warming will cause the AMOC to slow, but concrete evidence of such a slowdown has been scarce. Srokosz and Bryden review a decade of observations of the AMOC that reveal an unexpected amount of variability over time scales from seasonal to decadal, as well as a general weakening over this time. Science , this issue 10.1126/science.1255575

Observations and numerical modeling experiments provide evidence for links between variability in the Atlantic meridional overturning circulation (AMOC) and global climate patterns. Reduction in the strength of the overturning circulation is thought to have played a key role in rapid climate change in the past and may have the potential to significantly influence climate change in the future, as noted in the last two Intergovernmental Panel on Climate Change (IPCC) assessment reports (Houghton et al.; Solomon et al.). Both IPCC reports also highlighted the significant uncertainties that exist regarding the future behavior of the AMOC under global warming. Model results suggest that changes in the AMOC can impact surface air temperature, precipitation patterns, and sea level, particularly in areas bordering the North Atlantic, thus affecting human populations. Here, the current understanding of past, present, and future changes in the AMOC and the effects of such changes on climate are reviewed. The focus is on observations of the AMOC, how the AMOC influences climate, and in what way the AMOC is likely to change over the next few decades and the twenty-first century. The potential for decadal prediction of the AMOC is also discussed. Finally, the outstanding challenges and possible future directions for AMOC research are outlined.

Rogue waves are ocean surface waves larger than the surrounding sea that can pose a danger to ships and offshore structures. They are often deemed unpredictable without complex measurement of the wavefield and computationally intensive calculation, which is infeasible in most applications; consequently, there a need for fast predictors. Here we collate, quality control, and analyze the largest data set of single‐point field measurements from surface following wave buoys to search for predictors of rogue wave occurrence. We find that analysis of the sea state parameters in bulk yields no predictors, as the subset of seas containing rogue waves sits within the set of seas without. However, spectral bandwidth parameters of rogue seas display different probability distributions to normal seas, but these parameters are rarely provided in wave forecasts. When location is accounted for, trends can be identified in the occurrence of rogue waves as a function of the average sea state characteristics at that location. These trends follow a power law relationship with the characteristic sea state parameters: mean significant wave height and mean zero upcrossing wave period. We find that frequency of occurrence of rogue waves and their generating mechanism is not spatially uniform, and each location is likely to have its own unique sensitivities, which increase in the coastal seas. We conclude that forecastable predictors of rogue wave occurrence will need to be location specific and reflective of their generation mechanism. Therefore, given location and a sufficiently long historical record of sea state characteristics, the likelihood of occurrence can be obtained for mariners and offshore operators.

A new method for the detection of sea ice using GNSS‐R (Global Navigation Satellite Systems Reflectometry) is presented and applied to 33 months of data from the U.K. TechDemoSat‐1 mission. This method of sea ice detection shows the potential for a future GNSS‐R polar mission, attaining an agreement of over 98% and 96% in the Antarctic and Arctic, respectively, when compared to the European Space Agency's Climate Change Initiative sea ice concentration product. The algorithm uses a combination of two parameters derived from the delay‐Doppler Maps to quantify the spread of power in delay and Doppler. Application of thresholds then allows sea ice to be distinguished from open water. Differences between the TechDemoSat‐1 sea ice detection and comparison data sets are explored. The results provide information on the seasonal and multiyear changes in sea ice distribution of the Arctic and Antarctic. Future potential and applications of this technique are discussed.

This article provides a brief overview of AMOC science organized collaboratively between the UK RAPID and US AMOC Programs (with partners internationally) during the past 16 years as reflected in the set of synthesis and review articles in the AGU special issue entitled “Atlantic Meridional Overturning Circulation: Reviews of Observational and Modeling Advances.” The article highlights the programs' initial motivations and summarizes the successful implementation of the pan‐Atlantic AMOC observing system, efforts to assess the state, variability, and changes in AMOC, advances in understanding AMOC variability mechanisms and predictability, and illumination of AMOC impacts on global and regional climate, sea level, and ecosystems.

We present the first examples of Global Navigation Satellite Systems‐Reflectometry (GNSS‐R) observations of hurricanes using spaceborne data from the UK TechDemoSat‐1 (TDS‐1) mission. We confirm that GNSS‐R signals can detect ocean condition changes in very high near‐surface ocean wind associated with hurricanes. TDS‐1 GNSS‐R reflections were collocated with International Best Track Archive for Climate Stewardship (IBTrACS) hurricane data, MetOp ASCAT A/B scatterometer winds, and two reanalysis products. Clear variations of GNSS‐R reflected power (σ⁰) are observed as reflections travel through hurricanes, in some cases up to and through the eye wall. The GNSS‐R reflected power is tentatively inverted to estimate wind speed using the TDS‐1 baseline wind retrieval algorithm developed for low to moderate winds. Despite this, TDS‐1 GNSS‐R winds through the hurricanes show closer agreement with IBTrACS estimates than winds provided by scatterometers and reanalyses. GNSS‐R wind profiles show realistic spatial patterns and sharp gradients that are consistent with expected structures around the eye of tropical cyclones.