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Sand seas are vast expanses of Earth’s surface containing large areas of aeolian dunes—topographic patterns manifest from above-threshold winds and a supply of loose sand. Predictions of the role of future climate change for sand-sea activity are sparse and contradictory. Here we examine the impact of climate on all of Earth’s presently-unvegetated sand seas, using ensemble runs of an Earth System Model for historical and future Shared Socioeconomic Pathway (SSP) scenarios. We find that almost all of the sand seas decrease in activity relative to present-day and industrial-onset for all future SSP scenarios, largely due to more intermittent sand-transport events. An increase in event wait-times and decrease in sand transport is conducive to vegetation growth. We expect dune-forming winds will become more unimodal, and produce larger incipient wavelengths, due to weaker and more seasonal winds. Our results indicate that these qualitative changes in Earth’s deserts cannot be mitigated.

 

Wind‐blown sand dunes are both a consequence and a driver of climate dynamics; they arise under persistently dry and windy conditions, and are sometimes a source for airborne dust. Dune fields experience extreme daily changes in temperature, yet the role of atmospheric stability in driving sand transport and dust emission has not been established. Here, we report on an unprecedented multiscale field experiment at the White Sands Dune Field (New Mexico, USA), where by measuring wind, humidity and temperature profiles in the atmosphere concurrently with sediment transport, we demonstrate that a daily rhythm of sand and dust transport arises from nonequilibrium atmospheric boundary layer convection. A global analysis of 45 dune fields confirms the connection found in situ between surface wind speed and diurnal temperature cycles, revealing an unrecognized climate feedback that may contribute to the growth of deserts on Earth and dune activity on Mars.

 

Previous research demonstrates that domain experts, like ordinary participant populations, are vulnerable to decision bias. Here, we examine susceptibility to bias amongst expert field scientists. Field scientists operate in less predictable environments than other experts, and feedback on the consequences of their decisions is often unclear or delayed. Thus, field scientists are a population where the findings of scientific research may be particularly vulnerable to bias. In this study, susceptibility to optimism, hindsight, and framing bias was evaluated in a group of expert field geologists using descriptive decision scenarios. Experts showed susceptibility to all three biases, and susceptibility was not influenced by years of science practice. We found no evidence that participants' vulnerability to one bias was related to their vulnerability to another bias. Our findings are broadly consistent with previous research on expertise and decision bias, demonstrating that no expert, regardless their domain experience, is immune to bias.

 

Aeolian dune fields are self‐organized patterns formed by wind‐blown sand. Dunes are topographic roughness elements that impose drag on the atmospheric boundary layer (ABL), creating a natural coupling between form and flow. While the steady‐state influence of drag on the ABL is well studied, nonequilibrium effects due to roughness transitions are less understood. Here we examine the large‐scale coupling between the ABL and an entire dune field. Field observations at White Sands, New Mexico, reveal a concomitant decline in wind speed and sand flux downwind of the transition from smooth playa to rough dunes at the upwind dune‐field margin, that affects the entire∼10‐km ‐long dune field. Using a theory for the system that accounts for the observations, we generalize to other roughness scenarios. We find that, via transitional ABL dynamics, aeolian sediment aggradation can be influenced by roughness both inside and outside dune fields.

 

Abstract Wherever a loose bed of sand is subject to sufficiently strong winds, aeolian dunes form at predictable wavelengths and growth rates. As dunes mature and coarsen, however, their growth trajectories become more idiosyncratic; nonlinear effects, sediment supply, wind variability and geologic constraints become increasingly relevant, resulting in complex and history-dependent dune amalgamations. Here we examine a fundamental question: do aeolian dunes stop growing and, if so, what determines their ultimate size? Earth’s major sand seas are populated by giant sand dunes, evolved over tens of thousands of years. We perform a global analysis of the topography of these giant dunes, and their associated atmospheric forcings and geologic constraints, and we perform numerical experiments to gain insight on temporal evolution of dune growth. We find no evidence of a previously proposed limit to dune size by atmospheric boundary layer height. Rather, our findings indicate that dunes may grow indefinitely in principle; but growth depends on morphology, slows with increasing size, and may ultimately be limited by sand supply. 

Abstract How do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore closely parallels classic search and foraging behavior. Here we conduct a novel simulated data foraging study—and a complementary real-world case study—to determine how spatiotemporal data collection decisions are made in field sciences, and how search is adapted in response to in-situ data. Expert geoscientists evaluated a hypothesis by collecting environmental data using a mobile robot. At any point, participants were able to stop the robot and change their search strategy or make a conclusion about the hypothesis. We identified spatiotemporal reasoning heuristics, to which scientists strongly anchored, displaying limited adaptation to new data. We analyzed two key decision factors: variable-space coverage, and fitting error to the hypothesis. We found that, despite varied search strategies, the majority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, due to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions. We found that novice undergraduates used the same heuristics as expert geoscientists in a simplified version of the scenario. We believe the findings from this study could be used to improve field science training in data foraging, and aid in the development of technologies to support data collection decisions. 

One of the simplest questions in riverine science remains unanswered: “What determines the width of rivers?” While myriad environmental and geological factors have been proposed to control alluvial river size, no accepted theory exists to explain this fundamental characteristic of river systems. We combine analysis of a global dataset with a field study to support a simple hypothesis: River geometry adjusts to the threshold fluid entrainment stress of the most resistant material lining the channel. In addition, we demonstrate how changes in bank strength dictate planform morphology by exerting strong control on channel width. Our findings greatly extend the applicability of threshold channel theory, which was originally developed to explain straight gravel-bedded rivers with uniform grain size and stable banks. The parsimonious threshold-limiting channel model describes the average hydraulic state of natural rivers across a wide range of conditions and may find use in river management, stratigraphy, and planetary science. 

Natural environments are often filled with obstacles and disturbances. Traditional navigation and planning approaches normally depend on finding a traversable “free space” for robots to avoid unexpected contact or collision. We hypothesize that with a better understanding of the robot–obstacle interactions, these collisions and disturbances can be exploited as opportunities to improve robot locomotion in complex environments. In this article, we propose a novel obstacle disturbance selection (ODS) framework with the aim of allowing robots to actively select disturbances to achieve environment-aided locomotion. Using an empirically characterized relationship between leg–obstacle contact position and robot trajectory deviation, we simplify the representation of the obstacle-filled physical environment to a horizontal-plane disturbance force field. We then treat each robot leg as a “disturbance force selector” for prediction of obstacle-modulated robot dynamics. Combining the two representations provides analytical insights into the effects of gaits on legged traversal in cluttered environments. We illustrate the predictive power of the ODS framework by studying the horizontal-plane dynamics of a quadrupedal robot traversing an array of evenly-spaced cylindrical obstacles with both bounding and trotting gaits. Experiments corroborate numerical simulations that reveal the emergence of a stable equilibrium orientation in the face of repeated obstacle disturbances. The ODS reduction yields closed-form analytical predictions of the equilibrium position for different robot body aspect ratios, gait patterns, and obstacle spacings. We conclude with speculative remarks bearing on the prospects for novel ODS-based gait control schemes for shaping robot navigation in perturbation-rich environments.