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Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models

It can be challenging to understand why Earth system models (ESMs) produce specific results because one can arrive at the same result simply by changing the values of the parameters. In our paper, we demonstrate that it is possible to use machine learning to figure out how and why particular components of an ESM (such as biology or ocean circulations) affect the output. This work could be applied to observations to improve the accuracy of the formulations used in ESMs.

The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation

Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. The model will ultimately help to improve projections of sea level rise and climate change.

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.

Deep oceanic submarine fieldwork with undergraduate students: an immersive experience with the Minerve software

We use the Minerve virtual reality software to bring undergraduate students to an unusual field trip at 1200 m below sea level in the Lesser Antilles area. This region is located above an active subduction zone responsible for intense volcanic and seismic activity. In particular, we focus on the Roseau submarine fault that ruptured during the Mw 6.3 Les Saintes earthquake and presented a fresh scarp that the students can analyse and map in VR. They compile their results in a GIS project.

SELF v1.0: a minimal physical model for predicting time of freeze-up in lakes

The time when lakes freeze varies considerably from year to year. A common way to predict it is to use negative degree days, i.e., the sum of air temperatures below 0°C, a proxy for the heat lost to the atmosphere. Here, we show that this is insufficient as the mixing of the surface layer induced by wind tends to delay the formation of ice. To do so, we developed a minimal model based on a simplified energy balance, which can be used both for large-scale analyses and short-term predictions.

Extreme metrics from large ensembles: investigating the effects of ensemble size on their estimates

We address the question of how large an initial condition ensemble of climate model simulations should be if we are concerned with accurately projecting future changes in temperature and precipitation extremes. We find that for most cases (and both models considered), an ensemble of 20-25 members is sufficient for many extreme metrics, spatial scales and time horizons. This may leave computational resources to tackle other uncertainties in climate model simulations with our ensembles.

Assessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States

Modeling the hydrologic cycle at high resolution and at large spatial scales is an incredible opportunity and challenge for hydrologists. In this paper, we present the results of a high-resolution hydrologic simulation configured over the contiguous United States. We discuss simulated water fluxes through groundwater, soil, plants, and over land, and we compare model results to in situ observations and satellite products in order to build confidence and guide future model development.

Orogenic lithosphere and slabs in the greater Alpine area – interpretations based on teleseismic P-wave tomography

Solid Earth
DOI 10.5194/se-12-2633-2021
9 February 2022

New images from the multi-national AlpArray experiment illuminate the Alps from below. They indicate thick European mantle descending beneath the Alps and forming blobs that are mostly detached from the Alps above. In contrast, the Adriatic mantle in the Alps is much thinner. This difference helps explain the rugged mountains and the abundance of subducted and exhumed units at the core of the Alps. The blobs are stretched remnants of old ocean and its margins that reach down to at least 410 km.

The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content

We show that the way that the air–sea heat flux is treated in ocean models means that the model’s temperature variable should be interpreted as being Conservative Temperature, irrespective of whether the equation of state used in an ocean model is EOS-80 or TEOS-10.

Sand mining far outpaces natural supply in a large alluvial river

Unsustainable sand mining poses a threat to the stability of river channels. We use satellite imagery to estimate volumes of material removed from the Mekong River, Cambodia, over the period 2016–2020. We demonstrate that current rates of extraction now exceed previous estimates for the entire Mekong Basin and significantly exceed the volume of sand naturally transported by the river. Our work highlights the importance of satellite imagery in monitoring sand mining activity over large areas.

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