Highlight articles

Towards an objective assessment of climate multi-model ensembles – a casestudy: the Senegalo-Mauritanian upwelling region

The most robust representation of climate is usually obtained by averaging a large number of simulations, thereby cancelling individual model errors. Here, we work towards an objective way of selecting the least biased models over a certain region, based on physical parameters. This statistical method based on a neural classifier and multi-correspondence analysis is illustrated here for the Senegalo-Mauritanian region, but it could potentially be developed for any other region or process.

RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting

In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly outperformed the benchmark models at all lead times up to 60 min. Yet, an undesirable property of RainNet predictions is the level of spatial smoothing. Obviously, RainNet learned an optimal level of smoothing to produce a nowcast at 5 min lead time.

TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI):motivations and protocol version 1.0

Atmospheric characterization of rocky exoplanets orbiting within the habitable zone of nearby M dwarf stars is around the corner with the James Webb Space Telescope (JWST), expected to be launch in 2021.
Global climate models (GCMs) are powerful tools to model exoplanet atmospheres and to predict their habitability. However, intrinsic differences between the models can lead to various predictions. This paper presents an experiment protocol to evaluate these differences.

An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&C v1.0)

We developed a novel urban ecohydrological model (UT&C v1.0) that is able to account for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements in three cities in different climates (Singapore, Melbourne, Phoenix) and can be used to assess urban climate mitigation strategies that aim at increasing or changing urban green cover.

Can subduction initiation at a transform fault be spontaneous?

Solid Earth
DOI 10.5194/se-11-37-2020
17 January 2020

We propose a new exploration of the concept of “spontaneous” lithospheric collapse at a transform fault (TF) by performing a large study of conditions allowing instability of the thicker plate using 2-D thermomechanical simulations. Spontaneous subduction is modelled only if extreme mechanical conditions are assumed. We conclude that spontaneous collapse of the thick older plate at a TF evolving into mature subduction is an unlikely process of subduction initiation at modern Earth conditions.

A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?

The ocean plays a vital role in mitigating climate change by taking up atmospheric carbon dioxide (CO₂). Historically sparse ship-based measurements of surface ocean CO₂make direct estimates of CO₂exchange changes unreliable. We introduce a machine-learning ensemble approach to fill these observational gaps. Our method performs incrementally better relative to past methods, leading to our hypothesis that we are perhaps reaching the limitation of machine-learning algorithms’ capability.

The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model description

Our paper describes the Ecosystem Demography model. This computer program calculates how plants and ground exchange heat, water, and carbon with the air, and how plants grow, reproduce and die in different climates. Most models simplify forests to an average big tree. We consider that tall, deep-rooted trees get more light and water than small plants, and that some plants can with shade and drought. This diversity helps us to better explain how plants live and interact with the atmosphere.

The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 2: Model evaluation for tropical South America

The Ecosystem Demography model calculates the fluxes of heat, water, and carbon between plants and ground and the air, and the life cycle of plants in different climates. To test if our calculations were reasonable, we compared our results with field and satellite measurements. Our model predicts well the extent of the Amazon forest, how much light forests absorb, and how much water forests release to the air. However, it must improve the tree growth rates and how fast dead plants decompose.

How can geologic decision-making under uncertainty be improved?

Solid Earth
DOI 10.5194/se-10-1469-2019
3 September 2019

In this paper, we outline the key insights from decision-making research about how, when faced with uncertainty, humans constrain decisions through the use of heuristics (rules of thumb), making them vulnerable to systematic and suboptimal decision biases. We also review existing strategies to debias decision-making that have applicability in the geosciences, giving special attention to strategies that make use of information technology and artificial intelligence.

Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0)

MIMI v1.0 was designed for use within Earth system models to simulate the 3-D emission, atmospheric processing, and deposition of iron and its soluble fraction. Understanding the iron cycle is important due to its role as an essential micronutrient for ocean phytoplankton; its supply limits primary productivity in many of the world’s oceans. Human activity has perturbed the iron cycle, and MIMI is capable of diagnosing many of these impacts; hence, it is important for future climate studies.

GMPV Geochemistry, Mineralogy, Petrology & Volcanology Highlight articles

Highlight articles

Towards an objective assessment of climate multi-model ensembles – a casestudy: the Senegalo-Mauritanian upwelling region

RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting

TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI):motivations and protocol version 1.0

An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&C v1.0)

Can subduction initiation at a transform fault be spontaneous?

A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?

The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model description

The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 2: Model evaluation for tropical South America

How can geologic decision-making under uncertainty be improved?

Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0)

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