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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.

Acidification of the Nordic Seas

Ocean acidification, a direct consequence of the CO2 release by human activities, is a serious threat to marine ecosystems. In this study, we conduct a detailed investigation of the acidification of the Nordic Seas, from 1850 to 2100, by using a large set of samples taken during research cruises together with numerical model simulations. We estimate the effects of changes in different environmental factors on the rate of acidification and its potential effects on cold-water corals.

Late Neogene evolution of modern deep-dwelling plankton

Deep-living organisms are a major yet poorly known component of ocean biomass. Here we reconstruct the evolution of deep-living zooplankton and phytoplankton. Deep-dwelling zooplankton and phytoplankton did not occur 15 Myr ago, when the ocean was several degrees warmer than today. Deep-dwelling species first evolve around 7.5 Myr ago, following global climate cooling. Their evolution was driven by colder ocean temperatures allowing more food, oxygen, and light at depth.

Representativeness assessment of the pan-Arctic eddy covariance site network and optimized future enhancements

Thawing of Arctic permafrost soils could trigger the release of vast amounts of carbon to the atmosphere, thus enhancing climate change. Our study investigated how well the current network of eddy covariance sites to monitor greenhouse gas exchange at local scales captures pan-Arctic flux patterns. We identified large coverage gaps, e.g., in Siberia, but also demonstrated that a targeted addition of relatively few sites can significantly improve network performance.

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.

Rhizodeposition efficiency of pearl millet genotypes assessed on a short growing period by carbon isotopes (δ13C and F14C)

SOIL
DOI 10.5194/soil-8-49-2022
13 April 2022

Unravelling relationships between plant rhizosheath, root exudation and soil C dynamic may bring interesting perspectives in breeding for sustainable agriculture. Using four pearl millet lines with contrasting rhizosheaths, we found that δ13C and F14C of root-adhering soil differed from those of bulk and control soil, indicating C exudation in the rhizosphere. This C exudation varied according to the genotype, and conceptual modelling performed with data showed a genotypic effect on the RPE.

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.

Not all biodiversity rich spots are climate refugia

Biodiversity is under threat from the effects of global warming, and assessing the effects of climate change on areas of high species richness is of prime importance to conservation. Terrestrial and freshwater rich spots have been and will be less affected by climate change than other areas. However, marine rich spots of biodiversity are expected to experience more pronounced warming.

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.

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.

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