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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.

Autonomous methane seep site monitoring offshore western Svalbard: hourly to seasonal variability and associated oceanographic parameters

Natural sources of atmospheric methane need to be better described and quantified. We present time series from ocean observatories monitoring two seabed methane seep sites in the Arctic. Methane concentration varied considerably on short timescales and seasonal scales. Seeps persisted throughout the year, with increased potential for atmospheric release in winter due to water mixing. The results highlight and constrain uncertainties in current methane estimates from seabed methane seepage.

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.

Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

This study explores the Arctic sea level variability depending on different timescales and the relation to temperature, salinity and mass changes, identifying key parameters and regions that need to be observed co-ordinately. The decadal sea level variability reflects salinity changes. But it can only reflect salinity change at periods of greater than 1 year, highlighting the requirement for enhancing in situ hydrographic observations and complicated interpolation methods.

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.

Rectified tidal transport in Lofoten–Vesterålen, northern Norway

Ocean Science
DOI 10.5194/os-17-1753-2021
16 February 2022

Non-linear tidal dynamics can produce prominent time-mean transport in coastal regions where strong tidal currents interact with topography. We investigate tidal-induced transport using a tidally driven ocean model for Lofoten-Vesterålen in northern Norway and find that both tidal pumping and tidal rectification can play an important role for time-mean transport in the region. The study emphasizes the importance of non-linear tidal dynamics for time-mean transport in complex coastal regions.

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.

Dynamics of fortnightly water level variations along a tide-dominated estuary with negligible river discharge

Understanding tidal hydrodynamics is essential for water resources management in estuarine environments. In this study, we propose an analytical model to examine the fortnightly water level variations due to tidal motions alone in tide-dominated estuaries. Details of the analytical model show that changes in the mean depth or length of semi-arid estuaries affect the fortnightly tide amplitude, which has significant potential impacts on the estuarine ecosystem management.

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.

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