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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.

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

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.

CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States">Read more

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.

fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model

FV3GFS is a weather and climate model written in Fortran. It uses Fortran so that it can run fast, but this makes it hard to add features if you do not (or even if you do) know Fortran. We have written a Python interface to FV3GFS that lets you import the Fortran model as a Python package. We show examples of how this is used to write “model” scripts, which reproduce or build on what the Fortran model can do. You could do this same wrapping for any compiled model, not just FV3GFS.

A discontinuous Galerkin finite-element model for fast channelized lava flows v1.0

Lava flows present a natural hazard to communities around volcanoes and are usually slow-moving (< 1-5 cm/s). Lava flows during the 2018 eruption of Kilauea volcano, Hawai’i, however, reached speeds as high as 11 m/s. To investigate these dynamics we develop a new lava flow computer model that incorporates a nonlinear expression for the fluid viscosity. Model results indicate that the lava flows at Site 8 of the eruption displayed shear thickening behavior due to the flow’s high bubble content.

FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration

This paper presents an update of the FaIR simple climate model, which can estimate the impact of anthropogenic greenhouse gas and aerosol emissions on the global climate. This update aims to significantly increase the structural simplicity of the model, making it more understandable and transparent. This simplicity allows it to be implemented in a wide range of environments, including Excel. We suggest that it could be used widely in academia, corporate research, and education.

JlBox v1.1: a Julia-based multi-phase atmospheric chemistry box model

As our knowledge and understanding of atmospheric aerosol particle evolution and impact grows, designing community mechanistic models requires an ability to capture increasing chemical, physical and therefore numerical complexity. As the landscape of computing software and hardware evolves, it is important to profile the usefulness of emerging platforms in tackling this complexity. With this in mind we present JlBox v1.1, written in Julia.

A process-based evaluation of the Intermediate Complexity Atmospheric Research Model (ICAR) 1.0.1

This study conducts an in-depth process-based evaluation of the Intermediate Complexity Atmospheric Research (ICAR) model, employing idealized simulations to increase the understanding of the model and develop recommendations to maximize the probability that its results are correct for the right reasons. The results show that when model skill is evaluated from statistical metrics based on comparisons to surface observations only, such an analysis may not reflect the skill of the model in capturing atmospheric processes like gravity waves and cloud formation.

ICAR) 1.0.1">Read more

Development of a MetUM (v 11.1) and NEMO (v 3.6) coupled operational forecastmodel for the Maritime Continent – Part 1: Evaluation of ocean forecasts

This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models.

NEMO (v 3.6) coupled operational forecastmodel for the Maritime Continent – Part 1: Evaluation of ocean forecasts">Read more

Coordinating an operational data distribution network for CMIP6 data

The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive.

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