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The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It adds data assimilation capability to the Norwegian Earth System Model version 1 (NorESM1) and has contributed output to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). We describe the system and evaluate its baseline, reanalysis and prediction performance.

A new simple climate model, MCE, was developed. It can emulate the basic behavior of comprehensive climate models in a minimal way with sufficient accuracy, providing a reasonable way to assess climate change mitigation scenarios in terms of consistency with long-term temperature goals. The model's simple structure is suitable for building probability distributions of key model parameters, such that they reflect uncertainty ranges of multiple climate projections and observed warming trends.

We performed a WRF model study to figure out the mechanism of how the change of the minimum eddy diffusivity (Kzmin) in the planetary boundary layer (PBL) closure scheme affects the simulated near-surface temperature in Beijing area of China. Moreover, the influence of changing Kzmin on the temperature prediction under different surface categories was investigated. It was also shown that a functional-type Kzmin improves the model performance in capturing the temperature change of this area.

Applying the chemistry package from WRF-Chem into the Flow-following finite-volume Icosahedra Model, we essentially make it possible to explore the importance of different levels of complexity in gas and aerosol chemistry, as well as in physics parameterizations on the interaction processes in global modeling systems. The model performance validated by the Atmospheric Tomography Mission aircraft measurements in 2016 summer shows good performance in capturing the aerosol and gas-phase tracers.

Timber plantations are an increasingly important source for roundwood production, next to harvest from natural forests. However, so far timber plantations are underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.

The paper proposes an ensemble based analysis framework ESIAS-chem for time- and altitude-resolved volcanic ash emission fluxes and its uncertainty. The core of the algorithm is an ensemble Nelder-Mead optimization algorithm accompanied by a particle filter update. The performed notional experiments demonstrate the high accuracy of ESIAS-chem in analyzing the vertically resolved volcanic ash in the atmosphere. Further, the system is in general able to estimate the emission fluxes properly.

Road traffic emissions of nitrogen oxides, volatile organic compounds and carbon monoxide produce ozone in the troposphere and thus influence Earth's climate. To assess the ozone response to a broad range of mitigation strategies for road traffic, we developed a new chemistry-climate response model called TransClim. It bases on lookup-tables containing climate-response relations and thus is able to quickly determine the climate response of a mitigation option.

The region of southern Africa (SAF) is highly vulnerable to the impacts of climate change and is projected to experience severe precipitation shortages in the coming decades. Reliable climatic information is therefore necessary for the optimal adaptation of local communities. In this work we show that regional climate models are reliable tools for the simulation of precipitation over southern Africa. However, there is still a great need for the expansion and maintenance of observational data.

Cosmic Ray Neutron Sensors estimate root-zone soil moisture at sub-kilometre scales. There are national-scale networks of these sensors across the globe, however methods for converting neutron signals to soil moisture values are inconsistent. This paper describes our open-source python tool that processes raw sensor data into soil moisture estimates. The aim is to allow a user to ensure they have a harmonized dataset, along with informative metadata, to facilitate both research and teaching. 

Structured-mesh ocean models are still more mature in terms of functionality due to their long development history. However, also the unstructured-mesh ocean models acquire new features and catch up in their functionality. This paper continues the work by Scholz et al. (2019) in documenting the features available in FESOM2.0. It focuses on two aspects: (1) is partial bottom cells and embedded sea ice, (2) deals with mixing parameterizations enabled through the use of the CVMIX package.

For realistically simulating the recently observed distributions of dissolved ²³⁰Th and ²³¹Pa in the ocean, we highlight the importance of the removal process of ²³¹Pa and ²³⁰Th at the seafloor (bottom scavenging) and the dependence of scavenging efficiency on particle concentration. We show that consideration of these two processes can well reproduce not only the oceanic distribution of ²³¹Pa and ²³⁰Th but also the sedimentary ²³¹Pa / ²³⁰Th ratios.

The Community Multiscale Air Quality (CMAQ) modeling system extended for hemispheric-scale applications (H-CMAQ) incorporated the satellite-constrained degassing SO₂ emissions from 50 volcanos across the northern hemisphere. The impact on tropospheric sulfate aerosol (SO₄²⁻) is assessed for 2010. Although the considered volcanic emissions occurred at or below the middle of free troposphere (500 hPa), SO₄²⁻ enhancements of more than 10 % were detected up to the top of free troposphere (250 hPa).

Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to select, add, and scale emissions from different sources through a configuration file with no change to the model source code. We demonstrate the implementation of HEMCO in several models, all sharing the same HEMCO Core code and database library.

Ice sheets are an important component of the climate system and interact with the atmosphere through albedo variations and changes in the surface height. On very long timescales, it is impossible to directly couple ice sheet models with climate models and other techniques have to be used. Here we present a novel coupling method between ice sheets and the atmosphere by making use of an emulator to simulate ice sheet-climate interactions for several million years.

The model's ability to reproduce the state of the simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses K-means algorithm for clustering of model errors. All available data that fall into model domain and simulation period is incorporated into the skill assessment. The clustered errors are used for the spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.

We review the main assumptions and empirical values in convection parameterizations, highlighting their impacts on model outputs. We emphasize the
importance of observations to improve our current understanding of the physics of convection. This contribution fills a gap in current literature. It can help modelers to develop more accurate predictions of the time and location of convection and its subsequent development into severe weather. 
 

The Andes act as a wall in atmospheric flows and plays an important role in the weather of South America, being currently under-represented in weather and climate models. In this work, we propose grids that better capture the mountains and, using idealized dynamical models, study the effects caused by the use of such grids. While possibly improving forecasts for short periods, the grids introduce spurious numerical (non-physical) effects which can demand added caution of model developers.

Reduced-complexity air quality models are less computationally intensive and easier to use. We developed a reduced-complexity air quality intervention model over China, InMAPv1.6.1-China, to rapidly predict the air quality and estimate the health impacts of emission sources in China. We believe that this work will be of great interest to a broad audience, including environmentalists in China, and scientists in relevant fields in both national and local institutes. 

Over the past decade, understanding of isoprene oxidation has improved and proper representation of isoprene oxidation and isoprene-derived SOA (iSOA) formation in canopy-chemistry models is now recognized to be important for an accurate understanding of forest-atmosphere exchange. The updated FORCAsT version 2.0 improves the estimation of some isoprene oxidation products, and is one of the few canopy models currently capable of simulating SOA formation from monoterpenes and isoprene.

Wildfire is a devastating earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses, and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine learning-based wildfire surrogate model within an existing earth system model and achieved high accuracy.

This contributes to the research on uncertainty prediction, important either for determining the weather today or for estimating the risk in prediction. The problem is that uncertainty prediction is numerically very expensive. An alternative has been proposed where the uncertainty is presented in a simplified form where only the dynamics of certain parameters are required. This tool allows to determine the symbolic equations of these parameter dynamics as well as its numerical computation.

Sedimentary carbonate plays a central role to regulation of Earth’s carbon cycle and climate, and also serves as an archive of paleo environments hosting various elements/isotopes. To help obtain “true” environmental changes from carbonate records over diagenetic distortion, a new model IMP has been developed. Signal tracking is enabled by simulating diagenesis of multiple carbonate particles and different styles of particle mixing by benthos are also reflected with a variable transition matrix.

We introduce the first toolbox that allows exploring the benefit of air pollution mitigation scenarios in the every-day air quality forecasts through a web interface. The toolbox relies on the joint use of Chemistry-transport models (CTMs) and machine learning approaches.

Sea ice modelling is an important part of Earth System Models (ESMs). The results of ESMs are used by the Intergovernmental Panel on Climate Change in their reports. In this study we present an improvement to calculate the exchange of nutrients between the ocean and the sea ice. This nutrient exchange is an essential process to keep the ice associated ecosystem functioning. We found out that previous calculation methods may underestimate the primary production of the ice-associated ecosystem.

The CHIMERE chemistry-transport model is presented in its new version, V2020r1. Many changes are proposed compared to the previous version. It includes online modelling, new parametetrizations for aerosols, new emissions schemes, a new parameter file format, the subgrid scale variability of urban concentrations and new transport schemes.

Cropland is the major hotspot for N₂O emission as affected by multiple agricultural practices. Because of the varying magnitudes of N₂O emissions across observation sites and periods, it is difficult to quantify the N₂O budget at a large scale. A process-based biogeochemical model, TRIPLEX-GHG, was incorporated with major agricultural practices. By comparing the modeled and measured data, we found that the TRIPLEX-GHGv2.0 is capable to provide reasonable estimations of N₂O flux from cropland.

Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is “hidden” underground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (“model evaluation”).

The ocean heat content (OHC) based on OFES1 and OFES2 were examined from 1960 through 2016, with marked differences founded, especially in the Atlantic Ocean. The OFES1 generally better agrees with observation-based results. Heat transports could only partially account for the differences, which leads us to infer that the different vertical mixing are the potential causes. This study advances the understanding of OFES data and is expected to be an important reference for the OFES users.

This is a proof of concept and review paper of spatial agents with initial research focusing on geomodelling. The results may be of interest to others working on complex regional geological modelling with sparse data. Structural agent based swarming behaviour is key to advancing this field. The study provides groundwork for research in structural geology 3D modelling with spatial agents. This work was done with Netlogo a free agent modelling platform used mostly for teaching complex systems.