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This study applies novel artificial intelligence-based models which allow the identification of one specific weather system which affects the midlatitude circulation. We show that the models yield similar results as their trajectory-based counterpart which requires data at higher spatio-temporal resolution and is computationally more expensive. Overall, we aim to show how deep learning methods can be used efficiently to support process-understanding of biases in weather prediction models.

The green's functions methodology offers a systematic, easy to implement, computationally cheap, scalable, and extendable method to tune uncertain parameters in models accounting for the dependent response of the model to a change in various parameters. In this paper, we successfully show for the first time that long-term errors in earth system models can be considerably reduced using Green's functions methodology. The method can be easily applied to any model containing uncertain parameters.

Tracer transport in atmosphere models can be computationally expensive. We describe a flexible and efficient interpolation semi-Lagrangian method, Islet. It permits using up to three grids that share an element grid: a dynamics grid for computing quantities such as the wind velocity; a physics parameterizations grid; and a tracer grid. Islet performs well on a number of validation problems and achieves high performance on up to 27,600 NVIDIA V100 GPUs on the Summit supercomputer.

We wanted to examine how the chosen measurement data and calibration process itself affects soil organic carbon model calibration. In our results we found that there is benefit in using data multiple litterbag decomposition experiments simultaneously, even with the required assumptions. Additionally due to the amount of noise and uncertainties in the system, more advanced calibration methods should be used to parameterize the models.

The purpose of our paper is to show our experiences with a new algorithm for detecting of pressure centers based only on mean sea level pressure. While other methods usually focus only on the detection of cyclones, our approach is suitable for finding anticyclones centers as well. Our method is easy to implement with only a few parameters and is based only on standard image processing algorithms. When compared to the manual analysis provided by Met Office, the agreement is around 85 % to 90 %.

This paper describes a simplified interface for implementing and testing new chemical reactions within the reactive transport simulator PFLOTRAN. The manuscript describes the interface, providing example code for the interface. The paper includes several chemical reactions implemented through the interface.

With LSAT PM we introduce an open-source, standalone, easy-to-use application that supports scientific principles of openness, knowledge integrity, and replicability. Doing so, we want to share our experience in the implementation of heuristic and data-driven landslide susceptibility assessment methods such as Analytic Hierarchy Process, Weights of Evidence, Logistic Regression, and Artificial Neural Networks. A test dataset is available.

Cliff base and top lines that delimit coastal cliff faces are usually manually digitized based on maps, aerial photographs, terrain models, etc. However, manual mapping is time consuming and depends on mapper's decisions and skills. To increase objectivity and efficiency of cliff mapping, we developed CliffDelineaTool, an algorithm that identifies cliff base and top positions along cross-shore transects using elevation and slope characteristics.

Physical processes in weather systems importantly affect the midlatitude large-scale circulation. This study introduces an artificial intelligence-based framework which allows the identification of an important weather system – the so-called warm conveyor belt – at comparably low computational costs and from data at low spatial and temporal resolution. The framework thus newly enables the systematic investigation of WCBs in large data sets such as climate model projections.

This research assimilated the Global Precipitation Measurement (GPM) satellite-retrieved ocean surface meteorology data into the Weather Research and Forecasting (WRF) model with the Gridpoint Statistical Interpolation (GSI) system for two snowstorms during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field experiments. The result indicated positive impact of the data for short-term forecasts for both heavy snowfall events.

Reservoir hydrodynamic is often described in numerical models differing in dimensionality. 1D and 2D models assume homogeneity along the unresolved dimension. We compare the performance of three models: 1 to 3 dimensions. All models presented reasonable results for seasonal temperature dynamics. Neglecting longitudinal transport resulted in the largest deviations in temperature. Flow velocity could only reproduced by the 3D model. Our results support the selection of models and their assessment.

Most hydrodynamic models were initially developed based in marine environments. They can not be directly applied to large lakes. Based on field observations and numerical experiments of a large shallow lake, we developed a hydrodynamic model by adopting the new schemes of wind stress, wind waves and turbulence for large lakes. Our model can greatly improve the simulation of lake currents. This study will be a reminder to limnologist to prudently use ocean models to study lake hydrodynamics.

We modify the numerical model of forest growth MAIDENiso by explicitly simulating snow. This allows us to use the model in boreal environments, where snow is dominant. We tested the performance of the model before and after adding snow, using it at two Canadian sites to simulate tree-ring isotopes and comparing with local observations. We found that modelling snow improves significantly the simulation of the hydrological cycle, the plausibility of the model, and the simulated isotopes.

This paper describes a comprehensive model update to the boundary-layer schemes. Because the involved parameterisations are all built on widely applied frameworks, the here described modifications are applicable to many NWP and climate models. The model update contains substantial modifications to the cloud, turbulence and convection schemes and leads to a substantial improvement on several aspects of the model, especially low cloud forecasts.

The implementation of biological N fixation process to LPJ-GUESS in this study provides an opportunity to quantify N fixation rates between legumes, as well as to better estimate grain legumes' production on global scale. It also helps to predict and detect the potential contribution of N-fixing plants as 'green manure' to reducing or removing the use of N fertilizer in global agricultural systems, considering different climate conditions, management practices, and land-use change scenarios.

Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyze these across European forests. We find that uncertainties are dominantly induced by parameters related to water and mortality and climate with increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty, but also modifies uncertainties in other ecosystem processes.

We here evaluate the ability of the CMCC-CM2 climate model participating to the last Coupled Model Intercomparison Project effort, providing climate simulations for the last IPCC assessment report (AR6), in representing extreme events of precipitation and temperature. The 1/4 degree resolution version of the atmospheric model provides better results than the high resolution one (one degree) for temperature extremes. This is not the case of precipitation extremes.

We provide a geospatial modeling approach to sample training images to fill large scale irregular geophysical data gaps in West Antarctica. Using a repository of global topographic training images, our approach models high-resolution basal topography and quantifies the geospatial uncertainty . It generated multiple high-resolution topographic realizations to investigate the impact of basal topographic uncertainty on critical subglacial hydrological flow patterns associated with ice velocity.

This article describes a new version of IFS-AER, the tropospheric aerosol scheme that is used to provide global aerosol products within the Copernicus Atmosphere Monitoring Service (CAMS). Several components of the model have been updated such as the dynamical dust and sea-salt aerosol emission schemes. New deposition schemes have also been incorporated, but are not yet used operationally. This new version of IFS-AER have been evaluated and shown to have a greater skill than previous versions.

This paper presents the implementation of container technology for the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), a unified atmospheric model that can be used as a global, a global-nest, and a regional model for weather-to-seasonal prediction. Container technology makes SHiELD cross-platform and easy to use, which opens opportunities for collaborative research and development. Performance and scalability of the containerized SHiELD are evaluated and discussed.

This paper describes the way the Copernicus Atmosphere Monitoring Service (CAMS) produces forecasts of volcanic SO₂. These forecast are provided routinely every day. They are cerated by blending SO₂ data from satellite instruments (TROPOMI and GOME-2) with the CAMS model. We show that the quality of the CAMS SO₂ forecasts can be improved if additional information about the height of volcanic plumes is provided in the satellite data.

We present the technical coupling between a land surface model (CLM5) and the Parallel Data Assimilation Framework (PDAF). This coupling enables measurement data to update simulated model states and parameters in a statistically optimal way. We demonstrate the viability of the model framework using an application in a forested catchment where the inclusion of soil water measurements significantly improved the simulation quality.

The article focuses on the exploitation of radar polarimetry for model evaluation of stratiform precipitation. The model exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g., fragmentation due to ice-ice collisions), and use of more reliable snow scattering models in forward operator to draw valid conclusions.

Our atmosphere is a chaotic system, where even a tiny change can have a big impact. This makes it difficult to assess if small changes, such as the move to a new hardware architecture, will significantly affect a weather and climate model. We present a methodology that allows to objectively verify this. The methodology is applied to several test cases, showing a high sensitivity. Results also show that a major system update of the underlying supercomputer did not significantly affect our model.

We present a global version of the Ecosystem Demography (ED) model which can track vegetation 3-D structure and scale up ecological processes from individual vegetation to ecosystem scale. Model evaluation against multiple benchmarking dataset demonstrate the model capabilities to simulate global vegetation dynamics across a range of temporal and spatial scales. With this version, ED has potential to be linked with remote sensing observations to address key scientific questions.

SCSOFS operated in NMEFC of China can provide daily updated marine forecasting in SCS for the future 5 days since 2013. Comprehensive updates have been conducted to the configurations of SCSOFS’s physical model and data assimilation scheme in order to improve forecasting skills. Three most sensitive updates are highlighted. Scientific comparison and accuracy assessment results indicate that remarkable improvements have been achieved in SCSOFSv2 with respect to the original version SCSOFSv1.

Production of satellite products rely on information from different centers. By coupling a weather model and an orbit determination solver we eliminate the dependence on one of the centers. The coupling has proven to be possible in the first stage, where no formatting has been applied to any of the models involved. This opens a window for further development and improvement to a coupling that has proven to be as good as the predecessor model.

We revise the roughness of the land surface in the climate model CESM. Guided by observational data, we increase the surface roughness of forests and decrease the one of bare soil, snow, ice, and crops. These modifications alter simulated temperatures and wind speeds at and above the land surface considerably, in particular over desert regions. The revised model represents the diurnal variability of the land surface temperature better compared to satellite observations over most regions.

Our study introduces the SPAC-3Hpy model, a ready-to-use open-access model that simulates the water fluxes across the soil, roots, and stem. To test the model capabilities, we tested it against exact solutions and a case study. The model presented considerably small errors when compared to the exact solutions and was able to correctly represent transpiration patterns when compared to experimental data. The results show that SPAC-3Hpy can correctly simulate above- and below-ground water transport.

In this study, we developed a novel modelling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational effeciency through parallel computation. The test results in the Yuehe River Basin, China, show a good predicative capability for cascading flood–landslide events.

This paper developed a novel quantitative decoupling analysis (QDA) method to quantify the contributions of emission, meteorology, chemical reaction, and their nonlinear interactions on PM_2.5 and applied it to a pollution episode in Beijing. This method can provides the researchers and policy makers with valuable information for understanding of key factors to heavy pollution, but also help the modelers to find out the sources of uncertainties among numerical models.

This article is a technical note on the aerosol absorption and scattering calculations of the Community Radiative Transfer Model (CRTM) v2.2 and v2.3. It also provides guidance for prospective users of the CRTM aerosol option and Gridpoint Statistical Interpolation (GSI) aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are also briefly discussed.

This paper describes the validation of Area of Habitat (AOH) maps produced for terrestrial birds and mammals. The main objective of the validation was to asses the accuracy of these maps based on independent data. We used the open access data from repositories like ebird and gbif to check if our maps were a better reflection of species' distribution than random. When points were not available we used logistic models to validate the AOH maps. We found majority of AOH maps have high accuracy.

1. A physically-based distributed QMG model is effectively devoloped. 2. This model requires only a small amount of data for modelling in karst areas. 3. Eighteen satisfactory flood simulation results indicate this QMG model is feasible. 4. An improved chaotic particle swarm optimization algorithm is effective for parameter optimization.

Most current generation climate and weather models have a relatively simplistic description of snow and snow-atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow-cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore under-explored phenomena in the cryosphere.