Prescribing a fixed grassland density in the ORCHIDEE model limits its ability to capture grassland dynamics, leading to unrealistic mortality, especially in semi-arid grasslands. We proposed a dynamic density approach where a positive density-precipitation relationship emerges. This method improves spatial pattern, significantly reduces mortality, sustains productivity, and raises the aridity threshold above which frequent mortality events occur in grasslands.

Using a neural network model, county-level weather forecasts was achieved in the Western US. By combining traditional forecasting data with actual weather observations, the AI system achieved better temperature predictions at local scales. While showed promise for temperature forecasting, it still had difficulty accurately predicting extreme rainfall events. The research advances weather forecasting capabilities, potentially helping communities prepare for severe weather conditions.

Climate change affect lakes by including not just the lakes themselves but also the land areas that drain into them. These surrounding areas influence how much water and nutrients flow into lakes which in turn impact water quality. Here, water fluxes from land, derived from a global hydrological model where water fluxes are modelled at the grid scale, were used to estimate streamflow inputs to lakes from their catchments. Using data from 70 Swedish lakes, we showed that our method works well.

Accurately predicting how permafrost will thaw with land surface models is a grand challenge in Earth science. We created a new computer model by rebuilding a traditional physics model to work with artificial intelligence. Our results show this new approach is much faster and more reliable for tuning model parameters with data. This provides a better tool to build the next generation of climate models and improve predictions of permafrost's future.

Considering the crucial role of high-resolution surface observation temperature data in the study of surface atmospheric temperature in Marine areas, we propose a new two-stage deep learning model. This model is used to fill in the ocean surface temperature data that is missing in satellite observations due to the orbital gap of polar-orbiting satellites. 

The ocean plays an important role in regulating the Earth's climate by storing excess atmospheric carbon in its interior mainly through the sinking of small organic particles originating from planktonic organisms. Once produced these particles are constantly being transformed (sticking together, breaking into smaller pieces and being consumed by microbes). Here, we try to understand the dynamics of these processes by testing a variety of factors, such as stickiness and water column mixing.

Simple Climate Models (SCMs) are widely used tools to explore how Earth's climate may change in the future. In recent decades, the number and types of SCMs have increased significantly, hindering efforts to understand cross-model differences. In this study, we provide an overview of the main principles guiding climate simulation by SCMs, as well as a description of most high-profile SCMs. This work offers a clear reference to support the informed use of these important tools.

This paper presents a framework for landslide rapid detection using radar and deep learning, trained and tested on data from ≈73000 landslides across diverse regions in the world. The method showed high accuracy and rapid response potential regardless of weather and illumination conditions. By overcoming the limits of optical satellite imagery, it offers a powerful tool for timely landslide disaster response, benefiting disaster management and advancing methods for monitoring hazardous terrains.

We conducted sensitivity experiments to examine how different vertical coordinates influence the representation of water masses and tides using a high-resolution regional ocean model for the Northwest Pacific. We found that the choice of vertical coordinate  strongly affects the degree of artificial mixing, which in turn changes how well the model reproduces key ocean features. This highlights the importance of selecting a vertical coordinate when developing regional ocean models.

A new inventory of anthropogenic emissions, the China INtegrated Emission Inventory (CINEI), was developed in this study to better represent emission sectors, chemical speciation and spatiotemporal variations in China. Compared to simulations driven by global inventories, CINEI demonstrated better numerical modeling performance in ozone and its precursors (nitrogen dioxide and carbon monoxide). This study provides valuable insights for designing ozone mitigation strategies.

The erosion of surface snow by the wind is an important process for the Antarctic surface mass balance. This study presents the first development of a parameterisation of blowing snow for a global climate model. Simulations avec evaluated using measurements in Antarctica. Results show an overall decrease of the snow accumulation in the escarpment region of the ice sheet due to snow erosion and an increase at the coast due to blowing snow deposition and increase in precipitation.

Arctic sea ice recedes, and is thus more exposed to waves, which can fracture continuous pack ice into smaller floes. These are more mobile and easier to melt. Ice fracture itself is not well understood, because of harsh field conditions. We propose a novel criterion parametrising this process, and incorporate it into a numerical model that simulates wave propagation. This criterion can be compared to existing ones. We relate our results to lab experiments, and find qualitative agreement.

Subsurface injection of carbon dioxide (CO₂) can be used for a variety of purposes including geologic carbon storage and enhanced oil recovery. Recently, CO₂ injection into reactive host rocks has been explored as a way to transform CO₂ into dense solid minerals. We present a simulation framework for modeling flow of CO₂ due to injection and subsequent reactions that take place to mineralize CO₂.

Traditional climate models struggle to accurately represent storm clouds, leading to large rainfall biases over East Asia. To address this, we used a multiscale modeling framework that embeds a high-resolution cloud model into each grid cell of the Chinese Academy of Sciences Earth System Model. This approach greatly improves the simulation of East Asian precipitation.

This study introduces the Modified Spatial Efficiency metric to more rigorously evaluate how well climate models reproduce observed spatial patterns, addressing a long-standing challenge in model assessment. It demonstrates robust performance across a wide range of conditions, capturing spatial structures in an intuitive and physically meaningful way. This new metric offers researchers an improved tool for evaluating and inter-comparing climate models.

Concentration of deformation is difficult to capture accurately in computer simulations. We present a number of challenges associated with concentrated viscous deformation and demonstrate strategies to overcome them. The strategies include automatic selection of appropriate time steps to react to rapid changes in model behavior, automatic rescaling to avoid rounding errors, and three methods to prevent model instability. This way, we are able to accurately capture very fast viscous deformation.

The model simulates the evolution of primary and secondary pollutants via gas-phase chemistry, aerosol dynamics (including ultrafine particles), and intra-particle reactions. It uses a sectional approach for size and composition, includes a wall-loss module, and links gas-phase mechanisms of different complexity to secondary organic aerosol formation. Representation of particle phase composition allows viscosity and non-ideality to be taken into account.

This study present a data assimilation system that combines ocean observational data with ocean model results to better understand the ocean and predict its future state. The method uses a three dimensional incremental variational approach focusing on the physical relationships between all the state vector variables errors. Testing in the Mediterranean Sea showed that a complex sea level operator based on a barotropic model works best.

We use a state-of-the-art numerical chemistry-climate model to study the atmospheric sulfur dioxide budget. We simulate the atmospheric concentration of sulfur dioxide (SO₂) and corresponding sulfur deposition fluxes and compare the results with observational data from a satellite instrument and with ground-based in-situ measurements. For the evaluation of the simulated atmospheric lifetime of SO₂, we also simulate the fate of SO₂ emitted by two volcanic eruptions that happened in 2019.

This study explores a new tiling scheme within the HydroBlocks Land Surface Model to represent local, regional and intermediate subsurface flow. Using high-resolution environmental data, the scheme defines parameterized flow units, enabling water and energy flux simulations. Compared against a benchmark simulation, the multiscale scheme demonstrates strong agreement in spatial mean, standard deviation, and temporal variability, showcasing its potential for large-scale hydrological simulation.

We propose TCKF1D-Var, a thermodynamic-constrained variational framework for ground-based microwave radiometer retrievals. Using virtual potential temperature, a ratio-based cost function, and a microphysics closure, it reduces biases relative to ERA5 and 1D-Var, improves cloud liquid water representation, and enhances heavy rainfall precursors, extending lead times. This approach strengthens continuous profiling and supports high-impact weather nowcasting.

Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP (The Inter-Sectoral Impact Model Intercomparison Project) groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.

With CORDEX-CMIP6, ensembles of regional climate projections enable analyses on regional climate change. We present a regional coupled ocean-atmosphere model setup for Europe, tailored to provide consistent climate change information for the North and Baltic Seas. The simulation effectively captures the mean climate, variability, and extremes such as storm surges and marine heatwaves. Using this setup, we will contribute climate projections to EURO-CORDEX.