The current crop of models assessed by the Intergovernmental Panel on Climate Change to produce their assessment reports lack endogenous process-based representations of climate-driven changes to human activities, limiting understanding of the feedback between climate and humans. FRIDA (Feedback-based knowledge Repository for IntegrateD Assessments) v2.1 integrates these systems and generate results that suggest standard scenarios the shared socioeconomic pathways baseline scenarios may overestimate economic growth, highlighting the importance of feedbacks for realistic projections and informed policymaking.

The European spruce bark beetle (SBB) has, in recent years, been the most important disturbance agent in many European forests. We implemented a SBB module in a dynamic vegetation model and calibrated it against observations from Sweden, Switzerland, Austria and France. The start and duration of outbreaks triggered by storm damage and the increased damage driven by recent warm and dry periods were reasonably well simulated, although the spread was reflected in uncertain parameter estimates.

We propose an idealized model of pole-to-pole ocean dynamics designed as a testbed for eddy parameterizations across a range of horizontal scales. While computationally affordable, it is able to capture key metrics of the climate system. By comparing simulations at low, intermediate, and high horizontal resolution, we demonstrate its utility for evaluating eddy parameterizations, in terms of both their effect on the mean state and diagnosis of the unresolved eddy fluxes they aim to represent.

UFS-RAQMS incorporates the Real-time Air Quality Modeling System (RAQMS) stratosphere/troposphere chemistry into the existing NOAA Global Ensemble Forecast System (GEFS-Aerosols) version of NOAA's Unified Forecast System (UFS). Chemical data assimilation using TROPOMI CO column observations is conducted during the July–August–September 2019 period. Comparison of the CO column with independent measurements shows a systematic low bias in biomass burning CO emissions without assimilation.

We describe a new development in a state-of-the-art computer atmosphere model, which follows the movement of the model’s water. This provides an efficient way to track all the model's rain and snow back to the average location of the evaporative source, as shown in a present-day simulation. The new scheme can be used in simulations of the future to predict how sources of regional rain or snowfall might change owing to human actions, providing useful information for water management purposes.

Understanding porous fluid flow is key for many geology applications. Traditional methods cannot resolve cases with sharp discontinuities in hydraulic/mechanical properties across those layers. Here we present a new space-time method that can handle such discontinuities. This approach is coupled with trace element transport. Our study reveals that the layering of rocks significantly influences the formation of fluid-rich channels and the material distribution adjacent to discontinuities.

Wetlands absorb carbon dioxide (CO₂), helping slow climate change, but they also release methane, a potent warming gas. We developed a collection of AI-based models to estimate magnitudes of CO₂ and methane exchanged between the land and the atmosphere, for wetlands on a regional scale. This approach helps to inform land-use planning, restoration, and greenhouse gas accounting, while also creating a foundation for future advancements in prediction accuracy.

This study develops a novel physics-based weather prediction model using artificial intelligence development platform, achieving high accuracy while maintaining strict physical conservation laws. Our algorithms are optimized for modern super computers, enabling efficient large-scale weather simulations. A key innovation is the model's inherent differentiable nature, allowing seamless integration with AI systems to enhance predictive capabilities through machine learning techniques.

We developed a new numerical age solver for a pseudo-steady flow tube of an ice sheet. Thanks to a new coordinate system which tracks the trajectories and a change of the time variable, our scheme combines the advantages of Eulerian and Lagrangian schemes: no numerical diffusion and no dilution of tracers. Our model is so fast that it is easy to optimize its parameters. Our model is made available to the ice sheet community as an easy to use open-source software coded in python.

We have developed a new feature in the atmosphere and ocean components of the Community Earth System Model version 2 by implementing ultraviolet (UV) radiation inhibition of photosynthesis of four marine phytoplankton functional groups represented in the Marine Biogeochemistry Library. The new feature is tested with varying levels of UV radiation, and it will enable an analysis of an asteroid impact’s effect on the ozone layer and how that affects the base of the marine food web.

In this paper, we present pyPDAF, a Python interface to the parallel data assimilation framework (PDAF) allowing for coupling with Python-based models. We demonstrate the capability and efficiency of pyPDAF under a coupled data assimilation setup.

Weather forecasts sometimes show high-frequency noise degrading predictions. Our study reveals stripe patterns arise from mismatches between dynamic and physical calculations in models. Simplified experiments demonstrate that adjusting their connection eliminates stripes. This advances numerical weather prediction understanding, aiding forecasters and the public. Our diagnostic methods provide a framework for solving this global meteorological modeling challenge.

We present METEORv1.0.1, a climate model emulator, that can be trained on full spatially resolved and widely available climate model data to reproduce climate variables and make predictions from unseen emission trajectories. The methodology identifies patterns with timescales of impact for one or more forcers using idealised experiments and anomaly calculations. Results for precipitation and temperature show good model performance and can reproduce hysteresis for overshoot scenarios.

This study introduces a timescale-aware bias-correction framework to enhance Earth system model assessments, vital for the geoscience community. By decomposing model outputs into oscillatory components, we preserve critical information across various timescales, ensuring more reliable projections. This improved reliability supports strategic decisions in sectors such as agriculture, water resources, and disaster preparedness.

The impact of increasing mass loss from the Greenland and Antarctic ice sheets has not so far been included in historical climate model simulations. This paper describes the protocols and data available for modeling groups to add this anomalous freshwater to their ocean modules to better represent the impacts of these fluxes on ocean circulation, sea ice, salinity and sea level.

Simulations of convective precipitation events with microphysics schemes of varying complexity are statistically evaluated against polarimetric radar observations. Convective precipitation is potentially hazardous and difficult to predict. Cloud microphysics schemes contribute significantly to this uncertainty. Depending on the microphysics used, the simulated precipitation distribution varies significantly. The main reason for these differences are the underlying rain drop size distributions.

This study integrates the extreme weather index and deep learning algorithms with the World Food Studies Simulation Model (WOFOST), proposing the WOFOST-EW v1. WOFOST-EW significantly improves the simulation of winter wheat growth under extreme weather conditions, providing more accurate predictions of phenology and yield. As extreme weather events become more frequent, WOFOST-EW provides a key tool for agricultural development.

Measuring biodiversity is necessary to assess its loss, evolution, and role in ecosystem functions. Satellites image the whole terrestrial surface and could cost-efficiently map plant diversity globally. However, developing the necessary methods lacks consistent and sufficient field measurements. Thus, we propose using a simulation tool that generates virtual ecosystems, with species properties and functions varying in response to meteorology and the respective remote sensing imagery.

Tiny air particles impact air quality and climate change. Our study improved their prediction in eastern cities by modeling two key formation processes: ions + sulfuric acid + ammonia (daytime) and sulfuric acid + dimethylamine (morning/evening). This improved model increases predictions by 36–84 % in Beijing and Nanjing. These advancements enable better demonstrate how these chemical processes significantly influence China eastern cities' particulate pollution.

This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land Information System (LIS) version 7.5.2 to streamline the synchronization, development, and maintenance of Noah-MP within LIS and to enhance their interoperability and applicability. The model benchmarking and evaluation results reveal key model strengths and weaknesses in simulating land surface quantities and show implications for future model improvements.

The SAPRC Atmospheric Chemical Mechanism Generation System (MechGen) generates explicit chemical reaction mechanisms for organic compounds. MechGen has been used for decades in the development of the widely used SAPRC mechanisms. This paper, detailing the software system, and a companion paper, detailing the chemical basis, represent the first complete documentation of MechGen. This paper includes examples and instructions for generating explicit and reduced mechanisms.

A plume model is developed to simulate aerosol microphysics in a dispersing aircraft plume, including interactions between ice crystals and aerosols in vortex regime. Compared to an instantaneous dispersion approach, the plume approach estimates 15 % lower aviation aerosol number concentrations, due to more efficient coagulation at plume scale. The model is sensitive to background conditions and initialization parameters, such as ice crystal number concentration and fuel sulfur content.