Abstract. Developing and evaluating spatial efficiency metrics is essential for assessing how well climate or other models of the Earth's system reproduce the observed patterns of variables like precipitation, temperature, atmospheric pollutants, and other environmental data presented in a gridded format. In this study, we propose a new metric, the Modified Spatial Efficiency (MSPAEF), designed to overcome limitations identified in existing metrics, such as the Spatial Efficiency (SPAEF), the Wasserstein Spatial Efficiency (WSPAEF), or the Spatial Pattern Efficiency metric (Esp). The performance of MSPAEF is systematically compared to these metrics across a range of synthetic data scenarios characterized by varying spatial correlation coefficients, biases, and standard deviation ratios. Results demonstrate that MSPAEF consistently provides robust and intuitive performance, accurately capturing spatial patterns under diverse conditions. Additionally, two realistic but synthetic case studies are presented to further evaluate the practical applicability of the metrics. In both examples, MSPAEF delivers results that align with intuitive expectations, while the other metrics exhibit limitations in identifying specific features in at least one case. Finally, as a real-world application, we rank global Coupled Model Intercomparison Project phase 6 (CMIP6) model data according to their skill in representing precipitation and temperature using the four different metrics. This application highlights that the MSPAEF rankings are most similar with Esp with a normalized absolute ranking difference of 2.8 for precipitation, and 3.8 for temperature. These findings highlight the added value of the MSPAEF metric in evaluating spatial distributions and its potential to be used in climate or other environmental model evaluation or inter-comparison exercises.

Abstract. Secondary organic aerosol (SOA) is a major component of particulate matter but is often underpredicted in chemistry climate models. Recent advances in measuring and resolving the chemically complex structure of intermediate volatile organic compounds (IVOC) have shown that IVOCs, despite their high SOA yields, have long been underrepresented in models. These compounds are key precursors of SOA from emissions in the road transport sector and significantly influence SOA formation. Understanding vehicle emissions, their chemistry, and their SOA-forming potential is essential for accurately estimating their contributions to atmospheric SOA and global organic aerosol loads. To improve this understanding, we have updated the organic module ORACLE in the global chemistry climate model EMAC. The existing IVOC representation was based on scaled organic carbon (OC) emissions and a highly parameterized volatility basis set (VBS) which underestimated global IVOC emissions, particularly those from gasoline combustion, and oversimplified their chemistry. Here, we replaced this approach with a lumped species framework, in which experimental data for gasoline and diesel emissions were grouped into seven lumped species based on their chemical properties and hydroxylation potentials. These species were linked to adjusted emission inventories for regional diesel and gasoline consumption. A 10 year simulation with the updated ORACLE-IVOC model resulted in significant changes. The global atmospheric burden of road transport IVOC-derived SOA (SOA-iv) increased by 1 order of magnitude, from 0.014 to 0.13 Tg. The composition of road transport organic aerosol (OA) shifted, with SOA-iv contributing 2.5 to 13 times more than the primary organic aerosol (POA) and SOA derived from semi-volatile organic compounds combined. In the results using the previous model, this ratio was between 0.4 and 1.1. The geographical distribution of OA also changed. Regions rich in gasoline relative to diesel emissions experienced higher concentration increases, and remote areas experienced elevated concentrations due to more efficient long-range transport of the new lumped IVOC species. Overall, these changes led to a significant increase in the contribution of road transport to total anthropogenic SOA-iv from an average value of 3 % to 35 %.

Abstract. A new configuration of the Australian Community Climate and Earth System Simulator coupled model, ACCESS-CM2, with a higher resolution ocean-sea ice component at 0.25° is introduced. The higher resolution ACCESS-CM2-025 model was developed to better represent the ocean mesoscale and expand the scope of climate modelling research applications. The individual model components have not been changed compared with ACCESS-CM2-1, the existing lower resolution version of the model at 1°, which was one of Australia's contributions to the World Climate Research Program's Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper assesses the simulated climate for a 500 years present-day run in ACCESS-CM2-025 against observations, the lower resolution ACCESS-CM2-1 model, and two ocean-sea ice models using the same model components and comparable grid resolutions but with prescribed atmospheric forcing. ACCESS-CM2-025 is more energetic and performs better in regions of elevated ocean mesoscale variability such as at western boundary currents. The higher resolution ACCESS-CM2-025 also features a more realistic ENSO life cycle and seasonality, with a reduced biennality, which is common in the lower resolution ACCESS-CM2-1. Both ACCESS-CM2 models share many biases, particularly near the sea surface and also affecting sea ice coverage, reflecting insufficiency in the atmospheric model component. While ACCESS-CM2-025 exhibits improved time-mean deep convection, sea ice, and mixed layer depth in the North Atlantic, it also experiences multidecadal variability, which is evident in many variables, including the Atlantic Meridional Overturning Circulation.

Abstract. The impact of road traffic on local climate has often been overlooked, being modelled as an aggregated sensible heat flux released into the atmosphere, although it has multiple effects including turbulence, heat from energy inefficiencies of vehicles, tyre friction, snow compaction, and shadowing. These effects can impact road surface conditions and exacerbate the phenomenon of Urban Heat Island (UHI). This study aims to improve the representation of traffic impacts in the Town Energy Balance (TEB) V9.0 urban climate model. Particular attention has been paid to preserve physical consistency among the parameterisations of tyre friction, turbulence, energy inefficiencies, and radiation impacts of the road traffic within the model. In addition, a method has been developed to model the average engine efficiency of the entire automobile fleet with internal combustion engines (ICEs) using the Worldwide Harmonized Light vehicles Test Cycles (WLTC). The new parameterisations are evaluated using observations from two road weather stations in southern Finland, Nupuri and Palojärvi, which are characterised by clear commuting patterns. To evaluate the new traffic parameterisation, road surface temperature (RST) differences between the two road carriageways are used to isolate the traffic-induced effects from the natural factors. The results show that the new parameterisation is able to simulate the traffic-induced impacts on road surface temperatures. In addition, wind-induced impact and rolling friction have been shown to drive traffic effects on RST. Taking explicitly into account the traffic impacts might be better suited to simulate their actual impacts on the local scale.

Abstract. The Sahel region is characterized by its semi-arid climate and open-canopy agroforestry systems, which play an important role in global carbon dynamics. Parkland agroforestry has the potential to sequester carbon at an average rate of 0.4 tC ha−1 yr−1, which, if expanded to its maximum potential extent, would correspond to an additional carbon stock of approximately 558 TgC compared to treeless croplands. However, land surface models (LSM) used in global climate modeling struggle to represent carbon dynamics in these ecosystems due to the inadequate representation of deep-roots tapping groundwater during dry periods, key environmental control for many agroforestry systems such as the widespread parklands based on the phreatophytic species Faidherbia albida. This study explores the sensitivity of Faidherbia albida parklands to tree density and water availability (rainfall and soil water content in the capillary fringe of the groundwater table) using a new configuration of the ORCHIDEE LSM. To this aim, the ORCHIDEE LSM was modified to simulate the growth of Faidherbia albida by simulating its inverted phenology based on forced temporal series of soil water content of soil layers between 4 and 5 m and water saturation below 5 m and by adjusting the photosynthesis and carbon allocation parameters for Faidherbia albida and associated crops. The model was evaluated against independent eddy covariance and meteorological data from the Niakhar agroforestry site in Senegal. Simulation outputs were analyzed in terms of leaf area index (LAI), gross primary productivity (GPP), latent heat (LE), sensible heat (H) and net radiation (Rn). The model simulated tree GPP of 4.08 ± 0.21 tC ha−1 yr−1 compared to observed GPP of 5.06 ± 0.49 tC ha−1 yr−1. For croplands, the model produced GPP of 7.97 ± 0.89 tC ha−1 yr−1 compared to observed values of 7.78 ± 1.75 tC ha−1 yr−1. Simulations revealed that tree density positively influenced annual carbon uptake but reduced crop harvest at highest tree densities, indicating a trade-off between carbon sequestration and crop yield. Sensitivity analyses showed that interannual variability in soil water content in the capillary fringe of the groundwater table and rainfall influenced differently crop, tree and ecosystem carbon and energy fluxes. Despite its strengths, the model exhibited limited responsiveness of tree productivity to soil water content variability in the capillary fringe of the groundwater table, highlighting the need for enhanced representation of water uptake by tree roots in the model. These findings emphasize the importance of accurately modeling both surface soil water and groundwater dynamics and phenology to predict the responses of semi-arid agroforestry systems to climate variability. This study enhances our understanding of carbon and energy flux partitioning in complex, water-stressed and groundwater dependent agroforestry systems.

Abstract. We develop a new Earth System model configuration framed into the ICON architecture, which provides the baseline for the next generation of climate predictions and projections (hereafter ICON XPP – where XPP stands for eXtended Predictions and Projections). ICON XPP comprises the atmospheric component of the numerical weather prediction (ICON NWP), the ICON ocean and land surface components, and an ensemble-variational data assimilation system, all adjusted to an Earth System model for pursuing climate research and operational climate forecasting. Two baseline configurations are presented: (1) a 160 km atmosphere and a 40 km ocean resolution, and (2) 80 km atmosphere and 20 km ocean resolution. A CMIP DECK (Diagnostic, Evaluation and Characterization of Klima) experimentation framework is used for a first evaluation. ICON XPP depicts the basic properties of the coupled climate. The pre-industrial climate shows a top-of-atmosphere balanced radiation budget and a mean global near-surface temperature of 13.8–14.0 °C. The ocean shows circulation strengths in the range of the observed values, such as the AMOC at 16–18 Sv and the flows through the common passages. The current climate is characterized by a trend in the global mean temperature of ∼ 1.2 °C since the 1850s, similar to reference datasets. Regionally, the hydroclimate differs greatly from observed conditions. For example, the inter-tropical convergence zone (ITCZ) has a double peak and a wet southern subtropical branch across the oceans. Further, the Southern Ocean sea surface temperature has a strong positive mean bias with temperatures up to 5 °C higher than observations. Dynamical processes, such as El Niño/Southern Oscillation (ENSO) performs similarly to CMIP6-like coupled models. Tropical waves and the Madden-Julian Oscillation are well captured, and the 40 km atmospheric configuration has a spontaneous weak quasi-biennial oscillation. The atmospheric dynamics in the northern extra-tropics of both configurations represent well the position of the jet stream as well as the influences of the transient momentum transports and their feedbacks on the jet stream. Overall, ICON XPP performs similarly to climate models performed in CMIP6 making it a good basis for climate forecasts and projections, and climate research.

Abstract. Parameters in sea ice-ocean coupled models greatly affect the simulated evolution of the ocean and sea ice, and are typically tuned to bring the model state close to observations. Using an adjoint method, spatiotemporally varying parameters of an Arctic sea ice-ocean coupled model are optimized simultaneously with the initial conditions and atmospheric forcing by assimilating satellite and in-situ observations. The assimilation results show that the joint state and parameter estimation (SPE) substantially improves the sea ice concentration simulations. Particularly in October, when the ocean surface starts to refreeze, SPE reduces the lead closing parameter Ho (which determines the minimum ice thickness formed in the open water), thereby increasing the sea ice growth and facilitating the seasonal rapid sea ice recovery in the Arctic's Pacific sector. Comparisons with sea ice thickness observations from the moored upward-looking sonars and Ice Mass Balance buoys demonstrate that incorporating optimized model parameters into the coupled model also leads to better sea ice thickness estimation. Given that the optimal set of sea ice parameters may evolve alongside the thinning of Arctic sea ice, the adjoint-based SPE scheme has the potential to more accurately reconstruct the histical Arctic ocean and sea ice changes covering the satellite era, supporting research on Arctic sea ice and ocean variability.

Abstract. The equilibrium climate sensitivity (ECS) – the equilibrium global mean temperature response to a doubling of atmospheric CO2 – is a high-profile metric for quantifying the Earth system's response to human-induced climate change. A widely applied approach to estimating the ECS is the “Gregory method” (Gregory et al., 2004), which uses an ordinary least squares (OLS) regression between the net radiative flux, N, and surface air temperature anomalies, ΔT, from a 150 year experiment in which atmospheric CO2 concentrations are quadrupled. The ECS is determined by extrapolating the linear fit to N=0, i.e. the ΔT-intercept, indicating the point at which the system is back in equilibrium. This method has been used to compare ECS estimates across the CMIP5 and CMIP6 ensembles and will likely be a key diagnostic for CMIP7. Despite its widespread application, there is little consistency or transparency between studies in how the climate model data is processed prior to the regression, leading to potential discrepancies in ECS estimates. We identify 32 alternative data processing pathways, varying by differences in global mean weighting, net radiative flux variable, anomaly calculation method, and linear regression fit. Using 44 CMIP6 models, we systematically assess the impact of these choices on ECS estimates and calculate uncertainty ranges using two bootstrap approaches. While the inter-model ECS range is insensitive to the data processing pathway, individual outlier models exhibit notable differences. Approximating a model's native grid cell area (if irregular) with cosine of the latitude can decrease the ECS by 11 %, the choice of N-variable can change the ECS by 6 %, and some anomaly calculation methods can introduce spurious temporal correlations in the processed data. Beyond data processing choices, we also evaluate an alternative linear regression method – total least squares (TLS) – which has a more statistically robust basis than OLS. However, for consistency with previous literature, and given TLS may reduce the ECS compared to OLS (by up to 24 %), thereby making a known bias in the Gregory method worse, we do not feel there is sufficient clarity to recommend a transition to TLS in all cases. To improve reproducibility and comparability in future studies, we recommend a standardised Gregory method: weighting the global mean by cell area, using the top of the atmosphere (as opposed to the top of model) N-variable, and calculating anomalies by first applying a rolling average to the preindustrial control timeseries then subtracting from the raw CO2 quadrupling experiment. This approach accounts for model drift while reducing noise in the data to best meet the pre-conditions of the linear regression. While CMIP6 results of the multi-model mean ECS appear insensitive to these processing choices, similar assumptions may not hold for CMIP7, underscoring the need for standardised data preparation in future climate sensitivity assessments.

Abstract. Volatile organic compounds play a significant role in atmospheric chemistry, influencing air quality and climate change. Accurate prediction of their physical properties is essential for understanding their behavior. This paper introduces VaPOrS (Vapor Pressure in Organics via SMILES) as a comprehensive tool designed to process SMILES notation of organic compounds, identify key functional groups, and apply group-contribution methods for property estimation. The core innovation of VaPOrS lies in its self-contained functional group recognition algorithm, which eliminates dependence on external cheminformatics libraries. The current approach enables fully auditable, easily modifiable, and computationally efficient detection of 30 functional groups required by the SIMPOL method. Compared to existing tools, VaPOrS avoids heavy SMILES-to-graph conversions and can obviate interface overhead, providing orders-of-magnitude speedups for large-scale atmospheric modeling scenarios. While this first implementation focuses on the SIMPOL method for estimating saturation vapor pressure and enthalpy of vaporization, the framework is readily extendable to other group-contribution schemes and thermodynamic properties (e.g., partition coefficients, volatility basis set models, solubility, Henry's law constants). The tool has been validated against manually counted functional groups and experimental saturation vapor pressure data for a diverse set of compounds. Results demonstrate excellent agreement with both the original SIMPOL model and experimental observations, while comparisons with existing tools highlight the robustness and accuracy of the new parsing functions. VaPOrS thus provides a generalizable and computationally efficient platform for property prediction of large molecular datasets, facilitating integration into chemical transport and climate models and streamlining the analysis of thousands of organic compounds in atmospheric science applications.

Abstract. Simulations of dynamic global vegetation models (DGVMs) are typically conducted at a spatial resolution of 0.5°, while higher-resolution simulations remain uncommon. This coarse resolution eliminates detailed orographic features and hence, associated climate variability, which are especially pronounced in mountainous regions. The impact of disregarding such variability on vegetation dynamics has not been thoroughly examined. In this study, we explore the differences in vegetation outcomes between the DGVM LPJ-GUESS simulations conducted at high and low spatial resolutions. Using the CHELSA algorithm, we create an elevation-informed high-resolution climate dataset for a domain encompassing the European Union. Distinctive features of this algorithm include orographic nature of formation of precipitation, a negative derivative of temperatures with respect to elevation, and also, detailed consideration of shadowing and exposure of the terrain to the Sun in computations of solar radiation. We design a custom experiment protocol and use it to perform LPJ-GUESS simulations on both resolutions. Comparative analysis reveals significant systematic discrepancies between the two resolutions. In mountainous areas, all of the considered output variables show statistically significant differences. In particular, carbon pools are smaller on the high resolution, with the total carbon pool being 37 %–39 % smaller. Furthermore, we quantify the extent to which the under-representation of orographic climate variation affects regional predictions across the European Union. This is expressed as a difference in the total value, which ranges from −3.8 % for the net ecosystem productivity to 2.9 % for the litter and soil C pools. These values are found to be comparable to differences caused by miss-representation of water bodies and shorelines on the low resolution.