Journal of Advances in Modeling Earth Systems

Journal: Journal of Advances in Modeling Earth Systems

Publisher

American Geophysical Union

ISSN

1942-2466

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Publications 1 - 10 of 17

LWFBrook90.jl—Including Stable Water Isotopes in a Soil Vegetation Atmosphere Transport Model to Constrain Vertical Root Water Uptake Dynamics
Bernhard, Fabian Alexander; Knighton, James; Seeger, Stefan; et al. (2025)
Journal of Advances in Modeling Earth Systems
In water-limited ecosystems, soil water availability plays a pivotal role in determining the stress levels experienced by vegetation. Understanding the vertical distribution of roots and root water uptake (RWU) is essential for accurately predicting drought conditions using Soil Vegetation Atmosphere Transport (SVAT) models. However, quantifying RWU in environments poses a considerable challenge. Here, stable isotope signatures in water offer a promising avenue for inferring RWU. They can effectively trace the movement of precipitation through soil layers into trees. Combining isotopes with precipitation and soil moisture content enables inferring relative and absolute contributions of soil layers to RWU, thereby providing added benefit over mixing models. In this study, we extended the SVAT model LWF-Brook90 with transport of stable isotopes in water. The model was validated using measurements from a forest monitoring site and further test cases. Across calibration scenarios combining observed hydrometric and isotopic state variables as calibration targets, we compared model accuracy, predictive uncertainty, and parameter equifinality. Our results demonstrated that the model accurately reproduced observations and that overall model accuracy and precision could be improved by a multi-objective calibration approach combining isotopic and hydrometric time series. Isotopes specifically constrained parameters linked to RWU and preferential infiltration. Including isotopes reduced uncertainty in parameter estimates and model predictions and reduced parameter equifinality. Combining isotope mixing with SVAT models holds the potential to significantly enhance our mechanistic understanding of water fluxes in water-limited ecosystems, including dynamics of root water uptake, facilitating more accurate predictions of vegetation responses to changing environmental conditions.
A Numerical Analysis of Six Physics-Dynamics Coupling Schemes for Atmospheric Models
Ubbiali, Stefano; Schär, Christoph; Schlemmer, Linda; et al. (2021)
Journal of Advances in Modeling Earth Systems
Six strategies to couple the dynamical core with physical parameterizations in atmospheric models are analyzed from a numerical perspective. Thanks to a suitably designed theoretical framework featuring a high level of abstraction, the truncation error analysis and the linear stability study are carried out under weak assumptions. Indeed, second-order conditions are derived which are not influenced either by the specific formulation of the governing equations, nor by the number of parameterizations, nor by the structural design and implementation details of the time-stepping methods. The theoretical findings are verified on two idealized test beds. Particularly, a hydrostatic model in isentropic coordinates is used for vertical slice simulations of a moist airflow past an isolated mountain. Self-convergence tests show that the sensitivity of the prognostic variables to the coupling scheme may vary. For those variables (e.g., momentum) whose evolution is mainly driven by the dry dynamics, the truncation error associated with the dynamical core dominates and hides the error due to the coupling. In contrast, the coupling error of moist variables (e.g., the precipitation rate) emerges gradually as the spatio-temporal resolution increases. Eventually, each coupling scheme tends toward the formal order of accuracy, upon a careful treatment of the grid cell condensation. Indeed, the well-established saturation adjustment may cap the convergence rate to first order. A prognostic formulation of the condensation and evaporation process is derived from first principles. This solution is shown effective to alleviate the convergence issues in our experiments. Potential implications for a complete forecasting system are discussed.
Assessing Cloud Feedbacks Over the Atlantic With Bias-Corrected Downscaling
Liu, Shuchang; Zeman, Christian; Schär, Christoph (2025)
Journal of Advances in Modeling Earth Systems
Clouds exert a significant impact on global temperatures and climate change. Cloud-radiative feedback (CRF) is one of the major sources of climate change uncertainty. Understanding CRF is therefore crucial for accurate climate projections. Biases like the double-ITCZ problem in Global Climate Models (GCMs) hamper precise climate projections. Here, we explore a bias-corrected downscaling method to constrain the cloud feedback uncertainties in the tropical and sub-tropical Atlantic region. We use regional climate model (RCM) simulations with convection permitting resolution, driven by debiased driving fields from three different global climate models (GCMs). Bias-corrected downscaling significantly reduces biases in ITCZ intensity and position, eliminating the double-ITCZ bias across all six experiments (three GCMs for historical and future periods). We explore the new methodology's potential to investigate the CRF in comparison to that of the driving GCMs. Results indicate that additional GCMs and RCMs are necessary for a more comprehensive uncertainty estimation and more conclusive results, while our simulations suggest a potentially narrower range of CRF over the tropical and subtropical Atlantic, primarily due to an improved representation of stratocumulus clouds. Our study highlights the potential of bias-corrected downscaling in constraining the uncertainty of simulations and estimates of cloud feedback and equilibrium climate sensitivity. The results advocate for further simulations with additional RCMs and domains for a more comprehensive analysis.
Addressing Challenges in Simulating Inter–Annual Variability of Gross Primary Production
De, Ranit; Bao, Shanning; Koirala, Sujan; et al. (2025)
Journal of Advances in Modeling Earth Systems
A long-standing challenge in studying the global carbon cycle has been understanding the factors controlling inter–annual variation (IAV) of carbon fluxes, and improving their representations in existing biogeochemical models. Here, we compared an optimality-based model and a semi-empirical light use efficiency model to understand how current models can be improved to simulate IAV of gross primary production (GPP). Both models simulated hourly GPP and were parameterized for (a) each site–year, (b) each site with an additional constraint on IAV (), (c) each site, (d) each plant–functional type, and (e) globally. This was followed by forward runs using calibrated parameters, and model evaluations using Nash–Sutcliffe efficiency (NSE) as a model-fitness measure at different temporal scales across 198 eddy-covariance sites representing diverse climate–vegetation types. Both models simulated hourly GPP better (median normalized NSE: 0.83 and 0.85) than annual GPP (median normalized NSE: 0.54 and 0.63) for most sites. Specifically, the optimality-based model substantially improved from NSE of −1.39 to 0.92 when drought stress was explicitly included. Most of the variability in model performances was due to model types and parameterization strategies. The semi-empirical model produced statistically better hourly simulations than the optimality-based model, and site–year parameterization yielded better annual model performance. Annual model performance did not improve even when parameterized using . Furthermore, both models underestimated the peaks of diurnal GPP, suggesting that improving predictions of peaks could produce better annual model performance. Our findings reveal current modeling deficiencies in representing IAV of carbon fluxes and guide improvements in further model development.
ClimateBench v1.0: A Benchmark for Data-Driven Climate Projections
Watson-Parris, Duncan; Rao, Yuhan; Olivié, Dirk; et al. (2022)
Journal of Advances in Modeling Earth Systems
Many different emission pathways exist that are compatible with the Paris climate agreement, and many more are possible that miss that target. While some of the most complex Earth System Models have simulated a small selection of Shared Socioeconomic Pathways, it is impractical to use these expensive models to fully explore the space of possibilities. Such explorations therefore mostly rely on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Here we present ClimateBench – the first benchmarking framework based on a suite of Coupled Model Intercomparison Project, AerChemMIP and Detection-Attribution Model Intercomparison Project simulations performed by a full complexity Earth System Model, and a set of baseline machine learning models that emulate its response to a variety of forcers. These emulators can predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and aerosols, allowing them to efficiently probe previously unexplored scenarios. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, robustness and mathematical tractability. We hope that by laying out the principles of climate model emulation with clear examples and metrics we encourage engagement from statisticians and machine learning specialists keen to tackle this important and demanding challenge.
A Locally Smoothed Terrain-Following Vertical Coordinate to Improve the Simulation of Fog and Low Stratus in Numerical Weather Prediction Models
Westerhuis, Stephanie; Fuhrer, Oliver (2021)
Journal of Advances in Modeling Earth Systems
The correct simulation of fog and low stratus (FLS) is a difficult task for numerical weather prediction (NWP) models. The Swiss Plateau experiences many days with FLS in winter. Most NWP models employ terrain-following vertical coordinates. As a consequence, the typically flat cloud top is intersected by sloping coordinate surfaces above hilly terrain such as the Swiss Plateau. Horizontal advection across the sloping coordinate surfaces leads to spurious numerical diffusion which promotes erroneous FLS dissipation. To address this problem, we propose a new vertical coordinate formulation which features a local smoothing of the model levels. We demonstrate the positive impact of the new vertical coordinate formulation on a case study in detail and for a full month using the COSMO model. The improved vertical coordinate formulation is not yet sufficient to obtain perfect FLS forecasts, it is however a crucial aspect to consider on the way thereto.
Uncertainties related to the representation of momentum transport in shallow convection
Schlemmer, Linda; Bechtold, P.; Sandu, I.; et al. (2017)
Journal of Advances in Modeling Earth Systems
Convective momentum transport (CMT) has mostly been studied for deep convection, whereas little is known about its characteristics and importance in shallow convection. In this study, CMT by shallow convection is investigated by analyzing both data from large-eddy simulations (LESs) and reforecasts performed with the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). In addition, the central terms underlying the bulk mass-flux parametrization of CMT are evaluated offline. Further, the uncertainties related to the representation of CMT are explored by running the stochastically perturbed parametrizations (SPP) approach of the IFS. The analyzed cases exhibit shallow convective clouds developing within considerable low-level wind shear. Analysis of the momentum fluxes in the LES data reveals significant momentum transport by the convection in both cases, which is directed downgradient despite substantial organization of the cloud field. A detailed inspection of the convection parametrization reveals a very good representation of the entrainment and detrainment rates and an appropriate representation of the convective mass and momentum fluxes. To determine the correct values of mass-flux and in-cloud momentum at the cloud base in the parametrization yet remains challenging. The spread in convection-related quantities generated by the SPP is reasonable and addresses many of the identified uncertainties.
Global Mapping of Potential and Climatic Plant-Available Soil Water
Gupta, Surya; Lehmann Grunder, Peter Ulrich; Bickel, Samuel; et al. (2023)
Journal of Advances in Modeling Earth Systems
The estimation of plant-available soil water (PASW) is essential to quantify transpiration fluxes, the onset of heatwaves, irrigation water management, land-use decisions, vegetation ecology, and land surface memory in climate models. PASW is the amount of stored water available for plant use. It is broadly defined as the difference between soil water content at field capacity (FC) and wilting point (WP) in the root zone. The limiting states of FC and WP are linked to gravitational drainage and plant physiology and are often deduced from soil water characteristics at prescribed matric potentials (−3.3 and −150 m for FC and WP, respectively). Evidence suggests that static definition of FC at a constant matric potential ignores dynamic effects on FC attainment affected by soil and climate. Here, we revise the definition of PASW by considering (a) soil-specific dynamic effects and (b) local climate effects of evaporation and rainfall frequency on PASW. The new global PASW maps benefit from state-of-the-art soil maps, water characteristic parameterization, and incorporation of dynamic definition of FC. For completeness we provide static and dynamic PASW estimates. Globally, ice-free soils store up to 19,941 km³ of water in the top 1 m (representing 18% of annual terrestrial precipitation). Adjusted for local climatic conditions, global PASW storage rarely exceeds 14,853 km³ per year highlighting the need for multiple refilling of soil profiles. Differences between potential and climatic PASW are manifested primarily in arid regions (where soil profiles are rarely filled to capacity) whereas in humid regions differences in PASW storage are minor.
Modeling the Effect of Trees on Energy Demand for Indoor Cooling and Dehumidification Across Cities and Climates
Meili, Naika; Zheng, Xing; Takane, Yuya; et al. (2025)
Journal of Advances in Modeling Earth Systems
Increasing urban tree cover is a common strategy to lower urban temperatures and indirectly the building energy demand for air-conditioning (AC). However, urban vegetation leads to increasing humidity with potential negative effects on the AC dehumidification loads in hot-humid climates, an effect that has so far been unexplored. Here, we included a building energy model into the urban ecohydrological model Urban Tethys-Chloris (UT&C-BEM) to quantify the AC energy reduction effects of trees in seven hot cities with varying background humidity. A numerical experiment was performed simulating various urban densities and tree cover scenarios in the city-climates of Riyadh, Phoenix, Dubai, New Delhi, Singapore, Lagos, and Tokyo. The relative contribution of tree shade, air temperature reduction, and humidity increase on the AC energy reduction was further quantified. We found that well-watered trees provide the largest average summer AC energy reduction of -17% in the hot-dry climate (Riyadh, Phoenix). As tree shade is the dominant factor leading to the AC energy reduction in all city-climates, humid cities also show an average summer AC energy reduction ranging from -6% to -9%. However, increasing humidity is affecting AC dehumidification loads, especially under higher ventilation rates in humid climates and in these cities, AC energy reduction is most efficient with up to 40% tree cover. Additionally, we found that trees effectively reduce peak AC energy consumption due to higher shading effects in those hours. These results can inform urban planning strategies to maximize reduction in the AC energy demand using urban trees. Plain Language Summary Urban trees can provide multiple benefits, such as reducing temperature and potentially air-conditioning (AC) energy consumption, but they might increase humidity. During AC operation, air is not only cooled but also dehumidified, which requires energy, to prevent indoor mold formation and health problems. However, a quantification of the humidity effects of urban trees on the AC energy consumption in hot-humid cities has so far been lacking. Here, we quantify how urban trees influence the summer AC energy consumption in different climates (Riyadh, Phoenix, Dubai, New Delhi, Singapore, Lagos, and Tokyo). We found that well-watered trees lead to the largest average AC energy reduction of -17% in hot-dry cities. In all cities, tree shading is the dominant factor leading to reduced AC energy consumption. Because of this, we also simulated an average AC energy reduction in hot-humid cities of -6% to -9%. However, increasing humidity leads to raised energy consumption for dehumidification, especially when indoor-outdoor air exchange is high. In hot-humid cities, AC energy reduction due to trees is the most efficient with up to 40% tree cover. Trees also provide larger energy reduction during AC peak hours. These findings can inform urban planning strategies to maximize the ecosystem services provided by trees.
Assessment of Global Ocean Biogeochemistry Models for Ocean Carbon Sink Estimates in RECCAP2 and Recommendations for Future Studies
Terhaar, Jens; Goris, Nadine; Müller, Jens Daniel; et al. (2024)
Journal of Advances in Modeling Earth Systems
The ocean is a major carbon sink and takes up 25%–30% of the anthropogenically emitted CO₂. A state-of-the-art method to quantify this sink are global ocean biogeochemistry models (GOBMs), but their simulated CO₂ uptake differs between models and is systematically lower than estimates based on statistical methods using surface ocean pCO₂ and interior ocean measurements. Here, we provide an in-depth evaluation of ocean carbon sink estimates from 1980 to 2018 from a GOBM ensemble. As sources of inter-model differences and ensemble-mean biases our study identifies (a) the model setup, such as the length of the spin-up, the starting date of the simulation, and carbon fluxes from rivers and into sediments, (b) the simulated ocean circulation, such as Atlantic Meridional Overturning Circulation and Southern Ocean mode and intermediate water formation, and (c) the simulated oceanic buffer capacity. Our analysis suggests that a late starting date and biases in the ocean circulation cause a too low anthropogenic CO₂ uptake across the GOBM ensemble. Surface ocean biogeochemistry biases might also cause simulated anthropogenic fluxes to be too low, but the current setup prevents a robust assessment. For simulations of the ocean carbon sink, we recommend in the short-term to (a) start simulations at a common date before the industrialization and the associated atmospheric CO₂ increase, (b) conduct a sufficiently long spin-up such that the GOBMs reach steady-state, and (c) provide key metrics for circulation, biogeochemistry, and the land-ocean interface. In the long-term, we recommend improving the representation of these metrics in the GOBMs.

Publications 1 - 10 of 17

Journal: Journal of Advances in Modeling Earth Systems

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