Articles | Volume 12, issue 11
https://doi.org/10.5194/gmd-12-4585-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4585-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Nov 2019
Development and technical paper |
| 05 Nov 2019
| 05 Nov 2019
Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the northern Great Basin using the Ecosystem Demography (EDv2.2) model
Karun Pandit
CORRESPONDING AUTHOR
Department of Geosciences, Boise State University, 1910 University Dr,
Boise, ID 83725-1535, USA
Hamid Dashti
Department of Geosciences, Boise State University, 1910 University Dr,
Boise, ID 83725-1535, USA
Nancy F. Glenn
Department of Geosciences, Boise State University, 1910 University Dr,
Boise, ID 83725-1535, USA
Alejandro N. Flores
Department of Geosciences, Boise State University, 1910 University Dr,
Boise, ID 83725-1535, USA
Kaitlin C. Maguire
United States Geological Survey, Forest and Rangeland Ecosystem
Science Center, 970 Lusk St., Boise, ID 83706, USA
Douglas J. Shinneman
United States Geological Survey, Forest and Rangeland Ecosystem
Science Center, 970 Lusk St., Boise, ID 83706, USA
Gerald N. Flerchinger
United States Department of Agriculture, Agricultural Research
Service, 800 Park Blvd., Suite 105, Boise, ID 83712, USA
Aaron W. Fellows
United States Department of Agriculture, Agricultural Research
Service, 800 Park Blvd., Suite 105, Boise, ID 83712, USA
Related authors
Karun Pandit, Hamid Dashti, Andrew T. Hudak, Nancy F. Glenn, Alejandro N. Flores, and Douglas J. Shinneman
Biogeosciences, 18, 2027–2045, https://doi.org/10.5194/bg-18-2027-2021, https://doi.org/10.5194/bg-18-2027-2021, 2021
Short summary
Short summary
A dynamic global vegetation model, Ecosystem Demography (EDv2.2), is used to understand spatiotemporal dynamics of a semi-arid shrub ecosystem under alternative fire regimes. Multi-decadal point simulations suggest shrub dominance for a non-fire scenario and a contrasting phase of shrub and C3 grass growth for a fire scenario. Regional gross primary productivity (GPP) simulations indicate moderate agreement with MODIS GPP and a GPP reduction in fire-affected areas before showing some recovery.
Rainey Aberle, Ellyn Enderlin, Shad O'Neel, Caitlyn Florentine, Louis Sass, Adam Dickson, Hans-Peter Marshall, and Alejandro Flores
The Cryosphere, 19, 1675–1693, https://doi.org/10.5194/tc-19-1675-2025, https://doi.org/10.5194/tc-19-1675-2025, 2025
Short summary
Short summary
Tracking seasonal snow on glaciers is critical for understanding glacier health. Yet previous work has not directly compared machine learning algorithms for snow classification across satellite image products. To address this, we developed a new automated workflow for tracking seasonal snow on glaciers using several image products and machine learning models. Applying this method can help provide insights into glacier health, water resources, and the effects of climate change on snow cover.
Kachinga Silwimba, Alejandro N. Flores, Irene Cionni, Sharon A. Billings, Pamela L. Sullivan, Hoori Ajami, Daniel R. Hirmas, and Li Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-713, https://doi.org/10.5194/egusphere-2025-713, 2025
Short summary
Short summary
This study evaluates the influence of soil hydraulic parameterizations on soil moisture simulations in CLM5 across the CONUS (1980–2010) using Empirical Orthogonal Function (EOF) analysis. Results reveal significant regional discrepancies, particularly in the Great Plains, where parameter uncertainty drives biases in soil moisture variability. Comparisons with ERA5-Land highlight seasonal mismatches, underscoring the need for improved soil parameterization to enhance land surface model accuracy.
Lena Wang, Sharon Billings, Li Li, Daniel Hirmas, Keira Johnson, Devon Kerins, Julio Pachon, Curtis Beutler, Karla Jarecke, Vaishnavi Varikuti, Micah Unruh, Hoori Ajami, Holly Barnard, Alejandro Flores, Kenneth Williams, and Pamela Sullivan
EGUsphere, https://doi.org/10.5194/egusphere-2025-70, https://doi.org/10.5194/egusphere-2025-70, 2025
Short summary
Short summary
Our study looked at how different forest types and conditions affected soil microbes, and soil carbon and stability. Aspen organic matter led to higher microbial activity, smaller soil aggregates, and more stable soil carbon, possibly reducing dissolved organic carbon movement from hillslopes to streams. This shows the importance of models like the Microbial Efficiency – Matrix Stabilization framework for predicting CO2 release, soil carbon stability, and carbon movement.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, https://doi.org/10.5194/tc-18-5015-2024, 2024
Short summary
Short summary
Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev., 16, 6531–6552, https://doi.org/10.5194/gmd-16-6531-2023, https://doi.org/10.5194/gmd-16-6531-2023, 2023
Short summary
Short summary
It is important to know how well atmospheric models do in mountains, but there are not very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado River basin against the available data. The model works rather well, but there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we could not do before.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
Short summary
Short summary
We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Karun Pandit, Hamid Dashti, Andrew T. Hudak, Nancy F. Glenn, Alejandro N. Flores, and Douglas J. Shinneman
Biogeosciences, 18, 2027–2045, https://doi.org/10.5194/bg-18-2027-2021, https://doi.org/10.5194/bg-18-2027-2021, 2021
Short summary
Short summary
A dynamic global vegetation model, Ecosystem Demography (EDv2.2), is used to understand spatiotemporal dynamics of a semi-arid shrub ecosystem under alternative fire regimes. Multi-decadal point simulations suggest shrub dominance for a non-fire scenario and a contrasting phase of shrub and C3 grass growth for a fire scenario. Regional gross primary productivity (GPP) simulations indicate moderate agreement with MODIS GPP and a GPP reduction in fire-affected areas before showing some recovery.
Miguel A. Aguayo, Alejandro N. Flores, James P. McNamara, Hans-Peter Marshall, and Jodi Mead
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-451, https://doi.org/10.5194/hess-2020-451, 2020
Manuscript not accepted for further review
Roland Baatz, Pamela L. Sullivan, Li Li, Samantha R. Weintraub, Henry W. Loescher, Michael Mirtl, Peter M. Groffman, Diana H. Wall, Michael Young, Tim White, Hang Wen, Steffen Zacharias, Ingolf Kühn, Jianwu Tang, Jérôme Gaillardet, Isabelle Braud, Alejandro N. Flores, Praveen Kumar, Henry Lin, Teamrat Ghezzehei, Julia Jones, Henry L. Gholz, Harry Vereecken, and Kris Van Looy
Earth Syst. Dynam., 9, 593–609, https://doi.org/10.5194/esd-9-593-2018, https://doi.org/10.5194/esd-9-593-2018, 2018
Short summary
Short summary
Focusing on the usage of integrated models and in situ Earth observatory networks, three challenges are identified to advance understanding of ESD, in particular to strengthen links between biotic and abiotic, and above- and below-ground processes. We propose developing a model platform for interdisciplinary usage, to formalize current network infrastructure based on complementarities and operational synergies, and to extend the reanalysis concept to the ecosystem and critical zone.
Bangshuai Han, Shawn G. Benner, and Alejandro N. Flores
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-140, https://doi.org/10.5194/hess-2018-140, 2018
Manuscript not accepted for further review
Short summary
Short summary
Water scarcity is critical in semiarid regions with intensively managed water supplies. Climate change and water allocation laws, along with hydrological processes determine the spatial and temporal water scarcity patterns. We present a new efficient method that captures plausible variability of climate change, while explicitly capturing the complex irrigation activities as constrained by local water rights. It will be useful for semi-arid watersheds to project future water scarcity patterns.
Bangshuai Han, Shawn G. Benner, John P. Bolte, Kellie B. Vache, and Alejandro N. Flores
Hydrol. Earth Syst. Sci., 21, 3671–3685, https://doi.org/10.5194/hess-21-3671-2017, https://doi.org/10.5194/hess-21-3671-2017, 2017
Short summary
Short summary
The western US relies heavily on irrigation water which is allocated following local water rights. We develop and test a novel model that explicitly integrates water rights into a conceptual hydrologic model to spatially allocate irrigation water. The model well captures the timing and magnitude of irrigation water allocation in our study area and is applicable to semi-arid regions with similar water right regulations. The results could inform future water policies and management decisions.
A. A. Harpold, J. A. Marshall, S. W. Lyon, T. B. Barnhart, B. A. Fisher, M. Donovan, K. M. Brubaker, C. J. Crosby, N. F. Glenn, C. L. Glennie, P. B. Kirchner, N. Lam, K. D. Mankoff, J. L. McCreight, N. P. Molotch, K. N. Musselman, J. Pelletier, T. Russo, H. Sangireddy, Y. Sjöberg, T. Swetnam, and N. West
Hydrol. Earth Syst. Sci., 19, 2881–2897, https://doi.org/10.5194/hess-19-2881-2015, https://doi.org/10.5194/hess-19-2881-2015, 2015
Short summary
Short summary
This review's objective is to demonstrate the transformative potential of lidar by critically assessing both challenges and opportunities for transdisciplinary lidar applications in geomorphology, hydrology, and ecology. We find that using lidar to its full potential will require numerous advances, including more powerful open-source processing tools, new lidar acquisition technologies, and improved integration with physically based models and complementary observations.
Related subject area
Climate and Earth system modeling
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Impact of spatial resolution on CMIP6-driven Mediterranean climate simulations: a focus on precipitation distribution over Italy
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Statistical summaries for streamed data from climate simulations: One-pass algorithms (v0.6.2)
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
FACA v1 – Fully Automated Co-Alignment of UAV Point Clouds
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Short summary
Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
Short summary
Short summary
This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with vital physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
Short summary
Short summary
Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
Short summary
Short summary
We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
Short summary
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
Short summary
Short summary
We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
Short summary
Short summary
NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
Short summary
Short summary
We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
Short summary
Short summary
Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
Short summary
Short summary
We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ehsan Sharifi, Llorenç Lledó, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
Short summary
Short summary
To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Short summary
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Nick Schüßler, Jewgenij Torizin, Claudia Gunkel, Karsten Schütze, Lars Tiepolt, Dirk Kuhn, Michael Fuchs, and Steffen Prüfer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-209, https://doi.org/10.5194/gmd-2024-209, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
FACA – Fully Automated Co-Alignment – is a tool designed to generate co-aligned point clouds. We aim to accelerate the application of the co-alignment method and achieve fast results with evolving temporal data and minimal site-specific preparation. FACA offers multiple ways to interact with the workflow, enabling new users to quickly generate initial results through the custom interface, as well as integration into larger automated workflows via the command line. Test datasets are provided.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Cited articles
Ahrends, H. E., Etzold, S., Kutsch, W. L., Stockli, R., Bruegger, R.,
Jeanneret, F., Wanner, H., Buchmann, N., and Eugster, W.: Tree phenology and carbon
dioxide fluxes: use of digital photography for process-based interpretation
of the ecosystem scale, Clim. Res., 39, 261–274, 2009.
AmeriFlux: http://ameriflux.lbl.gov, last access: 7 May 2018.
Antonarakis, A. S., Munger, J. W., and Moorcroft, P. R.: Imaging
spectroscopy- and lidar-derived estimates of canopy composition and
structure to improve predictions of forest carbon fluxes and ecosystem
dynamics, Geophys. Res. Lett., 41, 2535–2542, https://doi.org/10.1002/2013GL058373,
2014.
Barnard, D. M. and Bauerle, W. L.: The implications of minimum stomatal
conductance on modeling water flux in forest canopies, J.
Geophys. Res.-Biogeo., 118, 1322–1333,
https://doi.org/10.1002/jgrg.20112, 2013.
Bonan, G. B., Williams, M., Fisher, R. A., and Oleson, K. W.: Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum, Geosci. Model Dev., 7, 2193–2222, https://doi.org/10.5194/gmd-7-2193-2014, 2014.
Bond-Lamberty, B., Fisk, J. P., Holm, J. A., Bailey, V., Bohrer, G., and Gough, C. M.: Moderate forest disturbance as a stringent test for gap and big-leaf models, Biogeosciences, 12, 513–526, https://doi.org/10.5194/bg-12-513-2015, 2015.
Botkin, D. B., Janak, J. F., and Wallis, J. R.: Rationale, Limitations, and
Assumptions of a Northeastern Forest Growth Simulator, IBM J.
Res. Dev., 16, 101–116, 1972.
Brabec, M. M.: Big Sagebrush (Artemisia tridentata) in a shifting climate
context: assessment of seedling responses to climate, Master's Thesis, Boise
State University, United States, 116 pp., 2014.
Bradley, B. A.: Assessing ecosystem threats from global and regional change:
hierarchical modeling of risk to sagebrush ecosystems from climate change,
land use and invasive species in Nevada, USA, Ecography, 33, 198–208, 2010.
Chambers, J. C., Miller, R. F., Board, D. I., Pyke, D. A., Roundy, B. A.,
Grace, J. B., Schupp, E. W., and Taush, R. J.: Resilience and Resistance of
Sagebrush Ecosystems: Implications for State and Transition Models and
Management Treatments, Range Ecol. Manage., 67, 440–454,
https://doi.org/10.2111/REM-D-13-00074.1, 2014.
Cleary, M. B., Pendall, E., and Ewers, B. E.: Aboveground and belowground
carbon pools after fire in mountain big sagebrush steppe, Range Ecol.
Manage., 63, 187–96, https://doi.org/10.2111/REM-D-09-00117.1, 2010.
Cleary, M. B., Naithani, K. J., Ewers, B. E., and Pendall, E.: Upscaling CO2
fluxes using leaf, soil and chamber measurements across successional growth
stages in a sagebrush steppe ecosystem, J. Arid Environ., 121, 43–51, 2015.
Combal, B., Baret, F., Weiss, M., Trubuil, A., Mace, D.,
Pragnere, A., Myneni, R., Knyazikhin, Y., and Wang, L.: Retrieval of canopy
biophysical variables from bidirectional reflectance using prior information
to solve the ill-posed inverse problem, Remote Sens. Environ., 84, 1–15, https://doi.org/10.1016/S0034-4257(02)00035-4, 2003.
Comstock, J. P. and Ehleringer, J. R.: Plant adaptation in the Great Basin and
Colorado Plateau, Great Basin Nat., 52, 195–215, 1992.
Connelly, J. W., Knick, S. T., Schroeder, M. A., and Stiver, S. J.:
Conservation Assessment of Greater Sage-grouse and Sagebrush Habitats,
Western Association of Fish and Wildlife Agencies, Unpublished Report,
Cheyenne, Wyoming, 2004.
Dietze, M. C., Serbin, S. P., Davidson, C., Desai, A. R., Fend, X., Kelly,
R., Kooper, R, LeBauer, D., Mantooth, J., McHenry, K., and Wang, D.: A
quantitative assessment of a terrestrial biospheremodel's data needs across
North American biomes, J. Geophys. Res.-Biogeo., 119, 286–300,
https://doi.org/10.1002/2013JG002392, 2014.
Dietze, M. C., Fox, A., Beck-Johnson, L. M., Betancourt, J. L., Hooten, M.
B., Jarnevich, C. S., Keitt, T. H., Kenney, M. A., Laney, C. M., Larsen, L.
G., Loescher, H. W., Lunch, C. K., Pijanowski, B. C., Randerson, J. T.,
Read, E. K., Tredennick, A. T., Vargas, R., Weathers, K. C., and White, E.
P.: Iterative near-term ecological forecasting: Needs, opportunities, and
challenges, P. Natl. Acad. Sci. USA, 115, 1424–1432,
https://doi.org/10.1073/pnas.1710231115, 2018.
Duursma, R. A., Blackman, C. J., Lopez, R., Martin-StPaul, N. K., Cochard,
H., and Medlyn, B. E.: On the minimum leaf conductance: its role in models of
plant water use, and ecological and environmental controls, New Phytol., online version, 221, 693–705,
https://doi.org/10.1111/nph.15395, 2019.
ED-2 model development team: Ecosystem Demography
model (ED-2) code repository, available at: https://github.com/EDmodel/ED2 (last access: 20 September 2019), 2014.
Farquhar, G. D. and Sharkey T. D.: Stomatal conductance and photosynthesis,
Ann. Rev. Plant Physiol., 33, 317–345, 1982.
Fellows, A. W., Flerchinger, G. N., Seyfried, M. S., and Lohse, K.: Data for
Partitioned Carbon and Energy Fluxes Within the Reynolds Creek Critical Zone
Observatory, Data set, https://doi.org/10.18122/B2TD7V,
2017.
Fer, I., Kelly, R., Moorcroft, P. R., Richardson, A. D., Cowdery, E. M., and Dietze, M. C.: Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation, Biogeosciences, 15, 5801–5830, https://doi.org/10.5194/bg-15-5801-2018, 2018.
Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P.,
Huntingford, C., Meir, P., and Woodward, F. I.: Assessing uncertainties in a
second-generation dynamic vegetation model caused by ecological scale
limitations, New Phytol., 187, 666–681, https://doi.org/10.1111/j.1469-8137.2010.03340.x, 2010.
Fisher, R. A., Koven, C. D., Anderegg, W. R. L, Christoffersen, B. O.,
Dietze, M. C., Farrior, C. E. Holm, J. A., Hurtt, G. C., Knox, R. G.,
Lawrence, P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D.,
Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K.,
Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu,
X., Zhang, T., and Moorcroft, P. R.: Vegetation demographics in Earth System
Models: A review of progress and priorities, Glob. Change Biol., 24, 35–24,
https://doi.org/10.1111/gcb.13910, 2018.
Flores, A., Masarik, M., and Watson, K.: A 30-Year, Multi-Domain
High-Resolution Climate Simulation Dataset for the Interior Pacific
Northwest and Southern Idaho, data set, https://doi.org/10.18122/B2LEAFD001, 2016.
Gill, R. A. and Jackson, R. B.: Global patterns of root turnover for
terrestrial ecosystems, New Phytol., 147, 13–31,
doi.org/10.1046/j.1469-8137.2000.00681.x, 2000.
Glenn, N.F., Spaete, L. P., Shrestha, R., Li, A., Ilangakoon, N., Mitchell,
J., Ustin, S. L., Qi, Y., Dashti, H., and Finan, K.: Shrubland Species
Cover, Biometric, Carbon and Nitrogen Data, Southern Idaho, 2014, ORNL DAAC,
Oak Ridge, Tennessee, USA, data set, https://doi.org/10.3334/ORNLDAAC/1503, 2017.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition
of the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hoffman, E. O. and Gardner, R. H.: Evaluation of Uncertainties in
Environmental Radiological Assessment Models, in: Radiological Assessments: a Textbook on Environmental Dose
Analysis, edited by: Till, J. E. and Meyer, H.
R., U.S. Nuclear Regulatory Commission Washington, DC, Report No.
NUREG/CR-3332, 1983.
Howard, J. L.: Artemisia tridentata subsp. Wyomingensis, in: Fire
Effects Information System, U.S. Department of Agriculture, Forest
Service, Rocky Mountain Research Station, Fire Sciences Laboratory
(Producer), available at:
https://www.fs.fed.us/database/feis/plants/shrub/arttriw/all.html (last
access: 20 August 2018), 1999.
Keating, E. H., Doherty, J., Vrugt, J. A., and Kang, Q. J.: Optimization
and uncertainty assessment of strongly nonlinear groundwater models with
high parameter dimensionality, Water Resour. Res., 46, W10517,
https://doi.org/10.1029/2009WR008584, 2010.
Kim, Y., Knox, R. G., Longo, M., Medvigy, D., Hutyra, L. R., Pyle, E. H.,
Wofsy, S. C., Bras, R. L., and Moorcroft, P. R.: Seasonal carbon dynamics
and water fluxes in an Amazon rainforest, Glob. Change Biol., 18,
1322–1334, https://doi.org/10.1111/j.1365-2486.2011.02629.x, 2012.
Knick, S. T. and Dobkin, D. S.: Teetering on the Edge or Too Late?
Conservation and Research Issues for Avifauna of Sagebrush Habitats, The
Condor, 105, 611–634, https://doi.org/10.1650/7329, 2003.
Knox, R. G., Longo, M., Swann, A. L. S., Zhang, K., Levine, N. M., Moorcroft, P. R., and Bras, R. L.: Hydrometeorological effects of historical land-conversion in an ecosystem-atmosphere model of Northern South America, Hydrol. Earth Syst. Sci., 19, 241–273, https://doi.org/10.5194/hess-19-241-2015, 2015.
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005.
Kwon, H., Pendall, E., Ewers, B. E., Cleary, M., and Naithani, K.: Spring
drought regulates summer net ecosystem CO2 exchange in a sagebrush-steppe
ecosystem, Agr. Forest Meteorol., 148, 381–391,
https://doi.org/10.1016/j.agrformet.2007.09.010 , 2008.
Kwon, B., Kim, H. S., Jeon, J., and Yi, M. J.: Effects of Temporal and
Interspecific Variation of Specific Leaf Area on Leaf Area Index Estimation
of Temperate Broadleaved Forests in Korea, Forests, 7, 215,
https://doi.org/10.3390/f7100215, 2016.
Lambrecht, S. C., Shattuckb, A. K., and Loik, M. E.: Combined drought and
episodic freezing effects on seedlings of low- and high-elevation subspecies
of sagebrush (Artemisia tridentata), Physiol. Plantarum, 130, 207–217, 2007.
LCC: Landscape Conservation Cooperatives: 2015 LCC Network Areas OGC
Webservices, available at:
https://www.sciencebase.gov/catalog/item/55c52e08e4b033ef5212bd75, last
access: 21 April 2018.
LeBauer, D. S., Wang, D., Richter, K. T., Davidson, C. C., and Dietze, M.
C.: Facilitating feedbacks between field measurements and ecosystem models,
Ecol. Monogr., 83, 133–154, https://doi.org/10.1890/12-0137.1, 2013.
Leuning, R.: A critical appraisal of a combined stomatal-photosynthesis
model for C3 plants, Plant Cell Environ., 18, 339–355, 1995.
Li, C., Sun, O. J., Xiao, C., and Han, X.: Differences in Net Primary
Productivity Among Contrasting Habitats in Artemisia ordosica Rangeland of
Northern China, Range Ecol. Manage., 62, 345–350,
https://doi.org/10.2111/07-084.1, 2009.
McArthur, E. D. and Plummer, A. P.: Biogeography and management of native
western shrubs: a case study, Section Tridentatae of Artemisia, Great Basin
Naturalist Memoirs, 2 , 15,
https://scholarsarchive.byu.edu/gbnm/vol2/iss1/15, 1978.
McIver, J. and Brunson, M.: Multidisciplinary, Multisite Evaluation of
Alternative Sagebrush Steppe Restoration Treatments: The SageSTEP Project,
Range Ecol. Manage., 67, 435–439, https://doi.org/10.2111/REM-D-14-00085.1,
2014.
Medvigy, D. M.: The state of the regional carbon cycle: Results from a
coupled constrained ecosystem-atmosphere model, PhD Thesis, Harvard Univ.,
Cambridge, MA, 2006.
Medvigy, D. M. and Moorcroft, P. R.: Predicting ecosystem dynamics at
regional scales: an evaluation of a terrestrial biosphere model for the
forests of northeastern North America, Phil. Trans. R. Soc. B, 367,
222–235, https://doi.org/10.1098/rstb.2011.0253, 2012.
Medvigy, D., Wofsy, S. C., Munger, J. W., Hollinger, D. Y., and Moorcroft,
P. R.: Mechanistic scaling of ecosystemfunction and dynamics in space and
time: Ecosystem Demography model version 2, J. Geophys. Res., 114, G01002,
https://doi.org/10.1029/2008JG000812, 2009.
Medvigy, D., Jeong, S. J., Clark, K. L., Skowronski, N. S., and Schäfer,
K. V. R.: Effects of seasonal variatio n of photosynthetic capacity onthe
carbon fluxes of a temperate deciduous forest, J. Geophys.
Res.-Biogeo., 118, 1703–1714, https://doi.org/10.1002/2013JG002421,
2013.
Miller, P. A. and Smith, B.: Modelling Tundra vegetation response to recent
Arctic warming, Ambio, 41, 281–291, 2012.
Miller R. F., Knick, S. T., Pyke, D. A., Meinke, C. W., Hanser, S. E., Wisdom, M. J., and Hild, A. L.: Characteristics of sagebrush habitats and limitations to long-term conservation, in: Greater
sage-grouse–ecology and conservation of a landscape species and its
habitats, edited by: Knick S. T. and Connelly, J. W., Studies in avian biology no. 38, University of California Press,
Berkeley, CA, USA, 145–185, 2011.
Mitchell, J. J., Glenn, N. F., Sankey, T. T., Derryberry D. R., Anderson, M.
O., and Hrusk, R. C.: Small-footprint Lidar Estimations of Sagebrush Canopy
Characteristics, Photogramm. Eng. Rem. S., 77,
521–530, 2011.
Mo, X., Chen, J. M., Ju, W., and Black, T. A.: Optimization of ecosystem
model parameters through assimilating eddy covariance flux data with an
ensemble Kalman filter, Ecol. Model., 217, 157–173,
https://doi.org/10.1016/j.ecolmodel.2008.06.021, 2008.
Moorcroft, P. R.: Recent advances in ecosystem-atmosphere interactions: an
ecological perspective, Phil. Trans. R. Soc. B, 270, 1215–1227, https://doi.org/10.1098/rspb.2002.2251, 2003.
Moorcroft, P. R., Hurtt, G. C., and Pacala, S. W.: A method for scaling
vegetation dynamics: the Ecosystem Demography model (ED), Ecol. Monogr., 71,
557–586, https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2,
2001.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models, Part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970.
Oleson, K., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven,
C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E.,
Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S.,
Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical
Description of version 4.5 of the Community Land Model (CLM), NCAR Technical
Note NCAR/TN-503+STR, Boulder, Colorado, 420 pp., 2013.
Olsoy, P. J., Mitchell, J. J., Levia, D. F., Clark, P. E., and Glenn, N. F.:
Estimation of big sagebrush leaf area index with terrestrial laser scanning,
Ecol. Indic., 61, 815–821,
https://doi.org/10.1016/j.ecolind.2015.10.034, 2016.
Pacala, S. W., Canham, C. D., and Silander Jr., J. A.: Forest models defined by field measurements. I. The design of a northeastern forest simulator, Can.
J. For. Res., 23, 1980–1988, 1993.
Pandit, K: Modified source codes for ED2 with shrub parameters, Zenodo, https://doi.org/10.5281/zenodo.3461233, 2019.
Pereira, F. F., Farinosi, F., Arias, M. E., Lee, E., Briscoe, J., and Moorcroft, P. R.: Technical note: A hydrological routing scheme for the Ecosystem Demography model (ED2+R) tested in the Tapajós River basin in the Brazilian Amazon, Hydrol. Earth Syst. Sci., 21, 4629–4648, https://doi.org/10.5194/hess-21-4629-2017, 2017.
Qi, Y., Dennison, P. E., Jolly, W. M, Kropp, R. C., and Brewer, S. C.:
Spectroscopic analysis of seasonal changes in live fuel moisture content and
leaf dry mass, Remote Sens. Environ., 150, 198–206,
https://doi.org/10.1016/j.rse.2014.05.004, 2014.
Qi, Y., Ustin, S. L., and Glenn, N. F.: Imaging Spectroscopic Analysis of
Biochemical Traits for Shrub Species in Great Basin, USA, Remote Sens., 10,
1621, https://doi.org/10.3390/rs10101621, 2018.
Quan, X., He, B., and Li, X.: A Bayesian Network-Based Method to Alleviate
the Ill-Posed Inverse Problem: A Case Study on Leaf Area Index and Canopy
Water Content Retrieval, IEEE T. Geosci. Remote,
33, 6507–6517, https://doi.org/10.1109/TGRS.2015.2442999, 2015
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet M., Berbigier
P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald,
T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila,
T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F.,
Ourcival, J. M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M.,
Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and
Valentini, R.: On the separation of net ecosystem exchange into assimilation
and ecosystem respiration: review and improved algorithm, Glob. Change
Biol., 11, 1424–39, https://doi.org/10.1111/j.1365-2486.2005.001002.x,
2005.
Richardson, A. D., Williams, M., Hollinger, D. Y., Moore, D. J. P., Dail, D.
B., Davidson, E. A., Scott, N. A., Evans, R. E., Hughes, H., Lee, J. T.,
Rodrigues, C., and Savage, K.: Estimating parameters of a forest ecosystem C
model with measurements of stocks and fluxes as joint constraints,
Oecologia, 164, 25–40, https://doi.org/10.1007/s00442-010-1628-y, 2010.
Schlaepfer, D. R., Lauenrotha, W. K., and Bradford, J. B.: Modeling
regeneration responses of big sagebrush (Artemisia tridentata) to abiotic
conditions, Ecol. Model., 286, 66–77,
https://doi.org/10.1016/j.ecolmodel.2014.04.021, 2014.
Schmidtlein, S., Tichy, L., Feilhauer, H., and Faude, U.: A brute-force
approach to vegetation classification, J. Veg. Sci., 21,
1162–1171, https://doi.org/10.1111/j.1654-1103.2010.01221.x, 2010.
Schroeder, M. A., Aldridge, C. L., Apa, A. D., Bohne, J. R., Braun, C. E.,
Bunnell, S. D., Connelly, J. W., Deibert, P. A., Gardner, S. C., Hilliard,
M. A., Kobriger, G. D., McAdam, S. M., McCarthy, C. W., McCarthy, J. J.,
Mitchell, D. L., Rickerson, E. V., and Stiver, S. J.: Distribution of
Sage-Grouse in North America, The Condor, 106, 363–376,
https://doi.org/10.1650/7425, 2004.
Schwantes, A. M., Swenson, J. J., and Jackson, R. B.: Quantifying
drought-induced tree mortality in the open canopy woodlands of central
Texas, Remote Sens.Environ., 181, 54–64,
https://doi.org/10.1016/j.rse.2016.03.027, 2016.
Sellers, P. J., Berry, J. A., Collatz, G. J., Field, C. B., and Hall, F. G.:
Canopy reflectance, photosynthesis, and transpiration, III. A reanalysis
using improved leaf models and a new canopy integration scheme, Remote
Sens. Environ., 42, 187–216, https://doi.org/10.1016/0034-4257(92)90102-P,
1992.
Seyfried, M. S., Harris, R., Marks, D., and Jacob, B.: A geographic database
for watershed research: Reynolds Creek Experimental Watershed, Idaho, USA,
Tech. Bull. NWRC 2000-3, 26 pp., Northwest Watershed Res. Cent., Agric. Res.
Serv., U.S. Dep. of Agric., Boise, Idaho, 2000
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplans, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic vegetation model, Glob.
Change Biol., 9, 161–185, 2003.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M.,
Duda, M. G., Huang, X., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3. NCAR Technical Note NCAR/TN-475+STR,
https://doi.org/10.5065/D68S4MVH, 2008.
Smith, B., Trentice, C. I., and Sykes, M. T.: Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: comparing two
contrasting approaches within European climate space, Global Ecol.
Biogeogr., 10, 621–637, 2001.
Steinberg, P. D.: Artemisia arbuscula, iFire Effects Information System, U.S. Department of Agriculture, Forest Service, Rocky Mountain
Research Station, Fire Sciences Laboratory (Producer), available at:
https://www.fs.fed.us/database/feis/plants/shrub/artarb/all.html (last
access: 27 July 2019), 2002.
Stephenson, G. R. (Ed.): CZO Dataset: Reynolds Creek – Geology, Soil Survey,
Vegetation, GIS/Map Data (1960–1970), available at:
http://criticalzone.org/reynolds/data/dataset/3722/, last access: 18 May
2018.
Sturges, D. L.: Soil water withdrawl and root characteristics of Big
Sagebrush, Am. Midl. Nat., 98, 257–274, 1977.
Tabler, R. D.: The root system of Artemisia tridentata at 9,500 feet in
Wyoming, Ecology, 45, 633–636, 1964.
Trugman, A. T., Fenton, N. J., Bergeron, Y., Xu, X., Welp, L. R., and
Medvigy, D.: Climate, soil organic layer, and nitrogen jointly drive forest
development after fire in the North American boreal zone, J.
Adv. Model. Earth Syst., 8, 1180–1209,
https://doi.org/10.1002/2015MS000576, 2016.
USDA: Plant materials Technical Note No. MT-114, Natural Resources
Conservation Service, United States Department of Agriculture, August 2016,
2016.
USDA: Soil Survey Staff, Natural Resources Conservation Service, United
States Department of Agriculture, Web Soil Survey, available at:
https://websoilsurvey.sc.egov.usda.gov/, last access: 28 March, 2018a.
USDA: The PLANTS Database, National
Plant Data Team, Greensboro, NC 27401-4901 USA, available at: http://plants.usda.gov, last access: 20 August, 2018b.
Wambura, F. J., Ndombab, P. M., Kongob, V., and Tumboc, S. D.: Uncertainty of
runoff projections under changing climate in Wami River sub-basin, J. Hydrol.-Reg. Studies, 4, 333–348, 2015.
Wang, D., LeBauer, D., and Dietze, M.: Predicting yields of short-rotation
hybrid poplar (Populus spp.) for the United States through model–data
synthesis, Ecol. Appl., 23, 944–958, 2013.
Waring, B. G., Averill, C., and Hawkes, C. V.: Differences in fungal and
bacterial physiology alter soil carbon and nitrogen cycling: insights from
meta-analysis and theoretical models, Ecol. Lett., 16, 887–894, https://doi.org/10.1111/ele.12125, 2016
Weiss, A. and Norman, J. M.: Partitioning solar radiation into direct and
diffuse, visible and near-infrared components, Agr. Forest Meteorol., 34,
205–213, 1985.
Wolf, A., Callaghan, T. V., and Larson, K.: Future changes in vegetation and
ecosystem function of the Barents Region, Clim. Change, 87, 51–73, 2008.
Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of parameter
uncertainties on the modeling of terrestrialbiosphere dynamics, Global
Biogeochem. Cy., 19, GB3020, https://doi.org/10.1029/2004GB002395, 2005.
Zhang, K., de Almeida Castanho, A. D., Galbraith, D. R., Moghim, S., Levine,
N. M., Bras, R. L., Coe, M. T., Costa, M. H., Malhi, Y., Longo, M., Knox, R.
G., McKnight, S., Wang, J., and Moorcroft, P. R.: The fate of Amazonian
ecosystems over the coming century arising from changes in climate,
atmospheric CO2, and land use, Glob. Change Biol., 21, 2569–2587,
https://doi.org/10.1111/gcb.12903, 2015.
Zhang, K., Ma. J., Zhu, G., Ma, T., Han, T., and Feng, L. L.: Parameter
sensitivity analysis and optimization for a satellite-based
evapotranspiration model across multiple sites using Moderate Resolution
Imaging Spectroradiometer and flux data, J. Geophys. Res.-Atmos., 122,
230–245, https://doi.org/10.1002/2016JD025768, 2017.
Zhang, Y, Guanter, L., Berry, J. A., Joiner, J., Tol, C. V. D., Huete, A.,
Gitelson, A., Voigt, M., and Kohler, P.: Estimation of vegetation
photosynthetic capacity from space-based measurements of chlorophyll
fluorescence for terrestrial biosphere models, Glob. Change Biol., 20,
3727–3742, https://doi.org/10.1111/gcb.12664, 2014.
Short summary
We explored shrub parameters representing sagebrush ecosystems within a dynamic vegetation model and estimated gross primary production (GPP) for two sagebrush sites in the northern Great Basin. Comparison with observations from eddy covariance (EC) tower data showed our modeled results were encouraging, although some seasonal underestimates were apparent. We believe our findings on preliminary parameterization of shrub PFT is an important step towards subsequent studies on shrubland ecosystems.
We explored shrub parameters representing sagebrush ecosystems within a dynamic vegetation model...
