Articles | Volume 11, issue 3
https://doi.org/10.5194/gmd-11-861-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-11-861-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
08 Mar 2018
Model description paper |
| 08 Mar 2018
Description and validation of the Simple, Efficient, Dynamic, Global, Ecological Simulator (SEDGES v.1.0)
Pablo Paiewonsky
CORRESPONDING AUTHOR
Department of Atmospheric and Environmental Sciences, State University
of New York at Albany, 1400 Washington Ave., Albany, NY 12222, USA
Oliver Elison Timm
Department of Atmospheric and Environmental Sciences, State University
of New York at Albany, 1400 Washington Ave., Albany, NY 12222, USA
Related authors
No articles found.
M. Heinemann, A. Timmermann, O. Elison Timm, F. Saito, and A. Abe-Ouchi
Clim. Past, 10, 1567–1579, https://doi.org/10.5194/cp-10-1567-2014, https://doi.org/10.5194/cp-10-1567-2014, 2014
Related subject area
Climate and Earth system modeling
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Dynamical Madden–Julian Oscillation forecasts using an ensemble subseasonal-to-seasonal forecast system of the IAP-CAS model
Implementation of a brittle sea ice rheology in an Eulerian, finite-difference, C-grid modeling framework: impact on the simulated deformation of sea ice in the Arctic
HSW-V v1.0: localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0
Updating the radiation infrastructure in MESSy (based on MESSy version 2.55)
An urban module coupled with the Variable Infiltration Capacity model to improve hydrothermal simulations in urban systems
Bayesian hierarchical model for bias-correcting climate models
Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4
Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification
TorchClim v1.0: a deep-learning plugin for climate model physics
Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0
Validating a microphysical prognostic stratospheric aerosol implementation in E3SMv2 using observations after the Mount Pinatubo eruption
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid–ammonia nucleation
Modeling biochar effects on soil organic carbon on croplands in a microbial decomposition model (MIMICS-BC_v1.0)
Hector V3.2.0: functionality and performance of a reduced-complexity climate model
Evaluation of CMIP6 model simulations of PM2.5 and its components over China
Assessment of a tiling energy budget approach in a land surface model, ORCHIDEE-MICT (r8205)
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0
A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)
Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)
LB-SCAM: a learning-based method for efficient large-scale sensitivity analysis and tuning of the Single Column Atmosphere Model (SCAM)
Quantifying the impact of SST feedback frequency on Madden–Julian oscillation simulations
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
A revised model of global silicate weathering considering the influence of vegetation cover on erosion rate
A radiative–convective model computing precipitation with the maximum entropy production hypothesis
Introducing the MESMER-M-TPv0.1.0 module: Spatially Explicit Earth System Model Emulation for Monthly Precipitation and Temperature
Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0
Machine learning parameterization of the multi-scale Kain–Fritsch (MSKF) convection scheme and stable simulation coupled in the Weather Research and Forecasting (WRF) model using WRF–ML v1.0
Impacts of spatial heterogeneity of anthropogenic aerosol emissions in a regionally refined global aerosol–climate model
cfr (v2024.1.26): a Python package for climate field reconstruction
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Evaluation of isoprene emissions from the coupled model SURFEX–MEGANv2.1
A comprehensive Earth system model (AWI-ESM2.1) with interactive icebergs: effects on surface and deep-ocean characteristics
The regional climate–chemistry–ecology coupling model RegCM-Chem (v4.6)–YIBs (v1.0): development and application
Coupling the regional climate model ICON-CLM v2.6.6 into the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical mixing parameterization on Arctic sea ice and upper ocean properties using the NEMO-SI3 model
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
Short summary
Short summary
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
Geosci. Model Dev., 17, 6249–6275, https://doi.org/10.5194/gmd-17-6249-2024, https://doi.org/10.5194/gmd-17-6249-2024, 2024
Short summary
Short summary
We give an overview of the Institute of Atmospheric Physics–Chinese Academy of Sciences subseasonal-to-seasonal ensemble forecasting system and Madden–Julian Oscillation forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its benefits but also biases, i.e., underdispersive ensemble, overestimated amplitude, and faster propagation speed when forecasting MJO. We provide a reason for these biases and prospects for further improvement of this system in the future.
Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
Short summary
Short summary
A new brittle sea ice rheology, BBM, has been implemented into the sea ice component of NEMO. We describe how a new spatial discretization framework was introduced to achieve this. A set of idealized and realistic ocean and sea ice simulations of the Arctic have been performed using BBM and the standard viscous–plastic rheology of NEMO. When compared to satellite data, our simulations show that our implementation of BBM leads to a fairly good representation of sea ice deformations.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
Short summary
Short summary
Large volcanic eruptions deposit material in the upper atmosphere, which is capable of altering temperature and wind patterns of Earth's atmosphere for subsequent years. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a test bed for climate attribution studies.
Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
Short summary
Short summary
Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
Geosci. Model Dev., 17, 5821–5849, https://doi.org/10.5194/gmd-17-5821-2024, https://doi.org/10.5194/gmd-17-5821-2024, 2024
Short summary
Short summary
We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
Short summary
Short summary
Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
Short summary
Short summary
Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
Short summary
Short summary
The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
Short summary
Short summary
We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
Short summary
Short summary
Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
Short summary
Short summary
Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
Short summary
Short summary
Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
Short summary
Short summary
Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
Short summary
The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
Short summary
Short summary
This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
Short summary
Short summary
We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
Short summary
Short summary
Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
Short summary
Short summary
To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
Short summary
Short summary
This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
Short summary
Short summary
Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
Short summary
Short summary
All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleußner
EGUsphere, https://doi.org/10.5194/egusphere-2024-278, https://doi.org/10.5194/egusphere-2024-278, 2024
Short summary
Short summary
Precipitation and temperature are two of the most impact-relevant climatic variables. Their joint distribution largely determines the division into climate regimes. Yet, projecting precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows to generate monthly means of local precipitation and temperature at low computational costs.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
Short summary
Short summary
We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
Short summary
Short summary
In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
Short summary
Short summary
Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
Short summary
Short summary
Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
Short summary
Short summary
Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
Short summary
Short summary
In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
Short summary
Short summary
We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
Short summary
Short summary
For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
EGUsphere, https://doi.org/10.5194/egusphere-2024-923, https://doi.org/10.5194/egusphere-2024-923, 2024
Short summary
Short summary
The regional Earth system model GCOAST-AHOI version 2.0 including the regional climate model ICON-CLM coupled with the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in the ICON-CLM model makes it more flexible to couple with an external ocean model and an external hydrological discharge model.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
Short summary
Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
Short summary
Short summary
We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
Short summary
Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
Short summary
Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2024-362, https://doi.org/10.5194/egusphere-2024-362, 2024
Short summary
Short summary
In this study, we improve an existing climate model to account for human water usage across domestic, industrial, and agriculture purposes. With the new capabilities, the model is now better equipped for studying questions related to water scarcity in both present and future conditions under climate change. Despite the advancements, there remains important limitations in our modelling framework which requires further work.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
Short summary
Short summary
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-36, https://doi.org/10.5194/gmd-2024-36, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-49, https://doi.org/10.5194/gmd-2024-49, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We study the parameters involved in the turbulent kinetic energy mixed layer penetration scheme of the NEMO model in Arctic sea ice-covered regions. This evaluation reveals the impact of these parameters on mixed layer depth, sea surface temperature and salinity, and ocean stratification. Our findings also demonstrate considerable impacts on sea ice thickness and sea ice concentration, emphasizing the importance of accurate ocean mixing representation in understanding Arctic climate dynamics.
Cited articles
Amthor, J. S. and Baldocchi, D. D.: Terrestrial higher plant respiration and
net primary production, in: Terrestrial global productivity, chap. 3, edited
by: Roy, J., Saugier, B., and Mooney, H. A., Academic Press, 33–59, 2001. a
Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C.,
Murray-Tortarolo, G., Papale, D., Parazoo, N. C., Peylin, P., Piao, S.,
Sitch, S., Viovy, N., Wiltshire, A., and Zhao, M.: Spatiotemporal patterns of
terrestrial gross primary production: A review, Rev. Geophys., 53,
785–818, 2015. a, b, c, d, e
Beljaars, A., Brown, A. R., and Wood, N.: A new parametrization of turbulent
orographic form drag, Q. J. Roy. Meteor. Soc., 130, 1327–1347, 2004. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H.,
Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N.,
Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C.
S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES),
model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4,
677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a, b
Betts, A. K. and Ball, J. H.: Albedo over the boreal forest, J. Geophys.
Res.-Atmos., 102, 28901–28909, 1997. a
Betts, R. A., Cox, P. M., Lee, S. E., and Woodward, F. I.: Contrasting
physiological and structural vegetation feedbacks in climate change
simulations, Nature, 387, 796–799, 1997. a
Blyth, E.: Estimating potential evaporation over a hill, Bound.-Lay.
Meteorol., 92, 185–193, 1999. a
Bonan, G. B., Pollard, D., and Thompson, S. L.: Effects of boreal forest
vegetation on global climate, Nature, 359, 716–718, 1992. a
Bond, W. J., Woodward, F. I., and Midgley, G. F.: The global distribution of
ecosystems in a world without fire, New Phytol., 165, 525–538, 2005. a
Bowring, S. P. K., Miller, L. M., Ganzeveld, L., and Kleidon, A.: Applying
the concept of “energy return on investment” to desert greening of the
Sahara/Sahel using a global climate model, Earth Syst. Dynam., 5, 43–53,
https://doi.org/10.5194/esd-5-43-2014, 2014. a
Boyce, C. K. and Lee, J.-E.: Plant Evolution and Climate Over Geological
Timescales, Ann. Rev. Earth Pl. Sc., 45, 61–87,
https://doi.org/10.1146/annurev-earth-063016-015629, 2017. a
Brovkin, V., Ganopolski, A., and Svirezhev, Y.: A continuous
climate-vegetation classification for use in climate-biosphere studies, Ecol.
Model., 101, 251–261, 1997. a
Brovkin, V., Bendtsen, J., Claussen, M., Ganopolski, A., Kubatzki, C.,
Petoukhov, V., and Andreev, A.: Carbon cycle, vegetation, and climate
dynamics in the Holocene: Experiments with the CLIMBER-2 model, Global
Biogeochem. Cy., 16, 1139, https://doi.org/10.1029/2001GB001662, 2002. a
Buckley, T. N. and Schymanski, S. J.: Stomatal optimisation in relation to
atmospheric CO2, New Phytol., 201, 372–377, 2014. a
Campbell, G. and Norman, J.: An introduction to environmental biophysics, 2nd
edn., Springer Science + Business Media Inc., New York, NY, USA, 286 pp.,
1998. a
Cao, L., Bala, G., Caldeira, K., Nemani, R., and Ban-Weiss, G.: Importance of
carbon dioxide physiological forcing to future climate change, P. Natl. Acad.
Sci. USA, 107, 9513–9518, 2010. a
Chen, F., Janjić, Z., and Mitchell, K.: Impact of atmospheric
surface-layer parameterizations in the new land-surface scheme of the NCEP
mesoscale Eta model, Bound.-Lay. Meteorol., 85, 391–421, 1997a. a
Chen, Y., Yang, K., Zhou, D., Qin, J., and Guo, X.: Improving the Noah land
surface model in arid regions with an appropriate parameterization of the
thermal roughness length, J. Hydrometeorol., 11, 995–1006, 2010. a
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M.
J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O.,
Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land
Environment Simulator (JULES), model description – Part 2: Carbon fluxes and
vegetation dynamics, Geosci. Model Dev., 4, 701–722,
https://doi.org/10.5194/gmd-4-701-2011, 2011. a
Cowan, I. and Farquhar, G.: Stomatal function in relation to leaf metabolism
and environment, in: Symposia of the Society for Experimental Biology, edited
by: Jennings, D., chap. 3, Cambridge University Press, Cambridge, UK,
471–505, 1977. a
Cramer, W., Kicklighter, D., Bondeau III, A., B. M., Churkina, G., Nemry, B.,
Ruimy, A., Schloss, A., and the participants of the Potsdam NPP model
intercomparison: Comparing global models of terrestrial net primary
productivity (NPP): overview and key results, Glob. Change Biol., 5, 1–15,
1999. a, b, c
Dai, A. and Trenberth, K. E.: Estimates of freshwater discharge from
continents: Latitudinal and seasonal variations, J. Hydrometeorol., 3,
660–687, 2002. a
De Kauwe, M. G., Kala, J., Lin, Y.-S., Pitman, A. J., Medlyn, B. E., Duursma,
R. A., Abramowitz, G., Wang, Y.-P., and Miralles, D. G.: A test of an optimal
stomatal conductance scheme within the CABLE land surface model, Geosci.
Model Dev., 8, 431–452, https://doi.org/10.5194/gmd-8-431-2015, 2015. a
de Pury, D. and Farquhar, G.: Simple scaling of photosynthesis from leaves to
canopies without the errors of big-leaf models, Plant Cell Environ., 20,
537–557, 1997. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, I., Biblot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Greer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011. a, b
DeFries, R. and Hansen, M.: ISLSCP II Continuous Fields of Vegetation Cover,
1992–1993, Oak Ridge National Laboratory Distributed Active Archive Center,
Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/931 (last access: February
2016), 2009. a, b, c
Dekker, S. C., de Boer, H. J., Brovkin, V., Fraedrich, K., Wassen, M. J., and
Rietkerk, M.: Biogeophysical feedbacks trigger shifts in the modelled
vegetation-atmosphere system at multiple scales, Biogeosciences, 7,
1237–1245, https://doi.org/10.5194/bg-7-1237-2010, 2010. a, b
Dietze, M. C.: Gaps in knowledge and data driving uncertainty in models of
photosynthesis, Photosynth. Res., 119, 3–14, 2014. a
Domec, J.-C., Warren, J., Meinzer, F., Brooks, J., and Coulombe, R.: Native
root xylem embolism and stomatal closure in stands of Douglas-fir and
ponderosa pine: mitigation by hydraulic redistribution, Oecologia, 141,
7–16, 2004. a
Domec, J.-C., Noormets, A., King, J. S., Sun, G., McNulty, S. G., Gavazzi,
M. J., Boggs, J. L., and Treasure, E. A.: Decoupling the influence of leaf
and root hydraulic conductances on stomatal conductance and its sensitivity
to vapour pressure deficit as soil dries in a drained loblolly pine
plantation, Plant Cell Environ., 32, 980–991, 2009. a
Dutra, E., Balsamo, G., Viterbo, P., Miranda, P. M., Beljaars, A., Schär,
C., and Elder, K.: An improved snow scheme for the ECMWF land surface model:
description and offline validation, J. Hydrometeorol., 11, 899–916, 2010. a
Dutra, E., Kotlarski, S., Viterbo, P., Balsamo, G., Miranda, P., Schär,
C., Bissolli, P., and Jonas, T.: Snow cover sensitivity to horizontal
resolution, parameterizations, and atmospheric forcing in a land surface
model, J. Geophys. Res.-Atmos., 116, D21109, https://doi.org/10.1029/2011JD016061, 2011. a
Essery, R.: Boreal forests and snow in climate models, Hydrol. Proc., 12,
1561–1567, 1998. a
Etheridge, D., Steele, L., Langenfelds, R., Francey, R., Barnola, J.-M., and
Morgan, V.: Historical CO2 records from the Law Dome DE08, DE08-2, and DSS
ice cores, Carbon Dioxide Information Analysis Center, Oak Ridge National
Laboratory, US Department of Energy, Oak Ridge, Tennesse, USA, 1998. a
Fekete, B. M., Vörösmarty, C. J., and Grabs, W.: High-resolution
fields of global runoff combining observed river discharge and simulated
water balances, Global Biogeochem. Cy., 16, 1042, https://doi.org/10.1029/1999GB001254,
2002. a, b, c, d
Fisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell,
N. G., Knox, R. G., Koven, C., Holm, J., Rogers, B. M., Spessa, A., Lawrence,
D., and Bonan, G.: Taking off the training wheels: the properties of a
dynamic vegetation model without climate envelopes, CLM4.5(ED), Geosci. Model
Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, 2015. a
Foley, A. M., Dalmonech, D., Friend, A. D., Aires, F., Archibald, A. T.,
Bartlein, P., Bopp, L., Chappellaz, J., Cox, P., Edwards, N. R., Feulner, G.,
Friedlingstein, P., Harrison, S. P., Hopcroft, P. O., Jones, C. D., Kolassa,
J., Levine, J. G., Prentice, I. C., Pyle, J., Vázquez Riveiros, N.,
Wolff, E. W., and Zaehle, S.: Evaluation of biospheric components in Earth
system models using modern and palaeo-observations: the state-of-the-art,
Biogeosciences, 10, 8305–8328, https://doi.org/10.5194/bg-10-8305-2013, 2013. a
Foley, J. A., Kutzbach, J. E., Coe, M. T., and Levis, S.: Feedbacks between
climate and boreal forests during the Holocene epoch, Nature, 371, 52–54,
1994. a
Franks, P. J., Adams, M. A., Amthor, J. S., Barbour, M. M., Berry, J. A.,
Ellsworth, D. S., Farquhar, G. D., Ghannoum, O., Lloyd, J., McDowell, N.,
Norby, R. J., Tissue, D. T., and von Caemmerer, S.: Sensitivity of plants to
changing atmospheric CO2 concentration: from the geological past to the
next century, New Phytol., 197, 1077–1094, 2013. a, b
Friend, A. D., Lucht, W., Rademacher, T. T., Keribin, R., Betts, R., Cadule,
P., Ciais, P., Clark, D. B., Dankers, R., Falloon, P. D., Ito, A., Kahana,
R., Kleidon, A., Lomas, M. R., Nishina, K., Ostberg, S., Pavlick, R., Peylin,
P., Schaphoff, S., Vuichard, N., Warszawski, L., Wiltshire, A., and Ian
Woodward, F.: Carbon residence time dominates uncertainty in terrestrial
vegetation responses to future climate and atmospheric CO2, P. Natl. Acad.
Sci. USA, 111, 3280–3285, 2014. a
Gibbs, H.: Olson's major world ecosystem complexes ranked by carbon in live
vegetation: An updated database using the GLC2000 land cover product
(NDP-017b), Carbon Dioxide Information Center, Oak Ridge National Laboratory,
Oak Ridge, Tennessee, USA, https://doi.org/10.3334/CDIAC/lue.ndp017.2006, 2006. a, b, c
Gifford, R. M.: Plant respiration in productivity models: conceptualisation,
representation and issues for global terrestrial carbon-cycle research,
Funct. Plant Biol., 30, 171–186, 2003. a
Giorgi, F. and Avissar, R.: Representation of heterogeneity effects in earth
system modeling: Experience from land surface modeling, Rev. Geophys., 35,
413–437, 1997. a
Guan, K., Pan, M., Li, H., Wolf, A., Wu, J., Medvigy, D., Caylor, K. K.,
Sheffield, J., Wood, E. F., Malhi, Y., Liang, M., Kimball, J. S., Saleska, S.
R., Berry, J., Joiner, J., and Lyapustin, A. I.: Photosynthetic seasonality
of global tropical forests constrained by hydroclimate, Nat. Geosci., 8,
284–289, 2015. a
Guswa, A.: Soil-moisture limits on plant uptake: An upscaled relationship for
water-limited ecosystems, Adv. Water Resour., 28, 543–552, 2005. a
Guswa, A. J., Celia, M., and Rodriguez-Iturbe, I.: Models of soil moisture
dynamics in ecohydrology: A comparative study, Water Resour. Res., 38, 1166,
https://doi.org/10.1029/2001WR000826, 2002. a, b
Hall, F. G., Brown de Colstoun, E., Collatz, G. J., Landis, D., Dirmeyer, P.,
Betts, A., Huffman, G. J., Bounoua, L., and Meeson, B.: ISLSCP Initiative II
global data sets: Surface boundary conditions and atmospheric forcings for
land-atmosphere studies, J. Geophys. Res.-Atmos., 111, D22S01,
https://doi.org/10.1029/2006JD007366, 2006. a, b, c
Heskel, M. A., Bitterman, D., Atkin, O. K., Turnbull, M. H., and Griffin,
K. L.: Seasonality of foliar respiration in two dominant plant species from
the Arctic tundra: response to long-term warming and short-term temperature
variability, Funct. Plant Biol., 41, 287–300, 2014. a
Hickler, T., Smith, B., Prentice, I. C., Mjöfors, K., Miller, P., Arneth,
A., and Sykes, M. T.: CO2 fertilization in temperate FACE experiments not
representative of boreal and tropical forests, Glob. Change Biol., 14,
1531–1542, 2008. a
Hikosaka, K. and Hirose, T.: Leaf angle as a strategy for light competition:
optimal and evolutionarily stable light-extinction coefficient within a leaf
canopy, Ecoscience, 4, 501–507, 1997. a
Holden, P. B., Edwards, N. R., Gerten, D., and Schaphoff, S.: A model-based
constraint on CO2 fertilisation, Biogeosciences, 10, 339–355,
https://doi.org/10.5194/bg-10-339-2013, 2013. a, b
Horton, D. E., Poulsen, C. J., and Pollard, D.: Influence of high-latitude
vegetation feedbacks on late Palaeozoic glacial cycles, Nat. Geosci., 3,
572–577, 2010. a
Houldcroft, C. J., Grey, W. M., Barnsley, M., Taylor, C. M., Los, S. O., and
North, P. R.: New vegetation albedo parameters and global fields of soil
background albedo derived from MODIS for use in a climate model, J.
Hydrometeorol., 10, 183–198, 2009. a
Huntingford, C., Blyth, E., Wood, N., Hewer, F., and Grant, A.: The effect of
orography on evaporation, Bound.-Lay. Meteorol., 86, 487–504, 1998. a
Jarvis, P. G. and McNaughton, K.: Stomatal control of transpiration: scaling
up from leaf to region, Adv. Ecol. Res., 15, 1–49, 1986. a
Javaux, M., Couvreur, V., Vanderborght, J., and Vereecken, H.: Root water
uptake: from three-dimensional biophysical processes to macroscopic modeling
approaches, Vadose Z. J., 12, 4, https://doi.org/10.2136/vzj2013.02.0042, 2013. a
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D.,
Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle, D.,
Gobron, N., Kiely, G., Kutsch, W., Lasslop, G.,Law, B. E., Lindroth, A.,
Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M.,
Vaccari, F., and Williams, C.: Global patterns of land-atmosphere fluxes of
carbon dioxide, latent heat, and sensible heat derived from eddy covariance,
satellite, and meteorological observations, J. Geophys. Res.-Biogeo., 116,
G00J07, https://doi.org/10.1029/2010JG001566, 2011. a, b, c, d, e, f
Kala, J., De Kauwe, M. G., Pitman, A. J., Lorenz, R., Medlyn, B. E., Wang,
Y.-P., Lin, Y.-S., and Abramowitz, G.: Implementation of an optimal stomatal
conductance scheme in the Australian Community Climate Earth Systems
Simulator (ACCESS1.3b), Geosci. Model Dev., 8, 3877–3889,
https://doi.org/10.5194/gmd-8-3877-2015, 2015. a
Kaplan, J. O.: Geophysical applications of vegetation modeling, PhD thesis,
Lund University, 2001. a
Kauppi, P. and Posch, M.: Sensitivity of boreal forests to possible climatic
warming, Clim. Change, 7, 45–54, 1985. a
Keeling, C. D., Bacastow, R. B., Bainbridge, A. E., Ekdahl, C. A., Guenther,
P. R., Waterman, L. S., and Chin, J. F.: Atmospheric carbon dioxide
variations at Mauna Loa observatory, Hawaii, Tellus, 28, 538–551, 1976. a
Kelliher, F., Leuning, R., Raupach, M., and Schulze, E.-D.: Maximum
conductances for evaporation from global vegetation types, Agr. Forest
Meteorol., 73, 1–16, 1995. a
Kleidon, A.: Quantifying the biologically possible range of steady-state soil
and surface climates with climate model simulations, Biologia, 61, 234–239,
2006a. a
Kleidon, A.: ISLSCP II Total plant-available soil water storage capacity of
the rooting zone, Oak Ridge National Laboratory Distributed Active Archive
Center, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1006, 2011. a, b, c
Klimarechenzentrum, D.: The ECHAM3 Atmospheric General Circulation Model,
Tech. Rep. 6, Max-Planck-Institut für Meteorologie, 1993. a
Knauer, J., Werner, C., and Zaehle, S.: Evaluating stomatal models and their
atmospheric drought response in a land surface scheme: A multibiome analysis,
J. Geophys. Res.-Biogeo., 120, 1894–1911, 2015. a
Köchy, M., Hiederer, R., and Freibauer, A.: Global distribution of soil
organic carbon – Part 1: Masses and frequency distributions of SOC stocks
for the tropics, permafrost regions, wetlands, and the world, SOIL, 1,
351–365, https://doi.org/10.5194/soil-1-351-2015, 2015. a
Krinner, G., Viovy, N., Noblet-Ducoudré, N. de, Oge?e, J., Polcher, J.,
Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic
global vegetation model for studies of the coupled atmosphere-biosphere
system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005.
Lawlor, D. and Cornic, G.: Photosynthetic carbon assimilation and associated
metabolism in relation to water deficits in higher plants, Plant Cell
Environ., 25, 275–294, 2002. a
Lee, E., Felzer, B. S., and Kothavala, Z.: Effects of nitrogen limitation on
hydrological processes in CLM4-CN, J. Adv. Model. Earth Syst., 5, 741–754,
https://doi.org/10.1002/jame.20046, 2013. a
Levis, S., Foley, J. A., and Pollard, D.: Large-scale vegetation feedbacks on
a doubled CO2 climate, J. Climate, 13, 1313–1325, 2000. a
Li, R. and Arora, V. K.: Effect of mosaic representation of vegetation in
land surface schemes on simulated energy and carbon balances, Biogeosciences,
9, 593–605, https://doi.org/10.5194/bg-9-593-2012, 2012. a, b
Lin, Y.-S., Medlyn, B. E., and Ellsworth, D. S.: Temperature responses of
leaf net photosynthesis: the role of component processes, Tree Physiol., 32,
219–231, 2012. a
Lin, Y.-S., Medlyn, B. E., Duursma, R. A., et al.: Optimal stomatal behaviour
around the world, Nat. Clim. Change, 5, 459–464, 2015. a
Linton, M., Sperry, J. S., and Williams, D.: Limits to water transport in
Juniperus osteosperma and Pinus edulis: implications for drought tolerance
and regulation of transpiration, Funct. Ecol., 12, 906–911, 1998. a
Lloyd, J. and Taylor, J.: On the temperature dependence of soil respiration,
Funct. Ecol., 8, 315–323, 1994. a
Long, S.: Modification of the response of photosynthetic productivity to
rising temperature by atmospheric CO2 concentrations: has its importance
been underestimated?, Plant Cell Environ., 14, 729–739, 1991. a
Loranty, M. M., Berner, L. T., Goetz, S. J., Jin, Y., and Randerson, J. T.:
Vegetation controls on northern high latitude snow-albedo feedback:
observations and CMIP5 model simulations, Glob. Change Biol., 20, 594–606,
2014. a
Louis, J., Tiedtke, M., and Geleyn, J.: A short history of the operational
PBL-parameterization at ECMWF, in: Proc. Workshop on Planetary Boundary Layer
Parameterization, ECMWF, Reading, UK, 59–79, 1982. a
Louis, J.-F.: A parametric model of vertical eddy fluxes in the atmosphere,
Bound.-Lay. Meteorol., 17, 187–202, 1979. a
Manabe, S.: Climate and the ocean circulation 1: i. The atmospheric
circulation and the hydrology of the earth's surface, Mon. Weather Rev., 97,
739–774, 1969. a
Mason, P.: The formation of areally-averaged roughness lengths, Q. J. Roy.
Meteor. Soc., 114, 399–420, 1988. a
Maxbauer, D. P., Royer, D. L., and LePage, B. A.: High Arctic forests during
the middle Eocene supported by moderate levels of atmospheric CO2,
Geology, 42, 1027–1030, 2014. a
McDowell, N., Pockman, W. T., Allen, C. D., Breshears, D. D., Cobb, N., Kolb,
T., Plaut, J., Sperry, J., West, A., Williams, D. G., and Yepez, E. A.:
Mechanisms of plant survival and mortality during drought: why do some plants
survive while others succumb to drought?, New Phytol., 178, 719–739, 2008. a
McLaughlin, B. C., Xu, C.-Y., Rastetter, E. B., and Griffin, K. L.:
Predicting ecosystem carbon balance in a warming Arctic: the importance of
long-term thermal acclimation potential and inhibitory effects of light on
respiration, Glob. Change Biol., 20, 1901–1912, 2014. a
Medlyn, B. E., De Kauwe, M. G., Lin, Y.-S., Knauer, J., Duursma, R. A.,
Williams, C. A., Arneth, A., Clement, R., Isaac, P., Limousin, J.-M.,
Linderson, M.-L., Meir, P., St. Paul, N.-M., and Wingate, L.: How do leaf and
ecosystem measures of water-use efficiency compare?, New Phytol., 216,
758–770, https://doi.org/10.1111/nph.14626, 2017. a, b
Melton, J. R. and Arora, V. K.: Sub-grid scale representation of vegetation
in global land surface schemes: implications for estimation of the
terrestrial carbon sink, Biogeosciences, 11, 1021–1036,
https://doi.org/10.5194/bg-11-1021-2014, 2014. a
Miralles, D. G., De Jeu, R. A. M., Gash, J. H., Holmes, T. R. H., and Dolman,
A. J.: Magnitude and variability of land evaporation and its components at
the global scale, Hydrol. Earth Syst. Sci., 15, 967–981,
https://doi.org/10.5194/hess-15-967-2011, 2011. a
Mooney, H. A., Björkman, O., and Collatz, G. J.: Photosynthetic
acclimation to temperature in the desert shrub, Larrea divaricata: I. Carbon
dioxide exchange characteristics of intact leaves, Plant Physiol., 61,
406–410, 1978. a
Mueller, B., Hirschi, M., Jimenez, C., Ciais, P., Dirmeyer, P. A., Dolman, A.
J., Fisher, J. B., Jung, M., Ludwig, F., Maignan, F., Miralles, D. G.,
McCabe, M. F., Reichstein, M., Sheffield, J., Wang, K., Wood, E. F., Zhang,
Y., and Seneviratne, S. I.: Benchmark products for land evapotranspiration:
LandFlux-EVAL multi-data set synthesis, Hydrol. Earth Syst. Sci., 17,
3707–3720, https://doi.org/10.5194/hess-17-3707-2013, 2013 a, b, c, d, e, f, g, h, i, j
NASA LP DAAC: MODIS Terra + Aqua BRDF/Albedo Snow-free Quality 16-Day L3
Global 0.05Deg CMG V005 (MCD43C2), NASA EOSDIS Land Processes DAAC, USGS
Earth Resources Observation and Science (EROS) Center, Sioux Falls, South
Dakota, available at: https://reverb.echo.nasa.gov/reverb/ (last
access: February 2016), 2008a. a, b
NASA LP DAAC: MODIS Terra + Aqua BRDF/Albedo 16-Day L3 Global 0.05Deg CMG
V005 (MCD43C3), NASA EOSDIS Land Processes DAAC, USGS Earth Resources
Observation and Science (EROS) Center, Sioux Falls, South Dakota, available
at: https://reverb.echo.nasa.gov/reverb/ (last access: December 2014),
2008b. a, b, c, d
Nilson, T.: A theoretical analysis of the frequency of gaps in plant stands,
Agr. Meteorol., 8, 25–38, 1971. a
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell,
G. V., Underwood, E. C., D'amico, J. A., Itoua, I., Strand, H. E., Morrison,
J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J.
F., Wettengel, W. W., Hedao, P., and Kassem, K. R.: Terrestrial Ecoregions of
the World: A New Map of Life on Earth A new global map of terrestrial
ecoregions provides an innovative tool for conserving biodiversity,
BioScience, 51, 933–938, 2001. a
Olson, J., Watts, J., and Allison, L.: Major world ecosystem complexes ranked
by carbon in live vegetation: A Database (NDP-017), Carbon Dioxide
Information Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA,
https://doi.org/10.3334/CDIAC/lue.ndp017 (last access: March 2016), 1985. a, b, c, d, e, f
Paiewonsky, P.: State dependency of the forest-tundra-short wave feedback:
comparing the mid-Pliocene and pre-industrial eras using a newly-developed
vegetation model, PhD thesis, State University of New York at Albany, 2017. a
Pavlick, R., Drewry, D. T., Bohn, K., Reu, B., and Kleidon, A.: The Jena
Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to
representing terrestrial biogeography and biogeochemistry based on plant
functional trade-offs, Biogeosciences, 10, 4137–4177,
https://doi.org/10.5194/bg-10-4137-2013, 2013. a, b
Piao, S., Sitch, S., Ciais, P., Friedlingstein, P., Peylin, P., Wang, X.,
Ahlström, A., Anav, A., Canadell, J. G., Cong, N., Huntingford, C., Jung,
M., Levis, S., Levy, P. E., Li, J., Lin, X., Lomas, M. R., Lu, M., Luo, Y.,
Ma, Y., Myneni, R. B., Poulter, B., Sun, Z., Wang, T., Viovy, N., Zaehle, S.,
and Zeng, N.: Evaluation of terrestrial carbon cycle models for their
response to climate variability and to CO2 trends, Glob. Change Biol., 19,
2117–2132, 2013. a, b
Pisek, J., Sonnentag, O., Richardson, A. D., and Mõttus, M.: Is the
spherical leaf inclination angle distribution a valid assumption for
temperate and boreal broadleaf tree species?, Agr. Forest Meteorol., 169,
186–194, 2013. a
Prentice, I. C., Cramer, W., Harrison, S. P., Leemans, R., Monserud, R. A.,
and Solomon, A. M.: Special Paper: A Global Biome Model Based on Plant
Physiology and Dominance, Soil Properties and Climate, J. Biogeogr., 19,
117–134, 1992. a
Prentice, I. C., Liang, X., Medlyn, B. E., and Wang, Y.-P.: Reliable, robust
and realistic: the three R's of next-generation land-surface modelling,
Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015,
2015. a, b
Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A.: Farming the
planet: 1. Geographic distribution of global agricultural lands in the year
2000, Global Biogeochem. Cy., 22, GB1003, https://doi.org/10.1029/2007GB002952, 2008. a
Raupach, M. and Finnigan, J.: Single layer models of evaporation from plant
canopies are incorrect but useful, whereas multilayer models are correct but
useless: discuss, Australian J. Plant Physiol., 15, 705–716, 1988. a
Rechid, D., Raddatz, T. J., and Jacob, D.: Parameterization of snow-free land
surface albedo as a function of vegetation phenology based on MODIS data and
applied in climate modelling, Theor. Appl. Climatol., 95, 245–255, 2009. a
Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M.,
Giorgetta, M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E.,
Rhodin, A., Schlese, U., Schulzweida, U., and Tompkins, A.: The atmospheric
general circulation model ECHAM 5 – PART I: Model description, Tech. Rep.
349, Max-Planck-Institut für Meteorologie, 2003. a, b
Sato, N., Sellers, P., Randall, D., Schneider, E., Shukla, J., Kinter III,
J., Hou, Y., and Albertazzi, E.: Effects of implementing the simple biosphere
model in a general circulation model, J. Atmos. Sci., 46, 2757–2782, 1989. a
Schaefer, K., Schwalm, C. R., Williams, C., et al.: A model-data comparison
of gross primary productivity: Results from the North American Carbon Program
site synthesis, J. Geophys. Res.-Biogeo., 117, G03010,
https://doi.org/10.1029/2012JG001960, 2012. a, b, c
Schlesinger, W. H. and Jasechko, S.: Transpiration in the global water cycle,
Agr. Forest Meteorol., 189, 115–117, 2014. a
Sellers, P., Bounoua, L., Collatz, G., Randall, D., Dazlich, D., Los, S.,
Berry, J., Fung, I., Tucker, C., Field, C., and Jensen, T.: Comparison of
Radiative and Physiological Effects of Doubled Atmospheric CO2 on Climate,
Science, 271, 1402–1406, 1996a. a
Sellers, P. J., Tucker, C. J., Collatz, G. J., Los, S. O., Justice, C. O.,
Dazlich, D. A., and Randall, D. A.: A revised land surface parameterization
(SiB2) for atmospheric GCMs – Part II: The generation of global fields of
terrestrial biophysical parameters from satellite data, J. Climate, 9,
706–737, 1996b. a, b
Sellers, P., Dickinson, R., Randall, D., Betts, A., Hall, F., Berry, J.,
Collatz, G., Denning, A., Mooney, H., Nobre, C., Sato, N., Field, C., and
Henderson-Sellers, A.: Modeling the exchanges of energy, water, and carbon
between continents and the atmosphere, Science, 275, 502–509, 1997. a
Serbin, S. P., Ahl, D. E., and Gower, S. T.: Spatial and temporal validation
of the MODIS LAI and FPAR products across a boreal forest wildfire
chronosequence, Remote Sens. Environ., 133, 71–84, 2013. a
Shinozaki, K., Yoda, K., Hozumi, K., and Kira, T.: A quantitative analysis of
plant form-the pipe model theory: I. Basic analyses, Jpn. J. Ecology, 14,
97–105, 1964. a
Shuttleworth, W. J.: Evaporation from Amazonian rainforest, Proc. R. Soc.
Lon. Ser.-A, 233, 321–346, 1988. a
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J., Levis, S., Lucht, W., Sykes, M., Thonicke, K., and Venevsky, S.:
Evaluation of ecosystem dynamics, plant geography and terrestrial carbon
cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9,
161–185, 2003. a
Sitch, S., Huntingford, C., Gedney, N., Levy, P., Lomas, M., Piao, S., Betts,
R., Ciais, P., Cox, P., Friedlingstein, P., Jones, C., Prentice, I., and
Woodward, F.: Evaluation of the terrestrial carbon cycle, future plant
geography and climate-carbon cycle feedbacks using five Dynamic Global
Vegetation Models (DGVMs), Glob. Change Biol., 14, 2015–2039, 2008. a, b
Sperry, J., Hacke, U., Oren, R., and Comstock, J.: Water deficits and
hydraulic limits to leaf water supply, Plant Cell Environ., 25, 251–263,
2002. a
Tian, H., Lu, C., Yang, J., Banger, K., Huntzinger, D. N., Schwalm, C. R.,
Michalak, A. M., Cook, R., Ciais, P., Hayes, D., Huang, M., Ito, A., Jain, A.
K., Lei, H., Mao, J., Pan, S., Post, W. M., Peng, S., Poulter, B., Ren, W.,
Ricciuto, D., Schaefer, K., Shi, X., Tao, B., Wang, W., Wei, Y., Yang, Q.,
Zhang, B., and Zeng, N.: Global patterns and controls of soil organic carbon
dynamics as simulated by multiple terrestrial biosphere models: Current
status and future directions, Global Biogeochem. Cy., 29, 775–792, 2015. a, b, c
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M.,
Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in
soil carbon simulations from CMIP5 Earth system models and comparison with
observations, Biogeosciences, 10, 1717–1736,
https://doi.org/10.5194/bg-10-1717-2013, 2013. a, b
Tyree, M. T. and Sperry, J. S.: Do woody plants operate near the point of
catastrophic xylem dysfunction caused by dynamic water stress? Answers from a
model, Plant Physiol., 88, 574–580, 1988. a
Vamborg, F. S. E., Brovkin, V., and Claussen, M.: The effect of a dynamic
background albedo scheme on Sahel/Sahara precipitation during the
mid-Holocene, Clim. Past, 7, 117–131, https://doi.org/10.5194/cp-7-117-2011,
2011. a, b
Vamborg, F. S. E., Brovkin, V., and Claussen, M.: Background albedo dynamics
improve simulated precipitation variability in the Sahel region, Earth Syst.
Dynam., 5, 89–101, https://doi.org/10.5194/esd-5-89-2014, 2014. a
Van Oijen, M., Schapendonk, A., and Höglind, M.: On the relative
magnitudes of photosynthesis, respiration, growth and carbon storage in
vegetation, Ann. Bot.-London, 105, 739–797, 2010. a
Wang-Erlandsson, L., van der Ent, R. J., Gordon, L. J., and Savenije, H. H.
G.: Contrasting roles of interception and transpiration in the hydrological
cycle – Part 1: Temporal characteristics over land, Earth Syst. Dynam., 5,
441–469, https://doi.org/10.5194/esd-5-441-2014, 2014. a, b
Willeit, M. and Ganopolski, A.: PALADYN v1.0, a comprehensive land
surface–vegetation–carbon cycle model of intermediate complexity, Geosci.
Model Dev., 9, 3817–3857, https://doi.org/10.5194/gmd-9-3817-2016, 2016 a, b
Wu, J., Albert, L. P., Lopes, A. P., Restrepo-Coupe, N., Hayek, M.,
Wiedemann, K. T., Guan, K., Stark, S. C., Christoffersen, B., Prohaska, N.,
Tavares, J. V., Marostica, S., Kobayashi, H., Ferreira, M. L., Campos, K. S.,
da Silva, R., Brando, P. M., Dye, D. G., Huxman, T. E., Huete, A. R., Nelson,
B. W., and Saleska, S. R.: Leaf development and demography explain
photosynthetic seasonality in Amazon evergreen forests, Science, 351,
972–976, 2016. a, b, c
Wu, X., Ju, W., Zhou, Y., et al.: Performance of linear and nonlinear
two-leaf light use efficiency models at different temporal scales, Remote
Sens., 7, 2238–2278, 2015. a
Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L. L., Baldocchi, D.,
Bernhofer, C., Gholz, H., Goldstein, A. H., Goulden, M. L., Hollinger, D. Y.,
Hu, Y., Law, B. E., Stoy, P. C., Vesala, T., Wofsy, S. C., and other
AmeriFlux collaborators: Deriving a light use efficiency model from eddy
covariance flux data for predicting daily gross primary production across
biomes, Agr. Forest Meteorol., 143, 189–207, 2007. a, b
Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law,
B., Arain, A., Beringer, J., Bernhofer, C., Black, A., Blanken, P. D.,
Cescatti, A., Chen, Y., Francois, L., Gianelle, D., Janssens, I. A., Jung,
M., Kato, T., Kiely, G., Liu, D., Marcolla, B., Montagnani, L., Raschi, A.,
Roupsard, O., Varlagin, A., and Wohlfahrt, G.: Global comparison of light use
efficiency models for simulating terrestrial vegetation gross primary
production based on the LaThuile database, Agr. Forest Meteorol., 192,
108–120, 2014. a
Zhang, X., Liang, S., Wang, G., Yao, Y., Jiang, B., and Cheng, J.: Evaluation
of the Reanalysis Surface Incident Shortwave Radiation Products from NCEP,
ECMWF, GSFC, and JMA Using Satellite and Surface Observations, Remote Sens.,
8, 225, https://doi.org/10.3390/rs8030225, 2016. a
Zhou, J., Poulsen, C. J., Rosenbloom, N., Shields, C., and Briegleb, B.:
Vegetation-climate interactions in the warm mid-Cretaceous, Clim. Past, 8,
565–576, https://doi.org/10.5194/cp-8-565-2012, 2012. a
Short summary
This paper presents a simple vegetation model for use as part of a climate or Earth system model. The model equations and their derivations are presented. We evaluate the model’s performance offline using near-present-day real-world datasets and deem it to be satisfactory. The model is useful because it is fast, easy to understand, and general in its formulations. It was developed to better simulate climate–vegetation feedbacks for answering paleoclimate research questions.
This paper presents a simple vegetation model for use as part of a climate or Earth system...
