Articles | Volume 16, issue 1
https://doi.org/10.5194/gmd-16-135-2023
© Author(s) 2023. 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-16-135-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
04 Jan 2023
Development and technical paper |
| 04 Jan 2023
| 04 Jan 2023
Climate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Yilun Han
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Yong Wang
CORRESPONDING AUTHOR
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Wenqi Sun
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Jianbo Deng
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Hunan Institute of Meteorological Sciences, Changsha, 410118, China
Daoming Wei
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
Ying Kong
College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000,
China
Bin Wang
Department of Earth System Science, Ministry of Education Key
Laboratory for Earth System Modeling, Institute for Global Change Studies,
Tsinghua University, Beijing, 100084, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese
Academy of Sciences, Beijing, 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy
of Sciences, Beijing, 100029, China
Related authors
No articles found.
Xiaoxuan Liu, Peng Zhu, Shu Liu, Le Yu, Yong Wang, Zhenrong Du, Dailiang Peng, Ece Aksoy, Hui Lu, and Peng Gong
Earth Syst. Dynam., 15, 817–828, https://doi.org/10.5194/esd-15-817-2024, https://doi.org/10.5194/esd-15-817-2024, 2024
Short summary
Short summary
An increase of 28 % in cropland expansion since 10 000 BCE has led to a 1.2 % enhancement in the global cropping potential, with varying efficiencies across regions. The continuous expansion has altered the support for population growth and has had impacts on climate and biodiversity, highlighting the effects of climate change. It also points out the limitations of previous studies.
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.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
Short summary
Short summary
C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
Short summary
Short summary
Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Yong Wang, Wenwen Xia, and Guang J. Zhang
Atmos. Chem. Phys., 21, 16797–16816, https://doi.org/10.5194/acp-21-16797-2021, https://doi.org/10.5194/acp-21-16797-2021, 2021
Short summary
Short summary
This study developed a novel approach to detect what rainfall rates climatologically are most efficient for wet removal of different aerosol types and applied it to a global climate model (GCM). Results show that light rain has disproportionate control on aerosol wet scavenging, with distinct rain rates for different aerosol sizes. The approach can be applied to other GCMs to better understand the aerosol wet scavenging by rainfall, which is important to better simulate aerosols.
Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma
Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, https://doi.org/10.5194/gmd-14-1575-2021, 2021
Short summary
Short summary
A stochastic deep convection parameterization is implemented into the US Department of Energy Energy Exascale Earth System Model Atmosphere Model version 1 (EAMv1). Compared to the default model, the well-known problem of
too much light rain and too little heavy rainis largely alleviated over the tropics with the stochastic scheme. Results from this study provide important insights into the model performance of EAMv1 when stochasticity is included in the deep convective parameterization.
Qiang Cheng, Juanjuan Liu, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-310, https://doi.org/10.5194/gmd-2018-310, 2018
Revised manuscript not accepted
Short summary
Short summary
Adjoint models are usually used to improve the weather forecast, but It's very time consuming. What we would like to do is determining how to significantly reduce the running cost of the adjoint model.The manuscript presented several methods. With them, we reduced the adjoint cost of the Weather Research and Forecasting plus (WRFPLUSV3.7) by almost half. Apparently, these are also productive in other applications in terms of adjoint model such as parameter estimation, singular vector etc.
Related subject area
Climate and Earth system modeling
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
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
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
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
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
Dynamic MJO forecasts using an ensemble subseasonal-to-seasonal forecast system of IAP-CAS model
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Localized injections of interactive volcanic aerosols and their climate impacts in a simple general circulation model
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
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.
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.
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.
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.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-1, https://doi.org/10.5194/egusphere-2024-1, 2024
Short summary
Short summary
This article gives an overview introduction of the IAP-CAS S2S (sub-seasonal to seasonal) ensemble forecasting system and MJO forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its advantages but also exhibits some biases, including underdispersive ensemble, overestimated amplitude and faster propagation speed when forecasting MJO. We also provide the explanation towards these biases and prospects for further improvement of this system in the future.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
Short summary
Short summary
Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
Short summary
Short summary
Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
EGUsphere, https://doi.org/10.5194/egusphere-2024-335, https://doi.org/10.5194/egusphere-2024-335, 2024
Short summary
Short summary
Large volcanic eruptions deposit material into the upper-atmosphere, which is capable of altering temperature and wind patterns of the Earth's atmosphere for years following. 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 testbed for climate attribution studies.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
Short summary
Short summary
Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
Short summary
Short summary
As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
Short summary
Short summary
A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
Short summary
Short summary
We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
Short summary
Short summary
This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Cited articles
Beaudoing, H., Rodell, M., and NASA/GSFC/HSL: GLDAS Noah Land Surface Model L4 monthly 1.0 x 1.0 degree V2.1, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/LWTYSMP3VM5Z, 2020.
CESM Software Engineering Group: CESM User’s Guide (CESM1.2 Release Series User’s Guide), https://www2.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html#download_ccsm_code, last access: 21 December 2022.
Chakraborty, T. and Lee, X.: Land Cover Regulates the Spatial Variability of
Temperature Response to the Direct Radiative Effect of Aerosols, Geophys.
Res. Lett., 46, 8995–9003, https://doi.org/10.1029/2019gl083812, 2019.
Dai, A.: Precipitation Characteristics in Eighteen Coupled Climate Models,
J. Climate, 19, 4605–4630, https://doi.org/10.1175/JCLI3884.1, 2006.
Duveiller, G., Forzieri, G., Robertson, E., Li, W., Georgievski, G., Lawrence, P., Wiltshire, A., Ciais, P., Pongratz, J., Sitch, S., Arneth, A., and Cescatti, A.: Biophysics and vegetation cover change: a process-based evaluation framework for confronting land surface models with satellite observations, Earth Syst. Sci. Data, 10, 1265–1279, https://doi.org/10.5194/essd-10-1265-2018, 2018.
Findell, K. L., Gentine, P., Lintner, B. R., and Kerr, C.: Probability of
afternoon precipitation in eastern United States and Mexico enhanced by high
evaporation, Nat. Geosci., 4, 434–439, https://doi.org/10.1038/ngeo1174, 2011.
Forzieri, G., Duveiller, G., Georgievski, G., Li, W., Robertson, E., Kautz,
M., Lawrence, P., Garcia San Martin, L., Anthoni, P., Ciais, P., Pongratz,
J., Sitch, S., Wiltshire, A., Arneth, A., and Cescatti, A.: Evaluating the
Interplay Between Biophysical Processes and Leaf Area Changes in Land
Surface Models, J. Adv. Model Earth Sy., 10, 1102–1126,
https://doi.org/10.1002/2018MS001284, 2018.
Forzieri, G., Miralles, D. G., Ciais, P., Alkama, R., Ryu, Y., Duveiller,
G., Zhang, K., Robertson, E., Kautz, M., Martens, B., Jiang, C., Arneth, A.,
Georgievski, G., Li, W., Ceccherini, G., Anthoni, P., Lawrence, P.,
Wiltshire, A., Pongratz, J., Piao, S., Sitch, S., Goll, D. S., Arora, V. K.,
Lienert, S., Lombardozzi, D., Kato, E., Nabel, J. E. M. S., Tian, H.,
Friedlingstein, P., and Cescatti, A.: Increased control of vegetation on
global terrestrial energy fluxes, Nat. Clim. Change, 10, 356–362,
https://doi.org/10.1038/s41558-020-0717-0, 2020.
Gao, Y., Leung, L. R., Zhao, C., and Hagos, S.: Sensitivity of U.S. summer
precipitation to model resolution and convective parameterizations across
gray zone resolutions, J. Geophys. Res.-Atmos., 122, 2714–2733,
https://doi.org/10.1002/2016jd025896, 2017.
Gelaro, R., McCarty, W., Suarez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy,
L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da
Silva, A., Gu, W., Kim, G. K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Global Modeling and Assimilation Office (GMAO): MERRA-2 instM_3d_asm_Np: 3d,Monthly mean,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/2E096JV59PK7, 2015a.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavgM_2d_flx_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/0JRLVL8YV2Y4, 2015b.
Hao, D., Bisht, G., Gu, Y., Lee, W.-L., Liou, K.-N., and Leung, L. R.: A parameterization of sub-grid topographical effects on solar radiation in the E3SM Land Model (version 1.0): implementation and evaluation over the Tibetan Plateau, Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, 2021.
Hao, D., Bisht, G., Huang, M., Ma, P. L., Tesfa, T., Lee, W. L., Gu, Y., and
Leung, L. R.: Impacts of Sub-Grid Topographic Representations on Surface
Energy Balance and Boundary Conditions in the E3SM Land Model: A Case Study
in Sierra Nevada, J. Adv. Model Earth Sy., 14, 4, https://doi.org/10.1029/2021ms002862, 2022.
Hao, Z., Hao, F., Xia, Y., Singh, V. P., Hong, Y., Shen, X., and Ouyang, W.:
A Statistical Method for Categorical Drought Prediction Based on NLDAS-2, J.
Appl. Meteorol. Clim., 55, 1049–1061, https://doi.org/10.1175/jamc-d-15-0200.1, 2016.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU
TS monthly high-resolution gridded multivariate climate dataset, Sci. Data,
7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Hartmann, D. L.: Global Physical Climatology, Academic Press, San Diego, America, 1994.
Hinkelman, L. M.: The Global Radiative Energy Budget in MERRA and MERRA-2:
Evaluation with Respect to CERES EBAF Data, J. Climate, 32, 1973–1994,
https://doi.org/10.1175/jcli-d-18-0445.1, 2019.
Huffman, G. J., Dan, B., Bolvin, A., Hsu, K., Joyce, R., and Xie, P.: Integrated Multi-satellitE Retrievals for GPM (IMERG), version 4.4. NASA's Precipitation Processing Center, ftp://arthurhou.pps.eosdis.nasa.gov/gpmdata/ (last access: 21 December 2022), 2014.
Jiménez, C., Prigent, C., Mueller, B., Seneviratne, S. I., McCabe, M.
F., Wood, E. F., Rossow, W. B., Balsamo, G., Betts, A. K., Dirmeyer, P. A.,
Fisher, J. B., Jung, M., Kanamitsu, M., Reichle, R. H., Reichstein, M.,
Rodell, M., Sheffield, J., Tu, K., and Wang, K.: Global intercomparison of
12 land surface heat flux estimates, J. Geophys. Res., 116, D02102,
https://doi.org/10.1029/2010jd014545, 2011.
Klein, S. A. and Hartmann, D. L.: The Seasonal Cycle of Low Stratiform
Clouds, J. Climate, 6, 1587–1606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2, 1993.
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K., and Janssen, P.: A
coupled data assimilation system for climate reanalysis, Q. J. Roy. Meteor.
Soc., 142, 65–78, https://doi.org/10.1002/qj.2629, 2016.
Lee, J. M., Zhang, Y., and Klein, S. A.: The effect of land surface
heterogeneity and background wind on shallow cumulus clouds and the
transition to deeper convection, J. Atmos. Sci., 76, 401–419,
https://doi.org/10.1175/JAS-D-18-0196.1, 2019.
Lee, W. L., Liou, K. N., Wang, C. c., Gu, Y., Hsu, H. H., and Li, J. L. F.:
Impact of 3-D Radiation-Topography Interactions on Surface Temperature and
Energy Budget Over the Tibetan Plateau in Winter, J. Geophys. Res.-Atmos.,
124, 1537–1549, https://doi.org/10.1029/2018jd029592, 2019.
Lee, X., Goulden, M. L., Hollinger, D. Y., Barr, A., Black, T. A., Bohrer,
G., Bracho, R., Drake, B., Goldstein, A., Gu, L., Katul, G., Kolb, T., Law,
B. E., Margolis, H., Meyers, T., Monson, R., Munger, W., Oren, R., Paw, U.
K., Richardson, A. D., Schmid, H. P., Staebler, R., Wofsy, S., and Zhao, L.:
Observed increase in local cooling effect of deforestation at higher
latitudes, Nature, 479, 384–387, https://doi.org/10.1038/nature10588, 2011.
Liu, S., Chen, M., and Zhuang, Q.: Aerosol effects on global land surface
energy fluxes during 2003–2010, Geophys. Res. Lett., 41, 7875–7881,
https://doi.org/10.1002/2014gl061640, 2014.
Liu, S., Liu, X., Yu, L., Wang, Y., Zhang, G. J., Gong, P., Huang, W., Wang,
B., Yang, M., and Cheng, Y.: Climate response to introduction of the ESA CCI
land cover data to the NCAR CESM, Clim. Dynam., 56, 4109–4127,
https://doi.org/10.1007/s00382-021-05690-3, 2021.
Liu, S., Wang, Y., Zhang, G. J., Wei, L., Wang, B., and Yu, L.: Contrasting
influences of biogeophysical and biogeochemical impacts of historical land
use on global economic inequality, Nat. Commun., 13, 2479,
https://doi.org/10.1038/s41467-022-30145-6, 2022.
Loeb, N. G., Kato, S., Loukachine, K., Manalo-Smith, N., and Doelling, D.
R.: Angular Distribution Models for Top-of-Atmosphere Radiative Flux
Estimation from the Clouds and the Earth's Radiant Energy System Instrument
on the Terra Satellite. Part II: Validation, J. Atmos. Ocean Tech., 24,
564–584, https://doi.org/10.1175/jtech1983.1, 2007.
Loeb, N. G., Lyman, J. M., Johnson, G. C., Allan, R. P., Doelling, D. R.,
Wong, T., Soden, B. J., and Stephens, G. L.: Observed changes in
top-of-the-atmosphere radiation and upper-ocean heating consistent within
uncertainty, Nat. Geosci., 5, 110–113, https://doi.org/10.1038/ngeo1375, 2012.
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G.,
Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth's
Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)
Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Climate, 31, 895–918,
https://doi.org/10.1175/jcli-d-17-0208.1, 2018.
Lothon, M., Campistron, B., Chong, M., Couvreux, F., Guichard, F., Rio, C.,
and Williams, E.: Life Cycle of a Mesoscale Circular Gust Front Observed by
a C-Band Doppler Radar in West Africa, Mon. Weather Rev., 139, 1370–1388,
https://doi.org/10.1175/2010MWR3480.1, 2011.
Lun, Y., Liu, L., Cheng, L., Li, X., Li, H., and Xu, Z.: Assessment of GCMs
simulation performance for precipitation and temperature from CMIP5 to CMIP6
over the Tibetan Plateau, Int. J. Climatol., 41, 3994–4018,
https://doi.org/10.1002/joc.7055, 2021.
Ma, J., Wang, H., and Fan, K.: Dynamic downscaling of summer precipitation
prediction over China in 1998 using WRF and CCSM4, Adv. Atmos. Sci., 32,
577–584, https://doi.org/10.1007/s00376-014-4143-y, 2015.
Mehran, A., AghaKouchak, A., and Phillips, T. J.: Evaluation of CMIP5
continental precipitation simulations relative to satellite-based
gauge-adjusted observations, J. Geophys. Res.-Atmos., 119, 1695–1707,
https://doi.org/10.1002/2013jd021152, 2014.
Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.:
Land-atmospheric feedbacks during droughts and heatwaves: state of the
science and current challenges, Ann. N. Y. Acad. Sci., 1436, 19–35,
https://doi.org/10.1111/nyas.13912, 2019.
Mueller, B. and Seneviratne, S. I.: Systematic land climate and
evapotranspiration biases in CMIP5 simulations, Geophys. Res. Lett., 41,
128–134, https://doi.org/10.1002/2013GL058055, 2014.
Na, Y., Fu, Q., and Kodama, C.: Precipitation Probability and Its Future
Changes From a Global Cloud-Resolving Model and CMIP6 Simulations, J.
Geophys. Res.-Atmos., 125, e2019JD031926, https://doi.org/10.1029/2019jd031926, 2020.
Novick, K. A., Biederman, J. A., Desai, A. R., Litvak, M. E., Moore, D. J.
P., Scott, R. L., and Torn, M. S.: The AmeriFlux network: A coalition of the
willing, Agr. Forest Meteorol., 249, 444–456,
https://doi.org/10.1016/j.agrformet.2017.10.009, 2018.
O'Brien, T. A., Collins, W. D., Kashinath, K., Rübel, O., Byna, S., Gu,
J., Krishnan, H., and Ullrich, P. A.: Resolution dependence of precipitation
statistical fidelity in hindcast simulations, J. Adv. Model Earth Sy., 8,
976–990, https://doi.org/10.1002/2016ms000671, 2016.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E.,
Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A.,
Decker, M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque,
J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S.,
Sakaguchi, K., Slater, A., Stockli, R., Wang, A., Yang, Z.-L., and Zeng, X.:
Technical description of version 4.0 of the Community Land Model (CLM), NCAR
Technical Note NCAR/TN-478+STR, 257 pp., 2010.
Osborn, T. J., Jones, P. D., and Joshi, M.: Recent United Kingdom and global
temperature variations, Weather, 72, 323–329, https://doi.org/10.1002/wea.3174, 2017.
Pielke, R. A.: Influence of the spatial distribution of vegetation and soils
on the prediction of cumulus Convective rainfall, Rev. Geophys., 39,
151–177, https://doi.org/10.1029/1999rg000072, 2001.
Pitman, A. J.: The evolution of, and revolution in, land surface schemes
designed for climate models, Int. J. Climatol., 23, 479–510,
https://doi.org/10.1002/joc.893, 2003.
Rieck, M., Hohenegger, C., and van Heerwaarden, C. C.: The Influence of Land
Surface Heterogeneities on Cloud Size Development, Mon. Weather Rev., 142,
3830–3846, https://doi.org/10.1175/mwr-d-13-00354.1, 2014.
Rochetin, N., Couvreux, F., and Guichard, F.: Morphology of breeze
circulations induced by surface flux heterogeneities and their impact on
convection initiation, Q. J. Roy. Meteor. Soc., 143, 463–478,
https://doi.org/10.1002/qj.2935, 2017.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Roc., 85, 381–394,
https://doi.org/10.1175/bams-85-3-381, 2004.
Rotenberg, E. and Yakir, D.: Contribution of semi-arid forests to the
climate system, Science, 327, 451–454, https://doi.org/10.1126/science.1179998, 2010.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer,
D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R.,
Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker,
E.: The NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208,
https://doi.org/10.1175/jcli-d-12-00823.1, 2014.
Stone, D. A., Risser, M. D., Angélil, O. M., Wehner, M. F., Cholia, S.,
Keen, N., Krishnan, H., O'Brien, T. A., and Collins, W. D.: A basis set for
exploration of sensitivity to prescribed ocean conditions for estimating
human contributions to extreme weather in CAM5.1-1degree, Weather and
Climate Extremes, 19, 10–19, https://doi.org/10.1016/j.wace.2017.12.003, 2018.
Su, F., Duan, X., Chen, D., Hao, Z., and Cuo, L.: Evaluation of the Global
Climate Models in the CMIP5 over the Tibetan Plateau, J. Climate, 26,
3187–3208, https://doi.org/10.1175/jcli-d-12-00321.1, 2013.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K. L.: A
Review of Global Precipitation Data Sets: Data Sources, Estimation, and
Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017rg000574, 2018.
Sun, W., Li, L., and Wang, B.: Reducing the biases in shortwave cloud
radiative forcing in tropical and subtropical regions from the perspective
of boundary layer processes, Sci. China Earth Sci., 59, 1427–1439,
https://doi.org/10.1007/s11430-016-5290-z, 2016.
Sun, W., Wang, B., Wang, Y., Zhang, G. J., Han, Y., Wang, X., and Yang, M.:
Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the
Atmosphere Improves Boreal Summer Land Precipitation Simulation with the
NCAR CESM1.2, Geophys. Res. Lett., 48, e2020GL090715, https://doi.org/10.1029/2020gl090715, 2021.
Tang, Y., Wen, X., Sun, X., and Wang, H.: Interannual variation of the Bowen
ratio in a subtropical coniferous plantation in southeast China, 2003–2012,
PLoS One, 9, e88267, https://doi.org/10.1371/journal.pone.0088267, 2014.
Taylor, C. M., Parker, D. J., and Harris, P. P.: An observational case study
of mesoscale atmospheric circulations induced by soil moisture, Geophys.
Res. Lett., 34, L15801, https://doi.org/10.1029/2007gl030572, 2007.
Waliser, D. E., Moncrieff, M. W., Burridge, D., Fink, A. H., and Yuter, S.:
The “year” of tropical convection (May 2008–April 2010): Climate
variability and weather highlights, B. Am. Meteorol. Soc., 93, 1189–1218,
https://doi.org/10.1175/2011bams3095.1, 2012.
Wang, Y., Zhang, G. J., and Craig, G. C.: Stochastic convective
parameterization improving the simulation of tropical precipitation
variability in the NCAR CAM5, Geophys. Res. Lett., 43, 6612-6619,
https://doi.org/10.1002/2016gl069818, 2016.
Wang, Y., Zhang, G. J., and He, Y. J.: Simulation of Precipitation Extremes
Using a Stochastic Convective Parameterization in the NCAR CAM5 Under
Different Resolutions, J. Geophys. Res.-Atmos., 122, 12875–12891,
https://doi.org/10.1002/2017jd026901, 2017.
Wang, Y., Zhang, G. J., and Jiang, Y.: Linking Stochasticity of Convection
to Large-Scale Vertical Velocity to Improve Indian Summer Monsoon Simulation
in the NCAR CAM5, J. Climate, 31, 6985–7002, https://doi.org/10.1175/jcli-d-17-0785.1,
2018.
Wang, Y., Xia, W., Liu, X., Xie, S., Lin, W., Tang, Q., Ma, H.-Y., Jiang,
Y., Wang, B., and Zhang, G. J.: Disproportionate control on aerosol burden
by light rain, Nat. Geosci., 14, 72–76, https://doi.org/10.1038/s41561-020-00675-z,
2021a.
Wang, Y., Zhang, G. J., Xie, S., Lin, W., Craig, G. C., Tang, Q., and Ma, H.-Y.: Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model, Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, 2021b.
Wei, L., Wang, Y., Liu, S., Zhang, G. J., and Wang, B.: Distinct roles of
land cover in regulating spatial variabilities of temperature responses to
radiative effects of aerosols and clouds, Environ. Res. Lett., 16, 124070,
https://doi.org/10.1088/1748-9326/ac3f04, 2021.
Xia, Y., Hao, Z., Shi, C., Li, Y., Meng, J., Xu, T., Wu, X., and Zhang, B.:
Regional and Global Land Data Assimilation Systems: Innovations, Challenges,
and Prospects, J. Meteorol. Res.-Prc., 33, 159–189,
https://doi.org/10.1007/s13351-019-8172-4, 2019.
Yin, M., Wang, Y., and Sun, W.: Climate Impacts of Parameterizing Subgrid Partitioning of Land Surface Heat Fluxes to the Atmosphere with the NCAR CESM1.2, Zenodo [data set], https://doi.org/10.5281/zenodo.6606418, 2022.
Yu, R., Li, J., Zhang, Y., and Chen, H.: Improvement of rainfall simulation
on the steep edge of the Tibetan Plateau by using a finite-difference
transport scheme in CAM5, Clim. Dynam., 45, 2937–2948,
https://doi.org/10.1007/s00382-015-2515-3, 2015.
Yue, S., Yang, K., Lu, H., Zhou, X., Chen, D., and Guo, W.: Representation
of Stony Surface-Atmosphere Interactions in WRF Reduces Cold and Wet Biases
for the Southern Tibetan Plateau, J. Geophys. Res.-Atmos., 126, e2021JD035291,
https://doi.org/10.1029/2021jd035291, 2021.
Zaitchik, B. F., Rodell, M., and Olivera, F.: Evaluation of the Global Land
Data Assimilation System using global river discharge data and a
source-to-sink routing scheme, Water Resour. Res., 46, W06507,
https://doi.org/10.1029/2009wr007811, 2010.
Zhou, X., Yang, K., Beljaars, A., Li, H., Lin, C., Huang, B., and Wang, Y.:
Dynamical impact of parameterized turbulent orographic form drag on the
simulation of winter precipitation over the western Tibetan Plateau, Clim.
Dynam., 53, 707–720, https://doi.org/10.1007/s00382-019-04628-0, 2019.
Zhou, X., Yang, K., Ouyang, L., Wang, Y., Jiang, Y., Li, X., Chen, D., and
Prein, A.: Added value of kilometer-scale modeling over the third pole
region: a CORDEX-CPTP pilot study, Clim. Dynam., 57, 1673–1687,
https://doi.org/10.1007/s00382-021-05653-8, 2021.
Zhu, Y.-Y. and Yang, S.: Evaluation of CMIP6 for historical temperature and
precipitation over the Tibetan Plateau and its comparison with CMIP5, Adv.
Clim. Change Res., 11, 239–251, https://doi.org/10.1016/j.accre.2020.08.001, 2020.
Short summary
All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the atmosphere, and thus their horizontal variations within the grid cell are averaged out. In this regard, a novel scheme considering the variation and partitioning of the surface heat fluxes within the grid cell is developed. The scheme reduces the long-standing rainfall biases on the southern and eastern margins of the Tibetan Plateau. The performance of key variables at the global scale is also evaluated.
All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the...
