Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-73-2021
© Author(s) 2021. 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-14-73-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales
Lawrence Livermore National Laboratory, Livermore, CA, USA
Chen Zhou
School of Atmospheric Sciences, Nanjing University, Nanjing, China
Yunyan Zhang
Lawrence Livermore National Laboratory, Livermore, CA, USA
Stephen A. Klein
Lawrence Livermore National Laboratory, Livermore, CA, USA
Mark D. Zelinka
Lawrence Livermore National Laboratory, Livermore, CA, USA
Xue Zheng
Lawrence Livermore National Laboratory, Livermore, CA, USA
Shaocheng Xie
Lawrence Livermore National Laboratory, Livermore, CA, USA
Wei-Ting Chen
Department of Atmospheric Sciences, National Taiwan University,
Taipei, Taiwan
Chien-Ming Wu
Department of Atmospheric Sciences, National Taiwan University,
Taipei, Taiwan
Related authors
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.
Shaoyue Qiu, Xue Zheng, Peng Wu, Hsiang-He Lee, and Xiaoli Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2025-3465, https://doi.org/10.5194/egusphere-2025-3465, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Model liquid water path (LWP) responses match satellite observations, showing decreased (increased) LWP for non-precipitating thin (precipitating) clouds, due to realistic simulations of non-precipitating and drizzling regimes. However, LWP increases for non-precipitating thick clouds, opposite to satellite-based decreases, due to excessive precipitation, moist bias in cloud-top humidity, and cloud droplet number–LWP relations from internal cloud processes, rather than aerosol-cloud interaction.
Xiaojian Zheng, Yan Feng, David Painemal, Meng Zhang, Shaocheng Xie, Zhujun Li, Robert Jacob, and Bethany Lusch
EGUsphere, https://doi.org/10.5194/egusphere-2025-3076, https://doi.org/10.5194/egusphere-2025-3076, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study combined satellite observation and climate model simulation to investigate the impact of aerosols on marine clouds over Eastern North Atlantic. Using regime-based analysis, we found that cloud responses to aerosols vary significantly across different meteorological patterns. Model generally captured observed trends but exaggerated the cloud responses, performing better for shallower stratiform clouds than deeper clouds. Our findings highlight the need for further model improvements.
Rachel Yuen Sum Tam, Timothy Myers, Mark Zelinka, Cristian Proistosescu, Yuan-Jen Lin, and Kate Marvel
EGUsphere, https://doi.org/10.5194/egusphere-2025-3177, https://doi.org/10.5194/egusphere-2025-3177, 2025
Short summary
Short summary
This work identifies the key driver to the change of present and future climate response, known as the pattern effect, by breaking down low-cloud feedback as the radiative changes to meteorology and the meteorology changes to warming using a cloud controlling factor framework. We identify inversion strength in the Southern Ocean and the South East Pacific as the main driver to the pattern effect, and larger uncertainty remains in the sensitivities of radiative flux to meteorology.
Beth Dingley, James A. Anstey, Marta Abalos, Carsten Abraham, Tommi Bergman, Lisa Bock, Sonya Fiddes, Birgit Hassler, Ryan J. Kramer, Fei Luo, Fiona M. O'Connor, Petr Šácha, Isla R. Simpson, Laura J. Wilcox, and Mark D. Zelinka
EGUsphere, https://doi.org/10.5194/egusphere-2025-3189, https://doi.org/10.5194/egusphere-2025-3189, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This manuscript defines as a list of variables and scientific opportunities which are requested from the CMIP7 Assessment Fast Track to address open atmospheric science questions. The list reflects the output of a large public community engagement effort, coordinated across autumn 2025 through to summer 2025.
Hsiang-He Lee, Xue Zheng, Shaoyue Qiu, and Yuan Wang
Atmos. Chem. Phys., 25, 6069–6091, https://doi.org/10.5194/acp-25-6069-2025, https://doi.org/10.5194/acp-25-6069-2025, 2025
Short summary
Short summary
The study investigates how aerosol–cloud interactions affect warm boundary layer stratiform clouds over the eastern North Atlantic. High-resolution weather model simulations reveal that non-rain clouds at the edge of cloud systems are prone to evaporation, leading to an aerosol drying effect and a transition of aerosols back to the accumulation mode for future activation. The study shows that this dynamic behavior is often not adequately represented in most previous prescribed-aerosol simulations.
Anna Zehrung, Andrew D. King, Zebedee Nicholls, Mark D. Zelinka, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2025-2252, https://doi.org/10.5194/egusphere-2025-2252, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The Gregory method is a common approach for calculating the equilibrium climate sensitivity (ECS). However, studies which apply this method lack transparency in how model data is processed prior to calculating the ECS, inhibiting replicability. Different choices of global and annual mean weighting, anomaly calculation, and linear regression fit can affect the ECS estimates. We investigate the impact of these choices and propose a standardised method for future ECS calculations.
Vincent Larson, Zhun Guo, Benjamin Stephens, Colin Zarzycki, Gerhard Dikta, Yun Qian, and Shaocheng Xie
EGUsphere, https://doi.org/10.5194/egusphere-2025-1593, https://doi.org/10.5194/egusphere-2025-1593, 2025
Short summary
Short summary
Global models of the atmosphere contain errors that lead to inaccurate simulations. A software tool ("QuadTune") is presented that attempts to mitigate some of the inaccuracies. It also displays diagnostic plots that provide hints about where the errors might lie in the model.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Mingxuan Wu, Hailong Wang, Zheng Lu, Xiaohong Liu, Huisheng Bian, David Cohen, Yan Feng, Mian Chin, Didier A. Hauglustaine, Vlassis A. Karydis, Marianne T. Lund, Gunnar Myhre, Andrea Pozzer, Michael Schulz, Ragnhild B. Skeie, Alexandra P. Tsimpidi, Svetlana G. Tsyro, and Shaocheng Xie
EGUsphere, https://doi.org/10.5194/egusphere-2025-235, https://doi.org/10.5194/egusphere-2025-235, 2025
Short summary
Short summary
A key challenge in simulating the lifecycle of nitrate aerosol in global climate models is to accurately represent mass size distribution of nitrate aerosol, which lacks sufficient observational constraints. We found that most climate models underestimate the mass fraction of fine-mode nitrate at surface in all regions. Our study highlights the importance of gas-aerosol partitioning parameterization and simulation of dust and sea salt in correctly simulating mass size distribution of nitrate.
Mark D. Zelinka, Li-Wei Chao, Timothy A. Myers, Yi Qin, and Stephen A. Klein
Atmos. Chem. Phys., 25, 1477–1495, https://doi.org/10.5194/acp-25-1477-2025, https://doi.org/10.5194/acp-25-1477-2025, 2025
Short summary
Short summary
Clouds lie at the heart of uncertainty in both climate sensitivity and radiative forcing, making it imperative to properly diagnose their radiative effects. Here we provide a recommended methodology and code base for the community to use in performing such diagnoses using cloud radiative kernels. We show that properly accounting for changes in obscuration of lower-level clouds by upper-level clouds is important for accurate diagnosis and attribution of cloud feedbacks and adjustments.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
Short summary
Short summary
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Tianning Su and Yunyan Zhang
Atmos. Chem. Phys., 24, 6477–6493, https://doi.org/10.5194/acp-24-6477-2024, https://doi.org/10.5194/acp-24-6477-2024, 2024
Short summary
Short summary
The planetary boundary layer is critical to our climate system. This study uses a deep learning approach to estimate the planetary boundary layer height (PBLH) from conventional meteorological measurements. By training data from comprehensive field observations, our model examines the influence of various meteorological factors on PBLH and demonstrates effectiveness across different scenarios, offering a reliable tool for understanding boundary layer dynamics.
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.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Shaoyue Qiu, Xue Zheng, David Painemal, Christopher R. Terai, and Xiaoli Zhou
Atmos. Chem. Phys., 24, 2913–2935, https://doi.org/10.5194/acp-24-2913-2024, https://doi.org/10.5194/acp-24-2913-2024, 2024
Short summary
Short summary
The aerosol indirect effect (AIE) depends on cloud states, which exhibit significant diurnal variations in the northeastern Atlantic. Yet the AIE diurnal cycle remains poorly understood. Using satellite retrievals, we find a pronounced “U-shaped” diurnal variation in the AIE, which is contributed to by the transition of cloud states combined with the lagged cloud responses. This suggests that polar-orbiting satellites with overpass times at noon underestimate daytime mean values of the AIE.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Mark D. Zelinka, Christopher J. Smith, Yi Qin, and Karl E. Taylor
Atmos. Chem. Phys., 23, 8879–8898, https://doi.org/10.5194/acp-23-8879-2023, https://doi.org/10.5194/acp-23-8879-2023, 2023
Short summary
Short summary
The primary uncertainty in how strongly Earth's climate has been perturbed by human activities comes from the unknown radiative impact of aerosol changes. Accurately quantifying these forcings – and their sub-components – in climate models is crucial for understanding the past and future simulated climate. In this study we describe biases in previously published estimates of aerosol radiative forcing in climate models and provide corrected estimates along with code for users to compute them.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
Short summary
Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
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.
Xue Zheng, Qing Li, Tian Zhou, Qi Tang, Luke P. Van Roekel, Jean-Christophe Golaz, Hailong Wang, and Philip Cameron-Smith
Geosci. Model Dev., 15, 3941–3967, https://doi.org/10.5194/gmd-15-3941-2022, https://doi.org/10.5194/gmd-15-3941-2022, 2022
Short summary
Short summary
We document the model experiments for the future climate projection by E3SMv1.0. At the highest future emission scenario, E3SMv1.0 projects a strong surface warming with rapid changes in the atmosphere, ocean, sea ice, and land runoff. Specifically, we detect a significant polar amplification and accelerated warming linked to the unmasking of the aerosol effects. The impact of greenhouse gas forcing is examined in different climate components.
Aurore Voldoire, Romain Roehrig, Hervé Giordani, Robin Waldman, Yunyan Zhang, Shaocheng Xie, and Marie-Nöelle Bouin
Geosci. Model Dev., 15, 3347–3370, https://doi.org/10.5194/gmd-15-3347-2022, https://doi.org/10.5194/gmd-15-3347-2022, 2022
Short summary
Short summary
A single-column version of the global climate model CNRM-CM6-1 has been designed to ease development and validation of the model physics at the air–sea interface in a simplified environment. This model is then used to assess the ability to represent the sea surface temperature diurnal cycle. We conclude that the sea surface temperature diurnal variability is reasonably well represented in CNRM-CM6-1 with a 1 h coupling time step and the upper-ocean model resolution of 1 m.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
Short summary
Short summary
An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Yu-Hung Chang, Wei-Ting Chen, Chien-Ming Wu, Christopher Moseley, and Chia-Chun Wu
Atmos. Chem. Phys., 21, 16709–16725, https://doi.org/10.5194/acp-21-16709-2021, https://doi.org/10.5194/acp-21-16709-2021, 2021
Short summary
Short summary
The impacts of increasing cloud condensation nuclei on summertime diurnal precipitation in weak synoptic weather over complex topography in Taiwan were investigated by applying object-based tracking analyses to semi-realistic large-eddy simulations. In hotspots of orographic locking processes, rain initiation is delayed, which prolongs the development of local circulation and convection. For this organized regime, the occurrence of extreme diurnal precipitating systems is notably enhanced.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
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.
Qi Tang, Michael J. Prather, Juno Hsu, Daniel J. Ruiz, Philip J. Cameron-Smith, Shaocheng Xie, and Jean-Christophe Golaz
Geosci. Model Dev., 14, 1219–1236, https://doi.org/10.5194/gmd-14-1219-2021, https://doi.org/10.5194/gmd-14-1219-2021, 2021
Peter A. Bogenschutz, Shuaiqi Tang, Peter M. Caldwell, Shaocheng Xie, Wuyin Lin, and Yao-Sheng Chen
Geosci. Model Dev., 13, 4443–4458, https://doi.org/10.5194/gmd-13-4443-2020, https://doi.org/10.5194/gmd-13-4443-2020, 2020
Short summary
Short summary
This paper documents a tool that has been developed that can be used to accelerate the development and understanding of climate models. This version of the model, known as a the single-column model, is much faster to run than the full climate model, and we demonstrate that this tool can be used to quickly exploit model biases that arise due to physical processes. We show examples of how this single-column model can directly benefit the field.
Cited articles
Adames, Á, F. and Kim, D.: The MJO as a Dispersive, Convectively Coupled Moisture Wave: Theory and Observations, J. Atmos. Sci., 73, 913–941, https://doi.org/10.1175/JAS-D-15-0170.1, 2016.
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak,
J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind,
J., Arkin, P., and Nelkin, E.: The version-2 Global Precipitation
Climatology Project (GPCP) monthly precipitation analysis (1979–present), J.
Hydrometeorol., 4, 1147–1167, 2003.
Ahn, M.-S., Kim, D., Sperber, K. R., Kang, I.-S., Maloney, E., Waliser, D.,
and Hendon, H.: MJO simulation in CMIP5 climate models: MJO skill metrics
and process-oriented diagnosis, Clim. Dynam., 49, 4023–4045,
https://doi.org/10.1007/s00382-017-3558-4, 2017.
Barton, N. P., Klein, S. A., Boyle, J. S., and Zhang, Y.: Arctic synoptic
regimes: Comparing domain-wide Arctic cloud observations with CAM4 and CAM5
during similar dynamics, J. Geophys. Res., 117, D15205,
https://doi.org/10.1029/2012JD017589, 2012.
Barton, N. P., Klein, S. A., and Boyle, J. S.: On the contribution of
longwave radiation to global climate model biases in Arctic lower
tropospheric stability, J. Climate, 27, 7250–7269, https://doi.org/10.1175/JCLI-D-14-00126.1, 2014.
Berg, L. K. and Kassianoy, E. I.: Temporal variability of fair- weather
cumulus statistics at the ACRF SGP site, J. Climate, 21, 3344–3358, 2008.
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus,
R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H.,
Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity,
Nat. Geosci., 8, 261–268, https://doi.org/10.1038/NGEO2398,
2015.
Chandra, A. S., Zhang, C., Klein, S. A., and Ma, H.-Y.: Low-cloud
characteristics over the tropical western Pacific from ARM observations and
CAM5 simulations, J. Geophys. Res.-Atmos., 120, 8953–8970, https://doi.org/10.1002/2015JD023369, 2015.
Chen, W.-T., Wu, C.-M., and Ma, H.-Y.: Evaluating the bias of South China
Sea summer monsoon precipitation associated with fast physical processes
using climate model hindcast approach, J. Climate, 32, 4491–4507,
https://doi.org/10.1175/JCLI-D-18-0660.1, 2019.
Ciesielski, P. E., Johnson, R. H., Jiang, X., Zhang, Y., and Xie, S.:
Relationships between radiation, clouds, and convection during DYNAMO, J.
Geophys. Res.-Atmos., 122, 2529–2548, https://doi.org/10.1002/2016JD025965, 2017.
Clothiaux, E. E., Ackerman, T. P., Mace, G. G., Moran, K. P., Marchand, R.
T., Miller, M., and Martner, B. E.: Objective determination of cloud heights
and radar reflectivities using a combination of active remote sensors at the
ARM CART sites, J. Appl. Meteorol., 39, 645–665, 2000.
Clothiaux, E. E., Miller, M. A., Perez, R. C., Turner, D. D., Moran, K. P.,
Martner, B. E., Ackerman, T. P., Mace, G. G., Marchand, R. T., Widener, K.
B., Rodriguez, D. J., Uttal, T., Mather, J. H., Flynn, C. J., Gaustad, K.
L., and Ermold, B.: The ARM millimeter wave cloud radars (MMCRs) and the
active remote sensing of clouds (ARSCL) value added product (VAP), U.S.
Department of Energy Tech. Memo. ARM VAP-002.1, 56 pp., 2001.
Covey, C., Gleckler, P. J., Doutriaux, C., Williams, D. N., Dai, A.,
Fasullo, J., Trenberth, K., and Berg, A.: Metrics for diurnal cycle of
precipitation: Toward routine benchmarks for climate models, J. Climate,
29, 4461–4471, 2016.
Dai, A.: Precipitation characteristics in eighteen coupled climate models,
J. Climate, 19, 4605–4630, 2006.
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, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hoìlm, E. V., Isaksen, L., Kallberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Theìpaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, 2011.
Gates, W. L.: AMIP: The Atmospheric Model Intercomparison Project, B.
Am. Meteorol. Soc., 73, 1962–1970, 1992.
Gerrity, J. P. and McPherson, R. D.: Noise analysis of a limited-area
fine-mesh prediction model, ESSA Technical Memoranda, WBTM NMC 46,
PB-191-188, 81 pp., 1970.
Golaz, J., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H., Lin, W., Lipscomb, W. H., Ma, P., Mccoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM coupled model version 1: Overview and evaluation at standard resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019.
Guilyardi, E., Wittenberg, A., Fedorov, A., Collins, M., Wang, C.,
Capotondi, A., van Oldenborgh, G. J., and Stockdale, T.: Understanding El
Niño in ocean-atmosphere general circulation models, B. Am. Meteorol.
Soc., 90, 325–340, 2009.
Hagos, S. M., Zhang, C., Feng, Z., Burleyson, C. D., DeMott, C., Kerns, B.,
Benedict, J., and Martini, M.: The impact of the diurnal cycle on the
propagation of Madden-Julian Oscillation convection across the Maritime
Continent, J. Adv. Model. Earth Sy., 8, 1552–1564, https://doi.org/10.1002/2016MS000725, 2016.
Hannah, W. M. and Maloney, E. D.: The moist static energy budget in NCAR
CAM5 hindcasts during DYNAMO, J. Adv. Model. Earth Sy., 6, 420–440,
2014.
Jakob, C.: Cloud cover in the ECMWF reanalysis, J. Climate, 12, 947–959,
1999.
Jiang, X.: Key processes for the eastward propagation of the Madden-Julian
Oscillation based on multimodel simulations, J. Geophys. Res.-Atmos., 122, 755–770, https://doi.org/10.1002/2016JD025955, 2017.
Jiang, X., Lau, N.-C., and Klein, S. A.: Role of eastward propagating
convection systems in the diurnal cycle and seasonal mean of summertime
rainfall over the U.S. Great Plains, Geophys. Res. Lett., 33, L19809,
https://doi.org/10.1029/2006GL027022, 2006.
Jiang, X., Waliser, D. E., Olson, W. S., Tao, W.-K., L'Ecuyer, T. S., Shige,
S., Li, K.-F., Yung, Y. L., Lang, S., and Takayabu, Y. N.: Vertical diabatic
heating structure of the MJO: Intercomparison between recent reanalyses and
TRMM estimates, Mon. Weather Rev., 139, 3208–3223, 2011.
Jiang, X., Waliser, D. E., Xavier, P. K., Petch, J., Klingaman, N. P.,
Woolnough, S. J., Guan, B., Bellon, G., Crueger, T., DeMott, C., Hannay, C.,
Lin, H., Hu, W., Kim, D., Lappen, C.-L., Lu, M.-M., Ma, H.-Y., Miyakawa, T.,
Ridout, J. A., Schubert, S. D., Scinocca, J., Seo, K.-H., Shindo, E., Song,
X., Stan, C., Tseng, W.-L., Wang, W., Wu, T., Wu, X., Wyser, K., Zhang, G.
J., Zhu, H.: Vertical structure and physical processes of the Madden-Julian
oscillation: Exploring key model physics in climate simulations, J. Geophys.
Res.-Atmos., 120, 4718–4748, https://doi.org/10.1002/2014JD022375, 2015.
Johnson, R. H. and Ciesielski, P. E.: Structure and Properties of
Madden–Julian Oscillations Deduced from DYNAMO Sounding Arrays, J. Atmos.
Sci., 70, 3157–3179, https://doi.org/10.1175/JAS-D-13-065.1,
2013.
Kato, S., Loeb, N. G., Fred, F. G., Doelling, D. R., Rutan, D. A., Caldwell,
T. E., Yu, L., and Weller, R. A.: Surface irradiances consistent with
CERES-derived top-of-atmosphere shortwave and longwave irradiances, J.
Climate, 26, 2719–2740, https://doi.org/10.1175/JCLI-D-12-00436.1, 2013.
Klein, S. A., Jiang, X., Boyle, J. S., Malyshev, S., and Xie, S.: Diagnosis
of the summertime warm and dry bias over the U.S. Southern Great Plains in
the GFDL climate model using a weather forecasting approach, Geophys. Res.
Lett., 33, L18805, https://doi.org/10.1029/2006GL027567, 2006.
Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J. S., and
Gleckler, P. J: Are climate model simulations of clouds improving? An
evaluation using the ISCCP simulator, J. Geophys. Res., 118, 1329–1342, https://doi.org/10.1002/jgrd.50141, 2013.
Klingaman, N. P., Woolnough, S. J., Jiang, X., Waliser, D., Xavier, P. K.,
Petch, J., Caian, M., Hannay, C., Kim, D., Ma, H.-Y., Merryfield, W. J.,
Miyakawa, T., Pritchard, M., Ridout, J. A., Roehrig, R., Shindo, E., Vitart,
F., Wang, H., Cavanaugh, N. R., Mapes, B. E., Shelly, A., and Zhang, G. J.:
Vertical structure and physical processes of the Madden–Julian oscillation:
Linking hindcast fidelity to simulated diabatic heating and moistening, J.
Geophys. Res.-Atmos., 120, 4690–4717, https://doi.org/10.1002/2014JD022374, 2015.
Lin, Y., Donner, L. J., Petch, J., Bechtold, P., Boyle J. S., Klein, S. A.,
Komori, T., Wapler, K., Willett, M., Xie, X., Zhao, M., Xie, S., McFarlane,
S. A., and Schumacher, C.: TWP-ICE global atmospheric model intercomparison:
convection responsiveness and resolution impact, J. Geophys. Res., 117, D09111, https://doi.org/10.1029/2011JD017018, 2012.
Loeb, G. N., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F.,
Kato, S., Manalo-Smith, N., and Wong, T.: Toward optimal closure of the
Earth's top-of-atmosphere radiation budget, J. Climate, 22, 748–766,
https://doi.org/10.1175/2008JCLI2637.1, 2009.
Ma, H.-Y.: A multi-year short-range hindcast experiment with CESM1, Lawrence Livermore National Laboratory, HPSS archive, available at: https://portal.nersc.gov/archive/home/h/hyma/www/CAPT/CAPT_Long, last access: 17 December 2020a.
Ma, H.-Y.: Initial conditions for the multi-year short-range hindcast experiment with CESM1, Lawrence Livermore National Laboratory, HPSS archive, available at: https://portal.nersc.gov/archive/home/h/hyma/www/CAPT/CAPT_Long/IC/, last access: 17 December 2020b.
Ma, H.-Y., Xie, S., Boyle, J. S., Klein, S. A., and Zhang, Y.: Metrics and
diagnostics for precipitation-related processes in climate model short-range
hindcasts, J. Climate, 26, 1516–1534, 2013.
Ma, H. Y., Xie, S., Klein, S. A., Williams, K. D., Boyle, J. S., Bony, S.,
Douville, H., Fermepin, S., Medeiros, B., Tyteca, S., and Watanabe, M.: On
the correspondence between mean forecast errors and climate errors in CMIP5
models, J. Climate, 27, 1781–1798, 2014.
Ma, H.-Y., Chuang, C. C., Klein, S. A., Lo, M.-H., Zhang, Y., Xie, S.,
Zheng, X., Ma, P.-L., Zhang, Y., and Phillips, T. J.: An improved hindcast
approach for evaluation and diagnosis of physical processes in global
climate models, J. Adv. Model. Earth Sy., 7, 1810–1827, https://doi.org/10.1002/2015MS000490, 2015.
Ma, H.-Y., Klein, S. A., and Xie, S.: Documentation for Multi-year (1997–2012) CAPT Hindcast Output, Lawrence Livermore National Laboratory, available at: https://portal.nersc.gov/archive/home/h/hyma/www/CAPT/CAPT_Long/CAPT_Long_output_cesm1_0_5_v5.pdf (last access: 17 December 2020), 2016.
Ma, H. Y., Klein, S. A., Xie, S., Zhang, C., Tang, S., Tang, Q., Morcrette,
C. J., Van Weverberg, K., Petch, J., Ahlgrimm, M., Berg, L. K., Cheruy, F.,
Cole, J., Forbes, R., Gustafson Jr., W. I., Huang, M., Liu, Y., Merryfield,
W., Qian, Y., Roehrig, R., and Wang, Y.-C.: CAUSES: On the role of surface
energy budget errors to the warm surface air temperature error over the Cen-
tral United States, J. Geophys. Res.-Atmos., 123, 2888–2909, https://doi.org/10.1002/2017JD027194, 2018.
Madden, R. A. and Julian, P. R.: Detection of a 40–50 day oscillation in
the zonal wind in the tropical Pacific, J. Atmos. Sci., 28, 702–708, 1971.
Madden, R. A. and Julian, P. R.: Description of global-scale circulation
cells in the tropics with a 40–50 day period, J. Atmos. Sci., 29,
1109–1123, 1972.
Medeiros, B., Williamson, D. L., Hannay, C., and Olson, J. G.: Southeast Pacific
stratocumulus in the Community Atmosphere Model, J. Climate, 25, 6175–6192,
2012.
Moncrieff,
M. W., Liu, C., and Bogenschutz, P.: Simulation, modeling, and
dynamically based parameterization of organized tropical convection for
global climate models, J. Atmos. Sci., 74, 1363–1380, 2017.
Morcrette, C. J., Van Weverberg, K., Ma, H. Y., Ahlgrimm, M., Bazile, E.,
Berg, L. K., Cheng, A., Cheruy, F., Cole, J., Forbes, R., and Gustafson Jr.,
W. I.: Introduction to CAUSES: Description of weather and climate models and
their near-surface temperature errors in 5 day hindcasts near the Southern Great Plains, J. Geophys. Res.-Atmos., 123, 2655–2683, https://doi.org/10.1002/2017JD027199, 2018.
National Center for Atmospheric Research (NCAR): CESM Models, UCAR/NCAR, Boulder, CO, available at: http://www.cesm.ucar.edu/models/cesm1.0/, last access: 17 December 2020a.
National Center for Atmospheric Research (NCAR): CESM Subversion input data repository, UCAR/NCAR, Boulder, CO, available at: https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/, last access: 17 December 2020b.
Neale, R. B., Chen, C. C., Gettelman, A., Lauritzen, P. H., Park, S.,
Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., Lamarque, J. F.,
and Marsh, D.: Description of the NCAR community atmosphere model (CAM 5.0),
NCAR Tech. Note NCAR/TN-486+ STR, 2010.
NOAA Physical Sciences Laboratory: NOAA Optimum Interpolation (OI) Sea Surface Temperature (SST) V2, NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, available at: https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html, last access: 17 December 2020.
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.
Phillips, T. J., Potter, G. L., Williamson, D. L., Cederwall, R. T., Boyle,
J. S., Fiorino, M., Hnilo, J. J., Olson, J. G., Xie, S., and Yio, J. J.:
Evaluating parameterizations in general circulation models: Climate
simulation meets weather prediction, B. Am. Meteorol. Soc., 85,
1903–1915, 2004.
Phillips, T. J., Klein, S. A., Ma, H.-Y., Tang, Q., Xie, S., Williams, I.
N., Santanello, J. A., Cook, D. R., and Torn, M. S.: Using ARM Observations
to Evaluate Climate Model Simulations of Land-Atmosphere Coupling on the
U.S. Southern Great Plains, J. Geophys. Res.-Atmos., 122, 11524–11548.
https://doi.org/10.1002/2017JD027141, 2017.
Powell, S. W. and Houze, R. A.: The cloud population and onset of the
Madden-Julian Oscillation over the Indian Ocean during DYNAMO-AMIE, J.
Geophys. Res.-Atmos., 118, 11979–11995, https://doi.org/10.1002/2013JD020421, 2013.
Qin, Y., Lin, Y., Xu, S., Ma, H.-Y., and Xie, S.: A diagnostic PDF cloud scheme to improve subtropical low clouds in NCAR Community Atmosphere Model (CAM5), J. Adv. Model. Earth Sys., 10, 320–341, 2018.
Rasch, P., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Burrows, S., Caldwell, P., Zhang, K., Easter, R., Cameron-Smith, P., Singh, B., Wan, H., Golaz, J.-C., Harrop, B., Roesler, E., Bacmeister, J., Larson, V., Evans, K., Qian, Y., Taylor, M., Leung, R., Zhang, Y., Brent, L., Branstettor, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J., Yoon, J., Zender, C., Bader, D., Flanner, M., Foucar, J., Jacob, R., Keen, N., Klein, S., Liu, X., Salinger, A., Shrivastava, M., and Yang, Y.: An Overview of the Atmospheric Component of the Energy Exascale Earth System Model, J. Adv. Model. Earth Syst., 11, 2377–2411, https://doi.org/10.1029/2019MS001629, 2019.
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.:
An improved in situ and satellite SST analysis for climate, J. Climate, 15,
1609–1625, 2002.
Rossow, W. B. and Schiffer, R. A.: Advances in Understanding Clouds from
ISCCP, B. Am. Meteorol. Soc., 80, 2261–2288, 1999.
Sobel, A. and Maloney, E.: An Idealized Semi-Empirical Framework for Modeling the Madden–Julian Oscillation, J. Atmos. Sci., 69, 1691–1705, https://doi.org/10.1175/jas-d-11-0118.1, 2012.
Sobel, A. and Maloney, E.: Moisture Modes and the Eastward Propagation of the MJO, J. Atmos. Sci., 70, 187–192, https://doi.org/10.1175/Jas-D-12-0189.1, 2013.
Sun, D.-Z., Thang, T., Covey, C., Klein, S. A., Collins, W. D., Hack, J. J.,
Kiehl, J. T., Meehl, G. A., Held, I. M., and Suarez, M.: Radiative and
dynamical feedbacks over the equatorial cold tongue: Results from nine
atmospheric GCMs, J. Climate, 19, 4059–4074, 2006.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, 2001.
Van Weverberg, K., Morcrette, C. J., Ma, H.-Y., Klein, S. A., and Petch, J.
C.: Using regime analysis to identify the contribution of clouds to surface
temperature errors in weather and climate models, Q. J. Roy. Meteor. Soc., 141, 3190–3206, https://doi.org/10.1002/qj.2603, 2015.
Van Weverberg, K., Morcrette, C. J., Petch, J., Klein, S. A., Ma, H. Y.,
Zhang, C., Xie, S., Tang, Q., Gustafson Jr., W. I., Qian, Y., and Berg, L.
K.: CAUSES: Attribution of surface radia- tion biases in NWP and climate
models near the U.S. South- ern Great Plains, J. Geophys. Res.-Atmos., 123,
3612–3644, https://doi.org/10.1002/2017JD027188, 2018.
Wan, H., Rasch, P. J., Zhang, K., Qian, Y., Yan, H., and Zhao, C.: Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models, Geosci. Model Dev., 7, 1961–1977, https://doi.org/10.5194/gmd-7-1961-2014, 2014.
Wheeler, M. C. and Hendon, H. H.: An all-season real-time multivariate MJO
index: Development of an index for monitoring and prediction, Mon. Weather
Rev., 132, 1917–1932, 2004.
Williams, K. D., Bodas-Salcedo, A., Déqué, M., Fermepin, S.,
Medeiros, B., Watanabe, M., Jakob, C., Klein, S. A., Senior, C. A., and
Williamson, D. L.: The Transpose-AMIP II Experiment and Its Application to
the Understanding of Southern Ocean Cloud Biases in Climate Models, J.
Climate, 26, 3258–3274, 2013.
Xavier, P. K., Petch, J. C., Klingaman, N. P., Woolnough, S. J., Jiang, X.,
Waliser, D. E., Caian, M., Cole, J., Hagos, S. M., Hannay, C., and Kim,
D.: Vertical structure and physical processes of the Madden-Julian
Oscillation: Biases and uncertainties at short range, J. Geophys.
Res.-Atmos., 120, 4749–4763, https://doi.org/10.1002/2014JD022718, 2015.
Xie, S., Zhang, M. H., Boyle, J. S., Cederwall, R. T., Potter, G. L., and
Lin, W. Y.: Impact of a revised convective triggering mechanism on CAM2
model simulations: results from short-range weather forecasts, J. Geophys.
Res., 109, D14102, https://doi.org/10.1029/2004JD004692, 2004.
Xie, S., Boyle, J. S., Klein, S. A., Liu, X., and Ghan, S.: Simulations of
Arctic mixed-phase clouds in forecasts with CAM3 and AM2 for M-PACE, J.
Geophys. Res., 113, D04211, https://doi.org/10.1029/2007JD009225, 2008.
Xie, S., Ma, H.-Y., Boyle, J. S., Klein, S. A., and Zhang, Y.: On the
correspondence between short- and long- timescale systematic errors in
CAM4/CAM5 for the Years of Tropical Convection, J. Climate, 25, 7937–7955,
2012.
Xie, S., Lin, W., Rasch, P. J., Ma, P.-L., Neale, R., Larson, V. E., Qian, Y., Bogenschutz, P. A., Caldwell, P., Cameron-Smith, P., Golaz, J.-C., Mahajan, S., Singh, B., Tang, Q., Wang, H., Yoon, J.-H., Zhang, K., and Zhang, Y.: Understanding cloud and convective characteristics in version 1 of the E3SM atmosphere model, J. Adv. Model. Earth Sy., 10, 2618–2644, https://doi.org/10.1029/2018MS001350, 2018.
Xie, S., Wang, Y.-C., Lin, W., Ma, H.-Y., Tang, Q., Tang, S., Zheng, X., Golaz, J.-C., Zhang, G., and Zhang, M.:
Improved Diurnal Cycle of Precipitation in E3SM with a Revised Convective
Triggering Function, J. Adv. Model. Earth Sy., 11, 2290–2310,
https://doi.org/10.1029/2019MS001702, 2019.
Xie, S. C., McCoy, R. B., Klein, S. A., Cederwall, R. T., Wiscombe, W. J.,
Clothiaux, E. E., Gaustad, K. L., Golaz, J. C., Hall, S. D., Jensen, M. P.,
Johnson, K. L., Lin, Y. L., Long, C. N., Mather, J. H., McCord, R. A.,
McFarlane, S. A., Palanisamy, G., Shi, Y., and Turner, D. D. D.: Arm Climate
Modeling Best Estimate Data a New Data Product for Climate Studies, B. Am.
Meteorol. Soc., 91, 13–20, https://doi.org/10.1175/2009bams2891.1, 2010.
Xu, W. and Rutledge, S. A.: Convective characteristics of the
Madden–Julian Oscillation over the Central Indian Ocean observed by
shipborne radar during DYNAMO, J. Atmos. Sci., 71, 2859–2877, 2014.
Yanai, M., Esbensen, S., and Chu, J.-H.: Determination of bulk properties
of tropical cloud clusters from large-scale heat and moisture budgets, J.
Atmos. Sci., 30, 611–627, 1973.
Yang, F., Pan, H., Krueger, S. K., Moorthi, S., and Lord, S. J.: Evaluation
of the NCEP Global Forecast System at the ARM SGP site, Mon. Weather Rev., 134, 3668–3690, 2006.
Zhang, C.: Madden–Julian Oscillation: Bridging weather and climate, B.
Am. Meteorol. Soc., 94, 1849–1870, https://doi.org/10.1175/bams-d-12-00026.1, 2013.
Zhang, C. and Ling, J.: Barrier effect of the Indo-Pacific Maritime
Continent on the MJO: Perspectives from tracking MJO precipitation, J.
Climate, 30, 3439–3459, 2017.
Zhang, K., Wan, H., Liu, X., Ghan, S. J., Kooperman, G. J., Ma, P.-L., Rasch, P. J., Neubauer, D., and Lohmann, U.: Technical Note: On the use of nudging for aerosol–climate model intercomparison studies, Atmos. Chem. Phys., 14, 8631–8645, https://doi.org/10.5194/acp-14-8631-2014, 2014.
Zhang, M., Xie, S., Liu, X., Lin, W., Zhang K., Ma, H.-Y., Zheng, X., and Zhang, Y.: Toward Understanding the Simulated Phase Partitioning of Arctic
Single-Layer Mixed-Phase Clouds in E3SM, Earth Space Sci., 7, e2020EA001125,
https://doi.org/10.1002/essoar.10502164.1, 2020.
Zhang, Y. and Klein, S. A.: Mechanisms affecting the transition from
shallow to deep convection over land: Inferences from observations of the
diurnal cycle collected at the ARM Southern Great Plains Site, J. Atmos.
Sci., 67, 2943–2959, 2010.
Zhang, Y., Xie, S., Klein, S. A., Marchand, R., Kollias, P., Clothiaux, E.
E., Lin, W., Johnson, K., Swales, D., Bodas-Salcedo, A., Tang, S., Haynes,
J. M., Collis, S., Jensen, M., Bharadwaj, N., Hardin, J., and Isom, B.: The
ARM cloud radar simulator for global climate models: Bridging field data and
climate models, B. Am. Meteorol. Soc., 99, 21–26, https://doi.org/10.1175/BAMS-D-16-0258.1, 2018.
Zhang, Y., Xie, S., Lin, W., Klein, S. A., Zelinka, M. D., Ma, P.-L., Rasch,
P. J., Qian, Y., Tang, Q., and Ma, H.-Y.: Evaluation of clouds in version 1
of E3SM Atmosphere Model with satellite simulators, J. Adv. Model. Earth
Sy., 11, 1253–1268, https://doi.org/10.1029/2018MS001562, 2019.
Zheng, X., Klein, S. A., Ma, H.-Y., Bogenschutz, P., Gettelman, A., and
Larson, V. E.: Assessment of marine boundary layer cloud simulations in the
Community Atmosphere Model with Cloud Layers Unified By Binormals and
updated microphysics scheme based on ARM observations from the Azores, J.
Geophys. Res.-Atmos., 121, 8472–8492, https://doi.org/10.1002/2016JD025274, 2016.
Zheng, X., Klein, S. A., Ma, H.-Y., Caldwell, P. M., Larson, V. E.,
Gettelman, A., and Bogenschutz, P.: A cloudy planetary boundary layer
oscillation arising from the coupling of turbulence with precipitation in
climate simulations, J. Adv. Model. Earth Sy., 9, 1973–1993, https://doi.org/10.1002/2017MS000993, 2017.
Short summary
We propose an experimental design of a suite of multi-year, short-term hindcasts and compare them with corresponding observations or measurements for periods based on different weather and climate phenomena. This atypical way of evaluating model performance is particularly useful and beneficial, as these hindcasts can give scientists a robust picture of modeled precipitation, and cloud and radiation processes from their diurnal variation to year-to-year variability.
We propose an experimental design of a suite of multi-year, short-term hindcasts and compare...