Articles | Volume 28, issue 4
https://doi.org/10.5194/npg-28-481-2021
© 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/npg-28-481-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Oct 2021
Research article |
| 04 Oct 2021
| 04 Oct 2021
A study of capturing Atlantic meridional overturning circulation (AMOC) regime transition through observation-constrained model parameters
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, 266100, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, 266100, China
Shaoqing Zhang
CORRESPONDING AUTHOR
Key Laboratory of Physical Oceanography, Ministry of
Education/Institute for Advanced Ocean Study/Frontiers Science Center for
Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China,
Qingdao, 266100, China
College of Oceanic and Atmospheric Sciences, Ocean University of
China, Qingdao, 266100, China
Pilot National Laboratory for Marine Science and Technology (QNLM),
Qingdao, 266237, China
International Laboratory for High-Resolution Earth System Model and
Prediction (iHESP), Qingdao, 266000, China
Yang Shen
College of Science, Liaoning University of Technology, Jinzhou,
121001, China
Yuping Guan
State Key Laboratory of Tropical Oceanography, Chinese Academy of
Sciences, Guangzhou, 510301, China
College of Marine Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Xiong Deng
College of Intelligent Systems Science and Engineering, Harbin
Engineering University, Harbin, 150001, China
Related authors
No articles found.
Feifan Yan, Hang Su, Yafang Cheng, Rujin Huang, Hong Liao, Ting Yang, Yuanyuan Zhu, Shaoqing Zhang, Lifang Sheng, Wenbin Kou, Xinran Zeng, Shengnan Xiang, Xiaohong Yao, Huiwang Gao, and Yang Gao
Atmos. Chem. Phys., 24, 2365–2376, https://doi.org/10.5194/acp-24-2365-2024, https://doi.org/10.5194/acp-24-2365-2024, 2024
Short summary
Short summary
PM2.5 pollution is a major air quality issue deteriorating human health, and previous studies mostly focus on regions like the North China Plain and Yangtze River Delta. However, the characteristics of PM2.5 concentrations between these two regions are studied less often. Focusing on the transport corridor region, we identify an interesting seesaw transport phenomenon with stagnant weather conditions, conducive to PM2.5 accumulation over this region, resulting in large health effects.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Dexun Chen, Yang Gao, Xiaopei Lin, Zhao Liu, and Xiaojing Lv
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-10, https://doi.org/10.5194/gmd-2024-10, 2024
Revised manuscript under review for GMD
Short summary
Short summary
The hardware-related perturbations caused by the heterogeneous many-core architectures can blend with software or human errors, which can affect the accuracy of the model consistency verification. We develop a deep learning-based consistency test tool for ESMs on the heterogeneous systems (ESM-DCT) and evaluate it in CESM on new Sunway system. The ESM-DCT can detect the existence of software or human errors when taking hardware-related perturbations into account.
Jiangyu Li, Shaoqing Zhang, Qingxiang Liu, Xiaolin Yu, and Zhiwei Zhang
Geosci. Model Dev., 16, 6393–6412, https://doi.org/10.5194/gmd-16-6393-2023, https://doi.org/10.5194/gmd-16-6393-2023, 2023
Short summary
Short summary
Ocean surface waves play an important role in the air–sea interface but are rarely activated in high-resolution Earth system simulations due to their expensive computational costs. To alleviate this situation, this paper designs a new wave modeling framework with a multiscale grid system. Evaluations of a series of numerical experiments show that it has good feasibility and applicability in the WAVEWATCH III model, WW3, and can achieve the goals of efficient and high-precision wave simulation.
Chupeng Zhang, Shangfei Hai, Yang Gao, Yuhang Wang, Shaoqing Zhang, Lifang Sheng, Bin Zhao, Shuxiao Wang, Jingkun Jiang, Xin Huang, Xiaojing Shen, Junying Sun, Aura Lupascu, Manish Shrivastava, Jerome D. Fast, Wenxuan Cheng, Xiuwen Guo, Ming Chu, Nan Ma, Juan Hong, Qiaoqiao Wang, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 23, 10713–10730, https://doi.org/10.5194/acp-23-10713-2023, https://doi.org/10.5194/acp-23-10713-2023, 2023
Short summary
Short summary
New particle formation is an important source of atmospheric particles, exerting critical influences on global climate. Numerical models are vital tools to understanding atmospheric particle evolution, which, however, suffer from large biases in simulating particle numbers. Here we improve the model chemical processes governing particle sizes and compositions. The improved model reveals substantial contributions of newly formed particles to climate through effects on cloud condensation nuclei.
Zhenming Wang, Shaoqing Zhang, Yishuai Jin, Yinglai Jia, Yangyang Yu, Yang Gao, Xiaolin Yu, Mingkui Li, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 16, 705–717, https://doi.org/10.5194/gmd-16-705-2023, https://doi.org/10.5194/gmd-16-705-2023, 2023
Short summary
Short summary
To improve the numerical model predictability of monthly extended-range scales, we use the simplified slab ocean model (SOM) to restrict the complicated sea surface temperature (SST) bias from a 3-D dynamical ocean model. As for SST prediction, whether in space or time, the WRF-SOM is verified to have better performance than the WRF-ROMS, which has a significant impact on the atmosphere. For extreme weather events such as typhoons, the predictions of WRF-SOM are in good agreement with WRF-ROMS.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
Short summary
Short summary
To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Jingzhe Sun, Yingjing Jiang, Shaoqing Zhang, Weimin Zhang, Lv Lu, Guangliang Liu, Yuhu Chen, Xiang Xing, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 15, 4805–4830, https://doi.org/10.5194/gmd-15-4805-2022, https://doi.org/10.5194/gmd-15-4805-2022, 2022
Short summary
Short summary
An online ensemble coupled data assimilation system with the Community Earth System Model is designed and evaluated. This system uses the memory-based information transfer approach which avoids frequent I/O operations. The observations of surface pressure, sea surface temperature, and in situ temperature and salinity profiles can be effectively assimilated into the coupled model. That will facilitate a long-term high-resolution climate reanalysis once the algorithm efficiency is much improved.
Lu Yang, Hongli Fu, Xiaofan Luo, Shaoqing Zhang, and Xuefeng Zhang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-92, https://doi.org/10.5194/tc-2022-92, 2022
Revised manuscript not accepted
Short summary
Short summary
During the melting season in Arctic, sea ice thickness is difficult to detect directly by the satellite remote sensing. A bivariate regression model is put forward in this study to construct sea ice thickness. Comparisons with observations show that the new sea ice thickness data has some advantages over other data sets. The experiment shows that the model is expected to provide an available data for improving the forecast accuracy of sea ice variables in the Arctic sea ice melting season.
Shaoqing Zhang, Haohuan Fu, Lixin Wu, Yuxuan Li, Hong Wang, Yunhui Zeng, Xiaohui Duan, Wubing Wan, Li Wang, Yuan Zhuang, Hongsong Meng, Kai Xu, Ping Xu, Lin Gan, Zhao Liu, Sihai Wu, Yuhu Chen, Haining Yu, Shupeng Shi, Lanning Wang, Shiming Xu, Wei Xue, Weiguo Liu, Qiang Guo, Jie Zhang, Guanghui Zhu, Yang Tu, Jim Edwards, Allison Baker, Jianlin Yong, Man Yuan, Yangyang Yu, Qiuying Zhang, Zedong Liu, Mingkui Li, Dongning Jia, Guangwen Yang, Zhiqiang Wei, Jingshan Pan, Ping Chang, Gokhan Danabasoglu, Stephen Yeager, Nan Rosenbloom, and Ying Guo
Geosci. Model Dev., 13, 4809–4829, https://doi.org/10.5194/gmd-13-4809-2020, https://doi.org/10.5194/gmd-13-4809-2020, 2020
Short summary
Short summary
Science advancement and societal needs require Earth system modelling with higher resolutions that demand tremendous computing power. We successfully scale the 10 km ocean and 25 km atmosphere high-resolution Earth system model to a new leading-edge heterogeneous supercomputer using state-of-the-art optimizing methods, promising the solution of high spatial resolution and time-varying frequency. Corresponding technical breakthroughs are of significance in modelling and HPC design communities.
Jiangyu Li and Shaoqing Zhang
Geosci. Model Dev., 13, 1035–1054, https://doi.org/10.5194/gmd-13-1035-2020, https://doi.org/10.5194/gmd-13-1035-2020, 2020
Short summary
Short summary
Two assimilation systems developed using two nearly independent wave models are used to study the influences of various error sources including mode bias on wave data assimilation; a statistical method is explored to make full use of the merits of individual assimilation systems for bias correction, thus improving wave analysis greatly. This study opens a door to further our understanding of physical processes in waves and associated air–sea interactions for improving wave modeling.
Mingchen Ma, Yang Gao, Yuhang Wang, Shaoqing Zhang, L. Ruby Leung, Cheng Liu, Shuxiao Wang, Bin Zhao, Xing Chang, Hang Su, Tianqi Zhang, Lifang Sheng, Xiaohong Yao, and Huiwang Gao
Atmos. Chem. Phys., 19, 12195–12207, https://doi.org/10.5194/acp-19-12195-2019, https://doi.org/10.5194/acp-19-12195-2019, 2019
Short summary
Short summary
Ozone pollution has become severe in China, and extremely high ozone episodes occurred in summer 2017 over the North China Plain. While meteorology impacts are clear, we find that enhanced biogenic emissions, previously ignored by the community, driven by high vapor pressure deficit, land cover change and urban landscape contribute substantially to ozone formation. This study has significant implications for ozone pollution control with more frequent heat waves and urbanization growth in future.
Yuxin Zhao, Xiong Deng, Shaoqing Zhang, Zhengyu Liu, Chang Liu, Gabriel Vecchi, Guijun Han, and Xinrong Wu
Nonlin. Processes Geophys., 24, 681–694, https://doi.org/10.5194/npg-24-681-2017, https://doi.org/10.5194/npg-24-681-2017, 2017
Short summary
Short summary
Here with a simple coupled model that simulates typical scale interactions in the climate system, we study the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA. Results show that an optimal OTW determined from the de-correlation timescale provides maximal observational information that best fits the characteristic variability of the coupled medium during the data blending process.
Xiaolin Yu, Shaoqing Zhang, Xiaopei Lin, and Mingkui Li
Nonlin. Processes Geophys., 24, 125–139, https://doi.org/10.5194/npg-24-125-2017, https://doi.org/10.5194/npg-24-125-2017, 2017
Short summary
Short summary
Parameter estimation (PE) with a global coupled data assimilation (CDA) system can improve the runs, but the improvement remains in a limited range. We have to come back to simple models to sort out the sources of noises. Incomplete observations and the chaotic nature of the atmosphere have much stronger influences on the PE through the state estimation (SE) process. Here, we propose the guidelines of how to enhance the signal-to-noise ratio under partial SE status.
A. A. Yuxin Zhao, B. B. Xiong Deng, and C. C. Shuo Yang
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2015-76, https://doi.org/10.5194/npg-2015-76, 2016
Preprint withdrawn
Short summary
Short summary
An optimal time window centred at the assimilation time to collect measured data for an assimilation cycle, can improve the CDA analysis skill. We study the impact of optimal OTWs on the quality of parameter optimization and climate prediction in a simple coupled model. Results show that the optimal OTWs of valid atmosphere or ocean observations exist for the parameter being estimated and incorporating the parameter optimization will enhance the predictability both of the atmosphere and ocean.
G.-J. Han, X.-F. Zhang, S. Zhang, X.-R. Wu, and Z. Liu
Nonlin. Processes Geophys., 21, 357–366, https://doi.org/10.5194/npg-21-357-2014, https://doi.org/10.5194/npg-21-357-2014, 2014
Related subject area
Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Simulation
Leading the Lorenz-63 system toward the prescribed regime by model predictive control coupled with data assimilation
Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model
Quantum Data Assimilation: A New Approach to Solve Data Assimilation on Quantum Annealers
Reducing manipulations in a control simulation experiment based on instability vectors with the Lorenz-63 model
Control simulation experiments of extreme events with the Lorenz-96 model
A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe
Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis
Control simulation experiment with Lorenz's butterfly attractor
Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction
Fast hybrid tempered ensemble transform filter formulation for Bayesian elliptical problems via Sinkhorn approximation
Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales
Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty
Generalization properties of feed-forward neural networks trained on Lorenz systems
Fumitoshi Kawasaki and Shunji Kotsuki
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2024-4, https://doi.org/10.5194/npg-2024-4, 2024
Revised manuscript accepted for NPG
Short summary
Short summary
Recently, scientists have been looking into ways to control the weather to lead to a desirable direction for mitigating weather-induced disasters caused by torrential rainfall and typhoons. This study proposed using model predictive control, known as an advanced control method, to control a chaotic system. Through numerical experiments using a low-dimensional chaotic system, we demonstrated that the system can be controlled successfully with shorter forecasts compared to previous studies.
Kenta Kurosawa, Shunji Kotsuki, and Takemasa Miyoshi
Nonlin. Processes Geophys., 30, 457–479, https://doi.org/10.5194/npg-30-457-2023, https://doi.org/10.5194/npg-30-457-2023, 2023
Short summary
Short summary
This study aimed to enhance weather and hydrological forecasts by integrating soil moisture data into a global weather model. By assimilating atmospheric observations and soil moisture data, the accuracy of forecasts was improved, and certain biases were reduced. The method was found to be particularly beneficial in areas like the Sahel and equatorial Africa, where precipitation patterns vary seasonally. This new approach has the potential to improve the precision of weather predictions.
Shunji Kotsuki, Fumitoshi Kawasaki, and Masanao Ohashi
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2023-16, https://doi.org/10.5194/npg-2023-16, 2023
Revised manuscript accepted for NPG
Short summary
Short summary
In Earth science, data assimilation plays an important role in integrating real-world observations with numerical simulations for improving subsequent predictions. To overcome the time-consuming computations of conventional data assimilation methods, this paper proposes using quantum annealing machines. Using the D-Wave's quantum annealer, the proposed method found solutions with comparable accuracy to conventional approaches with significantly reduced computational time.
Mao Ouyang, Keita Tokuda, and Shunji Kotsuki
Nonlin. Processes Geophys., 30, 183–193, https://doi.org/10.5194/npg-30-183-2023, https://doi.org/10.5194/npg-30-183-2023, 2023
Short summary
Short summary
This research found that weather control would change the chaotic behavior of an atmospheric model. We proposed to introduce chaos theory in the weather control. Experimental results demonstrated that the proposed approach reduced the manipulations, including the control times and magnitudes, which throw light on the weather control in a real atmospheric model.
Qiwen Sun, Takemasa Miyoshi, and Serge Richard
Nonlin. Processes Geophys., 30, 117–128, https://doi.org/10.5194/npg-30-117-2023, https://doi.org/10.5194/npg-30-117-2023, 2023
Short summary
Short summary
This paper is a follow-up of a work by Miyoshi and Sun which was published in NPG Letters in 2022. The control simulation experiment is applied to the Lorenz-96 model for avoiding extreme events. The results show that extreme events of this partially and imperfectly observed chaotic system can be avoided by applying pre-designed small perturbations. These investigations may be extended to more realistic numerical weather prediction models.
Clara Deser and Adam S. Phillips
Nonlin. Processes Geophys., 30, 63–84, https://doi.org/10.5194/npg-30-63-2023, https://doi.org/10.5194/npg-30-63-2023, 2023
Short summary
Short summary
Past and future climate change at regional scales is a result of both human influences and natural (internal) variability. Here, we provide an overview of recent advances in climate modeling and physical understanding that has led to new insights into their respective roles, illustrated with original results for the European climate. Our findings highlight the confounding role of internal variability in attribution, climate model evaluation, and accuracy of future projections.
Dikraa Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas
Nonlin. Processes Geophys., 29, 329–344, https://doi.org/10.5194/npg-29-329-2022, https://doi.org/10.5194/npg-29-329-2022, 2022
Short summary
Short summary
This study introduces a well-known use of hybrid methods in data assimilation (DA) algorithms that has not yet been explored for precipitation analyses. Our approach combined an ensemble-based DA approach with an existing deterministically based DA. Both DA scheme families have desirable aspects that can be leveraged if combined. The DA hybrid method showed better precipitation analyses in regions with a low rate of assimilated surface observations, which is typically the case in winter.
Takemasa Miyoshi and Qiwen Sun
Nonlin. Processes Geophys., 29, 133–139, https://doi.org/10.5194/npg-29-133-2022, https://doi.org/10.5194/npg-29-133-2022, 2022
Short summary
Short summary
The weather is chaotic and hard to predict, but the chaos implies an effective control where a small control signal grows rapidly to make a big difference. This study proposes a control simulation experiment where we apply a small signal to control
naturein a computational simulation. Idealized experiments with a low-order chaotic system show successful results by small control signals of only 3 % of the observation error. This is the first step toward realistic weather simulations.
Juan Ruiz, Guo-Yuan Lien, Keiichi Kondo, Shigenori Otsuka, and Takemasa Miyoshi
Nonlin. Processes Geophys., 28, 615–626, https://doi.org/10.5194/npg-28-615-2021, https://doi.org/10.5194/npg-28-615-2021, 2021
Short summary
Short summary
Effective use of observations with numerical weather prediction models, also known as data assimilation, is a key part of weather forecasting systems. For precise prediction at the scales of thunderstorms, fast nonlinear processes pose a grand challenge because most data assimilation systems are based on linear processes and normal distribution errors. We investigate how, every 30 s, weather radar observations can help reduce the effect of nonlinear processes and nonnormal distributions.
Sangeetika Ruchi, Svetlana Dubinkina, and Jana de Wiljes
Nonlin. Processes Geophys., 28, 23–41, https://doi.org/10.5194/npg-28-23-2021, https://doi.org/10.5194/npg-28-23-2021, 2021
Short summary
Short summary
To infer information of an unknown quantity that helps to understand an associated system better and to predict future outcomes, observations and a physical model that connects the data points to the unknown parameter are typically used as information sources. Yet this problem is often very challenging due to the fact that the unknown is generally high dimensional, the data are sparse and the model can be non-linear. We propose a novel approach to address these challenges.
Michiel Van Ginderachter, Daan Degrauwe, Stéphane Vannitsem, and Piet Termonia
Nonlin. Processes Geophys., 27, 187–207, https://doi.org/10.5194/npg-27-187-2020, https://doi.org/10.5194/npg-27-187-2020, 2020
Short summary
Short summary
A generic methodology is developed to estimate the model error and simulate the model uncertainty related to a specific physical process. The method estimates the model error by comparing two different representations of the physical process in otherwise identical models. The found model error can then be used to perturb the model and simulate the model uncertainty. When applying this methodology to deep convection an improvement in the probabilistic skill of the ensemble forecast is found.
Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper
Nonlin. Processes Geophys., 27, 209–234, https://doi.org/10.5194/npg-27-209-2020, https://doi.org/10.5194/npg-27-209-2020, 2020
Short summary
Short summary
Geophysical flows span a broader range of temporal and spatial scales than can be resolved numerically. One way to alleviate the ensuing numerical errors is to combine simulations with measurements, taking account of the accuracies of these two sources of information. Here we quantify the distribution of numerical simulation errors without relying on high-resolution numerical simulations. Specifically, small-scale random vortices are added to simulations while conserving energy or circulation.
Sebastian Scher and Gabriele Messori
Nonlin. Processes Geophys., 26, 381–399, https://doi.org/10.5194/npg-26-381-2019, https://doi.org/10.5194/npg-26-381-2019, 2019
Short summary
Short summary
Neural networks are a technique that is widely used to predict the time evolution of physical systems. For this the past evolution of the system is shown to the neural network – it is
trained– and then can be used to predict the evolution in the future. We show some limitations in this approach for certain systems that are important to consider when using neural networks for climate- and weather-related applications.
Cited articles
Aksoy, A., Zhang, F., and Nielsen-Gammon, J. W.: Ensemble-based simultaneous
state and parameter estimation in a two-dimensional sea-breeze model, Mon.
Weather Rev., 134, 2951–2970, https://doi.org/10.1175/MWR3224.1, 2006a.
Aksoy, A., Zhang, F., and Nielsen-Gammon, J. W.: Ensemble-based simultaneous
state and parameter estimation with MM5, Geophys. Res. Lett., 33, L12801,
https://doi.org/10.1029/2006GL026186, 2006b.
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation,
Mon. Weather Rev., 129, 2884–2903,
https://doi.org/10.1175/1520-0493(2001)129<2884:Aeakff>2.0.Co;2, 2001.
Anderson, J. L.: A local least squares framework for ensemble filtering,
Mon. Weather Rev., 131, 634–642,
https://doi.org/10.1175/1520-0493(2003)131<0634:Allsff>2.0.Co;2, 2003.
Annan, J. D., Hargreaves, J. C., Edwards, N. R., and Marsh, R.: Parameter
estimation in an intermediate complexity earth system model using an
ensemble Kalman filter, Ocean Model., 8, 135–154,
https://doi.org/10.1016/j.ocemod.2003.12.004, 2005.
Ashkenazy, Y. and Tziperman, E.: A wind-induced thermohaline circulation
hysteresis and millennial variability regimes, J. Phys. Oceanogr., 37,
2446–2457, https://doi.org/10.1175/JPO3124.1, 2007.
Birchfield, G. E.: A coupled ocean-atmosphere climate model: temperature
versus salinity effects on the thermohaline circulation, Clim. Dynam., 4,
57–71, https://doi.org/10.1007/BF00207400, 1989.
Bisaillon, P., Sandhu, R., Khalil, M., Pettit, C., Poirel, D., and Sarkar,
A.: Bayesian parameter estimation and model selection for strongly nonlinear
dynamical systems, Nonlinear Dynam., 82, 1061–1080,
https://doi.org/10.1007/s11071-015-2217-8, 2015.
Broecker, W. S., Peng, T. H., Jouzel, J., and Russell, G.: The magnitude of
global fresh-water transports of importance to ocean circulation, Clim. Dynam., 4, 73–79,
https://doi.org/10.1007/BF00208902, 1990.
Brown, N. and Galbraith, E. D.: Hosed vs. unhosed: interruptions of the Atlantic Meridional Overturning Circulation in a global coupled model, with and without freshwater forcing, Clim. Past, 12, 1663–1679, https://doi.org/10.5194/cp-12-1663-2016, 2016.
Bryan, F.: High-latitude salinity effects and interhemispheric thermohaline
circulations, Nature, 323, 301–304, https://doi.org/10.1038/323301a0, 1986.
Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G., and Saba, V.: Observed
fingerprint of a weakening Atlantic Ocean overturning circulation, Nature,
556, 191–196, https://doi.org/10.1038/s41586-018-0006-5, 2018.
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation
in the geosciences: An overview of methods, issues, and perspectives, Wires.
Clim. Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018.
Castellana, D. and Dijkstra, H. A.: Noise-induced transitions of the
Atlantic Meridional Overturning Circulation in CMIP5 models, Sci. Rep., 10,
20040, https://doi.org/10.1038/s41598-020-76930-5, 2020.
Castellana, D., Baars, S., Wubs, F. W., and Dijkstra, H. A.: Transition
probabilities of noise-induced transitions of the Atlantic Ocean
circulation, Sci. Rep., 9, 20284,
https://doi.org/10.1038/s41598-019-56435-6, 2019.
Cessi, P.: A simple box model of stochastically forced thermohaline flow, J.
Phys. Oceanogr., 24, 1911–1920,
https://doi.org/10.1175/1520-0485(1994)024<1911:ASBMOS>2.0.CO;2, 1994.
Cunningham, S. A., Kanzow, T., Rayner, D., Baringer, M. O., Johns, W. E.,
Marotzke, J., Longworth, H. R., Grant, E. M., Hirschi, J. J. M., Beal, L.
M., Meinen, C. S., and Bryden, H. L.: Temporal variability of the Atlantic
meridional overturning circulation at 26.5∘ N, Science, 317,
935–938, https://doi.org/10.1126/science.1141304, 2007.
Delworth, T. L. and Greatbatch, R. J.: Multidecadal thermohaline circulation
variability driven by atmospheric surface flux forcing, J. Climate, 13,
1481–1495, https://doi.org/10.1175/1520-0442(2000)013<1481:Mtcvdb>2.0.Co;2, 2000.
Delworth, T. L., Manabe, S., and Stouffer, R. J.: Interdecadal variations of
the thermohaline circulation in a coupled ocean-atmosphere model, J.
Climate, 6, 1993–2011,
https://doi.org/10.1175/1520-0442(1993)006<1993:Ivottc>2.0.Co;2, 1993.
Delworth, T. L., Zeng, F., Vecchi, G. A., Yang, X., Zhang, L., and Zhang, R.:
The North Atlantic Oscillation as a driver of rapid climate change in the
Northern Hemisphere, Nat. Geosci., 9, 509–512,
https://doi.org/10.1038/ngeo2738, 2016.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J. Geophys.
Res., 99, 10143–10162, https://doi.org/10.1029/94jc00572, 1994.
Fürst, J. J. and Levermann, A.: A minimal model for wind- and
mixing-driven overturning: Threshold behavior for both driving mechanisms,
Clim. Dynam., 38, 239–260, https://doi.org/10.1007/s00382-011-1003-7, 2012.
Gordon, N. J., Salmond, D. J., and Smith, A. F. M.: Novel approach to
nonlinear/non-Gaussian Bayesian state estimation, IEE Proc. F, 140,
107–113, https://doi.org/10.1049/ip-f-2.1993.0015, 1993.
Gottwald, G. A.: A model for Dansgaard–Oeschger events and millennial-scale
abrupt climate change without external forcing, Clim. Dynam., 56, 227–243,
https://doi.org/10.1007/s00382-020-05476-z, 2021.
Guan, Y. P. and Huang, R. X.: Stommel's box model of thermohaline
circulation revisited – The role of mechanical energy supporting mixing and
the wind-driven gyration, J. Phys. Oceanogr., 38, 909–917,
https://doi.org/10.1175/2007jpo3535.1, 2008.
Han, G.-J., Zhang, X.-F., Zhang, S., Wu, X.-R., and Liu, Z.: Mitigation of coupled model biases induced by dynamical core misfitting through parameter optimization: simulation with a simple pycnocline prediction model, Nonlin. Processes Geophys., 21, 357–366, https://doi.org/10.5194/npg-21-357-2014, 2014.
Hansen, J. A. and Penland, C.: On stochastic parameter estimation using data
assimilation, Physica D, 230, 88–98,
https://doi.org/10.1016/j.physd.2006.11.006, 2007.
Hu, X. M., Zhang, F., and Nielsen-Gammon, J. W.: Ensemble-based simultaneous
state and parameter estimation for treatment of mesoscale model error: A
real-data study, Geophys. Res. Lett., 37, L08802,
https://doi.org/10.1029/2010gl043017, 2010.
Huang, R. X.: Ocean, energy flows in, in: Encyclopedia of energy, edited by: Cleveland, C. J., Elsevier, New York, USA, 497–509, https://doi.org/10.1016/B0-12-176480-X/00053-X, 2004
Huisman, S. E., den Toom, M., Dijkstra, H. A., and Drijfhout, S.: An
indicator of the multiple equilibria regime of the Atlantic meridional
overturning circulation, J. Phys. Oceanogr., 40, 551–567,
https://doi.org/10.1175/2009JPO4215.1, 2010.
Jackson, L. C.: Shutdown and recovery of the AMOC in a coupled global
climate model: The role of the advective feedback, Geophys. Res. Lett., 40,
1182–1188, https://doi.org/10.1002/grl.50289, 2013.
Jackson, L. C. and Wood, R. A.: Hysteresis and resilience of the AMOC in an
eddy-permitting GCM, Geophys. Res. Lett., 45, 8547–8556,
https://doi.org/10.1029/2018GL078104, 2018.
Jazwinski, A. H.: Stochastic processes and filtering theory, Academic Press,
New York, USA, 1970.
Kalman, R. E.: A new approach to linear filtering and prediction problems,
J. Basic Eng., 82, 35–45, https://doi.org/10.1115/1.3662552, 1960.
Kalman, R. E. and Bucy, R. S.: New results in linear filtering and
prediction theory, J. Basic Eng., 83, 95–108,
https://doi.org/10.1115/1.3658902, 1961.
Khalil, M., Sarkar, A., and Adhikari, S.: Nonlinear filters for chaotic
oscillatory systems, Nonlinear Dynam., 55, 113–137,
https://doi.org/10.1007/s11071-008-9349-z, 2009.
Kleppin, H., Jochum, M., Otto-Bliesner, B., Shields, C. A., and Yeager, S.:
Stochastic atmospheric forcing as a cause of Greenland climate transitions,
J. Climate, 28, 7741–7763, https://doi.org/10.1175/JCLI-D-14-00728.1, 2015.
Klockmann, M., Mikolajewicz, U., Kleppin, H., and Marotzke, J.: Coupling of
the subpolar gyre and the overturning circulation during abrupt glacial
climate transitions, Geophys. Res. Lett., 47, e2020GL090361,
https://doi.org/10.1029/2020GL090361, 2020.
Kondrashov, D., Sun, C., and Ghil, M.: Data assimilation for a coupled
ocean-atmosphere model. Part II: Parameter estimation, Mon. Weather Rev.,
136, 5062–5076, https://doi.org/10.1175/2008mwr2544.1, 2008.
Kuhlbrodt, T., Griesel, A., Montoya, M., Levermann, A., Hofmann, M., and
Rahmstorf, S.: On the driving processes of the Atlantic meridional
overturning circulation, Rev. Geophys., 45, RG2001,
https://doi.org/10.1029/2004RG000166, 2007.
Lambert, E., Eldevik, T., and Haugan, P. M.: How northern freshwater input
can stabilise thermohaline circulation, Tellus A, 68, 31051,
https://doi.org/10.3402/tellusa.v68.31051, 2016.
Liu, W., Liu, Z., and Hu, A.: The stability of an evolving Atlantic
meridional overturning circulation, Geophys. Res. Lett., 40, 1562–1568,
https://doi.org/10.1002/grl.50365, 2013.
Liu, W., Xie, S., Liu, Z., and Zhu, J.: Overlooked possibility of a collapsed
Atlantic Meridional Overturning Circulation in warming climate, Sci. Adv.,
3, e1601666, https://doi.org/10.1126/sciadv.1601666, 2017.
Liu, Y., Liu, Z., Zhang, S., Rong, X., Jacob, R., Wu, S., and Lu, F.:
Ensemble-based parameter estimation in a coupled GCM using the adaptive
spatial average method, J. Climate, 27, 4002–4014,
https://doi.org/10.1175/JCLI-D-13-00091.1, 2014a.
Liu, Y., Liu, Z., Zhang, S., Jacob, R., Lu, F., Rong, X., and Wu, S.:
Ensemble-based parameter estimation in a coupled general circulation model,
J. Climate, 27, 7151–7162, https://doi.org/10.1175/jcli-d-13-00406.1,
2014b.
Longworth, H., Marotzke, J., and Stocker, T. F.: Ocean gyres and abrupt
change in the thermohaline circulation: A conceptual analysis, J. Climate,
18, 2403–2416, https://doi.org/10.1175/JCLI3397.1, 2005.
Lorenz, E. N.: Deterministic nonperiodic flow, J. Atmos. Sci., 20,
130–141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2, 1963.
Lorenz, E. N.: Irregularity: a fundamental property of the atmosphere,
Tellus A, 36, 98–110,
https://doi.org/10.1111/j.1600-0870.1984.tb00230.x, 1984.
Lynch-Stieglitz, J.: The Atlantic meridional overturning circulation and
abrupt climate change, Annu. Rev. Mar. Sci., 9, 83–104,
https://doi.org/10.1146/annurev-marine-010816-060415, 2017.
Manabe, S. and Stouffer, R. J.: Two stable equilibria of a coupled
ocean-atmosphere model, J. Climate, 1, 841–866,
https://doi.org/10.1175/1520-0442(1988)001<0841:tseoac>2.0.co;2, 1988.
Marotzke, J. and Willebrand, J.: Multiple equilibria of the global
thermohaline circulation, J. Phys. Oceanogr., 21, 1372–1385,
https://doi.org/10.1175/1520-0485(1991)021<1372:Meotgt>2.0.Co;2, 1991.
Mecking, J. V., Drijfhout, S. S., Jackson, L. C., and Graham, T.: Stable AMOC
off state in an eddy-permitting coupled climate model, Clim. Dynam., 47,
2455–2470, https://doi.org/10.1007/s00382-016-2975-0, 2016.
Miller, R. N., Ghil, M., and Gauthiez, F.: Advanced data assimilation in
strongly nonlinear dynamical systems, J. Atmos. Sci., 51, 1037–1056,
https://doi.org/10.1175/1520-0469(1994)051<1037:ADAISN>2.0.CO;2, 1994.
Miller, R. N., Carter, E. F., and Blue, S. T.: Data assimilation into
nonlinear stochastic models, Tellus A, 51, 167–194,
https://doi.org/10.3402/tellusa.v51i2.12315, 1999.
Mitsui, T. and Crucifix, M.: Influence of external forcings on abrupt
millennial-scale climate changes: a statistical modelling study, Clim.
Dynam., 48, 2729–2749, https://doi.org/10.1007/s00382-016-3235-z, 2017.
Mu, M., Sun, L., and Dijkstra, H. A.: The sensitivity and stability of the
ocean's thermohaline circulation to finite-amplitude perturbations, J. Phys.
Oceanogr., 34, 2305–2315,
https://doi.org/10.1175/1520-0485(2004)034<2305:Tsasot>2.0.Co;2, 2004.
Munk, W. and Wunsch, C.: Abyssal recipes II: energetics of tidal and wind
mixing, Deep-Sea Res., 45, 1977–2010,
https://doi.org/10.1016/S0967-0637(98)00070-3, 1998.
Nilsson, J. and Walin, G.: Freshwater forcing as a booster of thermohaline
circulation, Tellus A, 53, 629–641,
https://doi.org/10.3402/tellusa.v53i5.12232, 2001.
Nilsson, J. and Walin, G.: Salinity-dominated thermohaline circulation in
sill basins: can two stable equilibria exist?, Tellus A, 62, 123–133,
https://doi.org/10.1111/j.1600-0870.2009.00428.x, 2010.
Peltier, W. R. and Vettoretti, G.: Dansgaard-Oeschger oscillations predicted
in a comprehensive model of glacial climate: A “kicked” salt oscillator in
the Atlantic, Geophys. Res. Lett., 41, 7306–7313,
https://doi.org/10.1002/2014GL061413, 2014.
Rahmstorf, S.: On the freshwater forcing and transport of the Atlantic
thermohaline circulation, Clim. Dynam., 12, 799–811,
https://doi.org/10.1007/s003820050144, 1996.
Roebber, P. J.: Climate variability in a low-order coupled atmosphere-ocean
model, Tellus A, 47, 473–494,
https://doi.org/10.3402/tellusa.v47i4.11534, 1995.
Rooth, C.: Hydrology and ocean circulation, Prog. Oceanogr., 11,
131–149, https://doi.org/10.1016/0079-6611(82)90006-4, 1982.
Rühlemann, C., Mulitza, S., Lohmann, G., Paul, A., Prange, M., and Wefer,
G.: Intermediate depth warming in the tropical Atlantic related to weakened
thermohaline circulation: Combining paleoclimate data and modeling results
for the last deglaciation, Paleoceanography, 19, PA1025,
https://doi.org/10.1029/2003PA000948, 2004.
Scott, J. R., Marotzke, J., and Stone, P. H.: Interhemispheric thermohaline
circulation in a coupled box model, J. Phys. Oceanogr., 29, 351–365,
https://doi.org/10.1175/1520-0485(1999)029<0351:ITCIAC>2.0.CO;2, 1999.
Sévellec, F. and Fedorov, A. V.: Millennial variability in an idealized
ocean model: predicting the AMOC regime shifts, J. Climate, 27,
3551–3564, https://doi.org/10.1175/JCLI-D-13-00450.1, 2014.
Shen, Y. and Guan, Y. P.: Feature of thermohaline circulation in two-layer
conceptual model based on energy constraint, Sci. China Earth Sci., 58,
1397–1403, https://doi.org/10.1007/s11430-015-5092-8, 2015.
Shen, Y., Guan, Y. P., Liang, C. J., and Chen, D. K.: A three-box model of
thermohaline circulation under the energy constraint, Chinese Phys. Lett.,
28, 059201, https://doi.org/10.1088/0256-307x/28/5/059201, 2011.
Smeed, D. A., McCarthy, G. D., Cunningham, S. A., Frajka-Williams, E., Rayner, D., Johns, W. E., Meinen, C. S., Baringer, M. O., Moat, B. I., Duchez, A., and Bryden, H. L.: Observed decline of the Atlantic meridional overturning circulation 2004–2012, Ocean Sci., 10, 29–38, https://doi.org/10.5194/os-10-29-2014, 2014.
Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J.: Obstacles to
high-dimensional particle filtering, Mon. Weather Rev., 136, 4629–4640,
https://doi.org/10.1175/2008mwr2529.1, 2008.
Stommel, H.: Thermohaline convection with two stable regimes of flow, Tellus
B, 13, 224–230, https://doi.org/10.3402/tellusb.v13i2.12985, 1961.
Stone, P. H. and Yao, M.-S.: Development of a two-dimensional zonally
averaged statistical-dynamical model. part III: The parameterization of the
eddy fluxes of heat and moisture, J. Climate, 3, 726–740,
https://doi.org/10.1175/1520-0442(1990)003<0726:DOATDZ>2.0.CO;2, 1990.
Stouffer, R. J., Yin, J., Gregory, J. M., Dixon, K. W., Spelman, M. J.,
Hurlin, W., Weaver, A. J., Eby, M., Flato, G. M., Hasumi, H., Hu, A.,
Jungclaus, J. H., Kamenkovich, I. V., Levermann, A., Montoya, M., Murakami,
S., Nawrath, S., Oka, A., Peltier, W. R., Robitaille, D. Y., Sokolov, A.,
Vettoretti, G., and Weber, S. L.: Investigating the causes of the response of
the thermohaline circulation to past and future climate change, J. Climate,
19, 1365–1387, https://doi.org/10.1175/JCLI3689.1, 2006.
Sun, C., Zhang, J., Li, X., Shi, C., Gong, Z., Ding, R., Xie, F., and Lou,
P.: Atlantic Meridional Overturning Circulation reconstructions and
instrumentally observed multidecadal climate variability: A comparison of
indicators, Int. J. Climatol., 40, 1–16, https://doi.org/10.1002/joc.6695,
2020.
Taboada, J. J. and Lorenzo, M. N.: Effects of the synoptic scale variability on the thermohaline circulation, Nonlin. Processes Geophys., 12, 435–439, https://doi.org/10.5194/npg-12-435-2005, 2005.
Tardif, R., Hakim, G. J., and Snyder, C.: Coupled atmosphere–ocean data
assimilation experiments with a low-order climate model, Clim. Dynam.,
43, 1631–1643, https://doi.org/10.1007/s00382-013-1989-0, 2014.
van Leeuwen, P. J.: Particle filtering in geophysical systems, Mon. Weather
Rev., 137, 4089–4114, https://doi.org/10.1175/2009mwr2835.1, 2009.
Weijer, W., Cheng, W., Drijfhout, S. S., Fedorov, A. V., Hu, A., Jackson, L.
C., Liu, W., McDonagh, E. L., Mecking, J. V., and Zhang, J.: Stability of the
Atlantic Meridional Overturning Circulation: A review and synthesis, J.
Geophys. Res.-Oceans, 124, 5336–5375,
https://doi.org/10.1029/2019JC015083, 2019.
Weir, B., Miller, R. N., and Spitz, Y. H.: A potential implicit particle method for high-dimensional systems, Nonlin. Processes Geophys., 20, 1047–1060, https://doi.org/10.5194/npg-20-1047-2013, 2013a.
Weir, B., Miller, R. N., and Spitz, Y. H.: Implicit estimation of ecological
model parameters, B. Math. Biol., 75, 223–257,
https://doi.org/10.1007/s11538-012-9801-6, 2013b.
Welander, P.: A simple heat-salt oscillator, Dynam. Atmos. Oceans, 6,
233–242, https://doi.org/10.1016/0377-0265(82)90030-6, 1982.
Welander, P.: Thermohaline effects in the ocean circulation and related
simple models, in: Large-Scale Transport Processes in Oceans and Atmosphere,
edited by: Willebrand, J. and Anderson, D. L. T., Springer, Dordrecht, the Netherlands, 163–200, https://doi.org/10.1007/978-94-009-4768-9_4, 1986.
Wu, X., Zhang, S., Liu, Z., Rosati, A., Delworth, T. L., and Liu, Y.: Impact
of geographic-dependent parameter optimization on climate estimation and
prediction: simulation with an intermediate coupled model, Mon. Weather
Rev., 140, 3956–3971, https://doi.org/10.1175/MWR-D-11-00298.1, 2012.
Wu, X., Zhang, S., Liu, Z., Rosati, A., and Delworth, T. L.: A study of
impact of the geographic dependence of observing system on parameter
estimation with an intermediate coupled model, Clim. Dynam., 40, 1789–1798,
https://doi.org/10.1007/s00382-012-1385-1, 2013.
Wu, X., Han, G., Zhang, S., and Liu, Z.: A study of the impact of parameter
optimization on ENSO predictability with an intermediate coupled model,
Clim. Dynam., 46, 711–727, https://doi.org/10.1007/s00382-015-2608-z, 2016.
Wunsch, C.: The work done by the wind on the oceanic general circulation, J.
Phys. Oceanogr., 28, 2332–2340,
https://doi.org/10.1175/1520-0485(1998)028<2332:TWDBTW>2.0.CO;2, 1998.
Wunsch, C. and Ferrari, R.: Vertical mixing, energy, and the general
circulation of the oceans, Annu. Rev. Fluid Mech., 36, 281–314,
https://doi.org/10.1146/annurev.fluid.36.050802.122121, 2004.
Yu, X., Zhang, S., Lin, X., and Li, M.: Insights on the role of accurate state estimation in coupled model parameter estimation by a conceptual climate model study, Nonlin. Processes Geophys., 24, 125–139, https://doi.org/10.5194/npg-24-125-2017, 2017.
Zhang, R.: Coherent surface-subsurface fingerprint of the Atlantic
meridional overturning circulation, Geophys. Res. Lett., 35, L20705,
https://doi.org/10.1029/2008GL035463, 2008.
Zhang, S.: A study of impacts of coupled model initial shocks and
state-parameter optimization on climate predictions using a simple
pycnocline prediction model, J. Climate, 24, 6210–6226,
https://doi.org/10.1175/jcli-d-10-05003.1, 2011a.
Zhang, S.: Impact of observation-optimized model parameters on decadal
predictions: Simulation with a simple pycnocline prediction model, Geophys.
Res. Lett., 38, L02702, https://doi.org/10.1029/2010gl046133, 2011b.
Zhang, S. and Anderson, J. L.: Impact of spatially and temporally varying
estimates of error covariance on assimilation in a simple atmospheric model,
Tellus A, 55, 126–147, https://doi.org/10.1034/j.1600-0870.2003.00010.x,
2003.
Zhang, S., Harrison, M. J., Rosati, A., and Wittenberg, A. T.: System design
and evaluation of coupled ensemble data assimilation for global oceanic
climate studies, Mon. Weather Rev., 135, 3541–3564,
https://doi.org/10.1175/mwr3466.1, 2007.
Zhang, S., Liu, Z., Rosati, A., and Delworth, T.: A study of enhancive
parameter correction with coupled data assimilation for climate estimation
and prediction using a simple coupled model, Tellus A, 64, 10963,
https://doi.org/10.3402/tellusa.v64i0.10963, 2012.
Zhang, X., Lohmann, G., Knorr, G., and Purcell, C.: Abrupt glacial climate
shifts controlled by ice sheet changes, Nature, 512, 290–294,
https://doi.org/10.1038/nature13592, 2014.
Zhang, X., Knorr, G., Lohmann, G., and Barker, S.: Abrupt North Atlantic circulation changes in response to gradual CO2 forcing in a glacial climate state, Nat. Geosci., 10, 518–523, https://doi.org/10.1038/ngeo2974, 2017.
Zhao, Y., Deng, X., Zhang, S., Liu, Z., and Liu, C.: Sensitivity determined
simultaneous estimation of multiple parameters in coupled models: part
I – based on single model component sensitivities, Clim. Dynam., 53,
5349–5373, https://doi.org/10.1007/s00382-019-04865-3, 2019.
Short summary
A general methodology is introduced to capture regime transitions of the Atlantic meridional overturning circulation (AMOC). The assimilation models with different parameters simulate different paths for the AMOC to switch between equilibrium states. Constraining model parameters with observations can significantly mitigate the model deviations, thus capturing AMOC regime transitions. This simple model study serves as a guideline for improving coupled general circulation models.
A general methodology is introduced to capture regime transitions of the Atlantic meridional...
