Articles | Volume 21, issue 10
https://doi.org/10.5194/cp-21-1801-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-21-1801-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Observation error estimation in climate proxies with data assimilation and innovation statistics
Institute of Advanced Academic Research, Chiba University, Chiba, Japan
Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Diego S. Carrió
Department of Physics, Universitat de les Illes Balears, Palma, Spain
Quentin Dalaiden
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Jarrah Harrison-Lofthouse
School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Melbourne, Australia
Shunji Kotsuki
Institute of Advanced Academic Research, Chiba University, Chiba, Japan
Center for Environmental Remote Sensing, Chiba University, Chiba, Japan
Kei Yoshimura
Institute of Industrial Science, The University of Tokyo, Kashiwa, Japan
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Shunji Kotsuki, Kenta Shiraishi, and Atsushi Okazaki
Geosci. Model Dev., 18, 7215–7225, https://doi.org/10.5194/gmd-18-7215-2025, https://doi.org/10.5194/gmd-18-7215-2025, 2025
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Artificial intelligence (AI) is playing a bigger role in weather forecasting, often competing with physical models. However, combining AI models with data assimilation, a process that improves weather forecasts by incorporating observation data, is still relatively unexplored. This study explored the coupling of ensemble data assimilation with an AI weather prediction model, ClimaX, which succeeded in employing weather forecasts stably by applying techniques conventionally used for physical models.
Kenta Kurosawa, Atsushi Okazaki, Fumitoshi Kawasaki, and Shunji Kotsuki
Nonlin. Processes Geophys., 32, 293–307, https://doi.org/10.5194/npg-32-293-2025, https://doi.org/10.5194/npg-32-293-2025, 2025
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We propose ensemble-based model predictive control (EnMPC), a novel method that improves the control of complex systems like the atmosphere by integrating control theory with data assimilation. Unlike traditional methods, which are computationally expensive, EnMPC uses ensemble simulations to efficiently handle uncertainties and optimize solutions. This approach reduces computational cost while maintaining accuracy, making it a promising step toward real-world applications in dynamic system control.
Fumitoshi Kawasaki, Atsushi Okazaki, Kenta Kurosawa, Tadashi Tsuyuki, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-1785, https://doi.org/10.5194/egusphere-2025-1785, 2025
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A major challenge in weather control aimed at mitigating extreme weather events is identifying effective control inputs under limited computational resources. This study proposes a novel control framework called model predictive control with foreseeing horizon, designed to efficiently control chaotic dynamical systems. Using a 40-variable chaotic dynamical model, the proposed method successfully mitigated extreme events and reduced computational cost compared to the conventional approach.
Takahito Mitsui, Shunji Kotsuki, Naoya Fujiwara, Atsushi Okazaki, and Keita Tokuda
EGUsphere, https://doi.org/10.5194/egusphere-2025-987, https://doi.org/10.5194/egusphere-2025-987, 2025
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Extreme weather poses serious risks, making prevention crucial. Using the Lorenz 96 model as a testbed, we propose a bottom-up approach to mitigate extreme events via local interventions guided by multi-scenario ensemble forecasts. Unlike control-theoretic methods, our approach selects the best control scenario from available options. Achieving up to 99.4 % success, it outperforms previous methods while keeping costs reasonable, offering a practical way to reduce disasters with limited control.
Shigeyuki Ishidoya, Satoshi Sugawara, and Atsushi Okazaki
Atmos. Chem. Phys., 25, 1965–1987, https://doi.org/10.5194/acp-25-1965-2025, https://doi.org/10.5194/acp-25-1965-2025, 2025
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The 18O/16O ratio of atmospheric oxygen, δatm(18O), is higher than that of ocean water due to isotopic effects during biospheric activities. This is known as the Dole–Morita effect, and its millennial-scale variations are recorded in ice cores. However, small variations of δatm(18O) in the present day have never been detected so far. This paper presents the first observations of diurnal, seasonal, and secular variations in δatm(18O) and applies them to evaluate oxygen, carbon, and water cycles.
Toshiyuki Ohtsuka, Atsushi Okazaki, Masaki Ogura, and Shunji Kotsuki
EGUsphere, https://doi.org/10.48550/arXiv.2405.19546, https://doi.org/10.48550/arXiv.2405.19546, 2024
Preprint withdrawn
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We utilize weather forecasts in the reverse direction and determine how much we should change the temperature or humidity of the atmosphere at a certain time to change the future rainfall as desired. Even though a weather phenomenon is complicated, we can superimpose the effects of small changes in the atmosphere and find suitable small changes to realize desirable rainfall by solving an optimization problem. We examine this idea on a realistic weather simulator and show it is promising.
Shunji Kotsuki, Kenta Shiraishi, and Atsushi Okazaki
Geosci. Model Dev., 18, 7215–7225, https://doi.org/10.5194/gmd-18-7215-2025, https://doi.org/10.5194/gmd-18-7215-2025, 2025
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Artificial intelligence (AI) is playing a bigger role in weather forecasting, often competing with physical models. However, combining AI models with data assimilation, a process that improves weather forecasts by incorporating observation data, is still relatively unexplored. This study explored the coupling of ensemble data assimilation with an AI weather prediction model, ClimaX, which succeeded in employing weather forecasts stably by applying techniques conventionally used for physical models.
Sai Prabala Swetha Chittella, Andrew Orr, Pranab Deb, and Quentin Dalaiden
EGUsphere, https://doi.org/10.5194/egusphere-2025-4292, https://doi.org/10.5194/egusphere-2025-4292, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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Precipitation plays a vital role in regulating Antarctica's ice sheet mass balance and ice shelf stability, with much of it coming from extreme events that also drive variability. We examined trends in precipitation and extremes using advanced methods and found that the increases are primarily driven by human influence, with greenhouse gases identified as the dominant factor.
Yusuke Hiraga, Jacqueline Muthoni Mbugua, Shunji Kotsuki, Yoshiharu Suzuki, Shu-Hua Chen, Atsushi Hamada, Kazuaki Yasunaga, and Takuya Funatomi
EGUsphere, https://doi.org/10.5194/egusphere-2025-3524, https://doi.org/10.5194/egusphere-2025-3524, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Can cloud seeding be a strategy to mitigate localized heavy rainfall disasters? Our numerical experiments showed that injecting large amounts of ice nuclei into convective clouds inhibited the growth of individual ice crystals to sizes sufficient for rainfall, thereby reducing rainfall in the worst-hit area and shifting it downstream. This shows promise for using cloud seeding to lessen rain-related disasters, although further studies are needed to confirm its broader effectiveness.
Kenta Kurosawa, Atsushi Okazaki, Fumitoshi Kawasaki, and Shunji Kotsuki
Nonlin. Processes Geophys., 32, 293–307, https://doi.org/10.5194/npg-32-293-2025, https://doi.org/10.5194/npg-32-293-2025, 2025
Short summary
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We propose ensemble-based model predictive control (EnMPC), a novel method that improves the control of complex systems like the atmosphere by integrating control theory with data assimilation. Unlike traditional methods, which are computationally expensive, EnMPC uses ensemble simulations to efficiently handle uncertainties and optimize solutions. This approach reduces computational cost while maintaining accuracy, making it a promising step toward real-world applications in dynamic system control.
Diego S. Carrió, Vincenzo Mazzarella, and Rossella Ferretti
Nat. Hazards Earth Syst. Sci., 25, 2999–3026, https://doi.org/10.5194/nhess-25-2999-2025, https://doi.org/10.5194/nhess-25-2999-2025, 2025
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Populated coastal regions in the Mediterranean are known to be severely affected by extreme weather events that are initiated over maritime regions. These weather events are known to pose a serious problem in terms of numerical predictability. Different data assimilation techniques are used in this study with the main aim of enhancing short-range forecasts of two challenging severe weather events.
Wenpeng Xie, Hongmei Li, and Kei Yoshimura
EGUsphere, https://doi.org/10.5194/egusphere-2025-3301, https://doi.org/10.5194/egusphere-2025-3301, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Forecasts of land–air heat and moisture exchange often depend on experts manually adjusting settings, causing inconsistencies. We developed a deep learning method that automatically calibrates these simulations using observations from twenty sites, reducing average errors by fifteen percent. It kept high accuracy at new forest locations while showing more variability at farms. This tool enables faster, more reliable forecasts without manual tuning.
Rikuto Nagai, Yang Bai, Masaki Ogura, Shunji Kotsuki, and Naoki Wakamiya
Nonlin. Processes Geophys., 32, 281–292, https://doi.org/10.5194/npg-32-281-2025, https://doi.org/10.5194/npg-32-281-2025, 2025
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Controlling chaotic systems is a key step toward weather control. The control simulation experiment (CSE) modifies weather systems using small perturbations, as shown in studies with the Lorenz-63 model. However, the effectiveness of CSE compared to other methods is unclear. This study evaluates CSE against model predictive control (MPC). Simulations reveal that MPC achieves higher success rates with less effort under certain conditions, linking control theory and atmospheric science.
Fumitoshi Kawasaki, Atsushi Okazaki, Kenta Kurosawa, Tadashi Tsuyuki, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-1785, https://doi.org/10.5194/egusphere-2025-1785, 2025
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A major challenge in weather control aimed at mitigating extreme weather events is identifying effective control inputs under limited computational resources. This study proposes a novel control framework called model predictive control with foreseeing horizon, designed to efficiently control chaotic dynamical systems. Using a 40-variable chaotic dynamical model, the proposed method successfully mitigated extreme events and reduced computational cost compared to the conventional approach.
Pascal Oettli, Keita Tokuda, Yusuke Imoto, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-1458, https://doi.org/10.5194/egusphere-2025-1458, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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A tropical cyclone is a circular air movement that emerges over warm waters of the tropical ocean and its movement is guided by complex interactions between the ocean and the atmosphere. To better understand this complexity, we adopted ideas and techniques from biology and bioinformatics, to have a fresh look at this matter. This led to the creation of "MeteoScape," a tool that calculates the probability of paths for tropical cyclones can take and visualize them in an understandable way.
Takahito Mitsui, Shunji Kotsuki, Naoya Fujiwara, Atsushi Okazaki, and Keita Tokuda
EGUsphere, https://doi.org/10.5194/egusphere-2025-987, https://doi.org/10.5194/egusphere-2025-987, 2025
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Short summary
Extreme weather poses serious risks, making prevention crucial. Using the Lorenz 96 model as a testbed, we propose a bottom-up approach to mitigate extreme events via local interventions guided by multi-scenario ensemble forecasts. Unlike control-theoretic methods, our approach selects the best control scenario from available options. Achieving up to 99.4 % success, it outperforms previous methods while keeping costs reasonable, offering a practical way to reduce disasters with limited control.
Shigeyuki Ishidoya, Satoshi Sugawara, and Atsushi Okazaki
Atmos. Chem. Phys., 25, 1965–1987, https://doi.org/10.5194/acp-25-1965-2025, https://doi.org/10.5194/acp-25-1965-2025, 2025
Short summary
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The 18O/16O ratio of atmospheric oxygen, δatm(18O), is higher than that of ocean water due to isotopic effects during biospheric activities. This is known as the Dole–Morita effect, and its millennial-scale variations are recorded in ice cores. However, small variations of δatm(18O) in the present day have never been detected so far. This paper presents the first observations of diurnal, seasonal, and secular variations in δatm(18O) and applies them to evaluate oxygen, carbon, and water cycles.
Sarah Wauthy and Quentin Dalaiden
EGUsphere, https://doi.org/10.5194/egusphere-2025-192, https://doi.org/10.5194/egusphere-2025-192, 2025
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The surface mass balance (SMB) is one of the main drivers of future Antarctic mass changes. The interannual variability of the SMB is dominated by precipitation and extreme precipitation events (EPEs). In this study, we analyze the role of precipitation and EPEs in the contrasting SMB trends observed in the ice-core records of three adjacent ice rises. Our results show that precipitation and EPEs alone cannot explain the observed contrasts and suggest that other processes may be at work.
Yuka Muto and Shunji Kotsuki
Hydrol. Earth Syst. Sci., 28, 5401–5417, https://doi.org/10.5194/hess-28-5401-2024, https://doi.org/10.5194/hess-28-5401-2024, 2024
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It is crucial to improve global precipitation estimates to understand water-related disasters and water resources. This study proposes a new methodology to interpolate global precipitation fields from ground rain gauge observations using ensemble data assimilation and the precipitation of a numerical weather prediction model. Our estimates agree better with independent rain gauge observations than existing precipitation estimates, especially in mountainous or rain-gauge-sparse regions.
Marie Genevieve Paule Cavitte, Hugues Goosse, Quentin Dalaiden, and Nicolas Ghilain
EGUsphere, https://doi.org/10.5194/egusphere-2024-3140, https://doi.org/10.5194/egusphere-2024-3140, 2024
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Ice cores in East Antarctica show contrasting records of past snowfall. We tested if large-scale weather patterns could explain this by combining ice core data with an atmospheric model and radar-derived errors. However, the reconstruction produced unrealistic wind patterns to fit the ice core records. We suggest that uncertainties are not fully captured and that small-scale local wind effects, not represented in the model, could significantly influence snowfall records in the ice cores.
Florian Pantillon, Silvio Davolio, Elenio Avolio, Carlos Calvo-Sancho, Diego Saul Carrió, Stavros Dafis, Emanuele Silvio Gentile, Juan Jesus Gonzalez-Aleman, Suzanne Gray, Mario Marcello Miglietta, Platon Patlakas, Ioannis Pytharoulis, Didier Ricard, Antonio Ricchi, Claudio Sanchez, and Emmanouil Flaounas
Weather Clim. Dynam., 5, 1187–1205, https://doi.org/10.5194/wcd-5-1187-2024, https://doi.org/10.5194/wcd-5-1187-2024, 2024
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Cyclone Ianos of September 2020 was a high-impact but poorly predicted medicane (Mediterranean hurricane). A community effort of numerical modelling provides robust results to improve prediction. It is found that the representation of local thunderstorms controlled the interaction of Ianos with a jet stream at larger scales and its subsequent evolution. The results help us understand the peculiar dynamics of medicanes and provide guidance for the next generation of weather and climate models.
Matthias Schneider, Kinya Toride, Farahnaz Khosrawi, Frank Hase, Benjamin Ertl, Christopher J. Diekmann, and Kei Yoshimura
Atmos. Meas. Tech., 17, 5243–5259, https://doi.org/10.5194/amt-17-5243-2024, https://doi.org/10.5194/amt-17-5243-2024, 2024
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Despite its importance for extreme weather and climate feedbacks, atmospheric convection is not well constrained. This study assesses the potential of novel tropospheric water vapour isotopologue satellite observations for improving the analyses of convective events. We find that the impact of the isotopologues is small for stable atmospheric conditions but significant for unstable conditions, which have the strongest societal impacts (e.g. storms and flooding).
Fumitoshi Kawasaki and Shunji Kotsuki
Nonlin. Processes Geophys., 31, 319–333, https://doi.org/10.5194/npg-31-319-2024, https://doi.org/10.5194/npg-31-319-2024, 2024
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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 proposes using the model predictive control (MPC), an advanced control method, to control a chaotic system. Through numerical experiments using a low-dimensional chaotic system, we demonstrate that the system can be successfully controlled with shorter forecasts compared to previous studies.
Toshiyuki Ohtsuka, Atsushi Okazaki, Masaki Ogura, and Shunji Kotsuki
EGUsphere, https://doi.org/10.48550/arXiv.2405.19546, https://doi.org/10.48550/arXiv.2405.19546, 2024
Preprint withdrawn
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We utilize weather forecasts in the reverse direction and determine how much we should change the temperature or humidity of the atmosphere at a certain time to change the future rainfall as desired. Even though a weather phenomenon is complicated, we can superimpose the effects of small changes in the atmosphere and find suitable small changes to realize desirable rainfall by solving an optimization problem. We examine this idea on a realistic weather simulator and show it is promising.
Shunji Kotsuki, Fumitoshi Kawasaki, and Masanao Ohashi
Nonlin. Processes Geophys., 31, 237–245, https://doi.org/10.5194/npg-31-237-2024, https://doi.org/10.5194/npg-31-237-2024, 2024
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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 quantum annealer, the proposed method found solutions with comparable accuracy to conventional approaches and significantly reduced computational time.
Ryan L. Fogt, Quentin Dalaiden, and Gemma K. O'Connor
Clim. Past, 20, 53–76, https://doi.org/10.5194/cp-20-53-2024, https://doi.org/10.5194/cp-20-53-2024, 2024
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Antarctic sea ice is rapidly changing, with record lows set in 2017, 2022, and 2023 following decades of increase. To place these changes in a longer historical context, reconstructions have been created; however, they are quite different prior to observations. Here we find that the differences are more strongly tied to the implied connection of each reconstruction with the atmospheric circulation rather than differences in seasonality or geographic representation.
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
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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.
Christian Ferrarin, Florian Pantillon, Silvio Davolio, Marco Bajo, Mario Marcello Miglietta, Elenio Avolio, Diego S. Carrió, Ioannis Pytharoulis, Claudio Sanchez, Platon Patlakas, Juan Jesús González-Alemán, and Emmanouil Flaounas
Nat. Hazards Earth Syst. Sci., 23, 2273–2287, https://doi.org/10.5194/nhess-23-2273-2023, https://doi.org/10.5194/nhess-23-2273-2023, 2023
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The combined use of meteorological and ocean models enabled the analysis of extreme sea conditions driven by Medicane Ianos, which hit the western coast of Greece on 18 September 2020, flooding and damaging the coast. The large spread associated with the ensemble highlighted the high model uncertainty in simulating such an extreme weather event. The different simulations have been used for outlining hazard scenarios that represent a fundamental component of the coastal risk assessment.
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
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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.
Di Wang, Lide Tian, Camille Risi, Xuejie Wang, Jiangpeng Cui, Gabriel J. Bowen, Kei Yoshimura, Zhongwang Wei, and Laurent Z. X. Li
Atmos. Chem. Phys., 23, 3409–3433, https://doi.org/10.5194/acp-23-3409-2023, https://doi.org/10.5194/acp-23-3409-2023, 2023
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To better understand the spatial and temporal distribution of vapor isotopes, we present two vehicle-based spatially continuous snapshots of the near-surface vapor isotopes in China during the pre-monsoon and monsoon periods. These observations are explained well by different moisture sources and processes along the air mass trajectories. Our results suggest that proxy records need to be interpreted in the context of regional systems and sources of moisture.
Diego S. Carrió
Nat. Hazards Earth Syst. Sci., 23, 847–869, https://doi.org/10.5194/nhess-23-847-2023, https://doi.org/10.5194/nhess-23-847-2023, 2023
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The accurate prediction of medicanes still remains a key challenge in the scientific community because of their poor predictability. In this study we assimilate different observations to improve the trajectory and intensity forecasts of the Qendresa Medicane. Results show the importance of using data assimilation techniques to improve the estimate of the atmospheric flow in the upper-level atmosphere, which has been shown to be key to improve the prediction of Qendresa.
Farahnaz Khosrawi, Kinya Toride, Kei Yoshimura, Christopher Diekmann, Benjamin Ertl, Frank Hase, and Matthias Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2022-1408, https://doi.org/10.5194/egusphere-2022-1408, 2022
Preprint withdrawn
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We assess with an Observation System Simulation Experiment the potential of mid-tropospheric water isotopologue data for constraining uncertainties in meteorological analysis fields in the tropics. Our assimilation experiments indicate that isotopologue observations have the potential to reduce the uncertainties of diabatic heating rates and precipitation in the tropics and in consequence offer potential for improving meteorological analysis in the tropical regions.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022, https://doi.org/10.5194/gmd-15-8325-2022, 2022
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Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Jeanne Rezsöhazy, Quentin Dalaiden, François Klein, Hugues Goosse, and Joël Guiot
Clim. Past, 18, 2093–2115, https://doi.org/10.5194/cp-18-2093-2022, https://doi.org/10.5194/cp-18-2093-2022, 2022
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Using statistical tree-growth proxy system models in the data assimilation framework may have limitations. In this study, we successfully incorporate the process-based dendroclimatic model MAIDEN into a data assimilation procedure to robustly compare the outputs of an Earth system model with tree-ring width observations. Important steps are made to demonstrate that using MAIDEN as a proxy system model is a promising way to improve large-scale climate reconstructions with data assimilation.
Janica C. Bühler, Josefine Axelsson, Franziska A. Lechleitner, Jens Fohlmeister, Allegra N. LeGrande, Madhavan Midhun, Jesper Sjolte, Martin Werner, Kei Yoshimura, and Kira Rehfeld
Clim. Past, 18, 1625–1654, https://doi.org/10.5194/cp-18-1625-2022, https://doi.org/10.5194/cp-18-1625-2022, 2022
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We collected and standardized the output of five isotope-enabled simulations for the last millennium and assess differences and similarities to records from a global speleothem database. Modeled isotope variations mostly arise from temperature differences. While lower-resolution speleothems do not capture extreme changes to the extent of models, they show higher variability on multi-decadal timescales. As no model excels in all comparisons, we advise a multi-model approach where possible.
Nicolas Ghilain, Stéphane Vannitsem, Quentin Dalaiden, Hugues Goosse, Lesley De Cruz, and Wenguang Wei
Earth Syst. Sci. Data, 14, 1901–1916, https://doi.org/10.5194/essd-14-1901-2022, https://doi.org/10.5194/essd-14-1901-2022, 2022
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Modeling the climate at high resolution is crucial to represent the snowfall accumulation over the complex orography of the Antarctic coast. While ice cores provide a view constrained spatially but over centuries, climate models can give insight into its spatial distribution, either at high resolution over a short period or vice versa. We downscaled snowfall accumulation from climate model historical simulations (1850–present day) over Dronning Maud Land at 5.5 km using a statistical method.
Farahnaz Khosrawi, Kinya Toride, Kei Yoshimura, Christopher J. Diekmann, Benjamin Ertl, Frank Hase, and Matthias Schneider
Weather Clim. Dynam. Discuss., https://doi.org/10.5194/wcd-2021-49, https://doi.org/10.5194/wcd-2021-49, 2021
Revised manuscript not accepted
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We assess with an Observation System Simulation Experiment the potential of mid-tropospheric water isotopologue data for constraining uncertainties in meteorological analysis fields in the tropics. Our assimilation experiments indicate that isotopologue observations have the potential to reduce the uncertainties of diabatic heating rates and meteorological variables in the tropics and in consequence offer potential for improving meteorological analysis in the tropical regions.
Hugues Goosse, Quentin Dalaiden, Marie G. P. Cavitte, and Liping Zhang
Clim. Past, 17, 111–131, https://doi.org/10.5194/cp-17-111-2021, https://doi.org/10.5194/cp-17-111-2021, 2021
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Polynyas are ice-free oceanic areas within the sea ice pack. Small polynyas are regularly observed in the Southern Ocean, but large open-ocean polynyas have been rare over the past decades. Using records from available ice cores in Antarctica, we reconstruct past polynya activity and confirm that those events have also been rare over the past centuries, but the information provided by existing data is not sufficient to precisely characterize the timing of past polynya opening.
Marie G. P. Cavitte, Quentin Dalaiden, Hugues Goosse, Jan T. M. Lenaerts, and Elizabeth R. Thomas
The Cryosphere, 14, 4083–4102, https://doi.org/10.5194/tc-14-4083-2020, https://doi.org/10.5194/tc-14-4083-2020, 2020
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Surface mass balance (SMB) and surface air temperature (SAT) are correlated at the regional scale for most of Antarctica, SMB and δ18O. Areas with low/no correlation are where wind processes (foehn, katabatic wind warming, and erosion) are sufficiently active to overwhelm the synoptic-scale snow accumulation. Measured in ice cores, the link between SMB, SAT, and δ18O is much weaker. Random noise can be removed by core record averaging but local processes perturb the correlation systematically.
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Short summary
Data assimilation (DA) has been used to reconstruct paleoclimate fields. DA integrates model simulations and climate proxies based on their error sizes. Consequently, error information is vital for DA to function optimally. This study estimated observation errors using "innovation statistics" and demonstrated that DA with estimated errors outperformed previous studies.
Data assimilation (DA) has been used to reconstruct paleoclimate fields. DA integrates model...