the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jan 2024
Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without Summer 1816
Abstract. The “Year Without Summer” of 1816 was characterized by extraordinarily cold and wet periods in Central Europe, and it was associated with severe crop failures, famine, and socio-economic disruptions. From a modern perspective and beyond its tragic consequences, the summer of 1816 represents a rare occasion to analyze the adverse weather (and its impacts) after a major volcanic eruption. However, given the distant past, obtaining the high-resolution data needed for such studies is a challenge. In our approach, we use dynamical downscaling, in combination with 3D-variational data assimilation of early instrumental observations, for assessing a cold-air outbreak in early June 1816. We find that the cold spell is well represented in the coarse-resolution 20th Century Reanalysis product, which is used for initializing the regional Weather Research and Forecasting Model. Our downscaling simulations (including a 19th-century land-use scheme) reproduce and explain meteorological processes well at regional to local scales, such as a foehn wind situation over the Alps with much lower temperatures on its northern side. Simulated weather variables, such as cloud cover or rainy days, are simulated in good agreement with (eye) observations and (independent) measurements, with small differences between the simulations with and without data assimilation. However, validations with partly independent station data show that simulations with assimilated pressure and temperature measurements are closer to the observations, e.g. regarding temperatures during the coldest night, for which snowfall as low as the Swiss Plateau was reported, and a rapid pressure increase thereafter. General improvements from data assimilation are also evident in simple quantitative analyses of temperature and pressure. In turn, data assimilation requires careful selection, preprocessing and bias-adjustment of the underlying observations. Our findings underline the great value of digitizing efforts of early instrumental data and provide novel opportunities to learn from extreme weather and climate events as far back as 200 years or more.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Supplement
(1463 KB) - BibTeX
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2918', Anonymous Referee #1, 28 Feb 2024
In this study, the authors test the ability of the WRF model to simulate a cold spell over the European Alps during the Year Without Summer (1816). For this purpose, the authors employ two different configurations of the model: a simulation including 3DVAR data assimilation and another one without. Results show that even if the simulation including data assimilation consumes more computational resources and needs a more careful set-up (the available stations must be carefully selected first), it improves the results compared to the simple WRF simulation. Both simulations can simulate the observed general weather conditions, but only the one including data assimilation is closer to observations in terms of temperature and pressure. Thus, the authors highlight the improvements obtained due to the data assimilation only, and the novel opportunities provided by the digitalization of early records to study previous weather events.
The manuscript follows a logical structure, and it fits into the scope of Climate of the Past. However, some major comments need to be addressed by the authors before the manuscript is ready for publication. Please take a look at my detailed comments in the attached pdf.
- AC1: 'Reply on RC1', Lucas Pfister, 26 Jun 2024
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RC2: 'Comment on egusphere-2023-2918', Anonymous Referee #2, 19 May 2024
The paper focuses on the cold-air outbreak in early June 1816, during the Year Without Summer, characterized by extraordinarily cold and wet periods in Central Europe. Using the Weather Research and Forecasting (WRF) Model and the 20th Century Reanalysis (20CR) product, the authors perform dynamical downscaling combined with data assimilation of early instrumental observations. The findings highlight the capability of the WRF model to reproduce regional to local meteorological processes and improve the accuracy of simulations when early pressure and temperature measurements are assimilated.The Year Without Summer, caused by the eruption of Mount Tambora in 1815, has been extensively studied in historical climatology. Previous research often relied on descriptive sources and early instrumental measurements aggregated on a monthly basis. This paper builds on these foundations by providing high-resolution, sub-daily weather simulations, adding significant detail to the understanding of climatic impacts during this period.The work provides a novel approach to analyzing historical weather events using a combination of dynamical downscaling and data assimilation. This method allows for a more detailed and accurate reconstruction of past weather events than was previously possible, offering new insights into the meteorological conditions of the Year Without Summer. The findings underscore the importance of digitizing early instrumental data and demonstrate the potential of modern numerical models in historical climatology.The manuscript is well-structured and thorough, presenting a detailed methodology and comprehensive results. The use of both qualitative and quantitative validation against independent historical observations strengthens the credibility of the findings. The careful selection and bias-correction of assimilated data ensure the quality and reliability of the simulations.While the manuscript is robust, a few areas suggested below could benefit from further clarification:P4, L105-107 mentions the use of weather diaries and records of eye observations regarding sunshine, cloudiness, precipitation, wind, and other variables. It would be helpful to (1) Specify how the qualitative descriptions are converted into comparable data points and any challenges faced during this process. (2) Clearly state how the digitized eye observations are integrated into the validation process of the simulations. Highlight any specific methodologies used to ensure the reliability of these qualitative data points.P5, Line 139-141, The manuscript mentions that the observation series are not homogenized, which could impact the reliability of the assimilated data. A discussion on the potential effects of this and any mitigation strategies would be beneficial.P8, Line 173-174, the manuscript mentions the use of the 20CR as initial and boundary conditions for the downscaling experiments, but it does not specify the exact variables utilized.The distinction between variables used for downscaling and those used for data assimilation is crucial, and the manuscript does emphasize the use of data assimilation for pressure and temperature. However, it lacks clarity on which variables are used exclusively for downscaling. To improve clarity and completeness, the manuscript should explicitly list the variables read from the 20CR for dynamical downscaling. This list should differentiate between variables used for initial and boundary conditions in downscaling and those used in data assimilation.P8, Line 182-184, the manuscript states that the WRF model employs three nested, limited-area domains with cell sizes of 27 km, 9 km, and 3 km. These domains are nested to refine the global information to regional and local scales. It could be helpful to describe the process of providing lateral boundary conditions for each nested domain. Specify how the data from the parent domains are used to initialize and drive the simulations of the nested domains. Consider including a diagram that illustrates the nesting process and the flow of lateral boundary conditions from the outermost to the innermost domains. This visual aid would help readers better understand the methodology.P8, Line 178, mentions "Here, we mainly use the mean of the 80 ensemble members." It is essential to introduce here how these ensembles are used, specifically whether they used the ensemble mean or individual ensemble members. In the later part of the manuscript, they indicate that the ensemble mean of the 80 ensemble members is primarily used for synoptic analyses and as initial and boundary conditions for the downscaling simulations. Considering the high variability on synoptic scale, can 80 ensemble mean on 6-hour time scale reflect the weather pattern specifically for June in 1816? It would be beneficial to discuss the implications of using the ensemble mean versus individual ensemble members. This could involve addressing the potential smoothing effects and how this choice impacts the simulation results and their interpretation.P13, Line 310, while Figure 2 effectively presents data for domains D02 and D03, it skips the outermost domain (D01) and the comparison with the 20CR data. Including D01 in the figure would help demonstrate the first step of the downscaling process, showing how well the WRF model captures large-scale atmospheric patterns compared to the 20CR data. This is crucial for validating the initial downscaling step and ensuring that the model accurately represents the broader atmospheric conditions before refining them in the nested domains. This also helps readers better understand the progressive refinement of the atmospheric data from the global scale (20CR) to the regional (D01) and local scales (D02 and D03), and demonstrates each step of the downscaling process enhances the transparency and credibility of the methodology. By the way, the 500 gph contours can not be observed in Fig2 (b) and (c).P17, Line 392, Figure 5 presents meteograms comparing station observations and measurements with WRF model outputs for various variables over a period of time. While it provides comprehensive data, the figure appears too busy, making it difficult to easily interpret the comparisons. I suggest to simplify the figure to make it easier for readers to extract key information. Can group related variables together to provide a clear comparison and emphasize the most important differences between observations and model outputs.P23, in the summary and conclusions, the manuscript briefly touches on the successful application of the methodology for the Year Without Summer but does not delve deeply into the challenges and considerations for applying these methods further back in time. To enhance the manuscript's discussion on the wider application of their methodology in past climates, it would be beneficial to include a more detailed exploration of the challenges and potential strategies for overcoming them. This can be done in the discussion or conclusion sections.Citation: https://doi.org/
10.5194/egusphere-2023-2918-RC2 - AC2: 'Reply on RC2', Lucas Pfister, 26 Jun 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2918', Anonymous Referee #1, 28 Feb 2024
In this study, the authors test the ability of the WRF model to simulate a cold spell over the European Alps during the Year Without Summer (1816). For this purpose, the authors employ two different configurations of the model: a simulation including 3DVAR data assimilation and another one without. Results show that even if the simulation including data assimilation consumes more computational resources and needs a more careful set-up (the available stations must be carefully selected first), it improves the results compared to the simple WRF simulation. Both simulations can simulate the observed general weather conditions, but only the one including data assimilation is closer to observations in terms of temperature and pressure. Thus, the authors highlight the improvements obtained due to the data assimilation only, and the novel opportunities provided by the digitalization of early records to study previous weather events.
The manuscript follows a logical structure, and it fits into the scope of Climate of the Past. However, some major comments need to be addressed by the authors before the manuscript is ready for publication. Please take a look at my detailed comments in the attached pdf.
- AC1: 'Reply on RC1', Lucas Pfister, 26 Jun 2024
-
RC2: 'Comment on egusphere-2023-2918', Anonymous Referee #2, 19 May 2024
The paper focuses on the cold-air outbreak in early June 1816, during the Year Without Summer, characterized by extraordinarily cold and wet periods in Central Europe. Using the Weather Research and Forecasting (WRF) Model and the 20th Century Reanalysis (20CR) product, the authors perform dynamical downscaling combined with data assimilation of early instrumental observations. The findings highlight the capability of the WRF model to reproduce regional to local meteorological processes and improve the accuracy of simulations when early pressure and temperature measurements are assimilated.The Year Without Summer, caused by the eruption of Mount Tambora in 1815, has been extensively studied in historical climatology. Previous research often relied on descriptive sources and early instrumental measurements aggregated on a monthly basis. This paper builds on these foundations by providing high-resolution, sub-daily weather simulations, adding significant detail to the understanding of climatic impacts during this period.The work provides a novel approach to analyzing historical weather events using a combination of dynamical downscaling and data assimilation. This method allows for a more detailed and accurate reconstruction of past weather events than was previously possible, offering new insights into the meteorological conditions of the Year Without Summer. The findings underscore the importance of digitizing early instrumental data and demonstrate the potential of modern numerical models in historical climatology.The manuscript is well-structured and thorough, presenting a detailed methodology and comprehensive results. The use of both qualitative and quantitative validation against independent historical observations strengthens the credibility of the findings. The careful selection and bias-correction of assimilated data ensure the quality and reliability of the simulations.While the manuscript is robust, a few areas suggested below could benefit from further clarification:P4, L105-107 mentions the use of weather diaries and records of eye observations regarding sunshine, cloudiness, precipitation, wind, and other variables. It would be helpful to (1) Specify how the qualitative descriptions are converted into comparable data points and any challenges faced during this process. (2) Clearly state how the digitized eye observations are integrated into the validation process of the simulations. Highlight any specific methodologies used to ensure the reliability of these qualitative data points.P5, Line 139-141, The manuscript mentions that the observation series are not homogenized, which could impact the reliability of the assimilated data. A discussion on the potential effects of this and any mitigation strategies would be beneficial.P8, Line 173-174, the manuscript mentions the use of the 20CR as initial and boundary conditions for the downscaling experiments, but it does not specify the exact variables utilized.The distinction between variables used for downscaling and those used for data assimilation is crucial, and the manuscript does emphasize the use of data assimilation for pressure and temperature. However, it lacks clarity on which variables are used exclusively for downscaling. To improve clarity and completeness, the manuscript should explicitly list the variables read from the 20CR for dynamical downscaling. This list should differentiate between variables used for initial and boundary conditions in downscaling and those used in data assimilation.P8, Line 182-184, the manuscript states that the WRF model employs three nested, limited-area domains with cell sizes of 27 km, 9 km, and 3 km. These domains are nested to refine the global information to regional and local scales. It could be helpful to describe the process of providing lateral boundary conditions for each nested domain. Specify how the data from the parent domains are used to initialize and drive the simulations of the nested domains. Consider including a diagram that illustrates the nesting process and the flow of lateral boundary conditions from the outermost to the innermost domains. This visual aid would help readers better understand the methodology.P8, Line 178, mentions "Here, we mainly use the mean of the 80 ensemble members." It is essential to introduce here how these ensembles are used, specifically whether they used the ensemble mean or individual ensemble members. In the later part of the manuscript, they indicate that the ensemble mean of the 80 ensemble members is primarily used for synoptic analyses and as initial and boundary conditions for the downscaling simulations. Considering the high variability on synoptic scale, can 80 ensemble mean on 6-hour time scale reflect the weather pattern specifically for June in 1816? It would be beneficial to discuss the implications of using the ensemble mean versus individual ensemble members. This could involve addressing the potential smoothing effects and how this choice impacts the simulation results and their interpretation.P13, Line 310, while Figure 2 effectively presents data for domains D02 and D03, it skips the outermost domain (D01) and the comparison with the 20CR data. Including D01 in the figure would help demonstrate the first step of the downscaling process, showing how well the WRF model captures large-scale atmospheric patterns compared to the 20CR data. This is crucial for validating the initial downscaling step and ensuring that the model accurately represents the broader atmospheric conditions before refining them in the nested domains. This also helps readers better understand the progressive refinement of the atmospheric data from the global scale (20CR) to the regional (D01) and local scales (D02 and D03), and demonstrates each step of the downscaling process enhances the transparency and credibility of the methodology. By the way, the 500 gph contours can not be observed in Fig2 (b) and (c).P17, Line 392, Figure 5 presents meteograms comparing station observations and measurements with WRF model outputs for various variables over a period of time. While it provides comprehensive data, the figure appears too busy, making it difficult to easily interpret the comparisons. I suggest to simplify the figure to make it easier for readers to extract key information. Can group related variables together to provide a clear comparison and emphasize the most important differences between observations and model outputs.P23, in the summary and conclusions, the manuscript briefly touches on the successful application of the methodology for the Year Without Summer but does not delve deeply into the challenges and considerations for applying these methods further back in time. To enhance the manuscript's discussion on the wider application of their methodology in past climates, it would be beneficial to include a more detailed exploration of the challenges and potential strategies for overcoming them. This can be done in the discussion or conclusion sections.Citation: https://doi.org/
10.5194/egusphere-2023-2918-RC2 - AC2: 'Reply on RC2', Lucas Pfister, 26 Jun 2024
Peer review completion
Journal article(s) based on this preprint
Data sets
Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without Summer 1816 Peter Stucki https://doi.org/10.48350/189671
Model code and software
Dynamical downscaling and data assimilation for a cold-air outbreak in the European Alps during the Year Without Summer 1816 Peter Stucki https://doi.org/10.48350/189671
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Peter Stucki
Yuri Brugnara
Renate Varga
Chantal Hari
Stefan Brönnimann
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3421 KB) - Metadata XML
-
Supplement
(1463 KB) - BibTeX
- EndNote
- Final revised paper