the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An assessment of the variability in temperature and salinity of the Baltic Sea from a simulation with data assimilation for the period 1990 to 2020
Abstract. A Baltic dataset covering 1990–2020 is reconstructed using a circulation model and data assimilation. Satellite observations of sea surface temperature and temperature and salinity (T/S) profiles are used to reduce model biases by a local Singular Evolutive Interpolated Kalman (SEIK) filter. The dataset is evaluated with assimilated T/S profiles and reprocessed grid observations, and the results demonstrate that the sea surface temperature, sea surface height, mixed layer depth, and vertical distribution of T/S are all reasonably reproduced. T/S trends at various depths in the Baltic sub-basins are analyzed from a reanalysis perspective, revealing a clear warming trend in recent decades, with a slight desalination trend in the northern Baltic Sea and a salination trend in the southern Baltic Sea. In particular, T/S trends of the Baltic Sea are larger in the south than in the north. In the Baltic Sea over the past 30 years, the temperature rises at a rate of 0.036 to 0.041 °C/year, with a larger warming trend below the thermocline than above it, while the salinity increases with a trend of -0.0036 to 0.049 PSU/year. In addition, seasonal variations are evident in the temperature at the surface, 60 m, and bottom, as well as in the surface salinity, whereas no clear seasonal variations are detected in the salinity below the surface and temperature at 100 m.
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Status: open (until 18 Jan 2025)
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CC1: 'Comment on egusphere-2024-3283', Nima Zafarmomen, 07 Nov 2024
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How does the choice of the Local Singular Evolutive Interpolated Kalman (LSEIK) filter impact the accuracy and stability of reanalysis, and what justifications were made for using a constant inflation factor rather than a spatially varying one?
Given that the reanalysis shows significant variability in simulation quality across different sub-basins, can the authors provide further details on how biases (especially for temperature and salinity) were corrected and what limitations persist in coastal and deepwater regions?
The study utilizes multiple validation datasets. How do differences between assimilated and validation datasets (e.g., OSISAF vs. CMEMS SST products) impact the interpretation of errors and the robustness of the results?
Given the reported warming and salinity trends, how do these findings compare with observed trends from other datasets covering different periods? Are the differences statistically significant and, if so, what could be contributing to these discrepancies?
The results indicate significant seasonal and depth-dependent variability in temperature and salinity. Can the authors elaborate on how these variations align with physical oceanographic processes and potential anthropogenic impacts on the Baltic Sea?
How do the authors address limitations of a 2-nautical mile horizontal resolution and the potential impacts of parameterization choices on model accuracy, particularly in areas with complex bathymetry?
Can more details be provided about the data quality control measures applied to observations before assimilation? How do these controls influence data coverage and assimilation accuracy, especially in areas with sparse data?
The study emphasizes the climate relevance of Baltic Sea warming and salinity trends. What specific recommendations can be made for policy or management actions based on the observed trends?
Were any sensitivity analyses performed on key model parameters or external forcing datasets to evaluate their influence on simulation accuracy and uncertainty in trend estimations?
How does this study's approach and findings compare to other similar long-term reanalysis efforts in the Baltic Sea? Are there specific methodological advantages that led to better accuracy or unique insights?
I appreciate the comprehensive approach taken in this study, particularly about the assimilation of temperature and salinity data. However, I believe that incorporating references to relevant works on streamflow assimilation could enrich the context and highlight parallel methodologies or complementary findings. For instance, streamflow assimilation studies often deal with similar challenges of data assimilation accuracy, bias correction, and spatiotemporal variability, which could offer valuable insights or comparisons. Including such references would provide a broader perspective on assimilation techniques and their applications, potentially benefiting both this study's conclusions and the wider research community. I strongly suggest citing below papers:
"Potential in improving monthly streamflow forecasting through variational assimilation of observed streamflow."
"Assimilation of sentinel‐based leaf area index for modeling surface‐ground water interactions in irrigation districts."
Citation: https://doi.org/10.5194/egusphere-2024-3283-CC1
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