Bayesian estimates of snow cover area in Eurasia in the 21st century based on the results of calculations with the CMIP6 ensemble of climate models
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Bayesian estimates of snow cover area in Eurasia in the 21st century based on the results of calculations with the CMIP6 ensemble of climate models |
| 2. | Creator | Author's name, affiliation, country | M. M. Arzhanov; A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences; Russian Federation |
| 2. | Creator | Author's name, affiliation, country | I. I. Mokhov; A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences; Lomonosov Moscow State University; Russian Federation |
| 2. | Creator | Author's name, affiliation, country | M. R. Parfenova; A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences; Russian Federation |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | snow cover area; global climate models; satellite data; Bayesian averaging |
| 4. | Description | Abstract | Based on the results of calculations with the ensemble of global climate models CMIP6, quantitative estimates of changes in the area of snow cover in Eurasia in the 21st century were obtained under scenarios SSP2-4.5 and SSP5-8.5 of anthropogenic impacts using the Bayesian averaging. The contribution (weight) of the models to the overall ensemble estimates was determined by accuracy of reproduction of the long-term average, trend, and interannual variability of the snow cover area in Eurasia by satellite data. The largest inter-model variations in estimates, the most significant of which were calculated for the summer and autumn months, are associated with the description of the trend component and inter-annual variability of the snow cover area of Eurasia, as well as with equally weighted averaging. It is shown that when using Bayesian weights, the uncertainty of snow cover area estimates can be halved compared to the ensemble average with equal model weights. The obtained ensemble estimates of the snow cover area using combined Bayesian weights exceed the corresponding estimates for equally weighted averaging. |
| 5. | Publisher | Organizing agency, location | The Russian Academy of Sciences |
| 6. | Contributor | Sponsor(s) |
RSF (19-17-00240) RSF (23-47-00104) |
| 7. | Date | (DD-MM-YYYY) | 27.06.2024 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | Research Article |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://snv63.ru/2686-7397/article/view/650034 |
| 10. | Identifier | Digital Object Identifier (DOI) | 10.31857/S2686739724010198 |
| 11. | Source | Title; vol., no. (year) | Doklady Rossijskoj akademii nauk. Nauki o Zemle; Vol 514, No 1 (2024) |
| 12. | Language | English=en | ru |
| 13. | Relation | Supp. Files |
Fig. 1. Normalized Bayesian weights of CMIP6 ensemble models (model numbers see Table 1): W1 (red), W2 (orange), W3 (green) and W4 (blue) for the area of snow cover in Eurasia in 2000-2019 under the scenarios “historical” and SSP2-4.5 (a) and “historical” and SSP5-8.5 (b). The horizontal line corresponds to the weight W0 = 1/N, N is the number of models in the ensemble. (1MB) Fig. 2. Changes in the area of snow cover (million km2) in Eurasia (sliding 11‑year averages) for different months according to calculations with an ensemble of climate models and the corresponding intra- assembly snow cover area (million km2) in Eurasia in the 21st century. for different months, obtained under scenarios SSP2-4.5 (a) and SSP5- 8.5 (b) using different Bayesian weights: W1 (red), W2 (orange), W3 (green), W4 ( blue), as well as equally weighted with weight W0 (black, hatching) in comparison with CDR satellite data (bold black curve). (1MB) |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
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