Revisiting online and offline data assimilation comparison for paleoclimate reconstruction: an idealized OSSE study Article Swipe
Atsushi Okazaki
,
Takemasa Miyoshi
,
Kei Yoshimura
,
Steven J. Greybush
,
Fuqing Zhang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/essoar.10504749.1
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/essoar.10504749.1
Data assimilation (DA) has been applied to estimate the time-mean state such as annual mean surface temperature for paleoclimate reconstruction. There are two types of DA for this purpose: online-DA and offline-DA. The online-DA estimates the time-mean states and the initial conditions for the next DA cycles while the offline-DA only estimates the former. If there is sufficiently long predictability in the system of interest compared to the temporal resolution of the observations, online-DA is expected to outperform offline-DA by utilizing information in the initial conditions. However, previous studies failed to show the superiority of online-DA when time-averaged observations are assimilated, and the reason has not been investigated thoroughly. This study compares online-DA and offline-DA and investigates the relation to the predictability using an intermediate complexity general circulation model with perfect-model observing system simulation experiments. The result shows that the online-DA outperforms offline-DA when the length of predictability is longer than the averaging time of the observations. We also found that the longer the predictability, the more skillful the online-DA. Here, the ocean plays a crucial role in extending predictability, which helps online-DA to outperform offline-DA. Interestingly, the observations of near-surface air temperature over land are found to be highly valuable to update the ocean variables in the analysis steps, suggesting the importance to use cross-domain covariance information between the atmosphere and the ocean when online-DA is applied to reconstruct paleoclimate.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1002/essoar.10504749.1
- OA Status
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- OpenAlex ID
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https://openalex.org/W4242217416Canonical identifier for this work in OpenAlex
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https://doi.org/10.1002/essoar.10504749.1Digital Object Identifier
- Title
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Revisiting online and offline data assimilation comparison for paleoclimate reconstruction: an idealized OSSE studyWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2020Year of publication
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2020-11-17Full publication date if available
- Authors
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Atsushi Okazaki, Takemasa Miyoshi, Kei Yoshimura, Steven J. Greybush, Fuqing ZhangList of authors in order
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https://doi.org/10.1002/essoar.10504749.1Publisher landing page
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1002/essoar.10504749.1Direct OA link when available
- Concepts
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Predictability, Data assimilation, Paleoclimatology, Computer science, Climatology, Meteorology, Environmental science, Geology, Mathematics, Statistics, Climate change, Geography, OceanographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.utilizing | 80 |
| abstract_inverted_index.variables | 205 |
| abstract_inverted_index.atmosphere | 220 |
| abstract_inverted_index.complexity | 125 |
| abstract_inverted_index.conditions | 41 |
| abstract_inverted_index.covariance | 216 |
| abstract_inverted_index.importance | 212 |
| abstract_inverted_index.offline-DA | 49, 78, 114, 142 |
| abstract_inverted_index.online-DA. | 169 |
| abstract_inverted_index.outperform | 77, 184 |
| abstract_inverted_index.resolution | 69 |
| abstract_inverted_index.simulation | 133 |
| abstract_inverted_index.suggesting | 210 |
| abstract_inverted_index.circulation | 127 |
| abstract_inverted_index.conditions. | 85 |
| abstract_inverted_index.information | 81, 217 |
| abstract_inverted_index.offline-DA. | 31, 185 |
| abstract_inverted_index.outperforms | 141 |
| abstract_inverted_index.reconstruct | 229 |
| abstract_inverted_index.superiority | 93 |
| abstract_inverted_index.temperature | 16, 192 |
| abstract_inverted_index.thoroughly. | 108 |
| abstract_inverted_index.assimilated, | 100 |
| abstract_inverted_index.assimilation | 1 |
| abstract_inverted_index.cross-domain | 215 |
| abstract_inverted_index.experiments. | 134 |
| abstract_inverted_index.intermediate | 124 |
| abstract_inverted_index.investigated | 107 |
| abstract_inverted_index.investigates | 116 |
| abstract_inverted_index.near-surface | 190 |
| abstract_inverted_index.observations | 98, 188 |
| abstract_inverted_index.paleoclimate | 18 |
| abstract_inverted_index.sufficiently | 57 |
| abstract_inverted_index.observations, | 72 |
| abstract_inverted_index.observations. | 156 |
| abstract_inverted_index.paleoclimate. | 230 |
| abstract_inverted_index.perfect-model | 130 |
| abstract_inverted_index.time-averaged | 97 |
| abstract_inverted_index.Interestingly, | 186 |
| abstract_inverted_index.predictability | 59, 121, 147 |
| abstract_inverted_index.predictability, | 164, 179 |
| abstract_inverted_index.reconstruction. | 19 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.24662121 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |