The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections
<p>Differences between paleoclimatic reconstructions are caused by two factors: the method and the input data. While many studies compare methods, we will focus in this study on the consequences of the input data choice in a state-of-the-art Kalman-filter paleoclimate data assimilation approac...
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| Series: | Climate of the Past |
| Online Access: | https://www.clim-past.net/16/1061/2020/cp-16-1061-2020.pdf |
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doaj-7bcc41dbdeb3409c806e43d80ce93e0b2020-11-25T03:32:54ZengCopernicus PublicationsClimate of the Past1814-93241814-93322020-06-01161061107410.5194/cp-16-1061-2020The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collectionsJ. Franke0J. Franke1V. Valler2V. Valler3S. Brönnimann4S. Brönnimann5R. Neukom6R. Neukom7R. Neukom8R. Neukom9F. Jaume-Santero10Institute of Geography, University of Bern, Bern, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandInstitute of Geography, University of Bern, Bern, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandInstitute of Geography, University of Bern, Bern, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandInstitute of Geography, University of Bern, Bern, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandDepartment of Geography, University of Zurich, Zurich, SwitzerlandDepartment of Geosciences, University of Fribourg, Fribourg, SwitzerlandDepartment of Earth Physics and Astrophysics, Universidad Complutense de Madrid, Madrid, Spain<p>Differences between paleoclimatic reconstructions are caused by two factors: the method and the input data. While many studies compare methods, we will focus in this study on the consequences of the input data choice in a state-of-the-art Kalman-filter paleoclimate data assimilation approach. We evaluate reconstruction quality in the 20th century based on three collections of tree-ring records: (1) 54 of the best temperature-sensitive tree-ring chronologies chosen by experts; (2) 415 temperature-sensitive tree-ring records chosen less strictly by regional working groups and statistical screening; (3) 2287 tree-ring series that are not screened for climate sensitivity. The three data sets cover the range from small sample size, small spatial coverage and strict screening for temperature sensitivity to large sample size and spatial coverage but no screening. Additionally, we explore a combination of these data sets plus screening methods to improve the reconstruction quality.</p> <p>A large, unscreened collection generally leads to a poor reconstruction skill. A small expert selection of extratropical Northern Hemisphere records allows for a skillful high-latitude temperature reconstruction but cannot be expected to provide information for other regions and other variables. We achieve the best reconstruction skill across all variables and regions by combining all available input data but rejecting records with insignificant climatic information (<span class="inline-formula"><i>p</i></span> value of regression model <span class="inline-formula">>0.05</span>) and removing duplicate records. It is important to use a tree-ring proxy system model that includes both major growth limitations, temperature and moisture.</p>https://www.clim-past.net/16/1061/2020/cp-16-1061-2020.pdf |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
J. Franke J. Franke V. Valler V. Valler S. Brönnimann S. Brönnimann R. Neukom R. Neukom R. Neukom R. Neukom F. Jaume-Santero |
| spellingShingle |
J. Franke J. Franke V. Valler V. Valler S. Brönnimann S. Brönnimann R. Neukom R. Neukom R. Neukom R. Neukom F. Jaume-Santero The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections Climate of the Past |
| author_facet |
J. Franke J. Franke V. Valler V. Valler S. Brönnimann S. Brönnimann R. Neukom R. Neukom R. Neukom R. Neukom F. Jaume-Santero |
| author_sort |
J. Franke |
| title |
The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections |
| title_short |
The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections |
| title_full |
The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections |
| title_fullStr |
The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections |
| title_full_unstemmed |
The importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections |
| title_sort |
importance of input data quality and quantity in climate field reconstructions – results from the assimilation of various tree-ring collections |
| publisher |
Copernicus Publications |
| series |
Climate of the Past |
| issn |
1814-9324 1814-9332 |
| publishDate |
2020-06-01 |
| description |
<p>Differences between paleoclimatic reconstructions are caused by two
factors: the method and the input data. While many studies compare
methods, we will focus in this study on the consequences of the
input data choice in a state-of-the-art Kalman-filter paleoclimate data
assimilation approach. We evaluate reconstruction quality in the
20th century based on three collections of tree-ring records: (1)
54 of the best temperature-sensitive tree-ring chronologies chosen
by experts; (2) 415 temperature-sensitive tree-ring records chosen
less strictly by regional working groups and statistical screening;
(3) 2287 tree-ring series that are not screened for climate
sensitivity. The three data sets cover the range from small sample
size, small spatial coverage and strict screening for temperature
sensitivity to large sample size and spatial coverage but no
screening. Additionally, we explore a combination of these data sets
plus screening methods to improve the reconstruction quality.</p>
<p>A large, unscreened collection generally leads to a poor
reconstruction skill. A small expert selection of extratropical
Northern Hemisphere records allows for a skillful high-latitude
temperature reconstruction but cannot be expected to provide
information for other regions and other variables. We achieve the
best reconstruction skill across all variables and regions by
combining all available input data but rejecting records with
insignificant climatic information (<span class="inline-formula"><i>p</i></span> value of regression model <span class="inline-formula">>0.05</span>) and removing duplicate records. It is important to use
a tree-ring proxy system model that includes both major growth
limitations, temperature and moisture.</p> |
| url |
https://www.clim-past.net/16/1061/2020/cp-16-1061-2020.pdf |
| work_keys_str_mv |
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