English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Climate field reconstructions (CFR) attempt to estimate spatiotemporal fieldsof climate variables in the past using climate proxies such as tree rings, icecores, and corals. Data Assimilation (DA) methods are a recent and promisingnew means of deriving CFRs that optimally fuse climate proxies with climatemodel output. Despite the growing application of DA-based CFRs, little isunderstood about how much the assimilated proxies change the statisticalproperties of the climate model data. To address this question, we propose arobust and computationally efficient method, based on functional data depth, toevaluate differences in the distributions of two spatiotemporal processes. Weapply our test to study global and regional proxy influence in DA-based CFRs bycomparing the background and analysis states, which are treated as two samplesof spatiotemporal fields. We find that the analysis states are significantlyaltered from the climate-model-based background states due to the assimilationof proxies. Moreover, the difference between the analysis and background statesincreases with the number of proxies, even in regions far beyond proxycollection sites. Our approach allows us to characterize the added value ofproxies, indicating where and when the analysis states are distinct from thebackground states.

Evaluating proxy influence in assimilated paleoclimate reconstructions -- Testing the exchangeability of two ensembles of spatial processes