The potential of coralline algae as an indicator of climate in the Southern Hemisphere and for the evaluation of global climate models: a New Zealand case study

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Name
The potential of coralline algae as an indicator of climate in the Southern Hemisphere and for the evaluation of global climate models: a New Zealand case study
Description
General circulation models (GCMs) are currently the most important tools for obtaining projections about future climate. In addition, they provide data input for regional atmospheric models that translate global climate change to regional and local scales where humans and environments face the impacts. To ensure the accurateness of their simulations, GCMs need to be evaluated as thoroughly as possible against past climate records, where one focus is on the so-called "historical period" (1850–present). However, the evaluation task is difficult for the period before World War II and earlier due to a frequent lack of reliable observations. This problem is exacerbated for the Southern Hemisphere, which has been notoriously understudied in comparison to the climate of the Northern Hemisphere.

In this DFG-funded project (grant no. 453305163), we utilize crustose coralline algae – a rather recently discovered proxy archive – to extend the observational time series of sea surface temperature (SST) in the New Zealand region back to ~1850. The SST reconstruction is then employed in GCM evaluation to assess their skill in representing the large-scale climate of New Zealand. Finally, high-resolution sensitivity simulations are obtained from a regional atmospheric model to investigate the added value of the advanced GCM selection to regional climate modeling.

In New Zealand, variations in SST are reflected on a variety of spatial and temporal scales and are statistically detectable through to temperature anomalies and changes in glacier mass balance in the high mountains of the Southern Alps. Therefore, a key focus of the regional atmospheric modeling is to investigate the physical mechanisms that transform large-scale SST signals into local, high-mountain climate and glacier mass anomalies. For this purpose, an accurate representation of SST by GCMs is crucial.

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