Abstract

Predicting the ambient environmental conditions in the coming several years to one decade is of key relevance for elucidating how deep-sea habitats, like for example sponge habitats, in the North Atlantic will evolve under near-future climate change. However, it is still not well known to what extent the deep-sea environmental properties can be predicted in advance. A regional downscaling prediction system is developed to assess the potential predictability of the North Atlantic deep-sea environmental factors. The large-scale climate variability predicted with the coupled Max Planck Institute Earth System Model with low-resolution configuration (MPI-ESM-LR) is dynamically downscaled to the North Atlantic by providing surface and lateral boundary conditions to the regional coupled physical-ecosystem model HYCOM-ECOSMO. Model results of two physical fields (temperature and salinity) and two biogeochemical fields (concentrations of silicate and oxygen) over 21 sponge habitats are taken as an example to assess the ability of the downscaling system to predict the interannual to decadal variations of the environmental properties based on ensembles of retrospective predictions over the period from 1985 to 2014. The ensemble simulations reveal skillful predictions of the environmental conditions several years in advance with distinct regional differences. In areas closely tied to large-scale climate variability and ice dynamics, both the physical and biogeochemical fields can be skillfully predicted more than 4 years ahead, while in areas under strong influence of upper oceans or open boundaries, the predictive skill for both fields is limited to a maximum of 2 years. The simulations suggest higher predictability for the biogeochemical fields than for the physical fields, which can be partly attributed to the longer persistence of the former fields. Predictability is improved by initialization in areas away from the influence of Mediterranean outflow and areas with weak coupling between the upper and deep oceans. Our study highlights the ability of the downscaling regional system to predict the environmental variations at deep-sea benthic habitats on time scales of management relevance. The downscaling system therefore will be an important part of an integrated approach towards the preservation and sustainable exploitation of the North Atlantic benthic habitats.

Highlights

  • The deep sea, which encompasses depths below around 200 m, was initially considered to be a “marine desert” with low density and biomass of benthic species (Sanders and Hessler, 1969)

  • We notice that Habitat 9, which is shallow with a mean depth of 200 m, free from the Atlantic meridional overturning circulation (AMOC) influence and covered by sea ice most of the year (Rudels, 1995), is characterized by high predictability for both the physical and biogeochemical fields

  • The assessment in this study provides a synoptic view of the spatial patterns of the predictability of deep-sea environmental factors over the widespread sponge habitats in the North Atlantic, providing the sponge community an indication to what extent the prediction of the environmental conditions can be used

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Summary

Introduction

The deep sea, which encompasses depths below around 200 m, was initially considered to be a “marine desert” with low density and biomass of benthic species (Sanders and Hessler, 1969). In the deep North Atlantic Ocean, numerous benthic habitats widely spread across a broad spectrum of geomorphological features such as shelves, slopes, seamounts, mid-ocean ridges, canyons and fjords (Cárdenas and Rapp, 2015; Roberts et al, 2018; Kazanidis et al, 2019a,b,c; Meyer et al, 2020a,b), which are associated with a wide range of depths through the mesopelagic and bathyal zones and even at abyssal and hadal depths (Vacelet and Custódio, 2007; Hestetun et al, 2019). Further they are of fundamental importance for the benthic–pelagic coupling and marine biogeochemical cycling (Pile and Young, 2006; Oevelen et al, 2009; Smith et al, 2009; Maldonado et al, 2019)

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