10.5061/DRYAD.4F98T
Freer, Jennifer J.
University of Bristol
Partridge, Julian C.
University of Western Australia
Tarling, Geraint A.
British Antarctic Survey
Collins, Martin A.
Centre for Environment, Fisheries and Aquaculture Science
Genner, Martin J.
University of Bristol
Data from: Predicting ecological responses in a changing ocean: the
effects of future climate uncertainty
Dryad
dataset
2018
Electrona antarctica
climate model
IPCC
sea surface temperature
2018-10-23T00:00:00Z
2018-10-23T00:00:00Z
en
https://doi.org/10.1007/s00227-017-3239-1
192218536 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Predicting how species will respond to climate change is a growing field
in marine ecology, yet knowledge of how to incorporate the uncertainty
from future climate data into these predictions remains a significant
challenge. To help overcome it, this review separates climate uncertainty
into its three components (scenario uncertainty, model uncertainty, and
internal model variability) and identifies four criteria that constitute a
thorough interpretation of an ecological response to climate change in
relation to these parts (awareness, access, incorporation, communication).
Through a literature review, the extent to which the marine ecology
community has addressed these criteria in their predictions was assessed.
Despite a high awareness of climate uncertainty, articles favoured the
most severe emission scenario, and only a subset of climate models were
used as input into ecological analyses. In the case of sea surface
temperature, these models can have projections unrepresentative against a
larger ensemble mean. Moreover, 91% of studies failed to incorporate the
internal variability of a climate model into results. We explored the
influence that the choice of emission scenario, climate model, and model
realisation can have when predicting the future distribution of the
pelagic fish, Electrona antarctica. Future distributions were highly
influenced by the choice of climate model, and in some cases, internal
variability was important in determining the direction and severity of the
distribution change. Increased clarity and availability of processed
climate data would facilitate more comprehensive explorations of climate
uncertainty, and increase in the quality and standard of marine prediction
studies.
processed CMIP5 SST projectionsThis file has projected annual mean Sea
Surface Temperature (SST) averaged over the time period 1981-2100. Data
are taken from 15 CMIP5 climate models under two emission scenarios, RCP
4.5 and RCP 8.5. Where possible, multiple realisations of a climate model
are included, as well as the mean output from these realisations. The
present day baseline annual mean SST layer (1982-2001) is also available.
Two spatial resolutions are available: 1x1 degree and 0.25x0.25 degree
created by a spline interpolation procedure in ArcGIS v.10.4.1. All
original CMIP5 data are available at
https://esgf-node.llnl.gov/projects/cmip5/. See README file for further
information.SST_projections.zip
Southern Ocean