Dataset Information

Downscaling global ocean climate models improves estimates of exposure regimes in coastal environments.

ABSTRACT: Climate change is expected to warm, deoxygenate, and acidify ocean waters. Global climate models (GCMs) predict future conditions at large spatial scales, and these predictions are then often used to parameterize laboratory experiments designed to assess biological and ecological responses to future change. However, nearshore ecosystems are affected by a range of physical processes such as tides, local winds, and surface and internal waves, causing local variability in conditions that often exceeds global climate models. Predictions of future climatic conditions at local scales, the most relevant to ecological responses, are largely lacking. To fill this critical gap, we developed a 2D implementation of the Regional Ocean Modeling System (ROMS) to downscale global climate predictions across all Representative Concentration Pathway (RCP) scenarios to smaller spatial scales, in this case the scale of a temperate reef in the northeastern Pacific. To assess the potential biological impacts of local climate variability, we then used the results from different climate scenarios to estimate how climate change may affect the survival, growth, and fertilization of a representative marine benthic invertebrate, the red abalone Haliotis rufescens, to a highly varying multi-stressor environment. We found that high frequency variability in temperature, dissolved oxygen (DO), and pH increases as pCO₂ increases in the atmosphere. Extreme temperature and pH conditions are generally not expected until RCP 4.5 or greater, while frequent exposure to low DO is already occurring. In the nearshore environment simulation, strong RCP scenarios can affect red abalone growth as well as reduce fertilization during extreme conditions when compared to global scale simulations.

SUBMITTER: Fagundes M

PROVIDER: S-EPMC7450070 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Downscaling global ocean climate models improves estimates of exposure regimes in coastal environments.

Fagundes Matheus M Litvin S Y SY Micheli F F De Leo G G Boch C A CA Barry J P JP Omidvar S S Woodson C B CB

Scientific reports 20200826 1

Climate change is expected to warm, deoxygenate, and acidify ocean waters. Global climate models (GCMs) predict future conditions at large spatial scales, and these predictions are then often used to parameterize laboratory experiments designed to assess biological and ecological responses to future change. However, nearshore ecosystems are affected by a range of physical processes such as tides, local winds, and surface and internal waves, causing local variability in conditions that often exce ...[more]

PMID: 32848179

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Project description:Land-use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land-use mapping. Assessments of land-use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land-use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land-use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km(2)) estimates of five land-use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio-realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine-grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land-use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio-realms produced global fine-grained layers from the 2005 time step of the Land-use Harmonization dataset. Coarse-scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R (2): 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land-use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the downscaled cropping layer with the geoWiki layer showed an R (2) improvement of 0.12 compared with the Land-use Harmonization data. The downscaling method presented here produced the first global land-use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures will enable the reporting of land-use impacts on biodiversity at a finer resolution than previously possible. Furthermore, the general method presented here could be useful to others wishing to downscale similarly constrained coarse-resolution data for other environmental variables.

Dataset Information

Downscaling global ocean climate models improves estimates of exposure regimes in coastal environments.

Publications

Downscaling global ocean climate models improves estimates of exposure regimes in coastal environments.

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