Abstract. Global projections for ocean conditions in 2100 predict that the North Pacific will experience some of the largest changes. Coastal processes that drive variability in the region can alter these projected changes but are poorly resolved by global coarse-resolution models. We quantify the degree to which local processes modify biogeochemical changes in the eastern boundary California Current System (CCS) using multi-model regionally downscaled climate projections of multiple climate-associated stressors (temperature, O2, pH, saturation state (Ω), and CO2). The downscaled projections predict changes consistent with the directional change from the global projections for the same emissions scenario. However, the magnitude and spatial variability of projected changes are modified in the downscaled projections for carbon variables. Future changes in pCO2 and surface Ω are amplified, while changes in pH and upper 200 m Ω are dampened relative to the projected change in global models. Surface carbon variable changes are highly correlated to changes in dissolved inorganic carbon (DIC), pCO2 changes over the upper 200 m are correlated to total alkalinity (TA), and changes at the bottom are correlated to DIC and nutrient changes. The correlations in these latter two regions suggest that future changes in carbon variables are influenced by nutrient cycling, changes in benthic–pelagic coupling, and TA resolved by the downscaled projections. Within the CCS, differences in global and downscaled climate stressors are spatially variable, and the northern CCS experiences the most intense modification. These projected changes are consistent with the continued reduction in source water oxygen; increase in source water nutrients; and, combined with solubility-driven changes, altered future upwelled source waters in the CCS. The results presented here suggest that projections that resolve coastal processes are necessary for adequate representation of the magnitude of projected change in carbon stressors in the CCS.
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Examining current bias and future projection consistency of globally downscaled climate projections commonly used in climate impact studies
The associated uncertainties of future climate projections are one of the biggest obstacles to overcome in studies exploring the potential regional impacts of future climate shifts. In remote and climatically complex regions, the limited number of available downscaled projections may not provide an accurate representation of the underlying uncertainty in future climate or the possible range of potential scenarios. Consequently, global downscaled projections are now some of the most widely used climate datasets in the world. However, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we explore the utility of two such global datasets (CHELSA and WorldClim2) in providing plausible future climate scenarios for regional climate change impact studies. Our analysis was based on three steps: (1) standardizing a baseline period to compare available global downscaled projections with regional observation-based datasets and regional downscaled datasets; (2) bias correcting projections using a single observation-based baseline; and (3) having controlled differences in baselines between datasets, exploring the patterns and magnitude of projected climate shifts from these datasets to determine their plausibility as future climate scenarios, using Hawaiʻi as an example region. Focusing on mean annual temperature and precipitation, we show projected climate shifts from these commonly used global datasets not only may vary significantly from one another but may also fall well outside the range of future scenarios derived from regional downscaling efforts. As species distribution models are commonly created from these datasets, we further illustrate how a substantial portion of variability in future species distribution shifts can arise from the choice of global dataset used. Hence, projected shifts between baseline and future scenarios from these global downscaled projections warrant careful evaluation before use in climate impact studies, something rarely done in the existing literature.
more » « less- NSF-PAR ID:
- 10477347
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Climatic Change
- Volume:
- 176
- Issue:
- 12
- ISSN:
- 0165-0009
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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