We developed short-term (2020-2050) and long-term (2070-2100) downscaled projections for SST and salinity in Puget Sound, using two GCM models, CNRM-CM6-1-HR model and GFDL-CM4. Over the 21st century, both models predict warming and freshening of Puget Sound. This general result is in-line with the Puget-Sound-specific findings in Khangaonkar et al. (2019) and the broader findings in the California Current (Xiu et al. 2018; Siedlecki et al. 2020). Warming and freshening is greater in the long-term than in the short-term. The severity of these effects is dependent on the CMIP6 emissions scenario. In ssp585, representing a fossil-fuel dependent, high emissions world, there will be greater warming and freshening than in ssp126, which represents a sustainable, low-emissions world (Riahi et al. 2017). The CNRM-CM6-1-HR model and the GFDL-CM4 model have strong agreement on SST increases and less agreement on salinity. Spatially, the projected SST increase in Puget Sound is the greatest in Hood Canal and South Basin, and the projected salinity decrease is the greatest in Main Basin.
The models differed in their predictions of future anomalies, though more in terms of salinity than SST. The GFDL-CM4 model had slightly warmer predictions compared to the CNRM-CM6-1-HR model, up to 0.4 ℃ greater (Table S2). Salinity patterns were more varied than SST, with the GFDL-CM4 model showing a slight increase in salinity in the short-term and the CNRM-CM6-1-HR showing greater long-term freshening, up to 1.4 units fresher (Table S2). This distinction between models does not mean one is necessarily more accurate than the other; it is likely a result of different ocean model component configurations. The CNRM-CM6-1-HR model uses the NOCS-ORCA1 configuration of the ocean model NEMO version 3.6 (Danabasoglu et al. 2014; Voldoire et al. 2019), and the GFDL-CM4 model uses the MOM6 configuration of OM4.0 (Adcroft et al. 2019; Held et al. 2019). The different parameterizations used in the GCMs could account for different model responses to the same forcing, as in the case of salinity (Zelinka et al. 2020; Brunner et al. 2020). Internal variability and potential biases in GCMs can be exacerbated at regional scales (Lehner et al. 2020; Brunner et al. 2020). Including additional models to expand a multi-model comparison would have helped understand how choice of GCM influences model predictions due to uncertainty in different model parameterizations (Tommasi et al. 2017). However, choosing the GCM that resolved Puget Sound with the best spatial resolution was more important for the purpose of this project in order to reduce uncertainty from interpolating. The GDFL-CM4 model and the CNRM-CM6-1-HR model were the only two models we found that spatially resolved part or all of the Salish Sea on their horizontal grid, participated in ScenarioMIP, and had the data needed available for download (Table S1).
Each scenario resulted in different SST and salinity anomalies for Puget Sound, and the anomalies increased from the low emissions scenario ssp126 up to the high emissions scenario ssp585. Within the ScenarioMIP framework, scenarios have been updated relative to CMIP5 to include both the Representative Concentration Pathways (RCPs) of radiative forcing (W/m2), and new Shared Socioeconomic Pathways (SSPs) that identify different societal outcomes over the 21st century (Fig. 2, O’Neill et al. 2014; Riahi et al. 2017). For the four different scenarios used in this study, ssp126 represents the most sustainable development, lowest emissions, reduced socioeconomic and political inequality, and the most reduced climate change impacts (Riahi et al. 2017). This scenario also coincides with the least amount of freshening and warming in Puget Sound. ssp245 is an intermediate scenario, where progress towards reducing emissions, protecting environmental systems, and reducing global inequality is achieved more slowly (Riahi et al. 2017). Intuitively, increases in SST and salinity are slightly greater in ssp245 than in ssp126. Nationalism dominates in ssp370, resulting in vast regional differences in the reduction of emissions, quality of life, and environmental degradation (Riahi et al. 2017). Thus, warming and freshening in Puget Sound intensifies even more than in ssp245. The greatest amount of warming and freshening in Puget Sound is predicted for ssp585, the business-as-usual scenario in which fossil-fuel reliant development occurs and energy intensive lifestyles become globally widespread (Riahi et al. 2017). The range of outcomes for Puget Sound are dependent upon which scenario the world follows over the 21st century.
The spatial differences in Fig. 7 are primarily generated from the MoSSea ROMS model (Sutherland et al. 2011), which simulates circulation in the Salish Sea. The warmest regions of downscaled projected SST, Hood Canal and South Basin (Fig. 1 and Fig. 7), are both connected to the Main Basin by narrow passages that are constrained in part by shallow sills. Surface warming in these regions is likely greater due to a more constricted water flow and a narrower topography. SST changes are less extreme in the northern basins, where there is strong tidal inflow from the Strait of Juan de Fuca into Puget Sound (~16000 m3s-1) (Khangaonkar et al. 2019). The freshening seen in the final interpolated values (Fig. 7) is greatest near areas of major river input, such as the Whidbey Basin, where the Skagit and Snohomish rivers have an average discharge of ~1000 m3s-1 (Sutherland et al. 2011). However, because in Puget Sound the tidal influence is stronger than river input, the effects of river discharge on salinity are only noticeable in regions nearest to river mouths. The average downscaled results echo the interpolated anomalies from the CMIP6 results because the Delta Method is adding a step increase to the ROMS time series. Imposing the anomalies to the ROMS time series allows us to see seasonal differences, as forced by the ROMS, and obtain high-resolution values for SST and salinity that are more useful to further ecosystem-based management applications, such as the Puget Sound Atlantis ecosystem model development (Morzaria-Luna et al. 2020b), than anomalies alone.
Patterns of warming ocean temperatures and freshening waters are driven by atmospheric and terrestrial conditions, which are also changing as a result of climate change. Globally, average atmospheric temperature has already increased 1℃ from pre-industrial levels, and future warming of 1.5℃ in Washington State is expected to lead to a 67% increase in hot days above 32℃, a 38% decrease in snowpack, and a 16% increase in winter streamflow (Snover et al. 2019). Precipitation conditions are expected to shift towards a rain-dominated watershed and are likely to be slightly wetter overall, but with 20-27% drier summers in the long term and increasing frequency of heavy precipitation events (Mauger et al. 2015). Alterations in streamflow and the amount of precipitation drive salinity changes in Puget Sound (Moore et al. 2015). Melting glaciers in the North Cascades and Olympic mountain ranges, as well as earlier spring streamflow, will shift the amount and timing of river input. Additionally, warming surface waters have the potential to increase stratification, which with overall freshening in Puget Sound, could cause changes in mixing; it is unknown how much salinity variations will affect stratification and mixing (Yang and Khangaonkar 2008; Mauger et al. 2015).
The empirical downscaling methodology used in this paper is an important first step to developing CMIP6 climate projections for Puget Sound until a more detailed dynamical downscaling, similar to the methodology used for the Salish Sea by Khangaonkar et al. (2019) for CMIP5, can be carried out. For the purpose of ecosystem assessment, conservation planning, and other regional applications, empirical downscaling provides a method of rapid climate assessment with high climate realism (Ramirez-Villegas and Jarvis 2010; Ekström et al. 2015). However, it is important to remember that empirical downscaling does not make GCM output more accurate or reliable (Jones 2013), which is why we chose to use models that resolved Puget Sound with the highest initial resolution available (25 km; Fig. S3). One limitation of empirical downscaling is that it cannot account for changes in variability on the local scale because in empirical downscaling, the climate signal is coming from the coarse resolution GCM (Ekström et al. 2015). Regional feedbacks, such as local precipitation changes or circulation changes, are not well-simulated by the GCM; this is another source of error (Mearns et al. 2003; Ekström et al. 2015). Furthermore, empirical downscaling assumes that the anomaly patterns will hold true in the future (Ramirez-Villegas and Jarvis 2010), which may not be the case for Puget Sound. Though we provide both short-term and long-term projections, we expect uncertainty to be higher in the long-term projections, though at this time scale the choice of downscaling method may be less critical. In particular, categorizing uncertainty in terms of scenario uncertainty, model uncertainty, and internal variability (Cheung et al. 2016), we expect scenario uncertainty to increase and dominate over the long-term - in other words, the global decisions about emissions become stronger drivers of predicted ocean conditions than decisions about GCMs and downscaling technique. Overall, empirical downscaling was chosen for the purpose of rapid, straightforward assessment, but dynamical downscaling for Puget Sound using CMIP6 models will be an important next step in developing more accurate SST and salinity predictions.
Improving accuracy in SST and salinity projections will be important in the coming decades because it will allow better evaluation of the ecosystem impacts of climate change in Puget Sound. In particular, understanding how climate change will affect all trophic levels and culturally significant species such as Chinook salmon, Pacific herring (Clupea pallasii), and Southern Residents is of key relevance to Puget Sound. Both Southern Residents and Puget Sound Chinook salmon are listed under the Endangered Species Act (as endangered and threatened, respectively), and Pacific herring are a critical forage species for salmon and protected seabirds and marine mammals. Increased ocean temperatures can be harmful to salmon during multiple stages of their life cycle, impacting spawning and migration, increasing mortality, and the risk of pathogens (Battin et al. 2007; Beauchamp and Duffy 2011; Mauger et al. 2015). Higher ocean temperatures could also lead to higher Pacific herring embryo mortality (Villalobos et al. 2020). Furthermore, the loss in snowpack will reduce salmon spawning area in rivers in the Puget Sound watershed, leading to an expected decline in salmon population (Battin et al. 2007). This has been identified as the highest Puget Sound salmon exposure risk (Crozier et al. 2019). Freshening and warming has the potential to lead to more stratification in Puget Sound, which directly affects primary production through a changing nutrient supply (Mauger et al. 2015; Xiu et al. 2018). Warming will also continue to change phytoplankton dynamics in Puget Sound, leading to increased harmful algal blooms, which can be toxic to fish (Moore et al. 2015; Southern Resident Orca Task Force 2019). The fate of Southern Resident orcas is fundamentally tied to Chinook salmon, the orca’s primary food source (Ford et al. 2010). Compounding ecosystem stressors may have rippling effects at every trophic level, potentially leading to largely reduced food resources for the Southern Residents (Southern Resident Orca Task Force 2019). Climate change will increase ecosystem vulnerability to other anthropogenic impacts, emerging from a 42% human population increase expected by 2050 (Puget Sound Regional Council 2019).
We produced downscaled anomalies for the whole Salish Sea and downscaled time series for Puget Sound. Downscaled anomalies can be used to assess changes in species distributions (Petatán-Ramírez et al. 2019), and to guide resource-specific, basin-specific, or ecosystem-scale climate adaptation and resilience strategies, which is an emerging priority for the Puget Sound Partnership, the State of Washington agency tasked with recovery of Puget Sound habitats, resources and services (Puget Sound Partnership 2018). The downscaled time series also will be used to drive scenarios using the Atlantis model for Puget Sound, a deterministic simulation model designed to support strategic decision making for marine resource management (Weijerman 2017). Within Atlantis, temperature directly affects primary production, respiration, and other metabolic processes, and both temperature and salinity can dictate habitat use (Audzijonyte et al. 2019). Therefore, downscaled ocean projections will directly influence simulated growth of individuals, population dynamics, and spatial and trophic relationships of species ranging from phytoplankton to fish and marine mammals (Fulton 2001). Initially, the downscaled time series derived here will be used to link scenarios of warming oceanography to Atlantis simulations that test to what extent ‘speeding up’ ecosystem-wide anabolic processes (e.g., gains due to higher growth rates) will be balanced by ‘speeding up’ catabolic processes (e.g., losses due to declines in assimilation rates or increases in predation mortality). Higher trophic level species including Southern Residents and salmon within Atlantis are affected both by their own direct physiological responses to temperature, and to the temperature-driven responses of the forage base. Results from Atlantis ecosystem model simulations will help inform recommendations for ecosystem-based management (EBM) in Puget Sound (Hamel et al. 2017), particularly when it comes to evaluating how climate change, management decisions, ecological changes, and other influences will effect Chinook salmon populations, as well as the population of one of their key predators: the endangered Southern Residents orcas (Morzaria-Luna et al. 2019, 2020a). Other Atlantis ecosystem modeling projects have used downscaled projections from GCMs to drive their models, such as the Benguela and Agulhas Currents Atlantis model (Ortega-Cisneros et al. 2018), California Current Atlantis model (Marshall et al. 2017), and the Nordic and Barents Sea Atlantis model (Hansen et al. 2019). Empirical downscaling is an effective way to obtain finer spatial scale resolutions needed for making ecosystem level analysis.