Editorial: Ecological Applications of Earth System Models and Regional Climate Models

Department of Biology, East Carolina University, Greenville, NC, United States, Department of Bioscience & Arctic Research Centre, Aarhus University, Aarhus, Denmark, Cooperative Institute for Climate, Ocean, and Ecosystem Studies (CICOES), University of Washington, Seattle, WA, United States, 4 Pacific Marine Environmental Laboratory (PMEL), National Oceanic and Atmospheric Administration (NOAA), Seattle, WA, United States, 5 Skidaway Institute of Oceanography, University of Georgia, Athens, GA, United States, Horn Porn Laboratory, University of Maryland Center for Environmental Science (UMCES), Cambridge, MD, United States


Ecological Applications of Earth System Models and Regional Climate Models
Earth system models (ESMs) that couple sub-models describing atmospheric and oceanic dynamics with models of the cryosphere and biosphere are increasingly used to project climate change. Regional climate models (RCMs) function similarly but focus on regional scales with finer resolution. Due to the inclusion of lower trophic levels in ESMs (phytoplankton, zooplankton), these models are increasingly applicable for addressing ecological questions. While ESMs and RCMs do not typically represent higher trophic levels, they provide insights through: (1) coupling with mechanistic upper trophic level models, and (2) providing outputs to parameterize statistical, habitat-based models. Both types of analyses are increasingly used to forecast the dynamics of commercially and ecologically important species for management (Payne et al., 2017;Tommasi et al., 2017;Jacox et al., 2020). There are challenges related to using ESMs to explore ecological questions due to their coarse spatial and taxonomic resolution and a lack of understanding by many ecologists of the structural differences among different ESMs (Kearney et al., 2021). This Research Topic (RT) emerged as a result of the 2014 Ecological Dissertations in Aquatic Sciences Symposium, which led to a manuscript (Asch et al., 2016) and a session at the 2019 Aquatic Sciences Meeting entitled "Ecological Applications of ESMs and RCMS." The RT includes papers from the 2019 conference session and additional contributions from the community.

REGIONS
In Figure 1, we grouped the papers in this RT based on region, trophic level, oceanic drivers of changes, and modeling approach. Five papers focused on the Northeast Pacific (California Current, Gulf of Alaska), three the Northeast Atlantic, two the Western Pacific, and three presented global analyses ( Figure 1A). Studies from the southern hemisphere were underrepresented. This is a pattern common to meta-analyses of marine global change biology (Mackas et al., 2012;Poloczanska et al., 2013). Also not represented was the Western Atlantic; these gaps might reflect regional research priorities and the availability of welldeveloped RCMs.

MODELING APPROACHES
Contributions were evenly distributed between global ESMs and RCMs ( Figure 1D). Some papers (Holdsworth et al.; Pozo Buil et al.) integrated across these models by using dynamical downscaling of global models to inform boundary conditions at the edges of RCMs. Within the ESM and RCM analyses, papers used diverse approaches to examine how physical and biogeochemical forcing impacts marine ecosystems ( Figure 1E). Some studies, such as Birkmanis et al., used species distribution modeling to statistically link environmental drivers with changes in habitat suitability. Other papers used a more mechanistic approach, such as trait-based models to investigate functional group dynamics (Petrik et al.)

INTEGRATING OCEAN OBSERVATIONS AND MODELS
In situ observations are often the most reliable measure of a variable. However, they can be costly to obtain and may lack resolution in space or time. Satellite products are useful for increasing coverage, but rely on calibration with in situ measurements (Behrenfeld and Falkowski, 1997). Although using satellite data requires additional training, integrating in situ and satellite approaches with modeling is a logical step forward and is illustrated with two papers on the North Sea. This focus on the North Sea may reflect its historical wealth of observations and heterogeneous oceanography, which is difficult for models to capture. North Sea mixing is controlled by tidal influence along the coast and convective forcing offshore, both of which affect biogeochemistry and phytoplankton biomass. Mészáros et al. integrated satellite, in situ, and model observations to describe more accurately the dynamic nature of the region and project changes in primary productivity. Biogeochemistry of the coastal North Sea is also highly influenced by terrestrial run-off. Xu et al. showed coastal and offshore sites had different historical trends in chlorophyll a, with decreasing concentrations offshore and increasing trends along the coast despite a decrease in nutrient supply. This result was counterintuitive, leading them to conclude that satellite and modeling data should be used together to reassess in situ monitoring locations.

SPECIES AND CLIMATE DRIVERS
Climate change is a multifaceted phenomenon that causes changes in diverse physico-chemical ocean characteristics. Papers covered a wide range of oceanic drivers affected by climate change that impact marine ecosystems ( Figure 1C). The most common drivers examined were changes in temperature and primary and secondary productivity. Notably missing were studies examining sea level rise, which has a substantial effect on habitat availability and coastal ecosystem functioning (Oppenheimer et al., 2019). This gap likely reflects that ESMs and RCMs operate at broader geographic scales than most analyses of sea level rise that focus on nearshore environments. Papers were nearly evenly distributed among examining nutrients and biogeochemical dynamics, primary production, and upper trophic levels ( Figure 1B). Several papers also investigated the dynamics of zooplankton, benthos, and fisheries.
The few examples focused on zooplankton may reflect that they tend to be poorly represented in both nutrient-phytoplanktonzooplankton-detritus models and upper trophic level models (Rose et al., 2010).
Six papers projected the responses of fish and crabs to future climate (Figure 1B). Use of Representative Concentration Pathway 8.5 was common across all analyses; several studies also included other Representative Concentration Pathways. These papers showed that the response of upper trophic levels to future This diagram contains pictorial representations of nutrients, primary producers, zooplankton, benthic organisms, top predators, and fisheries. Arrows depict flows between these trophic levels. If a study considered primary production mainly as a driver of ecological change at higher trophic levels, then this study is represented in this diagram as focusing on higher trophic levels rather than primary producers. (C) Drivers of ecological change examined in this RT. Temp, temperature; NPP, net primary production. (Continued) FIGURE 1 | This also includes studies examining chlorophyll concentration or secondary production; DO, dissolved oxygen; Nut, nutrients; pH, includes all studies examining ocean acidification and the carbonate chemistry system; Light, includes studies examining changes in turbidity or euphotic depth; Sal, salinity; Strat, stratification. This includes studies examining changes in mixed layer depth (MLD) since MLD is often measured as a function of stratification; Mixing, includes upwelling and mixing by eddies; Physio, includes physiological and metabolic rates; Econ, economic drivers; Bathy, bathymetry. If phytoplankton dynamics are examined by a study as the primary ecological variable of interest, then primary producers are classified as a response variable and not the underlying driver of change. (D) Venn diagram indicating how many studies utilized regional climate models (RCMs) and global earth system models (ESMs). The intersection between these categories includes dynamical downscaling studies that used outputs from ESMs to simulate future changes in regional climate. Studies that utilized RCMs but where downscaling was not a major focus of the manuscript were placed solely in the RCM category. Overall, RT papers were diverse in terms of modeling approach, focal ecosystems, and species examined. The research approaches described provide examples of how ESMs and RCMs can be coupled to models to address ecological questions related to climate change. Some missing topics include paleo-ecological studies, data assimilation, integration of data from autonomous observational platforms (Chai et al., 2020), and examination of climate variability. The recent publication of the 6th Assessment Report by the Intergovernmental Panel on Climate Change will provide additional impetus to continue the application of ESMs and RCMs to answer pressing ecological questions.

AUTHOR CONTRIBUTIONS
All authors contributed to writing and editing this editorial.