Plain Language Summary
Periods of extremely high ocean temperatures that persist for days to months, known as Marine Heatwaves (MHWs), can cause the loss of marine life and impact coastal communities and economies. Climate change is expected to drive substantial increases in the length, strength and frequency of MHWs this century. There has been less analysis, however, of the characteristics of individual MHWs, like the rate at which they develop. In this research, we examine how well climate models can simulate these characteristics and the implication for future projections. We find considerable biases in the simulation of some key MHW characteristics in parts of the ocean due to model limitations in capturing physical processes like surface winds along the equator. Most MHW characteristics like duration and total heat stress are projected to increase sharply this century, particularly for coral reefs and kelp forests, although the increases in some regions are likely overestimated due to model biases. Conversely, we project decreases in “priming” – periods of sub-lethal heat stress that help marine life prepare for heat waves. These findings identify regional errors to consider when interpreting MHW projections and can help researchers identify areas for improving model performance.
1. Introduction
Over the past few decades, marine heatwaves (MHWs) have become longer, stronger and more frequent (Frölicher et al., 2018; X. Li & Donner, n.d.; Oliver et al., 2018). These periods of anomalously high sea surface temperatures (SSTs) have severely affected marine ecosystems including changes in species distributions, mass mortality, loss of biomass, degradation of ecosystem function and decline in ecosystem services (Arias-Ortiz et al., 2018; Cheung et al., 2021; Smale et al., 2019). MHWs during the summer or warm-season, when temperatures are more likely to exceed organisms’ upper thermal tolerance, are a particular threat to habitat-forming systems in which the foundational species are vulnerable to heat stress. Warmwater coral reefs are susceptible to heat stress of as little as 1-2 °C above long-term average summer temperature, which can interrupt the symbiont relationship between coral and microalgae living in coral tissue, leading to the phenomenon known as coral bleaching. For example, more than 75% of warmwater coral reefs experienced some bleaching during 2014 and 2017, which caused mass loss of living coral and cascading effects on reef ecosystems (Hughes et al., 2017; W. J. Skirving et al., 2019; Sully et al., 2019). Kelp forests are also severely threatened by MHWs, which can cause mass mortality, changes in the food web and phase shifts to urchin-dominated systems (Arafeh-Dalmau et al., 2019; Filbee-Dexter et al., 2020; Rogers-Bennett & Catton, 2019; Smale, 2020).
It has been well documented that MHWs are likely to become more frequent, intensive and longer-lasting under climate change throughout the 21st century (Frölicher et al., 2018; Oliver et al., 2019). Most studies of the projected impacts of MHWs on marine ecosystems have focused on the frequency and intensity of MHWs, and not considered other properties which can affect marine ecosystems and organisms. For example, most projections of the effects of MHWs on coral reefs employ accumulated heat stress, a metric measuring the combination of duration and magnitude of heat stress as the indicator of coral bleaching conditions (Skirving et al., 2020), while the rate of heat stress development, which can influence mortality of coral reef fish (Genin et al., 2020), has not been assessed. In addition, there has been limited analysis of the duration of pre-MHW “priming” – a period of sub-lethal heat stress in advance of warm-season MHW development which can influence the response of corals and other marine organisms to severe heat stress (Ainsworth et al., 2016; Hilker et al., 2016). Evaluating these fine-scale MHW properties could help better understand and project how MHWs affect marine ecosystems.
Projections of MHW properties and their effects on marine ecosystems depend on the ability of models to represent the atmospheric and oceanic processes that influence MHW development and dissolution. While previous studies evaluated MHW projections with outputs from ensembles of General Circulation Models (GCMs) and Earth System Models (Frölicher et al., 2018; Oliver et al., 2019; Plecha et al., 2021), there has been less analysis of model biases in simulating the baseline characteristics of MHWs, and how such biases may affect future projections. Challenges in simulating air-sea interactions, the periodicity and diversity of El Niño / Southern Oscillation (ENSO) dynamics, and other key climate phenomena due to limits of model resolution and other factors can lead to regional biases in mean and seasonal SST (Brown et al., 2020; Guo et al., 2022; Jiang et al., 2021; G. Li & Xie, 2012; Toniazzo & Woolnough, 2014; Wang et al., 2014). These model biases could reduce accuracy of the projected MHW thermal properties and their ecological impacts (Hoeke et al., 2011; van Hooidonk & Huber, 2012). Though a large ensemble of models might present a more accurate representation of mean SSTs (Frölicher et al., 2016; Weigel et al., 2010), some of the process-derived biases in individual models cannot cancel each other out (Frölicher et al., 2018; Oliver et al., 2019). Evaluating the model biases can indicate key processes to target in model development, and identify biases to be considered when projecting local or regional ecological impacts of warm-season MHWs.
In this study, we aim to improve our understanding of the future thermal properties of warm-season MHWs by assessing their projected changes in light of historical model biases, using three CMIP6 models. First, we compare historical model simulations against observations to identify the regional biases in warm-season MHW properties, including the duration, peak intensity, accumulated heat stress, heating rate and duration of the priming period. Second, we evaluate future projections of warm-season MHW properties under three Shared Socio-Economic Pathways (SSPs). Third, we examine the MHW projections for coral reef and kelp systems worldwide considering the role of the regional model biases. We then discuss the possible physical drivers of regional model biases and disagreement between model projections.
2. Methods
2.1 Definition of warm-season MHW and the metrics characterizing its thermal properties
A warm-season MHW is defined here as a period of positive anomalies of daily SSTs or HotSpots (HS), relative to the thermal threshold known as the Maximum Monthly Mean (MMM), that represents the climatological warm-season SST and is commonly used for predicting coral bleaching. The MMM in each grid cell is calculated as the maximum from a 1985-2014 monthly mean SST climatology. To test the effects of theoretical acclimation or adaptation to warming by marine ecosystems, we repeat the analysis using a rolling climatology (Logan et al., 2014), in which the MMM is calculated based on the previous sixty year period.
We define a set of metrics for characterizing warm-season MHWs in terms of magnitude, duration, accumulated heat stress and heating rate (Table 1), following Li & Donner (2022). The duration of heat stress is described by Dc, the duration of continuous positive HS, and Dtot, the total number of days with positive HS. The “priming” period (Dp), a period of sub-lethal heat stress that might train marine organisms’ thermal tolerance (Hilker et al., 2016), is computed as the number of days from the first positive HS in a year to the onset of Dc. The accumulated heat stress over the continuous period (Dc) and for the annual total (Dtot) are described by the metrics Ac and Atot, respectively. As the total number of positive HS days (Dtot) is longer than or at minimum equal to the duration of continuous heat stress (Dc), the accumulated heat stress over all HS days is greater than or at minimum equal to that over the period of continuous heat stress. Finally, the heating rate (HRc) is the rate of warming from the start of the continuous heat stress period to the date of peak HS.