Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model

Abstract

Abstract. Marine ecosystems are increasingly stressed by human-induced changes. Marine ecosystem drivers that contribute to stressing ecosystems -including warming, acidification, deoxygenation and perturbations to biological productivity -can co-occur in space and time, but detecting their trends is complicated by the presence of noise associated with natural variability in the climate system. Here we use large initial-condition ensemble simulations with an Earth system model under a historical/RCP8.5 (representative concentration pathway 8.5) scenario over 1950-2100 to consider emergence characteristics for the four individual and combined drivers. Using a 1-standard-deviation (67 % confidence) threshold of signal to noise to define emergence with a 30-year trend window, we show that ocean acidification emerges much earlier than other drivers, namely during the 20th century over most of the global ocean. For biological productivity, the anthropogenic signal does not emerge from the noise over most of the global ocean before the end of the 21st century. The early emergence pattern for sea surface temperature in low latitudes is reversed from that of subsurface oxygen inventories, where emergence occurs earlier in the Southern Ocean. For the combined multiple-driver field, 41 % of the global ocean exhibits emergence for the 2005-2014 period, and 63 % for the 2075-2084 period. The combined multiple-driver field reveals emergence patterns by the end of this century that are relatively high over much of the Southern Ocean, North Pacific, and Atlantic, but relatively low over the tropics and the South Pacific. For the case of two drivers, the tropics including habitats of coral reefs emerges earliest, with this driven by the joint effects of acidification and warming. It is precisely in the regions with pronounced emergence characteristics where marine ecosystems may be expected to be pushed outside of their comfort zone determined by the degree of natural background variability to which they are adapted. The results underscore the importance of sustained multi-decadal observing systems for monitoring multiple ecosystems drivers.

Using present-day observations to detect when anthropogenic change forces surface ocean carbonate chemistry outside preindustrial bounds

“…2-4) compared to the CCSM3-based estimates (Cooley et al, 2009). Newer earth systems models may still underestimate the full magnitude of variability; however, they can illustrate the relative variability signal between different open ocean regions (Rodgers et al, 2015). Similar to the moored observations, Rodgers et al (2015) showed that arag variability is higher in the North Pacific and North Atlantic regions (KEO, Papa, Iceland) compared to subtropical and tropical regions (i.e., WHOTS, Stratus, BOBOA).…”

Contrasting futures for ocean and society from different anthropogenic CO 2 emissions scenarios

“…Anthropogenic trends in biogeochemical variablesnotably in pH, PCO 2 , and the saturation of calcite and aragonite-emerge from the noise of natural variability much faster than sea surface temperature (SST) (21). The combined changes in these parameters will be distinguishable from natural fluctuations in 41% of the global ocean within a decade (22), and the change in aragonite saturation over the industrial period has been more than five times greater than natural variability over the past millennium in many regions (15).…”

Inconsistent strategies to spin up models in CMIP5: implications for ocean biogeochemical model performance assessment

“…Information on spin-up protocols and model initialization is usually not taken into account in model intercomparison studies (e.g., Andrews et al, 2013;Bopp et al, 2013;Cocco et al, 2013;Frölicher et al, 2014;Gehlen et al, 2014;Keller et al, 2014;Resplandy et al, 2013Resplandy et al, , 2015Rodgers et al, 2015;. This information, if available, can only be found separately in the reference papers of individual models (e.g., Adachi et al, 2013;Arora et al, 2011;Collins et al, 2011;Dunne et al, 2013;Ilyina et al, 2013;Lindsay et al, 2014;Romanou et al, 2013;Séférian et al, 2013Séférian et al, , 2016Tjiputra et al, 2013;Vichi et al, 2011;Volodin et al, 2010;Watanabe et al, 2011;Wu et al, 2013).…”