Forecasting Global Warming

Hope, Allen; Canty, T. P.; Salawitch, R. J.; Tribett, W. R.; Bennett, Brian F.

doi:10.1007/978-3-319-46939-3_2

Cited by 9 publications

(2 citation statements)

References 105 publications

(166 reference statements)

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“…EMGC 160,000 sample members, retaining the 1,000 that minimize reduced-chi-squared between modeled and observed GMST and OHC from 1850 to 1999 Canty et al (2013), Hope et al (2017, 2020), and McBride et al (2021 FaIRv1.6.1 3,000 sample members retaining the 501 that minimize RMSE between modeled and observed 1850-2014 GMST Millar et al (2017) and FaIRv2.0.0-alpha 1,000,000 member raw ensemble, constrained with likelihood of 2010-2019 level and rate of attributable warming, calculated using the Global Warming Index methodology (Haustein et al, 2017). 5000 members randomly drawn from the constrained ensemble for use here.…”

Section: Introductionmentioning

confidence: 99%

Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections

et al. 2021

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Over the last decades, climate science has evolved rapidly across multiple expert domains.Our best tools to capture state-of-the-art knowledge in an internally self-consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850-1900, using an observationally based historical warming estimate of 0.8°C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.Plain Language Summary Our best tools to capture state-of-the-art knowledge are complex, fully coupled Earth System Models (ESMs). However, ESMs are expensive to run and no single ESM can easily produce responses which represent the full range of uncertainties. Instead, for some applications, computationally efficient reduced complexity climate models (RCMs) are used in a probabilistic setup. An example of these applications is estimating the likelihood that an emissions scenario will stay below a certain global-mean temperature change. Here we present a study (referred to as the Reduced Complexity NICHOLLS ET AL.

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Section: Introductionmentioning

confidence: 99%

Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections

et al. 2021

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“…Over recent years, many studies have highlighted the effects of global warming driven by climate change on marine environments (Doney et al 2012), which has led to a rise in the average water temperature worldwide (Vitousek 1994;Walther et al 2002;Hope et al 2017). An example of a marine ecosystem that has shown rapid responses to this kind of climate change effects is found at the sub-Antarctic Prince Edward Islands (PEI, Chown and Froneman 2009).…”

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Factors affecting trophic compositions of offshore benthic invertebrates at a sub‐Antarctic archipelago

Puccinelli

McQuaid

Ansorge

2018

Limnology & Oceanography

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Food availability is a key regulator of the distribution, metabolism, and success of benthic populations. In deep sea ecosystems, hydrodynamics and depth play fundamental roles in determining benthic food resources. Recent studies suggest that the Southern Ocean sub‐Antarctic front is shifting southward, with implications for primary production and food availability around the sub‐Antarctic Islands embedded in the Antarctic Circumpolar Current. We used fatty acid (FA) and stable isotope (SI) analyses to investigate the trophic pattern of benthic invertebrates and suspended particulate matter (SPM) at three Depths (shallow: 100 m, middle: 300 m, and deep: 600 m) in three hydrographic regions with different flow and productivity regimes around the Prince Edward Islands. Both region and depth affected the SI values of SPM, while feeding guild was the key factor influencing consumer trophic values. Depth affected the δ15N of all trophic groups and the FA compositions of suspension feeders. Deeper samples had a higher δ15N and showed a greater proportion of mono‐ and saturated fatty acids, reflecting greater remineralization of SPM and of food reaching the seafloor. Region affected the δ13C and FA values of SPM, suspension feeders and deposit feeder/scavengers, with differences between the interisland and open ocean regions. This was probably linked to the retention of nutrients and phytoplankton between the islands. Critically, the effects of depth and hydrographic region were taxon specific, indicating that long‐term responses to environmental change may have complex consequences for the feeding ecology and viability of benthic populations, with implications for the higher trophic levels that these populations support.

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