references.bib

@article{DRENKARD2021,
    author = {Drenkard, Elizabeth J and Stock, Charles and Ross, Andrew C and Dixon, Keith W and Adcroft, Alistair and Alexander, Michael and Balaji, Venkatramani and Bograd, Steven J and Butenschön, Momme and Cheng, Wei and Curchitser, Enrique and Lorenzo, Emanuele Di and Dussin, Raphael and Haynie, Alan C and Harrison, Matthew and Hermann, Albert and Hollowed, Anne and Holsman, Kirstin and Holt, Jason and Jacox, Michael G and Jang, Chan Joo and Kearney, Kelly A and Muhling, Barbara A and Buil, Mercedes Pozo and Saba, Vincent and Sandø, Anne Britt and Tommasi, Désirée and Wang, Muyin},
    title = {Next-generation regional ocean projections for living marine resource management in a changing climate},
    journal = {ICES Journal of Marine Science},
    volume = {78},
    number = {6},
    pages = {1969-1987},
    year = {2021},
    month = {06},
    abstract = {Efforts to manage living marine resources (LMRs) under climate change need projections of future ocean conditions, yet most global climate models (GCMs) poorly represent critical coastal habitats. GCM utility for LMR applications will increase with higher spatial resolution but obstacles including computational and data storage costs, obstinate regional biases, and formulations prioritizing global robustness over regional skill will persist. Downscaling can help address GCM limitations, but significant improvements are needed to robustly support LMR science and management. We synthesize past ocean downscaling efforts to suggest a protocol to achieve this goal. The protocol emphasizes LMR-driven design to ensure delivery of decision-relevant information. It prioritizes ensembles of downscaled projections spanning the range of ocean futures with durations long enough to capture climate change signals. This demands judicious resolution refinement, with pragmatic consideration for LMR-essential ocean features superseding theoretical investigation. Statistical downscaling can complement dynamical approaches in building these ensembles. Inconsistent use of bias correction indicates a need for objective best practices. Application of the suggested protocol should yield regional ocean projections that, with effective dissemination and translation to decision-relevant analytics, can robustly support LMR science and management under climate change.},
    issn = {1054-3139},
    doi = {10.1093/icesjms/fsab100},
    url = {https://doi.org/10.1093/icesjms/fsab100},
    eprint = {https://academic.oup.com/icesjms/article-pdf/78/6/1969/40489272/fsab100.pdf},
}






@article{STOCK2011,
title = {On the use of IPCC-class models to assess the impact of climate on Living Marine Resources},
journal = {Progress in Oceanography},
volume = {88},
number = {1},
pages = {1-27},
year = {2011},
issn = {0079-6611},
doi = {https://doi.org/10.1016/j.pocean.2010.09.001},
url = {https://www.sciencedirect.com/science/article/pii/S0079661110001096},
author = {Charles A. Stock and Michael A. Alexander and Nicholas A. Bond and Keith M. Brander and William W.L. Cheung and Enrique N. Curchitser and Thomas L. Delworth and John P. Dunne and Stephen M. Griffies and Melissa A. Haltuch and Jonathan A. Hare and Anne B. Hollowed and Patrick Lehodey and Simon A. Levin and Jason S. Link and Kenneth A. Rose and Ryan R. Rykaczewski and Jorge L. Sarmiento and Ronald J. Stouffer and Franklin B. Schwing and Gabriel A. Vecchi and Francisco E. Werner},
abstract = {The study of climate impacts on Living Marine Resources (LMRs) has increased rapidly in recent years with the availability of climate model simulations contributed to the assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Collaboration between climate and LMR scientists and shared understanding of critical challenges for such applications are essential for developing robust projections of climate impacts on LMRs. This paper assesses present approaches for generating projections of climate impacts on LMRs using IPCC-class climate models, recommends practices that should be followed for these applications, and identifies priority developments that could improve current projections. Understanding of the climate system and its representation within climate models has progressed to a point where many climate model outputs can now be used effectively to make LMR projections. However, uncertainty in climate model projections (particularly biases and inter-model spread at regional to local scales), coarse climate model resolution, and the uncertainty and potential complexity of the mechanisms underlying the response of LMRs to climate limit the robustness and precision of LMR projections. A variety of techniques including the analysis of multi-model ensembles, bias corrections, and statistical and dynamical downscaling can ameliorate some limitations, though the assumptions underlying these approaches and the sensitivity of results to their application must be assessed for each application. Developments in LMR science that could improve current projections of climate impacts on LMRs include improved understanding of the multi-scale mechanisms that link climate and LMRs and better representations of these mechanisms within more holistic LMR models. These developments require a strong baseline of field and laboratory observations including long time series and measurements over the broad range of spatial and temporal scales over which LMRs and climate interact. Priority developments for IPCC-class climate models include improved model accuracy (particularly at regional and local scales), inter-annual to decadal-scale predictions, and the continued development of earth system models capable of simulating the evolution of both the physical climate system and biosphere. Efforts to address these issues should occur in parallel and be informed by the continued application of existing climate and LMR models.}
}

@article{SCHOEMAN2023,
title = {Demystifying global climate models for use in the life sciences},
journal = {Trends in Ecology & Evolution},
volume = {38},
number = {9},
pages = {843-858},
year = {2023},
issn = {0169-5347},
doi = {https://doi.org/10.1016/j.tree.2023.04.005},
url = {https://www.sciencedirect.com/science/article/pii/S016953472300085X},
author = {David S. Schoeman and Alex Sen Gupta and Cheryl S. Harrison and Jason D. Everett and Isaac Brito-Morales and Lee Hannah and Laurent Bopp and Patrick R. Roehrdanz and Anthony J. Richardson},
}


@article{BRODIE2022,
author = {Brodie, Stephanie and Smith, James A. and Muhling, Barbara A. and Barnett, Lewis A. K. and Carroll, Gemma and Fiedler, Paul and Bograd, Steven J. and Hazen, Elliott L. and Jacox, Michael G. and Andrews, Kelly S. and Barnes, Cheryl L. and Crozier, Lisa G. and Fiechter, Jerome and Fredston, Alexa and Haltuch, Melissa A. and Harvey, Chris J. and Holmes, Elizabeth and Karp, Melissa A. and Liu, Owen R. and Malick, Michael J. and Pozo Buil, Mercedes and Richerson, Kate and Rooper, Christopher N. and Samhouri, Jameal and Seary, Rachel and Selden, Rebecca L. and Thompson, Andrew R. and Tommasi, Desiree and Ward, Eric J. and Kaplan, Isaac C.},
title = {Recommendations for quantifying and reducing uncertainty in climate projections of species distributions},
journal = {Global Change Biology},
volume = {28},
number = {22},
pages = {6586-6601},
keywords = {artificial intelligence, climate change, earth system models, extrapolation, fisheries, machine learning, species distribution models, virtual species},
doi = {https://doi.org/10.1111/gcb.16371},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.16371},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.16371},
abstract = {Abstract Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change—rather than accurately predict specific outcomes—it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling. The simulations encompass marine species with different functional traits and ecological preferences to more broadly address resource manager and fishery stakeholder needs, and provide a simulated true state with which to evaluate projections. We present our results relative to the degree of environmental extrapolation from historical conditions, which helps facilitate interpretation by ecological modelers working in diverse systems. We found uncertainty associated with species distribution models can exceed uncertainty generated from diverging earth system models (up to 70\% of total uncertainty by 2100), and that this result was consistent across species traits. Species distribution model uncertainty increased through time and was primarily related to the degree to which models extrapolated into novel environmental conditions but moderated by how well models captured the underlying dynamics driving species distributions. The predictive power of simulated species distribution models remained relatively high in the first 30 years of projections, in alignment with the time period in which stakeholders make strategic decisions based on climate information. By understanding sources of uncertainty, and how they change at different forecast horizons, we provide recommendations for projecting species distribution models under global climate change.},
year = {2022}
}


@article{MORLEY2020,
    author = {Morley, James W and Frölicher, Thomas L and Pinsky, Malin L},
    title = {Characterizing uncertainty in climate impact projections: a case study with seven marine species on the North American continental shelf},
    journal = {ICES Journal of Marine Science},
    volume = {77},
    number = {6},
    pages = {2118-2133},
    year = {2020},
    month = {08},
    abstract = {Projections of climate change impacts on living resources are being conducted frequently, and the goal is often to inform policy. Species projections will be more useful if uncertainty is effectively quantified. However, few studies have comprehensively characterized the projection uncertainty arising from greenhouse gas scenarios, Earth system models (ESMs), and both structural and parameter uncertainty in species distribution modelling. Here, we conducted 8964 unique 21st century projections for shifts in suitable habitat for seven economically important marine species including American lobster, Pacific halibut, Pacific ocean perch, and summer flounder. For all species, both the ESM used to simulate future temperatures and the niche modelling approach used to represent species distributions were important sources of uncertainty, while variation associated with parameter values in niche models was minor. Greenhouse gas emissions scenario contributed to uncertainty for projections at the century scale. The characteristics of projection uncertainty differed among species and also varied spatially, which underscores the need for improved multi-model approaches with a suite of ESMs and niche models forming the basis for uncertainty around projected impacts. Ensemble projections show the potential for major shifts in future distributions. Therefore, rigorous future projections are important for informing climate adaptation efforts.},
    issn = {1054-3139},
    doi = {10.1093/icesjms/fsaa103},
    url = {https://doi.org/10.1093/icesjms/fsaa103},
    eprint = {https://academic.oup.com/icesjms/article-pdf/77/6/2118/34162752/fsaa103.pdf},
}



@article{CHEUNG2016,
    author = {Cheung, William W. L. and Frölicher, Thomas L. and Asch, Rebecca G. and Jones, Miranda C. and Pinsky, Malin L. and Reygondeau, Gabriel and Rodgers, Keith B. and Rykaczewski, Ryan R. and Sarmiento, Jorge L. and Stock, Charles and Watson, James R.},
    title = {Building confidence in projections of the responses of living marine resources to climate change},
    journal = {ICES Journal of Marine Science},
    volume = {73},
    number = {5},
    pages = {1283-1296},
    year = {2016},
    month = {01},
    abstract = {The Fifth Assessment Report of the Intergovernmental Panel on Climate Change highlights that climate change and ocean acidification are challenging the sustainable management of living marine resources (LMRs). Formal and systematic treatment of uncertainty in existing LMR projections, however, is lacking. We synthesize knowledge of how to address different sources of uncertainty by drawing from climate model intercomparison efforts. We suggest an ensemble of available models and projections, informed by observations, as a starting point to quantify uncertainties. Such an ensemble must be paired with analysis of the dominant uncertainties over different spatial scales, time horizons, and metrics. We use two examples: (i) global and regional projections of Sea Surface Temperature and (ii) projection of changes in potential catch of sablefish (Anoplopoma fimbria) in the 21st century, to illustrate this ensemble model approach to explore different types of uncertainties. Further effort should prioritize understanding dominant, undersampled dimensions of uncertainty, as well as the strategic collection of observations to quantify, and ultimately reduce, uncertainties. Our proposed framework will improve our understanding of future changes in LMR and the resulting risk of impacts to ecosystems and the societies under changing ocean conditions.},
    issn = {1054-3139},
    doi = {10.1093/icesjms/fsv250},
    url = {https://doi.org/10.1093/icesjms/fsv250},
    eprint = {https://academic.oup.com/icesjms/article-pdf/73/5/1283/31230945/fsv250.pdf},
}



@article{THUILLER2019,
	title = {Uncertainty in ensembles of global biodiversity scenarios},
	volume = {10},
	issn = {2041-1723},
	url = {https://doi.org/10.1038/s41467-019-09519-w},
	doi = {10.1038/s41467-019-09519-w},
	abstract = {While there is a clear demand for scenarios that provide alternative states in biodiversity with respect to future emissions, a thorough analysis and communication of the associated uncertainties is still missing. Here, we modelled the global distribution of {\textasciitilde}11,500 amphibian, bird and mammal species and project their climatic suitability into the time horizon 2050 and 2070, while varying the input data used. By this, we explore the uncertainties originating from selecting species distribution models (SDMs), dispersal strategies, global circulation models (GCMs), and representative concentration pathways (RCPs). We demonstrate the overwhelming influence of SDMs and RCPs on future biodiversity projections, followed by dispersal strategies and GCMs. The relative importance of each component varies in space but also with the selected sensitivity metrics and with species’ range size. Overall, this means using multiple SDMs, RCPs, dispersal assumptions and GCMs is a necessity in any biodiversity scenario assessment, to explicitly report associated uncertainties.},
	number = {1},
	journal = {Nature Communications},
	author = {Thuiller, Wilfried and Guéguen, Maya and Renaud, Julien and Karger, Dirk N. and Zimmermann, Niklaus E.},
	month = mar,
	year = {2019},
	pages = {1446},
}



@article{TAYLOR2001,
author = {Taylor, Karl E.},
title = {Summarizing multiple aspects of model performance in a single diagram},
journal = {Journal of Geophysical Research: Atmospheres},
volume = {106},
number = {D7},
pages = {7183-7192},
doi = {https://doi.org/10.1029/2000JD900719},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2000JD900719},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2000JD900719},
abstract = {A diagram has been devised that can provide a concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference, and the ratio of their variances. Although the form of this diagram is general, it is especially useful in evaluating complex models, such as those used to study geophysical phenomena. Examples are given showing that the diagram can be used to summarize the relative merits of a collection of different models or to track changes in performance of a model as it is modified. Methods are suggested for indicating on these diagrams the statistical significance of apparent differences and the degree to which observational uncertainty and unforced internal variability limit the expected agreement between model-simulated and observed behaviors. The geometric relationship between the statistics plotted on the diagram also provides some guidance for devising skill scores that appropriately weight among the various measures of pattern correspondence.},
year = {2001}
}



@article{BI2020,
	title = {Configuration and spin-up of {ACCESS}-{CM2}, the new generation {Australian} {Community} {Climate} and {Earth} {System} {Simulator} {Coupled} {Model}},
	volume = {70},
	url = {https://doi.org/10.1071/ES19040},
	number = {1},
	journal = {Journal of Southern Hemisphere Earth Systems Science},
	author = {Bi, Daohua and Dix, Martin and Marsland, Simon and O'Farrell, Siobhan and Sullivan, Arnold and Bodman, Roger and Law, Rachel and Harman, Ian and Srbinovsky, Jhan and Rashid, Harun A. and Dobrohotoff, Peter and Mackallah, Chloe and Yan, Hailin and Hirst, Anthony and Savita, Abhishek and Dias, Fabio Boeira and Woodhouse, Matthew and Fiedler, Russell and Heerdegen, Aidan},
	year = {2020},
	pages = {225--251},
}



@article{BOUCHER2020,
author = {Boucher, Olivier and Servonnat, Jérôme and Albright, Anna Lea and Aumont, Olivier and Balkanski, Yves and Bastrikov, Vladislav and Bekki, Slimane and Bonnet, Rémy and Bony, Sandrine and Bopp, Laurent and Braconnot, Pascale and Brockmann, Patrick and Cadule, Patricia and Caubel, Arnaud and Cheruy, Frederique and Codron, Francis and Cozic, Anne and Cugnet, David and D'Andrea, Fabio and Davini, Paolo and de Lavergne, Casimir and Denvil, Sébastien and Deshayes, Julie and Devilliers, Marion and Ducharne, Agnes and Dufresne, Jean-Louis and Dupont, Eliott and Éthé, Christian and Fairhead, Laurent and Falletti, Lola and Flavoni, Simona and Foujols, Marie-Alice and Gardoll, Sébastien and Gastineau, Guillaume and Ghattas, Josefine and Grandpeix, Jean-Yves and Guenet, Bertrand and Guez, Lionel, E. and Guilyardi, Eric and Guimberteau, Matthieu and Hauglustaine, Didier and Hourdin, Frédéric and Idelkadi, Abderrahmane and Joussaume, Sylvie and Kageyama, Masa and Khodri, Myriam and Krinner, Gerhard and Lebas, Nicolas and Levavasseur, Guillaume and Lévy, Claire and Li, Laurent and Lott, François and Lurton, Thibaut and Luyssaert, Sebastiaan and Madec, Gurvan and Madeleine, Jean-Baptiste and Maignan, Fabienne and Marchand, Marion and Marti, Olivier and Mellul, Lidia and Meurdesoif, Yann and Mignot, Juliette and Musat, Ionela and Ottlé, Catherine and Peylin, Philippe and Planton, Yann and Polcher, Jan and Rio, Catherine and Rochetin, Nicolas and Rousset, Clément and Sepulchre, Pierre and Sima, Adriana and Swingedouw, Didier and Thiéblemont, Rémi and Traore, Abdoul Khadre and Vancoppenolle, Martin and Vial, Jessica and Vialard, Jérôme and Viovy, Nicolas and Vuichard, Nicolas},
title = {Presentation and Evaluation of the IPSL-CM6A-LR Climate Model},
journal = {Journal of Advances in Modeling Earth Systems},
volume = {12},
number = {7},
pages = {e2019MS002010},
keywords = {IPSL-CM6A-LR, climate model, climate metrics, CMIP6, climate sensitivity},
doi = {https://doi.org/10.1029/2019MS002010},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS002010},
eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019MS002010},
note = {e2019MS002010 10.1029/2019MS002010},
abstract = {Abstract This study presents the global climate model IPSL-CM6A-LR developed at Institut Pierre-Simon Laplace (IPSL) to study natural climate variability and climate response to natural and anthropogenic forcings as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, and wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double Intertropical Convergence Zone [ITCZ], frequency of midlatitude wintertime blockings, and El Niño–Southern Oscillation [ENSO] dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL-CM5A-LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850–2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed.},
year = {2020}
}