text:
@book{vanHeerden1998,
address = {Pretoria, South Africa},
author = {{Van Heerden}, Johan and Hurry, Lynn},
publisher = {Collegium},
title = {{Southern Africa's weather patterns: An introductory guide}},
year = {1998}
}
@article{Rosenzweig2008,
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Rosenzweig, Cynthia and Karoly, David and Vicarelli, Marta and Neofotis, Peter and Wu, Qigang and Casassa, Gino and Menzel, Annette and Root, Terry L. and Estrella, Nicole and Seguin, Bernard and Tryjanowski, Piotr and Liu, Chunzhen and Rawlins, Samuel and Imeson, Anton},
doi = {10.1038/nature06937},
eprint = {arXiv:1011.1669v3},
isbn = {0028-0836},
issn = {0028-0836},
journal = {Nature},
number = {7193},
pages = {353--357},
pmid = {18480817},
title = {{Attributing physical and biological impacts to anthropogenic climate change}},
url = {http://www.nature.com/doifinder/10.1038/nature06937},
volume = {453},
year = {2008}
}
@phdthesis{Cooper2014,
author = {Cooper, Kyle Francis},
pages = {59},
school = {University of Cape Town},
title = {{Evaluating global ocean reanalysis systems for the greater Agulhas Current System}},
type = {Masters Thesis},
year = {2014}
}
@article{Barsugli1998,
abstract = {Starting from the assumption that the atmosphere is the primary source of variability internal to the midlatitude atmosphere-ocean system on intraseasonal to interannual timescales, the authors construct a simple stochastically forced, one-dimensional, linear, coupled energy balance model. The coupled system is then dissected into partially coupled and uncoupled systems in order to quantify the effects of coupling. The simplicity of the model allows for analytic evaluation of many quantities of interest, including power spectra, total variance, lag covariance between atmosphere and ocean, and surface flux spectra. The model predicts that coupling between the atmosphere and ocean in the midlatitudes will enhance the variance in both media and will decrease the energy flux between the atmosphere and the ocean. The model also demonstrates that specification of historical midlatitude sea surface temperature anomalies as a boundary condition for an atmospheric model will not generally lead to a correct simulation of low-frequency atmospheric thermal variance. This model provides a simple conceptual framework for understanding the basic aspects of midlatitude coupled variability. Given the simplicity of the model, it agrees well with numerical simulations using a two-level atmospheric general circulation model coupled to a slab mixed layer ocean. The simple model results are also qualitatively consistent with the results obtained in several other studies in which investigators coupled realistic atmospheric general circulation models to ocean models of varying complexity. This suggests that the experimental design of an atmospheric model coupled to a mixed layer ocean model would provide a reasonable null hypothesis against which to rest for the presence of distinctive decadal variability.},
author = {Barsugli, Joseph J. and Battisti, David S.},
doi = {10.1175/1520-0469(1998)055<0477:TBEOAO>2.0.CO;2},
isbn = {0022-4928},
issn = {0022-4928},
journal = {Journal of the Atmospheric Sciences},
number = {4},
pages = {477--493},
title = {{The Basic Effects of Atmosphere–Ocean Thermal Coupling on Midlatitude Variability*}},
volume = {55},
year = {1998}
}
@article{Scavia2002,
author = {Scavia, Donald and Field, John C. and Boesch, Donald F. and Buddemeier, Robert W. and Burkett, Virginia and Cayan, Daniel R. and Fogarty, Michael and Harwell, Mark A. and Howarth, Robert W. and Mason, Curt and Reed, Denise J. and Royer, Thomas C. and Sallenger, Asbury H. and Titus, James G.},
doi = {10.1007/BF02691304},
issn = {0160-8347},
journal = {Estuaries},
month = {apr},
number = {2},
pages = {149--164},
title = {{Climate change impacts on U.S. Coastal and Marine Ecosystems}},
url = {http://link.springer.com/10.1007/BF02691304},
volume = {25},
year = {2002}
}
@article{Ambroise2000,
abstract = {This paper presents a new method for segmenting multispectral satellite images. The proposed method is unsupervised and consists of two steps. During the first step the pixels of a learning set are summarized by a set of codebook vectors using a Probabilistic Self-Organizing Map (PSOM, Statistique et methodes neuronales, Dunod, Paris, 1997). In a second step the codebook vectors of the map are clustered using Agglomerative Hierarchical Clustering (AHC, Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge, 1996). Each pixel takes the label of its nearest codebook vector. A practical application to Meteosat images illustrates the relevance of our approach.},
author = {Ambroise, Christophe and S??ze, Geni??ve and Badran, Fouad and Thiria, Sylvie},
doi = {10.1016/S0925-2312(99)00141-1},
isbn = {0925-2312},
issn = {09252312},
journal = {Neurocomputing},
keywords = {Cloud classification,Hierarchical clustering,Image segmentation,Kohonen maps},
number = {1-4},
pages = {47--52},
title = {{Hierarchical clustering of self-organizing maps for cloud classification}},
volume = {30},
year = {2000}
}
@article{Gibson2016a,
abstract = {New insight is provided regarding 21st century projections of synoptic scale atmospheric circulation over the New Zealand region. Daily mean sea level pressure patterns from a number of general circulation models (GCMs) participating in the 5th Coupled Model Intercomparison Project (CMIP5) were analysed through a self-organizing map (SOM) technique. In terms of simulating the synoptic climatology found in reanalyses, an initial model evaluation revealed quite substantial differences in model skill. Generally, those models with relatively high horizontal atmospheric resolution skilfully simulated the historical frequency and mean lifetime of large scale synoptic patterns over this region. Analysing future projections under this smaller subset of better-performing models indicated robust and quite substantial increases in the annual frequency of widespread anticyclonic conditions over New Zealand, accompanied by a general decrease in widespread low pressure. These projected changes in frequency of synoptic occurrences become the most pronounced towards the end of the 21st century and for winter, whereas changes in the average synoptic pattern lifetime remain small. While not robust for all models in this subset, significant 21st century trends in the intensification of geostrophic westerly flow conditions within certain synoptic patterns were also found. Linking synoptic patterns to surface climate variables has also provided evidence that these circulation changes may collectively manifest as a significant non-uniform climatic change across New Zealand. These findings should encourage further focused studies on circulation-relevant applications and impacts in a climate change context.},
author = {Gibson, Peter B. and Perkins-Kirkpatrick, Sarah E. and Renwick, James A.},
doi = {10.1002/joc.4604},
isbn = {1097-0088},
issn = {10970088},
journal = {International Journal of Climatology},
keywords = {CMIP5,circulation,climate change,climate impacts,projections,weather typing},
number = {12},
pages = {3934--3948},
title = {{Projected changes in synoptic weather patterns over New Zealand examined through self-organizing maps}},
volume = {36},
year = {2016}
}
@article{Lutjeharms2000a,
author = {Lutjeharms, J.R.E and Cooper, J and Roberts, M},
doi = {10.1016/S0278-4343(99)00092-8},
issn = {02784343},
journal = {Continental Shelf Research},
month = {apr},
number = {7},
pages = {737--761},
title = {{Upwelling at the inshore edge of the Agulhas Current}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0278434399000928},
volume = {20},
year = {2000}
}
@article{Gunter1951,
abstract = {The shallowness of Texas bay waters, their practically landlocked condition and the rapidity with which cold northers strike the coast are factors making the marine life of this area particularly subject to mortality from cold waves every few years. Since 1940 slight mortality was caused by cold waves in 1941 and 1949. Considerable damage was caused in 1947. The 1940 and 1951 episodes were catastrophic. Probably the 1940 instance was the most severe although the 1951 freeze was the hardest. Estimates made by biologists of the Game, Fish and Oyster Commission of the amount of fish killed in 1951 range from 60 million to 90 million pounds. It seems that if the damaging cold waves are preceded by other freezes, their destructive effect is lessened. Some animals escape to deeper water if the onset of the cold wave is slow and the rapidity with which the northers strike is a factor influencing the amount of mortality.},
author = {Gunter, Gordon},
isbn = {0012-9658},
journal = {Ecology},
number = {4},
pages = {731--736},
title = {{Destruction of fishes and other organisms on the south Texas coast by the cold wave of January 28-February 3, 1951}},
volume = {32},
year = {1951}
}
@misc{Oliver2017,
author = {Oliver, E. C. J. and Lago, V. and Holbrook, N. J. and Ling, S. D. and Mundy, C. N. and Hobday, A. J.},
doi = {10.4226/77/587e97d9b2bf9},
institution = {Institute for Marine and Antarctic Studies},
publisher = {University of Tasmania},
title = {{Eastern Tasmania Marine Heatwave Atlas}},
url = {http://metadata.imas.utas.edu.au/geonetwork/srv/eng/metadata.show?uuid=20188863-0af6-4032-98f8-def671cdaa58},
year = {2017}
}
@article{Unal2003,
abstract = {Climate zones of Turkey are redefined by using the mathematical methodology of cluster analysis. Data from 113 climate stations for temperatures (mean, maximum and minimum) and total precipitation from 1951 to 1998 are used after standardizing with zero mean and unit variance, to confirm that all variables are weighted equally in the cluster analysis. Hierarchical cluster analysis is chosen to perform the regionalization. Five different techniques were applied initially to decide the most suitable method for the region. Stability of the clusters is also tested. It is decided that Ward's method is the most likely to yield acceptable results in this particular case, as is often the case in climatological research. Seven different climate zones are found, as in conventional climate zones, but with considerable differences at the boundaries. Copyright {\textcopyright} 2003 Royal Meteorological Society},
author = {Unal, Yurdanur and Kindap, Tayfun and Karaca, Mehmet},
doi = {10.1002/joc.910},
isbn = {1097-0088},
issn = {08998418},
journal = {International Journal of Climatology},
keywords = {Climate zones,Cluster analysis,Turkey},
number = {9},
pages = {1045--1055},
title = {{Redefining the climate zones of Turkey using cluster analysis}},
volume = {23},
year = {2003}
}
@article{Stachowicz2002,
author = {Stachowicz, J. J. and Terwin, J. R. and Whitlatch, R. B. and Osman, R. W.},
doi = {10.1073/pnas.242437499},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
month = {nov},
number = {24},
pages = {15497--15500},
title = {{Linking climate change and biological invasions: Ocean warming facilitates nonindigenous species invasions}},
url = {http://www.pnas.org/cgi/doi/10.1073/pnas.242437499},
volume = {99},
year = {2002}
}
@article{Ramos2001,
abstract = {One of the main characteristics of the Mediterranean climate is the irregular distribution of rainfalls and its high variability from year to year, which makes it difficult to know if Mediterranean rainfalls are changing. In this work, a combination of divisive (K-means algorithm) and hierarchical (average and Ward's methods) cluster techniques were used to analyse rainfall distribution patterns over the year and their changes over time. The study was performed using daily data (1889-1999) from the Alt Pened??s region (NE Spain), and annual, spring and autumn rainfall were analysed. The variables included in the cluster analysis were spring and autumn rainfalls, which represent on average 70{\%} of the annual rainfall. The K-means and Ward's methods led to similar classifications of the observations, whereas the average method had a lesser discrimination power. However, the combination of all of them was useful in the identification of the rainfall distribution patterns over the years and their changes with time. Patterns including dry springs and wet autumns have increased during the last decade, although any consistent trend in annual rainfall was not found. ?? 2001 Elsevier Science B.V. All rights reserved.},
author = {Ramos, M. C.},
doi = {10.1016/S0169-8095(01)00065-5},
isbn = {0177-798X},
issn = {01698095},
journal = {Atmospheric Research},
keywords = {Clusters,Mediterranean climate,Rainfall distribution patterns,Rainfall trends},
number = {2},
pages = {123--138},
title = {{Divisive and hierarchical clustering techniques to analyse variability of rainfall distribution patterns in a Mediterranean region}},
volume = {57},
year = {2001}
}
@article{Minchin1987,
author = {Minchin, Peter R},
doi = {10.1007/BF00038690},
issn = {0042-3106},
journal = {Vegetatio},
keywords = {detrended correspondence analysis,gaussian ordination,indirect gradient analysis,multidimensional scaling,non-metric,ordination,principal co-ordinates analysis,principal components analysis,robustness,simulated data},
month = {apr},
number = {1-3},
pages = {89--107},
title = {{An evaluation of the relative robustness of techniques for ecological ordination}},
url = {http://link.springer.com/10.1007/BF00038690},
volume = {69},
year = {1987}
}
@article{Hewitson2002a,
abstract = {Self organizing maps (SOMs) are used to locate archetypal points that describe the multi-dimensional distribution function of a gridded sea level pressure data set for the northeast United States. These points-nodes on the SOM-identify the primary features of the synoptic-scale circulation over the region. In effect, the nodes represent a non-linear distribution of overlapping, non-discreet, circulation types. The circulation patterns are readily visualized in a 2-dimensional array (the SOM) that places similar types adjacent to one another and very different types far apart in the SOM space. The SOM is used to describe synoptic circulation changes over time, and to relate the circulation to January station precipitation data (for State College, Pennsylvania) in the center of the domain. The paper focuses on the methodology; however, the analysis suggests that circulation systems that promote precipitation have decreased over the last 40 yr-although January precipitation at State College has actually increased. Further analysis with the SOM indicates that this is due to a change in precipitation characteristics of the synoptic-scale circulation features, rather than to their frequency of occurrence.},
author = {Hewitson, B. C. and Crane, R. G.},
doi = {10.3354/cr022013},
isbn = {0936577X (ISSN)},
issn = {0936577X},
journal = {Climate Research},
keywords = {Climate change,Downscaling,Self-organizing maps,Synoptic classification,Synoptic climatology},
number = {1},
pages = {13--26},
title = {{Self-organizing maps: Applications to synoptic climatology}},
volume = {22},
year = {2002}
}
@article{Schlegel2017,
author = {Schlegel, Robert W and Oliver, Eric C J and Wernberg, Thomas and Smit, Albertus J},
doi = {10.1016/j.pocean.2017.01.004},
file = {:home/rws/Documents/PhD/Articles/Schlegel et al 2017 cooccurrence.pdf:pdf},
issn = {00796611},
journal = {Progress in Oceanography},
keywords = {climate change,co-occurrence,coastal,extreme events,in situ data,remotely-sensed sst},
pages = {189--205},
publisher = {Elsevier Ltd},
title = {{Nearshore and offshore co-occurrence of marine heatwaves and cold-spells}},
url = {http://dx.doi.org/10.1016/j.pocean.2017.01.004},
volume = {151},
year = {2017}
}
@article{Akinduko2016,
author = {Akinduko, Ayodeji A. and Mirkes, Evgeny M. and Gorban, Alexander N.},
doi = {10.1016/j.ins.2015.10.013},
issn = {00200255},
journal = {Information Sciences},
month = {oct},
pages = {213--221},
title = {{SOM: Stochastic initialization versus principal components}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0020025515007318},
volume = {364-365},
year = {2016}
}
@article{Blamey2015,
author = {Blamey, Laura K. and Shannon, Lynne J. and Bolton, John J. and Crawford, Robert J.M. and Dufois, Francois and Evers-King, Hayley and Griffiths, Charles L. and Hutchings, Laurence and Jarre, Astrid and Rouault, Mathieu and Watermeyer, Katherine E. and Winker, Henning},
doi = {10.1016/j.jmarsys.2014.11.006},
issn = {09247963},
journal = {Journal of Marine Systems},
month = {apr},
pages = {9--29},
title = {{Ecosystem change in the southern Benguela and the underlying processes}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S092479631400311X},
volume = {144},
year = {2015}
}
@article{Kumar2011,
abstract = {Identification of geographic ecoregions has long been of interest to environmental scientists and ecologists for identifying regions of similar ecological and environmental conditions. Such classifications are important for predicting suitable species ranges, for stratification of ecological samples, and to help prioritize habitat preservation and remediation efforts. Hargrove and Hoffman [1, 2] have developed geographical spatio-temporal clustering algorithms and codes and have successfully applied them to a variety of environmental science domains, including ecological regionalization; environmental monitoring network design; analysis of satellite-, airborne-, and ground-based remote sensing, and climate model-model and model-measurement intercomparison. With the advances in state-of-the-art satellite remote sensing and climate models, observations and model outputs are available at increasingly high spatial and temporal resolutions. Long time series of these high resolution datasets are extremely large in size and growing. Analysis and knowledge extraction from these large datasets are not just algorithmic and ecological problems, but also pose a complex computational problem. This paper focuses on the development of a massively parallel multivariate geographical spatio-temporal clustering code for analysis of very large datasets using tens of thousands processors on one of the fastest supercomputers in the world. ?? 2011 Published by Elsevier Ltd.},
author = {Kumar, Jitendra and Mills, Richard T. and Hoffman, Forrest M. and Hargrove, William W.},
doi = {10.1016/j.procs.2011.04.173},
isbn = {1877-0509},
issn = {18770509},
journal = {Procedia Computer Science},
keywords = {Data mining,Ecoregionalization,High performance computing,K-means clustering},
pages = {1602--1611},
title = {{Parallel k-Means Clustering for Quantitative Ecoregion Delineation Using Large Data Sets}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1877050911002316},
volume = {4},
year = {2011}
}
@book{Luning1990,
address = {New York (USA)},
author = {L{\"{u}}nning, K},
booktitle = {John Wiley and Sons Inc. USA},
isbn = {04-716-24349},
publisher = {Wiley},
title = {{Seaweds: their environment, biogeography and ecophysiology.}},
year = {1990}
}
@book{IPCC2014,
  title={Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change},
  author={Pachauri, Rajendra K and Allen, Myles R and Barros, Vicente R and Broome, John and Cramer, Wolfgang and Christ, Renate and Church, John A and Clarke, Leon and Dahe, Qin and Dasgupta, Purnamita and others},
  year={2014},
  publisher={IPCC}
}
@article{Dee2011,
author = {Dee, D. P. and Uppala, S. M. and Simmons, A. J. and Berrisford, P. and Poli, P. and Kobayashi, S. and Andrae, U. and Balmaseda, M. A. and Balsamo, G. and Bauer, P. and Bechtold, P. and Beljaars, A. C. M. and van de Berg, L. and Bidlot, J. and Bormann, N. and Delsol, C. and Dragani, R. and Fuentes, M. and Geer, A. J. and Haimberger, L. and Healy, S. B. and Hersbach, H. and H{\'{o}}lm, E. V. and Isaksen, L. and K{\aa}llberg, P. and K{\"{o}}hler, M. and Matricardi, M. and McNally, A. P. and Monge-Sanz, B. M. and Morcrette, J.-J. and Park, B.-K. and Peubey, C. and de Rosnay, P. and Tavolato, C. and Th{\'{e}}paut, J.-N. and Vitart, F.},
doi = {10.1002/qj.828},
issn = {00359009},
journal = {Quarterly Journal of the Royal Meteorological Society},
month = {apr},
number = {656},
pages = {553--597},
title = {{The ERA-Interim reanalysis: configuration and performance of the data assimilation system}},
url = {http://doi.wiley.com/10.1002/qj.828},
volume = {137},
year = {2011}
}
@article{Hutchings2009,
author = {Hutchings, L and van der Lingen, C D and Shannon, L J and Crawford, R J M and Verheye, H M S and Bartholomae, C H and van der Plas, a. K and Louw, D and Kreiner, A. and Ostrowski, M and Fidel, Q and Barlow, R G and Lamont, T and Coetzee, J and Shillington, F and Veitch, J and Currie, J C and Monteiro, P M S},
doi = {10.1016/j.pocean.2009.07.046},
isbn = {0079-6611},
issn = {00796611},
journal = {Progress in Oceanography},
number = {1-4},
pages = {15--32},
publisher = {Elsevier Ltd},
title = {{The Benguela Current: an ecosystem of four components}},
volume = {83},
year = {2009}
}
@article{Paliy2016,
author = {Paliy, O. and Shankar, V.},
doi = {10.1111/mec.13536},
issn = {09621083},
journal = {Molecular Ecology},
month = {mar},
number = {5},
pages = {1032--1057},
title = {{Application of multivariate statistical techniques in microbial ecology}},
url = {http://doi.wiley.com/10.1111/mec.13536},
volume = {25},
year = {2016}
}
@article{Schaeffer2017,
author = {Schaeffer, A. and Roughan, M.},
doi = {10.1002/2017GL073714},
issn = {00948276},
journal = {Geophysical Research Letters},
title = {{Sub-surface intensification of marine heatwaves off southeastern Australia: the role of stratification and local winds.}},
url = {http://doi.wiley.com/10.1002/2017GL073714},
year = {2017}
}
@article{Kohonen1998,
author = {Kohonen, Teuvo},
journal = {Neurocomputing},
number = {1},
pages = {1--6},
publisher = {Elsevier},
title = {{The self-organizing map}},
volume = {21},
year = {1998}
}
@article{Banzon2016,
author = {Banzon, V and Smith, T M and Chin, T M and Liu, C and Hankins, W},
doi = {10.5194/essd-8-165-2016},
journal = {Earth System Science Data},
number = {1},
pages = {165--176},
title = {{A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies}},
url = {https://www.earth-syst-sci-data.net/8/165/2016/},
volume = {8},
year = {2016}
}
@book{Tyson2000,
address = {Cape Town, South Africa},
author = {Tyson, Peter Daughtrey and Preston-Whyte, Robert A},
publisher = {Oxford University Press},
title = {{weather and climate of southern Africa}},
year = {2000}
}
@article{Hansen1981,
author = {Hansen, James and Johnson, D. and Lacis, A and Lebedeff, S. and Lee, P. and Rind, David and Russell, G.},
doi = {10.1126/science.213.4511.957},
isbn = {0631199810},
issn = {0036-8075},
journal = {Science},
number = {4511},
pages = {957--966},
pmid = {17789014},
title = {{Climate Impact of Increasing Atmospheric Carbon Dioxide}},
url = {http://www.sciencemag.org/cgi/doi/10.1126/science.213.4511.957},
volume = {213},
year = {1981}
}
@article{Boucek2016,
author = {Boucek, R. E. and Gaiser, E. E. and Liu, H. and Rehage, J. S.},
doi = {10.1002/ecs2.1455},
issn = {21508925},
journal = {Ecosphere},
month = {oct},
number = {10},
pages = {e01455},
title = {{A review of subtropical community resistance and resilience to extreme cold spells}},
url = {http://doi.wiley.com/10.1002/ecs2.1455},
volume = {7},
year = {2016}
}
@article{Mills2013,
author = {Mills, Katherine and Pershing, Andrew and Brown, Curtis and Chen, Yong and Chiang, Fu-Sung and Holland, Daniel and Lehuta, Sigrid and Nye, Janet and Sun, Jenny and Thomas, Andrew and Wahle, Richard},
title = {{Fisheries Management in a Changing Climate: Lessons From the 2012 Ocean Heat Wave in the Northwest Atlantic}},
doi = {10.5670/oceanog.2013.27},
issn = {10428275},
journal = {Oceanography},
number = {2},
pages = {191--195},
url = {https://tos.org/oceanography/article/fisheries-management-in-a-changing-climate-lessonsfrom-the-2012-ocean-heat-},
volume = {26},
year = {2013}
}
@article{Benthuysen2014,
abstract = {In austral summer 2011, an unprecedented Ningaloo Ni{\~{n}}o event occurred off the west coast of Australia, with sea surface temperature anomalies reaching 5. C and significant impacts on marine ecosystems. In this study, a high resolution ({\~{}}2. km) hydrodynamic model (Regional Ocean Modeling System) is used to simulate the variations in the near-surface temperature in the region from 2009 through mid-2011. Model results indicate that the peak temperatures in the broad mid-west coast of Australia during the event are predominantly due to poleward advection of warmer, tropical water (???2/3 contribution). In addition, positive air-sea heat flux into the ocean also contributes (???1/3 contribution) to the rise in temperature. The anomalous advection of warm water is caused by changes in the poleward flowing Leeuwin Current due to both local and remote wind forcing. In early 2011, the Leeuwin Current intensified owing to remote forcing by the equatorial easterly wind anomalies in the Pacific Ocean associated with the 2010-2011 La Ni{\~{n}}a. In addition, the southerly winds off the west coast of Australia weakened, allowing the Leeuwin Current to further intensify in speed at the peak of the event. Concurrently, the inshore, equatorward Capes Current was suppressed and reversed direction. The poleward flow over the shelf contributed to near-shore warming in contrast to cooling by upwelling and equatorward advection from the Capes Current in previous years.},
author = {Benthuysen, Jessica and Feng, Ming and Zhong, Liejun},
doi = {10.1016/j.csr.2014.09.014},
issn = {02784343},
journal = {Continental Shelf Research},
keywords = {Eastern boundary currents,La Ni??a,Leeuwin current,Marine heat wave},
pages = {232--246},
title = {{Spatial patterns of warming off Western Australia during the 2011 Ningaloo Ni{\~{n}}o: quantifying impacts of remote and local forcing}},
volume = {91},
year = {2014}
}
@article{Wernberg2016,
author = {Wernberg, Thomas and Bennett, Scott and Babcock, Russell C and Bettignies, Thibaut De and Cure, Katherine and Depczynski, Martial and Dufois, Francois and Fromont, Jane and Fulton, Christopher J and Hovey, Renae K and Harvey, Euan S and Holmes, Thomas H and Kendrick, Gary A and Radford, Ben and Santana-garcon, Julia and Saunders, Benjamin J and Smale, Dan A and Thomsen, Mads S},
doi = {10.1126/science.aad8745},
issn = {0036-8075},
journal = {Science},
number = {1996},
pages = {2009--2012},
title = {{Climate driven regime shift of a temperate marine ecosystem}},
volume = {149},
year = {2016}
}
@article{Castillo2010,
author = {Castillo, Karl D and Lima, Fernando P},
journal = {Limnology and Oceanography Methods},
keywords = {AVHRR,MODIS,SST,satellite,temperature},
pages = {107--117},
title = {{Comparison of in situ and satellite-derived (MODIS-Aqua/Terra) methods for assessing temperatures on coral reefs}},
volume = {8},
year = {2010}
}
@article{Hufkens2012,
author = {Hufkens, Koen and Friedl, Mark A. and Keenan, Trevor F. and Sonnentag, Oliver and Bailey, Amey and O'Keefe, John and Richardson, Andrew D.},
doi = {10.1111/j.1365-2486.2012.02712.x},
issn = {13541013},
journal = {Global Change Biology},
month = {jul},
number = {7},
pages = {2365--2377},
title = {{Ecological impacts of a widespread frost event following early spring leaf-out}},
url = {http://doi.wiley.com/10.1111/j.1365-2486.2012.02712.x},
volume = {18},
year = {2012}
}
@article{Gordon1988,
author = {Gordon, G. and Brown, A. S. and Pulsford, T.},
doi = {10.1111/j.1442-9993.1988.tb00993.x},
issn = {1442-9985},
journal = {Austral Ecology},
month = {dec},
number = {4},
pages = {451--461},
title = {{A koala (Phascolarctos cinereus Goldfuss) population crash during drought and heatwave conditions in south-western Queensland}},
url = {http://doi.wiley.com/10.1111/j.1442-9993.1988.tb00993.x},
volume = {13},
year = {1988}
}
@article{Burrough2001,
abstract = {Previous attempts to quantify topographic controls on vegetation have$\backslash$noften been frustrated by issues concerning the number of variables$\backslash$nof interest and the tendency of classification methods to create$\backslash$ndiscrete classes though species have overlapping property sets (niches).$\backslash$nMethods of fuzzy k-means classification have been used to address$\backslash$nclass overlap in ecological and geographical data but previously$\backslash$ntheir usefulness has been limited when data sets are large or include$\backslash$nartefacts that may occur through the derivation of topo-climatic$\backslash$nattributes from gridded digital elevation models. This paper presents$\backslash$nways to overcome these limitations using GIS, spatial sampling methods,$\backslash$nfuzzy k-means classification, and statistical modelling of the derived$\backslash$nstream topology. Using data from a ca. 3600 km2 forested site in$\backslash$nthe Greater Yellowstone Area, we demonstrate the creation of meaningful,$\backslash$nspatially coherent topo-climatic classes through a fuzzy k-means$\backslash$nclassification of topo-climatic data derived from 100 m gridded digital$\backslash$nelevation models (DEMs), these classes were successfully extrapolated$\backslash$nto adjacent areas covering a total of ca. 10 000 km2. Independently$\backslash$nderived land cover data and middle infrared corrected Landsat TM$\backslash$nderived estimates of Normalised Difference Vegetation Index (M{\_}NDVI)$\backslash$nat 575 independently sampled sites were used to evaluate the topo-climatic$\backslash$nclasses and test their extrapolation to the larger area. Relations$\backslash$nbetween topo-climatic classes and land cover were tested by x2 analysis$\backslash$nwhich demonstrated strong associations between topo-climatic class$\backslash$nand 11 of the 15 cover classes. Relations between M{\_}NDVI and topo-climatic$\backslash$nclasses proved to be stronger than relations between M{\_}NDVI and the$\backslash$nindependent cover classes.We conclude that the fuzzy k-means procedure$\backslash$nyields sensible and stable topo-climatic classes that can be used$\backslash$nfor the rapid mapping of large areas. The value of$\backslash$n$\backslash$nthesemethods for quantifying topographic controls on biodiversity$\backslash$nand the strength of their relationswith computed NDVI values warrant$\backslash$nfurther investigation.},
author = {Burrough, Peter A. and Wilson, John P. and {Van Gaans}, Pauline F M and Hansen, Andrew J.},
doi = {10.1023/A:1013167712622},
isbn = {0921-2973},
issn = {09212973},
journal = {Landscape Ecology},
keywords = {Digital elevation models,Fuzzy k-means,GIS,Landscape classification,Topo-climatic analysis,Vegetation mapping},
number = {6},
pages = {523--546},
title = {{Fuzzy k-means classification of topo-climatic data as an aid to forest mapping in the Greater Yellowstone Area, USA}},
volume = {16},
year = {2001}
}
@article{Beal2011,
abstract = {The Atlantic Ocean receives warm, saline water from the Indo-Pacific Ocean through Agulhas leakage around the southern tip of Africa. Recent findings suggest that Agulhas leakage is a crucial component of the climate system and that ongoing increases in leakage under anthropogenic warming could strengthen the Atlantic overturning circulation at a time when warming and accelerated meltwater input in the North Atlantic is predicted to weaken it. Yet in comparison with processes in the North Atlantic, the overall Agulhas system is largely overlooked as a potential climate trigger or feedback mechanism. Detailed modelling experiments--backed by palaeoceanographic and sustained modern observations--are required to establish firmly the role of the Agulhas system in a warming climate.},
author = {Beal, Lisa M. and {De Ruijter}, Wilhelmus P. M. and Biastoch, Arne and Zahn, Rainer and Cronin, Meghan and Hermes, Juliet and Lutjeharms, Johann and Quartly, Graham and Tozuka, Tomoki and Baker-Yeboah, Sheekela and Bornman, Thomas and Cipollini, Paolo and Dijkstra, Henk and Hall, Ian and Park, Wonsun and Peeters, Frank and Penven, Pierrick and Ridderinkhof, Herman and Zinke, Jens},
doi = {10.1038/nature09983},
isbn = {0028-0836},
issn = {0028-0836},
journal = {Nature},
number = {7344},
pages = {429--436},
pmid = {21525925},
title = {{On the role of the Agulhas system in ocean circulation and climate}},
url = {http://www.nature.com/doifinder/10.1038/nature09983},
volume = {472},
year = {2011}
}
@article{Wehrens2007,
abstract = {In this age of ever-increasing data set sizes, especially in the natural sciences, visu- alisation becomes more and more important. Self-organizing maps have many features that make them attractive in this respect: they do not rely on distributional assumptions, can handle huge data sets with ease, and have shown their worth in a large number of applications. In this paper, we highlight the kohonen package for R, which implements self-organizing maps as well as some extensions for supervised pattern recognition and data fusion.},
annote = {NULL},
author = {Wehrens, Ron and Buydens, Lutgarde M. C.},
doi = {10.18637/jss.v021.i05},
isbn = {1548-7660},
issn = {15487660},
journal = {Journal of Statistical Software},
keywords = {classification,clustering,self-organizing maps,visualisation},
number = {5},
pages = {1--19},
title = {{Self- and super-organizing maps in R: The kohonen package}},
volume = {21},
year = {2007}
}
@article{Garrabou2009,
author = {Garrabou, J. and Coma, R. and Bensoussan, N. and Bally, M. and Chevaldonn{\'{e}}, P. and Cigliano, M. and Diaz, D. and Harmelin, J. G. and Gambi, M. C. and Kersting, D. K. and Ledoux, J. B. and Lejeusne, C. and Linares, C. and Marschal, C. and P{\'{e}}rez, T. and Ribes, M. and Romano, J. C. and Serrano, E. and Teixido, N. and Torrents, O. and Zabala, M. and Zuberer, F. and Cerrano, C.},
doi = {10.1111/j.1365-2486.2008.01823.x},
isbn = {1365-2486},
issn = {13541013},
journal = {Global Change Biology},
keywords = {Disturbance,Effects of climate change,Marine biodiversity,Mortality outbreak},
number = {5},
pages = {1090--1103},
pmid = {7404},
title = {{Mass mortality in Northwestern Mediterranean rocky benthic communities: effects of the 2003 heat wave}},
volume = {15},
year = {2009}
}
@article{Sang2008a,
abstract = {Self-organizing maps (SOMs) are a technique that has been used with high-dimensional data vectors to develop an archetypal set of states (nodes) that span, in some sense, the high-dimensional space. Noteworthy applications include weather states as described by weather variables over a region and speech patterns as characterized by frequencies in time. The SOM approach is essentially a neural network model that implements a nonlinear projection from a high-dimensional input space to a low-dimensional array of neurons. In the process, it also becomes a clustering technique, assigning to any vector in the high-dimensional data space the node (neuron) to which it is closest (using, say, Euclidean distance) in the data space. The number of nodes is thus equal to the number of clusters. However, the primary use for the SOM is as a representation technique, that is, finding a set of nodes which representatively span the high-dimensional space. These nodes are typically displayed using maps to enable visualization of the continuum of the data space. The technique does not appear to have been discussed in the statistics literature so it is our intent here to bring it to the attention of the community. The technique is implemented algorithmically through a training set of vectors. However, through the introduction of stochasticity in the form of a space--time process model, we seek to illuminate and interpret its performance in the context of application to daily data collection. That is, the observed daily state vectors are viewed as a time series of multivariate process realizations which we try to understand under the dimension reduction achieved by the SOM procedure.},
archivePrefix = {arXiv},
arxivId = {0901.3494},
author = {Sang, Huiyan and Gelfand, Alan E. and Lennard, Chris and Hegerl, Gabriele and Hewitson, Bruce},
doi = {10.1214/08-AOAS174},
eprint = {0901.3494},
issn = {19326157},
journal = {Annals of Applied Statistics},
keywords = {Bivariate spatial predictive process,Markov chain monte carlo,Model choice,Space-time models,Vector autoregressive model},
number = {4},
pages = {1194--1216},
title = {{Interpreting self-organizing maps through space-time data models}},
volume = {2},
year = {2008}
}
@article{Schlegel2016,
author = {Schlegel, Robert W. and Smit, Albertus J.},
doi = {10.1175/JCLI-D-16-0014.1},
file = {:home/rws/Documents/PhD/Articles/Schlegel et al 2016 Trend{\_}analysis.pdf:pdf},
issn = {0894-8755},
journal = {Journal of Climate},
number = {24},
pages = {9113--9124},
title = {{Climate Change in Coastal Waters: Time Series Properties Affecting Trend Estimation}},
url = {http://journals.ametsoc.org/doi/10.1175/JCLI-D-16-0014.1},
volume = {29},
year = {2016}
}
@article{Oliver2017,
author = {Oliver, Eric C. J. and Benthuysen, Jessica A. and Bindoff, Nathaniel L. and Hobday, Alistair J. and Holbrook, Neil J. and Mundy, Craig N. and Perkins-Kirkpatrick, Sarah E.},
doi = {10.1038/ncomms16101},
issn = {2041-1723},
journal = {Nature Communications},
month = {jul},
pages = {16101},
title = {{The unprecedented 2015/16 Tasman Sea marine heatwave}},
url = {http://www.nature.com/doifinder/10.1038/ncomms16101},
volume = {8},
year = {2017}
}
@article{Deser2010,
abstract = {Patterns of sea surface temperature (SST) variability on interannual and longer timescales result from a combination of atmospheric and oceanic processes. These SST anomaly patterns may be due to intrinsic modes of atmospheric circulation variability that imprint themselves upon the SST field mainly via Surface energy fluxes. Examples include SST fluctuations in the Southern Ocean associated with the Southern Annular Mode, a tripolar pattern of SST anomalies in the North Atlantic associated with the North Atlantic Oscillation, and a pan-Pacific mode known as the Pacific Decadal Oscillation (with additional contributions from oceanic processes). They may also result from coupled ocean-atmosphere interactions, such as the El Nino-Southern Oscillation phenomenon in the tropical Indo-Pacific, the tropical Atlantic Nino, and the cross-equatorial meridional modes ill the tropical Pacific and Atlantic. Finally, patterns of SST variability may arise from intrinsic oceanic modes, notably the Atlantic Multidecadal Oscillation.},
archivePrefix = {arXiv},
arxivId = {arXiv:cond-mat/0402594v3},
author = {Deser, C and Alexander, M A and Xie, S P and Phillips, A S},
doi = {10.1146/annurev-marine-120408-151453},
eprint = {0402594v3},
file = {:home/rws/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Deser et al. - 2010 - Sea surface temperature variability patterns and mechanisms.pdf:pdf},
isbn = {1204081514},
issn = {1941-1405},
journal = {Annual Review of Marine Science},
keywords = {El Nino,Pacific Decadal Oscillation,atlantic multidecadal oscillation,climate variability,decadal oscillation,el-nino,enso-like variability,indian-ocean,nino-southern-oscillation,northern hemisphere winter,ocean-atmosphere interaction,pacific,thermohaline circulation,tropical atlantic},
pages = {115--143},
pmid = {21141660},
primaryClass = {arXiv:cond-mat},
title = {{Sea surface temperature variability: patterns and mechanisms}},
volume = {2},
year = {2010}
}
@article{Bond2015a,
author = {Bond, Nicholas A. and Cronin, Meghan F. and Freeland, Howard and Mantua, Nathan},
doi = {10.1002/2015GL063306},
issn = {00948276},
journal = {Geophysical Research Letters},
month = {may},
number = {9},
pages = {3414--3420},
title = {{Causes and impacts of the 2014 warm anomaly in the NE Pacific}},
url = {http://doi.wiley.com/10.1002/2015GL063306},
volume = {42},
year = {2015}
}
@article{Grebmeier2006,
author = {Grebmeier, J. M.},
doi = {10.1126/science.1121365},
issn = {0036-8075},
journal = {Science},
month = {mar},
number = {5766},
pages = {1461--1464},
title = {{A Major Ecosystem Shift in the Northern Bering Sea}},
url = {http://www.sciencemag.org/cgi/doi/10.1126/science.1121365},
volume = {311},
year = {2006}
}
@article{Chen2015a,
author = {Chen, Ke and Gawarkiewicz, Glen and Kwon, Young-Oh and Zhang, Weifeng G.},
doi = {10.1002/2014JC010547},
issn = {21699275},
journal = {Journal of Geophysical Research: Oceans},
month = {jun},
number = {6},
pages = {4324--4339},
title = {{The role of atmospheric forcing versus ocean advection during the extreme warming of the Northeast U.S. continental shelf in 2012}},
url = {http://doi.wiley.com/10.1002/2014JC010547},
volume = {120},
year = {2015}
}
@article{Khalsa1983,
annote = {NULL},
author = {Khalsa, S J S},
issn = {00270644},
journal = {Monthly Weather Review},
keywords = {Air temperature,Data,Heat flux,Humidity,Modeling,Ocean-atmosphere interaction,Relationships,Sea-surface temperature,Tropical Pacific},
pages = {954--966},
title = {{The role of sea surface temperature in large-scale air-sea interaction}},
url = {c:{\%}5CPublications{\%}5Ckhalsa-i1520-0493-111-5-954.pdf},
volume = {111},
year = {1983}
}
@article{Denny2009,
author = {Denny, Mark W. and Hunt, Luke J. H. and Miller, Luke P. and Harley, Christopher D. G.},
doi = {10.1890/08-0579.1},
issn = {0012-9615},
journal = {Ecological Monographs},
month = {aug},
number = {3},
pages = {397--421},
title = {{On the prediction of extreme ecological events}},
url = {http://doi.wiley.com/10.1890/08-0579.1},
volume = {79},
year = {2009}
}
@article{Road1998,
abstract = {Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distributio, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspace of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. It produces identical resullts irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate clusters in large high dimensional datasetss.},
author = {Road, Harry and Jose, San},
doi = {10.1145/276305.276314},
isbn = {0-89791-995-5},
issn = {01635808},
journal = {Proceedings of the 1998 ACM SIGMOD international conference on Management of data},
keywords = {FCA OntologyHandbook},
pages = {94--105},
title = {{Automatic Subspace Clustering Mining of High Dimensional Applications for Data}},
url = {http://portal.acm.org/citation.cfm?id=276314},
volume = {27},
year = {1998}
}
@article{Pearce2013,
abstract = {Record high ocean temperatures were experienced along the Western Australian coast during the austral summer of 2010/2011. Satellite-derived sea surface temperature (SST) anomalies in February 2011 peaked at 3. ??C above the long-term monthly means over a wide area from Ningaloo (22??S) to Cape Leeuwin (34??S) along the coast and out to {\textgreater}200km offshore. Hourly temperature measurements at a number of mooring sites along the coast revealed that the temperature anomalies were mostly trapped in the surface mixed layer, with peak nearshore temperatures rising to {\~{}}. 5. ??C above average in the central west coastal region over a week encompassing the end of February and early March, resulting in some devastating fish kills as well as temporary southward range extensions of tropical fish species and megafauna such as whale sharks and manta rays. The elevated temperatures were a result of a combination of a record strength Leeuwin Current, a near-record La Ni??a event, and anomalously high air-sea heat flux into the ocean even though the SST was high. This heat wave was an unprecedented thermal event in Western Australian waters, superimposed on an underlying long-term temperature rise. ?? 2012 Elsevier B.V..},
author = {Pearce, Alan F. and Feng, Ming},
doi = {10.1016/j.jmarsys.2012.10.009},
isbn = {09247963},
issn = {09247963},
journal = {Journal of Marine Systems},
keywords = {Continental shelves,Geographical distribution,La Ni??a,Leeuwin Current,Mortality,Temperature,Water currents},
pages = {139--156},
title = {{The rise and fall of the "marine heat wave" off Western Australia during the summer of 2010/2011}},
volume = {111-112},
year = {2013}
}
@article{Gunter1941,
abstract = {See full-text article at JSTOR},
author = {Gunter, Gordon},
doi = {10.2307/1932218},
isbn = {0012-9658},
issn = {00129658},
journal = {Ecology},
number = {2},
pages = {203--208},
title = {{Death of fishes due to cold on the Texas coast, January, 1940}},
volume = {22},
year = {1941}
}
@article{Jain2010,
abstract = {Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering. ?? 2009 Elsevier B.V. All rights reserved.},
archivePrefix = {arXiv},
arxivId = {arXiv:cond-mat/0402594v3},
author = {Jain, Anil K.},
doi = {10.1016/j.patrec.2009.09.011},
eprint = {0402594v3},
isbn = {9781424417360},
issn = {01678655},
journal = {Pattern Recognition Letters},
keywords = {Data clustering,Historical developments,King-Sun Fu prize,Perspectives on clustering,User's dilemma},
number = {8},
pages = {651--666},
primaryClass = {arXiv:cond-mat},
title = {{Data clustering: 50 years beyond K-means}},
volume = {31},
year = {2010}
}
@manual{Oksanen2017,
annote = {R package version 2.4-4},
author = {Oksanen, Jari and Blanchet, F Guillaume and Friendly, Michael and Kindt, Roeland and Legendre, Pierre and McGlinn, Dan and Minchin, Peter R and O'Hara, R B and Simpson, Gavin L and Solymos, Peter and Stevens, M Henry H and Szoecs, Eduard and Wagner, Helene},
title = {{vegan: Community Ecology Package}},
url = {https://cran.r-project.org/package=vegan},
year = {2017}
}
@article{Johnson2013,
abstract = {It is now widely recognized that El Ni{\~{}} no–Southern Oscillation (ENSO) occurs in more than one form, with the canonical eastern Pacific (EP) and more recently recognized central Pacific (CP) ENSO types receiving the most focus. Given that these variousENSO‘‘flavors'' may contribute to climate variability and long-term trends in unique ways, and that ENSO variability is not limited to these two types, this study presents a framework that treats ENSO as a continuum but determines a finite maximum number of statistically distinguishable representativeENSOpatterns.Aneural network–based cluster analysis called self-organizing map (SOM) analysis paired with a statistical distinguishability test determines nine unique patterns that characterize the September–February tropical Pacific SST anomaly fields for the period from 1950 through 2011. These nine patterns represent the flavors of ENSO, which include EP, CP, and mixed ENSO patterns. Over the 1950–2011 period, the most significant trends reflect changes in La Ni{\~{}} na patterns, with a shift in dominance of La Ni{\~{}} na–like patterns with weak or negative western Pacific warm pool SST anomalies until the mid-1970s, followed by a dominance of La Ni{\~{}} na–like patterns with positive western Pacific warm pool SST anomalies, particularly after the mid-1990s. Both an EP and especially a CP El Ni{\~{}} no pattern experienced positive frequency trends,but these trends are indistinguishable fromnatural variability.Overall, changes infrequencywithin the ENSOcontinuum contributed to the pattern of tropical Pacific warming, particularly in the equatorial eastern Pacific and especially in relation to changes of La Ni{\~{}} na–like rather than El Ni{\~{}} no–like patterns.},
author = {Johnson, Nathaniel C.},
doi = {10.1175/JCLI-D-12-00649.1},
isbn = {0894-8755},
issn = {08948755},
journal = {Journal of Climate},
number = {13},
pages = {4816--4827},
title = {{How many enso flavors can we distinguish?}},
volume = {26},
year = {2013}
}
@article{Biastoch2009,
abstract = {The transport of warm and salty Indian Ocean waters into the Atlantic Ocean-the Agulhas leakage-has a crucial role in the global oceanic circulation and thus the evolution of future climate. At present these waters provide the main source of heat and salt for the surface branch of the Atlantic meridional overturning circulation (MOC). There is evidence from past glacial-to-interglacial variations in foraminiferal assemblages and model studies that the amount of Agulhas leakage and its corresponding effect on the MOC has been subject to substantial change, potentially linked to latitudinal shifts in the Southern Hemisphere westerlies. A progressive poleward migration of the westerlies has been observed during the past two to three decades and linked to anthropogenic forcing, but because of the sparse observational records it has not been possible to determine whether there has been a concomitant response of Agulhas leakage. Here we present the results of a high-resolution ocean general circulation model to show that the transport of Indian Ocean waters into the South Atlantic via the Agulhas leakage has increased during the past decades in response to the change in wind forcing. The increased leakage has contributed to the observed salinification of South Atlantic thermocline waters. Both model and historic measurements off South America suggest that the additional Indian Ocean waters have begun to invade the North Atlantic, with potential implications for the future evolution of the MOC.},
author = {Biastoch, A and B{\"{o}}ning, C W and Schwarzkopf, F U and Lutjeharms, J R E and Boning, C W and Schwarzkopf, F U and Lutjeharms, J R E},
doi = {10.1038/nature08519},
isbn = {0028-0836},
issn = {1476-4687},
journal = {Nature},
keywords = {Africa,Atlantic Ocean,Computer Simulation,Foraminifera,Indian Ocean,Salinity,Seawater,Seawater: analysis,Seawater: chemistry,Temperature,Water Movements,Wind},
number = {7272},
pages = {495--8},
pmid = {19940923},
title = {{Increase in Agulhas leakage due to poleward shift of Southern Hemisphere westerlies.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19940923},
volume = {462},
year = {2009}
}
@article{Travis2003,
abstract = {Climate change and habitat destruction are two of the greatest threats to global biodiversity. Lattice models have been used to investigate how hypothetical species with different characteristics respond to habitat loss. The main result shows that a sharp threshold in habitat availability exists below which a species rapidly becomes extinct. Here, a similar modelling approach is taken to establish what determines how species respond to climate change. A similar threshold exists for the rate of climate change as has been observed for habitat loss-patch occupancy remains high up to a critical rate of climate change, beyond which species extinction becomes likely. Habitat specialists, especially those of relatively poor colonizing ability are least able to keep pace with climate change. The interaction between climate change and habitat loss might be disastrous. During climate change, the habitat threshold occurs sooner. Similarly, species suffer more from climate change in a fragmented habitat.},
author = {Travis, J. M. J.},
doi = {10.1098/rspb.2002.2246},
isbn = {0962-8452},
issn = {0962-8452},
journal = {Proceedings of the Royal Society B: Biological Sciences},
number = {1514},
pages = {467--473},
pmid = {12641900},
title = {{Climate change and habitat destruction: a deadly anthropogenic cocktail}},
url = {http://rspb.royalsocietypublishing.org/cgi/doi/10.1098/rspb.2002.2246},
volume = {270},
year = {2003}
}
@article{Burrows2011,
abstract = {Climate change challenges organisms to adapt or move to track changes in environments in space and time. We used two measures of thermal shifts from analyses of global temperatures over the past 50 years to describe the pace of climate change that species should track: the velocity of climate change (geographic shifts of isotherms over time) and the shift in seasonal timing of temperatures. Both measures are higher in the ocean than on land at some latitudes, despite slower ocean warming. These indices give a complex mosaic of predicted range shifts and phenology changes that deviate from simple poleward migration and earlier springs or later falls. They also emphasize potential conservation concerns, because areas of high marine biodiversity often have greater velocities of climate change and seasonal shifts.},
author = {Burrows, Michael T. and Schoeman, David S. and Buckley, Lauren B. and Moore, Pippa and Poloczanska, Elvira S. and Brander, Keith M. and Brown, Chris and Bruno, John F. and Duarte, Carlos M. and Halpern, Benjamin S. and Holding, Johnna and Kappel, Carrie V. and Kiessling, Wolfgang and O'Connor, Mary I and Pandolfi, John M. and Parmesan, Camille and Schwing, Franklin B. and Sydeman, William J. and Richardson, Anthony J.},
doi = {10.1126/science.1210288},
isbn = {1095-9203 (Electronic)$\backslash$r0036-8075 (Linking)},
issn = {0036-8075},
journal = {Science},
month = {nov},
number = {6056},
pages = {652--655},
pmid = {22053045},
title = {{The pace of shifting climate in marine and terrestrial ecosystems}},
url = {http://www.sciencemag.org/cgi/doi/10.1126/science.1210288 http://www.sciencemag.org/content/334/6056/652.abstract{\%}5Cnhttp://www.sciencemag.org/content/334/6056/652.full.pdf},
volume = {334},
year = {2011}
}
@article{Thomas2004,
abstract = {Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20{\%} of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37{\%} of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction approximately 18{\%}) than mid-range approximately 24{\%}) and maximum-change approximately 35{\%}) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.},
author = {Thomas, Chris D. and Cameron, Alison and Green, Rhys E. and Bakkenes, Michel and Beaumont, Linda J. and Collingham, Yvonne C. and Erasmus, Barend F. N. and de Siqueira, Marinez Ferreira and Grainger, Alan and Hannah, Lee and Hughes, Lesley and Huntley, Brian and van Jaarsveld, Albert S. and Midgley, Guy F. and Miles, Lera and Ortega-Huerta, Miguel A. and {Townsend Peterson}, A. and Phillips, Oliver L. and Williams, Stephen E.},
doi = {10.1038/nature02121},
isbn = {0028083614764},
issn = {0028-0836},
journal = {Nature},
number = {6970},
pages = {145--148},
pmid = {14712274},
title = {{Extinction risk from climate change}},
url = {http://www.nature.com/doifinder/10.1038/nature02121},
volume = {427},
year = {2004}
}
@article{Cavazos2000,
abstract = {This paper examines some of the physical mechanisms and$\backslash$nremote linkages associated with extreme wintertime$\backslash$nprecipitation in the Balkans. The analysis is assessed on$\backslash$ndaily timescales to determine the role of the circulation$\backslash$nand atmospheric moisture on extreme events, and also at$\backslash$nintraseasonal and interannual timescales to find possible$\backslash$nlinkages with the North Atlantic Oscillation (NAO) and the$\backslash$nArctic Oscillation (AO) patterns. A nonlinear$\backslash$nclassification known as the self-organizing map (SOM) is$\backslash$nemployed to obtain the climate modes and anomalies that$\backslash$ndominated during the 1980--93 period. An artificial neural$\backslash$nnetwork (ANN) is also used to derive daily precipitation at$\backslash$ngridpoint scale and at local scale in Bucharest, Romania.$\backslash$nOf the predictors used, 500--1000-hPa thickness, 700-hPa$\backslash$ngeopotential heights, and 700-hPa moisture are the most$\backslash$nimportant controls of daily precipitation. These results$\backslash$nare substantiated with the climate states from the SOM$\backslash$nclassification, which show strong meridional flow over$\backslash$ncentral and eastern Europe coupled to increased winter$\backslash$ndisturbances in the central Mediterranean and a tongue of$\backslash$nmoisture at the 700-hPa level from the eastern$\backslash$nMediterranean and the Black Sea during anomalously wet$\backslash$nevents in the Bulgarian region. Dry events are almost an$\backslash$ninverse of these conditions. Extreme events are further$\backslash$nmodulated by changes in the circulation associated with the$\backslash$nAO. In contrast, the NAO does not play a role on wintertime$\backslash$nprecipitation over the region. The ANN captures well$\backslash$nsynoptic events and dry spells, but tends to overestimate$\backslash$n(underestimate) small (large) events. This suggests a$\backslash$nproblem for area-averaged precipitation, which is already$\backslash$nbiased by its spatial resolution. However, comparison$\backslash$nbetween precipitation at Bucharest station and at its$\backslash$nnearest grid point shows that the performance of the ANN is$\backslash$nslightly better at gridpoint scale.},
author = {Cavazos, Tereza},
doi = {10.1175/1520-0442(2000)013<1718:USOMTI>2.0.CO;2},
isbn = {0894-8755},
issn = {08948755},
journal = {Journal of Climate},
number = {10},
pages = {1718--1732},
title = {{Using self-organizing maps to investigate extreme climate events: An application to wintertime precipitation in the Balkans}},
volume = {13},
year = {2000}
}
@article{Feng2013,
abstract = {Unprecedented warm sea surface temperature (SST) anomalies were observed off the west coast of Australia in February-March 2011. Peak SST during a 2-week period were 5°C warmer than normal, causing widespread coral bleaching and fish kills. Understanding the climatic drivers of this extreme event, which we dub "Ningaloo Ni{\~{n}}o", is crucial for predicting similar events under the influence of global warming. Here we use observational data and numerical models to demonstrate that the extreme warming was mostly driven by an unseasonable surge of the poleward-flowing Leeuwin Current in austral summer, which transported anomalously warm water southward along the coast. The unusual intensification of the Leeuwin Current was forced remotely by oceanic and atmospheric teleconnections associated with the extraordinary 2010-2011 La Ni{\~{n}}a. The amplitude of the warming was boosted by both multi-decadal trends in the Pacific toward more La Ni{\~{n}}a-like conditions and intraseasonal variations in the Indian Ocean.},
author = {Feng, Ming and McPhaden, Michael J and Xie, Shang-Ping and Hafner, Jan},
doi = {10.1038/srep01277},
isbn = {2045-2322},
issn = {2045-2322},
journal = {Scientific Reports},
pages = {1277},
pmid = {23429502},
title = {{La Ni{\~{n}}a forces unprecedented Leeuwin Current warming in 2011.}},
volume = {3},
year = {2013}
}
@manual{Smit2017,
address = {Belville, Cape Town, South Africa},
annote = {R package version 0.15.7},
author = {Smit, A. J. and Oliver, E. C. J. and Schlegel, R. W.},
organization = {University of the Western Cape},
title = {{RmarineHeatWaves: Package for the calculation of marine heat waves}},
url = {https://github.com/ajsmit/RmarineHeatWaves},
year = {2017}
}
@article{Jolly2005,
author = {Jolly, William M. and Dobbertin, Matthias and Zimmermann, Niklaus E. and Reichstein, Markus},
doi = {10.1029/2005GL023252},
issn = {00948276},
journal = {Geophysical Research Letters},
month = {sep},
number = {18},
pages = {n/a--n/a},
title = {{Divergent vegetation growth responses to the 2003 heat wave in the Swiss Alps}},
url = {http://doi.wiley.com/10.1029/2005GL023252},
volume = {32},
year = {2005}
}
@article{Walther2002,
author = {Walther, Gian-Reto and Post, Eric and Convey, Peter and Menzel, Annette and Parmesan, Camille and Beebee, Trevor J. C. and Fromentin, Jean-Marc and Hoegh-Guldberg, Ove and Bairlein, Franz},
doi = {10.1038/416389a},
issn = {0028-0836},
journal = {Nature},
month = {mar},
number = {6879},
pages = {389--395},
title = {{Ecological responses to recent climate change}},
url = {http://www.nature.com/doifinder/10.1038/416389a},
volume = {416},
year = {2002}
}
@article{Perkins2013,
abstract = {Despite their adverse impacts, definitions and measurements of heat waves are ambiguous and inconsistent, generally being endemic to only the group affected, or the respective study reporting the analysis. The present study addresses this issue by employing a set of three heat wave definitions, derived from surveying heat- related indices in the climate science literature. The definitions include three or more consecutive days above one of the following: the 90th percentile for maximum temperature, the 90th percentile for minimum tem- perature, and positive extreme heat factor (EHF) conditions. Additionally, each index is studied using a multiaspect framework measuring heat wave number, duration, participating days, and the peak and mean magnitudes. Observed climatologies and trends computed by Sen's Kendall slope estimator are presented for the Australian continent for two time periods (1951–2008 and 1971–2008). Trends in all aspects and definitions are smaller in magnitude but more significant for 1951–2008 than for 1971–2008. Considerable similarities exist in trends of the yearly number of days participating in a heat wave and yearly heat wave frequency, suggesting that the number of available heat wave days drives the number of events. Larger trends in the hottest part of a heat wave suggest that heat wave intensity is increasing faster than the mean mag- nitude. Although the direct results of this study cannot be inferred for other regions, the methodology has been designed as such that it is widely applicable. Furthermore, it includes a range of definitions that may be useful for a wide range of systems impacted by heat waves.},
author = {Perkins, S. E. and Alexander, L. V.},
doi = {10.1175/JCLI-D-12-00383.1},
file = {:home/rws/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Perkins, Alexander - 2013 - On the measurement of heat waves.pdf:pdf},
isbn = {08948755},
issn = {08948755},
journal = {Journal of Climate},
number = {13},
pages = {4500--4517},
pmid = {88906971},
title = {{On the measurement of heat waves}},
volume = {26},
year = {2013}
}
@article{Barrett2008,
author = {Barrett, J. E. and Virginia, R. A. and Wall, D. H. and Doran, P. T. and Fountain, A. G. and Welch, K. A. and Lyons, W. B.},
doi = {10.1111/j.1365-2486.2008.01641.x},
issn = {13541013},
journal = {Global Change Biology},
month = {oct},
number = {10},
pages = {2249--2261},
title = {{Persistent effects of a discrete warming event on a polar desert ecosystem}},
url = {http://doi.wiley.com/10.1111/j.1365-2486.2008.01641.x},
volume = {14},
year = {2008}
}
@article{Hobday2016,
  title={A hierarchical approach to defining marine heatwaves},
  author={Hobday, Alistair J and Alexander, Lisa V and Perkins, Sarah E and Smale, Dan A and Straub, Sandra C and Oliver, Eric C J and Benthuysen, Jessica A and Burrows, Michael T and Donat, Markus G and Feng, Ming and others},
  doi={10.1016/j.pocean.2015.12.014},
  journal={Progress in Oceanography},
  volume={141},
  pages={227--238},
  year={2016},
  publisher={Elsevier}
}
@incollection{Salinger2016,
title = "Chapter One - Decadal-Scale Forecasting of Climate Drivers for Marine Applications",
editor = "Barbara E. Curry",
series = "Advances in Marine Biology",
publisher = "Academic Press",
volume = "74",
pages = "1 - 68",
year = "2016",
issn = "0065-2881",
doi = "https://doi.org/10.1016/bs.amb.2016.04.002",
url = "http://www.sciencedirect.com/science/article/pii/S0065288116300025",
author = "J. Salinger and A.J. Hobday and R.J. Matear and T.J. O’Kane and J.S. Risbey and P. Dunstan and J.P. Eveson and E.A. Fulton and M. Feng and É.E. Plagányi and E.S. Poloczanska and A.G. Marshall and P.A. Thompson",
keywords = "Decadal, Forecasting, Climate drivers, Marine resources, Extremes, Australia, El Niño/Southern Oscillation, Southern Annular Mode, Indian Ocean Dipole, Interdecadal Pacific Oscillation",
abstract = "Climate influences marine ecosystems on a range of time scales, from weather-scale (days) through to climate-scale (hundreds of years). Understanding of interannual to decadal climate variability and impacts on marine industries has received less attention. Predictability up to 10 years ahead may come from large-scale climate modes in the ocean that can persist over these time scales. In Australia the key drivers of climate variability affecting the marine environment are the Southern Annular Mode, the Indian Ocean Dipole, the El Niño/Southern Oscillation, and the Interdecadal Pacific Oscillation, each has phases that are associated with different ocean circulation patterns and regional environmental variables. The roles of these drivers are illustrated with three case studies of extreme events—a marine heatwave in Western Australia, a coral bleaching of the Great Barrier Reef, and flooding in Queensland. Statistical and dynamical approaches are described to generate forecasts of climate drivers that can subsequently be translated to useful information for marine end users making decisions at these time scales. Considerable investment is still needed to support decadal forecasting including improvement of ocean-atmosphere models, enhancement of observing systems on all scales to support initiation of forecasting models, collection of important biological data, and integration of forecasts into decision support tools. Collaboration between forecast developers and marine resource sectors—fisheries, aquaculture, tourism, biodiversity management, infrastructure—is needed to support forecast-based tactical and strategic decisions that reduce environmental risk over annual to decadal time scales."
}
@article{Scannell2016,
abstract = {Extreme and large-scale warming events in the ocean have been dubbed marine heatwaves, and these have been documented in both the Northern and Southern Hemispheres. This paper examines the intensity, duration and frequency of positive sea surface temperature anomalies in the North Atlantic and North Pacific Oceans over the period 1950-2014 using an objective definition for marine heatwaves based on their probability of occurrence. Small-area anomalies occur more frequently than large-area anomalies, and this relationship can be characterized by a power-law distribution. The relative frequency of large- versus small-area anomalies, represented by the power-law slope parameter, is modulated by basin-scale modes of natural climate variability and anthropogenic warming. Findings suggest that the probability of marine heatwaves is a trade-off between size, intensity and duration, and that region specific variability modulates the statistical attributes of these events.},
author = {Scannell, Hillary A. and Pershing, Andrew J. and Alexander, Michael A. and Thomas, Andrew C. and Mills, Katherine E.},
doi = {10.1002/2015GL067308},
issn = {19448007},
journal = {Geophysical Research Letters},
keywords = {SST anomaly,climate variability,marine heatwave,power law},
number = {5},
pages = {2069--2076},
title = {{Frequency of marine heatwaves in the North Atlantic and North Pacific since 1950}},
volume = {43},
year = {2016}
}
@article{Stott2004,
author = {Stott, Peter A. and Stone, D. A. and Allen, M. R.},
doi = {10.1038/nature03089},
issn = {0028-0836},
journal = {Nature},
month = {dec},
number = {7017},
pages = {610--614},
title = {{Human contribution to the European heatwave of 2003}},
url = {http://www.nature.com/doifinder/10.1038/nature03089},
volume = {432},
year = {2004}
}
@article{Corte-Real1998,
author = {Corte-Real, Jo{\~{a}}o and Qian, Budong and Xu, Hong},
doi = {10.1002/(SICI)1097-0088(199805)18:6<619::AID-JOC271>3.0.CO;2-T},
issn = {08998418},
journal = {International Journal of Climatology},
keywords = {circulation pattern,climate change,k-means clustering,precipitation variability,regional,singular spectrum analysis},
number = {6},
pages = {619--635},
title = {{Regional climate change in Portugal: precipitation variability associated with large-scale atmospheric circulation}},
url = {http://onlinelibrary.wiley.com/doi/10.1002/(SICI)1097-0088(199805)18:6{\%}3C619::AID-JOC271{\%}3E3.0.CO;2-T/abstract http://www.scopus.com/scopus/inward/record.url?eid=2-s2.0-3543007704{\&}partnerID=40{\&}rel=R7.0.0},
volume = {18},
year = {1998}
}
@article{Smale2009,
author = {Smale, D A and Wernberg, T},
doi={10.3354/meps08132},
journal = {Marine Ecology Progress Series},
pages = {27--37},
title = {{Satellite-derived SST data as a proxy for water temperature in nearshore benthic ecology}},
volume = {387},
year = {2009}
}
@article{Gibson2016b,
author = {Gibson, Peter B. and Uotila, Petteri and Perkins-Kirkpatrick, Sarah E. and Alexander, Lisa V. and Pitman, Andrew J.},
doi = {10.1007/s00382-015-2961-y},
issn = {14320894},
journal = {Climate Dynamics},
number = {7-8},
pages = {2235--2251},
title = {{Evaluating synoptic systems in the CMIP5 climate models over the Australian region}},
volume = {47},
year = {2016}
}
@article{McBratney1985,
author = {McBratney, A. B. and Moore, A. W.},
doi = {10.1016/0168-1923(85)90082-6},
issn = {01681923},
journal = {Agricultural and Forest Meteorology},
number = {1-4},
pages = {165--185},
title = {{Application of fuzzy sets to climatic classification}},
volume = {35},
year = {1985}
}
@article{Cavole2016,
author = {Cavole, Leticia and Demko, Alyssa and Diner, Rachel and Giddings, Ashlyn and Koester, Irina and Pagniello, Camille and Paulsen, May-Linn and Ramirez-Valdez, Arturo and Schwenck, Sarah and Yen, Nicole and Zill, Michelle and Franks, Peter},
doi = {10.5670/oceanog.2016.32},
issn = {10428275},
journal = {Oceanography},
number = {2},
pages = {273--285},
title = {{Biological Impacts of the 2013–2015 Warm-Water Anomaly in the Northeast Pacific: Winners, Losers, and the Future}},
url = {https://tos.org/oceanography/article/biological-impacts-of-the-20132015-warm-water-anomaly-in-the-northeast-paci},
volume = {29},
year = {2016}
}
@misc{Krishnamurti1988,
author = {Krishnamurti, T N and Oosterhof, D K and Mehta, A V},
booktitle = {Journal of the atmospheric sciences},
doi = {10.1175/1520-0469(1988)045<1304:AIOTTS>2.0.CO;2},
isbn = {0022-4928},
issn = {0022-4928, 1520-0469},
number = {8},
pages = {1304--1322},
title = {{Air–Sea Interaction on the Time Scale of 30 to 50 Days}},
url = {http://dx.doi.org/10.1175/1520-0469(1988)045{\%}3C1304:AIOTTS{\%}3E2.0.CO;2},
volume = {45},
year = {1988}
}
@article{Cox2000,
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Cox, P M and Betts, R a and Jones, C D and Spall, S a and Totterdell, I J},
doi = {10.1038/35041539},
eprint = {arXiv:1011.1669v3},
isbn = {0028-0836},
issn = {0028-0836},
journal = {Nature},
number = {6809},
pages = {184--187},
pmid = {11089968},
title = {{Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model.}},
volume = {408},
year = {2000}
}
@article{Smit2013,
abstract = {Gridded SST products developed particularly for offshore regions are increasingly being applied close to the coast for biogeographical applications. The purpose of this paper is to demonstrate the dangers of doing so through a comparison of reprocessed MODIS Terra and Pathfinder v5.2 SSTs, both at 4 km resolution, with instrumental in situ temperatures taken within 400 m from the coast. We report large biases of up to +6°C in places between satellite-derived and in situ climatological temperatures for 87 sites spanning the entire ca. 2 700 km of the South African coastline. Although biases are predominantly warm (i.e. the satellite SSTs being higher), smaller or even cold biases also appear in places, especially along the southern and western coasts of the country. We also demonstrate the presence of gradients in temperature biases along shore-normal transects - generally SSTs extracted close to the shore demonstrate a smaller bias with respect to the in situ temperatures. Contributing towards the magnitude of the biases are factors such as SST data source, proximity to the shore, the presence/absence of upwelling cells or coastal embayments. Despite the generally large biases, from a biogeographical perspective, species distribution retains a correlative relationship with underlying spatial patterns in SST, but in order to arrive at a causal understanding of the determinants of biogeographical patterns we suggest that in shallow, inshore marine habitats, temperature is best measured directly.},
author = {Smit, Albertus J. and Roberts, Michael and Anderson, Robert J. and Dufois, Francois and Dudley, Sheldon F J and Bornman, Thomas G. and Olbers, Jennifer and Bolton, John J.},
doi = {10.1371/journal.pone.0081944},
file = {:home/rws/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Smit et al. - 2013 - A coastal seawater temperature dataset for biogeographical studies large biases between in situ and remotely-sensed.pdf:pdf},
issn = {19326203},
journal = {PLOS ONE},
number = {12},
pmid = {24312609},
title = {{A coastal seawater temperature dataset for biogeographical studies: large biases between in situ and remotely-sensed data sets around the coast of South Africa}},
volume = {8},
year = {2013}
}
@article{Lirman2011,
abstract = {Coral reefs are facing increasing pressure from natural and anthropogenic stressors that have already caused significant worldwide declines. In January 2010, coral reefs of Florida, United States, were impacted by an extreme cold-water anomaly that exposed corals to temperatures well below their reported thresholds (16°C), causing rapid coral mortality unprecedented in spatial extent and severity.},
author = {Lirman, Diego and Schopmeyer, Stephanie and Manzello, Derek and Gramer, Lewis J. and Precht, William F. and Muller-Karger, Frank and Banks, Kenneth and Barnes, Brian and Bartels, Erich and Bourque, Amanda and Byrne, James and Donahue, Scott and Duquesnel, Janice and Fisher, Louis and Gilliam, David and Hendee, James and Johnson, Meaghan and Maxwell, Kerry and McDevitt, Erin and Monty, Jamie and Rueda, Digna and Ruzicka, Rob and Thanner, Sara},
doi = {10.1371/journal.pone.0023047},
isbn = {1932-6203},
issn = {19326203},
journal = {PLOS ONE},
number = {8},
pmid = {21853066},
title = {{Severe 2010 cold-water event caused unprecedented mortality to corals of the Florida reef tract and reversed previous survivorship patterns}},
volume = {6},
year = {2011}
}
@misc{Frankignoul1985,
abstract = {The mechanisms that contribute to the generation and damping of large-scale mid-latitude sea surface temperature (SST) anomalies are discussed. The SST anomalies reflect primarily the response of the upper ocean to the changes in air-sea fluxes that are associated with daily weather fluctuations. Heat flux forcing is dominant in the lower middle latitudes, while wind-driven entrainment may be most effective in the high latitudes; advection by anomalous Ekman current is generally less important, and Ekman pumping is negligible. The SST anomalies decay in part because of entrainment effects associated with mixed-layer deepening and oceanic mixing and in part because of heat exchanges with the atmosphere. The three approaches commonly used to model the evolution of SST anomalies are reviewed: case studies based on monthly or seasonal anomaly maps of the large-scale SST and atmospheric anomalies, numerical simulations with one-dimensional mixed-layer models, and stochastic forcing models. We stress the similarities in the different approaches and discuss their main advantages and limitations. The response of the atmosphere to mid-latitude SST anomalies is considered. First, we discuss the poorly known relationship between SST anomalies and diabatic heating. Using a crude assumption for the air-sea coupling, we consider a two-layer quasi-geostrophic channel model and discuss the stationary wave response to SST anomaly forcing and the resulting air-sea feedback. It is found that the back interaction of the SST anomalies onto the atmosphere causes a weak SST anomaly damping at large scales and a strong one at small scales; the air-sea coupling should also act as an eastward propagator for the SST anomalies. The response of more realistic linear wave models to prescribed diabatic heating is then reviewed, and it is suggested that realistic mid-latitude SST anomalies have a weak influence on the atmospheric circulation, corresponding to changes in the geopotential height of 10–30 m at most. This order of magnitude is consistent with the results of general circulation model experiments and with the limited climate predictability associated with mid-latitude SST anomalies.},
author = {Frankignoul, Claude},
booktitle = {Reviews of Geophysics},
doi = {10.1029/RG023i004p00357},
isbn = {1944-9208},
issn = {19449208},
number = {4},
pages = {357--390},
title = {{Sea surface temperature anomalies, planetary waves, and air-sea feedback in the middle latitudes}},
volume = {23},
year = {1985}
}
@article{Wernberg2012a,
author = {Wernberg, Thomas and Smale, Dan A and Tuya, Fernando and Thomsen, Mads S and Langlois, Timothy J and de Bettignies, Thibaut and Bennett, Scott and Rousseaux, Cecile S},
doi = {10.1038/nclimate1627},
isbn = {1758-678X},
issn = {1758-678X},
journal = {Nature Climate Change},
number = {8},
pages = {78--82},
title = {{An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot}},
url = {http://www.nature.com/doifinder/10.1038/nclimate1627},
volume = {3},
year = {2012}
}
@article{Jentsch2007,
author = {Jentsch, A. and Kreyling, J. and Beierkuhnlein, C.},
doi = {10.1890/1540-9295(2007)5},
journal = {Frontiers in Ecology and the Environment},
number = {6},
pages = {315--324},
title = {{A new generation of climate-change experiments: events, not trends}},
volume = {5},
year = {2007}
}
@misc{Gibson2017,
author = {Gibson, Peter B. and Perkins-Kirkpatrick, Sarah E. and Uotila, Petteri and Pepler, Acacia S. and Alexander, Lisa V.},
booktitle = {Journal of Geophysical Research: Atmospheres},
doi = {10.1002/2016JD026256},
issn = {21698996},
keywords = {Circulation types,Climate dynamics,Heat waves,K-means,SOMs,Weather types},
title = {{On the use of self-organizing maps for studying climate extremes}},
year = {2017}
}
@article{Morioka2010,
abstract = {Using a non-linear statistical analysis called "self-organizing maps", the interannual sea surface temperature (SST) variations in the southern Indian Ocean are investigated. The SST anomalies during austral summer from 1951 to 2006 are classified into nine types with differences in the position of positive and negative SST anomaly poles. To investigate the evolution of these SST anomaly poles, heat budget analysis of mixed-layer using outputs from an ocean general circulation model is conducted. The warming of the mixed-layer by the climatological shortwave radiation is enhanced (suppressed) as a result of negative (positive) mixed-layer thickness anomaly over the positive (negative) SST anomaly pole. This contribution from shortwave radiation is most dominant in the growth of SST anomalies. In contrast to the results reported so far, the contribution from latent heat flux anomaly is not so important. The discrepancy in the analysis is explained by the modulation in the contribution from the climatological heat flux by the interannual mixed-layer depth anomaly that was neglected in the past studies. {\textcopyright} 2010 Springer-Verlag.},
author = {Morioka, Yushi and Tozuka, Tomoki and Yamagata, Toshio},
doi = {10.1007/s00382-010-0843-x},
issn = {09307575},
journal = {Climate Dynamics},
keywords = {Indian Ocean subtropical dipole,Mixed-layer heat budget,Self-organizing map},
number = {6},
pages = {1075--1088},
title = {{Climate variability in the southern Indian Ocean as revealed by self-organizing maps}},
volume = {35},
year = {2010}
}
@article{Manabe1967,
abstract = {Radiative convective equilibrium of the atmosphere with a given distribution of relative humidity is computed as the asymptotic state of an initial value problem. The results show that it takes almost twice as long to reach the state of radiative convective equilibrium for the atmosphere with a given distribution of relative humidity than for the atmosphere with a given distribution of absolute humidity. Also, the surface equilibrium temperature of the former is almost twice as sensitive to change of various factors such as solar constant, CO2 content, O3 content, and cloudiness, than that of the latter, due to the adjustment of water vapor content to the temperature variation of the atmosphere. According to our estimate, a doubling of the CO2 content in the atmosphere has the effect of raising the temperature of the atmosphere (whose relative humidity is fixed) by about 2C. Our model does not have the extreme sensitivity of atmospheric temperature to changes of CO2 content which was adduced by M{\"{o}}ller.},
author = {Manabe, Syukuro and Wetherald, Richard T.},
doi = {10.1175/1520-0469(1967)024<0241:TEOTAW>2.0.CO;2},
isbn = {0022-4928},
issn = {0022-4928},
journal = {Journal of the Atmospheric Sciences},
number = {3},
pages = {241--259},
title = {{Thermal Equilibrium of the Atmosphere with a Given Distribution of Relative Humidity}},
volume = {24},
year = {1967}
}
@article{Wernberg2011,
abstract = {Temperate Australia is a global hotspot for marine biodiversity and its waters have experienced well-above global average rates of ocean warming. We review the observed impacts of climate change (e.g. warming, ocean acidification, changes in storm patterns) on subtidal temperate coasts in Australia and assess how these systems are likely to respond to further change. Observed impacts are region specific with the greatest number of species responses attributable to climate change reported in south-eastern Australia, where recent ocean warming has been most pronounced. Here, a decline of giant kelp (Macrocystis pyrifera) and poleward range extension of a key herbivore (sea urchin) and other trophically important reef organisms has occurred. Although, evidence of changes on other coastlines around Australia is limited, we suggest that this is due to a lack of data rather than lack of change. Because of the east-west orientation of the south coast, most of Australia's temperate waters are found within a narrow latitudinal band, where any southward movement of isotherms is likely to affect species across very large areas. Future increases in temperature are likely to result in further range shifts of macroalgae and associated species, with range contractions and local extinctions to be expected for species that have their northern limits along the southern coastline. While there is currently no evidence of changes attributable to non-temperature related climate impacts, potentially due to a lack of long-term observational data, experimental evidence suggests that ocean acidification will result in negative effects on calcifying algae and animals. More importantly, recent experiments suggest the combined effects of climate change and non-climate stressors (overharvesting, reduced water quality) will lower the resilience of temperate marine communities to perturbations (e.g. storms, diseases, and introduced species), many of which are also predicted to increase in frequency and/or severity. Thus climate change is likely to, both by itself and in synergy with other stressors, impose change to southern Australian coastal species, including important habitat-forming algae and the associated ecological functioning of temperate coasts. Management of local and regional-scale stresses may increase the resistance of temperate marine communities to climate stressors and as such, provides an attractive tool for building resilience in temperate systems. ?? 2011 Elsevier B.V.},
author = {Wernberg, Thomas and Russell, Bayden D and Moore, Pippa J and Ling, Scott D and Smale, Daniel A and Campbell, Alex and Coleman, Melinda A and Steinberg, Peter D and Kendrick, Gary A and Connell, Sean D},
doi = {10.1016/j.jembe.2011.02.021},
isbn = {0022-0981},
issn = {00220981},
journal = {Journal of Experimental Marine Biology and Ecology},
keywords = {Climate impacts,Community ecology,Global warming,Macroalgae,Multiple stressors,Phase shift,Range contraction,Range extension,Trophodynamics},
number = {1-2},
pages = {7--16},
title = {{Impacts of climate change in a global hotspot for temperate marine biodiversity and ocean warming}},
volume = {400},
year = {2011}
}
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abstract = {Abstract A regionalization of the conterminous United States is accomplished using hierarchical cluster analysis on temperature and precipitation data. The “best” combination of clustering method and data preprocessing strategy yields a set of candidate clustering levels, from which the 14-, 25-, and 8-duster solutions are chosen. Collectively, these are termed the “reference clusterings.” At the 14-cluster level, the bulk of the nation is partitioned into four principal climate zones: the Southeast, East Central, Northeastern Tier, and Interior West clusters. Many small clusters are concentrated in the Pacific Northwest. The 25-cluster solution can be used to identify the subzones within the 14 clusters. At that more detailed level, many of the areally more extensive clusters are partitioned into smaller, more internally cohesive subgroups. The “best” clustering approach is the one that minimizes the influences of three forms of bias-methodological, latent, and information-for the dataset at hand. Source...},
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booktitle = {Journal of Climate},
doi = {10.1175/1520-0442(1993)006<2103:CZOTCU>2.0.CO;2},
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year = {1993}
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doi = {10.1007/s10584-016-1650-0},
isbn = {0165-0009$\backslash$r1573-1480},
issn = {15731480},
journal = {Climatic Change},
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pages = {101--114},
title = {{Natural hazards in Australia: heatwaves}},
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archivePrefix = {arXiv},
arxivId = {0901.3494},
author = {Sang, Huiyan and Gelfand, Alan E. and Lennard, Chris and Hegerl, Gabriele and Hewitson, Bruce},
doi = {10.1214/08-AOAS174},
eprint = {0901.3494},
issn = {19326157},
journal = {Annals of Applied Statistics},
keywords = {Bivariate spatial predictive process,Markov chain monte carlo,Model choice,Space-time models,Vector autoregressive model},
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keywords = {AMSR,AVHRR,SST,temperature},
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title = {{Daily high-resolution-blended analyses for sea surface temperature}},
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year = {2007}
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repository: robwschlegel/MHWdetection

repo_name: robwschlegel/MHWdetection