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Scott T, McCarroll RJ, Masselink G, Castelle B, Dodet G, Saulter A, Scaife AA, Dunstone N. Role of Atmospheric Indices in Describing Inshore Directional Wave Climate in the United Kingdom and Ireland. Earths Future 2021; 9:e2020EF001625. [PMID: 34222554 PMCID: PMC8244045 DOI: 10.1029/2020ef001625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 03/27/2021] [Accepted: 04/05/2021] [Indexed: 06/13/2023]
Abstract
Improved understanding of how our coasts will evolve over a range of time scales (years-decades) is critical for effective and sustainable management of coastal infrastructure. A robust knowledge of the spatial, directional and temporal variability of the inshore wave climate is required to predict future coastal evolution and hence vulnerability. However, the variability of the inshore directional wave climate has received little attention, and an improved understanding could drive development of skillful seasonal or decadal forecasts of coastal response. We examine inshore wave climate at 63 locations throughout the United Kingdom and Ireland (1980-2017) and show that 73% are directionally bimodal. We find that winter-averaged expressions of six leading atmospheric indices are strongly correlated (r = 0.60-0.87) with both total and directional winter wave power (peak spectral wave direction) at all studied sites. Regional inshore wave climate classification through hierarchical cluster analysis and stepwise multi-linear regression of directional wave correlations with atmospheric indices defined four spatially coherent regions. We show that combinations of indices have significant skill in predicting directional wave climates (R 2 = 0.45-0.8; p < 0.05). We demonstrate for the first time the significant explanatory power of leading winter-averaged atmospheric indices for directional wave climates, and show that leading seasonal forecasts of the NAO skillfully predict wave climate in some regions.
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Affiliation(s)
- T. Scott
- School of Biological and Marine SciencesUniversity of PlymouthPlymouthUK
| | - R. J. McCarroll
- School of Biological and Marine SciencesUniversity of PlymouthPlymouthUK
| | - G. Masselink
- School of Biological and Marine SciencesUniversity of PlymouthPlymouthUK
| | - B. Castelle
- UMR EPOCUniversity of Bordeaux/CNRSBordeauxFrance
| | - G. Dodet
- IFREMERCNRSIRDLaboratoire d'Océanographie Physique et SpatialeIUEMUniversity of BrestBrestFrance
| | | | - A. A. Scaife
- UK Met OfficeExeterUK
- College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK
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Reboita MS, Ambrizzi T, Crespo NM, Dutra LMM, Ferreira GWDS, Rehbein A, Drumond A, da Rocha RP, Souza CAD. Impacts of teleconnection patterns on South America climate. Ann N Y Acad Sci 2021; 1504:116-153. [PMID: 33914367 DOI: 10.1111/nyas.14592] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 02/16/2021] [Accepted: 02/24/2021] [Indexed: 11/29/2022]
Abstract
Oceanic heat sources disturb the atmosphere, which, to come back to its initial state, disperses waves. These waves affect the climate in remote regions, characterizing the teleconnection patterns. In this study, we describe eight teleconnection patterns that affect South America climate: the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO), the Tropical Atlantic Dipole (TAD), the South Atlantic Dipole (SAD), the Southern Annular Mode (SAM), the Madden-Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD). Precipitation and winds at 850-hPa anomalies, considering these teleconnection patterns in ENSO neutral periods, are also presented. Overall, southeastern South America and the north sector of the North and Northeast regions of Brazil are the most affected areas by the teleconnection patterns. In general, there is a precipitation dipole pattern between these regions during each teleconnection pattern.
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Affiliation(s)
| | - Tércio Ambrizzi
- Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Natália Machado Crespo
- Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Lívia Márcia Mosso Dutra
- Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, SP, Brazil
| | | | - Amanda Rehbein
- Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Anita Drumond
- Instituto de Ciências Ambientais, Químicas e Farmacêuticas, Universidade Federal de São Paulo, Diadema, SP, Brazil
| | - Rosmeri Porfírio da Rocha
- Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, São Paulo, SP, Brazil
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Litzow MA, Ciannelli L, Puerta P, Wettstein JJ, Rykaczewski RR, Opiekun M. Non-stationary climate-salmon relationships in the Gulf of Alaska. Proc Biol Sci 2018; 285:rspb.2018.1855. [PMID: 30404879 DOI: 10.1098/rspb.2018.1855] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 10/19/2018] [Indexed: 11/12/2022] Open
Abstract
Studies of climate effects on ecology often account for non-stationarity in individual physical and biological variables, but rarely allow for non-stationary relationships among variables. Here, we show that non-stationary relationships among physical and biological variables are central to understanding climate effects on salmon (Onchorynchus spp.) in the Gulf of Alaska during 1965-2012. The relative importance of two leading patterns in North Pacific climate, the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO), changed around 1988/1989 as reflected by changing correlations with leading axes of sea surface temperature variability. Simultaneously, relationships between the PDO and Gulf of Alaska environmental variables weakened, and long-standing temperature-salmon and PDO-salmon covariance declined to zero. We propose a mechanistic explanation for changing climate-salmon relationships in terms of non-stationary atmosphere-ocean interactions coinciding with changing PDO-NPGO relative importance. We also show that regression models assuming stationary climate-salmon relationships are inappropriate over the multidecadal time scale we consider. Relaxing assumptions of stationary relationships markedly improved modelling of climate effects on salmon catches and productivity. Attempts to understand the implications of changing climate patterns in other ecosystems might also be aided by the application of models that allow associations among environmental and biological variables to change over time.
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Affiliation(s)
- Michael A Litzow
- College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Kodiak, AK 99615, USA .,Farallon Institute for Advanced Ecosystem Research, Petaluma, CA 94952, USA
| | - Lorenzo Ciannelli
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97330, USA
| | - Patricia Puerta
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97330, USA
| | - Justin J Wettstein
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97330, USA.,Geophysical Institute, University of Bergen, 5020 Bergen, Norway.,Bjerknes Centre for Climate Research, 5020 Bergen, Norway
| | - Ryan R Rykaczewski
- Department of Biological Sciences, Marine Science Program, University of South Carolina, Columbia, SC 29208, USA
| | - Michael Opiekun
- Department of Biological Sciences, Marine Science Program, University of South Carolina, Columbia, SC 29208, USA
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Marami Milani MR, Hense A, Rahmani E, Ploeger A. Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow's Milk. Foods 2016; 5:E52. [PMID: 28231147 PMCID: PMC5302396 DOI: 10.3390/foods5030052] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 07/07/2016] [Accepted: 07/18/2016] [Indexed: 11/16/2022] Open
Abstract
This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow's milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R² (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R² (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.
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Affiliation(s)
- Mohammad Reza Marami Milani
- Department of Organic Food Quality and Food Culture, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany.
| | - Andreas Hense
- Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn, Germany.
| | - Elham Rahmani
- Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn, Germany.
| | - Angelika Ploeger
- Department of Organic Food Quality and Food Culture, University of Kassel, Nordbahnhofstr. 1a, 37213 Witzenhausen, Germany.
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Mahlstein I, Spirig C, Liniger MA, Appenzeller C. Estimating daily climatologies for climate indices derived from climate model data and observations. J Geophys Res Atmos 2015; 120:2808-2818. [PMID: 26042192 PMCID: PMC4445374 DOI: 10.1002/2014jd022327] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Accepted: 03/13/2015] [Indexed: 06/04/2023]
Abstract
UNLABELLED Climate indices help to describe the past, present, and the future climate. They are usually closer related to possible impacts and are therefore more illustrative to users than simple climate means. Indices are often based on daily data series and thresholds. It is shown that the percentile-based thresholds are sensitive to the method of computation, and so are the climatological daily mean and the daily standard deviation, which are used for bias corrections of daily climate model data. Sample size issues of either the observed reference period or the model data lead to uncertainties in these estimations. A large number of past ensemble seasonal forecasts, called hindcasts, is used to explore these sampling uncertainties and to compare two different approaches. Based on a perfect model approach it is shown that a fitting approach can improve substantially the estimates of daily climatologies of percentile-based thresholds over land areas, as well as the mean and the variability. These improvements are relevant for bias removal in long-range forecasts or predictions of climate indices based on percentile thresholds. But also for climate change studies, the method shows potential for use. KEY POINTS More robust estimates of daily climate characteristicsStatistical fitting approachBased on a perfect model approach.
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Affiliation(s)
- Irina Mahlstein
- Federal Office of Meteorology and Climatology MeteoSwiss Zurich, Switzerland
| | - Christoph Spirig
- Federal Office of Meteorology and Climatology MeteoSwiss Zurich, Switzerland
| | - Mark A Liniger
- Federal Office of Meteorology and Climatology MeteoSwiss Zurich, Switzerland
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Dippner JW, Kornilovs G, Junker K. A multivariate Baltic Sea environmental index. Ambio 2012; 41:699-708. [PMID: 22430308 PMCID: PMC3472018 DOI: 10.1007/s13280-012-0260-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2011] [Revised: 08/05/2011] [Accepted: 02/18/2012] [Indexed: 05/30/2023]
Abstract
Since 2001/2002, the correlation between North Atlantic Oscillation index and biological variables in the North Sea and Baltic Sea fails, which might be addressed to a global climate regime shift. To understand inter-annual and inter-decadal variability in environmental variables, a new multivariate index for the Baltic Sea is developed and presented here. The multivariate Baltic Sea Environmental (BSE) index is defined as the 1st principal component score of four z-transformed time series: the Arctic Oscillation index, the salinity between 120 and 200 m in the Gotland Sea, the integrated river runoff of all rivers draining into the Baltic Sea, and the relative vorticity of geostrophic wind over the Baltic Sea area. A statistical downscaling technique has been applied to project different climate indices to the sea surface temperature in the Gotland, to the Landsort gauge, and the sea ice extent. The new BSE index shows a better performance than all other climate indices and is equivalent to the Chen index for physical properties. An application of the new index to zooplankton time series from the central Baltic Sea (Latvian EEZ) shows an excellent skill in potential predictability of environmental time series.
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Affiliation(s)
- Joachim W. Dippner
- />Leibniz Institute for Baltic Sea Research Warnemünde, Seestr. 15, 18119 Rostock, Germany
| | - Georgs Kornilovs
- />Fish Resources Research Department, Institute of Food Safety, Animal Health and Environment, Daugavgrivas 8, Riga, 1048 Latvia
| | - Karin Junker
- />Leibniz Institute for Baltic Sea Research Warnemünde, Seestr. 15, 18119 Rostock, Germany
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Bultó PLO, Rodríguez AP, Valencia AR, Vega NL, Gonzalez MD, Carrera AP. Assessment of human health vulnerability to climate variability and change in Cuba. Environ Health Perspect 2006; 114:1942-9. [PMID: 17185289 PMCID: PMC1764156 DOI: 10.1289/ehp.8434] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
In this study we assessed the potential effects of climate variability and change on population health in Cuba. We describe the climate of Cuba as well as the patterns of climate-sensitive diseases of primary concern, particularly dengue fever. Analyses of the associations between climatic anomalies and disease patterns highlight current vulnerability to climate variability. We describe current adaptations, including the application of climate predictions to prevent disease outbreaks. Finally, we present the potential economic costs associated with future impacts due to climate change. The tools used in this study can be useful in the development of appropriate and effective adaptation options to address the increased climate variability associated with climate change.
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