The impact of global warming on sea surface temperature based El Niño–Southern Oscillation monitoring indices

Turkington, Thea; Timbal, Bertrand; Rahmat, Raizan

doi:10.1002/joc.5864

Cited by 28 publications

(20 citation statements)

References 43 publications

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“…In Figure 5d, rainfall coefficients are higher and are significant in determining malaria incidence rate per 1000 around Mombasa from 2000 to 2005, south of Lake Victoria from 2010 to 2015, while rainfall has smaller coefficients around Nairobi from 2005 to 2010. The ENSO event of 2015 is perhaps more significant in driving up malaria in the southern region of Lake Victoria, as noted in prior research [47,48]. There are some changes in the rainfall coefficient over the time-period, shown in Figure 5d, around northern Lake Victoria.…”

Section: Spatial Determinants Of Malariasupporting

confidence: 54%

“…Vegetation coefficients, shown in Figure 5c for 2015, are significant in determining malaria incidence rate per 1000 and are positive in the southern shore of Lake Victoria, and are negative on the northern shore of Lake Victoria. Prior studies highlight the complex nature of malaria vector breeding in the lake habitats and describe the role of short and tall grass as well as water hyacinths in the lake [47,48]. The vegetation index coefficient was positive for Mombasa in 2000.…”

Section: Spatial Determinants Of Malariamentioning

confidence: 99%

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Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Gopal

Chen

et al. 2019

IJERPH

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The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used—Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino–southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

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Section: Spatial Determinants Of Malariasupporting

confidence: 54%

Section: Spatial Determinants Of Malariamentioning

confidence: 99%

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Gopal

Chen

et al. 2019

IJERPH

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“…In this paper, Niño 3.4 and Niño 4 SSTs used in the correlation maps in Figure 5a and b and the wavelet analysis in Supplementary Figure S3 (available online) are from the ERSSTv5 dataset (https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5). Concerns about Niño 1+2, 3, 3.4 and 4 region SSTs derived from such datasets that have been interpolated, use multiple data sources, include satellite remote sensing, and display warming biases due to the effect of climate change, have been discussed and improvements suggested by papers such as Newman et al (2018) and Turkington et al (2019). To assess the potential effect of such influences on our results, a comparison was made with the seasonal rainfall correlations in Figure 5a and b and in Supplementary Figure S3 (available online), which used ERSSTv5, with the same correlations produced using the observations only Kaplan 1856–1991 (http://iridl.ldeo.columbia.edu/SOURCES/.Indices/.nino/.KAPLAN/; Kaplan et al, 1998) and the combined statistically infilled observations with statistically reduced and coarser resolution optimally interpolated Kaplan Extended SST v2 1856–2019 (http://iridl.ldeo.columbia.edu/SOURCES/.Indices/.nino/.EXTENDED/) Niño 3.4 and Niño 4 SST datasets (not shown).…”

Section: Methodsmentioning

confidence: 99%

“…ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5). Concerns about Niño 1+2, 3, 3.4 and 4 region SSTs derived from such datasets that have been interpolated, use multiple data sources, include satellite remote sensing, and display warming biases due to the effect of climate change, have been discussed and improvements suggested by papers such as Newman et al (2018) and Turkington et al (2019). To assess the potential effect of such influences on our results, a comparison was made with the seasonal rainfall correlations in Figure 5a and b and in Supplementary Figure S3 (available online), which used ERSSTv5, with the same correlations produced using the observations only Kaplan 1856-1991 (http://iridl.ldeo.columbia.edu/SOURCES/.Indices/.nino/.…”

Section: Instrumental Datamentioning

confidence: 99%

Placing the AD 2014–2016 ‘protracted’ El Niño episode into a long-term context

2019

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Although extended or ‘protracted’ El Niño and La Niña episodes were first suggested nearly 20 years ago, they have not received the attention of other ‘flavours’ of the El Niño–Southern Oscillation (ENSO) or low-frequency ‘ENSO-like’ phenomena. In this study, instrumental variables and palaeoclimatic reconstructions are used to investigate the most recent ‘protracted’ El Niño episode in 2014–2016, and place it into a longer historical context. Although just reaching the threshold for such an episode, the 2014–2016 ‘protracted’ El Niño had very severe societal, agricultural, environmental and ecological impacts, particularly in western Pacific regions like eastern Australia. We show that although ‘protracted’ ENSO episodes of either phase cause similar, near-global modulations of weather and climate as during more ‘classical’ events, impacts associated with ‘protracted’ episodes last longer, with strong influences in eastern Australia. The latter is a response to the dominance of Niño 4 sea surface temperature (SST) and associated atmospheric teleconnection anomalies during ‘protracted’ ENSO episodes. Importantly, while Niño 4 SST anomalies recorded during the austral summer of 2016 were the highest values on record, an analysis of long-term palaeoclimate records indicates that there may have been episodes of greater magnitude and duration than seen in instrumental observations. This suggests that shorter instrumental observations may underestimate the risks of possible future ENSO extremes compared with those observed from multi-century palaeoclimate records. Improved knowledge of ENSO and the potential to forecast ‘protracted’ episodes would be of immense practical benefit to communities affected by the severe impacts of ENSO extremes.

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“…We estimate the forced signal in the tropical Pacific with the method of Turkington et al (2019) and by using multimodel means. Turkington et al (2019) quantify the forced signal as the linear trend in tropical SSTs from 30° S to 30° N over 1962-2011, a period chosen such that there is little trend in the IPO. The rate of global warming, however, increased significantly in the early 1970s (Rahmstorf et al 2017) related to changes in anthropogenic forcing.…”

Section: Influence Of Forced Trend In Pacific Sstmentioning

confidence: 99%

Pacific variability reconciles observed and modelled global mean temperature increase since 1950

et al. 2020

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Global mean temperature change simulated by climate models deviates from the observed temperature increase during decadal-scale periods in the past. In particular, warming during the ‘global warming hiatus’ in the early twenty-first century appears overestimated in CMIP5 and CMIP6 multi-model means. We examine the role of equatorial Pacific variability in these divergences since 1950 by comparing 18 studies that quantify the Pacific contribution to the ‘hiatus’ and earlier periods and by investigating the reasons for differing results. During the ‘global warming hiatus’ from 1992 to 2012, the estimated contributions differ by a factor of five, with multiple linear regression approaches generally indicating a smaller contribution of Pacific variability to global temperature than climate model experiments where the simulated tropical Pacific sea surface temperature (SST) or wind stress anomalies are nudged towards observations. These so-called pacemaker experiments suggest that the ‘hiatus’ is fully explained and possibly over-explained by Pacific variability. Most of the spread across the studies can be attributed to two factors: neglecting the forced signal in tropical Pacific SST, which is often the case in multiple regression studies but not in pacemaker experiments, underestimates the Pacific contribution to global temperature change by a factor of two during the ‘hiatus’; the sensitivity with which the global temperature responds to Pacific variability varies by a factor of two between models on a decadal time scale, questioning the robustness of single model pacemaker experiments. Once we have accounted for these factors, the CMIP5 mean warming adjusted for Pacific variability reproduces the observed annual global mean temperature closely, with a correlation coefficient of 0.985 from 1950 to 2018. The CMIP6 ensemble performs less favourably but improves if the models with the highest transient climate response are omitted from the ensemble mean.

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The impact of global warming on sea surface temperature based El Niño–Southern Oscillation monitoring indices

Cited by 28 publications

References 43 publications

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

Placing the AD 2014–2016 ‘protracted’ El Niño episode into a long-term context

Pacific variability reconciles observed and modelled global mean temperature increase since 1950

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