Earth  ID: 4785

Sea Surface Temperature anomalies and patterns of Global Disease Outbreaks: 2009-2018 (4K version)

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The El Niño-Southern Oscillation (ENSO) phenomenon is an irregularly recurring climate pattern characterized by warmer (El Niño) and colder (La Niña) than usual ocean temperatures in the equatorial Pacific, which creates a ripple effect of anticipated weather changes in far-spread regions of Earth. Weather changes associated with the El Niño-Southern Oscillation phenomenon result in rainfall, temperature and environmental anomaly conditions worldwide that directly favor outbreaks of infectious diseases of public health concern.

During the last 20 years NASA scientist Dr. Assaf Anyamba and colleagues have been studying interannual climate variability patterns associated with El Niño by monitoring various climate datasets, among them land surface temperature and vegetation data from the Advanced High Resolution Radiometer (AVHRR) on board NOAA POES satelittes, the Moderate Resolution Imaging Spectroradiometer aboard NASA's Terra and Aqua satellites, and Sea Surface Temperature and precipitation anomaly datasets from NASA and the National Oceanic and Atmospheric Administration (NOAA). At the same time, the science team has been collecting, cataloguing and analyzing patterns of disease outbreaks worldwide.

This scientific study - the first one to comprehensively assess the public health impacts of the major climate event on a global scale - was published in the journal Nature Scientific Reports, with the title Global Disease Outbreaks Associated with the 2015-2016 El Niño event and is open access available. According to this study, the 2015-2016 El Niño event brought weather conditions that triggered disease outbreaks in ENSO teleconnected regions throughout the world.

The visualization showcases a global flat map with monthly Sea Surface Temperature (SST) anomaly data on the water, the locations of Global Disease Outbreaks of ten infectious diseases on land, along with a timeline plot of the ENSO Index (Niño 3.4 Index region SST anomaly) for the period 2009-2018 on the bottom. The Nino 3.4 Index region SST with extents (5N-5S, 120W-170W) is the box region, highlighted on the Pacific Ocean.

During ENSO events, SST anomalies influence the nature and patterns of rainfall, vegetation and land surface temperatures on the land surface, which in turn influence the disease outbreaks that are mapped on a global scale. The 11 diseases mapped on this visualization are: chinkungunya, cholera, dengue virus, hantavirus, respiratory illness, Rift Valley fever, Ross River virus, St. Louis encephalitis, tularemia and zika virus/

During the 2015-2016 El Nino event, which is manifested in the visualization with increased sea surface temperature anomaly (reds in Niño 3.4 Index Region), changes in precipitation, land surface temperatures and vegetation created and facilitated conditions for transmission of diseases, resulting in an uptick in reported cases for plague and hantavirus in Colorado and New Mexico (in 2015), cholera in East Africa’s Tanzania (during 2015 and 2016), and dengue fever in Brazil and Southeast Asia (during 2015), among others.

According to the study, El Niño-driven increase in rainfall and milder temperatures over the American Southwest, spurred vegetative growth, providing more food for rodents that carry hantavirus. A resulting rodent population explosion put them in more frequent contact with humans, who contract the potentially fatal disease mostly through rodent fecal or urine contamination. As their rodent hosts proliferated, so did plague-carrying fleas.

Regarding dengue outbreaks, the strong El Niño period produced higher than normal land surface temperatures and therefore drier habitats, which drew mosquitoes into populated, urban areas where there are open water storage containers providing ideal habitats for mosquito production. In addition, the higher the normal temperatures increase the maturation time of larvae to adult mosquitos and also induce frequent blood feeding/biting by mosquito vectors resulting in increased number of disease cases. The following 3 data driven visualizations demonstrate the complex relationships between the El Niño event in 2015-2016 and disease outbreaks of dengue in the South East Asia region:


The relationship between ENSO induced anomalous rainfall is clearly illustrated by outbreak patterns of Rift Valley fever (RVF) between Eastern and South Africa. During El Niño events, Eastern Africa (Southern Africa) receives persistent and above (below) normal rainfall which floods habitats of RVF vectors mosquitoes triggering hatching of RVF infected eggs, increase in mosquito populations and infections of domestic livestock and human populations with the RVF virus. During La Niña events, Southern Africa receives above-normal rainfall that sets the stage for RVF outbreak activity. The results is the sea-saw pattern exhibited by the ENSO events drives patterns of disease outbreaks in different regions around the world. To learn more about the relationship between ENSO and Rift Valley fever outbreaks, please see the following 3 data driven visualizations:


The strong relationship between El Niño events and disease outbreaks underscores the importance of seasonal forecasts. Since disease outbreaks typically manifest 2-3 months after the start of El Niño events, early and regular climate monitoring, paired with the use of monthly and seasonal climate forecasts become significant tools for disease control and prevention. Findings of the scientific study suggest that by monitoring monthly climate datasets, country public health agencies and organizations such as the United Nations' World Health Organization and Food and Agriculture Organizations, can utilize early warning forecasts to undertake preventive measures to minimize the spread of ecologically coupled diseases.



Data Sources:

  • Sea Surface Temperature (SST) data: The SST known as the NOAA OI.v2 SST monthly fields are derived by a linear interpolation of the weekly optimum interpolation (OI) version 2 fields to daily fields then averaging the daily values over a month. The analysis uses in situ and satellite SST's plus SST's simulated by sea-ice cover. Before the analysis is computed, the satellite data is adjusted for biases using the method of Reynolds (1988) and Reynolds and Marsico (1993). The SST dataset is available here

  • Disease Outbreak data were collected from the Program for Monitoring Emerging Diseases (ProMED), the Pan-American Health Organization (PAHO) online country reports, weekly summaries of disease outbreaks reported by the Department of Defense Armed Forces Health Surveillance Branch and from the World Organisation for Animal Health/Organisation mondiale de la santé animale (OIE).

  • SST ENSO index (Niño 3.4) for the period 2009-2018 is obtained from the NOAA National Center for Climate Prediction on-line archives. The warm (El Niño) and cold (La Niña) periods of ENSO events were determined using the Oceanic Niño Index (ONI) threshold of +/- 0.5oC based on centered 30-year base periods updated every 5 years. The ONI is a 3-month running mean of Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4) SST anomalies in the Niño 3.4 region (5 N-5 S, 120W-170W).




This rest of this webpage provides frames, movies, colorbar information and layers associated with the development of this data visualization.
 

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Visualization Credits

Lead Visualizer:
Helen-Nicole Kostis (USRA)

Visualizer:
Greg Shirah (NASA/GSFC)

Lead Scientists:
Assaf Anyamba (USRA)
Radina Soebiyanto (USRA)

Science Support:
Jennifer Small (SSAI)

Technical Support:
Laurence Schuler (ADNET)
Ian Jones (ADNET)

Project Support:
Eric Sokolowsky (GST)
Leann Johnson (GST)

Outbreak Data Curators:
Sarah Hutchinson (Intern)
Destini Garrison (Intern)

Please give credit for this item to:
NASA's Scientific Visualization Studio

Science Paper:
Anyamba, A., Chretien, J-P., Britch,S.C.,Soebiyanto, R.P., Small, J. L., Jepsen, P., Forshey, B.M., Sanchez, J.L., Smith, R.D., Harris, R., Tucker, C.J., Karesh, W.B., Linthicum, K.J. (2019). Global Disease Outbreaks Associated with the 2015-2016 El Niño Event. Nature Scientific Reports. 9:1930 | 10.1038/s41598-018-38034-z

Short URL to share this page:
https://svs.gsfc.nasa.gov/4785

Data Used:
Multiple/Multisensors including buoys, passive microwave sensors/Oceanic Nino Index (3 month running means of Extended Reconstructed Sea Surface Temperature (ERSST) v5 anomalies
Note: While we identify the data sets used in these visualizations, we do not store any further details nor the data sets themselves on our site.

Keywords:
DLESE >> Climatology
SVS >> El Nino
DLESE >> Environmental science
DLESE >> Human geography
SVS >> SST Anomaly
GCMD >> Earth Science >> Climate Indicators >> Drought Indices
GCMD >> Earth Science >> Climate Indicators >> Precipitation Indices
GCMD >> Earth Science >> Climate Indicators >> Teleconnections >> El Nino Southern Oscillation
GCMD >> Earth Science >> Human Dimensions >> Human Health >> Diseases/Epidemics
GCMD >> Earth Science >> Human Dimensions >> Human Health >> Public Health
SVS >> Hyperwall
NASA Science >> Earth
GCMD >> Earth Science >> Human Dimensions >> Public Health >> Diseases/epidemics
SVS >> Diseases
NASA Earth Science Focus Areas >> Climate Variability and Change

GCMD keywords can be found on the Internet with the following citation: Olsen, L.M., G. Major, K. Shein, J. Scialdone, S. Ritz, T. Stevens, M. Morahan, A. Aleman, R. Vogel, S. Leicester, H. Weir, M. Meaux, S. Grebas, C.Solomon, M. Holland, T. Northcutt, R. A. Restrepo, R. Bilodeau, 2013. NASA/Global Change Master Directory (GCMD) Earth Science Keywords. Version 8.0.0.0.0