We will look at the physics behind this phenomenon later, but teleconnections as the one described above represent a statistical relationship (i.e. correlation) from which causality cannot necessarily be inferred. Ocean surface temperatures, and most notably slight anomalies herein (+/- 1 to 3 degrees Celsius), can cause significant changes in the Southern Oscillation, the atmospheric component. The combination of these two, also referred to as El Niño/Southern Oscillation (ENSO), and it's effect on the African continent is the focus of our study. We will try to answer the question whether rainfall and vegetation growth in this continent is linked to El Niño. We structure our scientific activities following the GIScience process as proposed by Gahegan (2005) depicted in the chart below.
The null hypothesis (Ho) for research is that there is no systematic difference in the predictand (i.e. rainfall, NDVI) between the seasons grouped according to the different El Niño events (the predictor);
versus the alternative hypothesis (Ha), that there is systematic difference in the predictand (i.e. rainfall, NDVI) between the seasons grouped according to the different El Niño events (the predictor).
The ENSO cycle, and its effect on vegetation biomass in Africa, is best described through a combination of three components: data collected in the ocean, data collected in the atmosphere, and a terrestrial dataset collected over the African continent. All three data components are published using Distributed Data Access technology through the ITC TDS Catalog (http://thredds.itc.nl:8180/thredds/ensoCatalog.html). Finally, for each component three datasets were produced by sorting close analogue from historical cases: El Niño, La Niña, and one for Normal conditions. The resulting datasets are still large. In the ocean, they cover 30 N. Lat to 30 S. Lat, 60 W. Lon to 240 W. Lon, and 0-232 meters below sea level, with data every 2 degrees of latitude and longitude. There are 15 vertical points in the data. The atmosphere data covers the same range of latitude and longitude, but with data every 2.5 degrees of latitude and longitude. On land, the NDVI dataset geographically extends from 43.5 N. Lat to 42.1 S. Lat, 24.7 E. Lon to 64.7 W. Lon, with data every 8-km mapped using a conic, equal area projection (Albers Conical Equal Area).
Note: Distributed Data Access is a technology consisting of interconnected servers and clients, who communicate (requests for) data through the Internet. Servers provide capability to remotely sub-set data collections based on the user's spatial, temporal, and parameter requirements. Internet-bandwidth requirements are thus drastically reduced, often resulting in download sizes by a factor 10 smaller than the original. Software packages as FERRET, GrADS, matlab, IDL/ENVI, IDV, and through add-ons even packages such as ESRI ArcGIS, are examples of clients that 'understand how to access data remotely'. This feature is made possible through open-source software standards developed by Unidata (http://www.unidata.ucar.edu/) and the Open Geospatial Consortium (OGC, http://www.opengeospatial.org).
The atmospheric data originates from the National Center for Environmental Prediction (NCEP), which has been collecting, archiving and quality controlling data for the atmosphere over the entire globe for the last 50 years. Once the data is collected, the Climate Diagnostics Center of the National Oceanic and Atmospheric Administration (NOAA) makes it available to the public via the web . Our atmospheric data is divided into three 'events': normal conditions, El Niño conditions, and La Niña conditions. To make these events from a 50-year-long span of data, we binned each year into one of the three categories, based on the Southern Oscillation Index:
To ensure a clear signal, we only considered data from December to January, when ENSO phases peak. Then, we averaged all of the years in each bin to produce three atmospheric datasets of 'typical' El Niño, La Niña, and Normal Years.
The datasets relevant to this study also contains data from the ocean. Due to scarcity of oceanographic observation (see Figure 1.3), scientists use numerical models of the ocean instead to get a more complete dataset. The models are checked against the data that oceanographers do have and then used primarily to fill in the gaps in time and space. Our ocean data were kindly provided by Dr. Peter Gent (NCAR Climate and Global Dynamics Division) and were generated from the ocean component of the Climate System Model (CSM) (http://www.cgd.ucar.edu/csm/index.html) run by the National Center for Atmospheric Research (NCAR). As with the atmospheric data, the ocean data is binned and averaged to produce a 'typical' El Niño, La Niña ocean dataset.
Assuming NDVI is an accurate indicator of vegetation greenness and biomass, the NDVI dataset (http://iridl.ldeo.columbia.edu/SOURCES/.USGS/.ADDS/.NDVI/) we use for modelling the terrestrial component is based on satellite sensor observation from the Advanced Very High Resolution Radiometers (AVHRR) instrument aboard the National Oceanic and Atmospheric Administration (NOAA). This dataset was re-processed as described in Tucker et al. (2005) and originates from FEWS/Africa Data Dissemination Service (ADDS) located in Harare, Zimbabwe. The same dataset is mirrored at the IRI/LDEO Climate Data Library (http://iridl.ldeo.columbia.edu) of the International Research Institute for Climate and Society. The same Data Library holds one more datasets (predictand) relevant to our study, notably estimated rainfall as described in Love et al. (2004), both geographically covering the whole of Africa:
To remotely access these data sources, the data access URLs takes the following form (NDVI Dekadal Maximum 1981-2004):
dods://iridl.ldeo.columbia.edu/SOURCES/.USGS/.ADDS/.NDVI/.NDVIg/.dekadal/.maximum/dodsNDVI Dekadal Maximum 2004-now:
dods://iridl.ldeo.columbia.edu/SOURCES/.USGS/.ADDS/.NDVI/.NDVIrg/.dekadal/.maximum/dodsFor the RFE imagery the data access url takes the following form:
dods://prefix (instead of
http://) which signals the OPeNDAP data server a DODS-data object is being requested instead of the metadata (text). If no error occurs in accessing and subsetting the data, the server responds by returning a data object to the client. Try the same url, but now with the
http://prefix and observe the difference.
Now, having NDVI imagery for a 28-year-long timespan (since December 1981, when the first NOAA/AVHRR sensor was launched) we again binned each year into one of the three El Niño categories as with the atmospheric/oceanic components:
Also for the two sites in Kenya (Naivasha and Narok) interpretations from the data visualizations seem to be confirmed by the statistical test, and results for Narok remain negative.
Talking about predictive (GIS) models, eventually the 'real' proof of any concept lies in its adaptation in society. This is the case for ENSO in South-East Africa, such so that even major seed trade companies see stock values fluctuate due to (un)founded fears for another El Niño. For example, de Jager et al. (1998) based their weather forecast component of a calibrated CERES maize model on this same ENSO teleconnection, with the intention to forecast the extent and severity of drought in maize in the Free State Province of South Africa one month before the growing season starts. The high correlation value (r²=0.86) for simulated vs. actual maize yields is an encouraging sign. Because every month for every year can be placed into a particular analogue of months, weather parameters other than rainfall can also be placed together to take out daily values (i.e. temperature, solar radiation, etc.). These data can then be used as input for a crop growth simulation model, a procedure made possible by the Agro-meteorological Rainfall Analysis and Forecast model (ARAF) as developed by Venus (1999, 2000). Based on this model he proved that roughly 75% of the rainfall variability and a corresponding 80% of maize yield variability could be explained for Mashona-Land West Province, Zimbabwe. Further justification of this technique can be found in Meinke and Hammer (1998) who demonstrated that highly significant differences in peanut yields in Australia exist among seasons grouped according to the SOI phases of Stone (1996). Despite being data demanding, El Niño/Southern Oscillation (ENSO) based agro-ecosystems predictions remain useful, i.e. for preliminary estimations of regional agricultural productivition-levels, also since they are available well in advance of actual conditions to allow for adaptive management.
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