ENSO and IOD teleconnections for African ecosystems: evidence of destructive interference between climate oscillations

Williams, C. A.; Hanan, Niall P.

doi:10.5194/bg-8-27-2011

Cited by 46 publications

(24 citation statements)

References 88 publications

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“…In comparison with southern Africa, correlations between ENSO and vegetation photosynthetic activity are weakeraccording to Table 2, column 2, barely 10% of the pixels are significantly correlated with ENSO-and less spatially coherent. This is consistent with Propastin et al (2010) and Williams and Hanan (2011), who found West African vegetation to be less influenced by ENSO warm events than the southern Africa vegetation. 4).…”

Section: ) Southern Africasupporting

confidence: 90%

“…However, most of these analyses have been performed at seasonal to annual time scales (Williams and Hanan 2011;Kogan 2000) or have focused on the impact of particular ENSO events [e.g., the 1997/98 event in Anyamba et al (2001Anyamba et al ( , 2002; Verdin et al 1999] or on specific regions within Africa (e.g., Martiny et al 2009;Brown et al 2010) and rarely on the continent as a whole (e.g., Nicholson and Kim 1997). Impacts on climate have been assessed primarily by an analysis of rainfall and temperature data while impacts on the environment have been assessed primarily by an analysis of remotely sensed data of vegetation photosynthetic activity.…”

Section: Introductionmentioning

confidence: 99%

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Timing and Patterns of the ENSO Signal in Africa over the Last 30 Years: Insights from Normalized Difference Vegetation Index Data

et al. 2014

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A more complete picture of the timing and patterns of the ENSO signal during the seasonal cycle for the whole of Africa over the three last decades is provided using the normalized difference vegetation index (NDVI). Indeed, NDVI has a higher spatial resolution and is more frequently updated than in situ climate databases, and highlights the impact of ENSO on vegetation dynamics as a combined result of ENSO on rainfall, solar radiation, and temperature.The month-by-month NDVI-Niño-3.4 correlation patterns evolve as follows. From July to September, negative correlations are observed over the Sahel, the Gulf of Guinea coast, and regions from the northern Democratic Republic of Congo to Ethiopia. However, they are not uniform in space and are moderate (;0.3). Conversely, positive correlations are recorded over the winter rainfall region of South Africa. In October-November, negative correlations over Ethiopia, Sudan, and Uganda strengthen while positive correlations emerge in the Horn of Africa and in the southeast coast of South Africa. By December with the settlement of the ITCZ south of the equator, positive correlations over the Horn of Africa spread southward and westward while negative correlations appear over Mozambique, Zimbabwe, and South Africa. This pattern strengthens and a dipole at 188S is well established in February-March with reduced (enhanced) greenness during ENSO years south (north) of 188S. At the same time, at ;28N negative correlations spread northward. Last, from April to June negative correlations south of 188S spread to the north (to 108S) and to the east (to the south of Tanzania).

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Section: ) Southern Africasupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Timing and Patterns of the ENSO Signal in Africa over the Last 30 Years: Insights from Normalized Difference Vegetation Index Data

et al. 2014

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“…Generally, the results resonate with a broad literature indicating the connection between vegetation greenness and rainfall patterns in East Africa with the coupled ocean-Atmosphere phenomena of El Ni˜no Southern Oscillation (ENSO) and the Indian Ocean Dipole [47,48]. ENSO forcing is widely linked to atypical rainfall patterns [49] causing higher than normal photosynthetic activity across East and southern Africa [50].…”

Section: Resultssupporting

confidence: 78%

Centered Log-Ratio (clr) Transformation and Robust Principal Component Analysis of Long-Term NDVI Data Reveal Vegetation Activity Linked to Climate Processes

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2015

Climate

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Predicting the future climate and its impacts on the global environment is model based, presenting a level of uncertainty. Alternative robust approaches of analyzing high volume climate data to reveal underlying regional and local trends are increasingly incorporating satellite data. This study uses a centered log-ratio (clr) transformation approach and robust principal component analysis (PCA), on a long-term Normalized Difference Vegetation Index (NDVI) dataset to test its applicability in analyzing large multi-temporal data, and potential to recognize important trends and patterns in regional climate. Twenty five years of NDVI data derived by Global Inventory Modeling and Mapping Studies (GIMMS) from 1982 to 2006 were extracted for 88 subwatersheds in central Kenya and statistically analyzed. Untransformed (raw) and clr transformed NDVI data were evaluated using robust PCA. The robust PCA compositional biplots of the clr transformed long-term NDVI data demonstrated the finest spatial-temporal display of observations identifying climate related events that impacted vegetation activity and observed variations in greenness. The responses were interpreted as normal conditions, El Niño Southern Oscillation (ENSO) events of El Niño and La Niña, and drought events known to influence the moisture level and precipitation patterns (high, low, normal) and therefore the level of vegetation greenness (NDVI value). More drought events (4) were observed between 1990 and 2006, a finding corroborated by several authors and linked to increasing climate variability. Results are remarkable, emphasizing the need for appropriate data transformation prior to PCA, dealing with huge complex datasets, to enhance pattern recognition and meaningful interpretation of results. Through improved analysis of past data, uncertainty is decreased in modeling future trends. OPEN ACCESSClimate 2015, 3 136

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“…These are regions where hydroclimatic fluctuations are reported to be correlated with El Niño-Southern Oscillation (ENSO) and/or the Indian Ocean Dipole (or Zonal Mode) climate oscillations (Nicholson and Kim, 1997;Reason, 2002;Reason and Rouault, 2002;Richard et al, 2000). Recent work has demonstrated their sizeable effects on carbon exchanges Williams, 2010;Williams and Hanan, 2011) (Fig. 7).…”

Section: Interannual Variabilitymentioning

confidence: 99%

A full greenhouse gases budget of Africa: synthesis, uncertainties, and vulnerabilities

et al. 2014

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Abstract. This paper, developed under the framework of the RECCAP initiative, aims at providing improved estimates of the carbon and GHG (CO 2 , CH 4 and N 2 O) balance of continental Africa. The various components and processes of the African carbon and GHG budget are considered, existing data reviewed, and new data from different methodologies (inventories, ecosystem flux measurements, models, and atmospheric inversions) presented. Uncertainties are quantified and current gaps and weaknesses in knowledge and monitoring systems described in order to guide future requirements. The majority of results agree that Africa is a small sink of carbon on an annual scale, with an average value of Published by Copernicus Publications on behalf of the European Geosciences Union. R. Valentini et al.: A full greenhouse gases budget of Africa−0.61 ± 0.58 Pg C yr −1 . Nevertheless, the emissions of CH 4 and N 2 O may turn Africa into a net source of radiative forcing in CO 2 equivalent terms. At sub-regional level, there is significant spatial variability in both sources and sinks, due to the diversity of biomes represented and differences in the degree of anthropic impacts. Southern Africa is the main source region; while central Africa, with its evergreen tropical forests, is the main sink. Emissions from land-use change in Africa are significant (around 0.32 ± 0.05 Pg C yr −1 ), even higher than the fossil fuel emissions: this is a unique feature among all the continents. There could be significant carbon losses from forest land even without deforestation, resulting from the impact of selective logging. Fires play a significant role in the African carbon cycle, with 1.03 ± 0.22 Pg C yr −1 of carbon emissions, and 90 % originating in savannas and dry woodlands. A large portion of the wild fire emissions are compensated by CO 2 uptake during the growing season, but an uncertain fraction of the emission from wood harvested for domestic use is not. Most of these fluxes have large interannual variability, on the order of ±0.5 Pg C yr −1 in standard deviation, accounting for around 25 % of the year-toyear variation in the global carbon budget.Despite the high uncertainty, the estimates provided in this paper show the important role that Africa plays in the global carbon cycle, both in terms of absolute contribution, and as a key source of interannual variability.

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ENSO and IOD teleconnections for African ecosystems: evidence of destructive interference between climate oscillations

Cited by 46 publications

References 88 publications

Timing and Patterns of the ENSO Signal in Africa over the Last 30 Years: Insights from Normalized Difference Vegetation Index Data

Timing and Patterns of the ENSO Signal in Africa over the Last 30 Years: Insights from Normalized Difference Vegetation Index Data

Centered Log-Ratio (clr) Transformation and Robust Principal Component Analysis of Long-Term NDVI Data Reveal Vegetation Activity Linked to Climate Processes

A full greenhouse gases budget of Africa: synthesis, uncertainties, and vulnerabilities

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