scholarly journals Climate variability in central equatorial Africa: Influence from the Atlantic sector

2004 ◽  
Vol 31 (23) ◽  
Author(s):  
Martin C. Todd ◽  
Richard Washington
Keyword(s):  
Climate Variability ◽  
Atlantic Sector ◽  
Equatorial Africa ◽  
Central Equatorial Africa

Climate of Western and Central Equatorial Africa

2017 ◽  
Author(s):  
Amin Dezfuli
Keyword(s):  
Climate Variability ◽  
Annual Cycle ◽  
Intraseasonal Variability ◽  
Vertical Motion ◽  
Annual Rainfall ◽  
Mean Annual Temperature ◽  
Lightning Flash ◽  
Equatorial Africa ◽  
Equatorial Wave ◽  
Central Equatorial Africa

Western and Central Equatorial Africa (WCEA), home to the Congo rainforests, is the green heart of the otherwise dry continent of Africa. Despite its crucial role in the Earth system, WCEA’s climate variability has received little attention compared to the rest of Africa. Climate variability in the region is a result of complex interactions among various features acting on local and global scales. The mesoscale convective systems (MCSs) that have a preferentially westward propagation and present a distinct diurnal cycle are the main source of rainfall in the region. As a result of strong MCS activity, WCEA stands out as a convective anomaly within the tropics and experiences the world’s most intense thunderstorms as well as the highest lightning flash rates. The moisture of the region is supplied primarily from the Atlantic Ocean, with additional contributions from local recycling and East Africa. WCEA, in turn, serves as a moisture source for other parts of the continent. One striking characteristic of WCEA is its intrinsic heterogeneity with respect to interannual variability of rainfall, resulting in delineation of the region primarily in the zonal direction. This is in contrast to the meridionally oriented spatial variability of the annual cycle and underlines the fact that driving factors of the two can be quite different. The annual cycle is mainly determined by the seasonal excursion of the sun. However, the interannual and intraseasonal variability of the region are modulated by remote forcings from all three oceans, reflected via zonal atmospheric cells and equatorial wave dynamics. The local atmospheric jets and regional Walker-like circulations also contribute to WCEA’s climate variability by modulating the moisture transport and vertical motion. The region has experienced an increasing rate of deforestation in recent decades and has made a significant contribution to the global biomass burning emissions that can alter regional and global circulation, along with energy and water cycles. The mean annual temperature of the region has increased by about 1°C in the past 70 years. The annual rainfall over the same period presents a negative trend, though that is quite negligible in the eastern sector of the region.

scholarly journals Correction to: Assessing reanalysis data for understanding rainfall climatology and variability over Central Equatorial Africa

2019 ◽  
Vol 53 (7-8) ◽  
pp. 5139-5139
Author(s):  
Wenjian Hua ◽  
Liming Zhou ◽  
Sharon E. Nicholson ◽  
Haishan Chen ◽  
Minhua Qin
Keyword(s):  
Reanalysis Data ◽  
Equatorial Africa ◽  
Rainfall Climatology ◽  
Central Equatorial Africa

Satellite-based rainfall data reveal a recent drying trend in central equatorial Africa

2014 ◽  
Vol 126 (1-2) ◽  
pp. 263-272 ◽  
Author(s):  
Jeremy E. Diem ◽  
Sadie J. Ryan ◽  
Joel Hartter ◽  
Michael W. Palace
Keyword(s):  
Rainfall Data ◽  
Equatorial Africa ◽  
Drying Trend ◽  
Central Equatorial Africa

30,000-Year-Old Fossil Restionaceae Pollen from Central Equatorial Africa and Its Biogeographical Significance

1990 ◽  
Vol 17 (3) ◽  
pp. 307 ◽  
Author(s):  
R. Bonnefille ◽  
A. C. Hamilton ◽  
H. P. Linder ◽  
G. Riollet
Keyword(s):  
Equatorial Africa ◽  
Central Equatorial Africa

scholarly journals Assessing reanalysis data for understanding rainfall climatology and variability over Central Equatorial Africa

2019 ◽  
Vol 53 (1-2) ◽  
pp. 651-669 ◽  
Author(s):  
Wenjian Hua ◽  
Liming Zhou ◽  
Sharon E. Nicholson ◽  
Haishan Chen ◽  
Minhua Qin
Keyword(s):  
Reanalysis Data ◽  
Equatorial Africa ◽  
Rainfall Climatology ◽  
Central Equatorial Africa

Seasonal predictions of energy-relevant Essential Climate Variables through Euro-Atlantic Teleconnections

2020 ◽  
Author(s):  
Irene Cionni ◽  
Llorenç Lledó ◽  
Franco Catalano ◽  
Alessandro Dell’Aquila
Keyword(s):  
Climate Variability ◽  
Large Scale ◽  
Surface Wind ◽  
Seasonal Prediction ◽  
Energy Sector ◽  
Climate Variables ◽  
Atmospheric Variability ◽  
East Atlantic ◽  
Atlantic Sector ◽  
Prediction Systems

<p>Accurate and reliable information from climate predictions at seasonal time-scales can have an essential role to anticipate climate variability affecting supply of renewables energy and to stabilize and secure the energy network as a whole. A number of recognized modes of variability -often called teleconnections- explain a large part of Earth’s climate variations and represent an important source of climate predictability. The leading atmospheric variability modes in the Euro-Atlantic sector (EATC) affect surface variables such as 2 meters temperature, solar radiation downward, and surface wind anomalies in Europe.</p><p>Characterizing EATC in observations and assessing their simulation and prediction and their impact on the energy sector can help to better understand patterns of seasonal-scale inter annual variability in renewables resources and to consider to what extent this variability might be predictable up to several months in advance. Furthermore EATC can be used to formulate empirical prediction of local climate variability (relevant for the energy sector) based on the large scale atmospheric variability modes predicted by the forecast systems.</p><p>To achieve this goal we analyze reanalysis dataset ERA5 and the multi-system seasonal forecast service provided by the Copernicus Climate Data Store (C3S).</p><p>Geopotential height anomalies at 500 hPa have been employed to compute the four Euro-Atlantic teleconnections North Atlantic Oscillation, East Atlantic, Scandinavian and East Atlantic-West Russian. The impacts of those four variability modes on the energy - relevant<span>  </span>essential climate variables have been assessed in both observed and predicted system. We have found that the observed relationship between EATC patterns and surface impacts is not accurately reproduced by seasonal prediction systems. This opens the door to employ hybrid dynamical-statistical methods. The idea consists in combining the dynamical seasonal predictions of EATC indices with the observed relationship between EATCs and surface variables.<span>  </span>We reconstructed the surface anomalies for multiple seasonal prediction systems and benchmarked these hybrid forecasts with the direct variable forecasts from the systems and also with the climatology. The analysis suggest that predictions of energy relevant Essential Climate Variables are improved by the hybrid methodology in almost all Europe.<span> </span></p>

scholarly journals Reconciling North Atlantic climate modes: revised monthly indices for the East Atlantic and the Scandinavian patterns beyond the 20th century

2018 ◽  
Vol 10 (4) ◽  
pp. 2329-2344 ◽  
Author(s):  
Laia Comas-Bru ◽  
Armand Hernández
Keyword(s):  
Climate Variability ◽  
North Atlantic ◽  
Winter Season ◽  
East Atlantic ◽  
Atlantic Sector ◽  
The North ◽  
North Atlantic Climate ◽  
The North Atlantic ◽  
The North Atlantic Oscillation ◽  
Different Sources

Abstract. Climate variability in the North Atlantic sector is commonly ascribed to the North Atlantic Oscillation. However, recent studies have shown that taking into account the second and third mode of variability (namely the East Atlantic – EA – and the Scandinavian – SCA – patterns) greatly improves our understanding of their controlling mechanisms, as well as their impact on climate. The most commonly used EA and SCA indices span the period from 1950 to present, which is too short, for example, to calibrate palaeoclimate records or assess their variability over multi-decadal scales. To tackle this, here, we create new EOF-based (empirical orthogonal function) monthly EA and SCA indices covering the period from 1851 to present, and compare them with their equivalent instrumental indices. We also review and discuss the value of these new records and provide insights into the reasons why different sources of data may give slightly different time series. Furthermore, we demonstrate that using these patterns to explain climate variability beyond the winter season needs to be done carefully due to their non-stationary behaviour. The datasets are available at https://doi.org/10.1594/PANGAEA.892769.

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