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

Hua, Wenjian; Zhou, Liming; Nicholson, Sharon E.; Chen, Haishan; Qin, Minhua

doi:10.1007/s00382-018-04604-0

Cited by 69 publications

(70 citation statements)

References 56 publications

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“…On spatial characteristics, all reanalysis datasets, except ERA-Interim, capture the wetting trend in the Sahel, though generally over-or underestimating its magnitude. Other local trend patterns do not reflect the observational datasets and show large differences amongst reanalyses, in line with subregional studies in Ghana [21], and southern West Africa [62]. This highlights even larger uncertainties in reanalysis rainfall trends than were previously found amongst observational datasets over Africa [16], strongly discouraging the use of reanalysis rainfall fore trend studies.…”

Section: Discussionmentioning

confidence: 58%

West African Summer Monsoon Precipitation Variability as Represented by Reanalysis Datasets

Quagraine

Nkrumah

Klein

et al. 2020

Climate

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Focusing on West Africa, a region riddled with in situ data scarcity, we evaluate the summer monsoon monthly rainfall characteristics of five global reanalysis datasets: ERA5, ERA-Interim, JRA-55, MERRA2, and NCEP-R2. Their performance in reproducing the West African monsoon (WAM) climatology, interannual variability, and long-term trends for the main monsoon months are compared to gauge-only and satellite products. We further examine their ability to reproduce teleconnections between sea surface temperatures and monsoon rainfall. All reanalyses are able to represent the average rainfall patterns and seasonal cycle; however, regional biases can be marked. ERA5, ERA-Interim, and NCEP-R2 underestimate rainfall over areas of peak rainfall, with ERA5 showing the strongest underestimation, particularly over the Guinea Highlands. The meridional northward extent of the monsoon rainband is well captured by JRA-55 and MERRA2 but is too narrow in ERA-Interim, for which rainfall stays close to the Guinea Coast. Differences in rainband displacement become particularly evident when comparing strong El Niño Southern Oscillation (ENSO) years, where all reanalyses except ERA-Interim reproduce wetter Sahelian conditions for La Niña, while overestimating dry conditions at the coast except for NCEP-R2. Precipitation trends are not coherent across reanalyses and magnitudes are generally overestimated compared to observations, with only JRA-55 and NCEP-R2 displaying the expected positive trend in the Sahel. ERA5 generally outperforms ERA-Interim, highlighting clear improvements over its predecessor. Ultimately, we find the strengths of reanalyses to strongly vary across the region.

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Section: Discussionmentioning

confidence: 58%

West African Summer Monsoon Precipitation Variability as Represented by Reanalysis Datasets

Quagraine

Nkrumah

Klein

et al. 2020

Climate

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“…This supports previous findings of Congo westerlies occurring throughout the year and being most developed in July–September (Nicholson & Grist, ). And the Congo Basin as a moisture source is much more likely in SON than MAM (Hua et al, ). Differences in circulation between wet days and dry days produced 850‐hPa westerly anomalies for all seasons, with the possible exception of boreal winter in northwestern Uganda (Figure ).…”

Section: Discussionmentioning

confidence: 99%

“…Diem et al () examined western Uganda as a whole but did not examine changes in rainy and dry seasons. In addition, western Uganda has been included—often as a small part of a domain—in rainfall and circulation studies with diverse geographic scales; examples include Kenya and Uganda (Ongoma et al, ; Otieno & Anyah, ), eastern Africa (Nicholson, ), EEA (Gitau et al, ); the Greater Horn of Africa (Nicholson, ; Williams et al, ), and CEA or Congo Basin (Creese et al, ; Creese & Washington, ; Dezfuli & Nicholson, ; Hua et al, , ; Nicholson & Dezfuli, ). Not only is it not known how rainy‐season rainfall characteristics have changed over time within western Uganda, but it also is not known what atmospheric conditions lead to wet days and dry days within the rainy and dry seasons.…”

Section: Introductionmentioning

confidence: 99%

“…include Kenya and Uganda (Ongoma et al, 2018;Otieno & Anyah, 2013), eastern Africa (Nicholson, 2017), EEA (Gitau et al, 2018); the Greater Horn of Africa (Nicholson, 2014;Williams et al, 2012), and CEA or Congo Basin (Creese et al, 2019;Creese & Washington, 2018;Hua et al, 2018Hua et al, , 2019. Not only is it not known how rainy-season rainfall characteristics have changed over time within western Uganda, but it also is not known what atmospheric conditions lead to wet days and dry days within the rainy and dry seasons.…”

Section: Introductionmentioning

confidence: 99%

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Rainfall Characteristics and Trends—and the Role of Congo Westerlies—in the Western Uganda Transition Zone of Equatorial Africa From 1983 to 2017

Diem

Sung

Konecky³

et al. 2019

JGR Atmospheres

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While long‐term rainfall trends and related atmospheric dynamics have been researched over the past several decades across equatorial Africa, little is known about rainfall in western Uganda, a transition zone in the middle of the continent. Using satellite‐derived rainfall and reanalysis data from 1983 to 2017, this study examines atmospheric characteristics of seasons and multidecadal trends in rainfall. Most of the region has a biannual rainfall regime (i.e., the first rains within March–May and the second rains during August–November). Ascending (descending) air and increased (decreased) specific humidity are observed over western Uganda during the rainy (dry) seasons. Southeasterly air‐parcel back trajectories are common throughout western Uganda at all times except for the first dry season (i.e., December–February). For all seasons, wet days in western Uganda are characterized by increases in ascending air and specific humidity in addition to westerly flow anomalies. Wet days in most seasons also have a disproportionately high frequency of westerly back trajectories extending over the Congo Basin. These Congo westerlies are associated with more vertical ascent and a more humid middle troposphere compared to the other trajectories. Rainy seasons, especially the first rains, have gotten longer and wetter throughout western Uganda. The duration of the first rains increased by about 1 month over the 35 years; in turn, the rainfall total increased by approximately 70%. Rainfall also has increased for climatological seasons, with the exception being December–February. Increases in middle‐troposphere specific humidity and vertical ascent over time provide support for the wetting trends derived from the satellite‐derived rainfall data.

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“…We choose MERRA reanalysis since the analyses have been performed near the A-Train constellation track. Although large spread is found in the performance among different reanalysis products over the Congo basin (Cook & Vizy, 2016), Hua et al (2019) found out that MERRA2, when compared with all other major reanalysis data sets, is the best reanalysis product that reproduces the mean climatology and interannual variability over the Congo basin. They have also found out that MERRA2 data also have the smallest biases and root-mean-square error in describing the wind fields in the lower to middle-troposphere, thus making it suitable for our meteorological analysis.…”

Section: Merra2mentioning

confidence: 95%

Deep Convective Evolution From Shallow Clouds Over the Amazon and Congo Rainforests

Chakraborty

Jiang

et al. 2020

JGR Atmospheres

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Using satellite measurements from A-Train constellation and Global PrecipitationMeasurement mission, we investigate the relationships between the afternoon time shallow convective top height (CTH afternoon ) and the evening time deep convective storm top height (CTH evening ) and rain rate (RR evening ) over the Amazon and Congo regions. We use CloudSat cloud type stratus and stratocumulus as the shallow afternoon clouds. Our results indicate that the afternoon shallow clouds over the Congo region are associated with suppressed and weakened evening time deep convection, whereas shallow clouds over the Amazon region are associated with the growth of the evening time deep convection. Over the Congo region, we find that as CTH afternoon increases, shallow convective rain rate in the afternoon (RR afternoon ) increases. As a result, the evening time convective available potential energy (CAPE) as well as free tropospheric humidity (RH 700-300 ) decrease. Consequently, condensation occurring inside deep convection reduces and CTH evening as well as RR evening decrease over there. Over the Amazon region, however, RR afternoon does not vary significantly with CTH afternoon . As CTH afternoon increases, CAPE, RH 700-300 , and condensation occurring inside deep convection increase in the evening. As a result, deep convective CTH evening and RR evening increase with CTH afternoon over the Amazon basin. These dissimilarities in the ambient condition drive the shallow to deep convective evolution differently over these two rainforests. On the other hand, shallow clouds that remain shallow in the evening are associated with less CAPE and RH 700-300, RR afternoon, and RR evening . Although CAPE and RH 700-300 promote deep convection to a height cloud top height, high vertical wind shear inhibits deep convection.

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

Cited by 69 publications

References 56 publications

West African Summer Monsoon Precipitation Variability as Represented by Reanalysis Datasets

West African Summer Monsoon Precipitation Variability as Represented by Reanalysis Datasets

Rainfall Characteristics and Trends—and the Role of Congo Westerlies—in the Western Uganda Transition Zone of Equatorial Africa From 1983 to 2017

Deep Convective Evolution From Shallow Clouds Over the Amazon and Congo Rainforests

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