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Publications (3 of 3) Show all publications
Klutse, N. A., Sylla, M. B., Diallo, I., Sarr, A., Dosio, A., Diedhiou, A., . . . Buechner, M. (2016). Daily characteristics of West African summer monsoon precipitation in CORDEX simulations. Journal of Theoretical and Applied Climatology, 123(1-2), 369-386
Open this publication in new window or tab >>Daily characteristics of West African summer monsoon precipitation in CORDEX simulations
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2016 (English)In: Journal of Theoretical and Applied Climatology, ISSN 0177-798X, E-ISSN 1434-4483, Vol. 123, no 1-2, p. 369-386Article in journal (Refereed) Published
Abstract [en]

We analyze and intercompare the performance of a set of ten regional climate models (RCMs) along with the ensemble mean of their statistics in simulating daily precipitation characteristics during the West African monsoon (WAM) period (June-July-August-September). The experiments are conducted within the framework of the COordinated Regional Downscaling Experiments for the African domain. We find that the RCMs exhibit substantial differences that are associated with a wide range of estimates of higher-order statistics, such as intensity, frequency, and daily extremes mostly driven by the convective scheme employed. For instance, a number of the RCMs simulate a similar number of wet days compared to observations but greater rainfall intensity, especially in oceanic regions adjacent to the Guinea Highlands because of a larger number of heavy precipitation events. Other models exhibit a higher wet-day frequency but much lower rainfall intensity over West Africa due to the occurrence of less frequent heavy rainfall events. This indicates the existence of large uncertainties related to the simulation of daily rainfall characteristics by the RCMs. The ensemble mean of the indices substantially improves the RCMs' simulated frequency and intensity of precipitation events, moderately outperforms that of the 95th percentile, and provides mixed benefits for the dry and wet spells. Although the ensemble mean improved results cannot be generalized, such an approach produces encouraging results and can help, to some extent, to improve the robustness of the response of the WAM daily precipitation to the anthropogenic greenhouse gas warming.

National Category
Climate Research
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-2053 (URN)10.1007/s00704-014-1352-3 (DOI)000368715000028 ()
Available from: 2016-05-02 Created: 2016-05-02 Last updated: 2017-11-30Bibliographically approved
Favre, A., Philippon, N., Pohl, B., Kalognomou, E.-A., Lennard, C., Hewitson, B., . . . Cerezo-Mota, R. (2016). Spatial distribution of precipitation annual cycles over South Africa in 10 CORDEX regional climate model present-day simulations. Climate Dynamics, 46(5-6), 1799-1818
Open this publication in new window or tab >>Spatial distribution of precipitation annual cycles over South Africa in 10 CORDEX regional climate model present-day simulations
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2016 (English)In: Climate Dynamics, ISSN 0930-7575, E-ISSN 1432-0894, Vol. 46, no 5-6, p. 1799-1818Article in journal (Refereed) Published
Abstract [en]

This study presents an evaluation of the ability of 10 regional climate models (RCMs) participating in the COordinated Regional climate Downscaling Experiment-Africa to reproduce the present-day spatial distribution of annual cycles of precipitation over the South African region and its borders. As found in previous studies, annual mean precipitation is quasi-systematically overestimated by the RCMs over a large part of southern Africa south of about 20A degrees S and more strongly over South Africa. The spatial analysis of precipitation over the studied region shows that in most models the distribution of biases appears to be linked to orography. Wet biases are quasi-systematic in regions with higher elevation with inversely neutral to dry biases particularly in the coastal fringes. This spatial pattern of biases is particularly obvious during summer and specifically at the beginning of the rainy season (November and December) when the wet biases are found to be the strongest across all models. Applying a k-means algorithm, a classification of annual cycles is performed using observed precipitation data, and is compared with those derived from modeled data. It is found that the in-homogeneity of the spatial and temporal distribution of biases tends to impact the modeled seasonality of precipitation. Generally, the pattern of rainfall seasonality in the ensemble mean of the 10 RCMs tends to be shifted to the southwest. This spatial shift is mainly linked to a strong overestimation of convective precipitation at the beginning of the rainy season over the plateau inducing an early annual peak and to an underestimation of stratiform rainfall in winter and spring over southwestern South Africa.

National Category
Climate Research
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-2037 (URN)10.1007/s00382-015-2677-z (DOI)000371069900025 ()
Available from: 2016-05-03 Created: 2016-05-02 Last updated: 2017-11-30Bibliographically approved
Kalognomou, E.-A., Lennard, C., Shongwe, M., Pinto, I., Favre, A., Kent, M., . . . Buechner, M. (2013). A Diagnostic Evaluation of Precipitation in CORDEX Models over Southern Africa. Journal of Climate, 26(23), 9477-9506
Open this publication in new window or tab >>A Diagnostic Evaluation of Precipitation in CORDEX Models over Southern Africa
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2013 (English)In: Journal of Climate, ISSN 0894-8755, E-ISSN 1520-0442, Vol. 26, no 23, p. 9477-9506Article, review/survey (Refereed) Published
Abstract [en]

The authors evaluate the ability of 10 regional climate models (RCMs) to simulate precipitation over Southern Africa within the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. An ensemble of 10 regional climate simulations and the ensemble average is analyzed to evaluate the models' ability to reproduce seasonal and interannual regional climatic features over regions of the subcontinent. All the RCMs use a similar domain, have a spatial resolution of 50 km, and are driven by the Interim ECMWF Re-Analysis (ERA-Interim; 1989-2008). Results are compared against a number of observational datasets.In general, the spatial and temporal nature of rainfall over the region is captured by all RCMs, although individual models exhibit wet or dry biases over particular regions of the domain. Models generally produce lower seasonal variability of precipitation compared to observations and the magnitude of the variability varies in space and time. Model biases are related to model setup, simulated circulation anomalies, and moisture transport. The multimodel ensemble mean generally outperforms individual models, with bias magnitudes similar to differences across the observational datasets. In the northern parts of the domain, some of the RCMs and the ensemble average improve the precipitation climate compared to that of ERA-Interim. The models are generally able to capture the dry (wet) precipitation anomaly associated with El Nino (La Nina) events across the region. Based on this analysis, the authors suggest that the present set of RCMs can be used to provide useful information on climate projections of rainfall over Southern Africa.

Keywords
Climate prediction, Climate variability, Climatology, Regional models
National Category
Climate Research
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-340 (URN)10.1175/JCLI-D-12-00703.1 (DOI)000327054100016 ()
Available from: 2015-04-14 Created: 2015-03-31 Last updated: 2017-12-04Bibliographically approved
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7546-4430

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