2016
DOI: 10.1007/s00382-016-3237-x
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Feedback of observed interannual vegetation change: a regional climate model analysis for the West African monsoon

Abstract: over higher (lower) VF is 12 % higher than by chance. We attribute this behaviour to horizontal circulations driven by differential heating. Over more vegetated regions, the divergence of moist air together with lower sensible heat fluxes hinders the initiation of deep convection during the day. During the night, mature convective systems cause an increase in the number of rainy hours over these regions. We identify this feedback in both water-and energy-limited regions of West Africa. The inclusion of observe… Show more

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Cited by 37 publications
(30 citation statements)
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“…Also, to improve the representation of the diurnal cycle of precipitation and of extreme precipitation events in the models, convectionpermitting and coupled atmospheric-hydrological modelling experiments are pursued (Arnault et al, 2016;Klein et al, 2017;Naabil et al, 2017). The climate modelling efforts presented here are undertaken in parallel to the setup of a dense network of automatic weather stations in the region with the goal of assessing and reducing model uncertainties and biases.…”
Section: Discussionmentioning
confidence: 99%
“…Also, to improve the representation of the diurnal cycle of precipitation and of extreme precipitation events in the models, convectionpermitting and coupled atmospheric-hydrological modelling experiments are pursued (Arnault et al, 2016;Klein et al, 2017;Naabil et al, 2017). The climate modelling efforts presented here are undertaken in parallel to the setup of a dense network of automatic weather stations in the region with the goal of assessing and reducing model uncertainties and biases.…”
Section: Discussionmentioning
confidence: 99%
“…However, the carbon concentration still has a large uncertainty even in state‐of‐the‐art models coupled with the global carbon cycle. Many new discoveries concerning the response of vegetation to global warming were identified from ecosystem scale to leaf scale, although the processes are too complex to fully parameterize the processes in the model [ Yu et al ., ; Klein et al ., ]. For example, earlier spring leaf unfolding of plants in response to climate warming could be counteracted because many deciduous trees require chilling for dormancy release and warming induces reductions in chilling [ Fu et al ., ].…”
Section: Impacts Of Atmosphere‐land Interactionsmentioning
confidence: 98%
“…Mainly because of the large PET increase associated with rising SAT, a drier climate is projected for a substantial portion of drylands [e.g., Dai , , ; Hughes , ; Cook et al ., ; Scheff and Frierson , ; Huang et al ., ; Fu and Mao , ] and dryland expansion by 2100 is expected [ Feng and Fu , ; Fu et al ., ; Huang et al ., ]. These changes in temperature, precipitation, and PET may alter terrestrial ecosystems [ Osmond et al ., ; Kullman , ; Zeng and Yoon , ; Badreldin and Goossens , ; Sylla et al ., ; Yu et al ., ; Klein et al ., ; Seddon et al ., ; Sjögersten and Wookey , ; Wu et al ., ], hydrology [ Ohmura and Wild , ; Cudennec et al ., ; Angeler et al ., ; Hu et al ., ], and agricultural production [ El‐Beltagy and Madkour , ; Valizadeh et al ., ; Hadgu et al ., ] over drylands. Because of the drastic climate change and fragile ecosystems of drylands, a better understanding of dryland climate change and its impact is required for further research and policy making.…”
Section: Introductionmentioning
confidence: 99%
“…It has been shown that in West Africa the representation of convection (i.e.,convection‐permitting versus parameterized convection) has a more profound impact on the outgoing longwave radiation (Pearson et al, ) and rainfall (Marsham et al, ) than the improved resolution. Convection‐permitting models also improve the coupling of the rainfall with other parts of the Earth system, notably land surface heterogeneity (Klein et al, ; Maurer et al, ; Taylor et al, ), and as a consequence can bring improvements when used to drive impacts models, such as crop prediction models (Garcia‐Carreras et al, ).…”
Section: Introductionmentioning
confidence: 99%