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) and aerosol–radiative forcings ( Kim et al. 2010 ). These effects can potentially interact with each other. For example, the variability of land surface conditions can affect the circulation over the ocean, which in turn can modify the SSTs and indirectly affect conditions over land ( Ma et al. 2013 ). The existence of significant impacts on WAM rainfall of slowly varying climate subcomponents indicates the potential for useful long-range forecasts ( Vellinga et al. 2013 ; Gaetani and Mohino 2013
) and aerosol–radiative forcings ( Kim et al. 2010 ). These effects can potentially interact with each other. For example, the variability of land surface conditions can affect the circulation over the ocean, which in turn can modify the SSTs and indirectly affect conditions over land ( Ma et al. 2013 ). The existence of significant impacts on WAM rainfall of slowly varying climate subcomponents indicates the potential for useful long-range forecasts ( Vellinga et al. 2013 ; Gaetani and Mohino 2013
droughts in the region. Proposed drought–vegetation feedbacks include energy balance feedback pathways mediated by surface albedo (e.g., Charney 1975 ) or by changes in the Bowen ratio (e.g., Betts and Ball 1998 ), moisture convergence feedbacks associated with transpiration rate (e.g., Dirmeyer 1994 ), aerodynamic effects of surface roughness (e.g., Sud et al. 1988 ), influence on mesoscale atmospheric circulations (e.g., Avissar and Liu 1996 ), and influence on infiltration of precipitation into
droughts in the region. Proposed drought–vegetation feedbacks include energy balance feedback pathways mediated by surface albedo (e.g., Charney 1975 ) or by changes in the Bowen ratio (e.g., Betts and Ball 1998 ), moisture convergence feedbacks associated with transpiration rate (e.g., Dirmeyer 1994 ), aerodynamic effects of surface roughness (e.g., Sud et al. 1988 ), influence on mesoscale atmospheric circulations (e.g., Avissar and Liu 1996 ), and influence on infiltration of precipitation into
indicating that a more realistic representation of surface conditions reduces model biases, many current numerical models, particularly those used for operational forecasts, still employ fixed land-cover types. Hence, they are unable to represent the additional sources of interannual variability owing to land-cover changes, as a result of either land-use changes or the vegetation’s degree of stress (e.g., during droughts, wet periods, or insect outbreaks). In other words, models that do not include
indicating that a more realistic representation of surface conditions reduces model biases, many current numerical models, particularly those used for operational forecasts, still employ fixed land-cover types. Hence, they are unable to represent the additional sources of interannual variability owing to land-cover changes, as a result of either land-use changes or the vegetation’s degree of stress (e.g., during droughts, wet periods, or insect outbreaks). In other words, models that do not include
