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Land-Atmosphere Interactions and Sahel Precipitation

Land-Atmosphere Interactions and Sahel Precipitation. Andrea M. Sealy ASP/CGD Advanced Study Program Research Review March 29 th , 2007. Outline. Background Sahel rainfall climatology Land-atmosphere interactions Review of previous studies Land-atmosphere coupling

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Land-Atmosphere Interactions and Sahel Precipitation

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  1. Land-Atmosphere Interactions and Sahel Precipitation Andrea M. Sealy ASP/CGD Advanced Study Program Research Review March 29th, 2007

  2. Outline • Background • Sahel rainfall climatology • Land-atmosphere interactions • Review of previous studies • Land-atmosphere coupling • Soil moisture-rainfall feedback • Previous studies in context of current Work • Land surface impacts on Sahel precipitation and African easterly waves • Review • Objectives • Proposed analyses • Desert dust impacts on Sahel precipitation • Review • Objectives • Proposed analyses

  3. Background • Sea surface temperature and its impact on seasonal variability and predictability of precipitation has been focus of climate studies • Shukla et al. (2002) found many regions have strong response to anomalous sea surface temperature (SST) such as El Niño/Southern Oscillation phenomenon • West African precipitation suggested to be linked to Gulf of Guinea/Tropical Atlantic (Eltahir and Gong, 1996; Vizy and Cook, 2000) and Indian Ocean SSTs (Giannini et al., 2003) • Other factors such as land state variables (soil moisture, vegetation cover, albedo, dust) may also contribute to seasonal precipitation variability in the Sahel • Comprehensive understanding of the feedbacks between land and atmosphere is yet to be reached • Observational data of surface and sub-surface properties are often very scarce (e.g., for soil moisture an observation network over large areas is lacking) • Numerical results may differ and are model dependent

  4. Source: Legates and Wilmott (1990); 1920-1980 gridded precipitation estimates

  5. Soil moisture-rainfall feedback (Eltahir, 1998; Eltahir and Pal, 2001) Increase soil moisture Decrease surface albedo Decrease ratio of sensible to latent heat Increase lower level water vapor concentration Decrease ground and surface temperature Increase net surface shortwave radiation Increase net surface longwave radiation Increase total latent and sensible heat flux Increase lower level moist static energy Decrease stability Increase frequency and magnitude of local convective rainfall

  6. Indirect soil moisture ― precipitation feedback (from Dave Lawrence, CGD) • Over wet soil: • enhanced evaporation lower Bowen ratio  shallower and wetter boundary layer • darker soil (α ) and cooler surface temperatures  enhanced net surface radiation  • larger total heat flux into boundary layer • two factors combine to increase Moist Static Energy per unit mass of Boundary Layer air Theory developed in Betts and Ball (1995), Betts et al. (1996), Eltahir (1997), and Schär et al. (1999) supported by observations from FIFE, 1-d models, and regional climate models. LH SH SW LW RNET MSE / m3 BL air LH SH SW LW RNET MSE / m3 BL air dry , warm, bright soil wet , cool, dark soil

  7. Land-atmosphere coupling • Land-atmosphere coupling strength: the degree to which the atmosphere responds to anomalies in land surface state • (Koster et al., 2004) Global Land-Atmosphere Coupling Experiment (GLACE): An inter-comparison study across a range of atmospheric general circulation models • Regions with significant land-atmosphere coupling are identified from multi-model average (including West Africa) • These hot spots indicate where greater monitoring of soil moisture could yield the greatest return in seasonal forecasting • Results show a broad disparity in the inherent precipitation responses of the different models • NCAR’s Community Atmosphere Model (CAM3) showed high land-atmosphere coupling strength

  8. Koster (2004) shows the land-atmosphere coupling strength diagnostic for northern hemisphere summer.

  9. Land surface impact on diurnal cycle and easterly waves • Taylor and Clark, 2001 • Met Office Hadley Centre Unified Model (HaDAM3) • SPARSE vegetation (which is more realistic for Sahel region) • warmer and deeper planetary boundary layer • weaker diurnal cycle of precipitation • enhanced daily variability of precipitation • greater easterly wave activity • Results illustrate close coupling between land surface and atmosphere

  10. Precip Obs V850 Obs African easterly waves – Sahel 3 – 5 day period Does strong SM-P feedback (strong dependence of convection on surface fluxes) in CAM3 get in the way of precipitation response to AEWs? Source: David Lawrence, CGD/CCR Precip CAM3 V850 CAM3 Precip HadAM3 V700 HadAM3 Taylor and Clark, 2001

  11. How does this relate to current work? • NCAR’s Community Atmosphere Model (CAM3) exhibits greater land-atmosphere coupling than Hadley Centre model (from Koster et al., 2004) • How is easterly wave behavior influenced by land surface conditions? • What connection should be investigated? • Fluxes from land surface into atmosphere and how it affects boundary layer (smaller evaporation rates, warmer and deeper boundary layer, weaker diurnal rainfall cycle, greater AEW activity, more long lived rain events, Taylor and Clark 2001) • Main parameter to be changed and why? • Soil moisture (gradient), affects displacement/location, magnitude of AEJ which creates the environment for AEWs to develop (Cook, 1999) • Vegetation?

  12. Impact of dust radiative forcing • Yoshioka et al., submitted to Journal of Climate • Community Atmosphere Model (CAM3) • Model of Atmospheric Transport and Chemistry (MATCH) • Radiative forcing of dust acts to reduce average precipitation • More significant for interactive SST (Slab Ocean Model) than observed SST (Atmospheric Model Intercomparison Project) runs

  13. Difference between AMIP with shortwave and longwave dust feedback and no dust feedback

  14. Difference between SOM with shortwave and longwave dust feedback and no dust feedback

  15. How does this relate to current work? • To investigate and compare the impact of dust radiative forcing, sea surface temperature forcing and (dynamic) vegetation on Sahel precipitation • Community Atmosphere Model (CAM3) coupled with Dynamic Global Vegetation Model (DGVM) • Fifty year simulations, forced either by observed sea surface temperatures (Atmospheric Model Intercomparison Project/AMIP) or interactive SST using Slab Ocean Model (SOM). • Simulations (with DGVM) that will be used for analysis • AMIP with no dust (AMIPndDV) • SOM with no dust (SOMndDV) • AMIP with dust feedback (AMIPDV) • SOM with dust feedback (SOMDV) • Analyze and validate the rainfall signal in terms of amount/magnitude, geographical distribution, seasonal distribution and compare to observations • Analyze dust optical depth, geographical distribution, shortwave and longwave forcing and net radiative (shortwave + longwave) forcing • compare to previous studies and any differences explained based on model and dust parameterization used in the respective studies • Examine differences between the dust feedback and no dust simulations’ precipitation • differences in shortwave and longwave radiative forcing and near surface temperature • impact of dynamic vegetation we could compare the DGVM runs to (Yoshioka et al, submitted to J. Climate) runs done without DGVM that use default CAM vegetation

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