Land-Atmosphere Interactions Land-Atmosphere Interactions and Sahel Precipitationand Sahel Precipitation
Andrea M. SealyAndrea M. SealyASP/CGDASP/CGD
Advanced Study Program Research ReviewAdvanced Study Program Research ReviewMarch 29March 29thth, 2007, 2007
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OutlineOutline
• 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
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BackgroundBackground
• 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
Source: Legates and Wilmott (1990); 1920-1980 gridded precipitation estimatesSource: Legates and Wilmott (1990); 1920-1980 gridded precipitation estimates
0
1
2
3
4
5
6
7
8
mm/day
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
West Africa and Sahel Rainfall Climatology
West Africa
Sahel
Soil moisture-rainfall feedback (Eltahir, 1998; Eltahir and Pal, Soil moisture-rainfall feedback (Eltahir, 1998; Eltahir and Pal, 2001)2001)
Increase soil moisture
Decrease surface albedo Decrease ratio of sensible to latent heat
Increase lower levelwater vapor concentration
Decrease stability
Increase total latent and sensible heat flux
Increase frequency and magnitude of local convective rainfall
Decrease ground and surface temperature
Increase net surfaceshortwave radiation
Increase lower level moist static energy
Increase net surface longwave radiation
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Indirect soil moisture ― precipitation feedback (from Dave Lawrence, CGD)Indirect soil moisture ― precipitation feedback (from Dave Lawrence, CGD)
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.
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
dry wet , cool, dark soil, warm, bright soil
• two factors combine to increase Moist Static Energy per unit mass of Boundary Layer air
LH SH SW LW RNET MSE / m3 BL air
LH SH SW LW RNET MSE / m3 BL air
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Land-atmosphere couplingLand-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
Koster (2004) shows the land-atmosphere coupling strength diagnostic Koster (2004) shows the land-atmosphere coupling strength diagnostic for northern hemisphere summer.for northern hemisphere summer.
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Land surface impact on diurnal Land surface impact on diurnal cycle and easterly waves 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
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PrecipObs
V850
Obs
PrecipCAM3
V850
CAM3
PrecipHadAM3
V700
HadAM3
Taylor and Clark, 2001
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
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How does this relate to current work?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?
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Impact of dust radiative forcingImpact 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
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Difference between AMIP with shortwave and longwave dust feedback and no dust feedbackDifference between AMIP with shortwave and longwave dust feedback and no dust feedback
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Difference between SOM with shortwave and longwave dust feedback and no dust feedbackDifference between SOM with shortwave and longwave dust feedback and no dust feedback
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How does this relate to current work?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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