What is PRECIS? The Physical Parameters Boundary Conditions Why the Mediterranean Basin? The Sulfur Cycle & Aerosols Aerosols Impacts Data Analysis Sulfur Cycle Validation Future Scenarios Conclusions 2 2
The PRECIS (Providing REgionalg Climates for Impact Studies) RCM is driven by the GCM HadAM3P. (Gordon et al, 2000) It is an Atmospheric and a LSM of limited area. The model is described in 3 units: The dynamics; The physical parameterizations; ations; The Sulfur cycle 3 3
Clouds & precipitation Large Scale & Mass flux productive Convective Radiation includes the seasonal and diurnal cycles of insolation Boundary layer can occupy up to the bottom 5 model layers (Smith, 1990) Land Surface characteristics are prescribed according to climatological surface types Surface exchanges Land surface scheme is MOSES (Met Office Surface Exchange Scheme) (Cox et al, 1999) Gravity wave drag is applied to momentum components in the free atmosphere Palmer et al, 1986) 4 4
Model requires surface boundary conditions from surface temperatures and ice extents. Dynamical atmospheric information at the boundary of the model s domain are obtained from the LBCs which include - standard atmospheric variables of surface pressure - horizontal wind components - atmospheric temperature and humidity measures LBCs are updated every 6 hours of the model simulation time. Surface boundary conditions are updated every day. 5
Mediterranean enclosed by 3 major continents; surrounded almost entirely by mountains. This makes the Mediterranean very unique and sensitive to climate changes. Simulation details GCM-HadAM3P-150km PRECIS (v 1.7.2) used 1960-1990 (1-year spin up) Resolution: 0.44 x 0.44 100 cells x (50 km x 50 km) (57 N, 18 S, 16 W, 46 E) 6 6
Mode Diameter (μm) Lifetime Removal Process Nucleation <0.01 Minutes-Hours Coagulation Aitken 0.01-0.1 Hours-Days Coagulation Accumulation 0.1-1 Weeks Nucleation Coarse 1-10 Hours-Days Gravitation Giant >10 Hours-Days Gravitation ti The Sulfur Cycle is based 5 variables: SO 2, DMS and 3 modes of SO 2-4 (One dissolved and 2 free modes) Model simulates transport of above via horizontal and vertical advection, convection and turbulent mixing Aerosols act as CCN SO 2-42 increase droplet acidity (acid rain) Increase in aerosol causes climate impacts 7 7
Aerosol Impacts 8 Indirect: Cloud lifetime effect CLW #CCN Droplet size PPN Cloud Lifetime Cloud Thick- ness Indirect: Twomey (Cloud albedo) effect CLW (Fixed) #CCN Droplet size Cloud Albedo Cloud Albedo/ Thickness Day-time Direct: Scattering effect Solar Radiation SW Skin Temp. Direct: LW unaffected: Aerosol Opacity as λ Indirect: RH decreased: water vapour as condensation Max Temp. Night-time Thermal Radiation Min Temp. 8
Measured Modelled ESRL units PRECIS units Skin Temperature o C Surface (Skin) Temperature K Net SW Radiation Flux W m -2 Net Down Surface SW Flux W m -2 Net LW Radiation Flux W m -2 Net Down Surface LW Flux W m -2 DTR at 2 meters K DTR at 1.5 meters K Convective Precipitation kg m -2 s -1 Convective Rainfall Rate kg m -2 s -1 Cloud Water Content kg m -2 Cloud Liquid Water Content kg kg -1 Relative Humidity % Relative Humidity % 9
Measured Modelled Min = 107.4; Max = 234.7 Min = 68.9; Max = 256.4 1961-1990 10
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Statistics of Parameters Parameter Correlation Average Bias Max Bias Min Bias Conv PPN (x10 6 ) 0.47 3.08 1.48 5.80 DTR 0.11 1.98 0.32 3.52 Net LW 048 0.48 101 1.01 377 3.77 1.37 137 Net SW 0.59 10.45 6.99 15.63 RH 0.43 5.46 3.01 8.38 Surface T 0.13 0.82 1.59 0.05 Bias of Conv PPN, DTR, Net LW, Net SW, RH: Suggests SO 2-4 over-prediction Bias of Surface T: Possible result of Net LW over-prediction Bias of SO 2 : Suggests over-prediction in chemical reaction: SO 2 SO 4 2-13 13
Sulfate Abundance: 1. Aitken 2. Accumulation 3. Dissolved 14 14
Aerosol-sensitive parameters Effect Correlation Expected Relationship Direct Aitken v SW -0.76 Negative Direct Accum v SW -0.82 Negative Direct SW v Skin T 0.69 Positive Direct Aitken v Skin T -0.60 Negative Direct Accum v Skin T -0.64 Negative Direct Effect Strong correlations Expected relationships Strong evidence of effect 15 15
Aerosol-sensitive sensitive parameters Indirect Effects: Low detectability of effect Expected due to complex interactions Incorrect correlations: Model errors? Expected Effect Correlation Relationship Indirect Diss v Skin T -0.77 Negative Indirect Diss v SW -0.82 Negative Indirect SW v DTR 0.50 Positive Indirect CLW v DTR -0.59 Negative Indirect Diss v CLW 0.39 Positive Indirect CLW v SW -0.28 Negative Indirect Diss v DTR -0.40 Negative Cloud lifetime Diss v PPN 0.43 Negative Cloud lifetime CLW v PPN 0.18 Negative Indirect CLW v LW -0.26 Positive Indirect Diss v LW -0.60 Positive Indirect LW v DTR 0.47 Negative 16 16
Expected Effect Correlation Relationship Cloud lifetime Diss v PPN 0.43 (Negative) Cloud lifetime CLW v PPN 0.18 Negative Positive Consider a cloud of increasing CLW Cloud lifetime effect Increasing Aerosol Droplet size Precipitation Water-cycle effect Constant Aerosol Droplet size Precipitation Water-cycle effect >> Cloud lifetime effect 17 17
Expected Effect Correlation Relationship Indirect CLW v LW -0.26 Negative Positive Indirect Diss v LW -0 0.60 Negative Positive Indirect LW v DTR 0.47 Negative Positive Consider a cloud with dissolved aerosol Influenced by the indirect effect ( CLW/thickness/albedo) LW Min T. DTR LW Max T. DTR LW LW 18
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SO 2 mass mixing ratio at model level 5 20 20
SO 2 mass mixing ratio (ppb) at various model levels at grid point (37.45 N, 14.59 E) Location of Etna (dashed lines non volcanic activity) 21 21
SO 2 mass mixing ratio (ppb) at various model levels at grid point (37.20 N, 15.84 E) Location of Plume (dashed lines non volcanic activity) 22 22
Scenario Economic growth Population growth Focus A2 Slow High Slow technological change B2 Intermediate Intermediate Sustainability: economy, society, environment A1B Very Rapid Peaks mid-century; Efficient technology; declines after Non/Fossil balance 23
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Model is able to produce topographical detail Effect evidence and correlation: Direct: High evidence & Acceptable correlation Indirect: Low-Moderate evidence & correlation Bias suggests problems with the Sulfur cycle: 2 Possible over-estimation of direct SO 4 2- emissions Possible over-estimation of SO 2 SO 4 2- More validation and ensemble simulations are needed for confidence with climate change scenarios as based on the Sulfur cycle More detailed analysis is needed for chemistry in the vertical layers 26 26
Mr. Adam Gauci Mr. Pierre-Sandre Farrugia Dr. Louis Zammit Mangion David Hein David Hassell http://precis.metoffice.com/ 27
Noel Aquilina E: noel.aquilina@um.edu.mt T: +356 2340 3036
PRECIS MONITORING STATIONS 29
PRECIS MONITORING STATIONS 30