Parameterization for Atmospheric Radiation: Some New Perspectives

Transcription

1 Parameterization for Atmospheric Radiation: Some New Perspectives Kuo-Nan Liou Joint Institute for Regional Earth System Science and Engineering (JIFRESSE) and Atmospheric and Oceanic Sciences Department University of California, Los Angeles, CA, USA q Brutsaert s Contributions to Atmospheric Radiation q Atmospheric Compositions and Solar and Thermal IR Absorption Line Spectra q From Emissivity to Band Model to Correlated k- Distribution q Some Unsolved Problems in Radiative Transfer q Radiative Transfer in Mountains/Snow: Snow-Albedo Feedback and Land-Surface Processes

2 VOL. 11, NO. 5 WATER RESOURCES RESEARCH OCTOBER 1975 On a Derivable Formula for Long-Wave Radiation From Clear Skies WILFRIED BRUTSAERT School of Civil and Environmental Engineering, Cornell University, Ithaca, New York A derivation is presented for the effective atmospheric emissivity to predict downcoming long-wave radiation at ground level under a clear sky and for a nearly standard atmosphere. The results are in good Agreement with those obtainable with empirical formulae based on water vapor pressure and temperature. However, the proposed formulation has the advantage that its simple functional form is based on physical grounds without the need for empirical parameters from radiation measurements. Also, in contrast to the empirical equations, it may be adjusted in a simple way to reflect changes in climatic and atmospheric conditions.

3 Radiation Parameterization for Climate Models A schematic view of the components of the climate system, their processes and interactions (IPCC)

4 Thermal IR Radiation Spectrum Observed solar and thermal infrared spectra displaying principal absorbing gases and their spectral location. The IR spectrum was obtained from the Scanning High-resolution Interferometer Sounder (S-HIS), which measured the emitted thermal radiation between 3.3 and 18 µm, onboard the NASA ER-2 aircraft over the Gulf of Mexico southeast of Louisiana, on April 1, 2001 (courtesy of Allen Huang and Dave Tobin of the University of Wisconsin).

5 From Emissivity to Band Model to Correlated k-distribution!(#)= & 1 ( ) + (,)), -.(#)= 0 #!( ) + (,)= Δ+ exp(, # + 1,) 1+/Δ+ 0 1 exp[, #(.) 1,] 1. Fig. 4.5 (a) Absorption coefficient k ν in units of (cm atm) -1 as a function of wave number with a resolution of 0.01 cm -1 in the H 2 O rotational band with p = 600 mb and T = 260K. (b) The probability function f(k) of the absorption coefficient. (c) The cumulative probability function for f(k) shown in (b), plotted as a function of k. (d) Same as (c), except that values of the absorption coefficient are expressed as a function of g (after Liou 2002; see also Goody et al. 1989, Lacis and Oinas 1991, Fu and Liou 1992).

6 (After Charlock and Alberta 1996, Bull. Amer. Meteor. Soc.)

7 Some Unsolved Problems in Radiative Transfer and Climate Research q Radiative Transfer in Ice Crystal Clouds (Almost Resolved; Contrails and Contrail-Cirrus; Small Ice crystals): Cirrus-Aerosol Interaction, Cirrus- Water Vapor in the Tropic, Radiative Forcing q Radiative Transfer in Aerosols (Black Carbon and Dust Mixed with Snow Grains) q Radiative Transfer in the Atmosphere-Ocean System (3D): Surface Winds and Phytoplankton q Radiative Transfer in Mountains/Snow (3D) (Snow-Albedo Feedback): Some New Perspectives

8 Sierra Nevada (America) Rocky (America) Tibetan Plateau (China) Alps (Europe)

9 3D Radiative Transfer (Monte Carlo Photon Tracing) in Mountains: W/m 2 in Regional Surface Energy Balance (Liou et al. 2007; Lee et al. 2010, regression parameterization for use in WRF-CLM) Solar radiation: Direct: solar incident angle θ i Diffuse: sky view factor V d Direct reflected: terrain configuration factor C t Diffuse reflected: terrain configuration factor C t Coupled: terrain configuration factor C t Thermal infrared radiation: Emitted in the atmosphere or from the surface Starting location sampled from a set of pre-divided cubic cells Random direction and isotropic emission (emissivity & temperature) V d θ i 4 C t

10 Comparison of the deviations of the five flux components computed from Monte Carlo simulations (real values) and multiple regression equations (predicted values). The upper panel is for direct (left) and diffuse (right) fluxes. The middle panel is for directreflected (left) and diffusereflected (right) fluxes. The lower panel shows the coupled flux with a surface albedo of 0.1 (left) and 0.7 (right). The most important component is direct flux (~ 700 W/m 2 ), followed by direct-reflected flux (Lee et al. 2011). We have derived 5 universal regression equations for flux deviations which have the following general form: F * i = a i + Σ b ij y j, i = dir, dif, dir-ref, dif-ref, and coup, where a i is the intercept, y j is a specific variable, and b ij are regression coefficients. For example, for the deviation of direct flux, we have F * dir = a 1 + b 11 y 1 + b 12 y 2, where y 1 is the mean cosine of the solar zenith angle and y 2 is the mean sky view factor. This parameterization is applicable to clear as well as cloudy conditions using cloud optical depth as a scaling factor.

11 Connection to Surface Energy Balance Equation (Community Land Model, CLM <-> WRF) q Basic Equation ( S + S ) + L - L - ( H + H ) - ( λ E + λe ) = G Ø g v atm v g vap v g G = Ground Heat Flux ( = Ts / t) ( Sg + S v ) = Absorbed Solar Flux ( v = vegetation, g = ground): 3D Effect L Ø = Incident Longwave Flux: 3D Effect atm L = Emitted Longwave Flux: 3D Effect ( H g + H v ) = Sensible Heat Flux ( λ E + E ) ( v λ = Latent Heat Flux g λ = vap q 3D Mountain Effects S o certain coefficient) ( 3D, α)[1 - α( snow) ]; S = Incident Solar Flux, α = Snow Albedo o Solar Direct & Diffuse Beam (Visible & Near-IR): 3D Monte Carlo and Plane-Parallel Radiation Parameterizations q External & Internal Mixing of BC in Snow Grains α(grain Size, BC) = Snow Albedo: Optical Depth, Single-Scattering Albedo & Asymmetry Factor

12 Solar Flux Differences (3D-PP, W/m 2 ) for March 29, 2007 in WRF Simulations. Model Domain: Covering the Area from W and N. Horizontal Resolution: 30 km. Vertical Level: 28. Fu-Liou-Gu Radiation Scheme. Input: NCEP Final Analysis: Global, 1 Degree Resolution, Every 6 hours. 48-Hour Model Integration: Starting on March 29, 2007 at 0000 UTC. 9AM noon 3PM

13 Differences in sensible and latent heat Fluxes and surface temperature at 9 AM local time, March 29, 2007 in WRF simulations associated with the production of solar flux differences (3D-PP). Sensible Heat Flux (W/m 2 ) Latent Heat Flux (W/m 2 ) Surface Temperature ( o C)

14 An Illustration of Mountain/Snow-Albedo Feedback due to Absorbing Aerosols Anthropogenic (BC/Dust) Positive Feedback Wet/Dry Deposition Decrease in Snow Grain Purity (External/ Internal Mixing) Decrease in Snow Albedo/Cover (Snow is less Bright) 3D Radiative Transfer (?) Mountain Effect Absorbs more Incoming Sunlight? Surface Warming Known Global Warming (CO2)

15 3D Mountain/Snow & Absorbing Aerosols: A Combined Regional Climate System Solar Inputs Clouds (BC/Dust) Circulation 1 Dry Deposition BC/Dust Wet Deposition Glacier Trees Land Lake Ocean