Spatiotemporal characteristics of clouds over boreal Siberian zone - - PowerPoint PPT Presentation

Spatiotemporal characteristics of clouds over boreal Siberian zone for simulation of shortwave component of the radiative balance in the “forest-atmosphere” system

Zhuravleva T., Sklyadneva T. and Bedareva T. Institute of Atmospheric Optics SB RAS, Tomsk, Russia

Cloud radiative forcing

Clouds and climate

CRF=CRWSW + CRFLW, CRFSW=A – Aclr, CRFLW=FTOA,clr - FTOA

Estimates of annually mean global average values of CRF, W/m2

Basis Researcher Source CRFLW CRFSW CRF Simple Schneider(1972) 37.5

• 65
• 27.5

models Cess (1976) 45.5

• 44.5

+1 Satellites Ramanathan et

• al. (1989)

ERBE 31

• 48
• 17

Ardanuy et al. (1991) Nimbus 7 24

• 51
• 27

6 climate models Cess and Potter (1987) Variability range, January {23,55} {-74,- 45} {-34,-2} 19 climate models Cess et al. (1990) Variability range, July {13,48} average =29 {-70,- 33} average=

• 50

{-45,-2} average=

• 21

Ultimate goal of the work is:

• n the basis of numerical simulation, to estimate

(i) the radiation budget and (ii) cloud and aerosol radiative forcing in the boreal zone

• f Siberia
• to identify the regional features of RB, CRF, and ARF

Purpose of the first stage is:

• the development of radiation code
• selection of the input atmospheric parameters (cloud

characteristics)

Model of solar radiative transfer (IAOT radiation code, Tomsk, Russia) clouds – aerosol – atmospheric gases – underlying surface Radiative characteristics:

• fluxes and brightness fields
• spectral and integrated characteristics

( )( ) ( )( )

∑

= ↓ ↑ ↓ ↑

=

M i i

z F z F

1

0.2-5 µm, M=30 Accounting for the molecular absorption: k-distribution technique (HITRAN-04, MT_CKD) ) Monte Carlo method

IAOT radiation code

• 1. Horizontally homogeneous atmosphere

⊕

ω r

As Z X

top atm

H

( )

µ σ σ

j cl j s cl j cl

g

, , , ,

, ,

( ) ( )

µ ε σ µ σ σ

i R i m i R i a i s a i a

g g

, , , , , , ,

, , , , ,

bot i

H

top i

H

pi, Ti

• 2. Spatially inhomogeneous clouds: 3D cloud effects

IAOT radiation code

Radiative characteristics within one cloud realization

Stratocumulus clouds: Moeng et al., 1996:

64×64×16 (55 m×55 m×25 m)

• 3. Statistical theory of radiative transfer in clouds:

average (over cloud realizations) radiative characteristics

Poisson model of broken clouds: Analytical averaging of RTE, closed system of equations for first and second intensity moments; algorithms of MC method

Testing of IAOT radiation code

3D cloud effects: Intercomparison of 3D Radiation Codes Comparison of model calculations and measurement data

Data of field experiments:

• vercast one-layer low-level

clouds, ARM Southern Great Plains site, Oklahoma, USA, 1997-1998. Rotating Shadowband Spectroradiometer: 350-1075 nm, 512/1024 channels, direct, diffuse, total radiation Spectral fluxes in the cloudy atmosphere Li, Trishchenko, Cribb (Canada), Kiedron, Harrison (USA), Firsov, Zhuravleva

550 600 800 850 900 950 1000 1050 0,0 0,1 0,2 0,3 0,4 19.10.1997 Cloud layer: 0.58 - 0.85 km LWP=0.008 cm, ref=7.2 µm WVC=1.6 g/cm

2

ξ0=47

• Spectral fluxes, Wt/(m

2*nm)

Wavelength, nm

RSS MOTRAN4 (Cribb) IAOT

Input parameters

Clouds:

• optical thickness
• single scattering albedo
• phase scattering function (asymmetry factor)
• cloud fraction
• layer’s location (lower and upper boundaries)
• cloud horizontal sizes

Aerosol:

• optical thickness
• single scattering albedo
• phase scattering function (asymmetry factor)

Atmospheric gases:

• profiles of temperature, pressure, concentrations

Underlying surface:

• reflection’s law (Lambert)
• surface albedo

Input parameters

Cloud characteristics

MODIS (collection 5): 2000, April – 2008, … Spatial resolution – 1 degree, time resolution – 1 month

• cloud fraction
• cloud optical thickness (water and ice phases)
• cloud effective radius (water and ice phases)
• cloud top pressure
• aerosol optical depth (0.55 µm)
• water vapor content (cloudy and clear sky)

Software

Environment of code development: С++ Builder 5.0

Input parameters

Cloud fraction (Day and Night)

Input parameters

60 70 80 90 100 110 120 130 52 56 60 64 68

Longtitude, deg Latitude, deg

0,3 0,5 0,7 0,9 1 60 70 80 90 100 110 120 130 52 56 60 64 68

Longtitude, deg Latitude, deg

January, 2001-2008 Nmean=6.8, Nmin=4.3, Nmax=8.4 July, 2000-2007 Nmean=6.1, Nmin=3.8, Nmax=8

0,4 0,5 0,6 0,7 0,8 0,9 10 20 30 40 50 60

Frequency of occurence, % Cloud fraction

0,4 0,5 0,6 0,7 0,8 0,9 10 20 30 40 50 60

Frequency of occurence, % Cloud fraction

Comparison of surface observations and MODIS data

Input parameters

( ) ( ) ( ) ( )

( )

sat sat sat sur

N N N N − + = 10 05 . : 1972 , Mullamaa

• MODIS
• Surface observations

2005 2006 2007 0,4 0,6 0,8 1,0 Krasnojarsk: 56

• 02'N, 92
• 45'E

2005 2006 2007 0,4 0,6 0,8 1,0 Irkutsk: 52

• 16'N, 104
• 24'E

2005 2006 2007 0,4 0,6 0,8 1,0 2005 2006 2007 0,4 0,6 0,8 1,0 Tomsk, 56

• 26'N,84
• 58'E

Dem'janskoe, 59

• 34'N, 69
• 28'E

Input parameters January, 2001-2008 τmean=40, τmin=6.7, τmax=100 July, 2000-2007 τmean=19.2, τmin=13.7, τmax=27.4

Cloud optical thickness

60 70 80 90 100 110 120 130 52 56 60 64

Longtitude, deg

10 20 30 40 50 60 70 80 60 70 80 90 100 110 120 130 52 56 60 64 68

Longtitude, deg

10 15 20 25 30 10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 60 70

Frequency of occurence, % Cloud optical thickness

12 16 20 24 28 10 20 30 40 50 60 70

Cloud optical thickness Frequency of occurence, %

Input parameters

Spectral cloud optical characteristics

Optical thickness (extinction coefficient), single scattering albedo, phase scattering function (asymmetry factor)

• 1. Particle size distribution + refractive index => Mie theory
• 2. Parameterizations of optical characteristics
• 2a. Slingo, Scherker, 1984; Slingo, 1989: 4.2<ref<16.6 µm

( ) ( ) ∫ ∫

∞ ∞

=

2 3

dr r f r dr r f r ref

τi=LWP(ai+bi/ref); ωi=1-ci-diref; gi=ei+firef. τ=3LWP/(2ρref),

• 2b. Hu and Stamnes, 1993: 2.5<ref<60 mm

σi=LWC(a1,i×ref

b1,i + c1,i); ωi=1- a2,i×ref b2,i - c2,i; gi= a3i×ref b3,i + c3,i.

Input parameters January, 2001-2008 rmean=11.9, rmin=7.7, rmax=20.9 µm July, 2000-2007 rmean=12.9, rmin=11.2, rmax=14.5 µm

Effective radius, water phase

60 70 80 90 100 110 120 130 52 56 60 64

Longtitude, deg

10 12 14 16 18 20 60 70 80 90 100 110 120 130 52 56 60 64 68

Longtitude, deg

10 11 12 13 14 15 6 8 10 12 14 16 18 20 22 10 20 30 40 50 60 70

Effective radius, µm

10 11 12 13 14 15 16 10 20 30 40 50 60 70

Effective radius, µm

On calculation of photosynthetically active radiation in estimation of carbon balance parameters of surface ecosystems Atmospheric model

Cloud model – statistically homogeneous, based on the Poisson point fluxes on straight lines Aerosol model – continental aerosol (WCP, 1986) Gaseous model – H2O, O2, O3 (HITRAN-2000) Underlying surface – Lambertian law

Calculation technique: 400-700 nm

( ) ( ) ( ) ( )

∫ ∑

+

= = − = ∆ = =

+ =

1

; 3 , 2 , 1 , 100 , , ,

1 3 1

i i

i nm d z F z F z F z F

i i i i i PAR λ λ

λ λ λ λ λ

Comparison of model calculations and experimental data Input parameters

Cloud fraction N:

MODIS/TERRA Atmosphere monthly Global Product;

Aspect ratio γ=H/D:

γ = γ(N) (Shmetter, 1987), H – geometrical thickness, D – mean horizontal cloud size;

Surface albedo:

conifer, water, snow,ice (Hook, ASTER Spectral library)

20 40 60 80 100 120 140 20 40 60 80 100 120 140

Model calculations, W/m

2

Measurements, W/m

2

Measured and model-derived monthly mean downward PAR for BOREAS NSA, 2001–2003

Database for fast calculation of mean PAR values

for different geographic latitudes, months, surface types, solar zenith angles and cloud fraction East Siberia, 2001, July

Cloud fraction

90 92 94 96 98 100 102 104 106 108 50 52 54 56 58 60 62 64

Longtitude, grad Latitude, grad

0,30 0,35 0,40 0,45 0,50 0,55 0,60 0,65 0,70 0,75 0,80 0,85 0,90

90 92 94 96 98 100 102 104 106 108 50 52 54 56 58 60 62 64

Longtitude, grad Latitude,grad

70 75 80 85 90 95 100 105 110 115 120 125 130

PAR, W/m2

Conclusions

• 1. The developed radiation code allows us to efficiently calculate the

shortwave radiative fluxes at the different levels under conditions of the cloudy and clear-sky atmosphere

• 2. It is suggested to use as the source of data on the cloud

characteristics the data of satellite scanner MODIS (Spatial resolution – 1 degree, time resolution – 1 month)

Problems arising in choice of the input parameters:

• cloud optical characteristics – testing?
• aerosol optical characteristics and testing?
• reflection properties of underlying surface and testing?

Thank you for attention!

Clouds and climate

Earth radiation budget

Earth Radiation Budget Experiment data: A=238 W/m2 , FTOA=235 W/m2

Estimates of global average radiation budget according to model data and field measurements [Kiehl and Trenberth, 1997]. “Anomalous absorption” in clouds (Cess, Pilewski, Ramanathan, 1995):∼ 20-25 W/m2

ERB components

sur SW

A

, W/m2

atm SW

A

, W/m2

TOA

R Model calculations Variability range 151-174 65-89 0.3 Model calculations + satellite data Rossow and Zhang (1995) 165 46 0.31 Ground-based network measurements Ohmura and Gilgen (1993) 142

2 2

W/m 150 145 , W/m 85 : ns calculatio Model − = ⇓ = ⇑

sur SW atm SW

A A

Input parameters

52 56 60 64 68

2005: Nmean=6.6, Nmin=3.4, Nmax=8.5 Latitude, deg

0,3 0,5 0,7 0,9 1 52 56 60 64 68

2006: Nmean=6.9, Nmin=3.5, Nmax=9.2 Latitude, deg

60 70 80 90 100 110 120 130 52 56 60 64 68

2007: Nmean=7.3, Nmin=2.9, Nmax=9.6 Longitude, deg Latitude, deg

60 70 80 90 100 110 120 130 52 56 60 64 68

2008: Nmean=6.5, Nmin=4, Nmax=9.6 Latitude, deg Longitude, deg

Interannual variations of cloud fraction 2005-2008, January

52 56 60 64 68

2004: Nmen=5.9, Nmin=3.5, Nmax=8 Latitude, deg

0,3 0,5 0,7 0,9 1 52 56 60 64 68

2005: Nmean=5.8, Nmin=3.7, Nmax=8.6 Latitude, deg

60 70 80 90 100 110 120 130 52 56 60 64 68

2006: Nmean=6.1, Nmin=3.5, Nmax=9.2 Longitude, deg Latitude, deg

60 70 80 90 100 110 120 130 52 56 60 64 68

2007: Nmean=5.8, Nmin=3.4, Nmax=8.4 Latitude, deg Longitude, deg

Interannual variations of cloud fraction 2004-2007, July

Input parameters