• 沒有找到結果。

在本研究中只考慮三種陸面環境實驗組,雖然每組實驗中皆有六組系集成員 計算可能因為隨機過程對於對流發展強度等變異度,但仍然有一些問題需要探討 並做為未來研究的方向,問題條列如下:

1. 目前只有考慮陸面型態作為實驗的控制變因,但陸地有許多因子如土壤 濕度(soil moisture)、土壤性質(soil property)、地表綠色植物比(green fraction)等,也會改變陸地性質並透過陸地大氣交互作用影響對流發展。

2. 由於在模式設定上,大氣與陸地之間地面通量是每 30 秒(6 個時步)做一 次交換,而透過改變地面通量交換的頻率可能會影響到在 Coupled 實驗 的對流發展與降水強度,進而影響在不同陸面環境下 Coupled 和

Prescribed 實驗之間的降水日變化強度差異與敏感度。

3. 本研究所使用的空間水平解析度為 2 km,雖然恰可模擬對流發展過程,

但當提高解析度後可以掌握更細緻的對流結構(Tsai and Wu, 2014)。然而 同時也會增加地面通量在空間上的異質性(spatial heterogeneity),可能會 降低對流結構組織程度與降水強度(Wu et al., 2015),因此地面通量的異 質性的增加有可能放大或縮減 Coupled 與 Prescribed 之間降水差異的程度 與敏感性。

4. 在選定冷池強度與冷區邊界上,只考慮相對適合本實驗結果並主觀選定 門檻值。然而冷池強度與性質也被陸面環境影響,因此需要能考慮不同 環境的方式來選定冷池門檻值,如 Drager and van den Heever(2017)等研 究中定義冷池邊界方法。

參考文獻

Acono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D.

Collins, 2008, Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103

Antonelli M, Rotunno R., 2007: Large-eddy simulation of the onset of the sea breeze.

J Atmos Sci 64:4445–4457

Arakawa, A. and C. Wu, 2013: A Unified Representation of Deep Moist Convection in Numerical Modeling of the Atmosphere. Part I. J. Atmos. Sci., 70, 1977–1992.

Baker, R.D., B.H. Lynn, A. Boone, W. Tao, and J. Simpson, 2001: The Influence of Soil Moisture, Coastline Curvature, and Land-Breeze Circulations on

Sea-Breeze-Initiated Precipitation. J. Hydrometeor., 2, 193–211.

Bechtold, P., Chaboureau, J.-P., Beljaars, A., Betts, A. K., Köhler, M., Miller, M. and Redelsperger, J.-L. 2004 : The simulation of the diurnal cycle of convective

precipitation over land in a global model. Q.J.R. Meteorol. Soc., 130: 3119–3137.

doi:10.1256/qj.03.103

Betts, A. K., and C. Jakob, 2002: Study of diurnal cycle of convective precipitation over Amazonia using a single column model, J. Geophys. Res., 107(D23), 4732, doi:10.1029/2002JD002264.

Chen, F., K. Mitchell, J. Schaake, Y. Xue, H. Pan, V. Koren, Y. Duan, M. Ek, and A.

Betts, 1996: Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J. Geophys. Res., 101, 7251-7268.

Chen, F. and J. Dudhia, 2001a: Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Mon. Wea. Rev., 129, 569-585.

Chen M, Zhang H, Liu W, Zhang W., 2014: The Global Pattern of Urbanization and Economic Growth: Evidence from the Last Three Decades. PLoS ONE9(8): e103799.

https://doi.org/10.1371/journal.pone.0103799

Chien, M.-H., and C.-M. Wu, 2016: Representation of topography by partial steps using the immersed boundary method in a vector vorticity equation model (VVM), J.

Adv. Model. Earth Syst., 8, 212–223.

Cosby, B. J., G. M. Hornberger, R. B. Clapp, and T. R. Ginn, 1984: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res., 20, 682-690.

Cronin, T. W., Emanuel, K. A. and Molnar, P., 2015: Island precipitation enhancement and the diurnal cycle in radiative-convective equilibrium. Q.J.R. Meteorol. Soc., 141:

1017–1034. doi:10.1002/qj.2443

Crosman, E.T. & Horel, J.D. Boundary-Layer Meteorol, 2010: Sea and Lake Breezes: A Review of Numerical Studies Boundary-Layer Meteorol (2010) 137, 1-29, doi.org/10.1007/s10546-010-9517-9.

Deardorff, J. W., 1972: Parameterization of the planetary boundary layer for use in general circulation models, Mon. Weather. Rev., 100, 93–106.

Drager, A. J., and S. C. van den Heever, 2017 : Characterizing convective cold pools, J. Adv. Model. Earth Syst., 9, 1091–1115, doi:10.1002/2016MS000788.

Feng, Z., S. Hagos, A. K. Rowe, C. D. Burleyson, M. N. Martini, and S. P. de Szoeke, 2015: Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign, J. Adv. Model. Earth Syst., 7, 357–381, doi:10.1002/2014MS000384.

Guichard, F., Petch, J. C., Redelsperger, J.-L., Bechtold, P., Chaboureau, J.-P., Cheinet, S., Grabowski, W., Grenier, H., Jones, C. G., Köhler, M., Piriou, J.-M., Tailleux, R.

and Tomasini, M., 2004: Modelling the diurnal cycle of deep precipitating convection over land with cloud-resolving models and single-column models. Q.J.R. Meteorol.

Soc., 130: 3139–3172. doi:10.1256/qj.03.145

Hamada, A., Y. Murayama, and Y.N. Takayabu, 2014: Regional Characteristics of Extreme Rainfall Extracted from TRMM PR Measurements. J. Climate, 27, 8151–

8169.

H-C Lin, 2015: An idealized simulation to understand impact of land-air exchanging process on fine scale meteorological characteristics in Taiwan using VVM

Qian, J., 2008: Why Precipitation Is Mostly Concentrated over Islands in the Maritime Continent. J. Atmos. Sci., 65, 1428–1441.

Jacquemin, B., and J. Noilhan, 1990: Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set. Bound.-Layer Meteor., 52, 93-134.

Janjić, Z. I., 1990: The Step-Mountain Coordinate: Physical Package. Mon. Wea. Rev., 118,1429–1443.

Janjić, Z. I., 1994: The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes. Mon. Wea.

Rev., 122, 927–945.

Janjić, Z. I., 1996: The surface layer in the NCEP Eta Model, Eleventh Conference on Numerical Weather Prediction, Norfolk, VA, 19–23 August; Amer. Meteor. Soc., Boston, MA, 354–355.

Janjić, Z. I., 2002: Nonsingular Implementation of the Mellor–Yamada Level 2.5 Scheme in the NCEP Meso model, NCEP Office Note, No. 437, 61 pp.

Jung, J.-H., and A. Arakawa, 2008: A three-dimensional anelastic model based on the vorticity equation, Mon. Weather Rev., 136(1), 276–294.

Khairoutdinov, M., and D. Randall, 2006: High-resolution simulation of

shallow-to-deep convection transition over land, J. Atmos. Sci.,63(12), 3421–3436.

Kuo G.-T. and C.-M. Wu, 2015: What Causes the Precipitation Hotspots of Afternoon Thunderstorms over Taipei Basin?

Krueger, S. K., Q. Fu, K. Liou, and H.-N. S. Chin, 1995: Improvements of an

ice-phase microphysics parameterization for use in numerical simulations of tropical convection, J. Clim. Appl. Meteorol., 34(1), 281–287.

Mahrt, L., and M. Ek, 1984: The influence of atmospheric stability on potential evaporation. J. Climate Appl. Meteor., 23, 222-234.

Mahrt, L., and H. L. Pan, 1984: A two-layer model of soil hydrology. Bound.-Layer Meteor.,29, 1-20.

Monin, A.S. and A.M. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci., USSR, (151), 163–187 (in Russian).

Noilhan, J., and S. Planton, 1989: A simple parameterization of land surface processes for meteorological models. Mon. Wea. Rev., 117, 536-549.

Ogawa S, Sha W, Iwasaki T., 2003: A numerical study of the interaction of a

sea-breeze front with convective cells in the daytime boundary layer. J Meteorol Soc Jpn 81:635–651

Pan, H.-L., and L. Mahrt, 1987: Interaction between soil hydrology and boundary-layer development. Bound.-Layer Meteor., 38, 185-202.

Physick WL., 1980: Numerical experiments on the inland penetration of the sea breeze. Q J RoyMeteorol Soc 106:735–746

Rowe, A. K., and R. A. Houze Jr., 2015: Cloud organization and growth during the transition from suppressed to active MJO conditions, J. Geophys. Res. Atmos., 120, 10,324–10,350, doi:10.1002/2014JD022948.

Saito, K., T. Keenan, G. Holland, and K. Puri, 2001: Numerical Simulation of the Diurnal Evolution of Tropical Island Convection over the Maritime Continent. Mon.

Wea. Rev., 129, 378–400.

Schaake, J. C., V. I. Koren, Q. Y. Duan, K. Mitchell, and F. Chen, 1996: A simple water balance model (SWB) for estimating runoff at different spatial and temporal scales. J. Geophys. Res., 101, 7461-7475.

Tijm ABC, Van Delden AJ, Holtslag AAM., 1999b: The inland penetration of sea breezes. Contrib Atmos Phys 72:317–328

Tompkins, A.M., 2001a: Organization of Tropical Convection in Low Vertical Wind Shears: The Role of Water Vapor. J. Atmos. Sci., 58, 529–545.

Tompkins, A.M., 2001: Organization of Tropical Convection in Low Vertical Wind Shears: The Role of Cold Pools. J. Atmos. Sci., 58, 1650–1672,

Tompkins, A.M., 2001: Organization of Tropical Convection in Low Vertical Wind Shears: The Role of Cold Pools. J. Atmos. Sci., 58, 1650–1672

Tsai, W.-M., and C.-M. Wu, 2017: The environment of aggregated deep convection, J.

Adv. Model. Earth Syst., 9, doi:10.1002/2017MS000967.

Tsai, W.-M., and C.-M. Wu, 2014: The responses of extreme precipitation to organized convections using a cloud resolving model.

Tsai, J.-Y. and C.-M. Wu*, 2016: Critical Transitions of Stratocumulus Dynamical Systems. DYNAT.

Wu, C.-M., and A. Arakawa, 2010: Inclusion of Surface Topography into the Vector Vorticity Equation Model (VVM), J. Adv. Model. Earth Syst., 3, M04002.

Wu, C. and A. Arakawa, 2014: A Unified Representation of Deep Moist Convection in Numerical Modeling of the Atmosphere. Part II. J. Atmos. Sci., 71, 2089–2103.

Wu, C.-M., M.-H. Lo, W.-T. Chen, and C.-T. Lu, 2015: The impacts of heterogeneous land surface fluxes on the diurnal cycle precipitation: A framework for improving the GCM representation of land-atmosphere interactions, J. Geophys. Res.

Atmos., 120, 3714–3727.

表 1 大氣模式設定

表 2 陸地模式設定

Experiment Land type Soil type soil moisture (volumetric)

porosity Green fraction

U00 Urban sand -- -- 0.1

P05 Pasture sand 0.29 0.339 0.1 G08 Grass organic 0.32 0.439 0.65

Model (Atmosphere) Vector Vorticity cloud-resolving Model (VVM)

Model (Land) Noah Land Surface Model (LSM) v3.4.1 Horizontal grid points ‧ Resolutions 256 x 256 ‧ 2 km x 2 km

Vertical resolutions 34 levels , stretching grid (up to 17.5 km) Simulation duration 12 hours (0600 ~ 1800 LST)

Time step 5 s

Back ground Wind Uniform West Wind , 3.2 m/s Lateral boundary condition Double periodic

Surface flux update period 30 s (six time steps)

圖 2.1 大氣初始場之位溫與水氣混合比垂直分布。

紅線為位溫(potential temperature (K))。

藍線為水氣混合比(water vapor mixing ratio (g kg−1))。

Potential temperature (K)

Water vapor mixing ratio (g kg

−1

)

圖 2.2.1 陸地總地面通量之空間上平均時間序列圖。

實線為六個系集實驗時間序列平均,其中淺色區域為時間 序列的範圍。

圖 2.2.2 陸地總地面通量之空間上平均時間序列圖。實線為空間平 均,淺色區域為地面通量之空間標準差。

圖 2.3 五組陸面環境下的蒸發比(evaporative fraction)時序圖。

實線為六個系集實驗時間序列平均,其中淺色區域為時間序列的 範圍。

(mm/hr)

(m/s)

圖 3.1 在 Coupled 實驗下,都市島嶼的日 變化之空間分布圖。圖中左上角為時 間,黑底為陸地,藍底為海洋。Color shading 為地面降水。Gray shading 為雲 頂溫度。箭頭為風速風向之 Y 方向平均 後的近地面(0~300 公尺高)水平風,紅色 為西風,藍色為東風。

m)

圖 3.2 各陸面下 Coupled 實驗之

(a)左側海風移入距離時序圖,海風鋒面移入距離只顯示到兩側海風鋒面輻合的時刻,在此定義為近地 面(0~180 公尺高)及 Y 方向平均東西向風在下午陸地上東側發生最大水平輻合的時刻。

(b)節自 Crosman and Horel, 2010,其中整理四篇模擬海風移入距離時序圖,其中(a)(b)中紅色虛線方框 為相同時間和海風鋒面移入距離區間。 Antonelli and Rotunno, 2007

++ 線(上): high H (下): low H

圖 3.3 在各陸面下,Coupled 實驗的陸地平均降水時序圖。

淺色區域為六個系集實驗結果的範圍。

Precipitation

Precipitation (mm/hr)

U00 P05 G08

X (Km)

Y (Km)

圖 3.4 各陸面下,Coupled 實驗的降水(color shading)、雲頂溫度(gray shading) 與近地面(0~300 公尺)平均水平風速。時間點分別為 Initiation (早上 11 點)、

Condensation(下午 1 點)、Merge(海風鋒面輻合)與 Prec.(陸地平均最大降水時刻)。

X (km)

Height (km)

圖 3.5 各陸面的 Coupled 和 Prescribed 下,取 Y 方向平均的下午對流與海風鋒面輻合時的垂直 結構差異。Gray shading 及白色 contour 為雲水雲冰混合比,contour 等值線量值分別為 0.4,0.5 (gkg-1)。紅色打點為垂直速度超過 0.5(ms-1)區域、紅色 contour 分別為垂直速度 1,1.2 (ms-1)。

藍橘色 shading 及黑色 contour 為 X 方向風速。藍色打點為冷池邊界(-0.003 ms-1),藍色 contour 為冷池強度,等值線量值分別為 -0.01,-0.02 (ms-1)。

(m/s)

U00

P05

G08

Coupled Prescribed

Boundary Layer Height Cold pool intensity

Moist static energy (MSE) Lifted condensation level (LCL)

圖 3.6 各陸面的 Coupled 實驗中,陸地上平均 (a)邊界層高度(Boundary Layer height, BL)

(b)冷池強度(cold pool intensity) (邊界層高度、冷池強度定義詳見附錄) (c)近地面兩公里內平均濕靜能(moist static energy, MSE)

(d)舉升凝結面高度(Lifted of condensation level, LCL)。在此使用氣塊法(air parcel theorem)並利用 近地面(0~21 公尺高)的平均溫度和水氣混合比作為氣塊在地面之溫度與水氣混合比計算。

淺色區域為六個系集實驗結果的範圍。

(c) (d)

(a) (b)

The LAI impact on diurnal amplitude of precipitation

圖 3.7 各陸面下之陸地上

平均日降水時序變化差值(Coupled 減 Prescribed)。

中心黑點為六個系集實驗最大差值的平均,error bar 為系集實驗 差值之標準差。

(a)

(b)

(c)

圖 3.8 下午兩點到六點之間在(a) U00 (b) P05 (c) G08 陸面環境 的陸地降水強度機率分布圖。

U00 P05 G08 Coupled 23558 23074 26442 Prescribed 27093 26912 28239

圖 3.9 在 (a) U00, (b) P05, (c) G08 陸面環境下 Coupled 和 Prescribed 之雲

(雲水+雲冰混合比超過 10-5 kgkg-1) 尺寸機率分布圖(Probability of cloud-size distribution)。

(d)不同陸面環境下六個系集實驗的雲總個數平均。

(a) (b)

(c)

(d) Counts

U00 P05 G08 Coupled 144 143 119 Prescribed 82 110 90

圖 3.10 在 (a) U00, (b) P05, (c) G08 陸面下 Coupled 和 Prescribed 實驗的對流核心雲(convective core cloud) (雲水+雲冰混合比超過 10-5 kgkg-1且垂直速度超過 0.5 ms-1))尺寸機率分布圖。

(d) 不同陸面環境下六個系集實驗的對流核心雲大雲(尺寸超過 103 km3 ) 總個數平均。

(a) (b)

(c)

(d) Counts

圖 3.11 三種陸面型態下對流核心雲的下午兩點至六點之降水貢獻比例在 Coupled 和 Prescribed 實驗 的差異(Coupled 減 Prescribed)。

其中 Large 為對流核心雲(convective core cloud)尺寸超過 103 km3,Small 為尺寸小於 103 km3

U00

P05

G08

Coupled Prescribed

(m/s)

(K)

圖 3.12 在各陸面下 Coupled 和 Prescribed 實驗發生最大平均降水時,冷池強度和地面溫度距平 (地面溫度和陸地地面溫度平均之差值)。藍色 shading 為冷池強度,紅色 shading 為地面溫度距平。

X (Km)

Y (Km)

(a)

圖 3.13

(a) 在各陸面下 Coupled 和 Prescribed 實驗發生最大平均降水時冷池強度和地面溫度 距平(和陸地地面溫度平均之差值)區域分離比例(0 為完全重合、1 為完全分離)。

實心點為六個系集實驗之空間分離度平均,error bar 為系集實驗之空間分離度平 均的標準差。

(b)為空間分離度定義的示意圖。藍色為冷池強度超過 10 (ms-1),紅色為地面溫度距 平低於 -5 (K)。平均空間分離比例的計算為只考慮有數字的區域(0,1)(即不考慮 NaN

(b)

Urban

Pasture

Grass

圖 3.14 造成在陸面從偏乾轉變到偏濕時,Coupled 和 Prescribed 實驗中對流 集結過程差異的機制示意圖。水藍色區域為地面冷區(cold region)強度,藍色 實線為冷池鋒面(cold pool front)垂直剖面,藍色虛線為冷區造成上方大氣沉 降。

附 錄

1. 邊界層高度:

本研究使用的邊界層高度定義為網格點上,在某高度的虛位溫(virtual

potential temperature)第一次高於地面虛位溫 0.05 K 時,該高度即為網格點的邊界 層高度。 3.1 Thermodynamics

根據 Mahrt and Ek(1984)中可藉由土壤溫度(soil temperature)擴散方程計算地 面熱通量(ground heat flux):

C(θ)𝜕𝜕𝑇𝑇

θ: 土壤含水量(soil water content),被土壤性質決定(Cosby et al.1984) 在模式中第 i 層的土壤方程可改寫成

3.2 Hydrology D:土壤水耗散(soil water diffusivity)

K:液體傳導(hydraulic conductivity)

𝐹𝐹𝜃𝜃:土壤水的來源和流失(sources and sinks),如降水、蒸發和逕流。 總蒸發散量(Evapotranspiration, E)

E = Edir+ 𝐸𝐸𝑐𝑐+ 𝐸𝐸𝑡𝑡+ 𝐸𝐸𝑠𝑠𝑟𝑟𝑣𝑣𝑠𝑠 3.3.1 可感熱(sensible heat flux, SH)

SH = (w������) = −θs Ch𝐶𝐶𝑣𝑣𝑃𝑃𝑠𝑠

𝑅𝑅𝜃𝜃𝑣𝑣𝑣𝑣a− 𝜃𝜃𝑠𝑠)

𝐶𝐶:在地表之熱通量和水氣通量交換係數

𝐶𝐶𝑣𝑣:乾空氣之定壓比容 𝑃𝑃𝑠𝑠:地面氣壓

R: 乾空氣氣體常數

𝐶𝐶透過 Monin and Obuhkuv similarity theory(1954)為基底的 Eta surface layer scheme 計算出(Janjić,1990,1994,1996,2002)

下標符號 s:地面; a: 風速計高度

3.3.2 潛熱(latent heat flux, LH)

LH = (w������) = E = Eqs dir+ 𝐸𝐸𝑐𝑐+ 𝐸𝐸𝑡𝑡+ 𝐸𝐸𝑠𝑠𝑟𝑟𝑣𝑣𝑠𝑠

𝐸𝐸𝑐𝑐𝑟𝑟𝑣𝑣 = �1 − 𝜎𝜎𝑓𝑓�𝛼𝛼𝐸𝐸𝑣𝑣,其中α = 𝜃𝜃1− 𝜃𝜃𝑠𝑠 𝜃𝜃𝑣𝑣𝑟𝑟𝑓𝑓− 𝜃𝜃𝑠𝑠

𝐸𝐸𝑣𝑣:potential evaporation,透過 Penman-based 能量平衡方法(Mahrt and Ek 1984)算 出。

𝑅𝑅𝑐𝑐:植物的阻抗值(canopy resistance)。在 Jacquemin and Noilhan(1990)中

Rc = 𝑅𝑅𝑐𝑐𝑐𝑐𝑟𝑟𝑟𝑟 LAI: leaf area index

qs(𝑇𝑇𝑣𝑣): 在溫度為Ta時的飽和水汽混合比

4. 更多陸面環境對於降水日變化的影響

此部分的陸面環境設定除了不同陸面型態之外,也有改變土讓濕度、地表綠 色植物比(green fraction)等變數,並在這些環境之下看 Coupled 與 Prescribed 之間降水日變化差異的程度,但沒有系集成員的統計結果。

Evaporative fraction

(Wm-2 )

LST (hr) LST (hr)

Total surface flux

The LAI impact on diurnal amplitude of precipitation

N

黑點為 Coupled 與 Prescribed 實驗之間降水日變化 最大值之差異,error bar 為降水日變化差異的標準 差。

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