Climate Simulations of the 20th Century by using NCC/IAPT63 Model

Ying XU, Xuejie GAO, Yong LUO , Panmao ZHAI and Yihui DING

(National Climate Center, China Meteorological Administration, Beijing)

OutlineOutlineModel Description

Experiment design

Result analysis

Summary

Future plan

Model Description

Model DescriptionModel Description

The atmospheric component of the model (AGCM) : 16 levels with a horizontal resolution of 1.875 degree of latitude by 1.875 degree of longitude, which produces a global grid of 192×96 grid cells;

The reference pressure is 1013.25hPa, time integration step is 22.5 minutes

Model DescriptionModel Description

The oceanic component of the model (OGCM) was developed by IAP (Institute of Atmospheric Physics, CAS). It uses a triangular-truncated spectral horizontal grid system, with same horizontal resolution as the AGCM.

Altogether 30 vertical levels, of which 20 levels for the upper ocean 1000m.

Model DescriptionModel Description

Following parameterization schemes of physical  processes are included: 

radiation scheme:  cloud and radiation processes and the scatter process of cloud were considered.

The GHG absorbers include H2O, CO2 and O3 and direct influence of aerosols is prescribed

vertical diffusion: turbulent vertical fluxes were simulated through the process of vertical diffusion

Model DescriptionModel Description

gravity wave drag convection: mass flux scheme for deep, shallow

and mid-level convection clouds were represented by a bulk model and include updraft and downdraft mass fluxes

land-surface processes sea-ice: thermodynamic sea-ice

Part I: Two simulations for climate variation from 1900~2000 year using the NCC/IAPT63 coupled model based on two different radiation schemes

Part II: Four simulations from 1948~2003 forced by HADISST and Sea Ice data. Two simulations have been completed, and another two are undergoing. Simulations were based on forcing from change in CO2, ozone, but not including that of aerosols, volcano and solar radiation.

Reference period: 1971~2000

ExperimentsExperiments

Result Analysis

Part I

Simulation of climate variation from 1900~2000 year using NCC/IAPT63

coupled model

Change of temperature and precipitation over Globe in 20th century (unit: ) (w.r.t.1961~1990)℃

-1.5

-1

-0.5

0

0.5

1

Gl obe-TGl obe-OBS

Gl obe-Pr

-0. 8

-0. 6

-0. 4

-0. 2

0

0. 2

0. 4

0. 6

0. 8

1

1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Gl obe-Pr

Change of temperature and precipitation over China in 20th century (unit: ) (w.r.t.1961~1990)℃

-1. 5

-1

-0. 5

0

0. 5

1

1. 5

1900

1903

1906

1909

1912

1915

1918

1921

1924

1927

1930

1933

1936

1939

1942

1945

1948

1951

1954

1957

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

Chi na-TChi na-OBS

-15

-10

-5

0

5

10

15

20

1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

Chi na-PrChi na-OBS

Long-term trend of annual mean T and Pr(%/100a)(1901~2000)

Global Simu.

Global OBS China-simu. China-OBS

T 0.86 0.67 0.89 0.34

Pr. 0.2 3.6 1.3

Correlation between simulation and observation in last 100 years

Globe East Asia China

T 0.67 0.55 0.31

Pr. 0.03 0.0 -0.02

Part I summary

• NCC/IAPT63 model was applied for transient climate change experiments on the 20th century. Experiments include anthropogenic forcing, but do not include natural forcing and troposphere aerosols.

• In these preliminary experiments, variation in global annual mean surface temperature seems to be generally agreed with observations.

.

Part II

Experiments forced by HADISST1.1

for the 1948~2003

MSLP (Jan)

MSLP (Jul)

MSLP (annual mean)

1 月份 30年平均海平面气压场

7月份 30年平均海平面气压场

低压位于高纬的海洋区，沿海岸线等压线密集。在欧亚大陆和北美大陆以及副热带大西洋地区是地面高压带。在数值实验中，只在下边界是加热里作用时，也能模拟出和真实情况相似的场，说明热力在维持高纬海洋区地面地压时很重要。低低

高

高

高

低

高高

海平面气压于 1月份近于相反，在欧亚大陆上是庞大的热低压，而在两大洋上为强大的副热带高压，这反映了热力强迫的显著差异。地形强迫主要反映在山脉两侧海平面气压梯度的差异上。在高空西风带地区，通过山脉海平面气压从西向东减小。

低

Distribution of 850hPa height field (1971~2000 mean) in Jan.

Simulation observation

Distribution of 850hPa height field (1971~2000 mean) in Jul.

Simulation observation

Distribution of annual mean heights at 850hPa

Simulation observation

Distribution of 500hPa height in Jan (1971~2000 )

Simulation observation

Distribution of 500hPa heights in Jul (1971~2000)

Simulation observation

Distribution of annual mean 500hPa heights

(1971~2000 )Simulation observation

Mean Jan 850hPa wind fields (1971~2000 )

Simulation observation

Mean Jul 850hPa wind fields (1971~2000)

Simulation observation

Annual mean 850hPa wind field (1971~2000)

Simulation observation

Mean Jan 200hPa wind field (1971~2000)

Simulation observation

Mean Jul. 200hPa wind field from (1971~2000)

Simulation observation

30 year mean U lat.-P cross section in January

Simulation observation

30year mean U lat.-P section plane in July

Simulation observation

30year mean Temperature lat.-P cross section for January

Simulation observation

30year mean Temperature lat.-P cross section for July

Simulation observation

Annual mean 30year mean Temperature Lat.-P cross section

Simulation observation

Surface air temperature in January

Simulation observation

Surface air temperature in July

Simulation observation

Annual mean surface air temperature(1971~2000)

Simulation observation

Mean State of January Precipitation

Mean State of July Precipitation

Mean State of Annual Precipitation

Month mean time-series of global surface temperature from 1948~2003

Annual mean global surface temperature during 1948~2003

Simulation and Observation of Annual Global Mean LandSurface Temperature by using HADAM3 (Ref: 1961-1990) -

-0.8-0.6-0.4-0.2

00.20.40.60.8

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

All Forcing Observation

Part II summary

• HADISST1.1 dataset is being used in some simulations of climate change of 20th Century

• Results of simulation for mean state seems to be encouraging

• Simulations for climate variations need to be greatly improved

Future plan

• Further simulations

• To improve the simulation results

• More thorough and robust analysis