Training Seminar on Climate

Analysis using Reanalysis Data

- Exordium of this seminar -

Norihisa FUJIKAWA

Climate Prediction Division,

Japan Meteorological Agency

TCC Training Seminar

Training Seminar on Climate Analysis using Re-analysis Data

Tokyo, 1-4 Dec. 2009

Purpose of this seminar

Let’s cultivate our sensibility

for climate analysis!

At first

Climate mainly depends on geographic distribution of Continents and Oceans

JRA-25 Atlas

http://ds.data.jma.go.jp/gmd/jra

/atlas/eng/atlas-tope.htm

Surface

Temperature

Precipitation

August

January August

Tokyo Teheran Lisbon

Tokyo Teheran Lisbon

Do you know the climate in Japan ?

Climates are quite different among cities even if they lie at the same latitude.

Jan

Jan

Jan

Aug

Aug

Aug

Do you exactly know the climate in Japan ? Hythergraph(ClimoGraph)

0

50

100

150

200

250

300

350

400

450

0 10 20 30Temperature (℃)

Pre

cip

itati

on (

mm

)

Tokyo

Takada

Jan

Jan Winter Monsoon

Local topography has a large influence on the local climate.

SLP normal (Jan)

Eurasia

Central mountains of Japan Sea of Japan Pacific

North-westerly

Monsoon

(getting wet)

Cumulonimbus

Dry Monsoon

Heavy Snow

Do you exactly know the climate in Japan ?

Hythergraph(ClimoGraph)

0

50

100

150

200

250

300

350

400

450

0 10 20 30Temperature (℃)

Pre

cip

itati

on (

mm

)

Tokyo

Takada

SLP normal (Jan)

Jan

Jan Winter Monsoon

Tokyo

Takada Mountains

Local topography has a large influence on the local climate.

To understand the climate, it is

necessary to know how continental

scale geography and local topography

make influence on the climate there.

Next,

what kinds of phenomena

should we focus on ?

Tempospatial diagram of climatic phenomena

1 Min 1 Hour 1 Day 1 Week 1 Month 1Season 1 Year 10 Years

1000km

10000km

100m

1km

10km

100km

Tornado

Cumulo-

nimbus

Localized

heavy rain

Tropical

Cyclone

low (high)

pressure

Blocking

MJO Monsoon El Nino Decadal

Long Short

La

rge

S

mall

Target of Climate Monitoring

Extreme Weather

Example for multi-scale

interaction of climate system

El Nino

Probability density of a monsoon activity

Normal

year

Change the shape of distribution

Probability density of a MJO activity

Normal

year

Change the shape of distribution

Probability density of convective activity

Normal

year

Change the shape of distribution

Precipitation

Each probability

density has a tempo

spatial structure.

Statistical relationship

Time average at each grid

El Nino

Probability density of a monsoon activity

Normal

year

Change the shape of distribution

Probability density of a MJO activity

Normal

year

Change the shape of distribution

Probability density of convective activity

Normal

year

Precipitation

Each probability

density has a tempo

spatial structure.

Positive Correlation

Time average at each grid

Negative Correlation

Change the shape of distribution at each grid

El Nino

Probability density of a monsoon activity

Normal year

Change the shape of distribution

Probability density of a MJO activity

Normal

year

Change the shape of distribution

Probability density of convective activity

Normal

year

Change the shape of distribution

Precipitation Statistical relationship

Time average at each grid

Decadal

Oscillation

Global

Warming

Probability density of a ENSO activity

Normal

year

El Nino

Probability density of a monsoon activity

Normal

year

Change the shape of distribution

Probability density of a MJO activity

Normal

year

Change the shape of distribution

Probability density of convective activity

Normal

year

Change the shape of distribution

Precipitation

Time average at each grid

Each probability

density has a tempo

spatial structure.

Statistical relationship

Predictability over a season

Predictability within a season

Predictability within a month

Predictability within a few days

Program of Basic Understanding

for Climate Monitoring

• Outline of Climate System Monitoring

• Global Warming Trend and Decadal Oscillation

• Interannual Variability

• Intra-seasonal Variability

• Prediction skill of the seasonal prediction model

And then,

Let’s move onto the ITACS world!

Showing examples

analyzed with ITACS

Showing examples

analyzed with ITACS

JMA Organization

Global Environment

and Marine Department Climate Prediction Division

Climate Prediction Division

Tokyo

Climate

Centre

Administration Director

WMO

NMHSs

Programs

Global Warming Climate

monitoring

Climate diagnosing

Reanalysis ( JRA-25 )

Seasonal forecast Numerical

model developing ENSO

monitoring & forecast

RCC

GPC

GSN-MC CBS-LC