Second Lecture Series

in South Africa

October 11−17, 2015

Report on

Lectures in South Africa

October 11−17, 2015, Limpopo Univ.

Moto Ikeda

Yushi Morioka

Jayanthi Venkata Ratnam

Tak Ikeda

Climate variability/change

and Public health

Lecture

Climate Change & Global Warming

Moto Ikeda from Tokyo, Japan

Is global warming real?

What is the mechanism of global warming?

Radiation balance in the atmosphere

Absorption

& heating

Longwave radiation Shortwave radiation

Carbon dioxide

Questions on Global warming

• Is global warming real? YES

• Is it because of CO2? YES

• How much will warming proceed?

• How much should we reduce CO2 emission?

• Is it economically feasible?

• We need more scientific proof rather than “CO2

will be as much as the dinosaur era”.

More rain or less rain? (IPCC Report)

Atmosphere (830 x 10**9 ton=830 x 109)

Ocean

(40000 x 10**9 ton)

Anthropogenic

8 x 10**9

Global Carbon Cycle existing amount and annual flux

revised by AR5

80 x

10**9

120

x 1

0**9

82 x

10**9

122 x

10**9

Terrestrial ecosystem (600 x 10**9 ton)

Soil (3000 x 10**9 ton)

Nature is

important!

River 1 x 10**9 ton

Facts of global warming

• Mechanisms

CO2 increases by human activities.

Outgoing longwave radiation is absorbed by

CO2, and emits it back to the earth surface.

Carbon cycle among atmosphere, land and ocean

• Learn from old time

Higher CO2 in the dinosaur era

Lower CO2 during the glacier period

Impacts on global environmental change

• Tropical disease expands to mid-latitudes due to warming.

• Food production is decreased by precipitation change and soil moisture decrease.

• In subpolar regions, winter becomes shorter, and snow depth decreases.

• Damage in coastal areas by sea level rise

• More damage by unpredictable weather??

• More damage on developing countries

Health: global warming expands malaria

Feedbacks among Urgent Issues

Water resource

Global warming Biodiversity

Energy

& resources

Food

Industrial development

Globalization

Health

Once each issue gets worse, it worsens the others.

Can we solve all issues at once?

Kyoto Protocol

• CO2 emission = 1 tonC／(person･year) as global

average

• 3 tC/(p･y) in Japan, 6 tC/(p･y) in the US

• Reduction of emission by 6% of 1990 amount

before 2010

• No duty on developing countries (China & India)

• US has left from Kyoto Protocol.

• At COP17, Japan expressed withdrawal in the

extended period after 2013.

Kyoto Protocol and its Mechanism

• It makes easier to reduce CO2 emission.

• Joint Efforts

Developed countries (Annex I) can transfer

their reduction and not-yet-achieved

reduction.

• Clean Development Mechanism（CDM）

Annex I countries can buy absorption by

sink from non-Annex I countries.

Some conditions have to be satisfied.

Beyond Kyoto Protocol

• What will come in 2050-2100?

• The global population has increased by a factor of 2.4 in last 50 years, and will increase.

• Some of the developing countries will become developed countries in 50 years, and emit a large amount of carbon dioxide.

• Global warming will change the pattern of rain.

• Biodiversity is reduced and food supply is limited.

• Human health is damaged.

What should we do?

• How can we slow down CO2 increase?

• Technological development: transfer from

developed countries to developing countries

• Renewable energy (wind power, solar power, bio-

energy etc.)

• Changes in our concept and life style: possible ?

• Enhance Kyoto Protocol mechanism: Clean

Development Mechanism for developing

countries to sell carbon emission saving to

developed countries

Lecture

Climate variabilities around South Africa ~The fundamental nature of modeling

and its usefulness~

Yushi Morioka

JAMSTEC/APL

Weather and Climate

Today’s weather Annual mean climate

day-month timescale season-decade timescale

Weather is affected

by atmospheric variability

(e.g. Low/High)

Climate is affected

by oceanic variability

(e.g. El Nino)

News24 CSIR

Oceanic variability

affecting global and local climate

Accurate prediction of oceanic variability is

important for skillful climate prediction.

Warm

Cold

El Nino is developing in the tropical Pacific !

Current status of global ocean

Numerical simulation for climate

3. Earth Simulator

2. Governing equations

4. Predicted climate

1. Ocean and Atmosphere

El Nino

Peak in autumn, then

decay in next spring

Indian Ocean Dipole

Prediction results

Peak in autumn, then

decay in winter

Predicted climate (Dec-Feb)

Global: warmer climate

SA: warmer, drier

JP: drier

Introduction to Statistical downscaling and Dynamical downscaling

J. V. Ratnam

Application Laboratory, Japan Agency for Marine-Earth Science and Tech

(JAMSTEC) Japan

Global Models

The global General Circulation Models (GCMs) are run at coarser horizontal resolutions

http://serc.carleton.edu/eet/envisioningclimatechange/part_2.html

Downscaling

• The global General Circulation Models (GCMs) are run at coarser horizontal resolutions.

• To get the weather/climate forecast at regional scales, the coarser GCM forecasts are downscaled.

• Downscaling can be either statistical or Dynamical. • Statistical downscaling assumes that the relationship between the larger

scale variables such as pressure and the actual rainfall measured at one particular station will always be the same.

• Embed a regional climate model in a GCM

Global forecasts at coarser Resolution

Statistical Downscaling Dynamical Downscaling

Regional/Local Forecasts

or

Observed Large scale variable for eg. Pressure

Observed local rainfall

Statistical model

Forecast Large scale variable

Forecast local rainfall

Statistical downscaling

• Regional or local climate information is derived by first determining the statistical model which relates large-scale climate variable to regional and local variables.

• Then the large scale output of GCM simulation is fed into this statistical model to estimate the corresponding local and regional climate characteristics.

Statistical Model: a) Weather Typing b) Weather generators c) Regression models

Courtesy: Google earth

Global Model (100km)

Regional Model 1st Domain (27km)

Regional Model 2nd Domain (9km)

Dynamical downscaling • Regional climate model at high resolution embedded into the GCM output • Multiple two-way interacting domains can be used for downscaling.

Advantages and disadvantages Statistical downscaling Advantages: a) The statistical models are computationally inexpensive. b) The models provide site specific information required for impact study. Disadvantages: a) The relationship developed for the present climate may not be true for the future climate. b) Results are dependent on the statistical method used for downscaling. Dynamical downscaling Advantages: a) Consistent with GCM b) Resolve local processes responsible for the rainfall. Disadvantages: a) The results depend on how good the GCM is in representing the large scale processes. b) Results depend on the choice of domain. c) Computationally expensive

Application of Regional downscaling

The downscaled results are used to drive hydrological models.

Limpopo River Basin www.limpoporak.org

Application of Regional downscaling The global seasonal forecasts are downscaled for local climate change.

Current and possible future impacts and vulnerabilities associated with climate variability and climate change for Africa. Source: Adapted from IPCC Fourth Assessment Report (2007), Working Group 2: Impacts, Adaptation and Vulnerability, Chapter 9, Figure 9.5

What we plan to do under the joint AMED/JICA - DST SA project

Picture Courtesy: Google earth

SINTEX-F

WRF 27km

WRF 9km

Malaria Model

http://users.ictp.it/~tompkins/vectri/

• Seasonal Forecasts from SINTEX-F CGCM will be downscaled using both Statistical and Dynamical downscaling methods.

• The downscaled results will be used for driving Disease models such as Malaria model with focus on Limpopo province.

http://www.jamstec.go.jp/frcgc/research/d1/iod/sintex_f1_forecast.html.en

Summary

• To get a local forecast from a global model, the technique of downscaling is used.

• The downscaling can be applied by using statistical techniques or by using regional climate models

• Statistical techniques are easy to use, but the regional models are physically consistent.

• The downscaled products are used at many forecasting centers for daily weather forecast/ hydrology forecast/ climate forecasts.

The 5W1H of Statistics By Tak Ikeda

tak.Ikeda@jamstec.go.jp

STATISTICS

WHAT

WHERE

WHEN

WHO

WHY

HOW

Outline: I will introduce the basics of statistics and how it is used in different scenarios, starting from the everyday things around us to more specific areas of research. If you are working or studying in the field of statistics, or already using statistics as a tool in your research or study, then GREAT (and keep learning)! If statistics is new to you, then I hope you learn something here. I am 95% confident that you will have to use statistics at some point of your life.

To learn more about statistics, you should take a course on it, or do some reading. I often make use of the sites below. Links: Online stats textbooks http://onlinestatbook.com/ http://www.statsoft.com/Textbook Forums http://stats.stackexchange.com http://stackoverflow.com/questions/tagged/r Social network www.researchgate.net Useful R related links https://cran.r-project.org/web/views/ http://www.r-bloggers.com http://www.ats.ucla.edu/stat/r/

5W1H Topics

• WHAT is statistics? • WHERE is statistics used around us? • WHO uses statistics? • WHEN can you use statistics? • WHY use statistics? • HOW to do statistics and interpret results

WHAT is statistics? “Statistics is the study of the collection, analysis, interpretation, presentation and organization of data.” - The Oxford Dictionary of Statistical Terms

• A “multi-tool” that can be used in any discipline. We could: • calculate probabilities • measure performance

• quantify uncertainty

• learn data

• predict the future

• make better decisions…

WHERE is statistics used around us?

• Statistics South Africa • Sports • Scientific research

WHEN can you use statistics?

You will need: • Some math skills • Calculator or computer software

(e.g. R, Stata, SPSS, MS Excel) • Data • Research question

WHO uses statistics?

•A “multi-tool” to: • calculate probability (or risk) of getting a disease – epidemiologist

• measure performance of 2 insect repellents – lab technician

• quantify uncertainty of future earthquake – seismologist

• learn data to decide what product to sell to who – data scientist

• predict the future of stock exchange – economist

• make better decisions, like which pair of shoes to buy – you

WHY use statistics?

• Give more meaning to data

• Describe the data -> trend, variability, correlation, patterns, etc

• Make inferences

-> generalizations of a population from a sample

• Make predictions -> future events in terms of probability, confidence

levels, uncertainty

Example – Risk of getting malaria Data: • Monthly malaria cases in Vhembe • For years 1998 to 2014

=> What is the relationship between

the number of malaria cases and temperature/precipitation?

Logarithm of malaria count

Precipitation

Temperature

Data are shown here by graphs and numbers. Intercepts and slopes are calculated from regression of Logarithm of malaria count on three variables: (1) Precipitation, (2) Temperature and (3) Multiplication of Precipitation by Temperature.

log.count = 5.296 + 0.135 x tempc + 0.011 x prcpc – 0.004 x tempc x prcpc • At mean temp and mean prcp: log.count = 5.296 (count = 200) • temp increase of 1 deg, no change in prcp: 5.296 + 0.135*1 + 0.011*0 – 0.004*1*0 = 5.431 (count = 228) • prcp increase by 30mm, no change in temp: 5.296 + 0.135*0 + 0.011*30 – 0.004*0*30 = 5.626 (count =278)

Results from regression model * temp.c = temperature - mean temperature

Let’s look at the effect of the interaction term (multiplication of precipitation and temperature): • At temp increase of 3 deg and prcp change of X, : 5.296 + 0.135*3 + 0.011*X – 0.004*3*X =5.296 + 0.405 + 0.011*X – 0.012*X =5.701 - 0.001*X Increase (decrease) in precipitation yields decrease (increase) in count. This suggests that warmer and drier conditions (like El Nino) => increase in malaria.