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CSIRO OCEANS AND ATMOSPHERE
Climate Projections for the
Philippine Climate Change
Adaptation Project
(PhilCCAP): Summary
Jack Katzfey
24 December 2015
GEF Grant No. TF096649
Citation
Katzfey JJ (2015) Climate Projections for the Philippine Climate Change Adaptation Project (PhilCCAP):
Summary. CSIRO, Australia.
Copyright and disclaimer
© 2015 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication
covered by copyright may be reproduced or copied in any form or by any means except with the written
permission of CSIRO.
Important disclaimer
CSIRO advises that the information contained in this publication comprises general statements based on
scientific research. The reader is advised and needs to be aware that such information may be incomplete
or unable to be used in any specific situation. No reliance or actions must therefore be made on that
information without seeking prior expert professional, scientific and technical advice. To the extent
permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for
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compensation, arising directly or indirectly from using this publication (in part or in whole) and any
information or material contained in it.
Acknowledgment
This workshop was funded by GEF Grant No TF096649 of the Philippine Climate Change Adaptation Project
(PhilCCAP).
We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is
responsible for the Coupled Model Intercomparison Project (CMIP), and we thank the climate modelling
groups for producing and making available their model output. For CMIP the U.S. Department of Energy's
Program for Climate Model Diagnosis and Intercomparison provides coordinating support and has led
development of software infrastructure in partnership with the Global Organization for Earth System
Science Portals. This report is a contribution to the Commonwealth Scientific Industrial Research
Organization (CSIRO) Oceans and Atmosphere business unit.
The Government of the Republic of the Philippines has received financing from the Global Environment
Facility (GEF) through the World Bank toward the cost of the Philippine Climate Change Adaptation Project
(PhilCCAP), from which this project was funded.
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Executive Summary
The Philippines are highly vulnerable to current climate risks and future climate change. On average, twenty tropical
cyclones enter the Philippine area of responsibility each year, with eight or nine crossing at least part of the country.
The country is also periodically affected by the El Niño-Southern Oscillation (ENSO) phenomenon, which causes
prolonged wet and dry seasons that contribute to a contraction in GDP and a dramatic drop in agricultural
production (Philippines Initial National Communication, 1999). From 1990 to 2003, the estimated damage due to
ENSO-related drought was more than US$370 million.
The objective of Component 3 of the Philippine Climate Change Adaptation Plan (PhilCCAP) Enhanced Provision of
Scientific Information for Climate Risk Management project is to improve the access of end users, especially in the
agriculture and natural resources sectors, to more reliable scientific information and enable more rapid and accurate
decision making for climate risk management. More specifically, it would provide data support for the
mainstreaming activities in Component 1 of PhilCCAP and the adaptation interventions to be carried out at the local
level under Component 2, , and strengthen the capabilities of the institutions having responsibilities in this area—
including the Philippines Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA).
Downscaled projections of future climate for the Philippines show a wide range of changes in temperature and
rainfall. It is important to note that climate changes by the end of the century under a lower emissions scenario are
likely to be similar in character, but lower in magnitude, than under a high emissions scenario. The multi-model
median or mean changes are presented, along with the 10th and 90th percentiles in order to capture the spread
among the model projections and provide some measure of uncertainty in the projections. Results were generated
for the whole of the Philippines as well as for the three sub-regions: Cagayan Province, Jalaur, and Surigao del
Norte. In this summary, only partial results for the whole of the Philippines are presented.
Some key results are:

Increases in temperature by 1.5-3°C for RCP4.5 and 2.5-5°C for RCP8.5, though with decreased warming later
in the century with RCP4.5, related to the CO2 concentrations not increasing. The temperature continues to
increase (and at an even faster rate) with the increasing CO2 concentrations with RCP8.5. Similar changes
are seen to maximum and minimum temperatures.

Changes in temperature extremes similar to changes in the mean temperature, with an increase in the
number of hot days. As indicated by the number of days with maximum temperatures greater than 35°C,
the simulations project that, even though there are only a very few hot days in the current climate, there is a
significant increase late in the century with RCP 8.5 in the northern Philippines (up to nearly 40 days per year
in Cagayan Province).

Generally small changes in mean annual rainfall, but with a non-significant drying trend, except in the
northern Philippines. The decreases are evident in all seasons except MAM, when little change is projected.

Trends for the extreme rainfall statistics broadly similar to the projected mean rainfall changes, with little
significant change projected. This is interesting since with warmer temperatures, the holding capacity of the
air is larger, potentially allowing for greater rainfall. The decrease in the number of tropical cyclones (noted
later) is possibly related to these changes. The number of rain days (days with greater than 1 mm) show
large spatial variability, with both increases and decreases in the change signals. While the number and
length of consecutive dry days (not shown) do not change much, the number of consecutive wet days does
decrease in the southern Philippines.

Little projected change in surface relative humidity over the Philippines, though slight increases are projected
over the oceans.

Slight decreases in wind speed across the Philippines.

Little change in solar radiation, but with slight increase in the southern Philippines.

Changes in surface pressure, with a slight strengthening of the mean meridional pressure gradient across the
Philippines.
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
Decrease in the number of tropical cyclones across the region. The intensity of the average tropical cyclone is
projected to decrease as well, by various measures. No analysis was completed for projected changes in the
more intense cyclones.
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Average air temperature
The average annual surface temperature shows a large amount of warming by the end of the century, with all
models agreeing on warming, as evidenced in the time series plots (Figure 1-1), especially with higher greenhouse
gas concentrations (RCP8.5). The 90th percentile warming approaches 5 °C in the northern Philippines by the end of
the century with RCP8.5. Under RCP4.5, the rate of warming decreases after mid-century, while there is a steady
warming under RCP8.5. These are consistent with the expected radiative forcing resulting from the CO2
concentrations for the two RCPs.
RCP4.5
RCP8.5
Philippines
ANN
1-1: Time series plots of change in the annual average air temperatures (°C) for the Philippines for RCP4.5 (left column) and
RCP8.5 (right column). Black line is mean, red line is 90th percentile, and blue line is 10th percentile. Solid lines show the 10year running mean while dashed lines show annual values. Dashed black line is zero mark.
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Daily Rainfall
Unlike temperature, the projected changes in annual average daily rainfall show a larger range of responses, with
both increases and decreases dependent upon which model simulation is used. The median changes generally show
decreases over Philippine land areas, but some models show increases (as indicated by the 90th percentile changes)
and some show decreases (as indicated by the 10th percentile changes) (Figures 2-1 and 2-2).
DAILY RAINFALL RATE: 2080-2099 average minus 1986-2005 average
RCP4.5
Ensemble mean
RCP8.5
50 %ile changes
Ensemble mean
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50 %ile changes
10 %ile changes
10 %ile changes
90 %ile changes
90 %ile changes
2-1: Average ensemble mean daily rainfall rate (mm/day) for 1986-2005 (top left), and 50th (top right), 10th (bottom left) and
90th (bottom right) percentile changes for 2080-2099 relative to 1986-2005 for RCP4.5 (left) and RCP8.5 (right) based upon the
six CCAM simulations.
TIME SERIES Of CHANGE IN ANNUAL AVERAGE DAILY RAINFALL RATE
RCP4.5
RCP8.5
Philippines
ANN
2-2: Time series plots of change in the annual average daily rainfall rate (mm/day) for the Philippines for RCP4.5 (left column)
and RCP8.5 (right column). Black line is median change, red line is 90th percentile change, and blue line is 10th percentile
change. Solid lines show the 10-year running mean. Dashed black line is zero mark.
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CWD: Consecutive wet days
The number of consecutive wet day periods (CWD; periods more than 5 days long with rainfall greater than or equal
to 1 mm/day)(Figure 3-1) and the change in their maximum length (Figure 3-2) generally show little change across
the Philippines. The 50th percentile shows decreases in the number, but small changes in the maximum length of
consecutive wet days by the end of the century with RCP8.5. Note that these time series plots indicate that there is
a spread of projected changes, with some models showing increases and others decreases in the number of events.
RCP4.5
RCP8.5
Philippines
ANN
3-1: Time series plots of change in the annual number of consecutive wet days periods of more than 5 days for the Philippines
and the three subregions shown in Figure 1-2 for RCP4.5 (left column) and RCP8.5 (right column). Black line is median (50th
percentile change), red line is 90th percentile change, and blue line is 10th percentile change. Solid lines show the 10-year
running mean. Dashed black line is zero mark.
RCP4.5
RCP8.5
Philippines
ANN
3-2: Time series plots of change in annual maximum length of consecutive wet days for the Philippines for RCP4.5 (left
column) and RCP8.5 (right column). Black line is median change, red line is 90th percentile change, and blue line is 10th
percentile change. Solid lines show the 10-year running mean. Dashed black line is zero mark.
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Number of days with more than 100 mm
precipitation (PD100)
The annual number of precipitation days with 100 mm or more rainfall (PD100) are projected to decrease, especially
with RCP8.5 (Figure 4-1, right column). These results are also consistent with the decreases in number of 5-day
rainfall amounts greater than 50 mm, the 95th percentile rainfall amounts and number of days with 20 mm or more
rainfall. Note that by the end
RCP4.5
RCP8.5
Philippines
ANN
4-1: Time series plots of change in the annual number of days with greater than 100 mm of rain for the Philippines for RCP4.5
(left column) and RCP8.5 (right column). Black line is median change, red line is 90th percentile change, and blue line is 10th
percentile change. Solid lines show the 10-year running mean. Dashed black line is zero mark.
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Tropical cyclone statistics
The number of tropical cyclones (density per 1° x 1° grid box) is projected to decrease by the end of the century for
RCP8.51, with only a few models projecting a slight increase (Figure 5-1). This might partly explain the projected
decrease in rainfall and wind speed, and the increase in solar radiation over the Philippines by the end of the century
shown in the six CCAM simulations produced for this study.
Ensemble mean 1986-2005
50th percentile change
10th percentile change
90th percentile change
5-1: Average tropical cyclone density (number per degree box) for Southeast Asia for the 1986-2005 period (top left), and 50th
(top right), 10th (bottom left) and 90th (bottom right) percentile changes for 2080-2099 relative to 1986-2005 for RCP8.5 based
upon the six CCAM simulations.
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Validation of the tropical cyclone density in the simulations was presented in the Technical Report for the High-resolution Climate Projections for Vietnam
project (Katzfey et al., 2014) and is not discussed here.
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Summary
The Philippines are highly vulnerable to current climate risks and future climate change because of the number of
tropical cyclones that enter the Philippine area of responsibility each year and the periodical effects of the El NiñoSouthern Oscillation (ENSO) phenomenon, which causes prolonged wet and dry seasons that contribute to a
contraction in GDP and a dramatic drop in agricultural production (Philippines Initial National Communication, 1999).
As part of Component 3 of the Philippine Climate Change Adaptation Plan (PhilCCAP) Enhanced Provision of Scientific
Information for Climate Risk Management project, high-resolution (10 km) projections of the future climate of the
Philippines were produced to improve the access of end users, especially in the agriculture and natural resources
sectors, to more reliable scientific information and aid in climate risk management. This report summarises the
results of downscaling the simulations of six CMIP5 global climate models (GCMs) using CSIRO’s regional climate
model (RCM), CCAM, for two emission scenarios (RCP 4.5 and RCP 8.5). Although results were generated for the
whole of the Philippines as well as for the three sub-regions (Cagayan Province, Jalaur, and Surigao del Norte), only
partial results for the whole of the Philippines are presented in this short summary.
Some key results are:

Increases in mean temperature by 1.5-3°C for RCP 4.5 and 2.5-5°C for RCP 8.5, though with decreased
warming later in the century with RCP4.5, with similar changes in maximum and minimum temperatures.

Similar changes in temperature extremes, with an increase in the number of hot days, especially in the
northern Philippines (up to nearly 40 days per year in Cagayan Province under RCP8.5).

Generally small changes in mean annual rainfall, with a non-significant drying trend in most seasons, except
in the northern Philippines. The number of days with greater than 1 mm precipitation shows great
variability, with both increases and decreases.

Little significant change in trends for extreme rainfall.

Little change in the number and length of consecutive dry days, but decrease in the number of consecutive
wet days in the southern Philippines.

Little change in surface relative humidity.

Slight decreases in wind speed projected across the Philippines.

Little change in solar radiation, but with slight increase in the southern Philippines.

Some surface pressure changes, with a slight strengthening of the mean meridional pressure gradient across
the Philippines.

A decrease in the number of tropical cyclones across the region, and a decrease in their intensity.
Maps of the change signal for the range of indices such as consecutive dry days (CDD) or Hot Days (days over 35°C)
calculated for this project can be used to determine the change in risk associated with each index. In addition to
multi-model median changes (50th percentile), the 10th and 90th values presented here show the range of possible
changes, and are a measure of uncertainty.
The results provide some measure of the projected changes to risks associated with climate change across the
Philippines. However, due to the limited number of downscaled GCMs and the use of only one downscaling model,
the results need to be used with caution. The much larger range of available GCMs should be investigated, as well as
the use of other downscaling methods and models. It is important to note that assumptions about emissions make a
significant difference to the amount of climate change projected by the end of the century. Consultation with
climate experts in PAGASA before using this and any projections for impact assessments is strongly encouraged2.
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All data generated for the project were provided to PAGASA, who have been trained in use of the scripts to generate the various indices and plots at the final
project workshop in Manila.
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Future research should focus on the physical understanding of the projected changes in order to gain confidence in
the changes. Other lines of evidence should also be considered (as discussed in IPCC, 2013), such as current trends,
changes in vertical structure of the atmosphere and how this might be related to the changes indicated here.
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References
IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M.
Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 1535 pp
Katzfey, JJ, McGregor, JL and Suppiah, R (2014) High-resolution climate projections for Viet Nam: Technical report.
CSIRO, Australia. 352 pp.
http://www.hpsc.csiro.au/users/kat024/final%20summaries/VN_TechnicalReport_266pp_WEB.pdf.
Philippines Initial National Communication, 1999. http://unfccc.int/resource/docs/natc/phinc1.pdf
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Oceans and Atmosphere
Jack Katzfey
t +61 3 9239-4562
e [email protected]
w http://people.csiro.au/K/J/Jack-Katzfey.aspx
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