* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download CSIRO_PHILCCAP_Summary_FNL
ExxonMobil climate change controversy wikipedia , lookup
Global warming hiatus wikipedia , lookup
Climate engineering wikipedia , lookup
Climate governance wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Climate change denial wikipedia , lookup
Politics of global warming wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Climate sensitivity wikipedia , lookup
Economics of global warming wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Global warming wikipedia , lookup
Physical impacts of climate change wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Climate change in Australia wikipedia , lookup
Climate change adaptation wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Solar radiation management wikipedia , lookup
General circulation model wikipedia , lookup
Climate change in Saskatchewan wikipedia , lookup
Climate change feedback wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate change in the United States wikipedia , lookup
Climate change and poverty wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
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 any consequences, including but not limited to all losses, damages, costs, expenses and any other 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. ii 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. 1 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. 2 1 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. 2 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 3 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. 4 3 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. 5 4 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. 6 5 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. 1 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. 7 6 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. 2 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. 8 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. 9 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 10 CONTACT US FOR FURTHER INFORMATION t 1300 363 400 +61 3 9545 2176 e [email protected] w www.csiro.au Oceans and Atmosphere Jack Katzfey t +61 3 9239-4562 e [email protected] w http://people.csiro.au/K/J/Jack-Katzfey.aspx YOUR CSIRO Australia is founding its future on science and innovation. Its national science agency, CSIRO, is a powerhouse of ideas, technologies and skills for building prosperity, growth, health and sustainability. It serves governments, industries, business and communities across the nation. 12