Gale Warning
Issued at: 5:00 PM TODAY , 05 April 2017
Gale Warning # 10 (Final)
For:

Gale Warning in PDF file





Monthly Climate Assessment and Outlook

Issued: 07 April 2017

Monthly Rainfall Forecast
RAINFALL FORECAST  (April - September 2017) 
UPDATED: 05 April 2017 (next update 26 May 2017)


Regional Rainfall Forecast
Issued: 05 April 2017
Valid for: April-September 2017
Farm Weather Forecast and Advisories
ISSUED              :  8AM, THURSDAY, APRIL 27, 2017
VALID UNTIL      :  8AM, FRIDAY, APRIL 27, 2017
FWFA:  N0. 17-117


Ten-Day Regional Agri-Weather Information

DEKAD NO. 12 April 21 - 30, 2017

PHILIPPINE AGRI-WEATHER FORECAST

      The weather systems that are expected to affect the whole country within the ten-day period are ridge of high pressure area, easterlies and low pressure area.

Partly cloudy to cloudy skies with isolated rainshowers and thunderstorms mostly in the afternoon or evening are expected over the whole country during the early and the middle days of the forecast period. On the latter part of the period, a possible low pressure area will affect the eastern section of Visayas and Mindanao which will bring cloudy skies with light to moderate rains. The rest of the country will have partly cloudy to cloudy with isolated rainshowers and thunderstorms mostly in the afternoon or evening.

Winds blowing from the southeast to south will prevail over Luzon and Visayas and coming from the east to northeast over Mindanao. The whole archipelago will have slight to moderate seas during the early and middle days of the dekadal period. On the latter part of the dekad the seaboards of Northern Luzon, eastern Visayas and Mindanao will have moderate to occasionally rough seas while the rest of the country will have slight to moderate seas.

            The eastern and central equatorial Pacific is in neutral condition.


Seasonal Climate Outlook
Issued:  20 January 2017
FOR January - June 2017
PDF 




Astronomical Diary
Issue for April 2017
Lyrids Meteor, Jupiter, Saturn will be found at about 19 degrees above the east southeastern horizon and...




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Introduction

Climate change is happening now. Evidences being seen support the fact that the change cannot simply be explained by natural variation. The most recent scientific assessments have confirmed that this warming of the climate system since the mid-20th century is most likely to be due to human activities; and thus, is due to the observed increase in greenhouse gas concentrations from human activities, such as the burning of fossil fuels and land use change. Current warming has increasingly posed quite considerable challenges to man and the environment, and will continue to do so in the future. Presently, some autonomous adaptation is taking place, but we need to consider a more pro-active adaptation planning in order to ensure sustainable development.

What does it take to ensure that adaptation planning has a scientific basis? Firstly, we need to be able to investigate the potential consequences of anthropogenic or human induced climate change and to do this, a plausible future climate based on a reliable and accurate baseline (or present) climate must be constructed. This is what climate scientists call a climate change scenario. It is a projection of the response of the climate system to future emissions or concentrations of greenhouse gases and aerosols, and is simulated using climate models. Essentially, it describes possible future changes in climate variables (such as temperatures, rainfall, storminess, winds, etc.) based on baseline climatic conditions.

The climate change scenarios outputs (projections) are an important step forward in improving our understanding of our complex climate, particularly in the future. These show how our local climate could change dramatically should the global community fail to act towards effectively reducing greenhouse gas emissions.

 





Climate Change Scenarios

As has been previously stated, climate change scenarios are developed using climate models (UNFCCC). These models use mathematical representations of the climate system, simulating the physical and dynamical processes that determine global/regional climate. They range from simple, one-dimensional models to more complex ones such as global climate models (known as GCMs), which model the atmosphere and oceans, and their interactions with land surfaces. They also model change on a regional scale (referred to as regional climate models), typically estimating change in areas in grid boxes that are approximately several hundred kilometers wide. It should be noted that GCMs/RCMs provide only an average change in climate for each grid box, although realistically climates can vary considerably within each grid. Climate models used to develop climate change scenarios are run using different forcings such as the changing greenhouse gas concentrations. These emission scenarios known as the SRES (Special Report on Emission Scenarios) developed by the Intergovernmental Panel on Climate Change (IPCC) to give the range of plausible future climate. These emission scenarios cover a range of demographic, societal, economic and technological storylines . They are also sometimes referred to as emission pathways. Table 1 presents the four different storylines (A1, A2, B1 and B2) as defined in the IPCC SRES.


Climate change is driven by factors such as changes in the atmospheric concentration of greenhouse gases and aerosols, land cover and radiation, and their combinations, which then result in what is called radiative forcing (positive or warming and negative or cooling effect). We do not know how these different drivers will specifically affect the future climate, but the model simulation will provide estimates of its plausible ranges.

A number of climate models have been used in developing climate scenarios. The capacity to do climate modeling usually resides in advanced meteorological agencies and in international research laboratories for climate modeling such as the Hadley Centre for Climate Prediction and Research of the UK Met Office (in the United kingdom), the National Center for Atmospheric Research and the Geophysical Fluid Dynamics Laboratory (in the United States), the Max Planck Institute for Meteorology (in Germany), the Canadian Centre for Climate Modeling and Analysis (in Canada), the Commonwealth Scientific and Industrial Research Organization (in Australia), the Meteorological Research Institute of the Japan Meteorological Agency (in Japan), and numerous others. These centers have been developing their climate models and continuously generate new versions of these models in order address the limitations and uncertainties inherent in models.

For the climate change scenarios in the Philippines presented in this Report, the PRECIS (Providing Regional Climates for Impact Studies) model was used. It is a PC-based regional climate model developed at the UK Met Office Hadley Centre for Climate Prediction and Research to facilitate impact, vulnerability and adaptation assessments in developing countries where capacities to do modeling are limited. Two time slices centered on 2020 (2006-2035) and 2050 (2036-2065) were used in the climate simulations using three emission scenarios; namely, the A2 (high-range emission scenario), the A1B (medium- range emission scenario) and the B2 (low-range emission scenario).

The high-range emission scenario connotes that society is based on self-reliance, with continuously growing population, a regionally-oriented economic development but with fragmented per capita economic growth and technological change. On the other hand, the mid-range emission scenario indicates a future world of very rapid economic growth, with the global population peaking in mid-century and declining thereafter and there is rapid introduction of new and more efficient technologies with energy generation balanced across all sources. The low-range emission scenario, in contrast, indicates a world with local solutions to economic, social, and environmental sustainability, with continuously increasing global population, but at a rate lower than of the high-range, intermediate levels of economic development, less rapid and more diverse technological change but oriented towards environment protection and social equity. 


To start the climate simulations or model runs, outputs (climate information) from the relatively coarse resolution GCMs are used to provide high resolution (using finer grid boxes, normally 10km-100km) climate details, through the use of downscaling techniques. Downscaling is a method that derives local to regional scale (10km-100km x 10km-100km grids) information from larger-scale models (150km-300km x 150km-300km grids) as shown in Fig.1. The smaller the grid, the finer is the resolution giving more detailed climate information.
 


The climate simulations presented in this report used boundary data that were from the ECHAM4 and HadCM3Q0 (the regional climate models used in the PRECIS model software). 


To run regional climate models, boundary conditions are needed in order to produce local climate scenarios. These boundary conditions are outputs of the GCMs. For the PRECIS model, the following boundary data and control runs were used:


For the high-range scenario, the GCM boundary data used was from ECHAM4. This is the 4th generation coupled ocean-atmosphere general circulation model, which uses a comprehensive parameterization package developed at the Max Planck Institute for Meteorology in Hamburg, Germany. Downscaling was to a grid resolution of 25km x 25km; thus, allowing more detailed regional information of the projected climate. Simulated baseline climate used for evaluation of the models capacity of reproducing present climate was the 1971-2000 model run. Its outputs were compared with the 1971-2000 observed values.
For the mid-range scenario, the GCM boundary data was from the HadCM3Q0 version 3 of the coupled model developed at the Hadley Centre. Downscaling was also to a grid resolution of 25km x 25km and the same validation process was undertaken.
For running the low-range scenario, the same ECHAM4 model was used. However, the validation process was only for the period of 1989 to 2000 because the available GCM boundary data in the model was limited to this period.
The simulations for all 3 scenarios were for three periods; 1971 to 2000, 2020 and 2050. The period 1971 to 2000 simulation is referred to as the baseline climate, outputs of which are used to evaluate the models capacity of reproducing present climate (in other words, the control run). By comparing the outputs (i.e., temperature and rainfall) with the observed values for the 1971 to 2000 period, the models ability to realistically represent the regional climatological features within the country is verified. The differences between the outputs and the observed values are called the biases of the model. The 2020 and 2050 outputs are then mathematically corrected, based on the comparison of the models performance.

The main outputs of the simulations for the three SRES scenarios (high-range, mid-range and low-range) are the following:
projected changes in seasonal and annual mean temperature;
projected changes in minimum and maximum temperatures;
projected changes in seasonal rainfall; and
projected frequency of extreme events.
The seasonal variations are as follows:
the DJF (December, January, February or northeast monsoon locally known as amihan) season;
the MAM (March, April, May or summer) season;
the JJA (June, July, August or southwest monsoon season, or habagat) season; and
the SON (September, October, November or transition from southwest to northeast monsoon) season.


On the other hand, extreme events are defined as follows:
extreme temperature (assessed as number of days with maximum temperature greater than 35 ºC, following the threshold values used in other countries in the Asia Pacific region);
dry days (assessed as number of dry days or day with rainfall equal or less than 2.5mm/day, following the World Meteorological Organization standard definition of dry days used in a number of countries); and
extreme rainfall (assessed as number of days with daily rainfall greater than 300mm, which for wet tropical areas, like the Philippines, is considerably intense that could trigger disastrous events).


Modeling of our future climate always entails uncertainties. These are inherent in each step in the simulations/modeling done because of a number of reasons. Firstly, emissions scenarios are uncertain. Predicting emissions is largely dependent on how we can predict human behavior, such as changes in population, economic growth, technology, energy availability and national and international policies (which include predicting results of the international negotiations on reducing greenhouse gas emissions). Secondly, current understanding of the carbon cycle and of sources and sinks of non-carbon greenhouse gases are still incomplete. Thirdly, consideration of very complex feedback processes in the climate system in the climate models used can also contribute to the uncertainties in the outputs generated as these could not be adequately represented in the models.


But while it is difficult to predict global greenhouse gas emission rates far into the future, it is stressed that projections for up to 2050 show little variation between different emission scenarios, as these near-term changes in climate are strongly affected by greenhouse gases that have already been emitted and will stay in the atmosphere for the next 50 years. Hence, for projections for the near-term until 2065, outputs of the mid-range emission scenario are presented in detail in this Report.

Ideally, numerous climate models and a number of the emission scenarios provided in the SRES should be used in developing the climate change scenarios in order to account for the limitations in each of the models used, and the numerous ways global greenhouse gas emissions would go. The different model outputs should then be analyzed to calculate the median of the future climate projections in the selected time slices. By running more climate models for each emission scenarios, the higher is the statistical confidence in the resulting projections as these constitute the ensemble representing the median values of the model outputs.

The climate projections for the three emission scenarios were obtained using the PRECIS model only due to several constraints and limitations. These constraints and limitations are:

Access to climate models: at the start, PAGASA had not accessed climate models due to computing and technical capacity requirements needed to run them;
Time constraints: the use of currently available computers required substantial computing time to run the models (measured in weeks and months). This had been partly addressed under the capacity upgrading initiatives being implemented by the MDGF Joint Programme which include procurement of more powerful computers and acquiring new downscaling techniques. Improved equipment and new techniques have reduced the computing time requirements to run the models. However, additional time is still needed to run the models using newly acquired downscaling techniques; and

The PAGASA strives to improve confidence in the climate projections and is continuously exerting efforts to upgrade its technical capacities and capabilities. Models are run as soon as these are acquired with the end-goal of producing an ensemble of the projections. Updates on the projections, including comparisons with the current results, will be provided as soon as these are available.  


The IPCC stresses that there is a large degree of uncertainty in predicting what the future world will be despite taking into account all reasonable future developments. Nevertheless, there is high confidence in the occurrence of global warming due to emissions of greenhouse gases caused by humans, as affirmed in the IPCC Fourth Assessment Report (AR4). Global climate simulations done to project climate scenarios until the end of the 21st century indicate that, although there are vast differences between the various scenarios, the values of temperature increase begin to diverge only after the middle of this century (shown in Fig.3). The long lifetimes of the greenhouse gases (in particular, that of carbon dioxide) already in the atmosphere is the reason for this behavior of this climate response to largely varying emission scenarios. 

Model outputs that represent the plausible local climate scenarios in this Report are indicative to the extent that they reflect the large-scale changes (in the regional climate model used) modified by the projected local conditions in the country.

It also should be stressed further that confidence in the climate change information depends on the variable being considered (e.g., temperature increase, rainfall change, extreme event indices, etc.). In all the model runs regardless of emission scenarios used, there is greater confidence in the projections of mean temperature than that of the others. On the other hand, projections of rainfall and extreme events entail consideration of convective processes which are inherently complex, and thus, limiting the degree of confidence in the outputs. 


Climate scenarios are commonly required in climate change impact, vulnerability and adaptation assessments to provide alternative views of future conditions considered likely to affect society, systems and sectors, including a quantification of climate risks, challenges and opportunities. Climate scenario outputs could be used in any of the following:

to illustrate projected climate change in a given administrative region/province;
to provide data for impact/adaptation assessment studies;
to communicate potential consequences of climate change (e.g., specifying a future changed climate to estimate potential shifts in say, vegetation, species threatened or at risk of extinction, etc.); and
for strategic planning (e.g., quantifying projected sea level rise and other climate changes for the design of coastal infrastructure/defenses such as sea walls, etc.).


 





Climate Projections

Projections on seasonal temperature increase and rainfall change, and total frequency of extreme events nationally and in the provinces using the mid-range scenario outputs are presented in this chapter. A comparison of these values with the high- and low- range scenarios in 2020 and 2050 is provided in the technical annexes.

It is to be noted that all the projected changes are relative to the baseline (1971-2000) climate. For example, a projected 1.0 C-increase in 2020 in a province means that 1.0 C is added to the baseline mean temperature value of the province as indicated in the table to arrive at the value of projected mean temperature. Therefore, if the baseline mean temperature is 27.8 C, then the projected mean temperature in the future is (27.8 C + 1.0 C) or 28.8 C.

In a similar manner, for say, a +25%-rainfall change in a province, it means that 25% of the seasonal mean rainfall value in the said province (from table of baseline climate) is added to the mean value. Thus, if the baseline seasonal rainfall is 900mm, then projected rainfall in the future is 900mm + 225mm or 1125mm.

This means that we are already experiencing some of the climate change shown in the findings under the mid-range scenario, as we are now into the second decade of the century. Classification of climate used the Corona's four climate types (Types I to IV), based on monthly rainfall received during the year. A province is considered to have Type I climate if there is a distinct dry and a wet season; wet from June to November and dry, the rest of the year. Type II climate is when there is no dry period at all throughout the year, with a pronounced wet season from November to February. On the other hand, Type III climate is when there is a short dry season, usually from February to April, and Type IV climate is when the rainfall is almost evenly distributed during the whole year. The climate classification in the Philippines is shown in Fig.16.  
 


All areas of the Philippines will get warmer, more so in the relatively warmer summer months. Mean temperatures in all areas in the Philippines are expected to rise by 0.9 C to 1.1 C in 2020 and by 1.8 C to 2.2 C in 2050. Likewise, all seasonal mean temperatures will also have increases in these time slices; and these increases during the four seasons are quite consistent in all parts of the country. Largest temperature increase is projected during the summer (MAM) season.


Generally, there is reduction in rainfall in most parts of the country during the summer (MAM) season. However, rainfall increase is likely during the southwest monsoon (JJA) season until the transition (SON) season in most areas of Luzon and Visayas, and also, during the northeast monsoon (DJF) season, particularly, in provinces/areas characterized as Type II climate in 2020 and 2050. There is however, generally decreasing trend in rainfall in Mindanao, especially by 2050. 
 
There are varied trends in the magnitude and direction of the rainfall changes, both in 2020 and 2050. What the projections clearly indicate are the likely increase in the performance of the southwest and the northeast monsoons in the provinces exposed to these climate controls when they prevail over the country. Moreover, the usually wet seasons become wetter with the usually dry seasons becoming also drier; and these could lead to more occurrences of floods and dry spells/droughts, respectively.

Hot temperatures will continue to become more frequent in the future. Fig.19 shows that the number of days with maximum temperature exceeding 35 C (following value used by other countries in the Asia Pacific region in extreme events analysis) is increasing in 2020 and 2050.
 

Heavy daily rainfall will continue to become more frequent, extreme rainfall is projected to increase in Luzon and Visayas only, but number of dry days is expected to increase in all parts of the country in 2020 and 2050. Figures 20 and 21 show the projected increase in number of dry days (with dry day defined as that with rainfall less than 2.5mm) and the increase in number of days with extreme rainfall (defined as daily rainfall exceeding 300 mm) compared with the observed (baseline) values, respectively. 

 

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050
under the medium-range emission scenario in the provinces in Region 1 are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces
, the projections are added to the observed values (presented in each of the tables).

For example, in Pangasinan, the projected values in 2020 are:

DJF mean temperature = (25.0 C + 0.9 C) = 25.9 C;
DJF rainfall = {19.4mm+19.4(54.3%)mm} = (19.4+10.5)mm or 29.9mm;
number of days with Tmax > 35 C in Dagupan City during the 2006-2035 period (centered at 2020) = 2,265;
number of dry days in Dagupan City during the 2006-2035 period (centered at 2020) = 6,443; and
number of days with rainfall > 300mm in Dagupan City during the 2006-2035 period (centered at 2020) =13.
 
 
 
 
Note:
  • For northern La Union, use values of Vigan.
  • For southern La Union, use values of Dagupan.
Back to map

 
The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 2 are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables).

For example, in Cagayan, the projected values in 2020 are:

DJF mean temperature in 2020 = (24.5 C+0.8 C) = 25.3 C;
DJF rainfall in 2020 = {284.4mm+284.4(6.9%)mm} = (284.4+19.6)mm or 304mm;
number of days with Tmax > 35 C in Aparri during the 2006-2035 (centered at 2020) = 1,276;
number of dry days in Aparri during the 2006-2035 period (centered at 2020) = 6,498; and
number of days with rainfall > 300mm in Aparri during the 2006-2035 period (centered at 2020) = 16. 
 



Note:
  • For northern Cagayan, use values of Aparri.
  • For southern Cagayan, use values of Tuguegarao.
  • For Isabela, use values of Tuguegarao.
  • For Nueva Vizcaya, use values of Tuguegarao.
  • For Quirino, use values of Baler.
Back to map

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 3 are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables).

For example, in Nueva Ecija, the projected values in 2020 are:

DJF mean temperature = (25.3 C+0.9 C) = 26.2 C;
DJF rainfall = {155.2mm+155.2(7.5%)mm} = (155.2+11.6)mm or 166.8mm;
number of days with Tmax > 35 C in Cabanatuan during the 2006-2035 period (centered at 2020) = 3,271;
number of dry days in Cabanatuan during the 2006-2035 period (centered at 2020) = 6,117; and
number of days with rainfall > 300mm in Cabanatuan during the 2006-2035 period (centered at 2020) = 2.




Note:
  • For Tarlac, use values of Clark.
  • For Bulacan, use values of Science Garden



The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 4-A are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 

For example, in Quezon, the projected values in 2020 are:

DJF mean temperature = (25.1 C+0.9 C) = 26.0 C;
DJF rainfall = {827.7mm+827.7(-6.5%)mm} = (827.7-53.8)mm or 763.9mm;
number of days with Tmax > 35 C in Casiguran during the 2006-2035 period (centered in 2020) = 1,720; number of dry days in Casiguran during the 2006-2035 period (centered at 2020) = 4,520; and
number of days with rainfall > 300mm in Casiguran during the 2006-2035 period (centered at 2020) = 20.


Note:
  • For northern portion of Quezon, use values of Casiguran.
  • For middle part of Quezon, use values of Infanta.
  • For southern portion of Quezon, use Tayabas.
  • For Laguna, use values of Ambulong.

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 4-B are presented in Table a, Table b and Table c, respectively.
To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 
For example, in Oriental Mindoro, the projected values in 2020 are:

DJF mean temperature = (26.4 C+0.8 C) = 27.2 C;
DJF rainfall = {260.3mm+260.3(-3.2%)mm} = (260.3-8.3)mm or 252.0mm;
number of days with Tmax > 35 C in Calapan during the 2006-2035 period (centered in 2020) = 440;
number of dry days in Calapan during the 2006-2035 period (centered at 2020) = 7,057; and
number of days with rainfall > 300mm in Calapan during the 2006-2035 period (centered at 2020) = 4.




Note:
  • For main island of Palawan, use values of Puerto Princesa.
  • For Marinduque, use values of Calapan.

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 5 are presented in Table a, Table b and Table c, respectively.
To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 
For example, in Albay, the projected values in 2020 are:

DJF mean temperature = (25.6 C+0.9 C) = 26.5 C;
DJF rainfall = {739.8mm+ 739.8(+0.5%)mm} = (739.8 +3.7)mm or 743.5mm;
number of days with Tmax > 35 C in Legaspi City during the 2006-2035 period (centered at 2020) = 683;
number of dry days in Legaspi City during the 2006-2035 period (centered at 2020) = 3,698; and
number of days with rainfall > 300mm in Legaspi City during the 2006-2035 period (centered at 2020) = 4.




Note:
  • For northern portion of Camarines Sur, use values of Daet.
  • For southern portion of Camarines Sur, use values of Legaspi.
  • For northern portion of Sorsogon, use values of Legaspi.
  • For south western portion of Sorsogon, use values of Masbate.
  • For south eastern portion of Sorsogon, use values of Catarman.

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 6 are presented in Table a, Table b and Table c, respectively. To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). For example, in Iloilo province, the projected values in 2020 are:

DJF mean temperature = (26.4 C+1.0 C) = 27.4 C;
DJF rainfall = {324.8mm+(324.8+1.2%)mm} = (324.8 +3.9) or 325.7mm;
number of days with Tmax > 35 C in Iloilo City during the 2006-2035 period (centered at 2020) = 1,431;
number of dry days in Iloilo City during the 2006-2035 period (centered at 2020) = 5,227; and
number of days with rainfall > 300mm in Iloilo City during the 2006-2035 period (centered at 2020) = 3.



Note:
  • For Aklan, use values of Roxas City.
  • For Antique, use values of Iloilo City.
  • For Negros Occidental, use values of Iloilo City.

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 7 are presented in Table a, Table b and Table c, respectively. To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). For example, in Bohol province, the projected values are:

DJF mean temperature = (26.6 C +0.9 C) = 27.5 C;
DJF rainfall = {376.1mm+376.1(9.8%)mm} = (376.1+36.9m) or 413mm;
number of days with Tmax > 35 C in Tagbilaran City during the 2006-2035 period (centered at 2020) = 1,710;
number of dry days in Tagbilaran City during the 2006-2035 period (centered at 2020)= 6,836; and
number of days with rainfall > 300mm in Tagbilaran City during the 2006-2035 period (centered at 2020) = 1.






Note:
  • For Siquijor, use values of Dumaguete.

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 8 are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 

For example, in Leyte province, the projected values in 2020 are:

DJF mean temperature = (26.4 C + 0.9 C)=27.3 C;
DJF rainfall = {689.5mm+689.5(3.0%)mm} = (689.5+20.7)mm or 710.2mm;
number of days with Tmax > 35 C in Tacloban City during the 2006-2035 period (centered at 2020) = 1,398;
number of dry days in Tacloban City during the 2006-2035 period (centered at 2020) = 5,199; and
number of days with rainfall > 300mm in Tacloban City during the 2006-2035 period (centered at 2020) = 7.





The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 9 are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 

For example, in Zamboanga del Sur province, the projected values in 2020 are:

DJF mean temperature = (26.8 C+0.9 C) = 27.7 C;
DJF rainfall = {294.5mm+294.5(11.2%)mm} = (294.5+33.0)mm or 327.0mm;
number of days with Tmax > 35 C in Zamboanga City during the 2006-2035 period (centered at 2020) = 114;
number of dry days in Zamboanga City during the 2006-2035 period (centered at 2020) = 7,058; and
number of days with rainfall > 300mm in Zamboanga City during the 2006-2035 period (centered at 2020) = 1.



Note:
  • Zamboanga Sibugay, use values of Zamboanga City.

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 10 are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 

For example, in Bukidnon province, the projected values in 2020 are:

DJF mean temperature = (25.1 C+1.0 C)= 26.1 C;
DJF rainfall = {329.7mm+329.7(2.9%)mm} = (329.7+9.6)mm or 339.3mm;
number of days with Tmax > 35 C in Malaybalay during the 2006-2035 period (centered at 2020) = 477;
number of dry days in Malaybalay during the 2006-2035 period (centered at 2020) = 3,977; and
number of days with rainfall > 300mm in Malaybalay during the 2006-2035 period (centered at 2020) = 1.





Note:
  • For western portion of Misamis Occidental, use values of Dipolog City.

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 11 are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 

For example, in Davao del Sur province, the projected values in 2020 are:

DJF mean temperature = (26.9 C+0.9 C) = 27.8 C;
DJF rainfall = {288.1mm+288.1(18.1%)mm} = (288.1+52.1)mm = 346.2mm;
number of days with Tmax > 35 C in Davao City during the 2006-2035 period (centered at 2020) = 2,981;
number of dry days in Davao City during the 2006-2035 period (centered at 2020) = 4,789; and
number of days with rainfall > 300mm during the 2006-2035 period (centered at 2020) = 0.





Note:
  • For Davao del Norte, Davao Oriental and Compostela valley, use values of Davao City.

The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Region 12 are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 

For example, in South Cotabato province, the projected values in 2020 are;

DJF mean temperature = (27.7 C +1.0 C) = 28.7 C;
DJF rainfall = {183.3mm+183.3 (10.1%)mm} = (183.3+18.5)mm or 201.8mm;
number of days with Tmax > 35 C in General Santos City during the 2006-2035 period (centered at 2020) = 3,748;
number of dry days in General Santos City during the 2006-2035 period (centered at 2020) = 7,526; and
number of days with rainfall > 300mm in General Santos City during the 2006-2035 period (centered at 2020) = 0.







The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in Caraga are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 

For example, in Surigao del Norte province, the projected values in 2020 are:

DJF mean temperature = (26.3 C+0.9 C) = 27.2 C;
DJF rainfall = {963.3mm+963.3 (4.0%)mm} = (963.3+38.5)mm or 1001.8mm;
number of days with Tmax > 35 C in surigao City during the 2006-2035 period (centered at 2020) = 225;
number of dry days in Surigao City during the 2006-2035 period (centered at 2020) = 6,054; and
number of days with rainfall > 300mm in Surigao City during the 2006-2035 period (centered at 2020) = 2.






The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in ARMM are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). 

For example, in Maguindanao province, the projected values in 2020 are:

DJF mean temperature = (27.6 C +1.0 C) = 28.6 C;
DJF rainfall = {225.3mm+225.3 (6.3%)mm} = (225.3+14.2)mm or 239.5mm;
number of days with Tmax > 35 C in Cotabato City during the 2006-2035 period (centered at 2020) = 3,382;
number of dry days in Cotabato City during the 2006-2035 period (centered at 2020) = 5,471; and
number of days with rainfall > 300 days in Cotabato City during the 2006-2035 period (centered at 2020) = 3.






The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in CAR are presented in Table a, Table b and Table c, respectively.

To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables).


DJF mean temperature = (27.6 C +1.0 C) = 28.6 C;
DJF rainfall = {225.3mm+225.3 (6.3%)mm} = (225.3+14.2)mm or 239.5mm;
number of days with Tmax > 35 C in Cotabato City during the 2006-2035 period (centered at 2020) = 3,382;
number of dry days in Cotabato City during the 2006-2035 period (centered at 2020) = 5,471; and
number of days with rainfall > 300 days in Cotabato City during the 2006-2035 period (centered at 2020) = 3. 





The projected seasonal temperature increase, seasonal rainfall change and frequency of extreme events in 2020 and 2050 under the medium-range emission scenario in the provinces in NCR are presented in Table a, Table b and Table c, respectively. To use the tables and arrive at values of seasonal mean temperature and seasonal rainfall in 2020 and 2050 in any of the provinces, the projections are added to the observed values (presented in each of the tables). For example, in Metro Manila, the projected values in 2020 are:

DJF mean temperature = (26.1 C+1.0 C) = 27.1 C;
DJF rainfall = {107.5mm+107.5(-12.8%)mm} = (107.5-13.8)mm or 93.7mm;
number of days with Tmax > 35 C in Quezon City during the 2006-2035 period (centered at 2020) = 1,984;
number of dry days in Quezon City during the 2006-2035 period (centered at 2020) = 6,302; and
number of days with rainfall > 300mm in Quezon City during the 2006-2035 period (centered at 2020) = 8.




 
 
 




Impacts of Climate Change

Climate change is one of the most fundamental challenges ever to confront humanity. Its adverse impacts are already being seen and may intensify exponentially over time if nothing is done to reduce further emissions of greenhouse gases. Decisively dealing NOW with climate change is key to ensuring sustainable development, poverty eradication and safeguarding economic growth. Scientific assessments indicate that the cost of inaction now will be more costly in the future. Thus, economic development needs to be shifted to a low-carbon emission path.


In 1992, the United Nations Framework Convention on Climate Change (UNFCCC) was adopted as the basis for a global response to the problem. The Philippines signed the UNFCCC on 12 June 1992 and ratified the international treaty on 2 August 1994. Presently, the Convention enjoys near-universal membership, with 194 Country Parties.

Recognizing that the climate system is a shared resource which is greatly affected by anthropogenic emissions of greenhouse gases, the UNFCCC has set out an overall framework for intergovernmental efforts to consider what can be done to reduce global warming and to cope with whatever temperature increases are inevitable. Its ultimate objective is to stabilize greenhouse gas concentrations in the atmosphere at a level that will prevent dangerous human interference with the climate system.

Countries are actively discussing and negotiating ways to deal with the climate change problem within the UNFCCC using two central approaches. The first task is to address the root cause by reducing greenhouse gas emissions from human activity. The means to achieve this are very contentious, as it will require radical changes in the way many societies are organized, especially in respect to fossil fuel use, industry operations, land use, and development. Within the climate change arena, the reduction of greenhouse gas emissions is called mitigation.

The second task in responding to climate change is to manage its impacts. Future impacts on the environment and society are now inevitable, owing to the amount of greenhouse gases already in the atmosphere from past decades of industrial and other human activities, and to the added amounts from continued emissions over the next few decades until such time as mitigation policies and actions become effective. We are therefore committed to changes in the climate. Taking steps to cope with the changed climate conditions both in terms of reducing adverse impacts and taking advantage of potential benefits is called adaptation.


Responses of the local climate to the mid-range compared to the high- and low-range scenarios are as shown in Fig. 22 below. Although there are vast differences in the projections, the so-called temperature anomalies or difference in surface temperature increase begin to diverge only in the middle of the 21st century. As has already been stated, the climate in the next 30 to 40 years is greatly influenced by past greenhouse gas emissions. The long lifetimes of the greenhouse gases already in the atmosphere, with the exception of methane (with a lifetime of only 13 years), will mean that it will take at least 30 to 40 years for the atmosphere to stabilize even if mitigation measures are put in place, not withstanding that in the near future, there could be some off-setting between sulfate aerosols (cooling effect) and the greenhouse gas concentrations (warming effect).

 


A warmer world is certain to impact on systems and sectors; although, magnitude of impacts will depend on factors such as sensitivity, exposure and adaptive capacity to climate risks. In most cases, likely impacts will be adverse. However, there could be instances when likely impacts present opportunities for potential benefits as in the case of the so-called carbon fertilization effect in which increased carbon dioxide could lead to increased yield provided temperatures do not exceed threshold values for a given crop/cultivar.
 

 


In areas/regions where rainfall is projected to decrease, there will be water stress (both in quantity and quality), which in turn, will most likely cascade into more adverse impacts, particularly on forestry, agriculture and livelihood, health, and human settlement. Large decreases in rainfall and longer drier periods will affect the amount of water in watersheds and dams which provide irrigation services to farmers, especially those in rain fed areas, thereby, limiting agricultural production. Likewise, energy production from dams could also be rendered insufficient in those areas where rainfall is projected to decrease, and thus, could largely affect the energy sufficiency program of the country. Design of infrastructure, particularly of dams, will need to be re-visited to ensure that these will not be severely affected by the projected longer drier periods. 

In areas where rainfall could be intense during wet periods, flooding events would follow and may pose danger to human settlements and infrastructure, in terms of landslides and mudslides, most especially, in geologically weak areas. Additionally, these flooding events could impact severely on public infrastructure, such as roads and bridges, including classrooms, evacuation centers, and hospitals.

Adaptive capacity is enhanced when impact and vulnerability assessments are used as the basis of strategic and long-term planning for adaptation. Assessments would indicate areas where critical water shortages can be expected leading to possible reduction of water available for domestic consumption, less irrigation service delivery, and possibly, decreased energy generation in dams. Note that the adverse impacts would cascade, so that long-term pro-active planning for these possible impacts is imperative in order to be able to respond effectively, and avoid maladaptations. A number of adaptation strategies should be considered. Among the wide array of cost effective options are rational water management, planning to avoid mismatch between water supply and demand through policies, upgrading/rehabilitation of dams where these are cost-effective, changes in cropping patterns in agricultural areas, establishing rain water collection facilities, where possible, and early warning systems.
 

 


Changes in rainfall regimes and patterns resulting to increase/decrease in water use and temperature increases could lead to a change in the forests ecosystem, particularly in areas where the rains are severely limited, and can no longer provide favorable conditions for certain highly sensitive species. Some of our forests could face die-backs. Additionally, drier periods and warmer temperatures, especially during the warm phase of El Nino events, could cause forest fires. A very likely threat to communities that largely depend on the ecological services provided by forests is that they may face the need to alter their traditions and livelihoods. This change in practices and behavior can lead to further degradation of the environment as they resort to more extensive agricultural production in already degraded areas.

Adverse impacts on forestry areas and resources could be expected to multiply in a future warmer world. The value of impact and vulnerability assessments could not be underscored. These assessments would help decision makers and stakeholders identify the best option to address the different impacts on forest areas, watersheds and agroforestry. Indigenous communities have to plan for climate-resilient alternative livelihoods. Thus, it is highly important to plan for rational forest management, particularly, in protected areas and in ancestral domains. One of the more important issues to consider is how to safeguard livelihoods in affected communities so as not to further exacerbate land degradation. Early warning systems in this sector will play a very important role in forest protection through avoidance and control/containment of forest fires.



 

Agriculture in the country could be severely affected by temperature changes coupled with changes in rain regimes and patterns. Crops have been shown to suffer decreases in yields whenever temperatures have exceeded threshold values and possibly result to spikelet sterility, as in the case of rice. The reduction in crop yield would remain unmitigated or even aggravated if management technologies are not put in place. Additionally, in areas where rain patterns change or when extreme events such as floods or droughts happen more often, grain and other agricultural produce could suffer shortfalls in the absence of effective and timely interventions. Tropical cyclones, particularly if there will be an increase in numbers and/or strength will continue to exert pressure on agricultural production.


Moreover, temperature increases coupled with rainfall changes could affect the incidence/outbreaks of pests and diseases, both in plants and animals. The pathways through which diseases and pests could be triggered and rendered most favorable to spread are still largely unknown. It is therefore important that research focus on these issues.

In the fisheries sub-sector, migration of fish to cooler and deeper waters would force the fisher folks to travel further from the coasts in order to increase their catch. Seaweed production, already being practiced as an adaptation to climate change in a number of poor and depressed coastal communities could also be impacted adversely. 

Decreased yields and inadequate job opportunities in the agricultural sector could lead to migration and shifts in population, resulting to more pressure in already depressed urban areas, particularly in mega cities. Food security will largely be affected, especially if timely, effective and efficient interventions are not put in place. Insufficient food supply could further lead to more malnutrition, higher poverty levels, and possibly, heightened social unrest and conflict in certain areas in the country, and even among the indigenous tribes.

A careful assessment of primary and secondary impacts in this sector, particularly, in production systems and livelihoods will go a long way in avoiding food security and livelihood issues. Proactive planning (short- and long-term adaptation measures) will help in attaining poverty eradication, sufficient nutrition and secure livelihoods goals. There is a wide cross-section of adaptation strategies that could be put in place, such as horizontal and vertical diversification of crops, farmer field schools which incorporate use of weather/climate information in agricultural operations, including policy environment for subsidies and climate-friendly agricultural technologies, weather-based insurance, and others. To date, there has not been much R&D that has been done on inland and marine fisheries technologies, a research agenda on resilient marine sector could form part of long-term planning for this subsector.



The countrys coastal resources are highly vulnerable due to its extensive coastlines. Sea level rise is highly likely in a changing climate, and low-lying islands will face permanent inundation in the future. The combined effects of continued temperature increases, changes in rainfall and accelerated sea level rise, and tropical cyclone occurrences including the associated storm surges would expose coastal communities to higher levels of threat to life and property. The livelihood of these communities would also be threatened in terms of further stress to their fishing opportunities, loss of productive agricultural lands and saltwater intrusion, among others.

Impact and vulnerability assessment as well as adaptation planning for these coastal areas are of high priority. Adaptation measures range from physical structures such as sea walls where they still are cost-effective, to development/revision of land use plans using risk maps as the basis, to early warning systems for severe weather, including advisories on storm surge probabilities, as well as planning for and developing resilient livelihoods where traditional fishing/ agriculture are no longer viable.


Human health is one of the most vital sectors which will be severely affected by climate change. Incremental increases in temperatures and rain regimes could trigger a number of adverse impacts; in particular, the outbreak and spread of water-based and vector-borne diseases leading to higher morbidity and mortality; increased incidence of pulmonary illnesses among young children and cardiovascular diseases among the elderly. In addition, there could also be increased health risk from poor air quality especially in urbanized areas.


Surveillance systems and infrastructure for monitoring and prevention of epidemics could also be under severe stress when there is a confluence of circumstances. Hospitals and clinics, and evacuation centers and resettlement areas could also be severely affected under increased frequency and intensity of severe weather events.

Moreover, malnutrition is expected to become more severe with more frequent occurrences of extreme events that disrupt food supply and provision of health services. The services of the Department of Health will be severely tested unless early and periodic assessments of plausible impacts of climate change are undertaken.

 
 




Conclusion



Scientific assessments have indicated that the Earth is now committed to continued and faster warming unless drastic global mitigation action is put in place the soonest. The likely impacts of climate change are numerous and most could seriously hinder the realization of targets set under the Millennium Development Goals; and thus, sustainable development. Under the UNFCCC, Country Parties have common but differentiated responsibilities. All Country Parties share the common responsibility of protecting the climate system but must shoulder different responsibilities. This means that the developed countries including those whose economies are in transition (or the so-called Annex 1 Parties) have an obligation to reduce their greenhouse gas emissions based on their emissions at 1990 levels and provide assistance to developing countries (or the so-called non-Annex 1 Parties) to adapt to impacts of climate change.

In addition, the commitment to mitigate or reduce anthropogenic greenhouse gas emissions by countries which share the responsibility of having historically caused this global problem, as agreed upon in the Kyoto Protocol, is dictated by the imperative to avoid what climate scientists refer to as the climate change tipping point. Tipping point is defined as the maximum temperature increase that could happen within the century, which could lead to sudden and dramatic changes to some of the major geophysical elements of the Earth. The effects of these changes could be varied from a dramatic rise in sea levels that could flood coastal regions to widespread crop failures. But, it still is possible to avoid them with cuts in anthropogenic greenhouse gases, both in the developed and developing countries, in particular, those which are now fast approaching the emission levels seen in rich countries.

In the Philippines, there are now a number of assisted climate change adaptation programmes and projects that are being implemented. Among these are the Millennium Development Goals Fund 1656: Strengthening the Philippines Institutional Capacity to Adapt to Climate Change funded by the Government of Spain, the Philippine Climate Change Adaptation Project (which aims to develop the resiliency and test adaptation strategies that will develop the resiliency of farms and natural resource management to the effects of climate change) funded by the Global Environmental Facility(GEF) through the World Bank, the Adaptation to Climate Change and Conservation of Biodiversity Project and the National Framework Strategy on Climate Change (envisioned to develop the adaptation capacity of communities), both funded by the GTZ, Germany.




Climate Change in the Philippines 

PUBLIC WARNING 

It has come to the attention of the Office of the Administrator of PAGASA that a certain person has been using the name of Dr. Vicente B. Malano to solicit money from the contractors of PAGASA.

Dr. Malano wishes to inform the public that he has not authorized anyone to solicit money on his behalf and to warn everyone against dealing with unscrupulous activities of certain individuals.


payong pagasa on android The latest version of the official PAGASA app can now be downloaded on Google play store

Weather Division is now  ISO 9001:2008 Certified
Proof has been furnished by means of an audit that the requirements of ISO 9001:2008 are met.

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PAGASA S&T RELEASE
PRESS STATEMENT
10 April 2017
               
SPECIAL WEATHER OUTLOOK FOR THE LENTEN SEASON


Starting today (April 10) until Maundy Thursday (April 13), warm and dry weather conditions associated with sunny weather will prevail over the entire archipelago, with chances of some isolated rainshowers or thunderstorms due to the Ridge of High Pressure Area (HPA) and Easterlies.

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PAGASA S&T Release
PRESS STATEMENT
05 April 2017


TERMINATION OF THE NORTHEAST MONSOON

The resurgence of the Northeast Monsoon that affected the Northern and Central Luzon during the last few days has weakened.  Latest weather map analyses and numerical model outputs indicate
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