Marcelino Q. Villafuerte

This study introduces a novel high-resolution (0.01° × 0.01°, daily) observation-based gridded dataset called the ClimGridPh-Temp and investigates its spatiotemporal representativeness in capturing the daily maximum (Tx), minimum (Tn), and mean (Tm) near-surface air temperatures in the Philippines as compared to other existing gridded temperature products. Quality- controlled daily Tx and Tn observations from 47 synoptic stations (SYNOP) of the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) data were merged with the European Center for Medium-range Weather Forecasting fifth-generation reanalysis (ERA5) covering the period from 1991–2020. Ten (10) different combinations of interpolation and data merging techniques were tested, and the simple bias adjustment implemented with Shepard was identified as the best to construct the ClimGridPh-Temp dataset. The performance of the ClimGridPh-Temp in capturing the observed daily temperatures from independently selected agrometeorological stations (AGROMET) of PAGASA (12 for Tx and Tm, and 14 for Tn) was measured using four commonly used metrics – namely root mean squared error (RMSE), bias, Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2). Results revealed that, in comparison with other existing gridded daily temperature datasets (i.e. PGF, CPC, MERRA, CHELSA, and ERA5), ClimGridPh-Temp provides the best estimates of daily Tx, Tn, and Tm both spatially and temporally over the Philippines. ClimGridPh-Temp obtained the lowest median RMSE (1.63, 1.57, and 1.05 for Tx, Tn, and Tm, respectively), lowest mean bias of 0.49, 0.20, and 0.05 (for Tx, Tn, and Tm, respectively), NSE of 0.47, –0.02, and 0.40 (for Tx, Tn, and Tm, respectively), and highest R2 of 0.63, 0.47, and 0.61 (for Tx, Tn, and Tm, respectively) relative to the aforementioned gridded temperature datasets. These findings suggest that the ClimGridPh-Temp dataset offers research-quality data, which can be used in various applications, including the investigation of temperature trends and their variations at a localized fine-scale across the Philippines.

In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional timescales covered by weather forecasting in the Philippines, this study has examined the multi-week (i.e., from Week-1 to Week-4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely: NCEP Climate Forecast System version 2 (CFSv2), European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 06 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multi-week TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p<0.05) positional bias toward the right of observed tracks beyond Week-1, and that they tend to propagate slower than observations especially in Week-1 and Week-2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multi-week TC forecasts in the Philippines. In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional timescales covered by weather forecasting in the Philippines, this study has examined the multi-week (i.e., from Week-1 to Week-4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely: NCEP Climate Forecast System version 2 (CFSv2), European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 06 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multi-week TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p<0.05) positional bias toward the right of observed tracks beyond Week-1, and that they tend to propagate slower than observations especially in Week-1 and Week-2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multi-week TC forecasts in the Philippines.

This paper presents a comprehensive analysis of the effect of urbanization on the surface air temperature (SAT) from 1951 to 2018 in the Philippines. The daily minimum temperature (T_min) and daily maximum temperature (T_max) records from 34 meteorological stations were used to derive extreme temperature indices. These stations were then classified as urban or rural based on satellite night‐lights. The results showed a significant difference in the SAT trends between urban and rural stations, indicative of the effect of urbanization in the country. Larger and more significant warming trends were observed in indices related to T_min than those related to T_max. In particular, the effects of urbanization were significant in the annual index series of T_min, diurnal temperature range, minimum T_min, percentage of days when T_min was less than the 10th percentile (T_N10p), percentage of days when T_min was greater than 90th percentile (T_N90p), and the number of coldest nights. The effects of urbanization were not as clear on the index series of maximum T_max (T_Xx), minimum T_max (T_Xn), percentage of days when T_max was less than 10th percentile (T_X10p), and the number of hottest days. The effects of urbanization on the annual series of extreme temperature indices were statistically significant at the 95% confidence level, with the exception of T_max, T_Xn, T_Xx, T_X10p, and the number of hottest days. Further analysis revealed that the effect of urbanization was the greatest during the DJF (December–January–February) season. These findings serve as a baseline study that focuses on the countrywide effect of urbanization on SAT trends in the Philippines.

One- and two-week TC forecasts are evaluated MJO affects TC predictability in 2015 and 2017 over the Philippines Predictable TCs have similar characte...

Climatological seasonal changes of rainfall and lower tropospheric circulation in and around the Philippines are analysed by utilizing the TRMM 3B42 to obtain the 5‐day mean rainfall and ERA‐Interim wind data for the period 1998–2013. In particular, climatological onset and withdrawal processes of the southwest (SW) and northeast (NE) monsoon are investigated. It is found that the onset of the SW monsoon occurs abruptly in mid‐May almost simultaneously over the Philippines except in the southern region. The start of the SW monsoon in the Philippines occurs earlier in the north than in the south. In addition, a climatological increase in rainfall on the west coast precedes the SW monsoon arrival by approximately two pentads, due to tropical cyclone influences in some years. After the SW monsoon onset, the monsoon trough is located over the southern Philippines. In mid‐June, the monsoon trough begins to deepen, and migrates northward to the central Philippines, and easterly winds intrude shortly to the northern Philippines. Then, the entire Philippines is covered by the summer monsoon westerly in early July, which is followed by the rainfall peak over the west coast region occurring in early August. The SW monsoon begins to retreat from the north in mid‐September, and fully retreats from the Philippines rather suddenly in late September. In general, the rainfall amount in the west coastal region remains high for approximately 2 months after this wind reversal, showing relatively larger post monsoon rainfall due mainly to tropical cyclone effects. During the NE monsoon season, the rainfall centre in the east coast is located in the northern and central region until mid‐December. Afterwards it is anchored in the southern region from late December to mid‐March, indicating the fully established NE monsoon season.

To help meet increasing demands for high‐resolution climate change projections in the Philippines, this study provides the results of multiple dynamically downscaled climate model simulations for projected changes in rainfall and temperature over the country by the mid‐21st century (2036–2065) relative to the baseline period (1971–2000), under the RCP8.5 scenario. The model‐simulated seasonal means of temperature, rainfall, and low‐level wind patterns were first compared with observations during the baseline period. Comparisons made between the model‐derived and APHRODITE observation‐based gridded temperature and rainfall data indicate that the dynamically downscaled simulations provide an overall improvement from their driving global climate models in capturing the spatial patterns of rainfall over the country, and the spatial and temporal characteristics of the country's mean temperature. Future climate projections show that the country's climate is expected to become warmer by the mid‐21st century, with a multi‐model ensemble mean increase of 1.2 to 1.9°C, relative to the baseline period, projected for many parts of the country and across most seasons. Slightly higher increases are projected during the country's hottest season, March–April–May. However, there are large differences in the models' projected rainfall changes by the mid‐21st century across seasons and regions. For most parts of the country, the multi‐model ensemble includes simulations that show increases and simulations that show decreases in rainfall. Nevertheless, there is a tendency of model projections towards wetter conditions over northern and central sections of the country (particularly in the December–January–February season) and drier conditions in the southern region of the country in almost all seasons. The results demonstrate the need for communities in the Philippines to adapt to a future warmer climate and prepare for a range of possible future changes in rainfall and temperature.

The exigencies of the global community toward Earth system science will increase in the future as the human population, economies, and the human footprint on the planet continue to grow. This growth, combined with intensifying urbanization, will inevitably exert increasing pressure on all ecosystem services. A unified interdisciplinary approach to Earth system science is required that can address this challenge, integrate technical demands and long-term visions, and reconcile user demands with scientific feasibility. Together with the research arms of the World Meteorological Organization, the Young Earth System Scientists community has gathered early-career scientists from around the world to initiate a discussion about frontiers of Earth system science. To provide optimal information for society, Earth system science has to provide a comprehensive understanding of the physical processes that drive the Earth system and anthropogenic influences. This understanding will be reflected in seamless prediction systems for environmental processes that are robust and instructive to local users on all scales. Such prediction systems require improved physical process understanding, more high-resolution global observations, and advanced modeling capability, as well as high-performance computing on unprecedented scales. At the same time, the robustness and usability of such prediction systems also depend on deepening our understanding of the entire Earth system and improved communication between end users and researchers. Earth system science is the fundamental baseline for understanding the Earth’s capacity to accommodate humanity, and it provides a means to have a rational discussion about the consequences and limits of anthropogenic influence on Earth. Without its progress, truly sustainable development will be impossible.

This study investigates the changes in annual and seasonal maximum daily rainfall (RX1day) in Southeast Asia, obtained from gauge-based gridded precipitation data, to address the increasing concerns about climate change in the region. First, the nonparametric Mann–Kendall test was employed to detect significant trends in RX1day. Then, maximum likelihood modeling, which allows the incorporation of covariates in the location parameter of the generalized extreme value (GEV) distribution, was conducted to determine whether the rising global mean temperature, as well as El Niño–Southern Oscillation (ENSO), is influencing extreme rainfall over the region. The findings revealed that annual and seasonal RX1day is significantly increasing in Indochina and east-central Philippines while decreasing in most parts of the Maritime Continent during the past 57 yr (1951–2007). The trends in RX1day were further linked to the rising global mean temperature. It was shown that the location parameter of the GEV—and hence the RX1day on average—has significantly covaried with the annually averaged near-surface global mean temperature anomaly. Such covariation is pronouncedly observed over the regions where significant trends in RX1day were detected. Furthermore, the results demonstrated that, as ENSO develops in July–September, negative covariations between the location parameter of the GEV and the ENSO index, implying a higher (lower) likelihood of extreme rainfall during La Niña (El Niño), were observed over the Maritime Continent. Such conditions progress northward to the regions of Indochina and the Philippines as ENSO approaches its maturity in October–December and then retreat southward as the ENSO weakens in the ensuing seasons.

Statistical modelling of extreme values was used to detect potential changes in extreme rainfall in the Philippines and to investigate whether such changes are associated with the rising near‐surface global mean temperature and the El Niño–Southern Oscillation (ENSO). The generalized extreme value distribution was formulated to its stationary and non‐stationary forms, and then was fitted by the maximum likelihood method to the series of daily rainfall annual maxima (RX1day) spanning 100 years (1911–2010) at 23 meteorological stations across the country. Subsequently, statistically significant changes in extreme rainfall in the country were detected; such changes were further linked to the near‐surface global mean temperature and ENSO. Specifically, the study has revealed a country‐averaged increase in the median intensity of extreme rainfall by about 4.3% °C⁻¹ rise in the near‐surface global mean temperature. Furthermore, a seasonal influence of ENSO on extreme rainfall in the Philippines has been shown. In particular, the stations located in the northwest section of the country, where 75–100% of the RX1day occurred in the summer monsoon season (July–September) during the entire period of 1911–2010, showed an average increase in the median intensity of extreme rainfall by about 6.5% °C⁻¹ increase in the ENSO index. These findings imply a potential intensification and increase in the occurrence of extreme rainfall into the future as the global mean temperature continues to rise, and such trends should be considered in adaptation strategies to minimize the disasters caused by extreme rainfall events in the Philippines.