232 research outputs found

    AsiaPEX Challenges and Prospects in Asian Precipitation Research

    No full text
    The Asian Precipitation Experiment (AsiaPEX) was initiated in 2019 to understand terrestrial precipitation over diverse hydroclimatological conditions for improved predictions, disaster reduction, and sustainable development across Asia under the framework of the Global Hydroclimatology Panel (GHP)/Global Energy and Water Exchanges (GEWEX). AsiaPEX is the successor to GEWEX Asian Monsoon Experiment (GAME; 1995-2005) and Monsoon Asian Hydro-Atmosphere Scientific Research and Prediction Initiative (MAHASRI; 2006-16). While retaining the key objectives of the aforementioned projects, the scientific targets of AsiaPEX focus on land-atmosphere coupling and improvements to the predictability of the Asian hydroclimatological system. AsiaPEX was designed for both fine-scale hydroclimatological processes occurring at the land surface and the integrated Asian hydroclimatological system characterized by multiscale interactions. We adopt six approaches including observation, process studies, scale interactions, high-resolution hydrological modeling, field campaigns, and climate projection, which bridge gaps in research activities conducted in different regions. Collaboration with mesoscale and global modeling researchers is one of the core methods in AsiaPEX. We review these strategies based on the literature and our initial outcomes. These include the estimation and validation of high -resolution satellite precipitation, investigations of extreme rainfall mechanisms, field campaigns over the Maritime Continent and Tibetan Plateau, areas of significant impact on the entire AsiaPEX region, process studies on diurnal-to interdecadal-scale interactions, and evaluation of the pre-dictabilities of climate models for long-term variabilities. We will conduct integrated observational and modeling initiative, the Asian Monsoon Year (AMY)-II around 2025-28, whose strategies are the subregional observation platforms and integrated global analysis.11Nsciescopu

    Tropical cyclones over the western north Pacific since the mid-nineteenth century

    Get PDF
    Tropical cyclone (TC) activities over the western North Pacific (WNP) and TC landfall in Japan are investigated by collecting historical TC track data and meteorological observation data starting from the mid-nineteenth century. Historical TC track data and TC best track data are merged over the WNP from 1884 to 2018. The quality of historical TC data is not sufficient to count the TC numbers over the WNP due to the lack of spatial coverage and different TC criteria before the 1950s. We focus on TC landfall in Japan using a combination of TC track data and meteorological data observed at weather stations and lighthouses from 1877 to 2019. A unified TC definition is applied to obtain equivalent quality during the whole analysis period. We identify lower annual TC landfall numbers during the 1970s to the 2000s and find other periods have more TC landfall numbers including the nineteenth century. No trend in TC landfall number is detected. TC intensity is estimated by an annual power dissipation index (APDI). High APDI periods are found to be around 1900, in the 1910s, from the 1930s to 1960s, and after the 1990s. When we focus on the period from 1977 to 2019, a significant increasing trend of ADPI is seen, and significant northeastward shift of TC landfall location is detected. On the other hand, TC landfall location shifts northeastward and then southwestward in about 100-year interval. European and US ships sailed through East and Southeast Asian waters before the weather station network was established in the late nineteenth century. Then, we focus on TC events in July 1853 observed by the US Naval Japan Expedition of Perry's fleet and August 1863 by a UK Navy ship that participated in two wars in Japan. A TC moved slowly westward over the East China Sea south of the Okinawa Islands from 21 to 25 July 1853. Another TC was detected in the East China Sea on 15-16 August 1863 during the bombardment of Kagoshima in southern Japan. Pressure data are evaluated by comparing the observations made by 10 naval ships in Yokohama, central Japan during 1863-1864. The deviation of each ship pressure data from the 10 ships mean is about 2.7-2.8 hPa

    Synoptic Conditions and Potential Causes of the Extreme Heavy Rainfall Event of January 2009 Over Mindanao Island, Philippines

    Get PDF
    This study investigates the synoptic conditions that led to the heavy rainfall/flood (HRF) event in Mindanao Island, Philippines (122 −127°E; 5 −10°N), on January 2009 (JAN2009 HRF) that are less emphasized in previous works. Extensive flooding was reported over Cagayan de Oro City in the northern part of Mindanao, where the rainfall on January 10, 11, and 13, 2009, exceeded the 99th percentile of daily rainfall records of all January of the city from 1979 to 2017 by almost two times. A similar exceedance was also felt in Hinatuan station over the eastern coast of Mindanao Island on January 15, 2009. The interaction of a cold surge shearline over the northern Mindanao Island and the warm tropical easterlies led to enhanced moisture convergence. The warmer air mass is forced to ascend by the advancing colder air mass because it has lower density than the colder air mass. The enhanced moisture convergence and buoyancy difference by the air masses led to enhanced ascent and consequently rainfall along the cold surge shearline. Further analysis shows that enhanced anomalous easterly and northerly winds at 925 hPa are apparent over the Philippines. The anomalous easterly winds sustained the supply of warmer easterlies and collaboratively interacted with the northerly winds that supplied colder temperature air mass. The climatology of this HRF event was examined for all January from 1979 to 2017. The authors identified 15 other cases that are similar to the JAN2009 HRF event and performed lag composite analyses. The results show that the occurrence of these HRF events is facilitated by the southward expansion of the high-pressure system to the north of the Philippines, enhanced cold and warm temperature advections, and enhanced moisture convergence along the cold surge shearline. The results of this study have important implications for disaster mitigation during the northeast monsoon season when rainfall activities are, in general, less intensive over this region

    A Climatological Analysis of the Monsoon Break Following the Summer Monsoon Onset Over Luzon Island, Philippines

    Get PDF
    This study investigates the climatology of the monsoon break following the onset of the summer rainy season over Luzon Island (120–122.5°E, 13–22°N) in the Philippines from 1979–2017. The first post-onset monsoon break is remarkable in stations located over the north and central Luzon Island and occurs climatologically in early June. Composite analysis of the large-scale circulation features during the monsoon break period shows that this break is associated with the westward extension of the western North Pacific Subtropical High (WNPSH), which weakened the monsoon southwesterlies and induced enhanced low-level divergence over Luzon Island. The westward extension of the WNPSH may be facilitated by the phase change of the boreal summer intraseasonal oscillation (BSISO). About 59% (23/39) of the monsoon break cases occurred when suppressed convection, associated with the dry phases of the BSISO, is apparent over the western North Pacific. This suppressed convection favours the westward expansion of the WNPSH. With the occurrence of the monsoon break in early summer, the seasonal march of the early summer monsoon over the Philippines can be divided into three phases: (1) the monsoon onset phase, which occurs between mid to late May under the influence of the westerly/southwesterly low-level winds, (2) the monsoon break phase, when rainfall decreases over Luzon Island in early June, and (3) the monsoon revival phase, when rainfall increases again due to the intrusion of monsoon southwesterlies over the Philippines. This study highlights the complex features of the summer monsoon onset and the impact of the WNPSH on the local climate of the Philippines in early summer

    Development of an Updated Global Land In Situ‐Based Data Set of Temperature and Precipitation Extremes: HadEX3

    No full text
    We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version

    Development of an updated global land in situ‐based data set of temperature and precipitation extremes: HadEX3

    Get PDF
    We present the second update to a data set of gridded land‐based temperature and precipitation extremes indices: HadEX3. This consists of 17 temperature and 12 precipitation indices derived from daily, in situ observations and recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). These indices have been calculated at around 7,000 locations for temperature and 17,000 for precipitation. The annual (and monthly) indices have been interpolated on a 1.875°×1.25° longitude‐latitude grid, covering 1901–2018. We show changes in these indices by examining ”global”‐average time series in comparison with previous observational data sets and also estimating the uncertainty resulting from the nonuniform distribution of meteorological stations. Both the short and long time scale behavior of HadEX3 agrees well with existing products. Changes in the temperature indices are widespread and consistent with global‐scale warming. The extremes related to daily minimum temperatures are changing faster than the maximum. Spatial changes in the linear trends of precipitation indices over 1950–2018 are less spatially coherent than those for temperature indices. Globally, there are more heavy precipitation events that are also more intense and contribute a greater fraction to the total. Some of the indices use a reference period for calculating exceedance thresholds. We present a comparison between using 1961–1990 and 1981–2010. The differences between the time series of the temperature indices observed over longer time scales are shown to be the result of the interaction of the reference period with a warming climate. The gridded netCDF files and, where possible, underlying station indices are available from www.metoffice.gov.uk/hadobs/hadex3 and www.climdex.org.Robert Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101) and thanks Nick Rayner and Lizzie Good for helpful comments on the manuscript. Lisa Alexander is supported by the Australian Research Council (ARC) Grants DP160103439 and CE170100023. Markus Donat acknowledges funding by the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 Grant Reference RYC‐2017‐22964. Mohd Noor'Arifin Bin Hj Yussof and Muhammad Khairul Izzat Bin Ibrahim thank the Brunei Darussalam Meteorological Department (BDMD). Ying Sun was supported by China funding agencies 2018YFA0605604 and 2018YFC1507702. Fatemeh Rahimzadeh and Mahbobeh Khoshkam thank I.R. of Iranian Meteorological Organization (IRIMO) and the Atmospheric Science and Meteorological Organization Research Center (ASMERC) for Data and also sharing their experiences, especially Abbas Rangbar. Jose Marengo was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014‐1, FAPESP Grants 2014/50848‐9 and 2015/03804‐9, and the National Coordination for High Level Education and Training (CAPES) Grant 88887.136402‐00INCT. The team that worked on the data in West Africa received funding from the UK's National Environment Research Council (NERC)/Department for International Development DFID) Future Climate For Africa programme, under the AMMA‐2050 project (Grants NE/M020428/1 and NE/M019969/1). Data from Southeast Asia (excl. Indonesia) was supported by work on using ClimPACT2 during the Second Workshop on ASEAN Regional Climate Data, Analysis and Projections (ARCDAP‐2), 25–29 March 2019, Singapore, jointly funded by Meteorological Service Singapore and WMO through the Canada‐Climate Risk and Early Warning Systems (CREWS) initiative. This research was supported by Thai Meteorological Department (TMD) and Thailand Science Research and Innovation (TSRI) under Grant RDG6030003. Daily data for Mexico were provided by the Servicio Meteorológico Nacional (SMN) of Comisión Nacional del Agua (CONAGUA). We acknowledge the data providers in the ECA&D project (https://www.ecad.eu), the SACA&D project (https://saca-bmkg.knmi.nl), and the LACA&D project (https://ciifen.knmi.nl). We thank the three anonymous reviewers for their detailed comments which improved the manuscript.Peer ReviewedPostprint (published version
    corecore