Diurnal and Seasonal Variability of Near-Surface Temperature and Humidity in the Maritime Continent

P. T. May aMonash University, Clayton, Victoria, Australia

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B. Trewin bBureau of Meteorology, Docklands, Victoria, Australia

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J. R. Nairn cUniversity of Adelaide, Adelaide, South Australia, Australia

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B. Ostendorf cUniversity of Adelaide, Adelaide, South Australia, Australia

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Chun-Hsu Su bBureau of Meteorology, Docklands, Victoria, Australia

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A. Moise dCentre for Climate Research Singapore, Singapore

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Abstract

This work examines the diurnal and seasonal variability of near-surface temperature and humidity at several large areas with high population density within the Maritime Continent using the Bureau of Meteorology Atmospheric Regional Reanalysis (BARRA) 12-km-resolution dataset that covers the period 1990–2019. The diurnal cycle is examined in detail, with a key feature being the relatively small diurnal variation of the wet-bulb temperature TWB when compared with the temperature and dewpoint temperature TD. The diurnal variability is strongly modulated by the monsoons with their increased rainfall and cloud cover. The near-surface signals associated with the Madden–Julian oscillation across the domains are relatively weak. Dry and humid temperature extremes are examined for regional and seasonal variability. The dry and moist variable extremes occur at different times of year, but each have spatially coherent structure.

Significance Statement

This paper examines the climatological variations of near-surface temperature and humidity and their extremes in four locations in the “Maritime Continent.” This is important because there are significant variations potentially affecting human and ecosystem health and its resilience to climate change.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter May, peter.may@monash.edu

Abstract

This work examines the diurnal and seasonal variability of near-surface temperature and humidity at several large areas with high population density within the Maritime Continent using the Bureau of Meteorology Atmospheric Regional Reanalysis (BARRA) 12-km-resolution dataset that covers the period 1990–2019. The diurnal cycle is examined in detail, with a key feature being the relatively small diurnal variation of the wet-bulb temperature TWB when compared with the temperature and dewpoint temperature TD. The diurnal variability is strongly modulated by the monsoons with their increased rainfall and cloud cover. The near-surface signals associated with the Madden–Julian oscillation across the domains are relatively weak. Dry and humid temperature extremes are examined for regional and seasonal variability. The dry and moist variable extremes occur at different times of year, but each have spatially coherent structure.

Significance Statement

This paper examines the climatological variations of near-surface temperature and humidity and their extremes in four locations in the “Maritime Continent.” This is important because there are significant variations potentially affecting human and ecosystem health and its resilience to climate change.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter May, peter.may@monash.edu

1. Introduction

The so-called Maritime Continent has been a focus of research for decades because of its influence on global weather and climate (Ramage 1968; Yamanaka et al. 2018) as a region characterized by very large areas of deep tropical convection (Greenfield and Krishnamurti 1979; Webster and Stephens 1980) as well as significant long-standing biases in the modeling of the diurnal variation of convection (e.g., Slingo et al. 2004; Yoneyama and Zhang 2020). There have been numerous field programs studying various aspects of the monsoons and climatology in Southeast Asia and northern Australia including the Winter Monsoon Experiment (WMONEX), Maritime Continent Thunderstorm Experiment (MCTEX), and others (e.g., Webster and Stephens 1980; Keenan et al. 2000; May et al. 2008b; Vaughan et al. 2008) and more recently the Year of the Maritime Continent (e.g., https://www.jamstec.go.jp/ymc/ymc_sp_collection.html). The impact and characteristics of diurnal variability have also been a key research focus in order to understand the diurnal variation of clouds and precipitation including studies of the diurnal variation of land breezes (e.g., Slingo et al. 2004; May et al. 2012; Siswanto et al. 2016; Mori et al. 2018; Short et al. 2019; Yoneyama and Zhang 2020) and their seasonal variability (e.g., Yoden et al. 2017) while near-surface temperature and humidity variations have been less studied. Further, in order to issue extreme weather warnings to mitigate risks, it may be necessary to include humidity in heatwave severity forecast for tropical regions (Nairn et al. 2022). In addition to this, the region is home to very large urban populations that are potentially at risk from temperature and humidity trends associated with climate change (Sherwood and Huber 2010).

Ecosystems in the tropics are adapted to a much narrower range of temperatures and there is greater sensitivity to the impact of increased humidity on both human health and ecosystems (e.g., Matthews et al. 2017; Raymond et al. 2020). The low day-to-day variability in the tropics also means that a modest increase in mean values can be associated with very large increases in the number of days exceeding critical thresholds. For example, the Bureau of Meteorology Darwin (Australia) weather station annual mean maximum temperature between 2019 and 2021 was ∼1°–1.1°C above the long-term average, but all three years had more than 40 days above 35°C whereas no pre-2019 year had more than 30. For the latter reasons and to add to the understanding of the diurnal variability and its seasonal dependence, this study will examine the diurnal and seasonal variability of near-surface temperature and humidity using a high-resolution (∼12 km) reanalysis dataset (Su et al. 2019). The focus is on areas surrounding some major population centers impacted by monsoons and intraseasonal variability associated with the Madden–Julian oscillation (MJO) (Fig. 1).

Fig. 1.
Fig. 1.

Map showing the four regions centered on four tropical cites (Bangkok, Singapore, Jakarta, and Darwin). The blue stars mark the centers of the domains over their respective cites. The red dashed lines mark the cross sections for the Hovmöller diagrams in section 3. The points for Singapore and Darwin as well as an offshore location in the Darwin domain (red star) are discussed in detail in section 4.

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

The regional rainfall is dominated by the impact of monsoon circulations (Yoden et al. 2017). The mean low-level circulations in January and July (Fig. 2) illustrate the cross-equatorial flow and Austral summer monsoon in January as well as the northeast monsoon in Singapore. Rainfall in the northern area centered on Bangkok, Thailand, is primarily from the Indian southwest monsoon from May to October with the maximum rainfall in September (Fig. 3) and the southwesterly wind anomalies weakening substantially in October and easterlies being established across the northern part of the Bangkok domain by November. At the southern end of the Malay Peninsula, Singapore is influenced by the northern winter northeast monsoon and an associated peak in the monthly rainfall while the influence of the southwest monsoon on winds and monthly average rainfall is much less clear, albeit with rain throughout the year (Figs. 2, 3). The northeasterly flow in Singapore starts in November and persists through to March, but the February–March period is locally referred to as a dry monsoon phase, as seen with the relatively reduced rainfall in those months. The northeasterly flow feeds into the southern summer wet season peak in Jakarta, Indonesia, with northerlies and into the Australian monsoon. However, the seasonal cycle is not symmetric, with the maximum in convection gradually moving south during the northern summer monsoon but with a rapid transition at the end of the southern summer monsoon (Chang et al. 2005). Note there are significant sea and land breezes observed across the Maritime Continent with circulations extending over hundreds of kilometers to sea (Short et al. 2019) and inland across northern Australia (Physik and Smith 1985) that may or may not be well resolved in the reanalysis. The reversal of wet season flow for Darwin and Bangkok introduces the influence of drier continental air masses and potential for reduced humidity temperature extremes.

Fig. 2.
Fig. 2.

Near-surface wind fields from the BARRA reanalysis for the four domains averaged over (left) January and (right) July. Monsoons are strongest for Singapore, Jakarta, and Darwin in January and in July for Bangkok. The alternate months are relatively dry except for Singapore, which does not have a defined dry season. The rainfall for the domain city is given for that month (RR). The red dashed lines mark the cross sections for the Hovmöller diagrams in section 3.

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

Fig. 3.
Fig. 3.

Plots of the monthly area-averaged maximum (red) and minimum (blue) temperature (solid), wet-bulb temperature (dotted), and dewpoint temperature (dashed) for the four domains being discussed, with the (left) land points and (left center) sea points averaged separately, along with (right center) the average monthly cloud cover diurnal maxima (solid) and minima (dashed) for land (red) and sea (blue) as well as (right) rainfall amounts for the cities at the center of the domain.

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

2. Dataset and analysis

The core dataset being used for this study is the Bureau of Meteorology Atmospheric Regional Reanalysis (BARRA), which has a grid resolution of ∼12 km covering Southeast (SE) Asia and the Maritime Continent as well as northern Australia. The BARRA dataset is hourly covering the period from January 1990 to February 2019. The hourly data include the standard variables such as the instantaneous screen-level (1.5 m) temperature and humidity, 10 m wind, and surface pressure diagnosed from the model levels (Su et al. 2019). We also consider hourly averages of total cloud cover, net longwave (LW) and shortwave (SW) radiation. The dataset was developed using the Unified Model data assimilation and modeling system (Walters et al. 2017) and is nested within the ERA-Interim global reanalysis dataset (Dee et al. 2011). BARRA is fully documented by Su et al. (2019). As such, it makes use of a wide variety of observational platforms and produces a dynamically consistent analysis of a wide range of variables that includes data-sparse areas such as over the ocean so that the areas being discussed can extend beyond station based analyses to examine spatial patterns and over extended periods. The 12-km resolution of the regional reanalysis also means that topographic impacts including those associated with mountains and complex coastlines are better represented when compared with global reanalysis datasets. We have extracted sectors from the dataset for further analysis (Fig. 1).

Calculation of relative humidity were performed using the approximations of Buck (1981) for calculating vapor pressure, noting this gives answers very close to the Goff–Gratch formulation for the warm temperatures in these regions. The wet-bulb temperature TWB was then calculated using the approximations given in Stull (2011) and is summarized in appendix 2 of May et al. (2021).

This analysis uses values estimated for the screen level that is below the lowest sigma level data from the model grid. We have selected square domains of 5° of latitude and longitude centered on the tropical cities of Darwin, Singapore, Jakarta, and Bangkok (Fig. 1). These areas have been selected because Darwin is one of the longest and most detailed areas in the tropics for study and along with Singapore is a canonical tropical location, with particular relevance as an indicator of El Niño. The meteorology around Jakarta has also been extensively studied (e.g., Siswanto et al. 2016; Mori et al. 2018). The Jakarta and Bangkok domains center on key major SE Asian urban areas and provide a sample of key monsoon influences across the Maritime Continent including the northern summer and winter monsoons as well as cross-equatorial flow feeding the Australian monsoon. These domains cover a reasonable area so that the impact of local circulations and spatial structure around these urban areas is visible. The use of four domains provides a representative sample of different tropical climate types while keeping the scale of the analysis reasonable.

To check the reliability of the BARRA analyses for this purpose, comparisons with automatic weather station (AWS) data across northern Australia were made (May et al. 2021). Despite these being comparisons of a 12-km-resolution grid with point measurements, the overall agreement is very good with the covariation of T, TD, and TWB all greater than 0.9 and the standard deviations of the differences ∼1.5°–2°C. The correlations were almost unaffected when the seasonal cycle was removed. This illustrates that the reanalysis is accurately capturing the variations of T, TD, and TWB on all time scales. These comparisons do show some potentially important biases. In particular, during the northern Australian dry season, the amplitude of the diurnal cycle of T is overestimated by up to 2°C, but this was reduced to less than ∼0.5°C in the wet season. The biases of the diurnal cycle amplitude of TD and TWB were mostly less than ∼0.5°C giving confidence that the highest values of moist variables are reliable. While we do not have similar validation data for other sites, it is reasonable to assume that the behavior in the wet season is similar across the domains including year around in Singapore. Darwin has the strongest seasonal cycle of rainfall and cloud cover so the May et al. (2021) study probably represents the upper bound on biases for the other locations in their respective dry seasons.

3. Seasonal variability of the diurnal temperature characteristics

All of these locations are tropical and strongly affected by the monsoon conditions and associated systematic seasonal variation in cloud cover. All except Singapore have distinct drier seasons. Figure 3 shows the area-averaged mean T, TD, and TWB over the domains shown above for locations over land and water as a function of month as well as the minimum and maximum average daily cloud cover and monthly rainfall. The maximum values of the temperatures and the month they occur is summarized in Table 1. All the locations show a distinct spring maximum in temperature and in most cases a weaker autumn maximum, particularly over the sea. Singapore is an exception because it is very close to the equator and has a distinct double peak near the equinoxes. All sites show two seasonal peaks offshore related to but after the equinoxes. The monsoon wet season corresponds to a minimum in temperature with the more extensive cloud cover and heavy rain events when compared with nonmonsoonal periods. A winter minimum in the maximum temperatures in addition to the wet season minimum is also evident in the two most poleward domains, Bangkok and Darwin. Note there is little difference on average in cloud cover over the land and ocean parts of the domains, but there are significant differences in the temperature and humidity responses. The systematic variations in cloud cover affect the radiative fluxes at the surface and consequently the temperature. The high cloud cover periods correspond to low net shortwave radiation with values of the maximum in daily net SW radiation as low as 6–700 W m−2 over the land and values over the ocean being ∼100 W m−2 higher. Wet season net shortwave radiation is similar across domains with the largest fluxes being ∼−80 W m−2 over land and about −60 W m−2 over the ocean. In the dry seasons of Bangkok and Darwin the largest net LW values over land reach approximately from −150 to 200 W m−2 and with a much larger diurnal cycle. The smallest values during the wet season are ∼−20 and −30 W m−2 over the land and ocean, respectively.

Table 1

Maximum values of the monthly average maximum temperatures (°C) for land and ocean (in parentheses) grid points for the four domains being discussed along with the month in which these were recorded.

Table 1

The two most poleward regions have the largest seasonal temperature amplitudes, particularly in minimum temperatures. These plots also show the amplitude of the diurnal temperature cycle maximizes over land in the dry seasons. The amplitudes are much larger over land than the ocean with the lowest seasonal amplitude of diurnal range being at Singapore, which also has the weakest seasonal variation of rainfall. The amplitude of the diurnal temperature cycle over ocean is 1.6°C or less at all locations.

The seasonal variation of temperature over the ocean is marked, but with some differences when compared with the land. Over three of the domains there is a maximum in temperature after the equinoxes (May and November) with the exception being Darwin where the temperature variation is dominated by relatively low winter temperatures.

The two moist variables display very different behavior to the temperature in terms of the amplitude of the diurnal variations and their seasonal dependence. The two poleward domains show larger seasonal variations of both TWB and TD with the impact of the dry seasons being particularly evident. This time also marks the largest amplitude of the diurnal cycle of temperature as well as the moist variables. In all cases the diurnal variation of the moist variables over land is less than that of the temperature with the cycle of TWB being the smallest. This will be examined in more detail in section 4.

The amplitudes of the diurnal cycle are summarized in Table 2 with the amplitudes of the diurnal cycle given for the whole year as well as the wet and dry seasons. Note that the wet season extends slightly beyond the formal monsoon onset and cessations as described by the relevant national weather services, but the amplitude of the temperature and humidity variations on these cusp months is similar, illustrating the dominant effect of rainfall and associated cloud cover. The seasonality shows the magnitudes of the diurnal signal during rainy seasons is much less than for the pronounced dry seasons. Regardless of time of year the TWB has the smallest diurnal amplitude: about one-third that of the temperature and the TD has on average a slightly larger diurnal amplitude than the TWB at about one-half of the T temperature variation. The wet seasons are similar across the domains with Darwin slightly wetter over land while the dry season amplitude signal is most pronounced for the domains with the driest “dry” seasons—Darwin being greater than Bangkok, which in turn was more than Jakarta, with Singapore the smallest with no defined dry season.

Table 2

The amplitude of the diurnal range of the three temperature variables for the four domains during their respective wet and (relatively) dry seasons for land and ocean (in parentheses) data points.

Table 2

The land–sea contrasts and variations in the diurnal cycle can be illustrated using Hovmöller diagrams. Figures 4 and 5 show north–south cross sections over the center of the domains (including the cities) of the temperature and humidity variables along with the diurnal perturbations of the wind components for several days during January (Fig. 4) and July (Fig. 5) where data from all 29 years have been composited. The north–south sections are chosen as they provide a view of the land–sea contrast. Periods from individual years, while not shown, are noisier but similar, except when there is a major event such as a tropical cyclone (TC) where the wind, moisture and temperature perturbations are large (e.g., May et al. 2008a for TC Ingrid affecting the Darwin area in March 2005). Data for 2.5 days are shown, but the sequence changes little for longer periods within a given month. The largest amplitude temperature and moisture signals are seen in the poleward domains, particularly in their respective dry seasons. The meridional gradients are largest on the land–sea boundaries although there is a drying trend toward the inland areas for the poleward sites. Some topographic effects are evident, for example, with the cooler temperatures in the northern part of the Bangkok domain and over Java over high terrain. There is no evidence of any heat island impacts in these cross sections that have been observed in station data (e.g., Kataoka et al. 2009).

Fig. 4.
Fig. 4.

Hovmöller diagrams for north–south sections through the center of the domains with the time axis from left to right. The data from 1990 to 2018 are composited for each month, and data from January are shown for days 6–8 of each month (in local time) for (left) T, (left center) TD, (center) TWB, (right center) u′, and (right) υ′, where u and υ are the zonal and meridional wind components. The heavy black contours in the u and υ panels marks the zero-velocity line. The green and blue lines on the leftmost panels respectively show the land topography and ocean. The land topography is scaled so that the highest point corresponds to 0.1 days on the horizontal scale.

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

Fig. 5.
Fig. 5.

As in Fig. 4, but for July.

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

Systematic perturbations in the winds are seen over the oceans consistent with large penetration of both land and sea breezes as has been previously observed (Physik and Smith 1985; Short et al. 2019). This is particularly clear in the Darwin cross sections with southward flow extending inland during the day and the northward nighttime flow, particularly in July. All these locations show flow perturbations toward the inland during the day including across the relatively narrow island of Java during the day and stronger perturbations during drier seasons where the temperature and humidity (TD) variations are larger.

The diurnal patterns from Darwin and Jakarta are consistent with previous analyses based on shorter periods showing nocturnal cooling and moistening and afternoon heating associated with the development of diurnally modulated convection (e.g., May et al. 2012; Katsumata et al. 2018). These features extend to the midtroposphere (Katsumata et al. 2018). There is a sharp change in the zonal flow amplitude over the land when compared with observations in the wet season (cf. Katsumata et al. 2018). Part of this is likely associated with changes in surface roughness in the model and interactions with steep topography south of Jakarta. However, the total cloud cover does not systematically vary over the land–sea boundaries.

4. Case studies

a. Case study 1: Diurnal and seasonal variability at Darwin

The region around Darwin is one of the most intensively studied in the tropics. There have been numerous studies of its climate and typical modes of deep convection during the premonsoon buildup and actual monsoon periods (e.g., Keenan and Carbone 1992). May et al. (2012) also examined the diurnal variation of the low-level temperature structure over the ocean and land during the wet season noting the marked contrast in mixed layer depth and sharp transition offshore during the 2005/06 wet season as well as modulation of convection activity associated with monsoon bursts and breaks. This study also showed a distinct land–sea contrast in monsoonal rainfall with more rain over the ocean. The diurnal variability of convection and tropospheric structure during these convectively active periods has also been documented (Keenan et al. 1989; May et al. 2012). The buildup is characterized by suppressed rainfall conditions on the large scale but intense electrically active storms with a large proportion of very deep storms developing on the sea breeze and other local circulations (e.g., Keenan and Carbone 1992; May et al. 2002; May and Ballinger 2007). Convection in the monsoon is generally much more oceanic in character with less lightning and a lower fraction of very deep storms. The monsoon onset is variable and climatologically most likely in late December (Drosdowsky 1996), but Fig. 3 showed that the rainfall increases markedly during the premonsoon buildup from November to the onset.

To examine how this reflects on the details of the diurnal variation of the temperature variables, we have constructed composites of the diurnal cycle for each month and plotted contours of the variability as an anomaly around the monthly mean temperatures (plotted to the right) along with the monthly average maximum and minimum diurnal values (Figs. 68). We have examined an oceanic data point and one near the coast noting that Darwin is located on the coast (Fig. 1). Figure 6 shows these composite plots for a grid point on land near the coast (and the city of Darwin) while Fig. 7 is for a grid point offshore. It is worth noting that plots from other grid points near the coast and far offshore show almost identical patterns with only a slight decrease in amplitude of the diurnal variations when compared with farther from the coast, while on the land side there is a gradual intensification of the magnitude of the diurnal cycle of temperature inland and with higher peak temperatures. It is near the coast that there is a very rapid transition in behavior. Part of this could be a model response with the change in the surface and boundary layer, but a rapid transition was observed during the 2007 field experiment called the Tropical Warm Pool International Cloud Experiment (TWPICE; May et al. 2008b), and it is also consistent with station observational data.1 Further, there is a sharp demarcation of precipitation across the land–sea boundary (May et al. 2012).

Fig. 6.
Fig. 6.

(left) Diurnal composites of the T, TWB, and TD anomalies as a function of local time and month along with (right) the monthly mean values of a grid point over land close to Darwin city on the coast (12.475°S, 131.055°E). The mean maximum (red dotted), minimum (blue dotted), and mean Ts are shown.

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

Fig. 7.
Fig. 7.

As in Fig. 6, but for a grid point ∼150 km offshore from Darwin (11.375°S, 129.845°E).

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

Fig. 8.
Fig. 8.

As in Fig. 6, but for a land point near Singapore on the southern tip of the Malay Peninsula (1.385°N, 103.885°E).

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

The gross features of the diurnal temperature ranges as a function of season are consistent with the domain averages. The largest diurnal amplitudes occur in the dry season with larger amplitudes over land than offshore. The amplitude of the diurnal variation of T is ∼3 times that of the TWB. The maximum temperature over land as a function of season has a double peak structure as previously discussed, but note the minimum temperature has a single winter minimum when the skies are clear with relatively low TD. The seasonal cycle of T offshore has just a single summer peak.

The diurnal variation of temperature over land shows a consistent midafternoon maximum as is commonly observed while the ocean surface temperatures maximize around 2000 LT in the early evening. The variation in TD is in almost opposite phase with drying as the boundary layer warms and deepens and moistens during the evening with evapotranspiration and the formation of a surface-based inversion limiting vertical mixing. Again, the TD diurnal signal is strongest in the dry season, but the maximum amplitude of the diurnal variation is a month later than for the temperature. This antiphase diurnal relation between T and TD results in the amplitude of the TWB diurnal variation being relatively small and being less than 1°C during the monsoon. The amplitude of the diurnal signals is stronger as samples are taken farther inland, but this increase in amplitude is very gradual.

Offshore there is an abrupt transition to a much smaller diurnal variation in all the fields with the peak in temperature moved to later in the day (Fig. 7). The cool season temperatures and TD’s are slightly warmer than over the land. The TWB diurnal variation in the wet season at this point is less than 0.5°C

The low-level circulation is sharply different between the dry and monsoon seasons. The minimum amplitude in the diurnal cycle of temperature occurs in January and February—the climatological most likely period to have monsoonal conditions and the maximum in cloud cover and rainfall (Fig. 2).

The seasonal variations are also clear with the well-known maximum in temperature outside of the summer wet season in April and October with October being warmer because of extensive rain and cloud cover during the monsoon (May et al. 2012). The minimum temperature does not show that double peak, but rather a single minimum in winter. In contrast and as expected, the dewpoints maximize in the wet season. The wet-bulb temperatures similarly have their maximum during the wet season with a value of about 26°C. This is probably an underestimate as there is a mean bias in maximum TWB analyzed (May et al. 2021), but note this is smallest during the wet season with an amplitude of about 0.2°C.

b. Case study 2: Singapore

Singapore has a much more uniform rainfall and temperature throughout the year (Fig. 3) but is also affected by monsoon circulations (e.g., Chang et al. 2005) and has a distinct maximum in rainfall totals early in the northeast monsoon period (November–January) prior to a “dry monsoon” phase with reduced rainfall, but still northeasterly winds (February–March). The remainder of the year has a southeasterly flow to the east of Singapore, but it is still affected by the southwest monsoon in June–September. Rainfall is observed to fall primarily in the afternoon except for June and into the following months when there is a morning maximum but weaker average diurnal variability (Singapore Meteorological Service; http://www.weather.gov.sg/climate-climate-of-singapore/).

Two features are immediately evident in the diurnal composites of T, TWB, and TD (Fig. 8). The patterns in the diurnal variations are quite similar to Darwin, but the amplitude of the variations is reduced by a factor of about 3. The ocean points around Singapore are similar to those in Darwin (not shown). However, there are some differences. For example, the TD around Darwin is maximized in the wettest part of the year, while in Singapore the peak rainfall season over December to February has a pronounced minimum in TD. A secondary minimum is also seen around August during the southwest monsoon season.

5. Modulation by the MJO

Weather in the Maritime Continent is strongly affected by the MJO along with other equatorially trapped waves (e.g., Wheeler and Kiladis 1999). The MJO dominates rainfall predictability on intraseasonal time scales through the tropical Asian monsoon region (e.g., Rauniyar and Walsh 2011) as well as the diurnal cycle of that rainfall (e.g., Oh et al. 2012). Active MJO phases show a larger mean rainfall and increased cloudiness, particularly over the ocean and an increase in the amplitude of the diurnal rainfall cycle over the ocean and decrease over land. Heavy rain events have been linked to active MJO cycles including around Sumatra (Wu et al. 2013). The mechanisms for the MJO are still actively researched but the active phases are preceded by a tendency for moistening of the boundary layer (Seo and Kim 2003) and for convection to become increasingly deep as the active phase approaches and propagates past (e.g., Mahoney and Hartmann 1998; Johnson and Ciesielski 2013). However, while there is a clear signal in rainfall, topographic influences do disrupt the steady movement of the anomalous convective heating over the Maritime Continent and there tends to be a minimum in the MJO rainfall signal in this region (Hsu and Lee 2005). Further, the impact of the MJO on near-surface temperature and humidity is less well documented, although the midtropospheric moistening leading into the active phases is well observed. In some instances, other modes of variability may be more important than the MJO (e.g., Geng and Katsumata 2021).

The phase of the MJO is most often described using the real-time multivariate MJO (RMM) index of Wheeler and Hendon (2004). This is widely used as an operational forecast tool as well as for research and is a real-time product (Bureau of Meteorology; http://www.bom.gov.au/climate/mjo/). The RMM diagnoses eight phases of the MJO and a zero phase in which the MJO is defined as weak (magnitude of the RMM indices < 1). The “active” phase of the MJO with enhanced cloudiness and rainfall in the MC is dependent on the longitude. Following Rauniyar and Walsh (2011), the active phases as a function of longitude range are given in Table 3. We have stratified the data according to the MJO phase using the Bureau of Meteorology operational RMM index with a null phase (phase 0) corresponding to the RMM vector amplitude < 1.0. We also tested the data using different RMM amplitude thresholds for the null MJO, but the results did not vary significantly.

Table 3

Convectively active phase of the MJO for the different domains (adapted from Rauniyar and Walsh 2011).

Table 3

The mean maximum daily T, TD, and TWB has been calculated for data from each phase of the MJO averaged over the land and sea areas in the four domains (Fig. 9) during their respective wet seasons. Based on the previous work given the maximum in expected cloud cover for the active phases, you would expect a minimum in max T and a buildup of moisture (increasing TD) ahead of the peak in activity. However, the observed signals are much more mixed. Broadly the curve for Darwin fits this paradigm, but the other domains to the west within the core of the Maritime Continent are far more nuanced, perhaps representing the weakening of the MJO through the Maritime Continent that has been reported (Seo and Kim 2003; Hsu and Lee 2005; Wu and Hsu 2009). The curves for Bangkok are more aligned with Darwin well to the east and lag the active phase while the moisture signal fits the expectation around Singapore and Jakarta, but with little temperature signal. Note that we have already seen the relatively small variations in monthly rainfall variation in the Singapore domain along with reliable rain and cloud cover, so perhaps this is masking any temperature impact near the surface. These signals combine to produce a maximum in TWB during the active phases. Of note is the timing of the maximum values of T, TD, and TWB change very little with MJO phase except for Darwin’s maximum in TD moving later in the evening during active phases.

Fig. 9.
Fig. 9.

Maximum T, TWB, and TD averaged over all the (left) land and (right) ocean grid points for the four domains (Darwin—magenta, Jakarta—black, Singapore—blue, and Bangkok—green) for the eight MJO RMM phases (lines) and when there is no significant MJO (circles). The averages are for the domain wet seasons comprising December–March (Darwin), November–March (Jakarta), November–January (Singapore), and July–October (Bangkok).

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

6. Extreme events

The discussion so far has focused on the mean climatology over the Maritime Continent. This section will now examine heat extremes, noting extreme rainfall has been often studied (e.g., Wu et al. 2007, 2013). The discussion will quantify the most intense dry and moist heat extremes as cool extremes at these latitudes do not have high impacts on people and ecosystems. Extreme high TWB is of particular interest in a warming globe (Raymond et al. 2020) where sustained TWB values rising toward 35°C encroach upon the physical upper limit for heat tolerance (Sherwood and Huber 2010). However, sustained dry temperature extremes in the diurnal cycle are also of interest when accumulated sensible heat events normally experienced at higher latitudes are detected. In either case an approximation of diurnal/accumulated dry and moist extremes are extracted from daily [(max + min)/2] values of T and TWB (after Nairn and Fawcett 2015).

There is some complication with the most extreme TWB and TD data as there are occasional gridpoint storms in the reanalysis (Su et al. 2019) and these lead to erroneous and very high values of TD and TWB. Fortunately, they are rare for any given grid point. The approach here is to take data for months separately and to analyze the total data period. This gives ∼900 daily minimum, maximum, and average T, TD, and TWB values for each month. These data are sorted and the 1st and 99th percentile values over the total record for the 29-yr record for each month. Thus, we have these percentiles for each month and each grid point. The overall season patterns are not dissimilar to the mean seasonal cycle. To illustrate and expand on this we have calculated the median value over the land and the ocean sections of each domain for the 99th and 1st percentile. Extreme daily average temperatures are plotted as a function of month in Fig. 10.

Fig. 10.
Fig. 10.

The median of the greatest monthly daily [(max + min)/2] extreme (99%) values for (left) land and (right) sea grid points for the four domains Darwin (magenta) Jakarta (black), Singapore (blue), and Bangkok (green).

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

The most extreme daily average and maximum (not shown) temperatures were seen in the inland areas of the Darwin and Bangkok domains (Fig. 10) prior to and in their respective summer seasons with daily average temperatures observed over 32°C (maximum over 41°C), with daily TWB extremes lagged by a month. Even the cool seasons had daily temperatures as high as the high 20s to low 30s in degrees Celsius at all of the sites. The ocean data points were lower, but still with daily average temperatures in the high 20s.

The daily average TD data shows land locations reach values of at least 26°C during their wettest seasons with Jakarta having the lowest peak values of daily average TD. Unsurprisingly the seasonal cycle near Singapore is the smallest while the poleward domains have pronounced dry seasons and cool and dry extremes (not shown). All locations show similar extreme daily average TWB values of 26°–27°C over land and sea. This narrow seasonal and domain range of extreme daily average TWB is indicative of a more persistent peak humid environment consistent with chronic environmental and heat health exposure. It is notable that the most extreme daily average TD and TWB curves resemble each other and are different to the T curves showing that these maxima occur somewhat independently. Nairn et al. (2022) examined dry and humid Australian heatwaves using BARRA data. The tropics alone experienced extreme humid and dry heatwaves. In this study, high humidity heat extremes appear to be at least 5°C below the limits of human endurance.

The danger of unusually dry heat extremes is considered through comparison of 99th- and 1st-percentile daily T, TWB, and TD for 5° latitude, longitude grid regions centered on all four locations. Bangkok’s peak extreme dry heat in April is examined in Fig. 11, while Darwin’s November peak is examined in Fig. 12 (there is no evidence of extreme dry heat in Singapore or Jakarta). Daily TD < 10°C on the plains inland from Bangkok coincide with low to mid-30s (°C) extreme daily T, while near the coast and inland from Darwin extreme daily T is approximately 1°C lower, and daily TD < 10°C commences farther inland. Consequently, Bangkok TWB is approximately 6°C lower than Darwin on the coast during these events.

Fig. 11.
Fig. 11.

Maps of the highest value of the 99th and 1st percentile of daily average T, TD, and TWB for each grid point in the Bangkok domain for April during the period 1990–2018.

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

Fig. 12.
Fig. 12.

As in Fig. 11, but for the Darwin domain for November.

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

Thermal stress arising from daily T and TWB extremes around Bangkok and Darwin exhibit different characteristics that need to be accommodated in intervention strategies for protection against adverse health effects of high temperature and heatwaves. This is particularly important as maximum temperature rises beyond 35°C in low humidity, where apparent temperature will increase rather than cool when fans or environmental winds are present (Steadman 1994).

Given the high values of maximum and daily average T and TWB that have been observed in the reanalysis, the question is how robust these results are. To assess and understand these events and examine potential spatial signals associated with local circulations such as sea and land breezes as well as topography, we examine the spatial structure of the most extreme cases for each of the regimes. Figure 13 shows maps of the most extreme values of T, TD, and TWB for the Darwin domain. Note that these are spatially coherent over large portions of the domain. These areas of high T, TD, or TWB also tend to be from the same months (not shown), so the result is robust. This is true over the land and ocean, but the event associated with the maximum over different parts of the domain does vary.

Fig. 13.
Fig. 13.

Maps of the highest value of the monthly 99th percentile of T, TD, and TWB for each grid point in the Darwin domain during the period 1990–2018.

Citation: Journal of Applied Meteorology and Climatology 61, 11; 10.1175/JAMC-D-22-0032.1

As expected, there is more structure over the land than the ocean in all the fields. In Darwin, the maximum temperature increases inland in the savannah regions noting that the Darwin region is relatively flat, and therefore it would be expected to show weaker topographic signals than some of the other domains. The fields involving moisture in many respects have a more interesting structure with a maximum seen along the coast. These maxima are associated with onshore monsoonal flow during January and February and are particularly distinctive in the Darwin domain but are visible elsewhere. The monsoonal signal dominates any signal associated with the prevalence of coastal convection in this domain in the buildup and breaks of the monsoon when the large scale is convectively suppressed allowing for a warmer and moister boundary layer, but the local forcing produces intense storms (Keenan and Carbone 1992; Schafer et al. 2001; May et al. 2002; May and Ballinger 2007). Anecdotally it is the buildup season that is most unpleasant for people, but this appears consistent with the T extremes signal (with significant coastal humidity) rather than extremes in TD and TWB. The most extreme TD and TWB analyzed are on the eastern end of Tiwi Islands north of Darwin, which has strong sea-breeze interactions (e.g., Schafer et al. 2001), with a larger and more coherent area of extreme high values (TD > 29°C) in a west–east band slightly inland from the northern mainland coast. The latter area in the BARRA analysis is broadly consistent with surface observations, with observational data2 at Batchelor, Northern Territory, Australia (13.05°S, 131.03°E), in the middle of the area, showing a value of 29.0°C.

The fact that these high values of TD and TWB occur in January and February makes it most likely they are of monsoonal origin. There is a strong rainfall gradient observed across the coastal boundary during the monsoon with higher rainfall offshore, but the combination of onshore flow and high rainfall may be producing these extreme values (May et al. 2012).

The small-scale details vary in other domains, but the key results are the same. There is significant spatial continuity and some variations tied to topography, although the Darwin data showed the clearest example.

7. Conclusions

This paper has examined the seasonal and diurnal variations of temperature and humidity for key areas across the Maritime Continent. As expected, there is a strong monsoonal influence in all these fields tied to the low-level circulation and corresponding rainfall, cloud cover and resulting radiation. We have documented and quantified these variations in surface values in both the means and extremes. The maximum temperatures are typically prior to monsoon onset in these locations and near the equinoxes. There are clear peaks in the moist signals during the monsoons in all domains. This leads to significant extremes in TWB, with substantial areas near Darwin recording values in excess of 30°C. However, these were not coincident with the most extreme heat, which tended to be prior to the monsoon in all domains noting the risk that delayed monsoonal onset correlated with extremes can result in significant heat related deaths (Nissan et al. 2017).

The diurnal cycles have also been quantified with the ocean parts of the domains showing very similar diurnal variations and magnitudes. Over land, the most poleward locations have the strongest seasonal variation in the amplitude of the diurnal signals of all the variables. These poleward locations also have larger amplitude diurnal variation during the monsoon when compared with Jakarta. Singapore consistently has the smallest amplitudes. In all locations the amplitude of the diurnal cycle on T is approximately 2 times that of TD and 3 times the TWB. Case studies showed that the diurnal cycle of T and TD are in antiphase, and this ultimately leads to the low amplitude of the TWB diurnal signal.

We also examined the potential impact of the MJO on the temperature and humidity during the respective wet seasons. Any MJO signal was observed to be weak in the equatorial Maritime Continent but somewhat stronger around Darwin and Bangkok, noting the relative weakness in the MJO rainfall signal across the Maritime Continent.

The next stage of these analyses will be to explore any evidence for trends in T, TD, and TWB over the 30-yr reanalysis period and other datasets across the Maritime Continent to build on previous work examining temperature and rainfall trends (e.g., Hassim and Timbal 2019; Siswanto et al. 2016).

1

In 2019 and 2020 (years for which complete observational data are available from the Darwin Harbour site), the mean annual diurnal temperature range was 6.6°C at Darwin Harbour (on the coast), 9.5°C at Darwin Airport (about 5 km inland), and 14.4°C at Middle Point (about 40 km inland).

2

Hourly data are available at Batchelor from 1999 to 2018 and therefore cover a slightly shorter period than the BARRA reanalysis. The daily maximum dewpoint is derived from hourly data in the same way as for the reanalysis data used in Fig. 9.

Acknowledgments.

BARRA is produced by the Bureau of Meteorology in collaboration with Australian emergency service agencies and research institutions.

Data availability statement.

The BARRA: Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia dataset is available via the NCI Data Catalogue (https://doi.org/10.4225/41/5993927b50f53). Rainfall data for Jakarta and Bangkok were obtained from the Global Historical Climatology Network monthly (GHCNm) dataset from the National Centers for Environmental Information (NCEI). Rainfall for Darwin was collected from the Bureau of Meteorology and for Singapore from the Singapore Meteorological Service. MJO data were provided by the Australian Bureau of Meteorology (http://www.bom.gov.au/climate/mjo/).

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  • Fig. 1.

    Map showing the four regions centered on four tropical cites (Bangkok, Singapore, Jakarta, and Darwin). The blue stars mark the centers of the domains over their respective cites. The red dashed lines mark the cross sections for the Hovmöller diagrams in section 3. The points for Singapore and Darwin as well as an offshore location in the Darwin domain (red star) are discussed in detail in section 4.

  • Fig. 2.

    Near-surface wind fields from the BARRA reanalysis for the four domains averaged over (left) January and (right) July. Monsoons are strongest for Singapore, Jakarta, and Darwin in January and in July for Bangkok. The alternate months are relatively dry except for Singapore, which does not have a defined dry season. The rainfall for the domain city is given for that month (RR). The red dashed lines mark the cross sections for the Hovmöller diagrams in section 3.

  • Fig. 3.

    Plots of the monthly area-averaged maximum (red) and minimum (blue) temperature (solid), wet-bulb temperature (dotted), and dewpoint temperature (dashed) for the four domains being discussed, with the (left) land points and (left center) sea points averaged separately, along with (right center) the average monthly cloud cover diurnal maxima (solid) and minima (dashed) for land (red) and sea (blue) as well as (right) rainfall amounts for the cities at the center of the domain.

  • Fig. 4.

    Hovmöller diagrams for north–south sections through the center of the domains with the time axis from left to right. The data from 1990 to 2018 are composited for each month, and data from January are shown for days 6–8 of each month (in local time) for (left) T, (left center) TD, (center) TWB, (right center) u′, and (right) υ′, where u and υ are the zonal and meridional wind components. The heavy black contours in the u and υ panels marks the zero-velocity line. The green and blue lines on the leftmost panels respectively show the land topography and ocean. The land topography is scaled so that the highest point corresponds to 0.1 days on the horizontal scale.

  • Fig. 5.

    As in Fig. 4, but for July.

  • Fig. 6.

    (left) Diurnal composites of the T, TWB, and TD anomalies as a function of local time and month along with (right) the monthly mean values of a grid point over land close to Darwin city on the coast (12.475°S, 131.055°E). The mean maximum (red dotted), minimum (blue dotted), and mean Ts are shown.

  • Fig. 7.

    As in Fig. 6, but for a grid point ∼150 km offshore from Darwin (11.375°S, 129.845°E).

  • Fig. 8.

    As in Fig. 6, but for a land point near Singapore on the southern tip of the Malay Peninsula (1.385°N, 103.885°E).