ORIGINAL PAPER

Regional mean and variability characteristics of temperatureand precipitation over Thailand in 1961–2000 by a regionalclimate model and their evaluation

Kritanai Torsri & Mega Octaviani &Kasemsan Manomaiphiboon &

Sirintornthep Towprayoon

Received: 2 June 2012 /Accepted: 4 October 2012 /Published online: 1 November 2012# Springer-Verlag Wien 2012

Abstract This study presents the characterization of region-al means and variability of temperature and precipitation in1961–2000 for Thailand using regional climate modelRegCM3. Two fine-resolution (20 km) simulations forcedby ERA-40 reanalysis data were performed, with the defaultland covers and with a land-cover modification strategysuggested by a previous work. The strategy was shown tosubstantially alleviate the problem of systematic underesti-mation of temperature given by the default simulation, formost part of Thailand in both dry and wet seasons. Thedegree of bias in precipitation tends to vary differently inevery sub-region and season considered. The patterns ofseasonal variation of both climatic variables are acceptablyreproduced. Simulated 850-hPa winds have general agree-ment with those of ERA-40, but wind speed is overesti-mated over the Gulf of Thailand during the dry months,potentially bringing excessive moisture to and causing morerain than actual in the south. Long-term trends in tempera-ture are reasonably predicted by the model while those inobserved and simulated precipitations for upper Thailand

are in the opposite directions. Apart from the conventionalmethods used in characterization, spectral decompositionusing Kolmogorov–Zurbenko filters was applied to inspectthe model’s capability of accounting for variability (here, interms of variance) in both climatic variables on three temporalscales (short term, seasonal, and long term). The model wasfound to closely estimate the total variances in the originaltime series and fairly predict the relative variance contribu-tions on all temporal scales. The latter finding is in line withthe results from an additional spectral coherence analysis.Overall, the model was shown to be acceptably adequate foruse in support of further climate studies for Thailand, and itsevident strength is the capability of reproducing seasonalcharacteristics and, to a lesser degree, trends.

1 Introduction

It is commonly acknowledged that regional climate models(RCMs) are a technical tool to generate adequately detailedclimatic information over a limited area of interest, which isuseful to support impact assessments and planning activitiesrelated to climatic conditions and their changes at a regionallevel. Technically, they are used to downscale coarse-resolution meteorological data, i.e., global reanalysis dataor simulated output from global circulation models (GCMs),resulting in finer spatial (and temporal) meteorologicaldetails (Giorgi et al. 2001). Over the last years, regionalclimate studies have grown rapidly and covered variousparts of the world, and RCMs have shown to play aninstrumental role in various such studies for both past andfuture climates (e.g., Giorgi et al. 2001; Christensen et al.2007; Giorgi et al. 2012 and references therein). ForThailand, public awareness of climate change has increased

Electronic supplementary material The online version of this article(doi:10.1007/s00704-012-0782-z) contains supplementary material,which is available to authorized users.

K. Torsri :M. Octaviani :K. Manomaiphiboon (*) :S. TowprayoonThe Joint Graduate School of Energy and Environment (JGSEE),King Mongkut’s University of Technology Thonburi (KMUTT),126 Prachautit Rd., Bangmod, Tungkru,Bangkok 10140, Thailande-mail: kasemsan_m@jgsee.kmutt.ac.th

K. Torsri :M. Octaviani :K. Manomaiphiboon : S. TowprayoonCenter for Energy Technology and Environment,Ministry of Education,Bangkok, Thailand

Theor Appl Climatol (2013) 113:289–304DOI 10.1007/s00704-012-0782-z

steadily. Many public and private organizations and agen-cies have begun to look into this issue and support building,enhancing, and sharing knowledge, among various societalsectors, related to the country’s climate and its vulnerability(ONEP 2012). A need of more knowledge of past andpotential future climates is crucial to both governmentaland local authorities in support of their planning and devel-oping strategies for impact mitigation and human adapta-tion, which necessarily relies on RCMs as a scientificprediction tool.

Thailand is located in the tropics and its climate is pri-marily influenced by the two prevailing monsoons: south-west and northeast (TMD 2012). The former monsoon isresponsible for the wet season, lasting from mid-May tomid-October and bringing warm moist air from the IndianOcean and then rains for most of the country. The lattermonsoon causes the winter (or dry) season and lasts be-tween mid-October and mid-February, bringing dry cool airfrom China and South China Sea. The climate of Thailandvaries among sub-regions. Due to its long coastlines, thesouthern region tends to have maritime climate as opposedto continental climate in the other sub-regions. Climatevariability is also affected by, among other things, the El-Nino Southern Oscillation (ENSO) on the interannual timescale (Singhrattna et al. 2005; Limsakul and Goes 2008). InThailand, monitoring networks of surface stations has beenexpanded with time but stations with long-term recordingare limited (especially for temperature), posing some diffi-culties for long-term climate-related studies. A number ofstudies investigated how observed climatic elementschanged in Thailand from the mid last century, mostly forthe most fundamental two variables: temperature and pre-cipitation (e.g., Manton et al. 2001; Jutakorn et al. 2002;Singhrattna et al. 2005; Limsakul and Goes 2008; Takahashiand Yasunari 2008). Their analyses and findings generallyvary due to differences in amount of observation data incor-porated and their treatment and quality checking and climat-ic variables considered. Many reported warming trends ofdaily maximum and daily minimum temperature over thesecond half of the twentieth century in most parts of thecountry (Jutakorn et al. 2002; Limsakul and Goes 2008),while long-term trends of daily mean temperature were notas evident. For precipitation in the wet season, decreasingtrends were reported (Jutakorn et al. 2002; Singhrattna et al.2005; Takahashi and Yasunari 2008). Manton et al. (2001)also investigated certain extreme indices and reported de-creasing (increasing) trends in cool days and cold nights(hot days and warm nights) for Thailand and neighboringcountries.

The above motivations have become the essential driver ofregional climate modeling efforts recently initiated by theclimate-research community in Thailand. This study repre-sents one of such efforts, and it is a continuation of a previous

modeling work by Octaviani andManomaiphiboon (2011) (tobe referred to as OM11) who applied RegCM3 to Thailand.RegCM3 is the third version of the RCM of the Abdus SalamInternational Centre for Theoretical Physic (ICTP, Italy) (Palet al. 2007). It is a product of continued developments ofRegCM2 (Giorgi et al. 1993a, b), which is originally devel-oped in late 1980s (Giorgi and Bates 1989), and its latestupdate (version 4) (Giorgi et al. 2012) was released veryrecently. RegCM3 was here used since this work was plannedand designed in 2010 and implemented along with anotherRegCM3 study on potential climate change in Thailand to-wards the mid-twenty-first century under multiple future sce-narios. Both studies, as well as OM11, represent a small subsetof various climate-research studies intensively promoted andsupported by the Thai Government. RegCM3 has been widelyused worldwide (e.g., Gao et al. 2006, 2012; Im et al. 2006,2007; Halenka et al. 2006; Pal et al. 2007; Kueppers et al.2008; Song et al. 2008). However, a relatively small numberof studies using RegCM3 have been dedicated specifically forregions on the Indochinese Peninsula, e.g., Phan et al. (2009)and Ho et al. (2011) for Vietnam and OM11. In OM11, themodel performance on surface air temperature (shortly, tem-perature) and precipitation was assessed across all sub-regionsof Thailand but limited to only seasons in a short-term period(i.e., 1997–1999), through a series of fine-resolution (20 km)simulation experiments driven by reanalysis data. Moreover, astrategy to adjust or modify a land cover type designated in themodel to help alleviate the severe systematic underestimationof temperature was tried with some success.

One important aspect not covered by OM11 is the capa-bility of the model to reproduce interested climatic condi-tions of Thailand over a past but long-term period. Thisaspect is not only relevant in context of climate and climatechange but of practical importance in order to gain basicunderstanding of model biases and then a proper level ofconfidence and adequacy of the model and its results forproper use. Towards this, we considered a period of 1961–2000 (i.e., 40 years) and employed a similar modelingframework to that of OM11, with reanalysis data as perfectdriving fields. We specifically aim to assess regional andseasonal average characteristics of temperature and precip-itation simulated by RegCM3, and to assess the roles of theland cover modification strategy in improving simulatedtemperature. This long period also offers an opportunity toassess and compare trends in temperature and precipitation,inferred from observations and simulated results. It is ofinterest to employ both conventional and non-conventionalmethods in the evaluation of model performance. The non-conventional approach applied is spectral decomposition ondata time series, using some particular filters and thenallowing relative variability (specifically, variance) contri-butions by fluctuations on different time scales to be esti-mated and compared.

290 K. Torsri et al.

2 Methods and data

2.1 Model description and setup

RegCM3 is a hydrostatic, compressible and primitive-equation model with terrain-following sigma verticalcoordinates (Pal et al. 2007). The model dynamics isthe same as that of the hydrostatic version of theNCAR-Penn State University Mesoscale Model version5 (Grell et al. 1994). The radiation scheme included inthe model is the Community Climate Model version 3(Kiehl et al. 1996), the nonlocal planetary boundarylayer scheme of Holtslag et al. (1990) and theresolvable-scale precipitation parameterization of theSUBgrid EXplicit moisture scheme (Pal et al. 2000).For unresolved-scale (convective) precipitation process-es, three parameterization schemes are available: (1)Anthes–Kuo scheme (Anthes 1977), (2) Grell scheme(Grell 1993) with either the Arakawa and Schubert(1974) or the Fritsch and Chappell (1980) closureassumptions, and (3) MIT–Emanuel scheme (Emanuel1991). Land surface fluxes of energy, moisture, andmomentum are represented using the Biosphere-Atmosphere Transfer Scheme (BATS) version 1e(BATS1e; Dickinson et al. 1993). Two ocean surfaceflux schemes are available: the BATS scheme (Dickinson etal. 1993) or Zeng et al.’s (1998) scheme (shortly, the Zengscheme).

The model setup in this study mostly follows that inOM11, which is summarized as follows: two domainswere specified to support simulations: mother domain(D1) with a 60-km horizontal resolution and nesteddomain (D2) with a 20-km resolution (Fig. 1a, b). D1covers the entire Indochinese Peninsula, parts of SouthChina and South Asia. D2 covers Thailand, Laos,Cambodia, parts of Vietnam and Myanmar. Each do-main has 100×100 grid points with 23 vertical sigmalevels with the top pressure at 100 hPa. Note thatOM11 used 18 levels. For D1 simulations, meteorolog-ical fields from the European Centre for Medium RangeWeather Forecasts (ECMWF)’s reanalysis data (here,ERA-40) (Uppala et al. 2005) were used to provideinitial and lateral boundary conditions. The ERA-40data have a 2.5° horizontal resolution, 6-hourly inter-vals, and 23 pressure levels with the top pressure at10 hPa. Sea–surface boundary conditions are monthlyaverage 1° resolution Global Ice coverage and SeaSurface Temperature of the UK Meteorological Office(Rayner et al. 2006). Output from D1 simulations wasused as the driving fields for D2 simulations throughone-way double nesting with an exponential relaxationmethod (using a 12 grid-point wide buffer zone). Landuse/land cover data with a 10 min (2 min) resolution for

D1 (D2) were derived from the US Geological Survey(USGS) Global Land Cover Characterization database(Loveland et al. 2000) with BATS classification. TheUSGS topographic dataset, Global Topographic 30-arc-second data (GTOPO30) (USGS 1996) was used tospecify terrain elevations.

In our RegCM3 modeling, the Grell convective schemewith Arakawa–Schubert closure and the BATS ocean-fluxscheme were used. From OM11, no single combination ofconvective and ocean-flux schemes available in the modelwas found to be conclusively superior among the others.Importantly, every combination systematically underesti-mated temperature across Thailand, especially over upperThailand in the dry season. They further investigated theissue and noticed “irrigated cropland” being a most domi-nant land cover type in upper Thailand (see Fig. S1 in theElectronic supplementary material (ESM)). It is known thatBATS1e (RegCM3’s land surface scheme) supplements soilmoisture to irrigated cropland by forcing the soil moisture tofield capacity at all time steps, which tends to produce largeincreases in latent heat flux and in turn reduce simulatednear-surface temperature in the dry season (Kueppers et al.2008). OM11 attempted re-designating irrigated-croplandgrid points to “crop/mixed farming” (as a nearby alternative)and ran a short-term simulation test but only on the coarserdomain, finding substantial improvement on temperatureresults for most parts of Thailand without pronouncedeffects on precipitation. Accordingly, two long-term simu-lations were designed and performed in the current study:with the default land covers (RCM-D) and with the landcover modification (RCM-M). Each spans 1960–2000, withthe first year (i.e., 1960) being a model spin-up.

2.2 Observation data and model evaluation

In evaluating and discussing the model performance, simu-lated results from D2 were primarily considered unlessindicated otherwise. Regional and seasonal statistics wereused according to the four sub-regions (central–eastern,northeastern, northern, and southern) and the 3-month sea-sons of a year (December–January–February (DJF), March–April–May (MAM), June–July–August (JJA), andSeptember–August–November (SON)). Note that the firstthree sub-regions may be aggregately called upper Thailand.The periods of DJF and MAM have relatively low precipi-tation for most of Thailand (except for the southern sub-region) and here refer to the dry season and the other periodsrefer to the wet season. Daily mean temperature and dailyprecipitation data from surface monitoring stations of theThai Meteorological Department (TMD) were obtained tosupport comparison with model predictions. After generalquality checking and the standard normality homogeneitytest (Alexandersson 1986), data of 43 and 58 stations were

Regional mean and variability characteristics of temperature and precipitation over Thailand 291

finally used for temperature and precipitation, respectively(Fig. 1c, d). Each station contains at least 70 % as non-missing values over 1961–2000. Gridded data of tempera-ture and precipitation, developed by the University ofDelaware (UDEL) (Matsuura and Willmott 2009), were alsoused (here, version 2.01) as supplement to ensure consis-tency. The UDEL datasets are monthly means with a 0.5°grid resolution, have a global coverage only for lands, andcover the years 1900–2008.

In the evaluation of model predictability, regional andseasonal mean biases (MBs), visual plots, and trend

estimation by linear square fitting were used. MB is definedas follows:

MB ¼ 1

N

1

M

XN

j¼1

XM

i¼1

Pij � Oij

� �; ð1Þ

where Oij and Pij are the observed and predicted (or simu-lated) values on day i at station j, M is the total number ofdays in a particular period (e.g., a season), and N is the totalnumber of stations within a particular area (e.g., a sub-region). As in OM11, simulated results were bi-linearly

(a) (b)

(c) (d)

Fig. 1 Modeling domains and TMD stations used in the study: a D1, b D2, c temperature stations, and d precipitation stations

292 K. Torsri et al.

interpolated from neighboring grid points to station loca-tions when compared with the TMD data. For the UDELdata, simulated results were regridded onto the defaultUDEL grid before comparison. In addition to temperatureand precipitation, seasonal average upper wind fields simu-lated by the model were also compared with those derivedfrom the ERA-40 data, as in Alves and Marengo (2009),Phan et al. (2009), and OM11. Choices of isobaric level inplotting upper winds for climate analysis, often seen in theliterature, are 850, 500, and 200 hPa, for instance. In OM11,850- and 200-hPa winds were considered. Here, the formerlevel was representatively chosen for conciseness. It is gen-erally about or near a top height of the atmospheric bound-ary layer, and winds at this level are thus not muchinfluenced by surface friction and are possible for use tosuggest synoptic transport pathways of scalar quantities(e.g., moisture).

Besides the conventional evaluation above, it is of furtherinterest to extend the evaluation to variability, i.e., to assessthe capability of the model to reproduce the variability oftemperature and precipitation. Climate variability is a broadterm and generally has different meanings from one contextto another. However, they tend to reflect how a quantityvaries or fluctuates over time and/or space either explicitlyor implicitly. Here, variance was used as a quantitativeaggregate measure for variability in data. For a typicalclimatic variable, its time series comprise superimposedoscillations over different time scales (e.g., diurnal, monthly,seasonally, annual, and inter-annual). Those time scales offluctuation are induced by different physical processes orforcings, e.g., local/meso-scale processes, day–night radia-tive contrast, synoptic features, monsoons, and inter-annualoscillations. In the current context of regional climate mod-eling, one may desire to compare the total variances ofobserved and simulated values and see how close they are.However, more meaningful results may instead be gained bylooking at how the observed and simulated variances onindividual time scales compares, which in turn reflects theability of the model and its physics parameterizations toreproduce or account for variability due to processes asso-ciated with individual time scales. Technically, to do sorequires spectral decomposition. Eskridge et al. (1997)reviewed a number of technical methods of separating me-teorological variables into different temporal componentsand, based on performance and accuracy, suggestedKolmogorov–Zurbenko (KZ) filtering (Zurbenko 1986) asa practical method. The KZ method has been applied inmany fields, e.g., climate, air quality, and image processing(Yang and Zurbenko 2010; references therein). Besides be-ing simple and easy to use, two other strengths of the KZmethod are its applicability to data with missing values andits relatively accurate (or clean) filtering capability. Hogrefeet al. (2004) applied the method to assessing the variability

(i.e., variance) of temperature observed and simulated by amesoscale model driven by a GCM output.

Here, the KZ method was chosen for use to decomposedaily mean temperature and daily precipitation data (bothobserved and simulated). A concise description of the KZdecomposition is given as follows. A KZ filter, denoted byKZ(m, p), is defined as an iteration of a moving averagewith m (odd integer, ≥3) being the length of the movingaverage window and p (integer, ≥1) being the number ofpasses or iterations. The moving average at time (or step) t,denoted by y(t), is computed by

yðtÞ ¼ 1

mþ 1

Xm=2

i¼�m 2=

x t þ ið Þ; ð2Þ

with KZ(m, p), y(t) from the first pass becomes the input forthe second pass, and so on, until the pth pass. To filter fluctu-ation cycles of less than N days, the criterion m×p1/2≤N isapplied. Here, the KZ decomposition follows Eskridge et al.(1997). That is, an original time series, x(t), is decomposedinto three components:

xðtÞ ¼ wðtÞ þ sðtÞ þ eðtÞ; ð3Þwhere w(t), s(t), and e(t) correspond to the short-term, season-al, and long-term components, respectively. KZ(15, 5) wasused to filter out cycles of <33 days, thus removing high-frequency, meso- and synoptic-scale variations from the origi-nal time series, i.e., KZ(15, 5) on x(t) gives s(t) + e(t). KZ(365,3) was used to filter out cycles of <632 days (1.7 years), i.e., KZ(365, 3) on x(t) gives e(t). Thus, it is possible to say that e(t)approximately represents inter-annual fluctuations such asENSO. By simple algebra, w(t) and s(t) are readily obtained.Accordingly, s(t) represents fluctuations due to cycles between33 days and 1.7 years, within which seasonal and most intra-annual variations fall. The degree of separation of the threedifferent scales using the KZ method can be checked by com-paring between the variance of x, or Var(x), and the sum of thevariances of the three components, i.e., the closer, the betterseparation.

3 Results and discussion

3.1 Regional and seasonal means

The performance of RCM-D and RCM-M in terms of MB on40-year average temperature and precipitation in comparisonwith the TMD data, by sub-region and by season, is summa-rized in Table 1 and Fig. 2. It is evident that both simulationsshow systematic underestimation for all sub-regions and in allseasons. An exception is a small warm bias (0.2 °C) by RCM-M for the northeast, in MAM. The degree of bias is relativelylarge in RCM-D (e.g., MB0−4.2 to −1.9 °C for annual period

Regional mean and variability characteristics of temperature and precipitation over Thailand 293

or 12 months, shortly annual) and becomes satisfactorilysmaller in RCM-M (e.g., MB0−2.1 to −0.9 °C for annual)for all sub-regions and in all seasons. In addition, the bias isgenerally more intensified in upper Thailand (i.e., the central-eastern, northeastern, and northern sub-regions combined)than in the south, in case of RCM-D. The land-use modifica-tion strategy (i.e., RCM-M) effectively reduces the bias, espe-cially in upper Thailand (i.e., in absolute terms, 1.8–2.7 °C forannual and 0.3–3.8 °C over seasons), with a lesser degree inthe south (i.e., 0.2 °C for annual and 0.1–0.3 °C over seasons).By examining the dry season (DJF and MAM), RCM-Dperforms somewhat poorly in DJF (>4.0 °C as MB magni-tude) and MAM (>3.7 °C) over upper Thailand. With RCM-M, the results become improved over upper Thailand by asmuch as ∼4 °C in DJF and 2–4 °C in MAM, but less pro-nounced in the south (<0.3 °C). In the wet season (JJA andSON), the improvement by RCM-M is small-to-moderate (0.3–1.6 °C over upper Thailand and <0.3 °C over the south). Theseresults are generally in line with those by OM11 (based on theshort-term simulations only). Furthermore, the model predic-tions were compared against the UDEL data (Fig. 3). It wasfound that the performance varies across sub-regions, and thesystematic underestimation is still seen over Thailand. Similarto the TMD data, RCM-M produces smaller magnitudes, asevidently seen in most of upper Thailand. For the south, theresults also show some improvement but to a lesser extent. Forseasonal variations (i.e., annual cycles) of temperature (Fig. 2),the RCM-M predictions and both TMD and UDEL data agreewell with each other in that their values tend to follow thetypical patterns of temperature in each region and season. Forexample, DJF and MAM are the seasons with the lowest andhighest temperatures, respectively, for upper Thailand while themore uniform pattern is typical of the maritime climate of thesouth. The UDEL temperature is quite close to the TMDtemperature in the northeast but tends to be about 1–2 °C lower

than the TMD for the other sub-regions in all seasons. It isnoted that, in Fig. 2, each value (of a sub-region and a season)was computed into two steps: first, a regional daily value iscalculated as the pooled average of daily values (in a season)over stations/grid cells in a sub-region, and all regional dailyvalues are then averaged (given passing the 70/30 rule, i.e., atleast 70 % of data being available or non-missing). Regardingto the improved temperature results by RCM-M, a furtherexamination of the probability density functions (PDFs) oftemperature (Fig. 4a, c) shows a positive shift of density (i.e.,more positively skewed) in RCM-M for both upper Thailandand the south (more evidently for the former), as opposed to thePDFs of RCM-D and the TMD data being closer to normality.A closer look to the PDFs by season indicates that the shift isprimarily induced during the dry season, as seen in Fig. 4d, eshowing those in DJF (as dry months) and JJA (as wet months)for example. The shapes of all PDFs in upper Thailand arebroader with lower peaks than those in the south. The lesserimpact of RCM-M in the south is due likely to a small percent-age (21 %) of total land in the sub-region modified with thealternative land cover (as opposed to 60 % in upper Thailand).

For precipitation, the degrees and patterns of bias aregeographically and seasonally dependent, as opposed to tem-perature for which cold bias was systematically found(Table 1). Annual precipitation is overestimated (i.e., wet-biased) for the central–east (MB00.8–1.4 mm/day) andunderestimated (i.e., dry-biased) for the northeast and thenorth (MB0−1.9 to −0.2 mm/day), by both RCM-D andRCM-M. For seasonal precipitation, MB in DJF is generallysmall in terms of magnitude for most sub-regions (<0.2 mm/day) over upper Thailand, which is expected due to minimalprecipitation observed in this dry months (Fig. 2). In JJA,relatively large differences is seen in upper Thailand, particu-larly, in the central–east and northeast (MB magnitude03.5–4.9 mm/day). For the south, both RCM-D and RCM-M gives

Table 1 Mean biases of simulated daily mean temperature (°C) and daily precipitation (mm/day) relative to TMD data

Variable Period Central–East Northeast North South

RCM-D RCM-M RCM-D RCM-M RCM-D RCM-M RCM-D RCM-M

Temperature Annual −3.44 −1.67 −3.53 −0.87 −4.24 −2.05 −1.93 −1.72

DJF −4.01 −0.52 −4.08 −0.28 −4.74 −1.24 −2.01 −1.89

MAM −3.77 −1.35 −3.92 0.20 −4.60 −0.98 −2.46 −2.15

JJA −3.05 −2.76 −3.03 −1.85 −3.91 −3.34 −1.59 −1.32

SON −2.93 −2.02 −3.12 −1.56 −3.72 −2.62 −1.65 −1.53

Precipitation Annual 1.37 0.83 −1.80 −1.87 −0.20 −0.34 1.08 −0.37

DJF 0.12 −0.02 −0.04 −0.16 −0.08 −0.15 3.87 2.40

MAM 0.84 0.30 −1.92 −1.91 −0.80 −0.96 2.43 0.06

JJA 4.90 4.21 −3.47 −3.42 1.89 1.65 −0.86 −1.49

SON −0.43 −1.21 −1.75 −1.98 −1.82 −1.91 −1.09 −2.42

DJF December–January–February, MAM March–April–May, JJA June–July–August, SON September–August–November

294 K. Torsri et al.

overestimates in DJF and MAM (MB00.1–3.9 mm/day) andunderestimates in JJA and SON (MB0−2.4 to −0.9 mm/day).The impact of RCM-M cannot be clearly deduced for upperThailand (varying among seasons with <1 mm/day as differ-ences between MB values in RCM-D and RCM-M) but itbecomes more evident for the south towards less precipitation(e.g., by 2.4 mm/day in MAM and by 1.3 mm/day in SON).Accordingly, it is possible to say that no indication for RCM-M to induce a consistent and drastic change in performance onprecipitation from RCM-D was found, i.e., the land covermodification strategy applied here does not appear to stronglyaffect the model performance on precipitation. This is also

confirmed with the comparison with the UDEL data (Fig. 3).Similar MB patterns are shown in RCM-D and RCM-M, withthe former having larger magnitudes in most of the south andover some coastal areas in the central–east. For seasonalvariations (Fig. 2), both TMD and UDEL datasets generallyagree with each other, and RCM-M yields an acceptable fit forboth the north and the south but a relatively poor (here, under-estimated) fit for the northeast. It also well depicts the timingsof precipitation high and low for all sub-regions (e.g., those ofupper Thailand: high in JJA and low in DJF, in the south: highin SON and low in DJF). For the PDFs of observed andsimulated precipitations (Fig. 4b, d), all have single peaks

(a) Central-east, temperature (b) Central-east, precipitation

(c) Northeast, temperature (d) Northeast, precipitation

(e) North, temperature (f) North, precipitation

(g) South, temperature (h) South, precipitation

Fig. 2 Seasonal 40-year average temperature (°C) and precipitation (mm/day) for the central–east, northeast, north, and south

Regional mean and variability characteristics of temperature and precipitation over Thailand 295

and positively long (or heavy) tails, with lower peaks andheavier tails seen in the south. Those in upper Thailand lookcomparable. For the south, each of RCM-D and RCM-M PDFshows a positive modal shift with overestimation (of densityor occurrence frequency) over a light precipitation range of 1–2 to about 10 mm/day. In case of RCM-M, slight underesti-mation is seen over the long tail, and these overestimation andunderestimation appear to offset their biases, causing a lowtotal annual bias (−0.4 mm/day, see Table 1).

It is well acknowledged that model biases can be attrib-uted to various sources, e.g., smoothed terrains due to

domain gridding, intrinsic errors in input data for initialand boundary conditions, and, notably, imperfection ofphysical parameterizations. For Thailand, complex terrainsof mountains and valleys are present in the northern sub-region, and terrain smoothing may affect orographicallyinduced precipitation and mountain-related meteorology. Inthe study, the horizontal grid resolution of 20 km wasemployed (which is relatively fine in the regional climatemodeling context), and no substantial or severe biases insimulated precipitation were found for the sub-region (ab-solute MB <2 mm/day by the simulations over all seasons)

(a)Temperature (UDEL) MB (RCM-D) MB (RCM-M)

(b)Precipitation (UDEL) MB (RCM-D) MB (RCM-M)

Fig. 3 Forty-year average temperature (°C) and precipitation (mm/day) and mean biases of RCM-D and RCM-M with respect to UDEL data

296 K. Torsri et al.

(Table 1; Fig. 2f). Representation of 40-year average 850-hPa wind fields simulated by the model was also inspectedto see if substantial errors are present in the winds.Comparison of the wind fields from the ERA-40 data andthe RCM-M results (from D1) shows good similarity(Fig. 5, only those of DJF and JJA representatively shownas the respective peak dry and peak wet months formost part of Thailand). In DJF, both datasets exhibitwesterly winds over the Tibetan Plateau, and northwest-erly winds over the East China Sea, turning to north-easterly winds over the South China Sea and movingacross the Indochinese Peninsula. The northeasterlies

bring cold dry air to most parts of Thailand. A lowerportion of the northeasterlies moves across the Gulf ofThailand, bringing rains to the southern sub-regions, asshown in Fig. 5a. The northeasterlies by RCM-M ap-pear to be large (in terms of magnitude) relative tothose by ERA-40 over the Gulf of Thailand (as markedby an east-west aligned dark gray band covering theGulf and the entire southern sub-region). This couldcause more moisture fluxes into the south and partlyattributes larger precipitation than actual. In JJA, atypical feature of southwest monsoon are well capturedby both datasets, showing westerlies and southwesteries

(a) Upper Thailand, temperature (b) Upper Thailand, precipitation

(c) Southern sub-region, temperature (d) Southern sub-region, precipitation

(d) Same as (a), but only in DJF (e) Same as (a), but only in JJA

Fig. 4 Probability density functions of daily mean temperature (°C) and daily precipitation (mm/day) for upper Thailand (central–eastern,northeastern, and northern sub-regions combined) and southern sub-region over 1961–2000

Regional mean and variability characteristics of temperature and precipitation over Thailand 297

from the Indian Ocean, bringing moisture and thenprecipitation to most part of Thailand. For the central–eastduring JJA where the largest bias magnitude is seen (MB04.2 mm/day in RCM-M), the simulated winds appear to flowover the sub-region acceptably direction-wise and magnitude-wise, as compared with those by ERA-40, and the bias is thusnot due primarily to the wind fields but more likely to theconvective parameterization.

3.2 Trends and variability

The trends in temperature and precipitation were estimatedusing linear square fit for upper Thailand and the southernsub-region, as shown in Fig. 6. Each trend was derived froma series of annual values that were averaged from daily data(in case of the TMD data and the model predictions) ormonthly data (in case of the UDEL data), pooled from all

(a) ERA-40, DJF (b) RCM-M, DJF

(c) ERA-40, JJA (d) RCM-M, JJA

Fig. 5 Seasonal (DJF and JJA) 40-year average 850-hPa wind fields given by ERA-40 and RCM-M (D1)

298 K. Torsri et al.

stations or grid cells within a sub-region. In the following,only the RCM-M predictions are presented due to being ofimproved quality. The trend estimation was here limited to31 years (1970–2000) because a large amount of missingvalues exists in the TMD data for the pre-1970 period inmany stations and fails the 70/30 rule (i.e., <70 % of days ina year being non-missing to produce an annual value). Fortemperature, all estimated trends were found to be positive(i.e., increasing). Those given by TMD (0.17 °C/decade forupper Thailand and 0.23 °C/decade for the south) are(statistically) significant at a 5 % level from both t-test andMann–Kendall trend test. An exception is upper Thailandwhere the trend is only significant from the t-test. It isnoticed that trends given by TMD are higher than those byboth UDEL and RCM-M. However, the RCM-M trend forthe south (0.22 °C/decade) is significant and very close tothat by TMD. It is noted that these findings are in contrastwith Limsakul and Goes (2008) and Jutakorn et al. (2002)who reported negative (i.e., decreasing) trends of (dailymean) temperature in entire Thailand and upper Thailand,respectively. The contrast may be attributed partly to theirlonger periods of study (starting in 1951 for both studies)and different numbers of surface stations. For precipitationin upper Thailand, negative trends were observed in the

TMD data (−0.04 mm/day/decade) and the UDEL data(−0.23 mm/day/decade and significant) whereas RCM-Mis unable to capture these, oppositely yielding a significantpositive trend of 0.36 mm/day/decade. The trends for thesouth are in agreement in that all are small in magnitude andnot significant. As part of quality assurance, it is also advis-able to further examine temperature trends using the ERA-40 data since the data were used as the driving fields for thesimulations. Here, the ERA-40’s 2-m temperature field wasacquired through downloading from the ECMWF website(http://www.ecmwf.int) and regridded over upper Thailandand the maritime South sub-region, and their respectivetrends over 1970–2000 were estimated. It was found (detailsnot shown) that all trends given by the TMD, UDEL, andERA-40 data commonly share the same direction (i.e., pos-itive) and that those by the TMD data and the ERA-40 dataare relatively close in magnitude. Thus, for our Thailandapplication, it is not likely for the ERA-40 to produceserious or significant biases upon the trends derived fromthe simulated results.

The KZ method described in Section 2.2 was employedto assess the model’s ability to capture variability of tem-perature and precipitation on the three different time scales,i.e., short-term (ST), seasonal (SN), and long-term (LT). An

(a) Upper Thailand, temperature (b) Upper Thailand, precipitation

(c) Southern sub-region, temperature (d) Southern sub-region, precipitation

Fig. 6 Trends in annual temperature and precipitation. Parenthesizednumbers are trend values (i.e., linear square fit slopes), estimated over1970–2000. The units are °C/decade for temperature and mm/day/

decade for precipitation. Asterisks denote statistical significance at a5 % level by both the t-test and the Mann–Kendall trend test, anddouble asterisks denote statistical significance only by the t-test

Regional mean and variability characteristics of temperature and precipitation over Thailand 299

example of the time series of these components, obtainedfrom the KZ decomposition on observed TMD and simulat-ed RCM-M temperatures for upper Thailand, is given inFig. 7 (also see Fig. S2 in the ESM for additional plots). Asseen, the degree of separation into the three temporal com-ponents is somewhat high, i.e., close to total variance inevery time series (here, >89 %). In terms of total variance,RCM-M predicts well within 15 % of those in the observationsfor both variables in both upper Thailand and the south (see theparenthesized values in Fig. 8). Apart from total variances,comparing relative variance contributions (shortly, contribu-tions) from different temporal components helps indicate themodel’s ability to capture fluctuations (specifically, fluctuationenergy) due to physical processes associated with those com-ponents. For temperature, the model correctly gives the overallpattern of variance contributions, i.e., largest by SN (about 60–70 %) and followed next by ST and LT (Fig. 8). The modelcaptures well each individual contribution for upper Thailand.However, for the south, ST contribution is underestimated (by14 %) and SN contribution is in turn overestimated (by 13 %).For precipitation, in upper Thailand, the model under-predictsST contribution by 19 % and in turn over-predicts SN contri-bution by 15 % while better agreement between the observa-tions and the predictions in the south was found.

Alternative to the KZ approach, a spectral coherenceanalysis can be performed, in which a squared coherency(shortly, coherency) spectrum is constructed and then usedto examine the degree that a pair of time series are correlatedin terms of amplitudes of fluctuation (i.e., fluctuation

energy) as a function of frequency (von Storch and Zwiers1999, see Sections 11.4 and 12.5 therein). The spectral valueranges between 0 and 1 (the larger value, the higher corre-lation). The computation of a coherency spectrum is not asstraightforward, subject to choices of spectral estimator andnoise/bias handling. Here, the coherency spectra of theobservations and the model predictions were computed forthe same pairs of time series as in the KZ method, usingopen-source statistical analysis R package (R core develop-ment team 2011). In the spectral calculation, de-trending,zero padding, tapering, and a standard Daniel spectral esti-mator were applied. The resulting spectra are displayed inFig. 9. As seen, all spectra share a similar pattern in thatcoherency is high on the high end of frequencies and low onthe low end. In all spectra, coherency is elevated (>0.3) overfrequencies of <0.006 1/day (>5- or 6-month periods), i.e.,fluctuation energy better captured in the SN and LT scales.For precipitation in upper Thailand and temperature in thesouth, very low coherency (i.e., close to or below the zero-coherency threshold) is mostly present over frequencies of>0.033 1/day (equivalent to <1-month periods or the STscale). Over intermediate frequencies (0.006–0.1 1/day),coherency for precipitation in the south appears to improve,and, to a much larger degree, for temperature in upperThailand. In general, the coherency spectra shown are inagreement with the KZ results.

It is noted that the above-discussed results from the KZdecomposition (as well as the coherency spectra) indicatethe acceptable-to-good capability of the model in capturing

(a) Raw

(c) Seasonal (d) Long-term

(b) Short-term

Fig. 7 An example of KZ decomposition for daily mean temperature (°C) of TMD data and RCM-M results for upper Thailand. In (b), the TMDshort-term component is omitted to avoid line overcrowding

300 K. Torsri et al.

variances on the SN and LT scales. In addition, to assess thedegree of variance in the observations explained by themodel, the Pearson’s correlation coefficients of all pairs ofthe same time series were then calculated. In statistics,squared correlation equals to the fraction of explained var-iance. It was found (Figs. S2 and S3 in the ESM) thatcorrelation is moderate to strong (0.5–0.9) on the SN andLT scales for both sub-regions and both variables. An ex-ception is negative weak correlation (−0.2) of the LT pre-cipitation component in upper Thailand, consistent with thelong-term trend oppositely predicted by the model (Fig. 6b).

4 Conclusions

The main regional features of mean and variability of tem-perature and precipitation over Thailand in the past 40 yearsof 1961–2000 were studied by means of the fine-resolutionRegCM3 modeling driven by the ERA-40 reanalysis data.The one-way double nesting downscaled the reanalysis datato the 60-km resolution and then to 20-km resolutions. Thestudy ran two long-term simulations, with the default landcovers (RCM-D) and with a land cover modification strate-gy (RCM-M), and their results were compared with the

(a) Temperature

(b) Precipitation

Fig. 8 Relative variancecontributions (in percent) ofthree temporal componentsafter KZ decomposition to totalvariances in daily TMD dataand RCM-M results. Parenthe-sized numbers are total varian-ces in individual time series andhave the units of (°C)2 for tem-perature and (mm/day)2 forprecipitation

Regional mean and variability characteristics of temperature and precipitation over Thailand 301

observation TMD and the gridded UDEL data. A number ofconventional and non-conventional measures were used incharacterizing the features and in the model evaluation.Below is a summary of key findings from the study:

(1) For temperature, RCM-D yields serious systematicunderestimation (e.g., as much as about 5 °C in termsof bias magnitude), particularly, in upper Thailand.The land cover modification strategy by RCM-M suc-cessfully alleviates the problem by increasing temper-ature to a more acceptable range of bias (∼0–2 °C asmagnitude) for almost all seasons and sub-regions.Overall bias and PDF examinations indicate that thestrategy tends to strongly affect dry-season temperaturein upper Thailand but its impact on the south is not asstrong due possibly to relatively limited areas re-designated with the substitute land cover and its un-derlying maritime climate. The model captures thetypical seasonal variation of temperature in everysub-region.

(2) For precipitation, biases are geographically and sea-sonally dependent. In terms of annual amount, bothsimulations give overestimates in the central-east andunderestimates in the north and northeast. The strategy(i.e., RCM-M) yields less precipitation in the south butdoes not play an influential role in upper Thailand. Theseasonal variation of precipitation in each sub-region isalso captured.

(3) Seasonal 850-hPa wind fields (here, in DJF and JJA)are acceptably reproduced but the strength of north-easterly winds over the Gulf of Thailand in DJF areoverestimated, potentially bringing more moisture toland and then precipitation overestimation over thesouth in this season.

(4) The increasing trends in observed annual temperatureare fairly reproduced by RCM-M. The precipitationtrends estimated from the observations and the modelpredictions are similar (small and not statistically sig-nificant) for the south but are in opposite directions forupper Thailand.

(a) Upper Thailand, temperature (b) Upper Thailand, precipitation

(c) Southern sub-region, temperature (d) Southern sub-region, precipitation

Fig. 9 Squared coherency spectra (unitless) of paired daily TMD data and daily RCM-M results for upper Thailand and southern sub-region,derived using 1970–2000 data. Dashed lines mark zero squared coherency at a 95 % confidence level

302 K. Torsri et al.

(5) RCM-M closely estimates total variances in the origi-nal time series of both variables. Furthermore, it fairlypredicts the relative variance contributions on the tem-poral scales decomposed using the KZ method. TheKZ results are also in general agreement with theresults from the spectral coherence analysis.

In concluding remark, the long-term RegCM3 simulationstudy presented here is devoted to Thailand in particular.The performance of the model associated with daily meantemperature and daily precipitation was evaluated. Themodel was shown to be technically adequate for use, thoughnot perfect, in support of further climate studies for thecountry. The capability of reproducing seasonal character-istics and, to a lesser degree, trends was found to be themodel’s strength. These characteristics are of fundamentalinterest in context of climate change. Lastly, it is fair to saythat there are many other aspects related to regional climateof Thailand not included here (e.g., extreme climatic con-ditions), then open to future possibilities for investigation.

Acknowledgments The authors sincerely thank the Thai Meteoro-logical Department for the observation data, the Abdus Salam ICTP forthe availability of RegCM3 and its user support, and the ECMWF forthe ERA-40 reanalysis data. We thank Dr. Gao Xuejie (China Meteo-rological Administration), Dr. Robert H. B. Exell (JGSEE), Dr. Atsa-mol Limsakul (Department of Environmental Quality Promotion), andDr. Jerasorn Santisirisomboon (Ramkhamhaeng University) for theiruseful comments given to the study. We also thank members at theJGSEE Computational Laboratory (Bang Khun Tien Campus) for theirgeneral assistance. This work was supported by the Joint GraduateSchool of Energy and Environment, the Postgraduate Education andResearch Development Office (under grant No. JGSEE/PROJECT/002-2011), the Thailand Research Fund (under grants No.RDG5030034 and RDG5050016), and the Asahi Glass Foundation.

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