sensitivity of seasonal climate and diurnal precipitation

CLIMATE RESEARCHClim Res

Vol. 52: 31–48, 2012doi: 10.3354/cr01049

Published March 22

1. INTRODUCTION

The climate of Central America is characterized bycomplex spatial and temporal features. Rainfall overCentral America comes in synoptic events ranging inintensity from easterly waves to hurricanes, butmuch of it is associated with moisture surges from thePacific or Caribbean that impinge upon its topo -graphy (Hastenrath 1967, Waylen et al. 1996, Peña &Douglas 2002). Moreover, mesoscale circulation fea-tures such as the Caribbean Low-Level Jet (CLLJ)also affect rainfall (Wang 2007, Wang & Lee 2007,Muñoz et al. 2008, Cook & Vizy 2010). On the west-

ern side of the Central American isthmus, the easternPacific inter-tropical convergence zone (ITCZ) bringsprecipitation as it migrates northward during thenorthern summer season (Hastenrath 1967). A partic-ularly interesting feature of Central America’s precip-itation patterns is that parts of the region display abimodal precipitation annual cycle, with maxima oc -curring in June and in either September or October,separated by a lull in July and August, with tropicalcyclone activity in the Caribbean following a similarpattern (Wang & Lee 2007). This period of lower rain-fall is called the mid-summer drought (MSD) (Mag-aña et al. 1999).

© Inter-Research 2012 · www.int-res.com*Email: [email protected]

Sensitivity of seasonal climate and diurnal precipitation over Central America to land and

sea surface schemes in RegCM4

G. T. Diro1,*, S. A. Rauscher2, F. Giorgi1, A. M. Tompkins1

1Earth System Physics Section, The Abdus salam ICTP, 34151 Trieste, Italy2Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

ABSTRACT: Multi-annual simulations over the Central America CORDEX domain are conductedwith the latest version of regional climate model RegCM4 driven by ERA-Interim reanalysis fields.The RegCM4 system can reproduce both the annual cycle and the spatial patterns of mean sum-mer precipitation over Central America and Mexico. Regional circulation features are also repro-duced, although the intensity of the Caribbean Low-Level Jet is underestimated and it is locatedtoo far south. Over most land areas, RegCM4 surface air temperatures are lower than observationsby 1 to 3°C, which however may also be related to biases in the reanalysis forcing data. The modelcan realistically simulate the amplitude of the convective diurnal cycle in areas where the convec-tive triggering is dominated by non-local gravity wave effects. However, the simulation of thephase of the diurnal cycle of convection is less satisfactory, with the peak precipitation occurringearlier than observed, a common fault in atmospheric models. Sensitivity experiments are carriedout to investigate the model sensitivity to land surface and a prognostic diurnal sea surface tem-perature scheme. Use of the Community Land Model (CLM) instead of the Bio sphere-AtmosphereTransfer Scheme (BATS) results in a warmer and drier land surface and a better simulation of theseasonal average spatial pattern of precipitation. However, with BATS, RegCM4 has a more real-istic simulation of the mid-summer drought over the region. The impact of the prognostic sea sur-face temperature (SST) scheme is generally small. In general, neither of these surface physicsupgrades results in a clearly superior model performance.

KEY WORDS: RegCM4 · CORDEX · Central America · Diurnal precipitation · Climate · DiurnalSST scheme · CLM

Resale or republication not permitted without written consent of the publisher

Contribution to CR Special 29 ‘The regional climate model RegCM4’

Clim Res 52: 31–48, 201232

Relatively few regional climate modeling studieshave been performed over domains that include Central America, and most have limited their ana -lyses to aspects of the seasonal mean climate. For in-stance, Her nandez et al. (2006) tested MM5 over asmall Cen tral American domain for a single year(1997). They showed large positive biases in precipi-tation compared to observations, particularly over theCarib bean Sea, although stations over land showedbetter agreement; MM5 simulated precipitation totalswithin 25% of the observed. Over a similarly smalldo main, Karmalkar et al. (2008) used the Hadley Cen-tre PRECIS model to simulate climate for the 20th(1961− 1990) and 21st century (2071−2100) at high resolution (0.22° grid-spacing) for the dry winter sea-son (December-January-February, DJF). They notedmo del deficiencies in simulating both temperatureand precipitation at high altitudes, with negative bi-ases in both cases. Martinez-Castro et al. (2006) com-pleted a sensitivity study of the Caribbean climate todomain size, resolution and convective schemes inRegCM3 for the 1993 summer period, finding sub-stantial sensitivity to all 3 factors ex plored. Tourigny& Jones (2009a,b) used the Rossby Centre regional at-mospheric model (RCA3) at 0.33° grid spacing and aseries (1979−2005) of 13 mo long re-initialized inte-grations (with a seasonal forecasting framework)forced by ECMWF re-analysis to examine the simu-lated climate over Mexico, Central America, theCaribbean, and northern South America. They foundthat the model captured both the precipitation an nualcycle over Mexico and the Caribbean and the precipi-tation anomalies in most areas associated with ENSOevents. However, the monthly precipitation minimumin July and August that is representative of the MSDclimate was not simulated by their model. Clearly,more work is needed to assess the performance of re-gional climate models (RCMs) in reproducing thecomplex climatic features of Central Ame rica, whichare affected by both remote processes related to largescale atmospheric and oceanic teleconnection pat-terns, such as ENSO and the North Atlantic Oscilla-tion (e.g. Hastenrath 1967, Enfield 1996, Giannini etal. 2000), and local processes due to complex topo-graphical, land use and coastline features (Hastenrath1967, Waylen et al. 1996, Peña & Douglas 2002).

Further emphasizing the need for regional climateassessments over Central America are recent studiessuggesting that Central America could be a climate-change hot-spot during the 21st century due to con-sistent model projections of future drying over theregion (e.g. Giorgi 2006, Neelin et al. 2006, Rauscheret al. 2008, 2011). Recognizing the importance of the

Central America region within the climate changecontext, the newly implemented international pro-gram Coordinated Regional Downscaling Ex peri -ment (CORDEX) (Giorgi et al. 2009; see also http:// wcrp.ipsl.jussieu.fr/SF_RCD_CORDEX.html),in cludes this region as one of the focus domains.These CORDEX experiments will provide an oppor-tunity to evaluate the performance of RCMs over theregion and to produce a new generation of RCM-based climate change projections. The first step inthe CORDEX protocol is to assess models using lat-eral boundary conditions from reanalysis of observa-tions. Here we present a detailed analysis of the per-formance of an RCM (the newly developed RegCM4;Giorgi et al. 2012, this Special), with a focus on theseasonal and diurnal cycle characteristics of precipi-tation over Central America. The analysis is con-ducted for the northern hemisphere summer months(June to September) since most of Central Americaexperiences its rainy season during this period (Has-tenrath 1967).

One aspect of regional climate that is receivingincreased attention is the diurnal cycle of precipita-tion. While it has not been conclusively demon-strated, it is claimed that a good simulation of thediurnal cycle is necessary for representing the meanclimate and its variability on longer time scales (e.g.Yang & Slingo 2001). A number of studies have ana-lyzed the diurnal cycle of precipitation using observa-tions and/or global and regional climate models (Wal-lace 1975, Dai et al. 1999, Dai 2001, Yang & Slingo2001, Collier & Bowman 2004, Dai & Trenberth 2004,da Rocha et al. 2009). Although these studies mostlyagreed that in many tropical regions, precipitationpeaks in late afternoon/early evening over land andlate night/early morning over ocean, they noted thatthere are regional differences because of land−seabreezes, mountain−valley circulations, and offshoreocean regions affected by gravity wave-triggeredcon vection (Mapes et al. 2003). Errors in the diurnalprecipitation cycle may affect the intensity and frequency of simulated precipitation. For example,many convection schemes allow convection to begintoo early in the day (related to the representation ofthe convection trigger; Dai & Trenberth 2004, Lin etal. 2000), and as a result over-estimate light precipita-tion events. While such errors may be masked inmonthly mean precipitation statistics, they can none -theless contribute to biases in other fields, such ascloudiness (Dai & Trenberth 2004). Moreover, anymodel deficiencies in physical parameterizations (e.g.boundary layer, convective parameterization) thatare related to the surface heating can negatively

Diro et al.: Sensitivity of Central American climate to surface schemes in RegCM4

impact the simulation of the diurnal cycle. Closer tothe study area of this paper, da Rocha et al. (2009)recently examined the diurnal cycle of precipitationusing RegCM3 over South America and found thatthe phase of RegCM3 matches observations (by cap-turing the afternoon peak) over the continental SouthAmerican convergence regions. For Central America,due in part to the topography of the isthmus, the diur-nal precipitation cycle is complex and shows bothsub-regional and seasonal variations (Curtis 2004).

While many previous studies of the diurnal cyclehave focused on deficiencies in the convective para-meterization physics itself, such as its sensitivity to en-trainment of dry air or triggering function (e.g. Betts &Jakob 2002), the atmosphere−surface inter action canalso be important for forcing diurnal variability aswell as for determining the mean climate. Thus, in ad-dition to evaluating the performance of RegCM4, thispaper investigates the model sensitivity to differentland surface schemes available in the model and tothe implementation of a simple prognostic schemethat represents the diurnal variation of SST.

2. DATA, MODEL AND METHODS

2.1. Data

Three observational datasets are used to evaluatethe model seasonal precipitation: the monthly meanproducts from the Tropical Rainfall Measuring Mis-sion (TRMM 3B43; Huffman et al. 2007), the ClimateRe search Unit (CRU; Mitchell & Jones 2005) and theGlobal Precipitation Climatology Project (GPCP;Adler et al. 2003) datasets, while the TRMM 3B42,which is a 3 hourly dataset, is used to validate the diurnal cycle of precipitation. The TRMM 3B42 is ahigh resolution (0.25° × 0.25°) near-global dataset,produced by optimally merging multi-satellite esti-mates to produce 3-hourly precipitation fields. TRMM3B43 is a monthly dataset produced by combining the3-hourly merged high-quality/infrared (IR) estimatesfrom 3B42 for the calendar month with monthly accu-mulated and analyzed rain gauge data. The TRMM3B42 data were chosen over the radar-only TRMM2A25, since the temporal sampling of precipitationfrom radar-only measurements is poor, with samplingfrequencies sometimes less than once per day, espe-cially towards the equator. Unless other estimates(e.g. from microwave or infrared data) are incorpo-rated, only a long term climatology of the diurnal cy-cle can be produced such as in Biasutti et al. (2011),who noted that the seasonality of the diurnal cycle

was a strong caveat of their own analysis. Seasonalrainfall statistics are also evaluated using 2 additionaldata sets. GPCP is a relatively coarse gridded (2.5° ×2.5°) monthly rainfall analysis produced by merginggauge measurements and satellite estimates of rain-fall (Adler et al. 2003). The CRU dataset includes 0.5°resolution gridded data derived from land stations forboth surface air temperature and pre cipitation(Mitchell & Jones 2005). Finally, where ap propriate,the model results are also compared with the corre-sponding fields from ERA-interim reanalysis (Sim-mons et al. 2007) that is used to drive the RegCM4.

2.2. Model setup and analysis

The climate model used here is the latest version ofthe International Centre for Theoretical Physics(ICTP) RCM (RegCM4), which is described by Giorgiet al. (2012). RegCM4 has a hydrostatic dynamicalcore (Grell et al. 1994) on an Arakawa B horizontalgrid, and a terrain following sigma coordinates. Inthe present setup, the model employs the radiationscheme of CCM3 (Kiehl et al. 1996), a modified ver-sion of the planetary boundary layer scheme of Holt-slag et al. (1990), the resolvable scale precipitationscheme of Pal et al. (2000) and a mixed convectionscheme using the MIT convection parameterizationof Emanuel (1991) over ocean areas and the schemeof (Grell 1993) over land areas. This configurationhas proven to be most effective over a number ofCORDEX domains (Giorgi et al. 2012). The control(CTRL) model integration uses Biosphere-Atmos-phere Transfer Scheme (BATS; Dickinson et al. 1993)to describe the land surface processes. This schemehas been used within the RegCM system for a num-ber of years. In addition, 2 sensitivity studies are per-formed which impact the surface coupling to theatmosphere over the land and ocean, respectively. (1)In the first experiment, the BATS land surfacescheme is re placed by the Community Land Model(CLM) version 3.5 (Oleson et al. 2008), which is amore ad vanc ed scheme including a more compre-hensive description of the surface energy and watercycles. (2) The second experiment focuses on the oce -an with the implementation of a diurnal cycle SSTscheme of Zeng & Beljaars (2005), which calculatesthe diurnal evolution of the ocean skin temperaturebased on energy budget considerations. The 2 sensi-tivity ex periments are hereafter referred to as theCLM and DCSST integrations, respectively.

Six hourly fields from ERA-interim reanalysis (Sim-mons et al. 2007) are used to provide the initial condi-

33

Clim Res 52: 31–48, 2012

tions and lateral boundary forcing for the regionalmodel in all experiments. The ERA-interim reanaly-sis was interpolated onto a 1.5° × 1.5° horizontal gridresolution and 37 levels in the vertical. SSTs are ob tained from the National Oceanic and Atmos-pheric Ad ministration (NOAA) optimal interpolationweekly SST data (Reynolds et al. 2002). Ten gridpoints in each direction are allocated for each lateralbuffer zone where an exponential nudging is used tocombine the model fields and the boundary condi-tions (Giorgi et al. 1993). The model domain followsthe CORDEX specifications and covers a large regionencompassing Central America (15° to 145° W and25° S to 42° N, Fig. 1), however the analysis fo cuseson the inner regions of the domain covering the Cen-tral American continental area and adjacent oceanregions (0° to 35° N, 30° to 130° W). FollowingCORDEX, the model horizontal grid spacing is 50 kmwith 18 sigma-pressure levels used in the vertical asin the standard configuration of the model. All theintegrations start at 00:00 h UTC on 1 January 1997and run continuously for 6 yr until 1 January 2003.The first year is excluded from the analysis to allowfor model spin-up.

A harmonic analysis method is used to measure thecoherence of the diurnal cycle between the simula-tions and observations (Wallace 1975, Dai 2001, Col-lier & Bowman 2004). The harmonic analysis of atime series is the decomposition of a periodic functioninto a sum of trigonometric functions as shown:

(1)

where tk = (00,03,06,09,12,15,18,21), the time inhours (UTC), Ai and φi represent the amplitude andthe phase of the i th harmonic respectively. R(tk) is the

estimated precipitation for each 3 h interval. R̂ is theaverage of the eight 3-hourly samples. The portion ofvariance explained by the ith harmonic wave is deter-mined by A2

i/2σ2, where σ is the standard deviationof the eight 3-hourly mean samples. First, a long term(seasonal) mean for the 8 time steps is computed.Then, a Fourier analysis is used to compute the ampli-tude and phase of the first harmonic (diurnal cycle).The phase of the first harmonic indicates the timingof maximum precipitation in the diurnal cycle.

3. RESULTS AND DISCUSSION

3.1. Seasonal features

3.1.1. Precipitation

The comparison of mean precipitation in the 3RegCM4 experiments with observations confirmsthat all simulations produce the observed precipita-tion maximum over the eastern Pacific and AtlanticITCZ regions, and over northern South America(Fig. 2). While the observed precipitation patternsare well reproduced, both the CTRL and DCSST ex -periments overestimate precipitation amounts (withup to 8 to 10 mm d−1 for the CTRL and 6 to 8 mm d−1

for the DCSST) off the west coast of Central Americaand over the north-northeastern coasts of South Ame -rica. Conversely, precipitation is somewhat under -estimated, by up to 2 mm d−1, over areas of the Gulfof Mexico, Cuba, Florida, the Yucatan Peninsula andthe eastern coasts of Central America and westernCaribbean islands.

A prominent and somewhat surprising finding ofFig. 2 is the strong response of the simulated precipi-tation over the equatorial Pacific and Atlantic oceanicre gions in the CLM simulation, in which the biasesover these regions are significantly reduced com-pared to the CTRL run, especially off the west coastof Central America and the northeastern coast ofSouth America.

In general, CLM tends to inhibit precipitation overland when compared to BATS, both over Central andSouth America. This is consistent with previous testsof CLM within the RegCM framework. For example,Steiner et al. (2009) found that the use of CLM re duced simulated precipitation over the West Africamon soon region and attributed this to soil−precipita-tion feedbacks. A more recent study with RegCM andCLM3.5 with a domain over the USA also showed awarm/dry bias over the centre of the country, linkedto soil moisture (Tawfik & Steiner 2011).

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Concerning the use of the diurnal cycle SST, itsmain effect, although not large, is to increase precipi-tation along the ITCZ core, both over the Pacific andthe Atlantic. The simulated diurnal cycle of SST is ofthe order of several tenths of a degree, and this is evi-dently sufficient to trigger increased convection par-ticularly during the day. We also note the substantialdifferences between the TRMM and GPCP observa-tions during this 5 yr averaging period, with TRMMindicating a more sharply defined and more intenseITCZ core. In addition to the use of different datasources and underlying retrieval algorithms, these dif-ferences could also be related to the order-of-magni-tude contrast in the resolution of the 2 products. Thisobservational uncertainty makes an unambiguous as-sessment of the model performance more difficult.

3.1.2. Annual cycle and MSD

The area-averaged (land-only grid points) simu-lated and observed annual cycle of rainfall for theCentral America and Mexico subregions (shown inFig. 1) is presented in Fig. 3. Over Central America,the observations show a bimodal annual cycle withpeak precipitation in June and September. Precipita-tion shows a relative minimum in July and August,during the MSD phenomenon. This bimodal pattern,which is more pronounced in the TRMM than theCRU data, is reproduced well by the RegCM4_CTRLand RegCM4_DCSST simulations, although there isa difference in the simulated and observed amount ofpeak precipitation in September. The CLM simula-tion captures the September peak but misses the

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Contour lines in the bottom-right panel: differences which are statistically significant at the 0.1 level

Clim Res 52: 31–48, 2012

June one and, more generally, shows an underesti-mation of precipitation over land.

The reduction in precipitation in the CLM simula-tion appears to lead to an overall reduction in dia-batic heating, as diagnosed by vertically averagedtemperature differences between the CLM andCTRL simulations (not shown). Consistent with thisnegative heating anomaly in the CLM simulation rel-ative to the CTRL run, there is a Gill-type response(Gill 1980) with overall easterly surface — and west-erly upper level — winds to the west of the negativeheating, and a pair of upper-level cyclones in the sub-tropics. Following the steady, linearized vorticityequation (Gill 1980), a negative heating anomaly andassociated column shrinking is balanced by equator-ward motion to the west of the forcing. A low-levelanticyclone and an upper-level cyclone to the west ofthe cooling comprise the baroclinic structure thatcharacterizes a Gill response to a negative heatinganomaly. Particularly in June, the location of thesecirculation anomalies (Fig. 4) appears to favor furtherprecipitation reduction, with maximum anticyclonicanomaly at 925 hPa over the west coast of Mexico.Associated with this anticyclonic anomaly, there isenhanced vertically integrated moisture divergenceover Central America and off its western coasts, andenhanced moisture convergence further south near5° N over the eastern tropical Pacific. Therefore, par-ticularly in June, the reduction in precipitation in theCLM run initiated by the land surface response feedsback to the regional circulation, further reducing pre-cipitation, even over adjacent oceanic regions. Theresponse over the ocean regions may be interpretedas a Gill response to the negative heating anomalyover the tropical American land regions.

Over the Mexico sub-domain, the bimodal natureof precipitation is not present, since part of this re -gion is affected by the North American monsoon,which has a precipitation annual cycle that is out ofphase with the MSD (Adams & Comrie 1997, Small etal. 2007). TRMM places a precipitation maximum inSeptember, while CRU places it in August. The CLMsimulation more closely follows the observed annualcycle of CRU, although the maximum in the modeloc curs in July. As TRMM, the CTRL run correctlyshows a precipitation maximum in September, butoverestimates precipitation. Overall, the annualcycle comparison suggests that there is no single con-figuration of RegCM4 that is best for both regions, al -though the CTRL configuration appears to betterreproduce the observed intra-seasonal fluctuations(i.e. the MSD) in the annual cycle.

As a measure of the MSD, Fig. 5 shows the spatialpattern in precipitation difference between July/August and June/September (monthly data used) (fol-lowing Small et al. 2007) for TRMM, and the CTRL,DCSST and CLM runs. The blue color in the plots indi-cates the precipitation deficit associated with theMSD. The TRMM data exhibit 2 main areas withMSD. One is located over southern Mexico, westernCentral America, and the Gulf of Tehuantepec, whilea less pronounced region is found over the Gulf ofMexico and the northern Caribbean. Conversely, apositive precipitation anomaly is found over the north-ern equatorial Pacific and over the western coasts ofMexico near the core region of the North Americanmonsoon system, which, as noted previously, has arainy season that is out of phase with the MSD.

All the RegCM4 simulations are able to reproducethe negative precipitation anomaly during the MSD

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over Central America, and the positive anomaly overthe north-equatorial Pacific and over the west coastof Mexico. The model, however, does not adequatelycapture the presence of the MSD when using theCLM scheme. Overall, the use of the BATS schemeap pears to produce a better agreement with observa-tions in terms of the intra-seasonal anomalies ana-lyzed in Fig. 5.

The MSD precipitation anomaly off the westerncoastal regions of Central America is associatedwith a low level (at 925 hPa) anticyclonic circulationanomaly over the region, i.e. a northerly anomalyover the Gulf of Mexico, easterly anomaly over theCaribbean and southerly anomaly over easternPacific along the west coast of the continent (Fig. 6).The easterly ano maly over the Caribbean suggeststhat the CLLJ is enhanced during the MSD. Thisrelationship be tween rainfall and the CLLJ is inagreement with the results of Wang (2007) whofound a negative correlation between rainfall overCentral America and CLLJ at the inter-annual timescale. All RegCM4 simulations show a similar circu-lation anomaly pattern compared to the reanalysis,although with a reduced magnitude. The inter com-parison of the 3 simulations shows that the anti -cyclonic anomaly (northerly over the Caribbean andsoutherly over the eastern Pacific and strongerCLLJ) is better reproduced by the RegCM CTRLand DCSST run, with the CLM run showing a muchweaker anomaly over the Carib bean. The weakerMSD in the CLM run may be re lated to the Gill-type response in the CLM run noted above, whichserves to reduce precipitation in the first peak inJune, which would reduce differences betweenJuly-August and June-September precipitation.

3.1.3. Temperature

In all 3 simulations the model is able to capture thespatial pattern of surface air temperature as drivenby the local topography (Fig. 7). However, several dif-ferences between the model integrations, reanalysis,and observations are found. Differences betweenreanalysis products and surface observations canderive from errors in reanalyses systems (from modeland/ or observational error), with Mooney et al.(2011) recently documenting pointwise root meansquare errors in ERA-Interim ranging from 0.2 to0.8 K for a series of mid-latitude weather stations, butalso from the fact that the interpolated/gridded obser-vational dataset also contains observational errors,especially in the parts of the region where stationdata is relatively sparse. First, it is noted that ERA-Interim produces lower temperatures than CRU overthe Amazon Basin and the Sierra Madre Occidentalof Mexico. The CTRL run also shows a similar coldbias distribution, in the order of 1 to 3°C, largelyagreeing with the ERA-Interim reanalysis.

The cold bias over mountainous regions is at leastpartially related to the well known valley bias in

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divergence (bottom)

Clim Res 52: 31–48, 2012

station-based observational networks, and has alsobeen noted in evaluations of other RCMs over Cen-tral American domains (Karmalkar et al. 2008). Overthe Amazon Basin the density of observing stations islow, making the CRU data rather uncertain. TheDCSST simulation has a limited impact on tempera-ture, mostly in the order of a few tenths of a degree.A greater sensitivity is found in the CLM run; the sim-ulated temperatures are higher over most parts ofMexico and northern South America compared to theCTRL run, and this helps in reducing the cold modelbias by 1 to 1.4°C compared to the CRU data.

3.1.4. Low level circulation

The low level circulation features (925 hPa) in theERA-Interim reanalysis and the 3 RegCM4 simula-tions are shown in Fig. 8. The dominant feature atthis level is the easterly jet core over the Atlanticwhich is associated with the subtropical AtlanticHigh. The easterlies reach their maximum (up to11 m s−1, i.e. the CLLJ; Amador 1998) between the

Caribbean highlands and South America. These east-erlies turn into a southeasterly flow over the Gulf ofMexico, bringing moisture to the southeastern US(Wang & Lee 2007, Muñoz et al. 2008, Cook & Vizy2010) via the Great Plains low level jet. On the Pacificside, the southerly cross-equatorial flow from thesouthern Pacific, the northerly flow from the northernPacific (which is associated with the Pacific subtropi-cal high) and the easterly flow from the Atlantic con-verge off the west coast of Central America. The east-erly flow from the Atlantic and the southwesterliesfrom the Pacific also converge over the northern partof South America. This indicates that the main areasof moisture convergence and precipitation during thenorthern hemisphere summer are off the west coastof Central America and northern South America.

The location of CLLJ is simulated reasonably wellby the 3 model versions, although the strength of thesimulated jet is weaker compared to the ERA-Interimreanalysis, and it is located slightly too far south, withthe core around 12° N. A vertical cross section of thezonal winds at 75° W (not shown) reveals that theCLLJ is slightly stronger in the CLM simulation than

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in the CTRL run, perhaps contributing to the lowerrainfall amounts over Central America in the CLMsimulation. This is consistent with Wang (2007), whofound (for August and September) a significant nega-tive correlation between the CLLJ and rainfall overCentral America, the eastern Caribbean Sea, andnorthwestern South America. The slight strengthen-ing of the jet in the CLM run may be related to thehigher surface temperatures over South America,since heating can strengthen the meridional pressuregradient over the Caribbean and hence the jet(Muñoz et al. 2008, Cook & Vizy 2010).

3.1.5. Surface energy budget

Since the effect of the DCSST run is small, theanalysis of surface fluxes concentrates on the compar-ison of the CTRL and CLM runs. As shown in Fig. 2,and discussed in Section 3.1, precipitation is substan-tially reduced in the CLM run compared to the CTRLrun. This change can be largely attributed to land-

atmosphere feedbacks, as the latent heat flux in theCLM simulation is much lower than in the CTRL sim-ulation (Fig. 9d). Due to the lower precipitation andcloud cover, net shortwave radiation at the surfaceincreases in the CLM simulation, while net longwaveradiation at the surface also increases due to highersurface temperatures.

CLM has a well-known dry bias in comparison toother land surface models, which has been partiallyattributed to its tendency to simulate mean globalevapotranspiration with low contributions from tran-spiration and high contributions from soil and canopyevaporation (Oleson et al. 2008, Lawrence & Chase2009). Reduced evaporation and drier soils can leadto a reduction in precipitation recycling (Schär et al.1999) and a decrease of the effciency of precipitationprocesses operating in a region (e.g. Betts et al. 1996).Similar feedbacks have been demonstrated in previ-ous RegCM4_CLM couplings (Steiner et al. 2009).This decrease in surface moisture availability, andhence precipitation, is substantial enough to modifythe circulation of the CLM simulation in a way that

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Clim Res 52: 31–48, 2012

appears to enhance the overall dryness of the CLMexperiment, as discussed in the previous subsection.

3.2. Diurnal cycle

3.2.1. Significance of the first harmonic

In order to ascertain how much the daily precipita-tion cycle is explained by the first harmonic, the per-centage of the total daily variance explained by it isshown in Fig. 10. During JJAS, the diurnal cycle fromthe 1st harmonic accounts for >70% of the dailymean variance over most of the continental domain,

suggesting that the first harmonic can be used as afirst order measure of the characteristics of the diur-nal precipitation cycle. In fact, a good agreement be -tween the simulated and observed explained vari-ance is found over land areas. In the TRMM dataset,a land–sea contrast can be observed in terms of themagnitude of this percentage, with lower values ofpercentage of variance explained by the first harmon-ics over ocean regions. Conversely, in the model theland–sea variation in the magnitude of variance ex -plained by the first harmonic is not evident. This sug-gests that the model has a substantial diurnal cycle(compared to semi-diurnal and higher order harmon-ics) over the oceans, whereas in TRMM semi-diurnal

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RegCM4_CTRL. The contour lines in the bottom-right panel represent values which are significant at 0.1 level


and higher order harmonics have a larger share inshaping the diurnal cycle over the ocean.

3.2.2. Amplitude of the first harmonic

Fig. 11 shows the JJAS seasonal mean normalizedamplitude of the first harmonic of the diurnal cycle.The diurnal amplitude is generally larger over landthan ocean areas in both TRMM and the RegCM4ex periments, consistent with previous studies usingobservations and models (e.g. Wallace 1975, Yang &Slingo 2001, Collier & Bowman 2004). Comparingthe RegCM4 simulations and TRMM data, the diur-nal amplitude is generally smaller in all RegCM4simulation than in the TRMM data, except over theeastern coast of Central America. However, theCLM run appears to produce larger diurnal ampli-tudes than the CTRL run. This underestimation ofthe diurnal cycle amplitude by models is not com-mon, especially in general circulation models(GCMs). In fact many GCM’s tend to overestimate

the amplitude of the precipitation diurnal cycle (e.g.Collier & Bowman 2004).

3.2.3. Phase of the first harmonic

Fig. 12 shows the spatial distribution of the time ofthe peak diurnal precipitation from the TRMM obser-vations and RegCM4 simulations. The time of peakprecipitation is converted from UTC to local solartime by subtracting lon/15 from UTC, where lon is a15 degree wide window centered at 15° W, 30° W,45° W, …,135° W longitudes. Observed precipitationpeaks in the early morning or early afternoon overmost ocean areas and in the evening over most landareas. There are significant spatial gradients in thepeak precipitation hours. For example, precipitationpeaks in early morning along the coastal oceanicregions of Central America and later in the morningor early afternoon further from the coasts into theGulf of Mexico and the equatorial ocean areas. Themodel reproduces some of these patterns, such as the

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Clim Res 52: 31–48, 2012

earlier peak times over ocean than over land and thecoast-to-open sea gradients. In general, however, themodel produces peak precipitation times earlier thanobserved. Over land, the peak precipitation time inthe simulations is around 15:00 to 17:00 h, while theob served values are mostly around 20:00 h or evenlater.

The discrepancy between TRMM and model simu-lations in the timing of maximum precipitation ispartly due to the fact that the TRMM data also in -clude precipitation estimates from the IR spectrum.Lin et al. (2000) have shown that the timingbetween the maximum surface precipitation and the

minimum outgoing longwave radiation or coldestcloud top surface (which is the basis of IR estimatesin TRMM 3B42) can have a lag time of 2 to 3 h. Inaddition, Nesbitt & Zipser (2003) also found thatTRMM estimates using radar and microwaveimagers place the pre cipitation maxima between17:00 and 19:00 h over Mexico, and suggested thatthe precipitation estimates from radar are superiorto those based on IR or microwave brightness tem-perature. However it should be noted that the tem-poral sampling of pre cipitation from radar measure-ments alone (such as TRMM 2A25) is poor becausesome points sample at a frequency of less than once

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Fig. 9. Net surface energy balance (W m−2) and surface moisture (mm) for RegCM4_CLM minus RegCM4_CTRL. (a) MeanJJAS net shortwave radiation, (b) net long wave radiation, (c) sensible heat flux, (d) latent heat flux. (e) Annual cycle for sur-face soil moisture (Central America; 85°−100°W, 10°−20°N) and (f) surface soil moisture (level 1). The contour lines represent

differences between RegCM4_CLM and RegCM4_CTRL runs that are statistically significant at 0.1 level


per day, especially to wards the equator. Thereforein order to obtain a time series of high temporal res-olution for studying the diurnal cycle, some otherestimates should be in corporated (e.g. from micro -wave or IR data) such as in TRMM 3B42; otherwise,only a climatology of the diurnal cycle can be pro-duced (Biasutti et al. 2011). For example, Biasutti etal. (2011) generated a high-resolution climatology of3-hourly gridded precipitation for Central Americaby combining the precipitation estimate in winterand summer with the assumption that in the tropicsthe characteristics of the diurnal cycle of convectionremain similar. However, the phase of the diurnalharmonic in Yang & Slingo (2001) shows a signifi-cant difference between winter and summer, espe-cially over Mexico, where the winter precipitationmaxima occur around midnight.

Over open ocean areas, on the other hand, the sim-ulated precipitation peaks in the early morning,while in the TRMM the peak is in the late morning orearly afternoon. The best agreement between obser-vations and simulations occurs over the Gulf of Mex-

ico and the coastal areas. The peak precipitation timeis also related to the convection trigger. For example,the Grell scheme — which is activated when suffi-cient buoyant energy lifts parcels above the free con-vection level — tends to generate precipitation in theearly afternoon over land (Dai et al. 1999). Inter- comparison of our simulations suggests that the useof CLM produces a slightly earlier precipitation peakthan BATS, and the use of the DCSST scheme pro-duces a later peak, than the standard configuration;however, these effects are not pronounced.

The diurnal cycle of precipitation can be deter-mined by both local and non-local effects, with con-vection propagating into the study area in meso scale-organized systems (Yang & Smith 2006). Pro pagatingsystems are revealed in a time– longi tude cross sec-tion of JJAS seasonal mean precipitation anomalies(for anomalies greater than the daily mean) aroundCentral America, averaged between 10° N and 20° Nfor the TRMM and RegCM4 simulations (Fig. 13). InFig. 13 the diurnal cycle is repeated twice for clarity.The TRMM data shows that in this region mesoscale

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Fig. 10. Percentage of variance explained by the 1st harmonic analysis for: (a) TRMM, (b) RegCM4_CTRL, (c) RegCM4_DCSST, and (d) RegCM4_CLM

Clim Res 52: 31–48, 201244

convective systems tend to initiate at 82.5° W at noon,then propagate westward, and reach 95° W at 03:00 hwith an average speed of ≈25 m s−1.

All 3 RegCM4 simulations reproduce this west-ward propagation, even though there is a phaseshift and a reduced intensity, especially around 90°to 91.5° W. A break in the propagation of rainfallanomalies in the simulation is observed during theearly evenings and this is more pronounced in theCLM run. This break in the propagation rainfallanomaly at about 91°W can be linked to the Yuca -tan Pen insula and Guatemalan highlands, since thebreaks are aligned with a secondary peak in topo-graphic height, which is calculated by averagingthe topography over the 10° to 20° N latitude band(Fig. 13).

4. CONCLUSIONS

In this paper we present a sensitivity study withthe newest version of the RegCM regional climatemodeling system, RegCM4 (Giorgi et al. 2012) run

over the Central America CORDEX domain. Thisregion is characterized by complex climatic featuresresulting from large-scale flow interactions andlocal processes related to topography and land–seacontrasts, and to date only few regional modelingstudies have focused on it. On the other hand, it is aregion identified as one of the most prominent cli-mate change hot-spots (Giorgi 2006). It is thus im -portant to test RCMs over this domain and to iden-tify major model deficiencies and areas in need ofimprovement. Specifically, the present study teststhe sensitivity of the model to land surface (CLM vs.BATS) and ocean SST (Standard vs. diurnal SST)parameterizations with a focus on the model repre-sentation of precipitation from the seasonal to thediurnal scale.

Overall, the model reproduces the spatial and sea-sonal patterns of precipitation over the region. Themodel captures the occurrence of the MSD over Cen-tral America, i.e. a break in the precipitation amountduring July and August, followed by a recovery inSeptember. Regional circulation features are alsoreproduced, although the intensity of the Caribbean



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Fig. 11. Normalized amplitude of the 1st harmonic analysis for (a) TRMM, (b) RegCM4_CT RL, (c) RegCM4_DCSST, and (d) RegCM4_CLM


Low-Level Jet is underestimated, and it is located toofar south.

The model shows a substantial sensitivity to theland surface scheme, through the interaction of thesurface energy and water budgets with the overlyingcirculations and convection. In general, the use ofCLM leads to reduced precipitation, associated withreduced evaporation and increased surface sensibleheat flux. This leads to increased agreement withobservations over some areas and decreased agree-ment over others. The choice of land surface schemeaffects not only precipitation over land, but also overoceans through a teleconnection involving regionalcirculations. The response over the ocean regionsmay be interpreted as a Gill response to the negativeheating anomaly over the tropical American landregions. Conversely, the effect of the diurnal SSTscheme is relatively minor.

Concerning the diurnal cycle of precipitation, themodel reproduced reasonably well the amplitude ofthe observed cycle and its land–ocean contrast, butshowed a systematic bias in the phase of the cycleconsisting of an earlier placement of the peak precip-

itation time in the model compared to observations.This bias was present when using both the BATS andCLM land surface modules, and is therefore probablyrelated to other components of the model physics,most likely involving the deep convection schemes.More research is needed to improve this problem,which is common to many global and regional cli-mate models.

As a summary assessment, the present version ofRegCM4 appears to provide a good representation ofthe basic features of the monsoon climate of CentralAmerica, although some systematic biases do persist,particularly at sub- diurnal scales. Neither of the landsurface schemes tested appears universally superiorover this region, and in particular it is highlightedthat the more complex CLM did not lead to a consis-tently improved model. Given the overall satisfactoryperformance of the model over this region, and keep-ing in view its main deficiencies at sub-daily scales,in future studies we plan to apply the RegCM4 mod-eling system to project climate changes over thisregion following the CORDEX protocol (Giorgi et al.2009).

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Clim Res 52: 31–48, 2012

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Submitted: April 26, 2011; Accepted: September 2, 2011 Proofs received from author(s): March 6, 2012

sensitivity of seasonal climate and diurnal precipitation

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