A Regional Climatography of West Nile, Uganda, to Support Human Plague Modeling

ANDREW J. MONAGHAN,* KATHERINE MACMILLAN,1 SEAN M. MOORE,*,1 PAUL S. MEAD,1

MARY H. HAYDEN,* AND REBECCA J. EISEN1

* National Center for Atmospheric Research,# Boulder, Colorado1 Centers for Disease Control and Prevention, Division of Vector Borne Infectious Diseases, Fort Collins, Colorado

(Manuscript received 13 September 2011, in final form 22 February 2012)

ABSTRACT

The West Nile region in northwestern Uganda is a focal point for human plague, which peaks in boreal

autumn and is spread by fleas that travel on rodent hosts. The U.S. Centers for Disease Control and Pre-

vention is collaborating with the National Center for Atmospheric Research to quantitatively address the

linkages between climate and human plague in this region. The aim of this paper is to advance knowledge of

the climatic conditions required to maintain enzootic cycles and to trigger epizootic cycles and ultimately to

target limited surveillance, prevention, and control resources. A hybrid dynamical–statistical downscaling

technique was applied to simulations from the Weather Research and Forecasting Model (WRF) to

generate a multiyear 2-km climate dataset for modeling plague in the West Nile region. The resulting dataset

resolves the spatial variability and annual cycle of temperature, humidity, and rainfall in West Nile relative to

satellite-based and in situ records. Topography exerts a first-order influence on the climatic gradients in West

Nile, which lies in a transition zone between the drier East African Plateau and the wetter Congo Basin, and

between the unimodal rainfall regimes of the Sahel and the bimodal rainfall regimes characteristic of

equatorial East Africa. The results of a companion paper in which the WRF-based climate fields were applied

to develop an improved logistic regression model of human plague occurrence in West Nile are summarized,

revealing robust positive associations with rainfall at the tails of the rainy season and negative associations

with rainfall during a dry spell each summer.

1. Introduction

Human plague is an often-fatal, fleaborne disease

caused by the bacterium Yersinia pestis. The rural West

Nile region of northwestern Uganda represents an epi-

demiological focus for plague, where humans are most

often exposed to Y. pestis during plague outbreaks in local

rat populations in which large numbers of rats die. The

infectious fleas on the dead rats are forced to seek alter-

native hosts, including humans. Previous work by the U.S.

Centers for Disease Control and Prevention (CDC) has

suggested that human plague cases in West Nile—which

typically occur at elevations above 1300 m and exhibit

a distinct seasonal cycle that corresponds to the annual

rainy season from August through October—are linked to

spatial and temporal climatic variability (Winters et al.

2009; Eisen et al. 2010; MacMillan et al. 2011). The CDC is

therefore developing models to examine quantitatively

the linkages between human plague case occurrence and

climatic variability in the West Nile region. The motiva-

tion for the work is to identify climatic variables that are

predictive of the spatial distribution of human plague

cases. Doing so advances our knowledge of the conditions

required to maintain enzootic cycles and may ultimately

be used to target limited surveillance, prevention, and

control resources to areas most at risk and to predict the

future range of human plague.

The fine-spatial-resolution climate data that are re-

quired for the CDC modeling effort were, until recently,

not available over the West Nile region because of the

sparse and unreliable observational network and because

existing atmospheric model datasets (such as atmospheric

reanalyses) are too coarse to resolve the complex orog-

raphy and climatic gradients in the region. An elevation

gradient of ;1000 m exists across the ;75 km 3 100 km

study region, which is situated on the East African Plateau

# The National Center for Atmospheric Research is sponsored

by the National Science Foundation.

Corresponding author address: Andrew J. Monaghan, National

Center for Atmospheric Research, P.O. Box 3000, Boulder, CO

80307-3000.

E-mail: monaghan@ucar.edu

VOLUME 51 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y JULY 2012

DOI: 10.1175/JAMC-D-11-0195.1

� 2012 American Meteorological Society 1201

and is bounded by the Albert Nile River to the east and

the Democratic Republic of Congo (DRC) to the west

(Fig. 1). Lake Albert, the northernmost in the chain of

the African Great Lakes that dot the Western Rift

Valley, lies to the south. To address the need for reliable

climate data to support human plague modeling efforts,

the Research Applications Laboratory of the National

Center for Atmospheric Research has simulated a high

quality, fine-resolution climate dataset for the greater

West Nile region. The dataset, which contains monthly

fields with 2-km spatial resolution for the period 1999–

2009, was generated with a hybrid dynamical–statistical

downscaling technique that is based on the Weather

Research and Forecasting Model (WRF; Skamarock

and Klemp 2008). In the following sections we 1) pro-

vide a literature-based overview of the climate of the

broader East African region and its primary large-scale

drivers, 2) describe the WRF-based dataset and assess

its veracity in comparison with satellite-derived and in

situ records, 3) employ the WRF-based dataset to ex-

amine the general climatic features of the greater West

Nile study region, and 4) summarize the successful ap-

plication of the dataset toward modeling the spatial

distribution of plague cases.

2. Overview of equatorial East African climate

The climate of equatorial East Africa (EEA: Uganda,

Kenya, and Tanzania) is broadly influenced by three

different air masses at low levels of the atmosphere: the

northeast monsoon, the southeast monsoon, and west-

erly to southwesterly flow often referred to as the Congo

air mass (e.g., Basalirwa 1995; Nicholson 1996). Both

monsoons transport relatively stable, dry air because

of their divergent nature (Griffiths 1972). Therefore,

rainfall associated with the monsoonal flow generally

requires mechanical uplift, either at the boundary be-

tween the monsoons—the intertropical convergence

zone (ITCZ)—or from localized topographic lifting

(Nicholson 1996). Rainfall seasonality is thus linked to

the transition seasons during which the ITCZ moves

northward or southward through EEA and flow asso-

ciated with the monsoons becomes more zonally (on-

shore) oriented (e.g., Hills 1979). Superimposed on the

monsoonal flow, episodic westerly incursions from the

humid and unstable Congo air mass can bring rainfall in

all but the driest months during boreal winter, during

which the ITCZ is far south of EEA (Thompson 1957;

Griffiths 1959; Okoola 1999). Interannual variations in

the relative influences of all three air masses in EEA are

governed by sea surface temperatures (SSTs) in the

tropical Atlantic, Pacific, and Indian Ocean basins and

their subsequent effect on the positions of the subtropical

anticyclones (e.g., Mutai and Ward 2000; Camberlin

et al. 2001).

Rainfall, because of its paramount influence on the

regional economies and livelihoods (e.g., Conway et al.

2005) and its comparatively large spatial and temporal

variability, is the focus of the majority of meteorological

literature that covers East Africa. In contrast, near-

surface temperature receives less attention because its

seasonal and interannual fluctuations are typically small

and its spatial variability is mainly a function of topog-

raphy (Griffiths 1972).1 Although the influence of the

synoptic-scale atmospheric circulation is largely consis-

tent throughout EEA, the spatial variability of rainfall is

inhomogeneous because of local-scale features such as

topography, large lakes, land cover, and proximity to

the coast (Plisnier et al. 2000; Anyah and Semazzi 2004;

Oettli and Camberlin 2005; Moron et al. 2007; Camberlin

et al. 2009; Molg et al. 2009; Hession and Moore 2011;

Moore et al. 2010). The seasonal cycle of rainfall varies

substantially by region as well. There are between 26

and 52 distinct rainfall zones in EEA (10–14 for Uganda

alone), with each one being distinguished by the timing

and frequency (uni-, bi-, or trimodal) of their annual

peaks (Griffiths 1972; Ogallo 1989; Basalirwa 1995). For

simplicity, a bimodal annual rainfall cycle is often em-

ployed to broadly categorize EEA climate into four

seasons, per the East African Meteorological Depart-

ment (1963): 1) a dry period from December to Febru-

ary when the ITCZ is far south of EEA; 2) the ‘‘long

rains’’ season from March to May that coincides with the

northward progression of the ITCZ through EEA dur-

ing boreal spring; 3) the second dry period that occurs

during June–August, when the ITCZ generally lies

northward of EEA; and 4) the ‘‘short rains’’ season from

September to November, associated with the southward

passage of the ITCZ back through EEA during boreal

autumn.

Large-scale influences on EEA climate variability—in

particular, rainfall—have primarily been explained in

terms of teleconnections arising from SST variability

in the tropical Atlantic, Pacific, and Indian Oceans (e.g.,

Mutai and Ward 2000). The influence of tropical At-

lantic Ocean SSTs on EEA rainfall is manifested pri-

marily through modulating the north–south position

of the ITCZ, which in turn has an impact on low-level

wind fields (Camberlin et al. 2001; Balas et al. 2007). In

1 Despite the comparatively modest amount of literature on

African temperature variability, it is noteworthy that recent studies

suggest that African agricultural yields may diminish substantially

under modest climatic-warming scenarios, even if rainfall remains

optimal for crops (e.g., Lobell et al. 2011).

1202 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 51

general, warm SST anomalies off the coast of equatorial

West Africa, combined with neutral to negative SST

anomalies in the subtropical Atlantic, are associated

with enhanced rainfall in EEA during both the long- and

short-rains seasons, with nearly zero lag (Nicholson and

Entekhabi 1987; Nicholson 1996). The Atlantic–EEA

teleconnection appears to be less robust, however, when

compared with the impacts of the Indian and Pacific

Oceans on EEA climate (Camberlin et al. 2001; Mutai

et al. 1998), which are described next.

The influence of the tropical Pacific Ocean on EEA

climate is through the El Nino–Southern Oscillation

(ENSO), the quasiperiodic (every 4–7 years) ocean–

atmosphere oscillation manifested in shifting sea surface

temperature anomalies in the tropical Pacific Ocean

(Walker 1924; Bjerknes 1969). In particular, ENSO

FIG. 1. (a) The WRF configuration with gradual nesting of spatial resolution from 54 (outer map) to 18 (orange

box), 6 (yellow box), and 2 (red box) km. (b) Terrain height (m MSL) on the 18-km domain. (c) Terrain height and

geographic features on the 2-km domain (the study region is outlined with the black dashed box). The color scale is

cropped below 700 m and above 2100 m to emphasize regional features.

JULY 2012 M O N A G H A N E T A L . 1203

warm events (above-average SSTs in the eastern tropical

Pacific) are positively correlated (zero-to-several-months

lag) with rainfall anomalies during the latter part of the

short-rains season, from October through December,

throughout EEA (Nicholson and Entekhabi 1987; Ogallo

1988; Nicholson and Kim 1997; Phillips and McIntyre

2000; Mutai and Ward 2000). This seasonal ENSO tel-

econnection has been linked to a wave train of horse-

shoe-shaped convective anomalies emanating from the

central Pacific Ocean (Mutai and Ward 2000). In contrast,

during the interim rainy season and the early portion of

the short-rains season, from July through September,

ENSO warm events tend to be negatively correlated

with rainfall anomalies, especially in the westernmost

regions of EEA, such as Uganda (Ogallo 1988; Phillips

and McIntyre 2000). In the dry and long-rains seasons of

the year from January to May, ENSO correlations with

EEA rainfall in general are comparatively weaker and

more spatially variable (Indeje et al. 2000; Camberlin

and Philippon 2002).

Tropical Indian Ocean variability is often manifested

as an ENSO-like coupled ocean–atmosphere oscillation

known as the Indian Ocean dipole (IOD), which is char-

acterized by warm (cool) SST anomalies in the north-

western (southeastern) equatorial Indian Ocean during

positive phases (Saji et al. 1999; Webster et al. 1999).

Greater rainfall occurs in EEA during positive IOD

phases, especially for the short-rains season in boreal

autumn, because of enhanced convergence and con-

vection over the warm SST pool in the northwestern

equatorial Indian Ocean and adjacent EEA (Birkett

et al. 1999; Hastenrath 2001, 2007; Ummenhofer et al.

2009; Hastenrath et al. 2010). The opposite is also true

for negative IOD phases, for which enhanced divergence

and subsidence over the same region lead to drought

conditions in EEA (Hastenrath et al. 2007, 2010). Black

et al. (2003) suggest that only ‘‘extreme’’ positive IOD

events, which persist for several months, lead to en-

hanced short rains over EEA, because only in these

events are the associated equatorial easterly anomalies

strong enough to enhance convection over EEA. Most

studies argue that the comparative influence of the IOD

on EEA rainfall is stronger than that of ENSO (e.g.,

Latif et al. 1999; Goddard and Graham 1999; Philippon

et al. 2002). However, although earlier studies suggest

that IOD variability is largely due to internal Indian

Ocean dynamics and thus is independent of ENSO

(Webster et al. 1999; Saji et al. 1999), more recent studies

suggest that the IOD and ENSO signals are closely cou-

pled (Black et al. 2003; Black 2005; Ummenhofer et al.

2009). In section 4 the linkages between rainfall and the

IOD, ENSO, and tropical Atlantic SST variability are

examined for the West Nile study region.

3. Methods

a. Atmospheric model configuration

The Advanced Research WRF version is a fully com-

pressible conservative-form nonhydrostatic atmospheric

model suitable for both research and weather-prediction

applications that has demonstrated ability for resolving

small-scale phenomena and clouds (Klemp et al. 2007;

Skamarock and Klemp 2008). WRF has multiple options

and choices for physical parameterizations (i.e., radiation,

cloud physics, cumulus clouds, and planetary boundary

layer turbulence) and is optimized for each application

on the basis of the prevailing weather conditions, loca-

tion, and goals of the project. To simulate realistically

the two-way land surface interactions with the atmo-

sphere, WRF is coupled to the ‘‘Noah’’ land surface

model (LSM) (e.g., Chen and Dudhia 2001). The Noah

LSM provides WRF with fluxes of energy and water

from the land surface, while also maintaining stores of

water and energy in four soil layers to a depth of 2 m.

A four-domain nested WRF configuration was employed,

with 56-, 18-, 6-, and 2-km spatial resolutions, respectively,

from the outermost to innermost domains (Fig. 1). One-

way nesting was employed. There were 35 vertical levels

from the surface to 50 hPa, with 7 levels in the lowest

1000 m. The initial and boundary conditions were

provided by the National Centers for Environmental

Prediction–U.S. Department of Energy Atmospheric

Model Intercomparison Project Reanalysis II (NCEP–

DOE II; Kanamitsu et al. 2002). NCEP–DOE II was

chosen because of the veracity of the results it produced

in the tropics for a recent downscaling experiment (Rife

et al. 2010; Monaghan et al. 2010). Daily-updated 0.258

SSTs for the oceans were specified with version 2 of the

National Oceanic and Atmospheric Administration

(NOAA) optimum-interpolation SST dataset (Reynolds

et al. 2002). Inland lake surface temperatures were up-

dated monthly with 0.058 monthly mean skin temperature

data (product ‘‘MOD11C3’’) derived from the Moderate

Resolution Imaging Spectroradiometer (MODIS) in-

strument flown aboard the National Aeronautics and

Space Administration (NASA) Terra satellite (Wan

2009). We employed the MOD11C3 fields because pre-

liminary simulations indicated that rainfall in the West

Nile region is sensitive to lake surface temperatures in

nearby Lake Albert, one of the African Great Lakes.

The MOD11C3 skin temperatures yielded more-realistic

(warmer) lake surface temperatures than those from the

coarser-resolution NCEP–DOE II skin temperatures,

leading to improved rainfall simulations by resolving a

dry rainfall bias. Anyah and Semazzi (2004) also noted

the strong sensitivity of EEA rainfall to lake surface

temperatures.

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Three-hourly ‘‘spunup’’ skin temperature, snow (wa-

ter equivalent), soil temperature, and soil moisture fields

from the 0.0258 Global Land Data Assimilation System

(GLDAS; Rodell et al. 2004), which employs the Noah

LSM that is also used in WRF, were used to initialize the

land surface and subsurface states for the Noah LSM in

WRF at the beginning of each month-long simulation.

These fields are available beginning on 24 February

2000. Therefore, because our simulations began in Jan-

uary of 1999, for the monthly simulations prior to March

of 2000 we employed the GLDAS data on the equiva-

lent day for the first available year for which data exist.

For example, we used the 30 April 2000 GLDAS fields

to initialize the land state for the month-long simulation

beginning on 30 April 1999. Rainfall totals during the

first available year of GLDAS (March 2000–February

2001) were average for 9 of the 12 months and were below

average for July, September, and October. Rainfall totals

during the months prior (January 1999–February 2000)

were nearly all average. Therefore, the impact on soil

conditions of using GLDAS fields from later months to

initialize the LSM in earlier months was likely minimal.

The chosen model physical parameterizations are

summarized in Table 1. Modifications were made to the

longwave physical parameterization, the Rapid Radia-

tive Transfer Model (RRTM; Mlawer et al. 1997), so

that carbon dioxide (CO2) was changed from a time-

invariant value of 360 ppm to annually varying values

ranging from 368 to 387 ppm for 1999–2009 on the basis

of the Mauna Loa, Hawaii, CO2 observations (Keeling

et al. 2009). Several options were tested for simulating

cumulus-scale precipitation, including 1) the updated

Kain–Fritsch parameterization (Kain 2004) for all four

domains, 2) the Grell–Devenyi parameterization (Grell

and Devenyi 2002) for all four domains, and 3) for the

6- and 2-km domains, no cumulus parameterization (i.e.,

assuming cumulus precipitation is resolved at these fine

spatial scales; in these cases the Kain–Fritsch parame-

terization was turned on in the outer 18- and 56-km

domains). It was found that the Grell–Devenyi param-

eterization for all four domains produced monthly

rainfall totals (spatial patterns and magnitude) that

most closely matched satellite–gauge precipitation

datasets. For the simulations in which the cumulus

parameterization was turned off on the 6- and 2-km

domains, monthly rainfall totals were unrealistically

low. The other parameterizations listed in Table 1 were

chosen because they yielded realistic simulations of

near-surface temperature and precipitation fields in

our preliminary simulations when compared with val-

idation datasets (see section 4), and these are the me-

teorological fields of primary interest for modeling cases

of human plague.

The final production simulations were performed for

month-long intervals; that is, 134 one-month simula-

tions were performed for 1999–2009, each beginning

on the last day of the previous month to allow 24 h for

spinup of the modeled fields. Because of computational-

resource constraints, we performed 1 yr of monthly

simulations—2006—with all four domains turned on;

the additional 10 yr of simulations were performed with

only the outer two domains (56 and 18 km) turned on,

and the comparatively expensive inner domains (6 and

2 km) turned off. A statistical technique, presented below,

was subsequently employed to downscale the 18-km

output to 2 km, for the 10-yr period in which the inner

domains were turned off, by calibrating the 18-km out-

put with the 2-km output for the overlap year of 2006.

Henceforth, we refer to the 2-km WRF-based dataset

resulting from the dynamical–statistical technique as

WRF-DS. The analysis of the climate of the West Nile

region of Uganda presented in this paper focuses on

the WRF-DS results because they are being employed

for the plague-modeling effort and because a detailed

study of the general climate of the West Nile region

covered by the WRF-DS domain has not previously

been performed.

b. Statistical downscaling

We statistically downscaled the WRF-simulated monthly

mean temperature Tavg, maximum temperature Tmax,

minimum temperature Tmin, relative humidity RHavg,

specific humidity Qavg, and total rainfall Ptot from 18

to 2 km using the bias-correction spatial disaggrega-

tion (BCSD) method of Wood et al. (2004). BCSD is

well suited for downscaling climate model output of

TABLE 1. Physical parameterizations used in WRF simulations.

Parameterization Choice Citation

Cloud microphysics Thompson Thompson et al. (2004)

Longwave radiation RRTM Mlawer et al. (1997)

Shortwave radiation Dudhia Dudhia (1989)

Surface layer Eta similarity Janjic (2002)

Land surface Noah LSM Chen and Dudhia (2001)

Planetary boundary layer Mellor–Yamada–Janjic Janjic (2002)

Cumulus clouds Grell–Devenyi Grell and Devenyi (2002)

JULY 2012 M O N A G H A N E T A L . 1205

comparatively coarse spatial resolution in regions where

in situ data are sparse, because finer-scale climate-model

fields can be used as the training dataset, in lieu of

gridded point-based observational data (which are not

available in our region of interest). Wood et al. (2004)

found that BCSD yielded better results for downscaled

rainfall and temperature fields when compared with

spatial linear interpolation and spatial disaggregation

without bias correction.

The BCSD method was applied to generate daily 2-km

WRF-DS fields for 1999–2009. Monthly and annual WRF-

DS means and totals were then calculated from the daily

fields. One important assumption of our bias correction is

that the 2-km fields with which we are calculating the

bias provide an accurate (unbiased) representation of

the rainfall, temperature, and humidity fields. Although

this clearly is not a perfect assumption, we demonstrate in

section 4 that our results compare well to satellite-based

and in situ observations of rainfall, temperature, and

humidity, especially given that the satellite-based and in

situ observations are subject to their own sources of

uncertainty. Another important assumption is that our

statistical downscaling technique, which is calibrated for

each month of 2006, is applicable to months in other

years. This assumption is implicitly tested by our vali-

dation analysis in section 4, but we test it here too. As

was done for 2006, we turned on all four domains for the

February and August WRF simulations for 2003 to

perform an independent comparison of the WRF-DS

fields with purely dynamically downscaled fields. Feb-

ruary and August were chosen because they represent

two extremes in the annual rainfall cycle and nearly two

extremes in the annual temperature cycle. The year 2003

was randomly chosen. The WRF-simulated fields versus

WRF-DS fields for February and August of 2003 are

shown for Tavg in Fig. 2 and Ptot in Fig. 3. The WRF-

simulated fields and WRF-DS fields visually appear to

be nearly identical for both Tavg and Ptot. The domain-

wide February (August) Spearman rank pattern cor-

relations are 0.995 (0.997) for Tavg and 0.944 (0.857)

FIG. 2. (left) The 2-km domain WRF-simulated 2003 monthly Tavg (8C) for (top) February and (bottom) August. (middle) As in the left

panels, but for WRF-DS Tavg. (right) The difference of WRF-DS minus WRF-simulated Tavg.

1206 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 51

for Ptot, confirming the similarity between the WRF-

simulated fields and WRF-DS fields. The difference

fields for Tavg are generally less than 60.48C over the

domain and are nearly zero within our study region

north of Lake Albert. The difference fields for Ptot are

large in February because they are expressed as per-

centages of total WRF-simulated rainfall, which is

very low: only 56% of grid boxes in the study region

fall within 625% of the simulated values. When ex-

pressed in absolute terms (not shown), however, the

differences are clearly small: 99% of grid boxes within

the study region are within 65 mm. In August, when

rainfall is near its peak, the percentage differences are

smaller than for February: 75% of grid boxes within

the study region fall within 625% of the simulated

values. In summary, this comparison for 2003 indicates

that the BCSD statistical downscaling technique is

robust and that the resulting uncertainty that is in-

troduced into the 2-km meteorological fields by choosing

2006 as the calibration year is reasonable in comparison

with the uncertainty among the satellite-based and in

situ estimates of Tavg and Ptot that will be presented in

section 4.

c. Comparison and validation data

Validating the WRF-DS results is challenging in

northwestern Uganda because there is a dearth of in situ

data, and therefore we employ the strategy of using

several temperature and precipitation datasets to help to

characterize uncertainty. The datasets used for com-

parison with and validation of the WRF-DS results are

summarized in Table 2. The first four rows summarize

the spatially explicit precipitation datasets. The primary

precipitation dataset we employ is the Climate Predic-

tion Center Morphing Technique (CMORPH), which

uses precipitation estimates from low-orbiting-satellite

microwave observations whose features are transported

by spatial propagation information from geostationary

infrared satellite data (Joyce et al. 2004). The version

of CMORPH used in this study has ;8-km resolution,

making it the best suited for comparison with the 2-km

WRF-DS rainfall. The second precipitation dataset is

FIG. 3. As in Fig. 2, but for Ptot (kg m22). Differences are expressed as a percentage of WRF-simulated Ptot.

JULY 2012 M O N A G H A N E T A L . 1207

the 0.258 Tropical Rainfall Measuring Mission Project

(TRMM) 3-hourly ‘‘TRMM and others rainfall estimate’’

(Huffman et al. 2007). The TRMM product combines

microwave and infrared information from a variety of

satellite instruments. The third precipitation dataset is

the 0.108 NOAA African Rainfall Climatology (ARC)

product (Love et al. 2004), which is a more temporally

stable version of the NOAA/Climate Prediction Center

operational daily precipitation estimate that supports

the U.S. Agency for International Development Famine

Early Warning System Network (Xie and Arkin 1996).

ARC rainfall estimates are based on the 3-hourly Geo-

stationary Operational Environmental Satellite pre-

cipitation index (Janowiak and Arkin 1991) and Global

Telecommunications System data. The fourth precipi-

tation dataset is the WorldClim 30 arc-s (;1 km) product,

a long-term estimate of monthly global precipitation that

is based on station records interpolated with thin-plate

smoothing splines (Hijmans et al. 2005). Dinku et al.

(2007) evaluated the first three datasets versus a rela-

tively dense station gauge network over the Ethiopian

highlands in East Africa for 10-day rainfall totals. They

found that CMORPH and TRMM compared reason-

ably to the gauge data according to a variety of statistical

measures while ARC had a relatively large mean error

and low correlation coefficient when compared with the

gauge data. The authors cautioned that the accuracy of

the precipitation datasets is probably regionally variable

because of the different methods and data sources each

employs. Asadullah et al. (2008) evaluated five rainfall

datasets, including CMORPH, TRMM, and ARC, over

Uganda and found that CMORPH and TRMM corre-

lated most closely with station records of rainfall amount

and occurrence. Like Dinku et al., however, Asadullah

et al. (2008) suggested that more than one rainfall dataset

should be used for any application because of the relative

strengths and weaknesses of each. Therefore, we employ

all four precipitation datasets described here to partially

account for methodological and data uncertainty in the

estimates.

We also employ two spatially explicit temperature

datasets. The first is the WorldClim 30 arc-s long-term

monthly-average near-surface temperature product

(Hijmans et al. 2005), which was created with the same

method that is described above for the WorldClim

precipitation. The second is the 0.058 monthly daytime

and nighttime clear-sky land surface skin temperature

from the MODIS instrument aboard the NASA Terra

satellite. We also use monthly temperature and precipi-

tation observations from the only known continuously

operating in situ weather station in the West Nile region:

the Arua, Uganda, airport (3.058N, 30.98E; Fig. 1). The

reliability of the Arua data cannot be confirmed; as will

be shown subsequently, however, the Arua data com-

pare reasonably to the other datasets in Table 2 when

those data are interpolated to the Arua coordinates. It is

noteworthy that we examined the regional representa-

tiveness of the Arua record prior to undertaking the

subsequent analysis. Monthly areal-averaged values of

temperature, humidity, and rainfall for the West Nile

region from the gridded datasets correlate strongly with

point values from Arua. Therefore, the Arua record is

considered to be representative of the entire West Nile

region in terms of its temporal variability.

4. Results and discussion

a. Climate of the West Nile region

The spatial plague modeling of MacMillan et al. (2012)

that is summarized in section 4c necessarily employed the

10-yr-average (1999–2008) 2-km WRF-DS fields for Ptot,

Tavg, Tmax, Tmin, RHavg, and Qavg. Therefore, in this

section we seek to describe the spatial variability and

annual cycle of the WRF-DS variables, specifically for

the West Nile region, from a climatological (i.e., multi-

year average) perspective and to assess their veracity

TABLE 2. Datasets used for comparison with and validation of WRF-DS.

Dataset Variables Citation Span Download source

CMORPH Rainfall (;8 km hourly) Joyce et al. (2004) Dec 2002–present http://www.cpc.ncep.noaa.gov/

products/janowiak/cmorph_

description.html (long-term

record by request)

TRMM Rainfall (product 3B42; 0.258 daily) Huffman et al. (2007) Jan 1998–present http://mirador.gsfc.nasa.gov

ARC Rainfall (0.18 daily) Love et al. (2004) Jan 1982–present ftp://ftp.cpc.ncep.noaa.gov/fews/

AFR_CLIM/DATA/

WorldClim Rainfall and near-surface temperature

(30 arc-s monthly)

Hijmans et al. (2005) — http://www.worldclim.org/

MODIS MOD11C skin temperature

(0.058 monthly)

Wan (2009) Mar 2000–present ftp://e4ftl01u.ecs.nasa.gov/

MOLT/MOD11C3.005

Arua weather

obs

Rainfall, near-surface air temperature,

and humidity

Unpublished Jan 1999–present — (obtained from CDC)

1208 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 51

relative to a variety of other datasets. Our climatography

is based on the full 11-yr WRF-DS dataset spanning 1999–

2009 (the 2009 simulations were performed in support of

related plague-modeling efforts but were not used for the

spatial plague-modeling effort described in section 4c).

Maps of 1999–2009 annual Tavg for WRF-DS and

WorldClim are presented in the top panels of Fig. 4. The

spatial patterns of monthly Tavg are similar to the annual

fields, and therefore they are not shown. In general, the

WRF-DS and WorldClim maps are remarkably similar

and show clearly the first-order effect of topography on

the spatial variability of Tavg. WRF-DS tends to have

a larger range between high and low temperatures along

the elevation gradients than do the WorldClim data.

This may be related to the differential spatial impact of

humidity and clouds on Tavg. Whereas WRF-DS resolves

these moisture variations, the interpolation method em-

ployed to generate the WorldClim data does not ex-

plicitly account for moisture variability, and therefore

the WorldClim spatial temperature patterns more closely

follow topographic variations. Within the West Nile study

region, annual Tavg in WRF-DS decreases from about

268C along the Albert Nile on the eastern boundary

(about 600 m MSL), to about 198C along the south-

western boundary with the DRC (about 1600 m MSL),

representing a substantial temperature gradient within a

relatively modest distance of about 75 km.

The annual cycle of Tmax, Tmin, and Tavg at Arua is

shown in the bottom panel of Fig. 4 for the observations,

WRF-DS, WorldClim, and MODIS. The MODIS data

are surface skin temperatures and thus are not strictly

comparable to the other near-surface temperature fields.

The MODIS data are included only to provide confir-

mation that the observations—which are subject to un-

certainty that is due to aging instrumentation that must

be manually read and digitized—are reasonable. It is

also noteworthy that the Arua observations were likely

used in the creation of WorldClim and that therefore

WorldClim may not provide an independent measure

of temperature at Arua in that regard. Relative to the

observations and WorldClim, WRF-DS Tmax has a cold

bias ranging from about 18C during the dry season

[December–February (DJF)] to about 28C during the

short-rains season (August–October). The cold bias is

manifested more subtly in Tavg and is nearly negligible

for Tmin. The cold bias may be related to excessive

cloudiness aloft, rather than to humidity near the sur-

face, because there is a dry bias in the near-surface hu-

midity fields (discussed below) that would tend to

promote warmer near-surface temperatures through

greater sensible heat flux partitioning.

The annual cycle of Tmax has an amplitude of ;58–

68C, with maxima during the dry season (DJF) and

minima during the period leading up to and including

the short-rains season (June–October). Temperatures

Tavg and Tmin have annual cycles with roughly the same

phase as Tmax but with smaller amplitudes of ;38–48C

and ;28C, respectively. The smaller annual amplitude

of Tmin relative to Tmax can be explained by compar-

ing the WRF-DS daytime [1500 local standard time

(LST)] and nighttime (0300 LST) surface energy balance

for February and August of 2006 (Table 3). During

February—a typical hot, dry month—the daytime sen-

sible heat flux consumes the majority of the net solar

radiation (QH 5 2404 W m22; negative fluxes point

away from the surface), driving Tmax higher. In contrast,

during August—a typical cool rainy month—the day-

time latent heat flux consumes most of the net solar

radiation (QE 5 2351 W m22) for evaporation, leaving

little energy to partition toward raising Tmax. This re-

versal of partitioning among the turbulent heat fluxes

between the dry and rainy seasons is responsible for the

comparatively large annual amplitude of Tmax. The in-

fluence of the nighttime surface energy balance on Tmin

is mechanistically different due to the absence of solar

radiation. Nighttime radiative heat loss L* is greater in

February than it is in August because higher surface

temperatures occur during the daytime in February. The

radiative heat loss is largely replaced by increased heat

flux upward from the ground to the surface QG, howe-

ver—a compensating effect that explains the relatively

small annual amplitude of Tmin.

Maps of 1999–2009 annual RHavg and Qavg for WRF-

DS are presented in the top panels of Fig. 5. As with

temperature, monthly humidity maps are not shown

because they exhibit spatial patterns that are similar to

those of the annual maps. There are no observationally

based spatial datasets of humidity for comparison to

confirm the veracity of WRF-DS. Inspection reveals

that RHavg increases and Qavg decreases along the east-

to-west elevation gradient within the West Nile study

area. This inverse relationship indicates that the spatial

variability of RHavg is strongly determined by the tem-

perature gradient (Fig. 4). In contrast, along the gently

sloping terrain of the DRC to the west of the study area,

Qavg and RHavg are positively associated (blue-shaded

areas), presumably because the moisture gradients

are largely determined by advective moisture trans-

port through the Congo air mass rather than being

topographically driven by temperature changes. (Figure

4 confirms that there is little temperature variability

across this region of the DRC.)

The 2007–09 annual cycles for WRF-DS and observed

daytime RHavg at Arua are shown in the bottom panel of

Fig. 5. Only 3 yr of daytime values (average of RH

at 0300 and 1500 UTC) are available in the Arua

JULY 2012 M O N A G H A N E T A L . 1209

observational record. The annual cycle of RHavg follows

that for Ptot (discussed below), having the lowest ob-

served values (;50%) during the dry season and the

highest values (;70%) during the short-rains season.

The WRF-DS annual cycle has a larger amplitude and a

clear dry bias during the dry season. The exact reasons for

the dry bias are unclear because of the numerous po-

tential causes (parameterization choices, uncertainty

FIG. 4. (top) The 1999–2009 Tavg (8C) for the (left) 2-km WRF-DS domain and (right) WorldClim. (bottom)

The 1999–2009 mean annual cycle of Tmax, Tavg, and Tmin at Arua for the observations (OBS), WRF-DS, MODIS

(2001–09 only), and WorldClim (time span varies). Pink shading denotes 61 std dev from the observed mean.

1210 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 51

in land surface parameters, etc.). Ruiz et al. (2010),

however, performed a series of WRF sensitivity studies

over South America from the equator southward and

found persistent near-surface dry humidity biases that

were associated with both LSM options available in

WRF (Noah and the Rapid Update Cycle). The dry

biases were not present when the authors employed the

simple five-layer slab treatment for the surface. They

suggested the biases may be related to the initialization

of soil moisture and its evolution in the LSMs, in ac-

cordance with other recent studies (e.g., Case et al.

2008). We employed spunup soil fields from GLDAS for

our initialization of the Noah LSM, as suggested by Ruiz

et al. (2010), which did not remedy the problem. Further

investigation of the near-surface dry bias in WRF-DS is

beyond the scope of this paper.

Maps of 2003–09 annual Ptot for WRF-DS, CMORPH,

TRMM, ARC, and WorldClim are presented in Fig. 6.

The shorter period was used for the spatial plots so as to

be comparable with the CMORPH record. The patterns

of rainfall across the domain are broadly similar, but

magnitudes vary substantially. The differences are partly

due to the differential spatial resolutions of the datasets

but not entirely; WorldClim has the highest spatial reso-

lution (;1 km) but has values that are characteristic of

lower-resolution products such as TRMM (;25 km).

One common feature among the datasets is the contrast

between the rainier Congo Basin in the western domain

that is fed by the humid Congo air mass and the drier

Western Rift Valley and East African Plateau in the

eastern domain, where orographic forcing plays an im-

portant role in mechanically lifting the relatively stable

air masses from the northeast and southeast monsoons

to generate rainfall (Nicholson 1996). The West Nile

study region straddles the boundary between these two

regimes. The sharp contrast is facilitated by the moisture

barrier formed by the mountains along the DRC–Uganda

border. An additional rain-shadow effect is caused by the

bisection of the Western Rift Valley through the East

African Plateau (location indicated by the dark green

valley of the Albert Nile River in Fig. 1c). The rain

shadow is evident by the line of rainfall minima that

extend from Lake Albert northward along the Albert

Nile into southern Sudan. Another common feature

among the datasets is a convective maximum to the east

of the West Nile region (;38N, 328E). Analysis of the

diurnal development of rainfall (not shown) indicates

that this maximum is linked to midday convective ac-

tivity that is triggered over the mountainous regions to

the east and northeast of the model domain—for ex-

ample, along the Uganda–Kenya border (Fig. 1b). The

prevailing easterly winds at mid- to low atmospheric levels

gradually propagate the convection toward the West

Nile region. The thunderstorms continue development

as they move westward and generally become fully de-

veloped in the late-evening and early-morning hours.

Maps of 2003–09 Ptot for WRF-DS and CMORPH

for four representative months are presented in Fig. 7.

Other datasets are excluded for simplicity and because

previous studies have suggested that CMORPH yields

realistic rainfall estimates over Ethiopia and Uganda in

East Africa (Dinku et al. 2007; Asadullah et al. 2008).

The patterns and magnitudes of monthly Ptot are similar

among CMORPH and WRF-DS, except in May when

CMORPH resolves a band of larger rainfall in the West

Nile region and throughout the northeastern portion of

the model domain. The seasonal migration of the ITCZ

is evident in the sequence of months shown in Fig. 7,

from January in boreal winter when it is far to the south,

to May when it is migrating northward through the study

region, to June when it is far enough northward to re-

duce rainfall slightly in the West Nile region (and more

so to the southeast), and finally to August when it is

again migrating southward though the region.

The 1999–2009 annual cycles of the median rainfall

totals and frequencies at Arua are shown in Fig. 8. The

subtle bimodal nature of West Nile rainfall is evident

by a short dip in June Ptot within what is otherwise

a gradual increase in rainfall beginning in March,

peaking in August–October, and sharply dropping

off in November (Fig. 8a). The pink shading, which

denotes the bounds between the 20th and 80th per-

centiles in the observed rainfall totals, indicates that

there is substantial year-to-year variability during the

peak of the rainy season, with the exception of October.

The comparatively small bounds about the October

median indicate that it is a consistently rainy month

from year to year. Overall, the shape of the annual

cycle and the dominance of the autumn rainy season

suggest that the West Nile region is in a transition zone

between the bimodal rainfall regimes that dominate

the equatorial regions and the unimodal regimes that

characterize Sahel rainfall (Nicholson 2000; Conway

et al. 2005).

TABLE 3. WRF-simulated components of the surface energy

budget at Arua for February and August 2006 for 0300 and

1500 LST. Notations are K* 5 net shortwave radiation; L* 5 net

longwave radiation; QG 5 ground heat flux; QH 5 sensible heat

flux; QE 5 latent heat flux; and QS 5 heat storage (residual).

Positive-valued terms are directed toward the surface.

Month Hour K* L* QG QH QE QS

Feb 0300 0 261 44 19 23 21

1500 700 2183 268 2404 250 24

Aug 0300 0 239 25 13 1 0

1500 609 288 232 2135 2351 4

JULY 2012 M O N A G H A N E T A L . 1211

The WRF-DS annual cycle of Ptot nearly mirrors the

Arua observations; however, the WRF-DS rainfall is

higher by ;50% during the rainiest months. It is not

clear what proportion of the bias is due to the model

simulations versus being due to undercatch by the Arua

rain gauge. Undercatch is a common issue with older

bucket or funnel gauges like the type used at Arua (e.g.,

Ciach 2003). That the other gridded rainfall datasets

FIG. 5. (top) The 1999–2009 annual (left) RHavg (%) and (right) Qavg (g kg21) for the WRF 2-km domain. (bottom) As in

Fig. 4, but for 2007–09 daytime (0900 and 1500 LST) RHavg at Arua for the observations and the 2-km WRF-DS domain.

1212 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 51

FIG. 6. The 2003–09 mean annual Ptot (kg m22) for (a) WRF-DS, (b) CMORPH, (c) TRMM, (d) ARC, and

(e) WorldClim. The datasets are summarized in Table 2.

JULY 2012 M O N A G H A N E T A L . 1213

have smaller values than WRF-DS during the rainy

season suggests that model bias plays a role, although

the fact that they resolve progressively smaller rainfall

totals as their resolution decreases suggests that spatial

resolution has an important affect on rainfall estimates

in this region. The rainfall-frequency data (Figs. 8b–d)

essentially have the same annual cycle as the totals, sug-

gesting that the type of rainfall (i.e., drizzles vs down-

pours) is consistent throughout the year. WRF-DS has a

clear bias toward simulating too many days with low

rainfall (Figs. 8b,c) and too few days with high rainfall,

which is a common issue for atmospheric models (e.g.,

Dai 2006). It is also possible that the near-surface dry

bias noted above (Fig. 5) affects the WRF-DS rainfall

frequency distribution.

In summary, we find that the 2-km WRF-DS climate

fields resolve the 11-yr-average spatial variability and

annual cycle of temperature, humidity, and rainfall rea-

sonably in comparison with satellite-based and in situ

records. Given the uncertainty in all of the datasets,

especially rainfall, differences between WRF-DS and

other data do not necessarily infer that WRF-DS is less

accurate than the data. For example, it is possible that

relative to the other datasets WRF-DS may more re-

alistically depict the nonlinear local-scale processes that

lead to the rainfall patterns shown in Fig. 6. Regardless

of which dataset is examined, it is clear that the spatial

variability and annual cycle of the temperature, humidity,

and rainfall gradients in the West Nile region are

strongly influenced by terrain. With respect to rainfall,

the West Nile region appears to be in a transition zone

between the drier East African Plateau and the wetter

Congo Basin. In addition, the annual cycle of Arua

rainfall suggests that the region is within a north-to-

south transition zone between the unimodal rainfall re-

gimes of the Sahel and the bimodal equatorial regimes.

Such complexity can drive substantial interannual vari-

ability, as is evident by the large 20th and 80th percentile

bounds during the rainiest months (Fig. 8), and likely

confounds the influence of large-scale climate drivers such

as ENSO, which we now examine in section 4b.

b. Tropical-ocean influence on West Nile rainfall

In this paper we apply the WRF-DS fields to model

the long-term-average spatial variability of human plague

as a function of climatic fields. In future work, we aim to

model the temporal variability of plague over the West

Nile region and, if possible, to predict the annual plague

season with several months of lead time so that years

with high plague risk can be identified in advance. In

support of these future efforts and given the linkages

between plague and rainfall (described in section 4c),

here we examine the impact of teleconnections arising

from tropical-ocean variability on rainfall in the West

Nile region up to 1 yr in advance, by correlating regional

rainfall with a variety of SST and SST–atmosphere

FIG. 7. The 2003–09 mean monthly Ptot (kg m22) for (top) WRF-DS and (bottom) CMORPH for December, May, June, and September.

1214 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 51

indices for the tropical Pacific, Atlantic, and Indian

Oceans (Table 4). We employ the regionally represen-

tative time series data from the Arua observational re-

cord, WRF-DS, and satellite-based time series from

ARC and TRMM. CMORPH was not used because the

record is too short to provide meaningful results. We

compared the regional WRF-DS time series with the

point-based time series from Arua and found that the

Arua record is highly representative of the interannual

rainfall variability across the plague study region. Three-

month running means of monthly rainfall totals, as well

as frequency of days per month with rainfall .2, .10,

and .20.0 mm, were detrended and normalized and

then correlated with detrended and normalized 3-month

running means of each tropical-ocean index for 1999–

2009. There were only modest differences among the

correlation patterns for rainfall totals when compared

with rainfall frequency (the frequency data provide in-

sight into associations that are related to rainfall inten-

sity, such as extremes). Therefore, the results discussed

here focus on rainfall totals (i.e., Ptot).

Figure 9 shows the results for the two indices that

we found to have the most robust correlation with

West Nile rainfall: the Nino-3.4 and the tropical North

Atlantic (TNA) indices. Although most of the ENSO-

based indices yielded similar correlation patterns, the

correlations with West Nile rainfall were strongest for

the Nino-3.4 index, a broad measure of SSTs in the

equatorial central Pacific Ocean. Of interest is that most

of the literature suggests that the strongest ENSO tele-

connection with EEA rainfall is manifested as a positive

correlation for lags from zero to several months during

FIG. 8. The 1999–2009 median annual cycle of (a) Ptot (kg m22) and frequency of days with rainfall of greater than

(b) 2, (c) 10, and (d) 20 mm at Arua. CMORPH data span 2003–09 only. Pink shading indicates the region between

the observed 20th and 80th percentiles.

JULY 2012 M O N A G H A N E T A L . 1215

the latter part of the short-rains season from October to

December (e.g., Ogallo 1988). In the West Nile region,

there are positive correlations during October–December,

but they are statistically insignificant among all four rain-

fall datasets for the 1999–2009 period. There is a clear

positive relationship between ENSO and West Nile rain-

fall that appears to be especially robust among the four

datasets during March–April, with rainfall lagging

ENSO by ;0–11 months. Other studies have also sug-

gested that the EEA long rains are correlated with

ENSO (e.g., Camberlin and Philippon 2002); the results

were highly variable in space, however, and no conclu-

sions were drawn specifically for the West Nile region.

The correlations between the TNA and West Nile

rainfall yield a pattern that is similar to those for ENSO

during approximately January–September, which is

consistent with literature that notes the correlation be-

tween tropical northern Atlantic SSTs and ENSO

(Enfield and Mayer 1997). The periods having the most

robust TNA–rainfall correlations overlap with those for

Nino-3.4 but remain statistically significant for a shorter

period. The strongest correlations occur during March–

April when rainfall lags the TNA by about 2–4 months.

Of interest is that no consistent correlations between

Indian Ocean SST variability and West Nile rainfall

were found (not shown), despite strong relationships

that have been found for other regions of EEA pre-

viously (e.g., Birkett et al. 1999). It has been suggested

that only very strong Indian Ocean dipole events have

significant impacts on EEA rainfall (Black et al. 2003),

and therefore it is possible that the weak correlations

with the dipole mode index (DMI) partly result from

the relatively short 11-yr correlation window, for which

only a handful of strong IOD events occurred.

In summary, our results suggest that the signs of the

correlations between ENSO and West Nile rainfall are

similar to those found in other regions of EEA but that

the most significant relationships are for the long-rains

season in March–April rather than the end of the short-

rains season as found elsewhere in EEA. The differen-

tial influence of teleconnections on West Nile rainfall

relative to other regions of EEA may be partly due to

the location of the West Nile region along the west-

ernmost reaches of EEA and the Western Rift Valley,

where the influence of the moist, unstable Congo air

mass from central equatorial Africa likely has a larger

influence on weather. SST variability in the tropical

northern Atlantic likewise is significantly correlated

with March–April rainfall in the West Nile region 2–4

months in advance but is also correlated with ENSO

TABLE 4. List of climate indices that are based on tropical-ocean and ocean–atmosphere variability. All datasets were obtained from the

NOAA/Environmental Systems Research Laboratory (http://www.esrl.noaa.gov/psd/data/climateindices/list/), with the exception of the

DMI, which was obtained from the Japan Agency for Marine Earth Science and Technology (http://www.jamstec.go.jp/frcgc/research/d1/

iod/HTML/Dipole%20Mode%20Index.html).

Dataset Acronym Description Citation

Tropical northern

Atlantic index

TNA Anomaly of monthly mean tropical Atlantic

SSTs from 5.58 to 23.58N and from

158 to 57.58W

Enfield et al. (1999)

Tropical southern

Atlantic index

TSA Anomaly of monthly mean SST from

08 to 208S and from 108E to 308W

Enfield et al. (1999)

Nino-1.2 index Nino-1.2 Monthly mean tropical Pacific SST from

08 to 108S and from 908 to 808W

NOAA Climate Diagnostics

Bulletin (various)

Nino-3 index Nino-3 Monthly mean tropical Pacific SST from

58N to 58S and from 1508 to 908W

NOAA Climate Diagnostics

Bulletin (various)

Nino-3.4 index Nino-3.4 Monthly mean tropical Pacific SST from

58N to 58S and from 1708 to 1208W

Barnston et al. (1997)

Nino-4 index Nino-4 Monthly mean tropical Pacific SST from

58N to 58S and from 1608E to 1508W

NOAA Climate Diagnostics

Bulletin (various)

Trans-Nino index TNI Standardized Nino-1.2 minus Nino-4 with

5-month running mean, then restandardized

Trenberth and Stepaniak (2001)

Southern Oscillation

index

SOI Difference of standardized Tahiti and Darwin

monthly mean sea level pressure

Ropelewski and Jones (1987)

Multivariate ENSO

index

MEI A combination of sea level pressure, near-surface

winds and air temperature, SSTs, and cloud

fraction in the tropical Pacific

Wolter and Timlin (1998)

Bivariate ENSO time

series

BEST A combination of the Nino-3.4 index and SOI Smith and Sardeshmukh (2000)

Dipole mode index DMI Monthly mean difference of tropical SSTs in the

western (108S–108N, 508–708E) and eastern

(08–108S, 908–1108E) Indian Ocean

Saji et al. (1999)

1216 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 51

FIG. 9. Pearson’s correlation coefficients for 1999–2009 monthly Ptot at Arua (for the ob-

servations, WRF-DS, TRMM, and ARC) vs the 0–12-month lagged monthly (left) Nino-3.4

and (right) TNA indices. Three-month running means are applied to both the rainfall data and

the indices. Means are centered about the month shown on the abscissa; for example, values

centered on February (F) correspond to the JFM running-mean total rainfall. Lags are also with

respect to the center month. Hatching (in red and blue contours) indicates statistical signifi-

cance (correlation r . 60.6) per the Student’s t test.

JULY 2012 M O N A G H A N E T A L . 1217

variability. Given these relationships, it may be possible to

predict the long rains in the West Nile region several

months in advance, although the utility of such predictions

in support of projecting human plague risk is uncertain.

c. Modeling human plague occurrence withWRF-derived climate data

In this section we briefly summarize the application of

the 2-km WRF-DS climate fields to model the spatial

distribution of human plague cases in the West Nile

region. The reader is referred to the companion study by

MacMillan et al. (2012) for a detailed description of the

plague model and method.

Approximately 2000 suspected or probable plague

cases occurred in the West Nile region from 1999 to 2007

(Winters et al. 2009). To ensure an accurate plague-case

dataset, during the 2008–09 plague season, which spanned

approximately from August through March, laboratory

confirmation was conducted for clinically suspected pla-

gue cases. Likewise, control (nonoccurrence) locations

were determined by mapping locations with similar ac-

cess to health care but for which no cases of clinically

diagnosed plague were reported from 1999 to 2008. A

total of 36 case and 72 control points were used in our

model development (Eisen et al. 2010).

Logistic regression modeling was applied to a vari-

ety of landscape variables (e.g., elevation) and the

1999–2008 average monthly WRF-DS Tmax, Tmin, Tavg,

RHavg, Qavg, and Ptot fields at each case and control

location. Prescreening was performed to reduce the

number of climatic variables by discarding strongly

correlated fields. Several logistic regression models

were then developed and tested for goodness of fit, a

subgroup of leading models was retained, and finally

the most parsimonious model was identified with Akaike’s

information criterion (Akaike 1974).

The leading logistic regression model reveals that

within high-elevation sites (above 1300 m) plague risk is

positively associated with rainfall during February, Oc-

tober, and November and is negatively associated with

rainfall during June. The model explanatory variables

are consistent with prior studies that associate plague

with higher-elevation, rainy areas in Africa (e.g., Davis

1953). The overall model accuracy is 94% (Table 5),

which is a substantial improvement relative to the 81% in

a previous model that did not employ climate data (Eisen

et al. 2010). The revised model retains sensitivity from the

previous model in which 89% of case locations were

correctly classified by the model as elevated risk. Using

WRF-DS predictors, specificity is improved by nearly

20%, with 90% of control locations correctly classified

as low risk. The negative predictive value (NPV), which

measures the percentage of time that a model correctly

classifies control locations as low risk, was similar to the

previous model. The positive predictive value (PPV),

which assesses the accuracy of the model for predicting

case occurrence, is improved by 22% using WRF-de-

rived predictors. As expected when modeling the oc-

currence of a rare disease, PPV is low relative to other

model diagnostics in this and the previous model.

Taken together, our model predictors suggest that

areas that receive increased but not continuous rainfall

provide ecologically conducive conditions for Y. pestis

transmission in the West Nile region. We seek to de-

velop a hypothesis to explain why these specific rainfall

associations in February, October, November, and June

are associated with the spatial distribution of plague

cases in the West Nile region. February is the driest

month, and subsequently rainfall begins gradually in-

creasing in March until it peaks during August–October,

and it begins decreasing again in November. Therefore,

above-average rainfall during February, and again in

October–November, may extend the growing season at

both of its tails (in turn providing more agricultural

forage near huts for plague-infested rats). June repre-

sents a comparatively dry spell between the two rainier

periods in March–May and July–October. Drier June

conditions could provide the essential increase in sun-

light for photosynthetic cycles during a critical growth

stage for crops or other vegetation that in turn attract

plague-infected rats or could provide adequate condi-

tions for drying crops for long-term storage in or near

living huts (which also attracts rats). Evidence for the

latter is suggested by Roberts (1936), who noted that

excessive rainfall in Kenya in the 1920s led to abundant

harvests but did not allow adequate time to dry crops

and prevent spoilage. An ongoing field program in

Uganda and concurrent efforts to model the temporal

variability of human plague will allow us to test and

refine our hypothesis.

5. Conclusions

In this paper we describe a 2-km WRF-based data-

set generated with a hybrid statistical–dynamical

TABLE 5. Comparison of performance statistics between the

MacMillan et al. (2012) and Eisen et al. (2010) plague risk models.

The MacMillan et al. (2012) model employed the WRF-DS climate

fields that are described in the study presented here. Metrics are

described in the text. All units are in percent.

Model Accuracy Sensitivity Specificity PPV NPV

MacMillan

et al. (2012)

94 89 90 82 94

Eisen et al. (2010) 81 89 71 60 93

1218 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 51

downscaling technique and assess its veracity in com-

parison with satellite-derived and in situ records. We

find that WRF-DS resolves the spatial variability and

annual cycle of temperature, humidity, and rainfall

reasonably when compared with satellite-based and in

situ records, albeit with some biases—most notably a

near-surface humidity dry bias that may be linked to the

Noah LSM. WRF-DS, along with the observationally

based datasets, is used to examine the general climatic

features of the greater West Nile region. The climatic

gradients in the West Nile region are strongly influenced

by terrain. The West Nile region lies in a transition

zone between the drier East African Plateau and the

wetter Congo Basin. The annual rainfall cycle is largely

unimodal—similar to the Sahel to the north—with a dry

season from December to February followed by grad-

ually increasing rainfall from March through the boreal

autumn short-rains peak. A modest dry spell in June

imparts a slight bimodal aspect to the annual rainfall

cycle that is characteristic of rainfall in many other

regions of EEA. As shown above, gaining an under-

standing of these climatic features, especially the rainfall

variability, has been important for interpreting our

plague-modeling results and for developing a hypothesis

to explain plague seasonality.

We examine the influence of tropical-ocean vari-

ability from the Atlantic, Pacific, and Indian Oceans

on West Nile rainfall and find a positive correlation

between March–April rains and ENSO (through the

Nino-3.4 index) that extends from zero lag to at least

several months of lag, suggesting the possibility for

seasonal predictions, although it is not yet clear whether

such predictions might be beneficial for modeling hu-

man plague. Of interest is that other studies find the

strongest associations between ENSO and EEA in bo-

real autumn. We propose that the differential influence

of the ENSO teleconnection relative to other regions of

EEA may be partly due to the location of the West Nile

region within the complex climatic transition zone dis-

cussed above.

We summarize the application of the 2-km WRF-DS

climate fields by MacMillan et al. (2012) to model the

spatial distribution of human plague cases in the West

Nile region. It is not clear whether biases noted in some

of the WRF-DS fields may have an impact on the plague-

model results. Employing WRF-DS fields improves the

overall plague-model accuracy by 13% relative to a

previous model that did not employ climate data (Eisen

et al. 2010), however. Human plague cases are positively

associated with rainfall during February, October, and

November and are negatively associated with rainfall

during June (during the brief dry spell between the long

rains and short rains). Our results suggest that areas that

receive increased but not continuous rainfall provide

ecologically conducive conditions for Y. pestis trans-

mission in the West Nile region. Continued efforts to

refine our hypothesis and improve our understanding of

the conditions required to maintain enzootic cycles may

ultimately be used to target limited surveillance, pre-

vention, and control resources to areas most at risk or to

predict the future range of human plague.

Acknowledgments. This work was funded by the CDC

and the U.S. Agency for International Development

through an interagency agreement with the National

Science Foundation. Doctor Daniel Steinhoff provided

useful comments. Sources and citations for datasets

employed in this paper are described within. Special

thanks are given to Russell Enscore and Jeff Borchert

of CDC, and to the Ugandan Virus Research Institute,

for providing in situ weather data from the airfield at

Arua. Doctor Kevin Griffith (CDC) provided invalu-

able insight and assistance in coordinating field work.

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