Land Surface Feedbacks on Spring Precipitation in the Netherlands

EMMA E. DANIELS AND RONALD W. A. HUTJES

Earth System Science Group, Wageningen University and Research Center, Wageningen, Netherlands

GEERT LENDERINK

Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands

REINDER J. RONDA AND ALBERT A. M. HOLTSLAG

Meteorology and Air Quality Group, Wageningen University and Research Center, Wageningen, Netherlands

(Manuscript received 9 April 2014, in final form 22 August 2014)

ABSTRACT

In this paper, the Weather Research and Forecasting (WRF) Model is used to investigate the sensitivity of

precipitation to soil moisture and urban areas in the Netherlands. The average output of a 4-day event during

10–13 May 1999 for which the individual days had similar synoptical forcing is analyzed. Four simulations

are conducted to test the impact of soil moisture changes on precipitation. A positive soil moisture–

precipitation feedback is found, that is, wet (dry) soils increase (decrease) the amount of precipitation. Three

additional experiments are executed, two in which urban areas in the Netherlands are expanded and one

where urban areas are completely removed. Expansion of urban areas results in an increase of the sensible

heat flux and a deeper planetary boundary layer, similar to reducing soil moisture. Expanding urban areas

reduces precipitation over the Netherlands as a whole, but the local response is not clear. Within existing

urban areas,mean andmaximum temperature increases of 0.4 and 2K, respectively, are found under an urban

coverage scenario for 2040. The ratio of evapotranspiration to precipitation (a measure of the soil moisture–

precipitation feedback) in the urbanization experiments is only about one-third (23%) of that in the soil

moisture experiments (67%). Triggering of precipitation, on the other hand, is relatively high in the urban

expansion experiments. The effects of reduced moisture availability and enhanced triggering in the urban

expansion experiments compensate each other, leading to the moderate reduction in precipitation.

1. Introduction

The land surface directly affects the surface energy

budget and corresponding surface energy fluxes through

the partitioning of incoming solar radiation at the sur-

face. The land surface, in this context, refers to both the

(water content of the) underlying soil and the surface

properties implied by land use and land cover. Urban

areas, for example, have a high albedo, surface rough-

ness, and large heat-storage capacity compared to rural

areas (Oke 1982). In addition, the large impervious

fraction in urban areas reduces moisture available for

evaporation. The influence of large urban areas has been

investigated and proven for temperature (e.g., Kalnay

and Cai 2003; Oke 1982) as well as precipitation. Both

the onset and timing of precipitation can be influenced

(e.g., Niyogi et al. 2011; Schmid and Niyogi 2013) as well

as the amount of precipitation and area where pre-

cipitation occurs (e.g., Rozoff et al. 2003; Shem and

Shepherd 2009; Shepherd et al. 2010). However, to our

knowledge, there is only one study that has investigated

the effect of urban areas on precipitation in Europe as

a whole, and there are no studies for the Netherlands.

Trusilova et al. (2008) compared simulations with cur-

rent urban cover to simulations in which all urban areas

were removed. They found that inclusion of urban areas

resulted in a small decrease of total precipitation in

Europe. Expanding urban cover, by 40% compared to

Denotes Open Access content.

Corresponding author address: Emma E. Daniels, Earth System

Science Group, Wageningen University and Research Center

(WUR), P.O. Box 47, 6700 AA Wageningen, Netherlands.

E-mail: emma.daniels@wur.nl

232 JOURNAL OF HYDROMETEOROLOGY VOLUME 16

DOI: 10.1175/JHM-D-14-0072.1

� 2015 American Meteorological Society

the current situation, reduced total precipitation further

in January while no clear signal was found in July

(Trusilova et al. 2009). In general, the impacts of land

use and land cover changes are likely to become more

significant in the coming decades (Pielke et al. 2007).

In addition, variations in land surface properties can

feed back on themselves through the changes they im-

pose on the near-surface climate. These feedbacks are

mainly controlled by the surface albedo, soil moisture

(SM) content, and consequent energy partitioning of

latent and sensible heat. For example, a reduction in soil

moisture and consequent decrease in evapotranspiration

[latent heat flux (LH)] leads to increases in sensible heat

flux (HFX) and near-surface air temperature. Higher

temperatures, in turn, increase the evaporative demand

and latent heat flux, which subsequently reduces soil

moisture. This constitutes a positive soil moisture–

temperature feedback.

Seneviratne et al. (2010) have written an extensive

review on how soil moisture feedbacks affect both near-

surface air temperature and precipitation. In earlier

work, the soil moisture–precipitation feedback was at-

tributed to a direct contribution of regional evapotrans-

piration to precipitation enhancement in the same region

(e.g., Brubaker et al. 1993; Eltahir and Bras 1996). More

recently, however, the role of indirect interactions in the

formation of precipitation and the stability of the

boundary layer have received more attention (e.g., Ek

andHoltslag 2004; Findell and Eltahir 2003; Hohenegger

et al. 2009; Santanello et al. 2009; Schär et al. 1999). Aprerequisite for the formation of clouds and subsequent

precipitation is high relative humidity at the top of the

planetary boundary layer (PBL). Dry soils have a rela-

tively high sensible heat flux and consequently higher

surface temperatures and deeper PBLs. Because of the

relatively low temperatures at the top of deep PBLs,

moisture transported by uprising thermals can easily

condensate and form clouds. Wet soils, on the other

hand, have a much shallower PBL in general. However,

over wet soils, cloud formation can occur because of

a moister PBL resulting from the high latent heat flux at

the surface. In both cases, the lifting condensation level

(LCL) can be reached below the top of the PBL, thus

allowing cloud formation. The feedback arising from this

mechanism can be of either sign, because precipitation

can be triggered over both dry and wet soils. Indeed,

both positive and negative soil moisture–precipitation

feedbacks have been found in observational and mod-

eling studies (e.g., Boé 2013; Hohenegger et al. 2009;

Santanello et al. 2009; Taylor et al. 2012; Tuinenburg

et al. 2011).

Urban areas in theNetherlands have strongly expanded

in the last century, a trend that is likely to continue

in the (near) future. Urban areas made up around 8% of

the total area in the Netherlands in 2000 and are pro-

jected to increase to 12% in 2040 (Dekkers et al. 2012)

under theGlobal Economy scenario (Janssen et al. 2006).

This national scenario is consistent with the Special Re-

port on Emissions Scenarios (SRES) A2 scenario. Ur-

banization has mostly taken place in the Randstad,

a conurbation consisting of the four largest Dutch cities

mainly located along the west coast of the Netherlands.

At the same time, precipitation has gradually increased

over the last half century by almost 16%, especially along

the west coast (Buishand et al. 2013). The impact of sea

surface temperature (SST) changes on precipitation in

the Netherlands has recently been investigated (Attema

and Lenderink 2014; Lenderink et al. 2009) and appears

to have played a major role. In this study, however, we

will investigate the impact of urbanization and soil

moisture conditions on precipitation in the Netherlands,

in isolation of other climatological conditions like SST

and circulation.

We will first introduce the selection of the case study

period, themodel setup, and experiments. Hereafter, the

model results from the reference run are compared to

observations. Next, the outcome of the sensitivity anal-

ysis, the resulting soil moisture–precipitation feedback,

and the effects of urban areas are evaluated, followed by

the discussion and conclusions.

2. Methods

a. Study area

Our study area, the Netherlands, is located in north-

west Europe along the North Sea and the Atlantic

Ocean (see Fig. 1). The vicinity to these water bodies has

great influence on weather in the Netherlands and causes

the typical mild winters and wet summers associated

with a sea climate. The spatial distribution of pre-

cipitation varies strongly between different seasons. In

winter and summer, precipitation is relatively uniform,

whereas a strong coastal gradient exists in spring and

autumn, though of the opposite sign (Attema and

Lenderink 2014). In spring, the relatively low SST sup-

presses shower activity over sea and near the coast. The

onset of precipitation is only triggered after air has

traveled over land for several kilometers and has suffi-

ciently warmed. The subsequent growth of the PBL

enables the formation of clouds and succeeding pre-

cipitation. As a consequence of this triggering mecha-

nism over land, precipitation in spring is up to 25% lower

near the coast than farther inland. The larger surface

roughness and hence lower wind speeds over land might

play a role here (Malda et al. 2007) in addition to the

temperature and associatedmoisture difference between

FEBRUARY 2015 DAN IEL S ET AL . 233

the land and sea surface. Considering the importance of

the land in triggering convective precipitation in spring,

we consider this period interesting to study the influence

of urbanization and soil moisture. In addition, large pre-

cipitation trends have been observed in spring (Daniels

et al. 2014), which still need to be explained.

b. Case selection

A useful criterion for the selection of a suitable case

study is the similarity of the synoptic conditions and

dominant westerly winds. Situations in which this cri-

terion is fulfilled are identified using a weather and

circulation type classification, namely, the 27-type

Jenkinson–Collison types (JCT) classification scheme.

This method was developed by Jenkinson and Collison

(1977) and is intended to provide an objective scheme

that acceptably reproduces the subjective Lambweather

types (Jones et al. 1993; Lamb 1950). For our purpose,

the JCT classification grid is altered from its original

domain to a smaller domain encompassing the Nether-

lands. The new domain ranges from 47.258 to 57.758Nand 38 to 12.758E, and the classification scheme is rerun

on 1200 UTCmean sea level pressure (MSLP) data from

the InterimEuropean Centre forMedium-RangeWeather

Forecasts (ECMWF) Re-Analysis (ERA-Interim; Dee

et al. 2011).

The 27 types of the JCT scheme are explained by

combining wind directions (west, northwest, north, etc.)

with cyclonic, straight, and anticyclonic flow, leaving three

classes for pure cyclonic days, pure anticyclonic days, and

a light indeterminate flow class, respectively. We look for

a case with southwestern, western, or northwestern wind,

because precipitation is commonly brought in from the

sea, and straight flow, because we are interested in land

surface effects and we expect the synoptic forcing to be

higher under (anti)cyclonic flow. The combination of

westerly winds and straight flow occurs relatively often,

about 20% of the time, so we have a large pool to sample

from. The selected period, 10–13 May 1999, has straight

southwestern flow on the first day and straight western

flow on the following 3 days. This period is selected be-

cause of the relatively unstable air and afternoon showers

that occurred, which makes it a suitable case for in-

vestigating land surface effects. Throughout the paper,

results are averaged over the whole of theNetherlands for

the entire 4-day period, unless otherwise specified.

c. Model setup

We use the nonhydrostatic Advanced Research

WeatherResearch andForecasting (WRF)Model (ARW,

version 3.4.1; Skamarock et al. 2008) on a single domain of

about 1000kmby 1000km.The domain is centered around

the Netherlands (see Fig. 1) and has a horizontal grid

spacing of 3km. The vertical grid contains 28 sigma levels,

of which the lowest 7 levels are below 1km to have finer

resolution in the PBL. Atmospheric and surface boundary

conditions are obtained from ECMWF’s operational data

archive every 6h. Unfortunately, soil moisture data were

not available from the operational archive and are there-

fore initialized using ERA-Interim data. Model output is

stored and analyzed on an hourly basis. The sensitivity

of the model to initial conditions was tested by altering

the boundary conditions at initialization up to 3 h be-

fore or after 1200 UTC. The differences in model

FIG. 1. Dominant land cover data in the Netherlands with the expanded urban areas in red and

blue. The inset shows the full model domain.

234 JOURNAL OF HYDROMETEOROLOGY VOLUME 16

output after these initial perturbations were very

small.

We use the Bougeault–Lacarrere PBL scheme

(Bougeault andLacarrere 1989), theWRF single-moment

6-class microphysics scheme (WSM6; Hong and Lim

2006), the Rapid Radiative Transfer Model GCM

(RRTMG) scheme for both long- and shortwave radia-

tion (Iacono et al. 2008), the Grell 3D cumulus parame-

terization scheme (Grell 1993; Grell and Devenyi 2002),

and the unified Noah land surface model (Tewari et al.

2004) with the urban canopymodel (UCM). The UCM is

a single-layermodel that has a simplified urban geometry.

Included in the UCM is shadowing from buildings; re-

flection of short- and longwave radiation; wind profile in

the canopy layer; and multilayer heat transfer equations

for roof, wall, and road surfaces (Kusaka and Kimura

2004; Kusaka et al. 2001). The standard values for urban

canopy parameters have been retained, although it is

known the UCM is sensitive to changes herein (Loridan

et al. 2010).

It is also well known that the choice of parameteri-

zation schemes in WRF can have a large effect on the

simulation (e.g., Jankov et al. 2005), but we have chosen

not to vary this. Mooney et al. (2013) investigated 12

combinations of parameterization schemes inWRFover

Europe. Their results indicated that the effect of dif-

ferent PBL schemes on precipitation is negligible while

the effect of the longwave radiation and microphysics

scheme is comparable, but much smaller than the effect

of the land surface model.

d. Model simulations

All model simulations were performed with 12-h

spinup time during which there was no precipitation.

The first experiment is a control simulation (reference;

REF) with land use from the standard U.S. Geological

Survey (USGS) map that is available within WRF. REF

has an average soil moisture content in the top layer (top

10 cm) of 0.30m3m23 after spinup. The next experiment

consists of a set of sensitivity simulations in which soil

moisture is initialized at a constant value for all land grid

points. If the value at initialization is above saturation or

belowwilting point for the local soil type, the land surface

model adjusts the soil moisture content during the spinup

period. The initial soil moisture content was varied, so we

have a very dry simulation with soil moisture near wilting

point (SM_0.13), a very wet simulation with soil moisture

near saturation (SM_0.35), and two values in between

these extremes (SM_0.18 and SM_0.25). The values in the

names of the different experiments denote the actual

average soil moisture content (m3m23) in the top layer

that was achieved after spinup. During the experiments,

soil moisture was allowed to evolve freely.

In the next set of experiments, we change the per-

centage of urban land cover in the Netherlands. The

urban fraction in the reference simulation, based on the

USGS map, is 2%, which is much lower than the real

urban fraction (8%). All urban land cover is treated

similarly and falls into the category high-intensity resi-

dential; this means that vegetation accounts for less than

20% and constructed materials account for 80%–100%

of the cover. Expanded urban land cover is calculated by

a simple function that checks neighboring cells and ex-

pands the urban fraction by as much as the sum of the

urban fractions in the eight neighboring cells. If urban

cover is increased, the fractions of nonurban land cover

in the cell are decreased proportionally. The resulting

fraction of land in the Netherlands where urban area is

dominant (see Fig. 1) is around 13%. This amount of

dominant urban area is similar to that achieved under

the Global Economy scenario in 2040 (12%; Dekkers

et al. 2012). We rerun the expansion function and in-

crease the dominant urban area to 24%.We will refer to

these simulations as URB_2040 and URB_FUT, re-

spectively. In our last experiment, all urban land cover in

the Netherlands is removed (NO_URB). In cells where

urban area is removed, the fractions of nonurban land

cover are increased proportionally. If the cell was en-

tirely urban (0.2%), land cover is replaced by dryland

cropland and pasture, the dominant land use type in the

Netherlands. All other variables in the sensitivity ex-

periments are identical to REF and no changes in an-

thropogenic heat or aerosols are modeled. An overview

of the eight conducted experiments and differences in

their parameters is given in Table 1.

3. Model evaluation

To evaluate the performance of the model, the results

from the reference simulation will first be compared to

observations (OBS). The results of the sensitivity ex-

periments will be discussed in the next chapter. After

a short description of the (synoptic) weather during the

selected case, we will evaluate the performance of the

TABLE 1. Overview of the conducted experiments and altered

variables. Boldface text denotes the reference simulation.

Expt

Soil moisture content

(m3m23) Urban area (%)

SM_0.13 0.13 As REF

SM_0.18 0.18 As REF

SM_0.25 0.25 As REF

SM_0.35 0.35 As REF

REF 0.30 2

NO_URB As REF 0

URB_2040 As REF 13

URB_FUT As REF 24

FEBRUARY 2015 DAN IEL S ET AL . 235

reference run in representing the temperature, MSLP,

and precipitation. Observations of precipitation are

available within the Netherlands at a 2.5-km resolution

on an hourly basis from (bias) corrected radar data

(Overeem et al. 2009). The radar data are remapped to

the 3-km resolution used in theWRFModel to allow for

comparison. Temperature (Haylock et al. 2008) and

MSLP (van den Besselaar et al. 2011) from the Euro-

pean daily high-resolution gridded dataset (E-OBS),

version 8.0, are used for further evaluation. Comparison

to these variables is done at the 0.258 resolution native toE-OBS.

a. Weather description

The month of May 1999 was, on average, very warm

(mean maximum temperature in De Bilt was 14.28Cwhereas 12.38C is the long-term average) and dry

(46mm precipitation in total whereas 57mm is the

long-term average). Precipitation mostly fell in small

showers, resulting in large local differences in pre-

cipitation amounts. During the period 10–14 May, the

weather in the Netherlands was determined by a de-

pression situated near 548N, 208W at 0000 UTC 10 May.

This depression traveled eastward and arrived at the

North Sea on 14 May. After the corresponding cold

front passed on 10May, a west-southwest current stayed

in place for the next 3 days. It was a sunny period with

normal temperatures (maxima around 168–188C and

minima around 88–108C) and afternoon showers that

developed from cumulus clouds.

b. Comparison to observations

Figure 2 shows the correlation and root-mean-square

error for minimum (Tmin), maximum (Tmax), and mean

(Tmean) temperature;MSLP; and precipitation (RAIN).

For temperature, the daily mean, minimum, and maxi-

mumare calculated before being averaged and compared

to the averaged observations. The results are generally

good: the correlation is 0.99 or higher and the variance in

the simulation is similar to that in temperature observa-

tions. MSLP is forced upon the model by the boundary

conditions and is represented very well in the output

compared to E-OBS. The correlation for precipitation is

lower than for the other variables, but still over 0.8, because

convective precipitation is one of themost difficult processes

to correctly represent in a model. In any case, our results

for precipitation are comparable with 5-day-averaged

results with WRF for Portugal (Soares et al. 2012).

c. Precipitation

Figure 3 shows the four day time series for precipitation

from hourly observations and REF averaged over the

Netherlands. On the first day, precipitation amounts in

REF are smaller than in observations because WRF

underestimates the duration of the precipitation event.

However, when averaged over the entire simulation pe-

riod, precipitation amounts are overestimated in REF. A

large proportion of the overestimation of precipitation by

the model occurs on the third and fourth days. On these

days, the onset of precipitation is too early and the sim-

ulated duration of precipitation is too long. The rate of

precipitation, on the other hand, is relatively well cap-

tured by the model throughout the entire simulation.

Likewise, the average coastal gradient in precipitation

amount is well reproduced by the model (see Fig. 4).

Little precipitation falls along the coast, moderate pre-

cipitation is found farther inland, and most precipitation

falls toward the eastern border and in the southernmost

tip of the country. Precipitation in the southernmost tip of

the country is most likely orography driven, which is well

captured by the model. In total, however, mean daily pre-

cipitation is overestimated by about 10%, and the model

underestimates precipitation in the center of the country.

4. Results

In section 4a, we provide the results from the sen-

sitivity experiments with a focus on the similarities

between the different sets of experiments. Section 4b

investigates soil moisture–precipitation feedbacks,

while section 4c focuses on the effects of expanding

urban areas.

a. Precipitation in the sensitivity experiments

In the sensitivity experiments we conduct, evapotrans-

piration is altered in two different ways: directly, through

FIG. 2. Taylor plot for RAIN; MSLP; and Tmin, Tmax, and Tmean

in REF compared to observations for 10–13 May 1999.

236 JOURNAL OF HYDROMETEOROLOGY VOLUME 16

the evaporative fraction in urban areas, and indirectly,

through changes in soil moisture. In our simulations,

a consistent reduction of precipitation under reduced soil

moisture and increased urban coverage is found (see

Fig. 5). Cumulative mean precipitation is about 12%

higher in the wettest and 24% lower in the driest experi-

ment compared to REF. If we consider that urban areas

reducewater availability for evaporation, expanding urban

areas can be regarded analogous to reducing soil moisture

because the fraction of evaporating surfaces in urban areas

FIG. 3. Time series of hourly accumulated precipitation in REF and radar observations over the

Netherlands for 10–13 May 1999.

FIG. 4. Mean daily precipitation in SM_0.13, REF, OBS, SM_0.35, and URB_FUT over the Netherlands for 10–13 May 1999.

FEBRUARY 2015 DAN IEL S ET AL . 237

is reduced to only 10%. In the urban expansion experi-

ments, a much smaller fraction of theNetherlands (0.1 and

0.22) is altered than in the SM experiments. Nonetheless,

the changes in mean precipitation are remarkably similar

to a first-order guess from the following estimation. If we

assume the reduction of the driest SM runs to be repre-

sentative for the response in the URB experiments, an

increase of 10% and 22% in urban area would yield a de-

crease of 2% [24%(0.1)] and 5% [24%(0.22)] in pre-

cipitation. Aggregated at the national scale, this analogy

seems to hold, as the simulation results show precipitation

in URB_2040 and URB_FUT is about 2% and 6% re-

duced, respectively, compared to REF. Note that pre-

cipitation in NO_URB is also slightly lower than in

REF, whereas a small increase in precipitation would be

expected.

The spatial patterns of precipitation (see Fig. 4) re-

main quite similar to REF in all of the sensitivity ex-

periments, though the amounts are locally enhanced or

reduced. For the drier runs, precipitation is shifted to-

ward the east and north and the maxima are located in

slightly different locations with regard to REF. The re-

duction of precipitation in the southernmost tip of the

country that can be seen in all of the SM experiments

could be due to the spatially uniform initialization of soil

moisture and is not seen in the URB experiments. The

urban expansion experiments have similar precipitation

as REF along the border, and the largest reductions are

seen spread over the center of the country. Overall, the

wetness or dryness at the start of a simulation appar-

ently influences the atmosphere in such a way that

precipitation is reduced (enhanced) over dry (wet) soils.

This is indicative of a positive soil moisture–precipitation

feedback.

b. Soil moisture–precipitation feedbacks

The strength of the soil moisture–precipitation feed-

back can be assessed using the ratio of evapotranspira-

tion to precipitation. The computed ratio for each of the

experiments is given in Fig. 6. The dashed lines are

computed by linear regression through the two sets of

experiments separately, both including REF. The ratio

of evapotranspiration to precipitation, that is, the slope

of the dashed lines, is about 67% for the SMexperiments

and 23% for the URB experiments. In other words, the

positive soil moisture–precipitation feedback is not as

strong in the URB experiments. Locally, urban areas

alter the atmosphere in a way that could constitute

a negative feedback, but the effects are not strong

enough in our simulations, and the positive feedback

remains dominant.

The mechanism underlying the weakening of the

positive feedback due to urban areas is illustrated in

Fig. 7. In the SM experiments, a reduction in soil mois-

ture leads to an increase in PBL height (PBLH). The

LCL, however, rises even faster. Thus, under dry con-

ditions, parcels from the surface do not reach the LCL as

easily as under higher soil moisture conditions. This

leads to a reduction in convective available potential

energy (CAPE) values. Similarly, in the URB experi-

ments, the increased urban area also leads to an

FIG. 5. Cumulative mean precipitation in the Netherlands for the

REF, SM, and URB experiments for 10–13 May 1999.FIG. 6. Ratio of evapotranspiration to precipitation for the

(black) SM and (blue) URB experiments in the Netherlands for

10–13 May 1999. Ratios of 100%, 60%, and 30% are given by the

dotted purple, red, and green lines, respectively.

238 JOURNAL OF HYDROMETEOROLOGY VOLUME 16

increase in PBLH. In terms of the reduction of LH, the

reduction in PBLH is similar to the SM experiments.

Yet, the rise in LCL is rather small in the URB exper-

iments and similar to the rise in PBLH. Thus, parcels

can still reach the LCL rather easily and convection is

not reduced as much as in the SM experiments. Con-

sequently, the urban expansion experiments have

stronger triggering of precipitation.

c. Effects of urban areas

Underlying the dissimilarities in atmospheric vari-

ables between the different sets of experiments is the

modification of energy partitioning at the surface in

urban areas. LH is reduced in urban areas because of the

lower evaporative fraction, and HFX is consequently

increased. The partitioning of these fluxes (see Table 2)

directly influences the near-surface temperature. In ad-

dition, temperature increases because of the higher

roughness and heat-storage capacity in urban areas. The

heat stored by buildings during daytime is returned

during the night. This can be seen in Fig. 8, which shows

the spatial and temporal difference in temperature be-

tween URB_2040 and NO_URB. Temperature within

urban areas and throughout the rest of the country is

most enhanced just after sunset. Comparing URB_2040

to REF (not shown here) shows the mean diurnal urban

heat island (UHI) increases with more than 0.4K within

existing urban areas. The maximum diurnal UHI under

urban coverage expected in 2040 is up to 2K higher

within existing urban areas.

The higher sensible heat flux and temperature in urban

areas affect the overhead and downwind atmosphere by

altering, among others, the LCL and PBLH. This can be

easily seen by comparing one of the urban expansion

experiments with NO_URB (see Fig. 9). In the URB

experiments, the PBLH is raisedmore than the LCL and

the level of free convection (LFC), effectively increasing

the potential for cloud formation and the triggering of

precipitation (see Table 2). These relatively high in-

creases in the URB experiments underlie the difference

in strength of the soil moisture–precipitation feedback.

The direct response on precipitation is not so clear,

however, and the spatial differences in precipitation are

not directly located near or downwind of cities (Fig. 9).

5. Discussion

In retrospect, the period we simulated in this study

belongs to what Findell and Eltahir (2003) would de-

scribe as atmospherically controlled. In an atmospherically

controlled situation, deep convection is triggered over both

dry and wet soils, and only the amount of rain can be im-

pacted by soil moisture conditions (Findell and Eltahir

2003). This is indeed what we find in our case, as wet soils

give larger precipitation amounts. The reduction of pre-

cipitation we find after expanding urban areas in this study

is similar to earlier European findings of Trusilova et al.

(2008, 2009), but in contrast with many other studies,

mostly situated in Asia and the United States (Han et al.

2014). This may be related to the typical dry surface con-

ditions around the bigger cities in the United States and

Asia, while in our case, the urban surroundings are much

wetter.

FIG. 7. Relative mean change in CAPE, LCL, and PBLH in re-

lation to the relative mean change in LH in the Netherlands for

10–13 May 1999. Linear regression lines are shown for URB

(dotted) and SM (dashed) experiments, both including REF.

TABLE 2. Mean daytime (0600–1800 UTC) values of several variables over the Netherlands for the conducted experiments. Variables are

LH, HFX, T2, percentage of time and area that the PBL top is over the LFC (PBL . LFC) and LCL (PBL . LCL), and RAIN.

Variable Unit SM_0.13 SM_0.18 URB_FUT SM_0.25 URB_2040 REF NO_URB SM_0.35

LH Wm22 100.2 128.3 125.4 146.0 138.4 154.6 157.6 163.2

HFX Wm22 156.7 127.1 117.1 108.1 109.6 100.6 98.6 88.1

T2 8C 16.4 15.9 15.7 15.6 15.6 15.5 15.4 15.2

PBL . LFC % 40.1 46.1 49.2 49.7 49.7 51.8 51.8 55.5

PBL . LCL % 63.3 70.7 76.0 75.9 76.4 77.4 77.3 80.7

RAIN mm 1.8 2.2 2.3 2.3 2.4 2.5 2.4 2.7

FEBRUARY 2015 DAN IEL S ET AL . 239

Our estimated ratio of evapotranspiration to pre-

cipitation is of similar size as the rough estimate forEurope

made by Schär et al. (1999). The ratio of evapotranspira-

tion to precipitation, unlike the recycling ratio (Budyko

1974; Eltahir and Bras 1994, 1996), combines precipitation

that truly originates from the region of interest with pre-

cipitation that originates from the sea but is triggered by

moist conditions over land (e.g., Bisselink and Dolman

2009). That this triggering of advected precipitation also

plays a role in our simulations can be inferred from the

difference in feedback strength between the different sets

of experiments. The same change in evaporation gives

a smaller precipitation reduction in the URB experiments

because of enhanced triggering.

With respect to temperature, the simulated average

and maximum diurnal UHI are in reasonable agree-

ment with observations of UHI in the Netherlands. We

find an average and maximum diurnal UHI of 0.4 and

1.3K, respectively. In summer, higher UHIs of 0.9 and

2.3K were measured (Steeneveld et al. 2011; Wolters

and Brandsma 2012). Wolters and Brandsma (2012),

however, show a clear dependency of UHI intensity on

wind speed, and measurements of the average UHI in

spring are approximately 30% lower than in summer.

Average wind speed during our case study was more

than 3m s21. Wind speeds like these could reduce the

UHI by another 50%, making our estimate comparable

with documented measurements. This suggests correct

heat fluxes are modeled (see also Fig. 2), which gives

confidence for the representation of other atmospheric

variables.

With respect to precipitation, however, there are some

obvious problems in the location of simulated precipitation

compared to radar observations. Although models are

often used to study changes in precipitation after ur-

banization, the correct representation of precipitation

remains challenging. Li et al. (2013), for example, show

that the biases inWRFwind fields play amajor role in the

biases in precipitation. Nonetheless, models are useful

tools in understanding the processes that govern (changes

in) precipitation, and we try to use it as such.

One shortcoming of the current model setup is the

usage of the standard USGS land use map, as it un-

derestimates urban land cover in the Netherlands. This

can easily be improved, but it might not have a large in-

fluence on precipitation (De Meij and Vinuesa 2014). In

addition, all urban areas are currently treated similarly in

the simulations, while WRF allows for a distribution into

three classes, adding a low-intensity residential and

commercial/industrial/transportation class.

Other factors that were not taken into account in this

study and have been shown to affect local precipitation

are urban pollution and aerosols (Guo et al. 2014; Han

et al. 2012). Aerosols typically reduce the size of cloud

droplets, initially reducing precipitation, and may in-

crease the probability of reaching super-cooled levels and

as such intensify downwind convective cells. However,

under very high aerosol concentrations, moisture can

be transported out of the storm in the ice phase, which

decreases precipitation efficiency (Carrio and Cotton

2011; Carrio et al. 2010). The forcing of aerosols on

precipitation seems to bemuch smaller than the forcing

of land use and land cover changes in an urban envi-

ronment, however (Hosannah andGonzalez 2014), and

we did not attempt to include these effects in the cur-

rent study.

FIG. 8. Difference in 2-m temperature (T2) in the Netherlands for 10–13 May 1999 between URB_2040 and

NO_URB, averaged over the entire country (mean), within urban areas (urban), and nonurban areas (rural)

(left) throughout the day and (right) at 2000 UTC. Urban areas are outlined in green in the spatial distribution

(right).

240 JOURNAL OF HYDROMETEOROLOGY VOLUME 16

6. Conclusions

This study investigates the effects of surface condi-

tions on precipitation in the Netherlands in isolation of

climatological changes like large-scale circulation and

SST. To investigate this, high-resolutionWRF simulations

are conducted with different soil moisture initializations

and urban cover scenarios for 10–13May 1999. In general,

in both the soil moisture and urbanization experiments,

we find a positive soil moisture–precipitation feedback. In

other words, precipitation is enhanced over wet soils and

reduced over dry soils or after urban expansion. Overall,

the sensitivity of precipitation to increased urban cover-

age is lower than to reduced soil moisture.

Underlying this difference in sensitivity, or feedback

strength, are differences in the response of the PBLH

and the LCL. In the soil moisture experiments, the LCL

increases more strongly than the PBL and a larger re-

duction in CAPE is found. In the urbanization experi-

ments, the PBL increases somewhat more than the LCL

and CAPE is not reduced as much. As a result, for a

similar reduction in evaporation, a smaller reduc-

tion in precipitation is achieved in the urbanization

experiments. Consequently, the ratio of evapotrans-

piration to precipitation, a measure of the soil moisture–

precipitation feedback, is about 67%, while it is only

23% in the urbanization experiments.

Acknowledgments. This study was supported by

the Dutch research program Knowledge for Climate.

We acknowledge E-OBS from the EU-FP6 project

ENSEMBLES (http://ensembles-eu.metoffice.com) and

the data providers from ECMWF, KNMI, and the

ECA&D project. We also appreciate the effort made by

the Cost733 project to harmonize weather type classifi-

cations (http://cost733.met.no) and make them available.

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