understanding near-surface turbulence fluxes using an open

Transcription

UNIVERSITY OF READING
Department of Meteorology
UNDERSTANDING NEAR-SURFACE
TURBULENCE FLUXES USING AN OPENPATH GAS ANALYSER
Isabella Van Damme
A dissertation submitted in partial fulfilment of the requirement for the degree
of Master of Science in Applied Meteorology
August 2010
Acknowledgment
I would like to thank my supervisors, Curtis Wood and Janet Barlow, for their guidance and
useful discussions and for giving me the opportunity to learn more about the fascinating
topic of boundary layer meteorology. The practical aspect of this work would not have been
possible without the assistance of Rosemary Wilson, who installed the instruments and the
equipment needed for calibration, and Ian Reed who never tiered of taking the sensor down
for me, no matter what the weather conditions were. Michael Stroud kindly provided me
with additional data from the Atmospheric Observation site and gave me information about
the practical aspects of different measurement techniques. Most of all, everybody always
welcomed my requests and questions and ensured I had an interesting and enjoyable
project.
Abstract
The concentrations of carbon dioxide (CO2) and water vapour were measured over 9 weeks
on the Atmospheric Observatory at the University of Reading using an open-path gas
analyser (OPGA). Fluxes were estimated from these measurements, combined with data
from a sonic anemometer, using the eddy correlation technique. The instrument
measurement error was 0.30 % on concentrations of CO2 and 1.3 % on water vapour.
Concentrations of CO2 were approximately 30 ppmv higher at night than during the day and
correspond to positive CO2 fluxes at night and negative fluxes during the day. This is
characteristic of a vegetated area and the diurnal variations are related to the atmospheric
boundary layer depth as well as respiration and absorption of CO2 through photosynthesis.
A source area analysis was not inconsistent with gas concentrations and particularly fluxes,
originating mainly from a vegetated area within 100 m from the sensor. The absolute
humidity, as measured with the OPGA, correlated well with results from a psychrometric
measurement and small differences are attributed to the choice of the coefficients used in
psychrometric method. The latent heat flux derived from the eddy correlation method was
up to 8 times smaller than fluxes derived from open-pan, Piche tube or Penman-Monteith
evaporation. The eddy correlation measurements indicate evaporation restricted by
moisture availability, mobility, plant physiology and atmospheric conditions while the other
methods represent unrestricted evaporation. Uncertainties associated with each method
lead to further differences. The hypothesis is put forward that the uncertainty associated
with the eddy correlation method originates mainly from post-field processing. Although
the measurements were carried out in an urban environment, the results are only
representative of the vegetated surroundings within 100 m from the sensor and indicate the
importance of intra-urban variation in surface characteristics as well as the need to select
appropriate measurement locations to obtain results that are representative of the wider
urban environment.
Table of Contents
Chapter 1 : Introduction ........................................................................................................... 1
Chapter 2 : Micrometeorology .................................................................................................. 7
2.1
The atmospheric boundary layer ............................................................................... 7
2.2
Latent heat ................................................................................................................. 9
2.3
Eddy correlation technique ...................................................................................... 12
2.3.1 Theory................................................................................................................. 12
2.3.2 Sources of uncertainty ....................................................................................... 13
2.3.3 Despiking ............................................................................................................ 16
2.3.4 Block averaging .................................................................................................. 16
2.3.5 Coordinate rotation ............................................................................................ 17
2.3.6 Quality control.................................................................................................... 18
Chapter 3 : Experimental work................................................................................................ 19
3.1
The observation site ................................................................................................. 19
3.2
Open-path gas analyser............................................................................................ 19
3.2.1 Principle .............................................................................................................. 19
3.2.2 Calibration .......................................................................................................... 21
3.2.3 Sources of measurement errors......................................................................... 22
3.3
Ultrasonic anemometer ........................................................................................... 23
3.4
Additional instrumentation ...................................................................................... 23
3.5
Methods ................................................................................................................... 24
3.6
Data handling ........................................................................................................... 25
Chapter 4 : Results and discussion .......................................................................................... 27
4.1
Sources of uncertainties ........................................................................................... 27
4.1.1 Instrumental uncertainty ................................................................................... 27
4.1.2 Contamination of the windows .......................................................................... 30
4.1.3 Friction velocity as a quality-control measure ................................................... 31
4.2
Diurnal variation in CO2 fluxes and concentrations ................................................. 32
4.3
Water vapour ........................................................................................................... 39
4.3.1 Comparison of absolute humidity measurements............................................. 39
4.3.2 Latent heat fluxes ............................................................................................... 42
Chapter 5 : Conclusions ........................................................................................................... 51
Appendix 1: CO2 concentration and fluxes in Mexico City and London .................................. 56
References ................................................................................................................................ 57
Chapter 1: Introduction
Atmospheric concentrations of carbon dioxide (CO2) have increased considerably since the
1950’s and the main cause has been attributed to anthropogenic sources. The absorption by
atmospheric CO2 of terrestrial infra-red radiation creates a greenhouse effect that results in
global average temperature rises (IPCC 2007). Atmospheric water vapour is the most
abundant and dominant greenhouse gas in the atmosphere, as it contributes 60% to the
total radiative forcing compared to 26% for CO2 gas under clear skies, and arises mainly from
natural evaporation of water (Kiehl; Trenberth 1997).
The saturated water vapour increases exponentially with temperature (e.g. according to the
Clausius-Clapeyron equation). This causes a non-linear feedback of water vapour in
response to temperature rises caused by other sources. Evaporation responds
instantaneously to changes in temperature and other atmospheric conditions and any
changes affect the hydrological cycle, which in turn influences cloud formation and the
ecosystem and leads to further climate feedbacks. A growing population and changing
lifestyles impact the landscape. Urban areas continue to expand, often at the cost of
surrounding agricultural land, while forests and other native landscapes are converted to
agricultural use to meet the increasing food production requirements. Changes in land use
alter the carbon cycle and the Bowen ratio (the ratio between sensible and latent heat
fluxes) (Baldocchi et al. 2001). Because of the tremendous impact of CO2 and water vapour
on Earth’s climate and ecosystem, and the chain of feedback mechanisms associated with
that, it is essential to continuously monitor the concentration of these greenhouse gases in
the atmosphere and understand their exchange between the surface and the atmosphere in
a variety of environments.
CO2 and water vapour are produced as by-products of fossil fuel burning and urban areas are
therefore major sources. Plants use CO2 and water vapour, together with light, to grow
through the process of photosynthesis. At night, CO2 and water vapour are released in a
process called respiration (Oke 1995). The CO2 exchange with the ecosystem has been
studied for many years with a range of techniques. The eddy covariance method, based on
the measurement of three-dimensional wind vectors and CO2 densities, is particularly
suitable as it can be used to study the CO2 exchange of the whole environment while
traditional methods are limited to exchange in small areas of leafs, plants or soil (Baldocchi
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2003). The CO2 flux measurements over vegetation based on eddy covariance have been
applied since the late 1950’s and have developed into a global network linked in the
FLUXNET program (Baldocchi et al. 2001) which currently includes over 500 measurement
sites around the globe. Only in the last 10 years have these measurement techniques been
applied to study the exchange in urban environments. Previously, urban CO2 fluxes were
derived from the estimates of emissions and sequestration (Grimmond et al. 2002). Longterm measurements of CO2 fluxes in urban environments are particularly relevant since the
main causes of increased [CO2] are associated with fossil fuel burning and changes in land
cover (IPCC 2007) with urban environments being a main source for both. In addition, rural
environments may be affected by advection from urban areas (George et al. 2007; Nemitz et
al. 2002; Rigby et al. 2008) .
The spatial variability of surface cover and roughness in urban areas poses particular
challenges to carry out studies representative of the whole urban area. The choice of spatial
and vertical location of the measurement equipment affects the measurements and
researchers often use multiple urban sites to cover a variety of urban environments (Offerle
et al. 2006). Measurement instruments must be placed in uniform areas at a height at least
double the height of the roughness elements and ideally above the roughness sublayer to
achieve a representative response of the local environment (Grimmond et al. 2002).
CO2 in urban environments
Studies of CO2 concentrations and fluxes in urban environments show large diurnal
variations. Similar trends were found in Chicago (Grimmond et al. 2002), Marseille
(Grimmond et al. 2004), Baltimore (George et al. 2007), London (Helfter et al. 2010; Rigby et
al. 2008) and Mexico City (Velasco et al. 2009) where the lowest concentrations are found in
the afternoon. This is associated with photosynthesis and especially the effective dispersion
of gasses in the daytime boundary layer aided by turbulent mixing. The greatest
concentrations of CO2 are measured during the night and in the morning. This is attributed
to night-time respiration from plants, a relatively shallow boundary layer and reduction of
turbulent mixing in the stable night-time atmosphere. An early-morning maximum is
associated with the morning rush hour when the deep boundary layer is not yet established.
The evening rush hour peak is often not observed and is explained by turbulent mixing and a
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deeper boundary layer later in the day. Measurements in Essen determined that 71% of the
near-surface [CO2] is affected most by traffic density and atmospheric stability (Henninger
2008).
Different weekend effects are observed with lesser concentrations during the
weekend in Baltimore but the greatest concentrations in Mexico City are observed on
Saturday. Here, the morning peak is greater on Saturdays and Sundays than during the
week and is attributed to the return home by night-time revellers, referred to as ‘the party
effect’. Nemitz et al. (2002) found in Edinburgh that traffic was the major source of CO2 but
wind speed and direction also play a role: low wind speeds lead to greater concentrations
while maritime wind and wind from sectors with parkland and residential areas decreased
the concentration.
Seasonal variability with greater CO2 concentrations during the winter in Florence (Matese
et al. 2009) were attributed to the use of domestic heating. The seasonal cycle in London
could be explained by changes in the hemispheric background level of [CO2], heating during
the winter and exchange by the biosphere, not only in London but in an area larger than
London. The meteorological conditions also played a role (Rigby et al. 2008).
In urban environments, CO2 fluxes remain positive throughout the day and urban
environments can be considered a net source of CO2. CO2 fluxes in Chicago did not show
diurnal variations while the largest emissions in Edinburgh, Marseille, Florence, London and
Mexico City were measured during the day. Similar to CO2 concentrations, diurnal variations
in CO2 fluxes in London were also found to be correlated with traffic while seasonal
variations were correlated with heating systems during the winter and photosynthetic
activity (Helfter et al. 2010; Rigby et al. 2008). Strong seasonal variations in CO2 fluxes were
also observed in Helsinki (Jarvi et al. 2009): large negative CO2 daytime fluxes, similar to
those observed over a beech forest, were measured when air temperatures exceeded 12°C.
The magnitude of the positive daytime fluxes increased as the air temperature decreased. In
all the above cases, the magnitude of the fluxes was found to be dependent on wind
direction.
Water vapour
Despite the importance of water vapour as a greenhouse gas and the role of latent heat
fluxes in potentially moderating urban heating effects, only three of the above mentioned
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urban studies refer to water vapour. George et al. (2007) found that the relative humidity is
the same in urban and nearby rural areas but the absolute humidity is higher in urban areas
since the temperature is higher. In Edinburgh (Nemitz et al. 2002), latent heat fluxes are
positive during the day and make a substantial contribution to the energy budget. The sum
of the latent heat and sensible heat fluxes was larger than the measured net radiation,
which hence suggests an additional heat input from the urban environment. Several studies
of the heat fluxes in North-American cities, Marseille and Lodz covering a range of urban
environments and climatic conditions, identified an inverse relation between the Bowen
ratio and the amount of vegetation (Grimmond et al. 2002; Grimmond et al. 2004; Newton
et al. 2007; Offerle et al. 2006). Flux partitioning as a consequence of variation in intraurban surface characteristics can affect the local climate and cause phenomena such as the
park breeze, when air flows from open vegetated areas to surrounding build-up areas as a
result of temperature differences between the two areas (Thorsson; Eliasson 2003). The
effect of climate is complex and temperature and humidity alone are not an indicator for the
energy fluxes as demonstrated in Miami (Newton et al. 2007). Although the climate was
subtropical, the latent heat flux and Bowen ratio are not dissimilar to those found for cities
in temperate climates and is attributed to heat storage in open water and wet soil, low
vapour pressure deficit and low coupling between the surface and the boundary layer due to
a relatively low roughness length in Miami.
Over vegetation, CO2 and water vapour fluxes are linked as photosynthesis and transpiration
occur through the same plant stomata. Changes in stomata density and stomata resistance
affect both fluxes.
Near-surface water vapour originates from evaporation which depends on meteorological
conditions, type of vegetation, surface cover and the amount of water available (Arnell
2002). Still air easily becomes saturated and prevents further evaporation. Turbulence
encourages evaporation by continuously removing the water vapour away from the surface.
Evaporation has long been studied in the context of the water balance, employing a range of
measurement techniques but is considered the most difficult component of the water
balance to measure (Arnell 2002). Several techniques are used but the open-pan
evaporation is one of the most popular since it is cheap and easy to use. Due to the difficulty
in measuring evaporation, mathematical equations are often used to estimate evaporation.
4
Many equations are empirical while others rely on meteorological observations. The
Penman-Monteith equation to estimate potential evaporation is most widely used, for
example in modelling of evaporation in climate models and estimating evaporation for
irrigation purposes recommended by the UN Food and Agriculture Organisation (Arnell
2002; FAO 1998).
Measurement techniques
Continuous and simultaneous measurements of CO2 concentrations ([CO2]) and water
vapour can be carried out with a closed or open-path gas analyser (OPGA) and both have
advantages and disadvantages. Fluxes can be estimated, using the eddy covariance
technique, when these instruments are combined with a fast-response anemometer that
measures the three-dimensional wind vectors. A closed-path analyser requires a pump and
associated tubing to suck in the air to be analysed. This introduces a delay and must be
corrected for to relate the measurements with the instantaneous wind vectors. Further
corrections must be applied because the interaction with the tubing may cause high
frequency losses (Aubinet et al. 2000). The pump installation needs additional maintenance
and the whole system requires frequent calibration but the advantage is that calibrations
can be carried out in-situ and can be automated. Open-path analysers measure the gas
densities directly and require less-frequent calibrations. These calibrations require human
interventions and introduce periods of at least one hour when data are not collected. Openpath analysers can suffer from environmental contaminations resulting in the rejection of
large numbers of unreliable data.
Project aims
The aim of this project is to evaluate the performance of an open-path CO2/H2O analyser in a
suburban environment and identify possible causes of uncertainty. The calibration drift will
be assessed with the aim to recommend a calibration interval. Of particular interest is the
instrument performance in the presence of rain, dew or fog since these weather conditions
are common in the UK. The results will be compared with data from other meteorological
measurements taken on the same site to validate the measurements or identify possible
reasons for observed differences. This work will particularly concentrate on the comparison
5
of different measurements of absolute humidity and latent heat fluxes. In addition, the
diurnal variations of CO2 concentrations and fluxes will be studied and compared to other
urban sites. Green spaces in urban environments could play an important role for the
overall urban carbon, heat and water vapour fluxes. The exchange of moisture and CO2 by
the vegetated soil together with the transpiration, photosynthesis and respiration by plants
create a completely different environment from the impermeable, heat absorbing/emitting
build environment associated with the urban heat island. The study site, which is
surrounded by a variety of vegetation within an urban area (see later), will enable us to
investigate this.
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Chapter 2: Micrometeorology
Micrometeorology deals with small-scale processes that occur in the atmospheric layer next
to Earth’s surface, i.e. the atmospheric boundary layer and more specifically, the surface
layer. The transport of energy and mass is a main topic of interest. The following chapter
describes the structure of the atmospheric boundary layer and measurement techniques
used in micrometeorology.
2.1 The atmospheric boundary layer
The atmospheric boundary layer is the layer formed as a consequence of the interaction
between the atmosphere and Earth’s surface. It is the layer where the exchange of mass,
heat and momentum occur. As a consequence, there is a diurnal variation in the structure of
the boundary layer. After sunrise, there is a continuous receipt of downwelling shortwave
radiation, usually resulting in a positive net radiation and thus heating of the surface,
creating buoyant thermal plumes and a well-mixed vertical layer. This daytime unstable
boundary layer grows through encroachment or entrainment reaching a height of 1 to 2 km
(in temperate latitudes) in the late afternoon (Foken 2008). Turbulent mixing is reduced as
a result of the radiative cooling of the surface becoming dominant in the evening and night.
Accordingly the boundary layer takes a new stable form and tends to shrinks to depths of 30
to 300 m in temperate latitudes.
The daytime boundary layer can be sub-divided in several layers (Figure 1). The layer
immediately next to the surface is the roughness sublayer (RSL). Air flows around the
roughness elements, i.e. individual obstacles such as trees, buildings and grass. This layer is
typically 2 to 5 times the mean height of the obstacles. The next layer, the surface layer,
comprises both the RSL and air above, reaching to about 10% of the height of the boundary
layer (Figure 1). In this layer, heat, wind and humidity change rapidly with altitude while
vertical fluxes vary by less than 10%. The near-constant fluxes enable flux measurements
within the surface layer to be related to surface fluxes. Surface heating during the day
causes a decrease of temperature and absolute humidity with height in the surface layer
leading to an unstable surface layer. Under these conditions, heat and vapour
7
Figure 1: Evolution of the structure of the daytime and night time boundary layers (Diagram copied from
http://www.smhi.se/sgn0106/if/FoUp/us/html/parameterization.html. Note: the diagramon this web site is incorrectly
attributed to Wyngaard, J., confirmed through personal communication with J. Wyngaard. The original source of the
diagram could not be located but the diagram is include here anyway because it give a clear overview of the boundary
layer structures. )
are transported vertically by eddy diffusion and the resulting convection leads to upward
(positive) sensible and latent heat fluxes. The ratio of sensible to latent heat, the Bowen
ratio, depends on the availability of moisture. Similar reasoning can be applied to the
transport of other substances, such as CO2. The direction and magnitude of the turbulent
transport of the gas molecules depend on the concentration gradient near the surface.
Small (a few mm), high-frequency eddies are found near the surface and these gradually
increase in size with altitude. The well-mixed layer extends from the surface layer to the top
of the boundary layer and occupies most of the volume in the daytime boundary layer
(Figure 1). Turbulence ensures that potential temperature, momentum and scalars are wellmixed and constant with height, while fluxes decrease with height. Large eddies dominate
this layer and eddies with a diameter equal to the height of the boundary layer can reach
down to the surface. The convective boundary layer is capped by a capping inversion with a
thickness of approximately 10% of the boundary layer (Figure 1). This temperature
8
inversion inhibits further vertical mixing keeping most tracers, such as pollution, within the
boundary layer.
Under idealized conditions (such as cloud-free rural), throughout the evening and night,
stable atmospheric conditions prevail while turbulence and vertical movement is
considerably reduced. The lack of vertical transport of fast moving upper air to the surface
means that nocturnal wind speeds near the surface are often less than during the day. The
dominance of radiative cooling of the surface can lead to a temperature inversion which
results in a downward (negative) sensible heat flux. Negative latent heat fluxes can occur
when the air temperatures drops down to the dew point temperature. A residual mixed
layer exists at higher levels, which is less turbulent than the daytime well-mixed layer, and is
capped by a capping inversion (Figure 1).
In analogy with the above description, an urban boundary layer develops over cities and
affects areas downwind. An internal boundary layer is created by the difference in surface
roughness and surface characteristics compared to a rural area (Oke 1995). Turbulence near
the surface depends on the height of buildings and the width of the streets, while sensible
heat fluxes are affected by the nature of the materials used in towns as well as the density of
the buildings. Water vapour originates from burning of fossil fuels, rivers, parks and water
for home, industrial and leisure use. The dearth of large areas of vegetation and the
impermeable nature of the building materials means that water storage is less than in rural
areas. The shortage of water to evaporate has a major impact on the urban climate since
the net radiation is primarily used to generate sensible heat fluxes and increase urban
temperatures and boundary-layer depths (Oke 1995). The concentration and fluxes of
pollutants and gasses, including CO2, are related to the atmospheric stability and structure,
the meteorological conditions as well as the available sources and sinks. CO2 is a long-lived,
chemically stable greenhouse gas and is only removed from the atmosphere through
photosynthesis by plants or absorption in the oceans over a decadal timescale (IPCC 2007).
2.2 Latent heat
Latent heat fluxes (FE) describe the vertical transport of water vapour taking into account the
heat required to evaporate the water from the surface
9
FE   w'q'
(1)
where  is the latent heat of evaporation in J kg-1, w’ is the vertical wind fluctuations in m s-1
and q’ is the fluctuation in absolute humidity in kg/m3. Evaporation of water depends on
the availability of moisture at the surface, the properties of the underlying surface, the
availability of energy to vaporise the water and turbulence to transport the vapour (Arnell
2002). Evaporation from soil depends on capillary transport of moisture to the surface and
depends on the soil type and texture. Evaporation is strongest during the day, when most
energy is available but can continue at night. Water vapour released by plants as part of the
photosynthesis process is called transpiration. Transpiration is related to CO2 exchange by
plants as both are controlled by photosynthesis and occur through the same stomata on
plant leaves. The combination of evaporation and transpiration is often referred to as
evapotranspiration.
The most popular measurement technique to measure evaporation is the open pan
evaporation. It is simple and cheap and is widely used by hydrological and meteorological
agencies (Arnell 2002). This technique is known to have high levels of uncertainty as water
can splash out, animals can drink from it, objects can fall into the pan and there are
inaccuracies associated with reading small differences in water level. Moreover, energy can
be stored in the water for later evaporation. The Piche-tube evaporation (see 3.4)
overcomes some of these problems as it is measured in a Stevenson screen. Both methods
measure evaporation from a water surface, which is unconstraint and is not representative
of a land-based environment. Evaporation is measured in mm water evaporated during a 24
hour (86400 s) period and converted to latent heat flux
FE  h w / 86400
(2)
where  is the latent heat of evaporation in J kg-1, h is the depth of evaporated water in m
and w is the density of water in kg m-3. Compared to open water, evaporation over land is
modified by the availability of moisture, the soil type and ground cover. The ground cover is
important as it affects evaporation through its albedo and porosity. Urban environments
consist of large areas of impermeable and often dark surfaces. Large latent heat fluxes can
therefore be expected after rain showers but low fluxes during dry periods as the surface
stores and transmits little water. Anthropogenic sources of water vapour in the urban
10
environment are a by-product of fuel burning. In parks and rural environments, the type of
vegetation also affects the albedo as well as turbulence. High levels of turbulence promote
better mixing and less resistance to transfer of moisture. The resistance to turbulent
diffusion is expressed as aerodynamic resistance ra (s m-1) and is inversely related to
windspeed and vegetation height (Monteith; Unsworth 2001)
{ln[( z  d ) / z 0 ]}2 {ln[( z  d ) / z 0 ]}
ra 

ku *
k 2u
(3)
where z is the measurement height, d is the zero-plane displacement height, z0 is the
aerodynamic roughness length, k is the von Karman constant, u is the wind velocity at height
z and u* is the friction velocity. This equation is only valid in neutral stability conditions.
Short grass has a higher aerodynamic resistance than tall trees, i.e. turbulent diffusion from
trees is easier than from grass. Values of ra over short grass range typically between 70 –
200 s/m for windspeeds between 5 and 1 m s-1. Transpiration of water from plants occurs
through stomata on leaves and stomatal resistance expresses the resistance to vapour flow
through the stomata. The stomatal resistance increases at high temperatures and in the
absence of soil moisture. Surface resistance rs (s m-1) describes stomatal resistance together
with resistance of vapour to flow through soil, in case not all the soil is covered by plants
(FAO 1998)
rs 
rl
LAI active
(4)
where rl is the stomatal resistance of an average leaf (s m-1) and LAIactive is the leaf area index
and is a dimensionless unit that expresses the leaf area per area of soil underneath it. For a
well-watered crop, rs varies between 100 to 25 s m-1 as the leaf area increases. The surface
resistance is specific for each plant variety and depends on the growth stage of the plants
and the plant density.
The aerodynamic resistance, surface resistance, available energy and vapour pressure deficit
are used in the Penman-Monteith equation to estimate potential evaporation (Monteith;
Unsworth 2001)
QE 
( Rn  G)   a C p (es  e) / ra
[   (1  rs / ra )]
11
(1)
where Rn is net radiation, G is the soil heat flux, a is mean air density at constant pressure,
Cp is the specific heat of the air, es is the saturated vapour pressure, e the vapour pressure, 
is the slope of the saturated vapour pressure-temperature relationship and  is the
psychrometric constant. The Penman-Monteith equation estimates potential evaporation
and does not represent true evaporation as assumptions are made that the soil is saturated
with water, plant roots can freely extract water and atmospheric conditions do not hinder
evaporation. Changes in turbulent conditions are not accounted for.
2.3 Eddy correlation technique
2.3.1 Theory
The eddy correlation method, also called the eddy covariance method, is used to measure
fluxes. Fluxes, caused by turbulent mixing, are a function of the vertical transport by eddies
of a concentration of a variable (Kaimal; Finnigan 1994) and can be derived from regular and
frequent measurements over time at a fixed observation point. The assumption is made
that Taylor’s frozen turbulence hypothesis is valid, i.e. the observed fluctuations over time at
a fixed point are associated with the advection of turbulence transported by the mean wind.
Turbulence at a point fluctuates, depending on the frequency of the eddies, and vertical
transport is only achieved if fluctuations in the vertical wind velocity correlate with
fluctuations in the variable of interest. The assumption is made that vertical transport is
proportional to the gradient of the mean variable. Reynolds’ decomposition is used to
separate the fluctuation X’ at a time point from the time-mean value of X as
Xi  X  X '
(2)
where Xi is the instantaneous value of a variable. The overbar indicates a time-average and
a prime represents a fluctuation from the mean. The flux FX is expressed as the covariance
between the fluctuations in the vertical wind component w’ and the fluctuations in a
variable X’, such as water vapour, CO2 or temperature, and is generally expressed as
FX  w' X '
Positive fluxes indicate a net transport to the atmosphere and negative fluxes denote
transport towards the surface.
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(7)
2.3.2 Sources of uncertainty
The eddy correlation method assumes that (i) the atmosphere is turbulent, (ii) advection is
negligible, i.e. steady-state conditions prevail, and (iii) the terrain is flat and homogeneous.
This implies that the mean vertical flow is assumed to be zero and vertical transport is only
caused by turbulence. Uncertainty due to advection can be reduced by selecting the
appropriate averaging time. Uneven terrain can be accounted for by coordinate rotation.
Errors also originate from spikes in the data and consequently the method used to remove
the spikes and fill in such missing data. The time period selected to average the fluctuations
adds further uncertainty. Coordinate rotation, block averaging, despiking and quality control
will be discussed in more detail in the following sections.
In addition to the causes of uncertainty listed above, many other parameters are reported in
the literature and by the instrument manufacture that can affect flux measurements and
relate to instrumentation, data handling or the flow itself. Eddy correlation measurements
require substantial post-field data corrections but there is no general agreement amongst
micrometeorologist on how best to compute near surfaces fluxes (Mahrt 2010). However, it
is recognised that more effort is needed to identify and quantify the uncertainties associated
with flux measurements (Richardson et al. 2006). The total error is a composite of all
measurement errors and is expressed as random and systematic errors. It is beyond the
scope of this work to give a detailed and comprehensive analysis of all the possible sources
of errors. A brief overview is given of some parameters while Table 1 gives a summary of
sources of errors to highlight the origin of uncertainty associated with flux measurements
using the eddy correlation method and the proposed corrections. The additive effect of the
individual errors could lead to errors of more than 100%. The sources of errors listed in
Table 1 mainly refer to systematic errors. A study of random errors by Richardson et al.
(2006) identified that the magnitude varies depending on season, wind speed and net
radiation. Average random errors up to 3.7 µmol s-1 m-2 in CO2 flux and 26 W m-2 in latent
heat flux were reported although errors up to 170 W m-2 were recorded when the net
radiation was higher than 400 W m-2. In most cases, there are several options for how to
correct and process the data and errors are also inherent in the applied corrections. For a
30-minute averaging method, different methodologies can result in a 10% deviation on
13
sensible heat fluxes and 15% on latent heat fluxes (Mauder et al. 2007). Errors associated
with instrument calibration, maintenance and hardware are not considered here.
Table 1: Sources of errors related to flux measurements derived from an open-gas analyser using the eddy correlation
technique
Error Source
Error Range
(%)
Correction
Reference
Optimize installation
Spectral correction in the high frequency range
Frequency response corrections
Adjust for delay
Digital sampling correction
Correction factor
(Foken 2008)
(Foken 2008)
0 - 15
0 - 25
Spike removal
Coordinate rotation
(Burba; Anderson 2007)
0 -50
Webb-Pearman-Leuning Correction
Correction factor
(Foken 2008)
(Jarvi et al. 2009)
0 -10
0-5
0 - 20
Sonic temperature correction
Band broadening correction
Different filling strategies
(Burba; Anderson 2007;
Foken 2008; Moffat et al.
2007)
Instrument installation
Sensor separation
Frequency response
Time delay
Digital sampling error
Loss of flux due to
path/volume averaging
Spikes
Unlevelled instrument and
flow
Density fluctuations
Sensor heating of gas analyser
Sonic heat error
Band Broadening
Missing data filling
Instrument temperature
fluctuations
Data averaging
Loss of low frequency turbulence
Loss of high frequency turbulence
Weak wind , low turbulence, storage, advection,
stationarity
Sonic temperature measurements conversion to
actual temperatures
Deviation from Gaussian
distribution (skewness, kurtosis)
5 - 30
5 - 10
Apply temperature control
 Appropriate time scale
 Ogive correction
Cospectral analysis and high pass
filter
Cospectral analysis and low pass
filter
 Apply a u* filter
 Stability and non-stationarity
tests and corrections
Apply correction factor
(Finnigan et al. 2003;
Mahrt 2010)
(Burba; Anderson 2007;
Kaimal; Finnigan 1994)
Use appropriate statistical methods
(Richardson et al. 2006)
(Finnigan 2008; Foken
2008; Mahrt 2010)
(Foken 2008)
Measurement errors can be limited by optimising the instrument installation to avoid any
interference with the measured properties. To prevent distorting the flow, the gas analyser
should be downwind from the sonic anemometer at a horizontal distance of 20 - 30 cm
according to Foken (2008a) but only 10 – 15 cm according to (Burba; Anderson 2007). A
sensor separation error still occurs in the covariance because the two instruments do not
14
measure at the same point in space (Burba; Anderson 2007). It is also recommended to
install the open-path analyser at or slightly below the sonic anemometer (Burba; Anderson
2007; Grelle; Burba 2007). Distortion of the vertical wind component can be reduced by
keeping the area and mast underneath the instruments clear. The measurement height
should be 2 to 5 times the canopy height and 5 to 18 times the instrument path length
(Burba; Anderson 2007). The instrument sampling rate must be sufficiently fast to include
even the smallest turbulent scales of motion and concentration fluctuations. Sources of
instrument error are reviewed in section 3.2.3.
The open-gas analyser measures gas densities in mmol per unit volume. Fluctuations in
pressure, temperature and water vapour result in changes in the volume, i.e. the air density,
and affects fluxes of CO2 and water vapour. The Webb, Pearman and Leuning (WPL)
correction (Webb et al. 1980) is usually applied although there is continuing discussion over
the validity of this correction (Foken 2008). The correction is dependent on the heat and
evaporation fluxes, the density of the measured gas and the air temperature. This means
that corrections of water vapour fluxes for changes in air density include the magnitude of
the water vapour flux itself in the calculations of the correction. The correction step must
therefore be iterative but this is not reported in the literature. Effects of pressure
fluctuations are not included in the WPL correction and it requires accurate values of heat
and evaporation fluxes. Further corrections are required because the open-path analyser
itself generates heat and affects the measurement volume. The sensible heat flux in the
optical path caused by electronic heating and radiation can be measured with a fine-wire
thermometer and should be used in the WPL equation instead of the ambient sensible heat
(Burba et al. 2008; Grelle; Burba 2007). The sensible heat flux in the optical path has been
reported to be up to 14% higher than the ambient heat flux (Burba et al. 2008). However,
this is only valid if the gas analyser is installed near-vertically. Compared to a closed-path
analyser, where the WPL correction need not be used, applying the standard WPL correction
can still lead to an underestimation of the CO2 flux by 19% while including the instrument
heating correction reduced this error to only 4% (Grelle; Burba 2007). The WPL density
correction is small during the growing season and large in winter when CO2 fluxes are small
(Burba; Anderson 2007). The lack of energy balance closure reported for near-surface flux
measurements using the eddy correlation method suggest that the energy fluxes are
15
underestimated and this will affect the accuracy of the WPL correction (Jarvi et al. 2009;
Wilson et al. 2002a). In addition, random errors on the energy fluxes can be substantial
(Richardson et al. 2006). Because of the large uncertainties on the parameters used in the
WPL correction and since the measurements reported in this work were taken during May to
July, the WPL correction will not be applied to the analyses presented in this dissertation.
The analysis of the WPL correction is included here as an example to illustrate that
corrections applied in response to errors in the eddy correlation method may not completely
rectify the errors and can even introduce further uncertainties. Due to time restrictions of
this project, only the corrections discussed in the next sections were applied in this project
as these were considered to be the most important.
2.3.3 Despiking
Spikes can be caused electronically but one of the major sources of errors in data from an
open-path gas analyser is caused by interference of particles or droplets in the optical path
of the instrument. Since the path is exposed to the environment, any object crossing the
path is registered as a spike. Precipitation, insects, pollen and dust passing through the path
cause temporary interference. The open-path gas analyzer sequentially rotates between
different wavelengths and any temporary obscuration may affect only one of the
wavelengths leading to a large difference, seen as a spike, between the absorption of the
reference and measurement wavelengths. The manufacturer claims that deposition on the
measurement window equally affects all wavelengths as long as the objects are stationary
and should not cause spikes. Some applications require continuous data sets and it is
therefore common practice to fill in missing data (Baldocchi et al. 2001). Different methods
can be used to eliminate spikes and replace the data (Goring; Nikora 2002). Spike
elimination is commonly based on removing data points that deviate by a multiple of the
standard deviations from the mean value. Many methods can be used for gap filling (Moffat
et al. 2007) but the most simple and commonly used methods use the average of the
nearest points, linear or non-linear interpolation or use averaged long term or
meteorological values.
2.3.4 Block averaging
Reynolds’ decomposition and flux calculations involve taking the arithmetic mean over a
certain time period. The time-scale chosen must be long enough to encompass the
16
frequency spectra of turbulences that will be encountered but not too long to avoid
including synoptic changes, advection and diurnal variations. The ability to measure high
frequency eddies is only determined by the sampling rate of the instruments and is not
affected by the choice of averaging time. The size of the smallest eddy that can be
measured is determined by the optical path length of the open-path gas analyser. A
sampling rate of 10 -20 Hz for near-surface measurements is suggested (Foken 2008). As
the optimum averaging time is dependent on the atmospheric stability and wind velocity, a
shorter averaging time can be used during the day and a longer time is required at night.
Although the appropriate averaging time is dependent on the local conditions and
measurement height, an averaging time of 30 min for near-surface measurements is
generally recommended (Foken 2008; Kaimal; Finnigan 1994). This time scale is sufficiently
long to encompass the turbulence representing the whole frequency spectrum, including
two or three large eddies. Under some circumstances it may be necessary to average over
longer time scales, especially over complex terrain and in a stable atmosphere (Finnigan et
al. 2003). Low values of fluxes, leading to a lack of energy balance closure, have been partly
attributed to the omission of low frequency turbulence and averaging times of 4 hours may
be required (Finnigan 2008).
2.3.5 Coordinate rotation
Eddies are transported horizontally with the mean wind in the horizontal plane and the
assumption is made that the mean vertical wind is negligible. The measurement of fluxes is
based on measuring fluctuations in transport vertical to the horizontal plane. The sonic
anemometer measures wind vector components relative to the orientation of the
instrument. The true vertical velocity is not measured due to tilting of the vertical
instrument axis and the horizontal wind may be distorted by the underlying terrain. This can
be corrected for by applying a correction. This involves rotating the x, y, z coordinate system
to align the x-axis with the mean wind vector. Several methods have been proposed to
calculate the tilt correction (Wilczak et al. 2001). The method applied in this work consists of
a double rotation of the anemometer axis. The sonic anemometer gives wind components
u, v, w in a right-handed coordinate system x, y, z. The first rotation rotates the coordinates
through and angle α around the z-axis so that the mean wind component v is zero. The
second rotation rotates the coordinates through an angle β around the y-axis and the mean
17
wind vector w is zero. The coordinate system is now aligned with the streamlines and the
new wind vector components u2, v2 and w2 relate to the mean sonic wind components
 u 2   cos  cos 
 v     sin 
 2 
 w2   cos  sin 
with   arctan(v / u ) and   arctan( w
sin  cos 
cos 
 sin  sin 
sin    u 
0    v 
cos    w 
(8)
u 2  v 2 ) . A third rotation is sometimes applied
to make the covariance between the w’ and v’ equal to zero. This third rotation is usually
very small and can even produce unphysical results (Finnigan et al. 2003). It is therefore
recommended only to apply the first two rotations and try to align the vertical anemometer
axis as close as possible perpendicular to the surface.
2.3.6 Quality control
The quality control step allows the removal of obviously-bad data. The most common
causes of bad data are related to precipitation and atmospheric conditions. Although spikes
have already been removed, the effect of precipitation or dew can still be seen in the
processed data as obviously-bad data points. In this work, data affected by precipitation
were removed based on a diagnostic value given by the open-path gas analyser.
Stable atmospheric conditions often occur at night. Wind speeds and turbulence can be
small and fluxes negligible while advection, storage near the surface and in the canopy as
well as drainage become important (Finnigan 2008). Data obtained under these conditions
are not an accurate measure of the exchange between the surface and the atmosphere and
result in an under-estimation of night time CO2 fluxes. Water vapour fluxes are not affected
since these are close to zero during the night (Baldocchi et al. 2001) . Several tests for
quality control purposes can be used (Aubinet et al. 2000; Baldocchi et al. 2001; Foken 2008;
Vickers et al. 2010). A commonly used criterion is to remove data that correspond to low u*
values. There is no general agreement on threshold level and the choice of threshold of u*
is based on the judgment of individual researchers and on the local conditions. Threshold
values of u* ranging between 0.02 and 0.86 m s-1, depending on the site and time of year,
have been suggested (Gu et al. 2005).
18
Chapter 3: Experimental work
3.1 The observation site
Observations were taken at the Atmospheric Observatory at the University of Reading
(51.44’N, 0.938’W, 66 m above mean sea level). The site is approximately 2 km to the southeast of the centre of Reading and 2.3 km to the north of the M4 motorway (Figure 2). The
nearest public road is approximately 240 m to the East. The instruments are installed over a
grassy surface. The site is in an open area surrounded by extensive and diverse vegetation
interspersed with buildings and a small lake 200 m away. This surface cover extends over
the whole university campus which has a surface area of 1.2 km2 and is surrounded by
residential areas. The nearest building is a 2-storey building approximately 50 m to the
south of the observatory while other buildings are at a distance of more than 100 m. A
roughness length z0 of 0.06 m (representing the grass) was used for this site, based on
previous measurements. Measurements were taken between the end of May and early July
2010.
3.2 Open-path gas analyser
3.2.1 Principle
An open-path analyser of CO2 and water vapour uses absorption of infrared radiation by
gasses at characteristic wavelengths to measure the densities of gasses in the atmosphere.
A Li-7500 open-path CO2/H2O analyser (LI-COR, USA) was used in this study to measure [CO2]
and water vapour in the near-surface atmosphere (Figure 3). It is a fast response instrument
and suitable for flux measurements. The instrument consists of a lower chamber that
contains the infra-red source and a chopper filter to alternate between different
wavelengths. The detector is housed in the upper chamber. The optical path is 10x125 mm
and is exposed to the atmosphere. The background is measured at the non-absorbing
reference wavelengths centred on 3.95 and 2.4 µm.
The absorption by CO2 or water
vapour is measured at 4.26 and 2.59 µm, respectively. Consecutive measurements are
carried out at the four wavelengths. The reference and the measurement wavelengths
follow the same optical path and any contamination should affect all measurements equally.
Partial obscuration of the window by stationary droplets should not affect the
measurements since the gas concentration is based on the ratio of the intensity of the
19
(a)
Atmospheric
Observatory
(b)
Town centre
University campus
Motorway, M4
Figure 2 : (a) Location of the Atmospheric Observatory within the University of Reading’s Whiteknights campus (green=
vegetation, blue= lake, orange= buildings, grey= roads/car park); (b) Location of the campus within the town of Reading
(Map: Digimap/ Ordnance Survey)
20
Figure 3: Cross section of a Li-cor 7500 open path CO2/H2O analyser showing the detector, at the top of the instrument,
and the main housing containing the infra-red source (LI-7500 Product Information).
reference and the absorption wavelengths. This is claimed to reduce the sensitivity to
individual water droplets and other contamination because it is assumed that the
contaminant equally affects all wavelengths. Particles moving through the optical path do
not affect the absorption at all of the wavelengths and leads to spikes (Heusinkveld et al.
2008).
3.2.2 Calibration
The instrument is calibrated in the factory and requires additional regular user calibrations.
The CO2 factory calibration uses 13 certified gas standards covering the range of 0 to 3000
ppm CO2. The calibration coefficients are obtained from a 5th order polynomial fit over the
whole calibration range to take variations with temperature and pressure into account.
Known water vapour densities are produced with a portable dew point generator, Li-610
(LICOR, USA).
Fifteen dew points between 0°C and 40°C are generated and a 3 rd order
21
polynomial is fitted to the data. The factory calibrations should remain constant for several
years. In addition, a user calibration must be carried out regularly to set the zero and span
parameters. This is a 2-point calibration to ensure that the instrument response agrees at 2
points with the factory calibration and is based on the assumption that the overall
polynomial fit does not change. The manufacturer recommends a weekly to monthly user
calibration depending on the operating environment (LI-7500 Instruction Manual). User
calibration also gives the opportunity to carry out general maintenance such as cleaning the
windows, which also improves the accuracy of the data.
3.2.3 Sources of measurement errors
The accuracy of the Li-7500 is mainly determined by the calibrations. The zero value should
be stable for several months but will eventually increase as the internal chemicals
deteriorate. The span is affected by temperature and the state of internal chemicals. These
consist of a CO2 absorber Ascarite II, a sodium hydroxide coated silica, and moisture
absorber magnesium perchlorate; they keep the instrument housing dry and free from CO2.
The span of the water vapour shows the largest amount of variability and a 10°C change in
temperature will change the H2O span by 1-2% (Li-7500 Instruction Manual). The accuracy
can be further improved by using high-accuracy calibration gases and carrying out multipoint calibrations that span the whole range of expected measurement values (Burns et al.
2009).
The largest source of measurement uncertainty arises from contamination or obstruction of
the optical path. Water droplets, dust, pollen, etc. flying through the optical path or
deposited on the optical windows affect the measured absorptance. The manufacturer
recommends regular cleaning of the windows, installing the instrument slightly tilted away
from the vertical and covering the windows with a hydrophobic wax (Li-7500 Instruction
Manual). The last two points encourage water droplets to run off the windows. An
instrument diagnostic value can be used to estimate the amount of contamination. Heating
of the detector to keep the window above the dew point has been recommended to prevent
dew formation and is claimed not to introduce significant errors in flux measurements
(Heusinkveld et al. 2008) . However, Grelle & Burba (2007) found that the CO2 flux was
underestimated by 66% when external heating was applied compared to underestimation of
only 19% without heating, relative to data from a closed-path gas analyser. Errors are
22
introduced as temperature fluctuations caused by the electronics or radiation can lead to
thermal expansion of the optical path and change the air density. Mean temperature
increases in the optical path up to 2°C compared to the ambient temperature have been
reported (Clement et al. 2009). A recently-developed modification to the open-path gas
analyser, where the optical path is enclosed by a thermally insulated shroud, eliminates the
negative effects of precipitation and surface heating and improves the overall data quality
(Clement et al. 2009). A sensor heating correction is proposed by Jarvi et al. (2009) while a
surface coating of the windows is recommended by Heusinkveld et al. (2008) to reduce solar
heating effects.
3.3 Ultrasonic anemometer
An ultrasonic anemometer (sometimes just called ‘sonic’) consists of an array of 3 pairs of
transducers vertically separated by approximately 200 mm. Each transducer acts alternately
as a receiver and an emitter. The time of flight for two ultrasound pulses (~ 100 kHz) to
travel in both directions between a pair of transducers is measured. The time of flight
depends on the distance between the transducers, the air velocity and the speed of sound.
The wind velocity between each pair of transducers is derived from the time of flight in both
directions between the transducers and eliminates the dependency on the speed of sound.
The speed of sound varies with temperature and humidity and is used to determine the
virtual temperature of the air. A transformation is applied to the wind velocities obtained in
the line between the transducer pairs to obtain the wind vectors u, v and w. This makes the
sonic anemometer a suitable instrument to measure turbulent fluctuations of the wind
velocity. An Omnidirectional (R3-50) Ultrasonic Anemometer (Gill Instruments Ltd, UK) was
used to measure the wind vectors.
3.4 Additional instrumentation
The data from the sonic anemometer and the open-path gas analyser were compared with
data obtained from a range of observations at the Atmospheric Observatory
(www.met.rdg.ac.uk/weatherdata). The absolute humidity is derived from the wet and drybulb temperatures measured with platinum resistance thermometers in a Large Stevenson
Screen. A cup anemometer (A100 Porton, Vector Instruments) placed at 3 m measured the
wind velocity. The net radiation is measured with a NR Lite net radiometer and a CNR1 4component radiometer (both from Kipp and Zonen), at a 1.5 m height. The ground heat flux
23
is measured with a ground heat flux plate. All the above measurements are recorded
continuously (1s) and are averaged to 5 minutes.
Evaporation is measured as open-pan and Piche tube evaporation. The open-pan measures
evaporation from a water surface directly exposed to the atmosphere. The open-pan
conforms to British standards and consists of a square measuring 1.83 m on each side
installed so that the top of the pan is flush with the ground level. The depth of the water is
measured with a hook gauge with graduations to 0.02 mm. The Piche tube measures the
evaporation of water from absorbent paper. It consists of a glass cylinder with a 10 mm
diameter and a height of 300 mm placed in a Large Stevenson Screen. The glass tube is
graduated in 1 mm steps. Absorbent paper rests on the open side of the tube and is wetted
as the tube is inverted when suspended on the closed side of the tube from a hook. The
water evaporated from the absorbent paper is continuously replenished from the water
column above. The evaporation is read as a decrease in water level in the glass tube. The
open-pan and Piche tube evaporation are recorded every day at 9 UTC and express the
evaporation over the previous 24 hours.
3.5 Methods
The sonic anemometer and the open-path gas analyser were placed on a 3 m mast with the
open-path gas analyser 200 mm to the east of the anemometer. The gas analyser was
positioned at a 15° angle from the vertical direction to facilitate water run-off. The
instrument was pointing north to reduce heating the window by direct sunlight. Data from
the gas analyser and the sonic anemometer were collected at a frequency of 20 Hz by the
CR1000 logger. Temperature fluctuations were derived from the sonic temperature. There
was also a separate thermistor (removed from the Licor control box) attached to the side of
the sonic anemometer. According to Aubinet et al. (2000), the sonic temperature becomes
more variable at wind speeds above 10 m/s because of mechanical deformation of the
anemometer. The wind speeds during our measurement period were generally very low
(see section 4.1.3).
Calibration of the open-path gas analyser was carried out weekly indoors (main teaching
laboratory) at room temperature. A calibration tube inserted into the optical path is flushed
with a calibration gas. The calibration tube encloses the optical path completely and
24
ensures only the supplied calibration gasses are exposed to the infra-red beam. The zero
point for CO2 and water vapour were adjusted using dry air (BOC, UK) with less than 1 ppm
CO2 and less than 2 ppm water. The span for CO2 was calibrated with carbon dioxide in air
(BOC, UK) certified to contain 1020 ppm CO2 but the uncertainty on this value is not known
(although the nominal request was in fact for 1000 ppm CO2 but the gas arrived with a
specific certificate indicating 1020 ppm). The dew point generator was used to calibrate the
water vapour span. Saturated water vapour is created at a set temperature, in this case
10°C or 15°C, and used to calibrate the open-path gas analyser. The concentration of CO2
and water vapour in the carbon dioxide or the water-vapour-saturated air was recorded
before and after calibration. The window was cleaned with a lens-cleaning cloth before
each calibration. A hydrophobic wax (Rain-X, Shell Car Care International) was applied to the
windows and the surrounding area according to the manufacturer’s instructions. The
internal chemicals were replaced before the measurement series started.
3.6 Data handling
The data of the sonic anemometer and the gas analyser were continuously recorded on a 2
GByte CompactFlash memory card on a CR1000 logger (program written in C-Basic by Curtis
Wood, as adapted from the ACTUAL project: www.actual.ac.uk) and were downloaded
regularly onto a computer. The raw binary data are converted to appropriate SI units with
the LoggerNet software (Campbell Scientific) and stored as ASCII files (TOA5 format). The
data files contained the time and date as well as values for the three wind vectors, the
temperature and CO2 and water vapour concentrations recorded at a frequency of 20 Hz.
Several free software packages are available to process the data and derive fluxes using the
eddy correlation method. Considering the wide range of options to process the data and
apply corrections (see 2.3), the available software was not used. Instead, a data-processing
and analysis program was written using Matlab (MathWorks) to ensure full control of the
process. Matlab code was written to process the data as discussed in Chapter 2 and analyse
the results. The data were processed in 24 hour blocks from midnight to midnight UTC. The
despiking process removed values above and below a certain threshold for each measured
parameter. The spikes were replaced by the average value of the last data point before and
the first data point after the removed spike. This process and the suitability of the threshold
levels were validated by visual inspection of the data in graphic format before and after
25
despiking. Data were averaged in blocks of 30 minutes, in line with general practice for near
surface fluxes. Two coordinate rotations were carried out to make the mean wind
components v and w equal to zero. A quality control step removed low quality data caused
by obstruction of the optical path and low turbulence. Obstruction of the optical path is
indicated by a diagnostic value from the OPGA. In the absence of contamination or
interference, the diagnostic value was stable at a value of 247. Data corresponding to
diagnostic values of less than 246 or more than 248 indicated contamination and were
removed. Data were also eliminated when the turbulence was very low and was based on
the threshold level of the friction velocity u*, chosen to be less than 0.1 m s-1. This value
was selected after evaluating several threshold levels, guided by values used in the
literature. The selected threshold removed the most unreliable data; higher threshold levels
did not appear to improve the data quality while removing a large number of data. Any data
removed during quality control or absent due to calibration time were not filled in because
the gaps often spanned several hours.
Additional data from the Atmospheric Observatory were obtained as Excel files (using the
online data extractor or from a memory card associated with the Met Mast) and were
converted into text files for analysis in Matlab. These data were processed in the same way
as the data from the OPGA and sonic anemometer to make them comparable. Statistical
analysis was carried out, employing standard statistical techniques, using Statgraphics
software (StatPoint Technologies, Inc.).
26
Chapter 4: Results and discussion
4.1 Sources of uncertainties
4.1.1 Instrumental uncertainty
A weekly user calibration of the open-path gas analyser was carried out over 9 weeks to
determine the zero and span calibration factors for CO2 and water vapour. The calibration of
the water vapour span was only carried out on 5 occasions because the instrument required
to generate saturated vapour was not available early in the project. The concentration used
to calibrate the instrument (1020 ppm) is more than double the natural levels of CO 2 and the
calibration range therefore covers the measurement range of interest. The water vapour
concentration used for calibration was determined by the dew point setting of the vapour
generator. The choice of dew point used was limited by the environmental conditions since
condensation had to be avoided. Natural water vapour levels of up to 14.5 g m -3 were
measured while calibration was generally carried out with 12.5 g m-3.
Table 2: Uncertainty on the calibration factors for the open-path gas analyser obtained from weakly calibrations.
Parameter
Number of
Mean
Standard
95% confidence interval
Coefficient of
observations
factor
deviation
on the mean
variation (%)
CO2 zero
9
0.9226
0.00021
± 0.00014
0.023
CO2 span
9
1.0062
0.00086
± 0.00056
0.086
H2O zero
9
0.8792
0.00294
± 0.00192
0.334
H2O span
5
1.0311
0.00551
± 0.00483
0.535
The zero and span factors for CO2 hardly varied from week to week and the coefficients of
variation were less than 0.1% (Table 2). The coefficient of variation for the zero and span
calibration factors of water vapour was a factor of 10 larger but still indicates little weekly
drift. The variations in the calibration factors were random over time and did not indicate a
systematic instrument drift (not shown). According to the manufacturer, the zero values are
expected to increase over time, as the internal chemicals lose their effectiveness, but the
time scale of our experiments may have been too short to observe this. The zero value for
CO2 was within the range of 0.85 – 1.1 given by the manufacturer but the zero value for
water vapour was outside the typical range of 0.65 – 0.85. It is not known if there are any
27
implications for the measurements but this value should be closely monitored. The span
values for both CO2 and water vapour were within the recommend range of 0.9 – 1.1.
Table 3: Impact of calibration on the measurement of a constant amount of CO2.(1020 ppm)
Parameter
Number of
Mean
Standard
95% confidence
Coefficient of
observations
concentration
deviation
interval on the
variation (%)
(ppm)
(ppm)
mean (ppm)
8
1019.3
2.88
± 2.00
0.28
9
1020.3
0.55
± 0.36
0.05
CO2 before
calibration
CO2 after
calibration
Table 4: Impact of calibration on the measurement of a constant amount of water vapour (dew point 15°C).
Parameter
Number of
Mean
Standard
95% confidence
Coefficient of
observations
concentration
deviation
interval on the mean
variation (%)
-3
H2O before
(g m )
(g m )
-3
(g m )
-3
3
12.76
0.172
± 0.195
1.4
3
12.50
0.0837
± 0.094
0.67
calibration
H2O after
calibration
The effect of changes in weekly calibration factors on the measured concentrations of CO 2
and water vapour was determined by measuring known concentrations of these gasses each
week before and after the instrument calibration. The coefficient of variation on these
measurements was generally larger than that of the calibration factors (Table 2, Table 3 and
Table 4). This suggests that the uncertainty in the measurement cannot be estimated from
the calibration uncertainty alone although the conditions of the calibration and the
concentration measurements were identical. Based on the 95% confidence interval, the
respective mean concentrations of CO2 and water vapour measured before and after
calibration were not significantly different. This was confirmed by applying a t-test to
compare the means. For both gasses, calibration resulted in a tighter distribution around
the mean, indicated by the smaller coefficient of variation, and the mean CO2 concentration
deviated less from the certified concentration of 1020 ppm, although the uncertainty on this
value is not known.
28
The reproducibility of the calibration was assessed by repeating the complete calibration and
verification procedures three times in succession. In general, the uncertainty on the
reproducibility was higher than on the weekly variability except for the water vapour
concentration after calibration (Table 3, Table 4 Table 5). The higher uncertainty may be
related to the low number of observations used in the reproducibility test. These results
indicate that the change over a week is lower than the error on the calibration procedure. It
may therefore be possible to calibrate the instrument less frequently but further work is
required to determine the maximum acceptable calibration interval without detrimental
effect on the measurements.
Table 5: Reproducibility of the calibration procedure determined by carrying out the whole calibration process three
times in succession.
Parameter
Number of
Mean
observations
Standard
95% confidence
Coefficient of
deviation
interval on the
variation (%)
CO2 zero
3
0.9234
0.00078
± 0.00088
0.084
CO2 span
3
1.0037
0.00322
± 0.00364
0.321
CO2 after
3
1021.1
2.57
± 2.91
0.25
H2O zero
3
0.8775
0.00365
± 0.00413
0.416
H2O span
3
1.0512
0.02281
± 0.02581
2.17
3
12.50
0.00115
± 0.00130
0.0092
calibration (ppm)
H2O after
-3
calibration (g m )
The overall error on the measurement, including calibration errors and weekly variations,
can be estimated from the standard deviation on the mean difference before and after
calibration relative to the mean concentration of the gas measured. The measurement error
for CO2 was found to be 0.30% and for water vapour 1.3% (Table 6). The random errors due
Table 6: Measurement errors on CO2 and water vapour measurements estimated from the standard deviation on the
mean difference before and after calibration relative to the concentration of the gas.
Mean difference
Standard
Mean
Measurement error
before and after
deviation on the
concentration
(%)
calibration
mean difference
CO2
0.95 ppm
3.04 ppm
1020.3 ppm
0.30%
Water vapour
-0.27 g m-3
0.157 g m-3
12.50 g m
Parameter
29
-3
1.3%
to the instrument are negligible compared to the natural range of gas concentrations that
the instrument must be able to measure and compared to other sources of uncertainty
associated with the eddy correlation method (see 2.3.2).
4.1.2 Contamination of the windows
Contamination of the windows was established on the basis of visual weekly inspections and
a diagnostic value recorded by the gas analyser where 247 indicated good conditions. Small
(approximately 1 mm diameter) droplets were noted on the instrument window after or
during precipitation but no other deposits were detected and the windows remained clean
between weekly inspection intervals. Deposits on the windows were thus only attributed to
water and were associated with dew and precipitation. The departure of the diagnostic
value from 247 coincided with rain events, as measured by rain gauges, but the
contamination remained, presumably, until the water droplets evaporated. Departures of
the diagnostic value also occurred when air temperatures reached the dew point
temperature, as measured on the Atmospheric Observatory. In the absence of condensation
or precipitation, the diagnostic value remained constant at 247 over the 2 month
measurement period. This suggests that deposition of dust and pollen, which were prevalent
during the measurement period, did not pose a problem. Coating of the window with a
hydrophobic wax apparently had no impact on the measurements and did not reduce the
contamination by water droplets. It is claimed that only large droplets which completely
obscure the window affect the measurements and small droplets on the windows should not
affect the measurements since the infra-red light beam should be equally reduced for all the
measurements, including the reference measurement, thereby cancelling out the effect of
partial obscuration. Our measurements showed that small droplets did reduce the quality of
the results and data obtained under these conditions had to be rejected. Based on this
criterion, no data had to be rejected on 22 days out of 36, while on the remaining days up to
56% of data had to be eliminated. Over the total measurement period, 7.7% of the data had
to be rejected due to precipitation or condensation. This level is low compared to 30% of
data rejected by dew over grassland reported by Heusinkveld et al. (2008). This low
contamination level was presumably achieved because the measurements were carried out
during a particularly dry period but it is probable that annual measurements will have higher
rejection levels.
30
4.1.3 Friction velocity as a quality-control measure
The wind speed, and thus turbulence, was on average low over the measurement period, as
expected for the time of year. The maximum mean windspeed during the day only reached
2.2 m s-1 and at night only 1.3 m s-1 (Figure 4). Based on windspeeds averaged over 30
minutes throughout the day over 38 days, 28 % of the windspeed was less than 1 m s-1 and
96 % was less than 3 m s-1. No average windspeed above 5 m s-1 was found for any 30
minute period.
th
th
Figure 4: Mean windspeeds over 38 days between the 25 of May and 12 of July derived from daily 30-min averaged
windspeeds after coordinate rotation. The error bars represent ± 1 standard deviation around the mean.
Eliminating data when u*<0.1 m s-1 together with the effect of precipitation resulted in
25.5% of data being rejected. The data lost during the downtime for calibration is not
included. No data needed to be rejected on only 1 day out of 36 (30th of May) while on
other days up to 73% was rejected. Low values of u* were predominantly encountered
during the night, which agrees with observations in London (Langford et al. 2010). The
rejection level of 25.5 % compares to the rejection of 44% of data collected with an openpath gas analyser in Helsinki (Jarvi et al. 2009). Differences in rejection criteria and
precipitation frequency make it difficult to compare rejection levels.
Compared to data rejected due to precipitation only, higher CO2 fluxes (Figure 5) were
obtained at night when all quality control parameters were taken into account. These
31
results agree with the FLUXNET observations that CO2 fluxes are underestimated in
nighttime stable conditions (Finnigan 2008) and necessitate the omission of fluxes obtained
when there is low turbulence. The choice of u* threshold used as a quality control
parameter is therefore important but there are no guidelines how to estimate the
appropriate threshold. The uncertainty associated with low turbulence and the choice of the
u* threshold can lead to an underestimation of respiration and has important consequences
for the carbon budget.
Figure 5: Comparison of average diurnal variation in CO2 fluxes with incorrect diagnostic values removed (red) and with
th
th
all quality control parameters applied (blue). The data were averaged over 31 days between the 25 of May and the 5
of July 2010. Error bars are not indicated for clarity.
4.2 Diurnal variation in CO2 fluxes and concentrations
The diurnal variation of CO2 concentrations and fluxes was estimated for a 31-day period
between the 25th of May and the 5th of July (inclusive). The CO2 concentration starts to
increase in the evening and continues to rise throughout the night to reach a maximum
mean concentration of 403 ppmv around sunrise (Figure 6). After sunrise, the concentration
drops rapidly until mid-morning followed by a slower decrease until late afternoon when the
lowest mean concentration of 375 ppmv is measured. The standard deviation represents
the spread of concentrations measured over the 31 day period at a given time point. The
daytime concentrations show little day-to-day variability with the coefficient of variation
reaching as low as 0.72%. More variability is observed at night although the maximum
32
coefficient of variation is still only 3.6%. The low temporal variability suggests that the
range of atmospheric and meteorological conditions encountered over the time period of
the measurements, have only a minor influence on the CO2 concentrations.
Figure 6: Average diurnal variation in CO2 concentration over 31 days between 25 May and 5 July 2010. The error bars
represent ± 1 standard deviation around the mean.
Figure 7: Average diurnal variation in CO2 fluxes over 31 days between 25 May and 5 July 2010. The error bars represent
± 1 standard deviation around the mean value.
33
Figure 8: Average diurnal variation in the Monin-Oblukhov stability parameter z/L over 31 days between 25 May and 5
July 2010. The error bars represent ± 1 standard deviation around the mean value.
The continuous increase in CO2 concentrations encountered at night coincides with positive
fluxes which indicate that CO2 is released at night (Figure 7). The fluxes become negative
shortly after sunrise and CO2 is absorbed throughout the day until the early evening. The
highest level of absorption occurs late morning. This pattern of CO2 absorption and release
is characteristic for an area dominated by photosynthesis (Monteith; Unsworth 2001).
Respiration occurs at night from plant tops, plant roots and soil organisms. After sunrise,
solar irradiance enables photosynthesis to begin and CO2 is absorbed. At the same time, the
daytime boundary layer develops and grows throughout the morning. The Monin-Obukhov
stability parameter z/L is used to estimate the atmospheric stability. The atmosphere is
considered to be unstable when z/L < 0, neutral for z/L 0 and stable for z/L >0.
Predominantly stable stratification occurs between 19.30 and 4.45 UTC and the atmosphere
is on average unstable during the day (Figure 8). The sunrise time varied between 0342 and
0355 UTC and sunset times between 2001 and 2023 UTC over the 31 day time period
considered here (obtained from the US Naval Oceanography Portal,
http://www.usno.navy.mil/USNO/astronomical-applications/data-services/rs-one-yearworld). This indicates that the atmospheric stability changed within 1 hour before sunset
and 1 hour after sunrise. At sunrise, photosynthesis starts to assimilate CO2 and the upward
flux decreases to zero approximately 1 hour after sunrise (Monteith; Unsworth 2001). The
34
reverse process takes place shortly before sunset. The unstable daytime conditions enable
a well-mixed layer to develop in the boundary layer and help to vertically spread any nearsurface gasses, including CO2, thereby reducing the near-surface concentrations. The
shallow boundary layer and stable conditions at night prevent the dispersion of CO2.
Photosynthesis and respiration together with atmospheric stability explain the observed
diurnal variations in CO2 concentrations and fluxes. It was not possible to separate the
relative importance of these contributing factors from the data available but a strong linear
correlation (R2= 0.9443, P <0.001) was found between the mean flux and mean z/L over 31
days (figure not shown). The time when the CO2 flux changes from positive to negative, and
vice versa, corresponds to the time when the Monin-Obukhov parameter changes sign but
this also coincides with the expected time when a different phase of the photosynthetic
pathway becomes active. Annual measurements that include the winter period when
photosynthesis is limited would enable to separate the relative effect of boundary layer
height and photosynthesis.
The diurnal patterns in CO2 concentration derived from measurements at the Atmospheric
Observatory in Reading does not show a peak that could be associated with the morning
rush hour which is observed in other urban measurements. This indicates that nearby
traffic does not have a large effect on the CO2 concentrations at the measurement site. The
magnitude of the CO2 concentration will not be compared with other towns because
differences may be caused by regional and seasonal variations and differences in
measurement height. The largest difference was found when comparing CO2 fluxes with
those measured in other urban environments. Carbon dioxide fluxes in cities are generally
reported to remain positive throughout the day (for example, see Appendix 1). Our flux
measurements follow the same trend as the CO2 fluxes measured during the summer at the
University of Helsinki, which is surrounded by buildings and extensive vegetation (Jarvi et al.
2009). The origin of the discrepancy between CO2 fluxes in Reading and most other urban
centres may be elucidated by examining more closely where the [CO2] measured by the
sensor originates from.
Point measurements do not measure concentrations or fluxes immediately below the sensor
but reflect the surface upwind. The area that contains sources and sinks that influence the
instrument at a certain height is referred to as the footprint (Foken 2008) or source area
35
(Schmid 1994). The concept of source area enables us to estimate the spatial scale of point
measurements. The source area is upwind from the sensor but its size and position are
constantly changing and depend on the wind velocity and direction, surface roughness and
atmospheric stability. Several models are available (Foken 2008) based on turbulent
diffusion in a Eulerian frame of reference or based on Lagrangian trajectory dispersion
models. Most models are derived assuming certain conditions, such as for use in forests or
during neutral stratification. The model SAM-2 developed by Schmid (1994) is based on
diffusion and was used in this work to estimate the source area of CO2 concentrations. The
footprint function used in the model describes the source area in terms of its special extent
and concentration distribution. The model assumes a flat surface with no obstacles that can
disturb the wind flow. It does not take changes in surface cover, i.e. roughness, into account.
The results are therefore only an approximation of the source area. The model requires the
input of the measurement height z (3 m), the roughness length z0 (0.06 m), the friction
velocity u*, the Obukhov length L and the standard deviation of the lateral wind fluctuations
v. The source area is aligned with the wind direction and elongated. The effect of
atmospheric stability on the source area is demonstrated for the 31 st of May 2010 when
average wind speeds up to 5 m s-1 were recorded from a predominantly north-westerly
direction (Figure 9). Under weakly unstable (z/L= -0.123) conditions, the main source of CO2
is within 8 to 42 m from the sensor with the maximum at 25 m (Figure 10a), i.e. mostly
within the enclosure of the observation site. The total source area extended to 467 m in
length and only covered an area of 0.095 km2 over vegetation and open water (Figure 9b).
Under weakly stable conditions (z/L= 0.107), the main source area was narrow and
elongated up to 108 m away over vegetation (Figure 9a), with the maximum at 48 m, while
the total source area extended to 1670 m (Figure 9b) and covered an area of 0.643 km2.
Increasing the stability increases the source area while increasing the instability decreases
the source area (Figure 10). Turbulence in an unstable atmosphere promotes vertical
transport and limits horizontal transport. Under stable conditions, vertical transport is low
and the vertical wind profile increases more rapidly with height leading to more dominant
horizontal transport and thus a larger source area. The area immediately underneath the
sensor did not contribute under any conditions. The low measurement height (3 m) explains
why only localised effects were measured which are dominated by the extensive
surrounding vegetation on the university campus and is not representative of a typical urban
36
NW wind
Sensor
0m
(a)
100 m
NW wind
Sensor
0m
(b)
700 m
Figure 9: Estimated source area for CO2 measurements at the University of Reading based on Schmid’s model (1994) for
-1
-1
-1
-1
stable (z/L= 0.107, u*= 0.225 m s , v=0.694 m s ) and unstable (z/L= -0.123, u*= 0.337 m s , v= 1.24 m s ) conditions
st
on the 31 of May 2010 with a north-westerly wind direction. (a) The environment surrounding the sensor consits of
vegetation (green) and a few buildings (pink). The solid eliptic lines indicate the area with the maximum contribution to
measured [CO2] in unstable (red) and stable (black) conditions. This corresponds to 10% of the relative effect level. The
dashed lines indicate the total source area (100% relative effect level) in unstable (red) and stable (black) conditions. (b)
Full extend of the total source areas as indicated in (a). (Map Source= Digimap/ Ordnance Survey).
37
Figure 10: Distance of the maximum impact source from the OPGA as a function of atmospheric stability expressed as
z/L.
The main contribution is always from within the campus, irrespective of wind direction, but
it may be expected that neighbouring residential areas should have a larger influence when
winds come from an east-north-easterly direction as this is closest to the sensor. A higher
measurement height will extend the source area (Schmid 1994) and measurements at a
height of 50 m are required to include Reading town centre in the source area when the
atmosphere is unstable. The dependence of the source area on atmospheric stability is in
line with results from other urban studies but the source area in those studies was larger
because the sensors were placed at 48 m (Velasco et al. 2009) and approximately 200 m
(Helfter et al. 2010; Langford et al. 2010). The wind direction was not found to influence the
CO2 concentrations and fluxes but this may be linked to the overall low windspeed during
the short measurement period. The effect of wind direction and velocity may become
noticeable for measurements during winter when wind speeds are greater.
The analysis of the source area for fluxes, using the model FSAM developed by Schmid
(1994), showed the same trends as that for concentration but the total source area was
smaller for the same atmospheric conditions (results not shown). For examples, the
maximum flux source area extended up to a maximum length of 82 m under unstable
conditions and 268 m under stable conditions compared to 467 m and 1670 m, respectively,
under the same conditions for the concentration source area. These results agree with the
findings of Schmid (1994) that the source area for fluxes is smaller by an order of magnitude.
38
The small flux source area indicates that the measured fluxes originate mainly from the
vegetated area immediately surrounding the measurement site with little influence from
nearby buildings or roads. This explains why the observed CO2 fluxes are representative of
the exchange over a vegetated area and not of that over an urban area.
4.3 Water vapour
4.3.1 Comparison of absolute humidity measurements
The open gas analyser measures the density of water vapour (mmol/m3) in the optical path
based on the amount of infra-red radiation absorbed by the water molecules in the beam.
The absolute humidity is the total mass of water vapour per unit volume of air (g/m3) and is
derived from this measurement by taking account of the molecular weight of water (1 mol
water = 18 g). Many methods are in use to measure the absolute humidity but the most
widely used is the psychrometric method (WMO 2008) and the absolute humidity estimated
from the open-path gas analyser will be compared here to this method. The psychrometric
method is based on the continuous measurements of the wet and dry bulb temperatures at
the Atmospheric Observatory, approximately 5 metres to the west of the open-path gas
analyser. Both temperatures are measured next to each other in a Large Stevenson Screen
with platinum resistance thermometers but the wet bulb thermometer is wrapped in a
cotton sleeve kept wet by capillary flow from a deionised water bath. Water evaporating
from the sleeve lowers the temperature and the amount of evaporation, and thus the
temperature depression, is proportional to the air humidity. The absolute humidity AH is
derived by the automated system (METFiDAS) from the following equations:


100e

AH  1000
 461.5(T  273) 
dry


(3)
where e is the vapour pressure (hPa) and Tdry is the dry bulb temperature (°C). The vapour
pressure in turn is derived from the dry and wet bulb (Twet) temperatures using the semiempirical Regnault’s equation:
e  es  0.8(Tdry  Twet )
(4)
where 0.8 is the psychrometric coefficient and es is the saturated vapour pressure (hPa) with
respect to Twet and is derived from the empirical Teten’s equation
39
 A  Twet 

es  eo  exp 
 Tw  T ' 
( 5)
where e0 = 6.122 hPa and is the reference saturation pressure at 0°C, A = 17.67 and T’=
243.5. The accuracy of the absolute humidity derived from equations 9 – 11 depends on
several factors. The psychrometric coefficient used is an empirical value determined for a
screen psychrometer and is here assumed to be constant although in reality it varies with air
temperature and humidity and, in particular, it does vary markedly for ventilation rates
below 3-5 m s-1 (WMO 2008). It is recommended that screen psychrometers are artificially
aspirated to keep the ventilation rate constant. The effect of atmospheric pressure on the
vapour pressure is not taken into account and A should in fact be multiplied by p/1000
where p is the atmospheric pressure in hPa (WMO 2008). The accuracy further depends on
the accuracy of the thermometers, the use of appropriate coefficients and the use of clean
water and a clean sleeve to measure Twet , which can affect the vapour pressure of the water.
The assumption that adequate ventilation occurs in the screen is not always met at low wind
speeds, which dominated over most of the measurement period and particularly at night
(Figure 4).
The absolute humidity measured with the gas analyser (AHG) was compared with data
derived from the psychrometric method (AHT) over 21 days when no rain occurred. The
two datasets showed a strong linear correlation (R2 = 0.9932) and the values were similar
indicated by a slope of 0.962 (standard error = 0.00377) with an intercept of 0.00356
(standard error = 0.0351) when x = AHG (figure not shown). The standard error on the
intercept does not indicate a significant difference but the standard error on the slope is
considerably smaller than the slope value and indicates that the two data sets are
significantly different. This relationship shows that AHT is approximately 0.96 times AHG.
This is confirmed by visually examining the daily humidity differences which confirm that the
observed AHG is generally slightly greater than AHT (Figure 11) but follow the same trends.
The sources of uncertainty described earlier associated with both types of measurement can
all contribute to the small differences observed between the two data sets. The
measurement error on the absolute humidity measured with the open-path gas analyser
was determined to be 1.3% (see Error! Reference source not found.). The uncertainty on the
40
values derived from the psychrometric measurements is not known and is influenced by
several factors. The
Figure 11: Comparison between the absolute humidity AHG measured with the open-path gas analyser (red) and AHT
th
derived from the wet and dry bulb temperatures (blue) on the 6 of June 2010.
calculation of AHT involves the use of several empirically determined constants and
coefficients. The psychrometric coefficient was not adjusted for temperature, humidity and
ventilation. The majority of windspeeds (96%) were less than 3 m s-1 and mostly below
5 m s-1, particularly at night (see section 4.1.3). The ventilation in the screen is likely to be
even lower. Temperature variations of more than 20°C, large humidity variations and low
windspeeds over the measurement period suggest that the use of a constant psychrometric
coefficient is not appropriate and may increase the uncertainty on the absolute humidity
derived by this method. Different values of e0, A and T’ are also in use and introduce further
uncertainty but the values used here are recommended by the World Meteorological
Organisation (WMO 2008). Overall, the uncertainty on AHT is likely to be larger than that on
AHG.
The dependence of the absolute humidity measured with OPGA on meteorological and
atmospheric conditions was explored by examining possible correlations between these
parameters. No meaningful correlations were found when the data were considered over
the whole day probably because synergistic effect of these parameters on a diurnal scale.
41
For example, cooler temperatures at night coincide with low wind speeds and a stable
atmosphere; it not possible to identify the effect of temperature alone. To limit the effect of
this interdependence, correlations around midday alone were investigated when
atmospheric variability is less than on a diurnal scale. The absolute humidity measured
between 1000 and 1400 UTC over 29 days with the OPGA correlates with air temperatures
(R2 = 0.6013, P < 0.001) and shows a negative trend with net radiation Rn (R2 = -0.3518,
P <0.001). Air humidity can increase as the saturated vapour pressure increases with
temperature, according to the Clausius Clapeyron equation, but high levels of radiation may
cause excessive surface heating and activate protective physiological plant mechanisms to
prevent evaporation.
4.3.2 Latent heat fluxes
4.3.2.1 Evaporation
To facilitate comparison of data in this section, evaporation is expressed as latent heat flux
obtained from evaporation measurements according to equation 2 and from water vapour
fluxes derived from the eddy correlation method using equation 1. Evaporation
measurements are based on one daily observation at 0900 UTC when the evaporation over
the previous 24 hours is recorded. The latent heat fluxes measured throughout the day by
the OPGA system were integrated over the same time period to determine the evaporation
during the same 24 hours. No correlation was found between the latent heat flux from the
open-pan or Piche tube and that from the gas analyser (Figure 12).
Figure 12: Comparison between the latent heat derived from open-pan and Piche tube evaporation with that derived
from the open-path gas analyser using the eddy correlation method. The linear regression line for each dataset is drawn
2
on the graph and the linear regression equations, together with the correlation coefficient R , are listed. A blue line or
box refers to correlations with the Piche tube and red refers to correlations with the Open-pan.
42
Moreover, the results from the open-pan and Piche tube were only weakly correlated with
one another (R2 = 0.3203) (figure not shown). Although both measure evaporation from a
water source, evaporation is generally higher from the Piche tube (Figure 12). The World
Meteorological Organisation (WMO 2008) recommends evaporation measurements should
have a 95% confidence interval of ± 0.1 mm but concedes that in practice 1 mm is more
achievable. Based on the graduation of the measurement devises, the uncertainty on
reading water levels on the observation site at the University of Reading is ± 0.5 mm for the
Piche tubes and 0.01 mm for the open pan. This results in an uncertainty of ± 14.3 W m-2
and ± 5 W m-2 on a latent heat flux of 100 W m-2 for the Piche tube and open pan,
respectively. Accounting for the uncertainty in reading the water level does not change the
overall correlation but does reduce the magnitude of the difference between the two
methods. The difference is not likely to be related to the under-compensation for
precipitation events in the open-pan as rain only occurred on a few days (Figure 13). More
than 1 mm of rain only occurred on 6 days during the measuring period. In the Piche tube,
evaporation of deionised water does not occur directly from a water surface but from
wetted absorbent paper. Evaporation of water in the open-pan from a large water surface,
Figure 13: Latent heat estimated from the open-path gas analyser, open-pan evaporation and Piche tube evaporation
between May and July 2010. The vertical black lines indicate days with minimum 0.5 mm rain.
43
although contaminated, may require more energy to overcome the surface tension of the
water compared to evaporation of water from wetted absorbent paper. The direct
radiation, wind and turbulence experienced by the open pan may still not be sufficient to
provide the additional energy.
Latent heat flux measured with the gas analyser is on average 8 times smaller than that
derived from the Piche tube and 7 times smaller than the open-pan (Figure 13). In these
methods, evaporation is not restricted by water availability while latent heat fluxes
measured by the gas analyser originate from many sources most of which are restricted by
water availability, mobility and plant physiology (see 2.2). The open-pan and Piche tube only
indicate how evaporation from a limited and specific water surface is affected by
meteorological conditions while the eddy correlation method indicates the evaporation from
the total complex environment. A direct comparison between the two types of
measurement is therefore of limited value as they are physically different measurements.
As discussed in section 4.2, the source area detected by the gas sensor is dominated by
vegetated soil with the main source derived from a grassy area. Water availability is
expected to be high after precipitation but no trend was found between latent heat flux and
precipitation or time since the last precipitation occurred (Figure 13). Infrequent and low
levels of precipitation, often less than 1 mm for less than 30 minutes, leading to depletion of
soil moisture may account for high levels of moisture absorption after precipitation and low
evaporation.
No significant correlations were found between the latent heat flux derived from the gas
analyser and wind speed, wind direction or humidity. The correlation (R 2 = 0.5146) between
temperature and latent heat flux was driven by the simultaneous diurnal variation of both
variables but did not relate to the temperature between 1000 and 1400 UTC (R2 = 0.1889).
Daytime maximum temperatures alone do not determine the magnitude of the latent heat
flux. Multiple regression between the available meteorological parameters and latent heat
flux was not able to produce a satisfactory model. These results indicate that latent heat
fluxes are driven by a complex interaction of multiple parameters and not only by
44
meteorological conditions; any model should include parameters representing the condition
of the surface.
4.3.2.2 Potential evaporation
Latent heat fluxes (E1) were derived from meteorological observations according to the
Penman-Monteith equation (equation 5). These were used together with net radiation (Rn)
and ground flux (G) measurements to estimate the sensible heat flux (H1) derived from the
energy balance where Rn = E1 + H1 + G. Latent heat (E2), discussed in the previous section,
and sensible heat (H2) fluxes were also obtained from the measurement system associated
with the OPGA based on the eddy correlation method.
Figure 14: Comparison of the latent heat fluxes estimated from the open-path gas analyser (E2) and the Penmanth
th
Monteith (PM) equation (E1) over 18 days between the 25 of May and the 11 of July 2010. The black limits indicate
the 95% confidence interval as an indication of the spread of the data at a time point over a period of 18 days.
Both datasets show that latent heat fluxes are positive during the day and close to zero at
night but they vary greatly in their magnitude with maximum daytime latent heat fluxes E1
approximately 7 times larger than E2 (Figure 14). Consequently, large differences are also
seen in the sensible heat fluxes (Figure 15 and Figure 16). A mean midday Bowen ratio (β=
H/E) of 0.26 when using the Penman-Monteith equation, confirms that this estimate
assumes low moisture restrictions and is typical for well-irrigated crops and grassland
(Wilson et al. 2002b) . In contrast, a mean maximum Bowen ratio of 4.9 was derived from
45
the eddy correlation method. This is representative of a dry environment and is
encountered over vegetation in arid regions and in a Mediterranean climate (Wilson et al.
2002b). For comparison, the mean Bowen ratio at midday in the different areas of Marseille
during spring and summer ranged between 5.5 and 3.8 depending on vegetation cover
(Grimmond et al. 2004) although higher midday Bowen ratios have been reported for several
North-American cities (Grimmond et al. 2002). The prevailing conditions over the
measurement period were dry and warm with only 2.6 mm rain over the 18 days considered
Figure 15: Mean latent heat E2 and sensible heat H2 derived from the open-path gas analyser using the eddy correlation
th
th
technique over 18 days between the 25 of May and the 11 of July 2010. The black limits indicate the 95% confidence
interval as an indication of the spread of the data at a time point over a period of 18 days.
46
Figure 16: Mean latent heat E1, derived from the Penman-Monteith method, and sensible heat H1 estimated from the
th
th
energy budget, averaged over 18 days between the 25 of May and the 11 of July 2010. The black limits indicate the
95% confidence interval as an indication of the spread of the data at a time point over a period of 18 days.
here and similarly low rainfall on intervening days. Although not identical to a
Mediterranean climate, the vegetation is less adapted to drought and heat and may respond
more quickly to these conditions. This was confirmed by the grass turning yellow over the
measurement period. Dry soil limits direct evaporation while plant stress, caused by lack of
water and high daytime temperatures, also restricts stomatal evaporation. The main source
area for the fluxes consists predominantly of grass-covered soil. Grassland is more sensitive
to soil moisture than trees because its shallow roots cannot access deep ground water
(Wilson et al. 2002b). This suggests that arid vegetation has not dissimilar surface properties
as impervious urban surfaces.
The surface resistance rs was assumed to be constant at 60 s m-1 for the calculation of E1. In
reality, rs depends on water availability and varies throughout the day as the stomatal
resistance changes. Seasonal differences associated with leaf area can be ignored here as all
the measurements were taken in late spring and early summer. An rs value of 60 s m-1 is
representative of midday conditions over well-irrigated crops but values of several hundred
s m-1 are found over other types of vegetation and 563 s m-1 was found over grassland under
very dry conditions (Wilson et al. 2002b). Increasing the surface resistance rs to 200 s m-1 to
represent dry conditions, although this may still be an underestimation, reduced the mean
midday latent heat flux by 29.5% (Table 7). This also includes the effect of changing the
measurement height z, used to calculate the aerodynamic resistance (Equation 3), to 3 m
which is the height at which the wind speed is measured. Originally, E1 assumed a
measurement height of 1.5 m and corresponds to the height of the temperature
measurement. The effect of changing z alone was small compared to the other effects (data
not shown). The estimate of ra was based on a roughness length z0 of 0.01 m to estimate E1
and applies to an open, level surface with short grass. However, the measurement
environment also contained long grass, shrubs, trees and buildings and a roughness length
of 0.06 m was used together with rs of 200 s m -1 and a measurement height of 3 m to
explore the uncertainty on ra. Using these assumptions, the latent heat flux decreased by
37.0% and increased the Bowen ratio from 0.26 to 1.1 (Table 7). This analysis indicates that
47
the surface resistance introduces more uncertainty on the latent heat flux calculations than
the aerodynamic resistance. The latent heat flux decreases logarithmically as rs and ra
increase and is particularly sensitive to changes in values within the range of interest for the
Table 7: Impact of changing the surface resistance rs, measurement height z and roughness length z0 on the mean
midday latent heat flux derived from the Penman-Monteith equation and on the Bowen ratio. Midday refers to the
period between 1000 and 1400 UTC. The standard deviation gives an indication of the spread of latent heat fluxes over
the measurement period and does not represent intrinsic measurement uncertainties.
Mean Midday
Standard deviation
Difference in latent
Latent heat flux
(W m-2)
heat flux
(W m-2)
E1: rs = 60 s m-1,
Bowen ratio
(%)
319
88.4
0.26
225
67.4
29.5
0.80
201
60.1
37.0
1.1
z = 1.5 m, z0 = 0.01 m
rs = 200 s m-1, z = 3 m,
z0 = 0.01 m
rs = 200 s m-1, z = 3 m,
z0 = 0.06 m
conditions applicable to the measurement site in Reading (Sinclair 2004). Moreover,
atmospheric stability was not taken into account when calculating ra and neutral stability
was assumed. We can conclude that a high level of uncertainty is associated with the
Penman-Monteith assumptions and the method used with constant parameters is not an
appropriate reference method. The modified values are still different by a factor of 4
compared to those derived from the OPGA system, although the parameters may still not
have been optimum, but it does suggest that other aspects apart from aerodynamic and
surface resistances as well as measurement uncertainty play a role.
The sensible heat H1 became negative around 1630 UTC, compared to 1945 UTC for H2, and
reached values as low as -80 W m-1 by 2000UTC ( Figure 15 and Figure 16). These values
appear highly questionable in a warm, dry season when sunset occurred as late as 2025 UTC
and, assuming all the measurements are correct, gives further indication that a high level of
uncertainty is associated with the latent heat flux derived from the Penman-Monteith
equation.
48
The estimation of H1 is based on the assumption of energy balance closure while the
measurements of H2 and E2 make no such assumptions. Accounting for a mean midday
ground flux of 46 W m-2, based on ground flux measurements (data not shown), a lack of
energy balance closure at midday of approximately 20% was found. This agrees with an
average energy imbalance in the order of 20% over vegetation obtained from a FLUXNET
study of 22 sites (Wilson et al. 2002a). In that study, the closure improved with increased
turbulence and a lack of closure of the energy balance was attributed to underestimating
turbulence. Foken (2008b) also suggests that the imbalance is not due to measurement
errors in the eddy correlation method or radiation measurements but attributes the lack of
closure to the omission of large scale eddies which have a significant contribution to the
energy exchange. This implies that longer block averaging periods should be considered. To
test this hypothesis, the measurements were re-analysed by averaging over 60 minutes and
these results were compared with the original data averaged over 30 minutes and
subsequently averaged over 1 hour, as described by Langford et al. (2010). Eddies with a
time period between 30 and 60 minutes increase the latent heat flux by 1.2% (Figure 17).
Figure 17: Comparison of latent heat derived from the OPGA averaged over 30 min and 1 hour. The data were obtained
th
th
on 18 days between the 25 of May and the 11 of July 2010. The analysis was based on 432 data points.
This compares to a 1% discrepancy found by Helfter et al. (2010) when the average time was
extended from 30 min to 2 hours for sensible heat fluxes in London measured at a height of
200 m. Langford et al. (2010) found sensible heat flux losses of 3.1% for a 25 min averaging
49
time compared to 60 min at the same site in London. Our results imply that hardly any large
scale turbulences have been filtered out by choosing a 30 min averaging period. Increasing
the averaging period further is unlikely to make up for the lack of energy balance closure
while increasing the risk of non-stationarity. In addition, the measurement uncertainty
associated with the OPGA instrument is very small (see 4.1) and cannot account for the lack
in energy closure. This leads to the hypothesis that the cumulative effect of different errors
related to data processing, as discussed in section 2.3.2, may be responsible for errors of up
to 20% and account for the lack of energy balance closure.
50
Chapter 5: Conclusions
The performance and stability of an open-path gas analyser that measures CO2 and water
vapour concentrations was examined over a 9 week period by measuring weekly known
amounts of gas and determining the calibration factors. The instrument’s values were stable
and the uncertainty due to the calibration only was less than 0.09% for CO2 and less than
0.6% for water vapour. This resulted in a measurement uncertainty of 0.3% on
concentrations of CO2 and 1.3% for water vapour. This random error is small compared to
the natural range of gas concentrations to be measured which makes the instrument
suitable for the measurement of [CO2] and water vapour. The main drawback of the OPGA is
that data collected during precipitation, dew or fog must be rejected. Droplets in the air in
the optical path or deposited on the windows lead to spurious data which have to be
removed. Installing the instrument at an angle away from the vertical direction and coating
with a hydrophobic wax did not prevent the deposition of droplets. Other substances, such
as pollen and dust, were not found to contaminate the window. The performance of the
instrument over 9 weeks suggests that weekly calibrations may not be necessary but further
work is required to determine the maximum acceptable calibration interval. The inability to
measure gasses accurately during precipitation and condensation events makes the OPGA
less suitable for continuous use in climates where these weather phenomena occur
frequently, such as in the UK, and the use of a protective shroud with the OPGA or a closedpath gas analyser may be more appropriate.
Apart from errors associated with the gas analyser, other sources of uncertainty must be
taken into account when the OPGA is used together with a sonic anemometer to estimate
fluxes of latent heat, sensible heat and CO2 using the eddy correlation method. Additional
errors are associated with the instrument installation on the measurement site and the
extensive post-field data processing that is required. Data processing, including correction
procedures, introduces uncertainty and can lead to a bias in the results. There are no agreed
procedures and individual researchers decide how best to handle their data. Many
discussions are ongoing on the validity of certain corrections or the best methods to employ.
An additional complicating factor is that the appropriate corrections are not generally
applicable but depend on the prevailing atmospheric conditions. One of the major sources
of uncertainty is related to flux measurements under low turbulent conditions, which occur
51
mainly during the night, and the choice of an acceptable u* threshold. This can lead to an
underestimation of CO2 respiration and has important consequences for the carbon budget.
The majority of the flux measurements and the discussions around data processing relate to
measurements over vegetation. These techniques have been adopted for urban
measurements but this area of research is still under-represented. It has not been possible
to define the overall magnitude of the uncertainty associated with flux measurements due
to the multitude of sources of error and their unknown synergistic effects but the possibility
of large errors must be considered when interpreting data.
Clear diurnal variations in CO2 concentrations and fluxes were observed with high
concentrations at night dropping sharply after sunrise to reach a minimum in the late
afternoon. This diurnal pattern correlated with positive fluxes at night combined with stable
atmospheric conditions and with negative fluxes and unstable conditions during the day.
These results are characteristic for a vegetated area and not for an urban environment. An
analysis of the source area of gas concentrations confirmed that the main source of the CO2
concentration measured by the instrument at a height of 3 m is within less than 110 m from
the sensor and extends mainly over vegetation in most directions. The source area for fluxes
was smaller by an order of magnitude and is even more strongly dominated by vegetation.
The size of the source area varies with smaller source areas contributing under unstable
atmospheric conditions than under stable stratification. The diurnal pattern in CO2
concentration and fluxes can therefore be attributed to the change in boundary layer depth,
which is related to the atmospheric conditions, combined with the effect of photosynthesis
and respiration over vegetation. These results suggest that extensive green spaces in an
urban environment have an important role to play in the overall carbon exchange of a city.
The absolute humidity derived from the psychrometric method was, on average, 0.96 times
that measured with the OPGA. The small discrepancy between the two measurements may
be related to instrument error and the constants used to derive the absolute humidity from
the wet and dry bulb measurements. Particularly, lack of sufficient ventilation in the screen
at low windspeeds and the use of a constant value for the psychrometer coefficient may be
inappropriate as this should be adjusted according to wind speed, temperature and
humidity.
52
Several methods can be used to estimate the latent heat flux but comparative studies are
difficult to find presumably because each method is based on widely different principles and
represent different aspects of latent heat flux in the environment. The comparison of the
latent heat flux derived from the eddy correlation technique and open pan, Piche tube and
Penman-Monteith evaporation identified that the latent heat flux from the eddy correlation
method is on average 4 to 8 times lower than that measured with the other methods. The
open pan and Piche tube indicate evaporation from an unrestricted, open water surface and
represent how evaporation from a specific water surface is affected by meteorological
conditions while the eddy correlation method indicates the evaporation from the total
complex environment. The Penman-Monteith method assumes a saturated surface and
measures potential and not actual evaporation. Comparison between these methods and
latent heat fluxes measured over vegetation during warm and dry conditions are bound to
show large discrepancies as the physical principals and assumptions of the measurements
differ. In addition, each of the measurements has considerable sources of uncertainties
associated with them related to measurement uncertainty, the parameters used to derive
the results, assumptions made about atmospheric stability and using constants where in
reality the values depend on the atmospheric conditions. The source area of the latent heat
flux derived from the eddy correlation method consists of vegetation, mainly grass with
some trees, where evaporation is restricted by moisture availability, mobility and plant
physiology as well as atmospheric conditions. The warm and dry conditions during the
measurement period lead to a Bowen ratio similar to values measured over arid and
Mediterranean regions or some urban centres with little vegetation. Overall, the energy
fluxes derived from the eddy correlation method best represent the fluxes of the total
environment surrounding the sensor. For this method, errors up to 20% assumed to
originate from the data processing step, must be taken into account and lead to
underestimation of energy fluxes and a lack of energy balance closure.
Latent heat and CO2 fluxes from the vegetated source area are linked as plant physiology
plays an important role for both. The low latent heat fluxes indicate that plants were highly
stressed and this should also reduce the CO2 fluxes. Considering that the grass was yellow, it
is assumed that most of the CO2 fluxes originated from the trees. Measurements when the
53
soil is well watered would indicate the potential for CO2 exchange and the relative
contributions from grassland and trees.
The results from this work indicate that the atmospheric exchange over extensive green
areas within an urban environment is representative of the vegetated area only. This shows
the significance of parks in towns and the need to consider the intra-urban surface cover in
urban studies. It also highlights the importance of choosing an appropriate measurement
site and height when aiming to measure processes in a build environment. Conclusions from
this work are limited because several parameters that can affect the observations only
varied within narrow limits as the measurement period covered just a few weeks during late
spring and early summer. An extended measurement period including winter and summer is
required to derive more conclusions about the impact of atmospheric conditions and large
variations in photosynthesis, surface cover and characteristics.
Suggestions for future work
The results from this project have indicated that this research area would benefit from
further investigations to get a more comprehensive understanding of urban environments,
instrumentation and data processing. Areas of interest are:

Determine the maximum acceptable interval for user calibration of the OPGA.

Evaluate the suitability of an OPGA during periods of frequent rain and in freezing
temperatures, i.e. under prevailing winter conditions in the south of England.

Explore the potential of using a shroud to protect the OPGA from precipitation and
condensation.

Optimise and extend the post-field data handling process or evaluate available software.

Validate the code used for data processing by analysing a ‘Gold Standard’ dataset
obtainable from AmeriFlux (http://public.ornl.gov/ameriflux/gold-open_path.shtml).

Extend the measurement period to include all seasons to investigate :
- Annual variations in concentrations and fluxes.
- The relative impact of photosynthesis and boundary layer height on variations in
CO2 fluxes by including periods with strong and weak photosynthetic activity.
- Effect of windspeed and direction on the source areas, fluxes and concentrations
by including periods with higher windspeeds.
54

Detailed investigation of the impact of green spaces as well as dispersed vegetation
throughout a city on the overall urban climate and carbon budget by comparing
atmospheric conditions in parks with surrounding build-up areas. The minimum area of
parkland with respect to the size of the city, which is required to have a beneficial effect
on the urban climate, should be determined for different climatic conditions and types of
vegetation. This could potentially be a tool for urban designers to mitigate the urban
heat effect.
55
(c)
(e)
CO2 concentration (ppmv)
(a)
Appendix 1: CO2 concentration and fluxes in Mexico City and London
-2 -1
CO2 flux (µmol m s )
-2 -1
CO2 flux (µmol m s )
Time of day (hours)
(b)
(d)
(f)
Time of day (hours)
Figure 18: Comparison of the average diurnal pattern of CO2 concentrations and CO2 fluxes in three towns. (a) is [CO2] and (b) flux in Reading, as reported in Figures 6 and 7. The
error bars represent ± 1 standard deviation. (c) is [CO2] and (d) fluxes in London ((Helfter et aI. 2010). The error bars in (c) represent ± 1 standard deviation. (e) is [CO2] and (f)
fluxes in Mexico City (Velasco et al. 2009). The gray shadow represents ±1 standard deviation from the total 2006 average and gives and indication of the day-to-day variability.
The timescale corresponds to the local standard time.
References
Arnell, N., 2002: Hydrology and Global Environmental Change. Pearson Education Limited,
346 pp.
Aubinet, M., and Coauthors, 2000: Estimates of the annual net carbon and water exchange
of forests: The EUROFLUX methodology. Advances in Ecological Research, Vol 30,
Academic Press Inc, 113-175.
Baldocchi, D., and Coauthors, 2001: FLUXNET: A new tool to study the temporal and spatial
variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities.
Bulletin of the American Meteorological Society, 82, 2415-2434.
Baldocchi, D. D., 2003: Assessing the eddy covariance technique for evaluating carbon
dioxide exchange rates of ecosystems: past, present and future. Global Change
Biology, 9, 479-492.
Burba, G., and D. Anderson 2007: Introduction to the Eddy Covariance Method: General
Guidelines and Conventional Workflow. Li-cor Biosciences.
Burba, G. G., D. K. McDermitt, A. Grelle, D. J. Anderson, and L. K. Xu, 2008: Addressing the
influence of instrument surface heat exchange on the measurements of CO2 flux
from open-path gas analyzers. Global Change Biology, 14, 1854-1876.
Burns, S. P., and Coauthors, 2009: An Evaluation of Calibration Techniques for In Situ Carbon
Dioxide Measurements Using a Programmable Portable Trace-Gas Measuring System.
Journal of Atmospheric and Oceanic Technology, 26, 291-316.
Clement, R. J., G. G. Burba, A. Grelle, D. J. Anderson, and J. B. Moncrieff, 2009: Improved
trace gas flux estimation through IRGA sampling optimization. Agricultural and Forest
Meteorology, 149, 623-638.
FAO, cited 2010: Crop evapotranspiration - Guidelines for computer crop water
requirements. FAO Irrigation and drainage paper 56. [Available online at
http://www.fao.org/docrep/x0490e/x0490e06.htm.]
Finnigan, J., 2008: An introduction to flux measurements in difficult conditions. Ecological
Applications, 18, 1340-1350.
Finnigan, J. J., R. Clement, Y. Malhi, R. Leuning, and H. A. Cleugh, 2003: A re-evaluation of
long-term flux measurement techniques - Part I: Averaging and coordinate rotation.
Boundary-Layer Meteorology, 107, 1-48.
Foken, T., 2008: Micro-meteorology. Springer Verlag, 306 pp.
George, K., L. H. Ziska, J. A. Bunce, and B. Quebedeaux, 2007: Elevated atmospheric CO2
concentration and temperature across an urban-rural transect. Atmospheric
Environment, 41, 7654-7665.
Goring, D. G., and V. I. Nikora, 2002: Despiking acoustic Doppler velocimeter data. Journal of
Hydraulic Engineering-Asce, 128, 117-126.
Grelle, A., and G. Burba, 2007: Fine-wire thermometer to correct CO2 fluxes by open-path
analyzers for artificial density fluctuations. Agricultural and Forest Meteorology, 147,
48-57.
Grimmond, C. S. B., T. S. King, F. D. Cropley, D. J. Nowak, and C. Souch, 2002: Local-scale
fluxes of carbon dioxide in urban environments: methodological challenges and
results from Chicago. Environmental Pollution, 116, S243-S254.
Grimmond, C. S. B., J. A. Salmond, T. R. Oke, B. Offerle, and A. Lemonsu, 2004: Flux and
turbulence measurements at a densely built-up site in Marseille: Heat, mass (water
and carbon dioxide), and momentum. Journal of Geophysical Research-Atmospheres,
109, 19.
Gu, L. H., and Coauthors, 2005: Objective threshold determination for nighttime eddy flux
filtering. Agricultural and Forest Meteorology, 128, 179-197.
Helfter, C., D. Famulari, G. J. Phillips, J. F. Barlow, and C. R. Wood, 2010: Controls of carbon
dioxide concentrations and fluxes above central London. In prepartion for submission
in Atmos Chem Phys.
Henninger, S., 2008: Analysis of near surface CO2 variability within the urban area of Essen,
Germany. Meteorologische Zeitschrift, 17, 19-27.
Heusinkveld, B. G., A. F. G. Jacobs, and A. A. M. Holtslag, 2008: Effect of open-path gas
analyzer wetness on eddy covariance flux measurements: A proposed solution.
Agricultural and Forest Meteorology, 148, 1563-1573.
IPCC, 2007: IPCC Fourth Assesment Report (AR4) - The Physical Science Basis. Cambridge
University Press, 996 pp.
Jarvi, L., and Coauthors, 2009: Comparison of net CO2 fluxes measured with open- and
closed-path infrared gas analyzers in an urban complex environment. Boreal
Environment Research, 14, 499-514.
Kaimal, J. C., and J. J. Finnigan, 1994: Atmospheric Boundary Layer Flows - Their structure and
measurement. Oxford University Press, 289 pp.
Kiehl, J. T., and K. E. Trenberth, 1997: Earth's annual global mean energy budget. Bulletin of
the American Meteorological Society, 78, 197-208.
Langford, B., and Coauthors, 2010: Fluxes and concentrations of volatile organic compounds
above central London, UK. Atmospheric Chemistry and Physics, 10, 627-645.
Mahrt, L., 2010: Computing turbulent fluxes near the surface: Needed improvements.
Agricultural and Forest Meteorology, 150, 501-509.
Matese, A., B. Gioli, F. P. Vaccari, A. Zaldei, and F. Miglietta, 2009: Carbon Dioxide Emissions
of the City Center of Firenze, Italy: Measurement, Evaluation, and Source
Partitioning. Journal of Applied Meteorology and Climatology, 48, 1940-1947.
Mauder, M., and Coauthors, 2007: The energy balance experiment EBEX-2000. Part II:
Intercomparison of eddy-covariance sensors and post-field data processing methods.
Boundary-Layer Meteorology, 123, 29-54.
Moffat, A. M., and Coauthors, 2007: Comprehensive comparison of gap-filling techniques for
eddy covariance net carbon fluxes. Agricultural and Forest Meteorology, 147, 209232.
Monteith, J. L., and M. Unsworth, 2001: Principles of Environmental Physics. Butterworth Heinemann, 291 pp.
Nemitz, E., K. J. Hargreaves, A. G. McDonald, J. R. Dorsey, and D. Fowler, 2002:
Meteorological measurements of the urban heat budget and CO2 emissions on a city
scale. Environmental Science & Technology, 36, 3139-3146.
Newton, T., T. R. Oke, C. S. B. Grimmond, and M. Roth, 2007: The suburban energy balance
in Miami, Florida. Geografiska Annaler Series a-Physical Geography, 89A, 331-347.
Offerle, B., C. S. B. Grimmond, K. Fortuniak, and W. Pawlak, 2006: Intraurban differences of
surface energy fluxes in a central European city. Journal of Applied Meteorology and
Climatology, 45, 125-136.
Oke, T. R., 1995: Boundary Layer Climates. Routledge, 435 pp.
58
Richardson, A. D., and Coauthors, 2006: A multi-site analysis of random error in tower-based
measurements of carbon and energy fluxes. Agricultural and Forest Meteorology,
136, 1-18.
Rigby, M., R. Toumi, R. Fisher, D. Lowry, and E. G. Nisbet, 2008: First continuous
measurements of CO2 mixing ratio in central London using a compact diffusion
probe. Atmospheric Environment, 42, 8943-8953.
Schmid, H. P., 1994: Source areas for scalars and scalar fluxes. Boundary-Layer Meteorology,
67, 293-318.
Sinclair, V., 2004: Boundary Layer Influences on Pollution in Reading, Department of
Meteorology, University of Reading, 48 pp
Thorsson, S., and I. Eliasson, 2003: An intra-urban thermal breeze in Goteborg, Sweden.
Theoretical and Applied Climatology, 75, 93-104.
Velasco, E., and Coauthors, 2009: Eddy covariance flux measurements of pollutant gases in
urban Mexico City. Atmospheric Chemistry and Physics, 9, 7325-7342.
Vickers, D., M. Gockede, and B. E. Law, 2010: Uncertainty estimates for 1-h averaged
turbulence fluxes of carbon dioxide, latent heat and sensible heat. Tellus Series BChemical and Physical Meteorology, 62B, 87-99.
Webb, E. K., G. I. Pearman, and R. Leuning, 1980: Correction of flux measurements for
density effects due to heat and wate vapour transfer. Quarterly Journal of the Royal
Meteorological Society, 106, 85-100.
Wilczak, J. M., S. P. Oncley, and S. A. Stage, 2001: Sonic anemometer tilt correction
algorithms. Boundary-Layer Meteorology, 99, 127-150.
Wilson, K., and Coauthors, 2002a: Energy balance closure at FLUXNET sites. Agricultural and
Forest Meteorology, 113, 223-243.
Wilson, K. B., and Coauthors, 2002b: Energy partitioning between latent and sensible heat
flux during the warm season at FLUXNET sites. Water Resources Research, 38.
WMO, 2008: WMO Guide to Meteorological Instruments and Methods of Observation - Part
I: Measurements of meteorological measurements. Seventh Edition ed., 369 pp.
59

understanding near-surface turbulence fluxes using an open

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