ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-3563-2016The contrasting roles of water and dust in controlling daily variations in
radiative heating of the summertime Saharan heat lowMarshamJohn H.j.marsham@leeds.ac.ukhttps://orcid.org/0000-0003-3219-8472ParkerDouglas J.ToddMartin C.BanksJamie R.BrindleyHelen E.Garcia-CarrerasLuishttps://orcid.org/0000-0002-9844-3170RobertsAlexander J.RyderClaire L.https://orcid.org/0000-0002-9892-6113National Centre for Atmospheric Science (NCAS), Leeds,
UKSchool of Earth and Environment, University
of Leeds, Leeds, UKDepartment of Geography, University of Sussex, Brighton,
UKSpace and Atmospheric Physics Group, The Blackett
Laboratory, Imperial College, London, UKDepartment of Meteorology, University of Reading, Reading,
UKJohn H. Marsham (j.marsham@leeds.ac.uk)17March20161653563357513May201516July201511February201620February2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/3563/2016/acp-16-3563-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/3563/2016/acp-16-3563-2016.pdf
The summertime Sahara heat low (SHL) is a key component of the West African
monsoon (WAM) system. Considerable uncertainty remains over the relative
roles of water vapour and dust aerosols in controlling the radiation budget
over the Sahara and therefore our ability to explain variability and trends
in the SHL, and in turn, the WAM. Here, new observations from Fennec
supersite-1 in the central Sahara during June 2011 and June 2012, together
with satellite retrievals from GERB, are used to quantify how total column
water vapour (TCWV) and dust aerosols (from aerosol optical depth, AOD)
control day-to-day variations in energy balance in both observations and
ECWMF reanalyses (ERA-I). The data show that the earth-atmosphere system is
radiatively heated in June 2011 and 2012. Although the empirical analysis of
observational data cannot completely disentangle the roles of water vapour,
clouds and dust, the analysis demonstrates that TCWV provides a far stronger
control on TOA net radiation, and so the net heating of the earth-atmosphere
system, than AOD does. In contrast, variations in dust provide a much
stronger control on surface heating, but the decreased surface heating
associated with dust is largely compensated by increased atmospheric
heating, and so dust control on net TOA radiation is weak. Dust and TCWV are
both important for direct atmospheric heating. ERA-I, which assimilated
radiosondes from the Fennec campaign, captures the control of TOA net flux
by TCWV, with a positive correlation (r=0.6) between observed and
modelled TOA net radiation, despite the use of a monthly dust
climatology in ERA-I that cannot capture the daily variations in dustiness.
Variations in surface net radiation, and so the vertical profile of
radiative heating, are not captured in ERA-I, since it does not capture
variations in dust. Results show that ventilation of the SHL by cool moist
air leads to a radiative warming, stabilising the SHL with respect to such
perturbations. It is known that models struggle to capture the advective
moistening of the SHL, especially that associated with mesoscale convective
systems. Our results show that the typical model errors in Saharan water
vapour will lead to substantial errors in the modelled TOA energy balance
(tens of W m-2), which will lead to errors in both the SHL and the WAM.
Introduction
The Sahara lies under the descending branch of the Hadley circulation and
during summer the intense solar heating combined with the arid environment
leads to large sensible surface heat fluxes and the formation the Saharan
heat low (SHL). This increases the pressure gradient from the Gulf of Guinea
to the Sahara, driving the West African monsoon (WAM); and variations in the
SHL modify the WAM on timescales from days to decades (Thorncroft and
Blackburn, 1999; Sultan and Janicot, 2003; Parker et al., 2005; Peyrillé
and Lafore, 2007; Biasutti et al., 2009; Lavaysse et al., 2009, 2010; Chauvin
et al., 2010; Xue et al., 2010; Martin and Thorncroft, 2014; Martin et al.,
2014; Dong and Sutton, 2015). There is a shortage of routine observations in
the SHL and substantial disagreements exist even between analyses (Marsham et
al., 2011; Roberts et al., 2015). The Fennec project aimed to better quantify
processes governing the Saharan atmosphere (Washington et al., 2012; Ryder et
al., 2015) and deployed an observational supersite-1 close to the
climatological centre of the SHL (Marsham et al., 2013a).
The radiative budget of the Sahara is significantly modulated by variations
in clouds, dust, and water vapour. Charney (1975) shows how the high albedo
and dry atmosphere can lead to a top-of-atmosphere (TOA) net radiative
cooling in July, with heating via subsidence, proposing a positive feedback
where dry soils with little vegetation generate high albedo, favouring
atmospheric descent and low rainfall. The dry atmosphere means that water
vapour provides a key control in the longwave with vapour at all levels
affecting top-of-atmosphere (TOA) outgoing longwave (Allan et al., 1999;
Brindley and Harries, 1998). Evan et al. (2015) suggest that the increasing
temperatures within the SHL over the past 30 years, key to the recovery of
the Sahel from drought, are driven by longwave impacts of increasing water
vapour, in the “Saharan Water Temperature” feedback and Dong and
Sutton (2015) propose a greenhouse-gas driven increase with a feedback
through water vapour. Shallow clouds on top of the deep dry boundary layer
(Cuesta et al., 2009) occur around 20 % of the time over the Sahara, with
mid-level clouds reducing net surface shortwave and increasing net surface
longwave in the Sahel (Stein et al., 2011; Bouniol et al., 2012). Dust
absorbs and emits longwave radiation (Haywood et al., 2005) and scatters and
absorbs shortwave (Ryder et al., 2013; Banks et al., 2014). At the TOA, and
over the bright Sahara, dust induces a warming as its longwave effects
dominate its shortwave effects (Balkanski et al., 2007; Yang et al., 2009).
Operational models use either prognostic dust or dust climatologies, but
struggle to capture variations in summertime dust, partly as cold-pool
outflows from convection (haboobs) provide a key uplift mechanism that is
missing in operational models that use parameterised moist convection (Marsham
et al., 2011, 2013a; Heinold et al., 2013).
At low levels the Sahara is cooled by advection from neighbouring moister and
cooler regions, including the WAM to the south. Representing the monsoon is a
challenge to models, partly because of the representation of convection, in
particular its diurnal timing and cold pools; the diurnal timing of Sahelian
moist convection affects the pressure gradient driving the monsoon,
modulating the flux of water vapour from the Sahel to the Sahara and hence
rainfall over the Sahel (Marsham et al., 2013b; Birch et al., 2014).
Furthermore cold pools form a significant component of the monsoon (Marsham
et al., 2013b) and also ventilate the Sahara from the Atlas in the north
(Emmel et al., 2010); most temperature and humidity biases in the Met Office
global model at the Fennec supersite-1 during June 2011 were caused by
missing cold pool advection (Garcia-Carreras et al., 2013). Similarly
ventilation of the Sahara by the Atlantic Inflow involves mesoscale flows
that are a challenge for global models (Grams et al., 2010; Todd et al.,
2013). Since clouds, water vapour and dust are all important to the Sahara's
radiative energy balance, such model errors in convection, clouds, haboobs
and advection of water vapour will all affect modelled radiative energy
balances and hence climate.
There is a clear need to establish the controls on the radiation budget over
the Sahara and evaluate models. In this paper we use observations of surface
radiative fluxes from Fennec supersite-1 in the central Sahara and retrievals
of TOA fluxes from satellite data to investigate how dust and water together
control the day-to-day variations in energy balance over the Fennec
supersite-1 in the summertime SHL region, and how this is represented in
ERA-Interim (ERA-I) reanalysis. Results in Sect. 3 show that TCWV (total column water vapour) and AOD are
correlated and we cannot completely isolate the effects of either TCWV or
dust. However, TCWV and AOD have sufficiently independent variations, and
sufficiently distinct impacts at solar and infrared wavelengths, which
conform with physical principles, that the results give unique insights into
their contrasting roles in the central Sahara. Section 2 describes methods,
Sect. 3 presents results, Sect. 4 contains discussion and conclusions are in
Sect. 5.
Method
We use data from Fennec supersite-1 in the central Sahara, located at
Bordj-Badji Mokhtar (BBM) at 21.4∘ N 0.9∘ E (in the very south of Algeria, close
to the triple point of Algeria, Mali and Niger), close to the SHL's
climatological centre and the dust maximum (Marsham et al., 2013a), together
with corresponding values from ERA-Interim (Dee et al., 2011), and satellite
TOA fluxes (Harries et al., 2005; Dewitte et al., 2008). These satellite TOA
fluxes are produced using a narrow to broad band conversion of SEVIRI
(Spinning Enhanced Visible and Infrared Imager) radiance measurements. These
are scaled by co-located GERB (Geostationary Earth Radiation Budget
experiment) measurements and converted into broadband fluxes measurements at
a horizontal resolution of 3 × 3 SEVIRI pixels (0.32–4 microns in
the shortwave, and 4–100 microns in the longwave). This enhancement gives a
spatial resolution of 9 km at nadir, compared with the 45 km of the native
GERB. These observed fluxes are from clear and cloudy skies, but we also use
the European Organisation for the Exploitation of Meteorological Satellites'
MPEF (Meteorological Product Extraction Facility) cloud mask as a simple
measure of cloud cover. ERA-Interim uses aerosol climatologies so it cannot
capture day-to-day variations in dust. Radiosonde data from the Fennec
supersite were assimilated into ERA-I, which will have improved its
representation of the thermodynamic profile (see Garcia-Carreras et al.,
2013, for impacts of assimilation of Fennec radiosondes on the Met Office
global forecast model).
Fennec data are from intensive observation periods (IOPs) in June 2011 and
2012, when a Cimel sun photometer provided aerosol optical depths (AODs) at
675 nm, a Kipp & Zonen radiometer mounted at 2 m provided measurements
of broad-band radiative fluxes and 3- to 6-hourly radiosonde observations were
available (Marsham et al., 2013a). The sun-photometer is part of the AERONET
program (Holben et al., 1998) and cloud-screened AOD retrievals are only
available during the day. Level-2 AOD data are not available for 2012 since
not all data meet level-2 requirements. However, the 0.675 nm AODs are still
reliable. We therefore use level-2 data for 2011 and level-1.5 for 2012,
noting that using only 2011 data does not affect our conclusions. We use the
radiosondes to compute column water vapour from the surface to 300 hPa (a
height consistently reached by the radiosondes), which we refer to simply as
“total column water vapour (TCWV)”. During June 2011 BBM was regularly
cooled by nocturnal monsoon flows and embedded cold pools giving substantial
variability in TCWV (Marsham et al., 2013a), and qualitatively similar
weather events were observed during June 2012.
In order to study the day-to-day variations in the energy budget, we average
all data to their daily means. Complete surface flux data were only available
for 11 days in June 2011 (9, 10, 18–20, 23–27, 30 June) and 25 days in
June 2012 (all except 4, 16–18, 30 June). Some dates had short data gaps
(around 2 hours on 2 days, but otherwise an hour or less) and these gaps
were interpolated across in order to include 7, 17, 21, 22 June 2011 and
16–17 June 2012. This gave an improved range of AODs, albeit with increased
uncertainties in surface fluxes. Fluxes from these days with some
interpolation are marked by squares in Figs. 1 to 4, and the effects of
interpolation are discussed in Sect. 3, where it is seen that results from
these days are physically consistent with other data from days without
interpolation. The surface flux data from Kipp and Zonen radiometers have
slightly different spectral ranges to the satellite-borne GERB: Kipp and
Zonen are 0.3 to 2.8 µm in the shortwave and 4.5 to 42 µm
in the longwave, whilst GERB is 0.32 to 4 µm and 4 to
100 µm. This means that the surface-based Kipp and Zonen can miss
up to 3.5 W m-2 net shortwave atmospheric heating as would be seen by
GERB and up to 3.8 W m-2 of the net longwave as would be seen by GERB
(Banks et al., 2014). This introduces errors of up to 4 W m-2 in our
inferred direct atmospheric radiative heating rates, but does not affect our
analysis and conclusions, which is focused on the controls on the variability
of these rates, rather than their absolute values.
Sun-photometer AODs were available from 8 June 2011 (with no observations on
13 June) and 1 to 28 June 2012 (with no observations on 17–19 June).
Radiosondes were available from 8 June 2011 and 1 to 26 June 2012. The number
of observations contributing to the daily mean is variable for AODs, since
observations are only made when it is cloud free, but all days except one had
at least eight AOD observations and the daily mean AOD range of 0.2 to 2.7 is
similar to that of the observation range in AODs (0.2 to 3.9) and the diurnal
cycle in AOD is weak (Marsham et al., 2013a; Banks et al., 2014). Overall
this gave 36 days with surface data, observed AOD and observed TCWV and
44 days with TCWV and AOD.
Gradients of best-fit straight lines (i.e. regression coefficients)
for listed relationships, values in [ ] are normalised by standard deviation
of TCWV or AOD. Values in ( ) are correlation coefficients (bold values are
significant at 90 % level). For ERA-I observed AODs are used. Standard
deviation in TCWV in ERA-I = 4.7 kg m-2 (4.5 for “All data”). For
observations 4.7 kg m-2 (4.4 for “All data”). Standard deviation in
AOD for observations is 0.68 (0.65 in “All data”).
Observations ERA-I Good surface dataAll dataGood surface dataAll dataTCWV : AOD (kg m-2)0.04 (0.29)0.04 (0.30)0.02 (0.16)0.02 (0.14)AOD : TOA Net (W m-2)5.3 [3.6] (0.26)3.5 [2.3] (0.17)1.7 (0.12)0.33 (0.02)AOD : TOA Net LW (W m-2)10.5 [7.2] (0.33)8.5 [5.5] (0.26)2.4 (0.12)1.0 (0.05)AOD : TOA Net SW (W m-2)-5.2 [-3.6] (-0.28)-5.0 [-3.3] (-0.26)-0.72 (-0.07)-0.70 (-0.06)TCWV : TOA Net (W kg-1)2.2 [10.4] (0.74)2.1 [9.2] (0.68)1.3 (0.66)1.4 (0.66)TCWV : TOA Net LW (W m-2 kg-1 m2)3.2 [15.0] (0.68)3.0 [13.3] (0.63)1.8 (0.61)2.0 (0.65)TCWV : TOA Net SW (W kg-1)-0.98 [-4.6] (-0.36)-0.9 [-4.1] (-0.33)-0.48 (-0.30)-0.49 (-0.30)TOA Net SW : TOA Net LW-1.38 (-0.80)-1.35 (-0.79)-1.44 (-0.78)-1.39 (-0.72)AOD : Surface Net (W m-2)-13.1 [-9.0] (-0.70)NA3.4 (0.34)3.2 (0.31)AOD : Surface Net SW (W m-2)-31.9 [-21.8] (-0.87)NA-1.6 (-0.12)-1.7 (-0.12)AOD : Surface Net LW (W m-2)20.7 [14.2] (0.81)NA5.0 (0.28)4.9 (0.27)TCWV : Surface Net (W kg-1)-0.20 [-0.96] (-0.07)NA0.76 (0.53)0.85 (0.57)TCWV : Surface Net LW (W kg-1)2.0 [9.3] (0.54)NA2.2 (0.84)2.2 (0.85)TCWV : Surface Net SW (W kg-1)-1.8 [-8.2] (-0.33)NA-1.4 (-0.69)-1.4 (-0.68)Surface Net SW : Surface Net LW-0.61 (-0.88)NA-1.1 (-0.83)-1.1 (-0.82)AOD : Atmospheric Net (W m-2)18.5 [12.1] (0.62)NA-1.75 (-0.13)-2.9 (0.21)AOD : Atmospheric Net LW (W m-2)-10.2 [-6.7] (-0.41)NA-2.65 (-0.18)-3.9 (-0.26)AOD: Atmospheric Net SW (W m-2)26.7 [17.5] (0.93)NA0.91 (0.13)1.0 (0.15)TCWV : Atmospheric Net (W kg-1)2.4 [10.7] (0.56)NA0.51 (0.26)0.54 (0.27)TCWV : Atmospheric Net LW (W kg-1)1.2 [5.4] (0.34)NA-0.41 (-0.20)-0.36 (-0.17)TCWV : Atmospheric Net SW (W kg-1)0.78 [3.4] (0.19)NA0.91 (0.93)0.90 (0.91)Atmos Net SW : Atmos Net LW-0.39 (-0.45)NA-0.73 (-0.35)-0.79 (-0.36)Results
In order to determine how the changing amounts of water and dust over BBM
affect the changing radiative heating at the surface, TOA and within the
atmosphere we analyse relationships between the daily means of key variables,
using both observed quantities and the equivalent from ERA-I at the location
of BBM. ERA-I uses a climatological AOD field and so cannot capture the
observed daily variability in AOD. This, in effect, represents a
quasi-control experiment for dust variability. Net fluxes are defined as
downward, with increased net downward flux corresponding to increased
shortwave heating or reduced longwave cooling. All correlations and slopes of
linear regression lines discussed are listed in Table 1 (correlations in bold
are significant at the 90 % level). Relationships are shown using days
where surface data are available (referred to as “Good surface data”), and
for all available data (“All data”) where surface flux data are not
required. The regressions are very similar whichever data set is used and
values in the text are for “Good surface data”, unless otherwise noted. The
effect of subsampling is small for ERA-I, showing that general lessons can be
drawn from the observational data, despite the limited time-span of the
data set. Similarly, for both June 2011 and 2012 analysed water vapour at 850
and 925 hPa and AODs from MISR, Deep blue (Terra and Aqua) and OMI are all
within 1 standard deviation of their mean values (not shown) and there is
no indication that the weather regimes affecting BBM in these periods were
anomalous.
Daily means of TCWV and AOD, pluses show days with complete surface
data, squares show days with some interpolation (see Sect. 2). Diamonds show
all data points (including days with no surface-flux data).
There are correlations between dust and water (discussed below) which mean
that effects of either cannot be completely isolated from the other, but
nevertheless the approach allows identification of how variations in these
variables affect radiative heating. Figure 1 shows that there is a
significant tendency for more dust with more TCWV, although there are a few
dry dusty days (correlation = 0.29). The use of surface flux data with
some interpolation (shown by squares) allows study of more days with high
AODs. The mechanisms underlying this correlation are understood: Marsham et
al. (2013a) shows how moist monsoon surges from the south are associated with
dust at BBM. This is because the moist surges are associated with both dusty
haboobs and moist nocturnal low-level jets (LLJs) that together dominate the
dust uplift at BBM in June 2011 (Marsham et al., 2013a; Allen et al., 2013).
The association between dust and water vapour is consistent with Fig. 16 in
Marsham et al. (2013a), which shows a statistical link between AOD and cloud
cover at BBM. Intense dust uplift does sometimes occur in dry air, however,
mainly in the dry Harmattan LLJs (Marsham et al., 2013a; Allen et al., 2013).
TOA fluxes, with symbols as in Fig. 1, showing daily means for days
with surface data. Dotted lines in (d) and (e) have a
gradient of -1.
Control of TOA net radiation by water (TCWV) and aerosols (AOD)
Daily mean net TOA radiation is always positive (i.e. downwards) and has a
mean value of 26 W m-2, i.e. there is warming of the earth-atmosphere
system throughout the period (Fig. 2a). Net heating varies between around 0
and 70 W m-2, or approximately 0 to 1.2 K day-1 if the heating
were distributed over the 5 km deep boundary layer.
There is a significant correlation of 0.74 between TCWV and TOA net
radiation. Figure 2a shows that TOA net downward radiation increases with
TCWV (and associated dust and cloud), with a regression coefficient of
+2.2 W kg-1. This is a result of a 3.2 W kg-1 increase in TOA
net longwave with TCWV in observations (Fig. S1a in the Supplement), from
water vapour, clouds (and associated dust) reducing TOA outgoing longwave.
This longwave TCWV effect dominates the decrease in net shortwave with
increased water vapour (-0.98 W kg-1, Fig. S1d), due to water vapour
and associated clouds and dust. The correlations are strongest between TCWV
and TOA net or longwave radiation (both 0.74 and 0.68), rather than TOA
shortwave (-0.36), since the water vapour directly affects the longwave,
while much of the shortwave effects of TCWV are indirect, occurring via
associated clouds and dust.
The correlation between AOD and TOA net radiation (Fig. 2b) is much weaker
than between TCWV and TOA net radiation (0.26 compared with 0.74). Figure 2b
shows that TOA net radiation increases with AOD (5.3 W m-2 per AOD,
comparable with Balkanski et al., 2007), but this relationship is complex and
its magnitude decreases to 3.5 W m-2 if all available data are used
(with a correlation of 0.17). The increase in net TOA radiation with AOD
occurs because the increase in TOA longwave (+10.5 W m-2 per AOD)
dominates the decrease in TOA net shortwave (-5.2 W m-2 per AOD;
Fig. S1b and e). The observed net effect of dust at TOA and the dominance of
the longwave for this effect are both consistent with previous studies
(Balkanski et al., 2007; Yang et al., 2009). Banks et al. (2014) show that in
clear sky the diurnal mean effect of dust at BBM is warming in the shortwave.
Therefore the observed reduced shortwave heating associated with dust
reported in Fig. 2 is likely a result of cross correlation of AOD and cloud.
This cloud, as well as the water vapour and dust, reduces outgoing longwave,
leading to a warming. The effects of AOD and TCWV variations on radiation
normalised by the standard deviation (σ) in either AOD or TCWV
(Table 1, values in square brackets) show that the variance in TCWV has a
much larger effect on TOA net radiation (10.4 W m-2 per σ)
than the variance in AOD (3.6 W m-2 per σ, or 2.3 W m-2
if “All data” are used), i.e. most day-to-day variations in net TOA
radiation are mostly controlled by TCWV, not AOD.
Figure 2d shows daily net shortwave heating is always greater than net
longwave cooling (the Earth-atmosphere system is warming in June). Daily
variations in shortwave are anti-correlated with variations in longwave such
that as daily net TOA shortwave decreases, the net longwave increases
(correlation of -0.80). In Fig. 2d, if the gradient is less than -1,
reducing the net shortwave will increase the net flux. The observed gradient
is -1.4, i.e. days with net shortwave reduced by combinations of dust and
cloud are associated with increased longwave heating (i.e. reduced longwave
cooling) from the water vapour, dust and cloud that more than compensates for
the decreased shortwave heating, resulting in greater net heating on these
days. Figure 2d shows how there is greater variance in daily longwave cooling
than shortwave warming and therefore, although they are coupled, variations
in longwave cooling make the larger contribution to variations in TOA net
radiation.
TCWV and aerosol effects at TOA in ERA-I
The ERA-I regression coefficients for TOA net radiation with TCWV of
1.3 W kg-1 (1.4 W kg-1 for all data) is similar to that
observed (2.2 W kg-1, 2.1 W kg-1 for all data, Fig. 2c and a).
ERA-I captures the sign of correlations of both TOA net longwave and
shortwave with TCWV, although it underestimates the magnitude of the
regression coefficients for both (1.8 W kg-1 in longwave for ERA-I,
compared with the 3.2 W kg-1 observed, and -0.48 W kg-1 in
shortwave for ERA-I compared with the -0.98 W kg-1 observed;
Fig. S1c and f). As observed, reduced net shortwave increases TOA net flux in
ERA-I (Fig. 2e, gradient of -1.4).
Even though it does not account for the daily variations in dust, ERA-I
captures much of the day-to-day variations in TOA net variation (correlations
with observations are 0.62 and 0.73 for “All data” and “Good surface
data”, not shown). Table 1 shows that the regression coefficients for ERA-I
fluxes with observed AODs are of the correct sign: this suggests that some of
the observed trends with AOD are due to associated water vapour and cloud
(captured at least to some extent by ERA-I), rather than dust. This is
consistent with the lower correlations between observed AOD and observed TOA
net flux (0.26) than between observed TCWV and observed TOA net flux (0.74),
discussed in the previous section.
The differences in the effects of TCWV in ERA-I and in observations are
likely because of both errors in clouds in ERA-I and its lack of variability
in dust. Detailed validation of model clouds over the bright dusty Sahara is
challenging and beyond the scope of this paper. Here, we note that ERA
captures day-to-day variations of mean cloud fraction (correlation with MPEF
cloud mask of 0.56), but mean cloud fraction in ERA-I is 0.22, much less than
the MPEF value of 0.53, although this value is likely biased high by dust.
Surface albedo in ERA-I is very close to observed, but TOA upward shortwave
in ERA-I is about 15 W m-2 less than in observations (although daily
maxima in these values are similar, not shown). These comparisons with data
both support the hypothesis that ERA-I underestimates cloud cover (consistent
with Dolinar et al., 2015, Fig. 4). The underestimate of the regression
coefficient of TOA net longwave with TCWV in ERA-I compared with observations
(1.8 compared with 3.2 W kg-1) is consistent with this suspected
underestimation of cloud cover in ERA-I and also the lack of dust associated
with TCWV reducing outgoing longwave (Haywood et al., 2005). However, in
ERA-I the underestimation of the magnitude of the regression coefficient of
TOA net longwave with TCWV (1.8 compared with 3.2 W kg-1) and shortwave
with TCWV (-0.48 compared with -0.98 W m-2) compensate to some
extent give a trend in TOA net radiation with TCWV of 1.3 W kg-1 in
ERA-I, close to the 2.2 W kg-1 observed.
As Fig. 2, but for surface fluxes.
Control of surface net radiation by TCWV and AOD
At the surface there is a strong and significant decrease in net radiation
with increasing AOD (Fig. 3b) with a regression coefficient of
-13.1 W m-2 per AOD. This is a result of compensating longwave and
shortwave effects, with the shortwave effect being largest: Table 1 (and
Fig. S2e) shows -31.9 W m-2 surface net shortwave per AOD, with dust
reducing solar heating at the surface (largely compensated by heating the
atmosphere above, comparing with -5.2 W m-2 TOA net shortwave per
AOD, Sect. 3.3). Table 1 (Fig. S2b) shows +20.7 W m-2 surface net
longwave per AOD, i.e. dust, together with the water vapour and cloud
associated with the dust, warms the surface in the longwave, but unlike at
TOA this does not compensate fully for the shortwave effects. The effects of
AOD on net, shortwave and longwave fluxes are consistent between the days
with some interpolated values (asterisks) and other days (pluses).
TCWV decreases surface net radiation by 0.20 W kg-1 (Fig. 3a). This is
a balance of +2.0 W kg-1 from the longwave and -1.8 W kg-1
from the shortwave, i.e. it is a small difference between two large numbers
(Fig. S2a and d). Impacts of TCWV on surface net heating are therefore a
subtle balance of water vapour, clouds and associated dust. If variations in
surface net radiation with AOD and TCWV are normalised by the standard
deviation in AOD or TCWV, variability in AOD is seen to dominate the
variations in surface net radiation (square brackets in Table 1). For the
impacts of TCWV, the days with some interpolated values at first appear to be
inconsistent with other days (Figs. 3a, S1a, d), but this is due to the high
AODs for these days, the effects of which are consistent with other data
(Figs. 3b, S2be, e).At the surface, although the observed shortwave and
longwave variations are anti-correlated (coefficient =-0.88), they
cancel to a much lesser extent than at TOA. Figure 3d shows how decreased
shortwave leads to increased net longwave, but this does not tend to
compensate fully (gradient of -0.61), so decreased shortwave
gives
decreased net surface radiation. As such, daily variability in surface net
radiation at BBM is influenced more by variability in the shortwave than the
longwave. Again data from days with some interpolation of surface fluxes
(squares) are consistent with other days (pluses).
The observed increase in surface net longwave with TCWV of 2.0 W kg-1
is within the range of 1.0 to 3.0 W kg-1 obtained by Evan et
al. (2015) for Tamanrasset from observations, analyses and radiative transfer
modelling. In summer at Tamanrasset TCWV might be expected to correlate with
AOD as it does at BBM, and dust and clouds associated with TCWV in reality,
but missing or under-estimated in analyses and radiative transfer modelling,
may account for the greater sensitivity of surface net longwave to TCWV in
observations compared with radiative transfer modelling and analyses, noted
by Evan et al. (2015). The BBM value of 2.0 W kg-1 is slightly lower
than the diurnal-mean observational value of 3.0 W kg-1 for
Tamanrasset obtained by Evan et al. (2015), which may reflect the greater
prevalence of clouds at the high-altitude Tamanrasset site, where mountains
trigger moist convection (Birch et al., 2012). The BBM results also suggest
that although the increases in net surface longwave with TCWV shown by Evan
et al. (2015) could largely be compensated by coincident decreases in net
surface shortwave (as at BBM), this is not expected at TOA, supporting the proposed role by Evan
et al. (2015) of water vapour in warming the SHL.
Effects of TCWV and AOD at the surface in ERA-I
Figure 3c shows that, in contrast with observations (Fig. 3a), ERA-I always
produces an increase in net surface radiation with increasing TCWV
(+0.76 W kg-1, compared with -0.20 W kg-1). Figure 3e
(gradient -1.1) shows that at the surface in ERA-I, unlike in observations,
decreased net shortwave is always compensated by increased net longwave (i.e.
reduced longwave cooling). This occurs since in ERA-I greater water vapour
leads to greater net surface longwave (i.e. reduced longwave cooling,
Fig. S2c), without the associated dust to reduce the net surface shortwave
(Fig. S2f): the net surface radiation in ERA-I depends largely on surface
longwave, whereas in observations it depends largely on the shortwave. As a
result, ERA-I, which uses a monthly dust climatology, fails to capture
day-to-day variations in surface net radiation, producing no correlation
(0.02) with observations.
As Fig. 2, but for inferred atmospheric heating (TOA flux minus
surface flux).
Although it does not affect the regression coefficients of surface fluxes
with TCWV and AOD discussed above, we note here that ERA-I surface net
longwave is on average 55 W m-2 less than observed, and this is almost
all from more upward longwave than observed (not shown). Due to the
non-linear nature of thermal emission, the 13 % error in upward longwave
can be caused by only a 3 % error in skin temperature (or from an error
in emissivity). Maximum values of daily ERA-I surface net shortwave are
similar to observed, but minima are higher, likely from missing dust and
cloud. These two errors lead to surface net radiation being around
34 W m-2 lower in ERA-I than observed.
Radiative heating of the atmosphere
The TOA and surface fluxes are differenced to give the radiative flux
convergence within the atmosphere, i.e. the direct radiative heating of the
atmosphere (Fig. 4). As expected the atmosphere is cooling in the longwave
and is heated in the shortwave. There are statistically significant positive
correlations between both TCWV or AOD (which are themselves correlated,
Fig. 1) and net radiative heating of the atmosphere (Fig. 4a and b). This is
consistent with the results of Slingo et al. (2006) and Slingo et al. (2009)
for dust over the Sahel. For AOD there is a strong correlation (0.93) with
shortwave atmospheric heating (Fig. S3e, 26.7 W m-2 per AOD,
comparable with Balkanski et al., 2007) that dominates the trend of net
longwave heating with AOD (Fig. S3c, -10.2 W m-2). There are
significant increases in net shortwave and net longwave radiative heating of
the atmosphere with increasing TCWV (Fig. S3a and d, Table 1). The longwave
effect (Fig. S3a) is much less clear than it is at TOA or at the surface,
since the effects at TOA and the surface (Figs. S1a and S2a) are similar (3.2
and 2.0 W m-2) and largely cancel each other.
When trends with TCWV and AOD are normalised by the standard deviations in
TCWV and AOD to allow comparison (results in square brackets in Table 1),
effects of AOD dominate those from TCWV, but this is much more pronounced in
the shortwave. The results therefore show significant shortwave heating of
the atmosphere by dust (consistent with Banks et al., 2014), consistent with
the large effect of AOD on surface net and surface net shortwave fluxes, with
much smaller effects at TOA. Decreases in surface heating associated with
dust are largely compensated by direct radiative heating of the atmosphere.
The shortwave heating from TCWV (correlation coefficient of only 0.19) is
similar to that in ERA (below and Fig. S3f) showing that is not just from
associated dust, but from shortwave absorption by water (although points with
unusually high shortwave heating are explained by AODs, Fig. S3d and e).
Figure 4d shows how increasing longwave cooling of the atmosphere is more
than compensated for by the corresponding increased shortwave heating
(gradient =-0.39); atmospheric heating is largely controlled by
effects of dust on the shortwave, whereas longwave atmospheric heating is
much less variable.
Atmospheric heating in ERA-I
ERA gives weaker longwave atmospheric cooling than observed and therefore
less net atmospheric cooling (Figs. 4c and S3c). Lacking the observed
variability in dust, ERA has little variability in atmospheric shortwave
heating, with almost no correlation of shortwave heating with observed AODs
(Table 1). ERA has a significant increase in shortwave atmospheric heating
with TCWV (Fig. S3f, 0.91 W kg-1) from absorption by water (similar to
that observed, Fig. S3d, 0.78 W kg-1). While observations have a
significant, but weak, positive correlation between TCWV and longwave
atmospheric heating (Fig. S3a, 0.34), ERA has a weak insignificant negative
correlation (Fig. S3c, -0.20). Effects are weak in both cases, since TOA
and surface longwave fluxes both respond similarly to TCWV and ERA is of
course lacking the variability in dust that correlates with TCWV and this may
contribute to the difference. Despite the weak variation in shortwave
atmospheric heating in ERA compared with observations, variations in
shortwave dominate the variations in net atmospheric heating, giving
increased net heating with increased TCWV (Fig. 4c). This is however much
weaker than observed (Fig. 4a), since ERA has much less variability in net
heating due to its use of a dust climatology.
Discussion
Since variability in water dominates day-to-day variability in net TOA
heating it is crucial for models to capture the water content of the SHL.
Small errors in TCWV, in the altitude of the water vapour, or in associated
cloud, could cause errors in clear-sky longwave radiation comparable with the
50 W m-2 from dust seen in Haywood et al. (2005), or the 20 to
40 W m-2 model bias that Allan et al. (2011) show can be removed by
the inclusion of dust. This paper shows that 50 W m-2 TOA net longwave
corresponds to around 16 kg m-2 water (based on the 3.2 W kg-1
dependence of TOA net longwave on TCWV, Table 1), roughly equivalent to
3 g kg-1 over the 5 km deep boundary layer. Roberts et al. (2015) show
that root mean square differences in analyses of WVMR at
20∘ N in the Sahara are around 1.5 g kg-1, and show a case
where differences between different analyses are around 4 g kg-1.
Garcia-Carreras et al. (2013) show a global model mean bias of around
1 g kg-1 at Fennec supersite-1 in June 2011 in the model first guess
(3-to-6 h forecast), despite assimilation of the Fennec radiosoundings.
Models struggle to capture monsoon flow that cools and moistens the SHL, in
particular from cold pools (Marsham et al., 2013b; Garcia-Carreras et al.,
2013). This study shows that errors in fluxes of water vapour will lead to a
compensating error of insufficient radiative heating from the absence of the
moister air. Model errors in dust will affect the vertical distribution of
heating and so also affect vertical mixing and dynamics.
The results give some insight into the Saharan BL energy budget during June
over BBM. We show TOA net radiative heating of around 26 W m-2. There
was an observed mean night-time cooling of around 4 K over an approximately
1 km depth every night (Marsham et al., 2013a), corresponding to around
50 W m-2 cooling (not all of this cooling is advective, some is
radiative). To compensate for this cooling an additional warming of around
20 W m-2 is required. Daily entrainment of free-tropospheric air will
raise the BL top, which is lowered by subsidence to give, in the long term, a
constant BL top. We can estimate the heating rate of the BL either from
entrainment or subsidence. The 24 h entrainment flux is perhaps
10 W m-2 (20 % of the 100 W m-2 surface flux for 12 h).
The 24 h subsidence of a lid of 5 K/100 m with 0.1 m s-1 is
5 W m-2. These simple estimates therefore leave a mis-match of around
10 W m-2, but show that all terms (net daytime radiative warming, net
night-time radiative and advective cooling, entrainment of warm subsiding
air) are all of a similar order of magnitude and significant.
Although modelling is needed to fully understand the observed effects of
water vapour on the radiation, the observations show that monsoon surges at
BBM are expected to have significant effects on radiative heating rates. In
June 2011 BBM experienced sudden moistenings of up to around 5 g kg-1
(Fig. 5, Marsham et al., 2013a). If we assume that a value of
2.5 g kg-1 is more representative of the change over the 5 km deep
Saharan BL (Fig. 3 in Marsham et al., 2013a, shows such monsoon surges tend
to directly affect the lower half of the 5 km layer) this gives a TOA net
radiative heating of around 28 W m-2 (based a TCWV of
12.5 kg m-2 and a dependence of net radiation on TCWV of
2.2 W kg-1, Table 1). If this heating is distributed over the 5 km
deep Saharan BL it will result in a warming of around 0.5 K day-1. It
will therefore take days for the additional radiative warming to compensate
for the cooling of a few degrees experienced in such events. This “radiative
rewarming timescale” may be one contributing factor (together with
timescales such as those for advection & mixing timescales and synoptic
features such as African Easterly waves) to the variability of the
3-to-30-day variability of the SHL observed by Lavaysse et al. (2010).
Schematic showing net radiation and implied tropospheric radiative
heating, in situations where either TCWV (top row) or AOD (bottom row) is
perturbed by plus or minus one standard deviation away from their mean state
(right and left columns respectively). Moist atmospheres tend to be dusty
and vice versa. Red numbers show net shortwave, purple numbers show net longwave and
black show net radiation. TOA and surface heating are shown by plus signs
with downward arrows. Values are shown at surface, TOA and for inferred
atmospheric radiative heating (“Atmos. Conv.”). Variance in TCWV has the
dominant effect on net TOA radiation, while variance in AOD has the dominant
effect on net surface radiation. Both TCWV and AOD are important for
atmospheric heating rates.
The observed net radiative heating of the SHL region observed at BBM during
June appears to contrast with Charney (1975), which shows heating from
subsidence and TOA cooling from radiation for the Sahara in July. However,
the Fennec supersite-1 is at the northern limit of the Inter Tropical
Discontinuity and regularly receives cold moist air from the south (Marsham
et al., 2013a). Charney (1975, Fig. 1) shows net TOA heating at the location of the Fennec supersite in July, with
TOA net cooling only north of around 22∘ N (interestingly the TOA
heating extends northeastwards over the Hoggar mountains, a region that
favours northward extent of moist monsoon air; Cuesta et al., 2010). It is
likely that further north away from the moistening from the monsoon the
warmer drier atmosphere will give greater longwave cooling and a net
radiative cooling, as shown by Charney (1975). This will be further
investigated.
Conclusions
We have used unique observations of surface energy balance, TCWV and AOD from
the central Sahara in June, together with retrievals from GERB, to
investigate controls on the day-to-day variations in radiative heating in the
SHL region. TOA fluxes show that on average the earth-atmosphere system is
warming (26 W m-2), the surface is warming (98 W m-2) and the
atmosphere is cooling (74 W m-2), with the longwave cooling and the
shortwave warming in each case. Although there are limits to the extent to
which our empirical approach can disentangle the roles of dust, cloud and
water vapour, largely due to correlations between these factors, the results
provide new insight into their roles in controlling the radiative balance of
the unique environment of the central Sahara (schematic in Fig. 5).
Water vapour and dust are observed to correlate in the central Sahara, likely
due to the uplift of dust in monsoon surges and haboobs (Bou Karam et al.,
2008; Marsham et al., 2008, 2013a). However, variations in water vapour (and
associated variables such as temperature and cloud) and not variations in
dust dominates day-to-day variability of TOA net radiation, and hence total
heating of the earth-atmosphere system. ERA-I captures the observed variation
in TOA net radiation (correlation with observations of around 0.65), despite
a monthly dust climatology in ERA-I, which cannot capture day-to-day
variations in dustiness. Variations in AOD dominate day-to-day variations in
surface net radiation, which unsurprisingly are not captured in ERA-I. If
effects from TCWV were simply due to correlated changes in AOD, or vice
versa, these contrasting roles of TCWV and AOD at the TOA and surface would
not be so distinct.
At TOA, on average, decreased shortwave heating gives greater net heating due
to associated increases in longwave heating. ERA-I captures this and the
overall impact of TCWV on TOA net radiation, with a mean increase in TOA net
radiation with TCWV of 1.3 W kg-1 compared with 2.2 W kg-1 in
observations. There are, however, compensating errors in the effects of TOA
net shortwave and longwave with TCWV in ERA-I. ERA-I under-estimates the
effects of TCWV on both TOA longwave and shortwave: it misses corresponding
variations in dust and although it captures much of the effects of water
vapour, it likely underestimates cloud (and significant uncertainties in
analysed water vapour persist at BBM, even when radiosondes are assimilated,
Garcia-Carreras et al., 2013).
At the surface, dust (and associated water vapour and cloud) decreases net
surface radiation in reality by around 13 W m-2 per AOD. Although
increasing TCWV reduces the surface longwave cooling, the effect of TCWV on
the net surface radiation is weak, variable and a subtle balance between the
competing effects of water vapour, clouds and dust (-0.2 W kg-1).
Unlike at the TOA, at the surface decreases in shortwave are on average not
compensated by increases in longwave, leading to decreased net radiation with
decreased shortwave. In contrast to the observations, ERA-I gives greater net
surface radiation with decreased surface shortwave: it is missing the effects
of varying dust and can only capture the effects of water and cloud, likely
underestimating cloud. This gives no correlation between ERA-I surface net
radiation and that observed and a mean heating of 98 W m-2 compared
with the observed value of 64 W m-2, due to an overestimation of
surface downward shortwave in ERA-I. Differences between TOA and surface
fluxes are used to infer atmospheric radiative heating. Effects from TCWV on
these are significant, but they are more strongly controlled by AODs, since
dust has a much greater effect on surface net radiation than TOA net
radiation, while effects of TCWV on TOA and surface heating are more similar.
The results show that, when the SHL is cooled by cold moist air from its
margins, the overall effect is to increase net TOA radiative heating,
rewarming the SHL, a feedback which stabilises the system, by rewarming the
cool air. This occurs in both reality and ERA-I. This ventilation by cold
air is, however, normally accompanied by clouds and dust, which together
reduce surface net radiation, which is not captured by ERA-I, as ERA-I is
missing the variations in dust (and likely under-predicts cloudiness). As a
result, even if ERA-I gives the correct TOA net radiation in response to
water vapour, it fails to distribute this heating correctly in the vertical,
with too much surface heating and insufficient boundary-layer heating. This
will destabilise the boundary-layer profile compared with reality, affecting
subsequent modelled dry and moist convection and therefore modelled
transport of heat, momentum, water vapour and dust.
Improved modelling of the energy budget of the SHL region is needed in models
to improve predictions of the WAM across timescales (e.g. Evan et al.,
2015). The results show that it is important that models used for predictions
can accurately capture the processes controlling the water vapour
distribution over the Sahara, as well as the dust. This capability is
currently questionable for water (Marsham et al., 2013b; Birch et al.,
2014; Garcia-Carreras et al., 2013; Roberts et al., 2015), clouds (Roehrig et
al., 2013; Stein et al., 2015) and dust (Evan et al., 2014), with many dust
errors coming from moist convection (Marsham et al., 2011; Heinold et al.,
2013). The results presented here therefore strongly motivate the need to
improve the representation of advection of water vapour, clouds and
convection in models.
The Supplement related to this article is available online at doi:10.5194/acp-16-3563-2016-supplement.
Acknowledgements
Fennec was funded by a NERC consortium grant (NE/G017166/1). We would like to
thank Azzendine Saci, Abdelkader Ouladichir, Bouzianne Ouchene,
Mohammed Salah-Ferroudj, Benyakoub Abderrahmane, Mohammmed Limam, and
Diali Sidali (ONM) and Richard Washington (University of Oxford) for their
contributions to setting up running the Fennec supersite, and indeed all at
ONM Algeria for their patience and hospitality during Fennec. We would like
to thank the AERONET PHOTONS team for their assistance with the Cimel Sun
photometer. Acknowledgment is made to the FGAM (Facility for Ground-Based
Atmospheric Measurement), NCAS (National Centre for Atmospheric Science) for
the use of the sodar, lidar, and radiosonde units. ECMWF data were provided
by the NCAS British Atmospheric Data Centre (BADC,
http://badc.nerc.ac.uk/). We thank the Royal Meteorological Institute
of Belgium for providing the GERB HR flux data. Marsham is a water@leeds
research fellow part funded by ERC grant 257543 “Desert Storms”, NCAS and
the NERC SWAMMA project (NE/L005352/1). We would like to thank Amato Evan and
two anonymous reviewers whose comments have improved the content and clarity
of the paper. Edited by: Y. Balkanski
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