ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-15095-2017Characterizing energy budget variability at a Sahelian site: a test of
NWP model behaviourMackieAnnaanna.mackie@ed.ac.ukhttps://orcid.org/0000-0001-6387-8521PalmerPaul I.https://orcid.org/0000-0002-1487-0969BrindleyHelenSchool of GeoSciences, The University of Edinburgh, Edinburgh, UKNational Centre for Earth Observation, The University of Edinburgh, Edinburgh, UKNational Centre for Earth Observation, Imperial College London, London, UKAnna Mackie (anna.mackie@ed.ac.uk)21December20171724150951511915June201730August201727October20174November2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/17/15095/2017/acp-17-15095-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/15095/2017/acp-17-15095-2017.pdf
We use observations of surface and top-of-the-atmosphere (TOA) broadband
radiation fluxes determined from the Atmospheric Radiation Measurement
programme mobile facility, the Geostationary Earth Radiation Budget
(GERB) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments and a range of meteorological
variables at a site in the Sahel to test the ability of the ECMWF Integrated
Forecasting System cycle 43r1 to describe energy budget variability. The
model has daily average biases of -12 and 18 W m-2 for outgoing
longwave and reflected shortwave TOA radiation fluxes, respectively. At the
surface, the daily average bias is 12(13) W m-2 for the longwave
downwelling (upwelling) radiation flux and
-21(-13) W m-2 for the shortwave downwelling (upwelling) radiation
flux. Using multivariate linear models of observation–model
differences, we attribute radiation flux discrepancies to physical processes,
and link surface and TOA fluxes. We find that model biases in surface
radiation fluxes are mainly due to a low bias in ice water path (IWP), poor
description of surface albedo and model–observation differences in surface
temperature. We also attribute observed discrepancies in the radiation
fluxes, particularly during the dry season, to the misrepresentation of
aerosol fields in the model from use of a climatology instead of a dynamic
approach. At the TOA, the low IWP impacts the amount of reflected shortwave
radiation while biases in outgoing longwave radiation are additionally
coupled to discrepancies in the surface upwelling longwave flux and
atmospheric humidity.
Introduction
The balance at the top of the atmosphere (TOA) between solar and thermal
radiation fluxes determines the energy budget of the climate system. A
proportion of the incoming solar radiation is reflected back into space, but
the majority is absorbed by the Earth and its atmosphere which subsequently
emit radiation at longer wavelengths. At the TOA, there are three broadband
radiation components: (1) incoming solar (often referred to as total solar
irradiance – TSI); (2) outgoing reflected solar (or reflected shortwave
radiation – RSR); and (3) thermal outgoing (or outgoing longwave
radiation – OLR). To be able to confidently describe future changes in climate,
climate models must be able to produce a realistic representation of this TOA
radiation budget and have some skill at simulating past states that can be
evaluated using data . Here, we use surface and TOA
radiation flux measurements to test the model skill of a numerical weather
prediction (NWP) model, the ECMWF Integrated Forecasting System (IFS).
Our understanding of the TOA radiation budget has been
vastly improved by dedicated satellite missions e.g.. These instruments typically have the
advantage of global spatial coverage over multi-year temporal coverage, making
them ideal for studying a wide spectrum of TOA flux variations
and evaluating climate models
. In this study, we use TOA broadband
radiation fluxes determined from the Geostationary Earth Radiation Budget
(GERB) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) instruments
. Although these instruments do not have
global coverage they have the advantage of high (15 min) temporal
resolution.
TOA radiation fluxes are determined by processes at the surface
and throughout the atmosphere. Measurements of surface radiation fluxes are
therefore helpful in modelling radiative processes. Surface measurements are
generally much sparser than satellite data, though some surface networks
exist, such as the Baseline Surface Radiation Network (BSRN;
) and the Atmospheric Radiation Measurement (ARM)
programme. The ARM programme includes three permanent sites and three ARM mobile
facilities (AMFs), which are deployed in different geographical locations.
Here, we use data from an AMF deployment in Niamey, Niger, during 2006
. Data collected during this campaign consist of not only
high-frequency surface radiation measurements but also coincident
measurements of atmospheric variables relevant to the study of radiation
transfer, including aerosol optical depth, atmospheric humidity, 2 m air
temperature and data from sonde ascents.
We use surface and TOA radiation flux measurements over Niamey to
evaluate the performance of the IFS
cycle 43r1 from the European Centre for Medium-Range Weather Forecasts
(ECMWF), a subsequent cycle to cycle 31r2 on which the ERA-Interim reanalysis is based.
Using the model helps us to link observed
radiation flux variations at the surface to TOA radiation fluxes
and to quantify the influence of radiative and non-radiative variables
on model error.
The combination of data available from the AMF and GERB/SEVIRI provides
valuable insight into radiative processes in a region where surface
measurements are scarce. In particular, the high temporal frequency of the
data allows us to look in detail at the relationships and dependencies
between key variables. It is worth noting that although this study is
necessarily limited to the one measurement site at Niamey, this location was
chosen carefully in order to sample the range of climatic conditions
typically experienced across the wider Sahelian region .
In the next section, we describe the study site at Niamey, Niger, and the
associated key components of radiative transfer. We present the data and
methods in Sect. . In Sect. , we present our
analysis of individual components of the radiation flux, including an
analysis of the model error. We provide conclusions in
Sect. .
Description of the Niamey study region
Niamey, Niger (13∘29′ N, 2∘11′ E), was selected for the first AMF deployment because the
characteristic climatology of the location exhibits strong variability that
substantially affects the corresponding behaviour of the TOA and surface
radiative fluxes. Regional dust storms and biomass burning plumes
significantly impact the energy budget , with
dry season aerosol loading composed of varying proportions of mineral dust
and biomass burning aerosol from agricultural fires .
Additionally, the annual progression of the Intertropical Front (ITF) drives
the west African monsoon (WAM) and imposes a strong seasonal cycle on
radiation fluxes due to the onset of the wet season from approximately
April to October .
The AMF deployment over Niamey was from late 2005 to early 2007. It included
measurements of a range of meteorological, thermodynamic and
radiative variables. The deployment was designed to coincide with the
availability of TOA broadband radiation fluxes from GERB. Data from
AMF and GERB could then be reconciled to identify problems in
radiative transfer schemes and numerical weather prediction
.
Overview of radiation and meteorological environments
Figure is used to present the key features of radiative
transfer of the Earth–atmosphere system, with the following section outlining
key aspects of this in Niamey. We refer the reader elsewhere for further, in-depth details e.g..
Simplified schematic of major processes controlling broadband
radiation fluxes. Red arrows indicate shortwave radiation fluxes; blue arrows
longwave radiation fluxes. For a detailed description of the arrows, please see
Sect. .
The incident solar radiation (TSI) enters the top of the atmosphere. A
fraction of the TSI is transmitted through the atmosphere, reaching the
surface as “direct” solar radiation (Fig. 1, arrow 1d). When water vapour (arrow 1a), cloud
(arrow 1b) or aerosol (arrow 1c) is present, a significant fraction of the incident solar
beam will be absorbed or scattered. Some of the scattered radiation will be
scattered down to the surface as “diffuse” solar radiation (arrows 2a, 2b, 2c).
The combination of the diffuse and direct downward radiation combine to form
the total downwelling solar, or shortwave, radiation at the surface (DSR; labelled 2 in Fig. 1).
A fraction of this downwelling solar radiation will be reflected, determined
by the surface albedo, as upwelling shortwave radiation (USR; labelled 3). The
fraction of the USR that is transmitted up through the atmosphere, combined
with the solar radiation scattered upwards by atmospheric molecules, clouds
and aerosol and escaping to space (arrows 4a, 4b), represents the reflected solar
radiation at the TOA (RSR; labelled 4).
Longwave radiation fluxes also depend on the meteorological
conditions. Resulting thermal emissions from the surface
(upwelling longwave radiation – ULR; labelled 5) can be absorbed by atmospheric molecules (predominantly water
vapour; arrow 5), clouds (arrow 5b) and dust aerosol (arrow 5c) depending on the
season. Subsequent emission of radiation from these
absorbers contributes to the downwelling longwave radiation at the
surface (DLR; arrows 6a, 6b, 6c) and outgoing longwave radiation (OLR; arrows 7a,
7b, 7c) at the TOA. The OLR also includes the fraction of radiation
emitted by the surface that is transmitted through the atmosphere and
escapes directly to space (arrow 5d). The surface also cools by sensible
heat flux (SHF; arrow 8a) or, depending on soil moisture content, latent
heat flux (LHF; arrow 8b).
There are distinct dry and wet seasons in Niamey, determined by the position
of the surface ITF, the boundary between the moist air coming from the
southwest from tropical Atlantic and the warmer, dry air coming from the
northeast from the Sahara . During 2006,
the first dry season ran from days 1 to 125, the wet season from days 126 to 300
and the second dry season from days 301 to 365, as determined by a sustained
dew point temperature of at least 15 ∘C .
Figure shows that during the first dry season, given
clear conditions, there is a steady increase in surface DSR as the year
progresses. Dry conditions will typically lead to a relatively constant surface
albedo such that RSR and USR (Fig. a and c) also
increase with time. Figure implies that the
increasing DSR results in surface warming, which in turn leads to enhanced
ULR. The presence of clouds, water vapour and aerosols leads to deviations
from this trend. Aerosols from dust storms, blown in from the Sahara,
decrease DSR , enhance DLR and
increase RSR. Periodic increases in cloudiness and total column water vapour
(TCWV) can lead to increased absorption of both long- and shortwave radiation.
This results in decreases in DSR, increases in RSR and increases in DLR due
to atmospheric warming.
Daily means of observed (blue, from AMF and GERB) and 43r1 (red)
shortwave radiation fluxes at Niamey during 2006. Lines become dashed when
values are from interpolation (see Sect. ). Plots show observed
and 43r1 (a) TOA reflected shortwave; (b) surface
downwelling shortwave; (c) surface upwelling shortwave radiation
fluxes; and the observation–43r1 difference of these same fluxes in plots
(d)–(f), respectively. Black diamonds on plots
(d)–(f) indicate model values outside of the observational
uncertainty range (horizontal dashed lines). Vertical dashed lines indicate
the beginning and end of the wet season.
After the northwards passing of the ITF over Niamey in April, Niamey enters
the wet season. The further northward the ITF migrates, the greater the
vertical extent of the moist air mass above Niamey. TCWV therefore peaks when
the ITF is at its most northerly, leading to a period with deep convective
clouds and increased precipitation . Greater cloud cover
leads to enhanced shortwave (SW) reflection back to space and atmospheric SW radiative
heating which reduce the DSR. Clouds and increased TCWV also absorb in the
longwave (LW), reducing OLR and enhancing DLR (Fig. ). Decreases
in DSR reduce the shortwave radiative heating of the surface, therefore
decreasing ULR. Increases in precipitation, and therefore higher soil
moisture, affect the partition between radiative, sensible and latent
heating.
The same as Fig. but for longwave fluxes. Plots
show observed and 43r1 (a) TOA outgoing longwave;
(b) surface downwelling longwave; (c) surface upwelling
longwave radiation fluxes; and the observation–43r1 difference of these
same fluxes in plots (d)–(f), respectively.
Overview of previous studies
There are a number of studies that have evaluated radiative processes in west
Africa as represented by various models, which point to the difficulties in
simulating the processes which control radiative transfer in the Sahel.
examine the impact of hydrological variables on broadband
atmospheric column divergence in Niamey using both the data from the AMF/GERB
and output from four global climate models (GCMs). They show that the
reasonably well-modelled net broadband divergence across the atmosphere is
the product of error cancellation of longwave and shortwave divergences. GCMs
such as these are not intended to exactly replicate the exact state of the
atmosphere but rather capture long-term spatial and temporal patterns.
Operational forecasts and reanalyses, however, assimilate observational data
regularly and aim to simulate the atmosphere as closely as possible. As
discuss, high temporal frequency observations at specific
points are ideal for comparison to reanalyses: not only do observational
constraints make the projections as realistic as possible, but reanalyses
often share aspects with GCMs which means evaluating them can simultaneously
improve our understanding of underlying models used to make climate
predictions.
There has also been some comparison of operational forecasts to data from the
AMF and GERB at Niamey during 2006. , in their
wider comparison of west African data to ECMWF's operational forecast,
briefly look at how well surface broadband fluxes are modelled in Niamey.
They note that there are periods in the dry season where the observed DSR
decreases which are not present in the model and attribute this primarily to
the use of a constant climatology for aerosol loading and missing cloudy
conditions. ERA-I has also been evaluated by other studies in west Africa
, which find that TCWV is well captured by the model and
that its role in controlling TOA net flux is more important than that of
dust.
Data and methodsData and their uncertainties
We use GERB-like high-resolution TOA broadband radiation fluxes, hereinafter
referred to as GERB fluxes, with a temporal resolution of 15 min and a
spatial resolution of 10 km at nadir . This product uses
SEVIRI measurements, passed through the GERB processing algorithms, to derive
broadband fluxes throughout the year.
Radiative and non-radiative data used from the AMF, with dates for
which the data stream is available in 2006. Data are available from the
ARM archive:
http://www.archive.arm.gov.
VariableData streamDescriptionFrequencyPeriod (2006-mm-dd)UncertaintyShortwave radiationflux (W m-2)qcrad1longM1.s2Up- and downwelling, at surface1 min avg01-01–12-319 W m-2Longwave radiation flux (W m-2)qcrad1longM1.s2Up- and downwelling, at surface1 min avg01-01–12-315 W m-2Temperature (∘C)nimmetM1.b1Temperature, air, at 2 m height1 min avg01-01–12-081 %TCWV (cm)nimsondewnpnM1.b1Temperature, dew point, at altitude6 h01-06–12-310.5 ∘CPressure, atmospheric, at altitude1 hPaTurbulent fluxesnim30qcecorM1.s1Latent heat flux30 min avg01-01–12-316 %(W m-2)Sensible heat fluxAerosol optical depthAOD-FLYNNAOD at 500 nm derived from MFRSR corrected1-day avg01-01–12-310.005
Table summarizes the surface radiative and non-radiative
variables that we use from the AMF during 2006. Direct, diffuse and total
shortwave fluxes were measured using a normal incidence pyrheliometer, and
shaded and unshaded pyranometers, respectively, while longwave fluxes were
measured using shaded and unshaded pyrgeometers. These were complemented with
inferences of turbulent heat fluxes (THFs) from an eddy correlation system.
From the ARM-standard meteorological instruments, we use 2 m air temperature.
The temperature and pressure measurements at altitude, required for TCWV
estimates, come from Vaisala RS-92 radiosonde ascents. We also use relative
humidity (RH) profiles from the sonde ascents to extract upper tropospheric
humidity (UTH), defined, following , as the average RH
between 500 and 200 hPa. Finally, we use aerosol optical depth (AOD) at 500 nm
from the multiple-frequency rotating shadow band radiometer (MFRSR),
corrected for forward scattering . In
addition to these surface-based measurements, we use ice water path (IWP) and
liquid water path (LWP) daily averages derived from SEVIRI provided by the
Climate Monitoring Satellite Application Facility (CMSAF, ).
When comparing AMF and GERB data, we consider two sources of error associated
with (1) the determination of the quantities being measured by an
instrument and (2) relating a point AMF measurement with a
GERB flux measurement that is representative of a much larger spatial scale
. estimate uncertainties in GERB
fluxes to be approximately 5 and 10 W m-2 for the short- and longwave,
respectively. However, argue that this underestimates the
uncertainty and estimate the instantaneous flux uncertainty to be 10 % for
both long- and shortwave fluxes. In this study, we use whichever of these is
larger on a particular day, along with the AMF uncertainties of 5 and
9 W m-2 for surface long- and shortwave fluxes following
. Uncertainties in other AMF variables are given in
Table , while those in IWP and LWP are provided by CMSAF and
have an annual mean of 0.021 and 0.015 kg m-2, respectively.
For ease of comparison, all data are processed into daily means.
Figures , and
show daily means of shortwave and longwave fluxes, and
non-radiative variables, respectively. For our analysis, as described below,
we use continuous data sets that are regularly spaced in time. We use the
period 7 January–8 December, determined by the availability of sonde and air
temperature data, and impute missing values. Missing values from the AMF data
are imputed using a linear interpolation. We also use a linear interpolation
for missing GERB data points for gaps of one data point; otherwise, we
use a climatology from 2005 to 2014. In the majority of cases, this corresponds
to a 9-year mean.
Observed and 43r1 non-radiative fluxes for Niamey during
2006. Plot (a) shows temperature and TCWV; (b) sensible and latent
heat fluxes; (c) aerosol optical depth; and (d) ice water path
and liquid water path. Dashed sections indicate imputed data, and
dashed vertical lines indicate the beginning and end of the wet
season.
Model and data analysis
We compare the daily means of the radiation and meteorological variables to
corresponding model output from IFS cycle 43r1 (Fig. ). We
use the Tco399 resolution of IFS cycle 43r1, with a global horizontal
resolution of approximately 29 km and 137 vertical levels. The radiation
scheme is called every hour, with approximate updates every model time step
(15 min) using the approach of . Both cycle 43r1 and
ERA-I use a climatological aerosol distribution , derived from
.
To evaluate the daily mean model radiative and non-radiative variables, we
use the square of Pearson's correlation coefficient (r2), which we assume is
statistically significant only if the p value < 0.001, and the root
mean square error (RMSE). We also use the average daily model bias, which we
define as
bias=∑inxiO-xiMn,
where xi is the variable in question on day
i, n denotes the number of days and the superscripts O and M denote
observation and model, respectively.
We use multivariate models to link observed and model variables. To build the
multivariate linear models for a particular variable, we identify potential
predictor variables based on the schematic in Fig. to
give a physical rationale for selection. There are two requirements for the
predictor variable to be included in the linear model to avoid overfitting:
first, the predictor variables must have a statistically significant
correlation with the dependent variable which also tests whether the linear
approximation is appropriate; second, the predictor variables must be
independent of each other . To achieve this, we first
perform a least-squares regression of the predictor variable on the dependent
variable and then between the selected predictor variables to ensure mutual
independence.
We select predictor variables according to the criteria above in order to
build linear models of the observed and 43r1 fluxes. This has two purposes:
not only does this highlight the relative importance of different predictor
variables in both the observations and 43r1, but it also indicates generally
how well a linear multivariate model is able to capture the variability.
Finally, we build models of the differences between observed and 43r1
variables: we define the observed–43r1 value to be the “discrepancy”. The
uncertainties in the linear models are derived from the measurement
uncertainties, propagated with the uncertainty from the linear model. We
evaluate model performance by assessing the variation in the discrepancy
which is explained by the linear model using the r2 value.
ResultsModel radiative and non-radiative variables
We begin with a comparison of observations to both 43r1 and ERA-I radiation
fluxes at the surface and the TOA for long- and shortwave flux observations
(Fig. ). ERA-I has a coarser spatial resolution, with a
horizontal grid of approximately 80 km, and 60 vertical levels
. ERA-I is also based on an IFS cycle 31r2, which was
operational a decade earlier than 43r1. Numerous improvements to the physics
and the dynamics of the model have been made in the intervening period, such
as changes to the convection scheme leading to a much better capturing of the
diurnal cycle of tropical convection . From
Fig. , we see that although there are some distinct changes
between the two cycles of the model, most notably in ULR and to a lesser
extent DLR (Fig. d and f), the behaviour of the two versions
of the model tends to be more similar to each other than to the matched
observations. Due to this similarity, we continue with comparisons of
observations to 43r1 only but note that our general conclusions are
applicable to ERA-I output. We explore reasons for the observation–model
discrepancies in Sect. .
Radiative variables
For the shortwave fluxes, the model has a negative bias for RSR
(Fig. a, d) and a positive model bias at the surface,
with annual average biases of -21 W m-2 in DSR
(Fig. b, e) and -13 W m-2 in USR
(Fig. c, f). For all shortwave fluxes, the
observations show large, day-to-day variations during the second part of the
wet season (approximately days 200–300) which are not reproduced by the
model.
For OLR, the model has a positive bias throughout the year
(Fig. a, d). However, the majority of the model points
lie within in the uncertainty bounds of the observations. In the wet season,
when large day-to-day variability is seen in the observations but not in the
model, differences can exceed the observational uncertainty. Here, the average
daily bias is -13 W m-2 (Table ). In contrast, at
the surface, there are larger biases in dry season longwave fluxes than in the
wet season, with modelled DLR and ULR consistently underestimated
(Fig. b, c, e, f). The correlation coefficients for
both the dry seasons are high (r2=0.89 and 0.76 for ULR and DLR in the
first dry season), suggesting that although the model has a significant
negative bias, it captures the dry season variability of the surface longwave
fluxes well. Wet season average bias in DLR and ULR is small at 0 and
1 W m-2, respectively, though this is due to cancellation of the model
underestimation of DLR and ULR in the first part of the wet season (days
126–200) with the overestimation in the second part of the wet season (days
200–300). All radiative variables show more variability in the observations
than in the model, reflecting the larger range of competing influences in
comparison to the idealized and less chaotic model.
Statistics from observation–43r1 comparison of radiative variables
for the whole year (days 7–341), first dry season (days 7–125), wet season
(days 126–301) and second dry season (days 302–341); Pearson's r2 value
(bold if significant to p≤0.001), the root mean square error and the
bias are all defined in Sect. .
VariableStatisticWholeFirst dryWetSecond dryyearseasonseasonseasonOLRr20.510.540.400.56RMSE (W m-2)24202910bias (W m-2)-12-12-13-5DLRr20.830.760.680.90RMSE (W m-2)23331218bias (W m-2)1329016ULRr20.450.890.350.94RMSE (W m-2)24302018bias (W m-2)1228-117RSRr20.310.510.210.01RMSE (W m-2)32194210bias (W m-2)1813248DSRr20.230.600.090.27RMSE (W m-2)47286013bias (W m-2)-21-13-30-3USRr20.430.640.140.33RMSE (W m-2)1712215bias (W m-2)-13-10-16-3Non-radiative variables
Figure presents observed and modelled 2 m air temperature,
TCWV, LHF, SHF, aerosol optical depth (AOD) and IWP and LWP, with mean
statistics shown in Table .
Statistics from observation–ERA-I comparison of non-radiative
variables for the whole year (days 7–341), first dry season (days 7–125), wet
season (days 126–301) and second dry season (days 302–341); Pearson's r2
value (bold if significant to p≤0.001), the root mean square error and
the bias are all defined in Sect. .
VariableStatisticWholeFirst dryWetSecond dryyearseasonseasonseasonTemperaturer20.600.890.560.92RMSE (∘C)2.12.12.20.9bias (∘C)0.01.8-1.30.3TCWVr20.800.520.740.94RMSE (cm)0.81.00.50.3bias (cm)0.20.8-0.20.3Latent heat fluxr20.660.100.430.36RMSE (W m-2)1772211bias (W m-2)-6-6-6-11Sensible heat fluxr20.130.520.060.13RMSE (W m-2)25232822bias (W m-2)1118520Ice water pathr20.130.390.070.00RMSE (kg m-2)0.130.020.180.01bias (kg m-2)0.050.010.080.01Liquid water pathr20.080.120.010.08RMSE (kg m-2)0.060.030.080.02bias (kg m-2)0.010.010.020.01
Air temperature at 2 m, Ta2, is lower than, but closely coupled
to, surface or skin temperature for which observations
are not available at the study site. We find that observed and model
Ta2 (Fig. a) follows a very similar pattern to
ULR (Fig. b), as expected. In particular, we find the
model generally underestimates observations during the dry season but with a
high correlation coefficient (r2=0.89). During the wet season, as with
ULR, the model values of Ta2 display less of the observed
day-to-day variability. The seasonal cycle in TCWV is similar to that in 43r1
(r2=0.80 for the whole year) but is much less variable than the
observations during the wet season.
Figure b shows modelled and observed THF. The model describes
66 % of observed LHF variation over the year, with 43 % of observed
variation described in the wet season but only 10 % of observed variation
described in the dry season. In contrast, the model captures only 13 % of
the annual variation of SHF with the 52 % of the observed variation in the
first dry season but only 6 % of the observed variation described during
the wet season.
For 500 nm AOD from the AMF (Fig. c), we find large values
(> 3) during the dry seasons and much less variability in the wet season.
The model uses aerosol climatology , which bears little
resemblance to the observations, and consistently underestimates the AOD
throughout the year.
The model–observation IWP and LWP, though significant at
the p≤0.001 level, capture only 13 and 8 % of the observed
variability, respectively (Fig. d). The model IWP and LWP
have a consistent low bias with respect to the observations
(Fig. ), particularly during the wet season. Although there
are significant correlations during the dry season (r2=0.39 and 0.08,
respectively) there are no significant correlations during the wet season
when there are large variations in the observations.
Figure shows that the model reproduces the observed
large-scale seasonal pattern of RH of a very dry lower
troposphere (700–1000 hPa) during the dry season, with large variations and
high RH throughout the troposphere during the wet season. The model has some
consistent differences to the observations with a generally negative bias
between 500 and 700 hPa and a positive bias between 200 and 400 hPa
(Fig. c).
(a) Observed and (b) 43r1 daily mean of relative
humidity
profiles from 1000 to 200 hPa above Niamey during 2006. Plot (c) is the observation–model relative humidity discrepancy.
Surface radiative flux discrepancies
We begin by examining the surface budget in both the model and the
observations. Differences in up- and downwelling fluxes lead to differences
in the surface energy budget (Fig. a), defined as the
difference between the downwelling energy flux (net downward shortwave
(DSR minus USR) plus DLR) and upwelling energy flux (ULR plus SHF plus LHF), using the
convention that downwelling fluxes are positive. We find that the
overestimation in model DLR and DSR outweighs the underestimation in ULR and
USR, leading to a generally positive downwelling flux in the model, in
contrast to the observed negative downwelling flux.
Surface and TOA energy budget at Niamey during 2006.
Plot (a) shows the net energy flux at the surface (downwelling
long- and shortwave surface radiation fluxes minus upwelling long- and
shortwave radiation surface fluxes and turbulent heat fluxes) for
observations (blue) and 43r1 (red). Plot (b) is the net flux at the
TOA (total solar irradiance minus reflected shortwave and outgoing longwave
radiation), also for observations and 43r1. Positive values for both indicate
more downwelling than upwelling energy flux. Dashed vertical lines indicate
the beginning and end of the wet season.
In the rest of this section, we take each of the surface fluxes in turn and
examine the relationship between observed and model fluxes with respect to
other variables, before using a multivariate model to interpret the
observation–model discrepancy. Equations for all discrepancy linear models
can be found in Appendix .
Surface downwelling shortwave radiation
We remove the effect of the changing TOA TSI over
the course of the year to emphasize the effect of the meteorological
controls, though simply refer to this (DSR minus TSI) as DSR for the purposes
of this section. We expect that the primary controls on the DSR reaching the
surface will be scattering and absorption from aerosols, water vapour and
clouds (Fig. ). Therefore, we examine the DSR in both the
observations and from 43r1 with relevant variables: AOD, LWP, IWP and TCWV.
Table presents statistics corresponding to surface shortwave
fluxes.
Shortwave surface fluxes: r2 values from correlations between
observed and 43r1 USR and DSR, and their discrepancy, to other variables for
the whole year (days 7–341), first dry season (days 7–125), wet season (days
126–301) and second dry season (days 302–341); statistically significant (to
p≤0.001) values are in bold. Italics indicate which variables were
used in the linear model.
As expected, the observational wet season variability in DSR is more closely
correlated to variables related to clouds (TCWV, IWP and LWP), while AOD is
more closely correlated to dry season variability. By combining IWP, TCWV and
AOD, we generate a linear model (similar to that used by )
which explains 70 % of the observed variability in DSR over the whole year
(Fig. a). Figure b shows the contributions to the
linear model, where negative values indicate that an increase in that
variable corresponds to a decrease in DSR. We repeat this process with the
corresponding model variables and find that TCWV and LWP have a higher
correlation coefficient with DSR than in the observations, especially in the
dry season which is most likely due to the poor representation of AOD
variability. We generate a linear model using IWP and TCWV, a combination
which gives high correlation coefficients throughout the year
(Fig. c, d).
Downwelling shortwave radiation minus total solar irradiance
(DSR–TSI), in observations and 43r1. Plot (a) shows DSR (blue) and the
linear model (red) of (DSR–TSI) built from a
linear combination of IWP (orange), TCWV (blue) and AOD (green) shown
in plot (b). Plot (c) shows the 43r1 (DSR–TSI) (pink) and the linear model of 43r1 (DSR–TSI)
(green) made up of IWP (orange) and TCWV (dark blue) in
plot (d). Plot (e) shows the observation–43r1 discrepancy in red, with
a linear model (blue) of this discrepancy built from discrepancies in IWP
(green), aerosol optical depth (orange) and LWP (purple) in plot
(f). Negative contributions in plots (b), (d) and
(f) indicate that an increase in that variable corresponds to a
decrease in DSR–TSI. Dashed lines indicate the beginning and end of the wet
season.
We perform linear regressions on the observation–model discrepancies in
AOD, TCWV, IWP and LWP with the discrepancy in DSR. The discrepancy in IWP
has a statistically significant correlation with that in DSR over the whole
year but particularly in the wet season (r2=0.54). The correlation
between the discrepancy in DSR and that in LWP is lower but still
significant, while the discrepancy in TCWV has no significant correlation.
The discrepancy in AOD has a significant correlation during the dry seasons
(r2=0.27 and 0.54, respectively). We combine the highest correlating
discrepancies, IWP, AOD and LWP, in a linear model (Fig. e)
which captures 56 % of the variability in the observation–model
discrepancy in DSR over the year. Figure f shows that during the
dry season the contribution to the linear model from AOD is largest, while
during the wet season IWP largely determines the variability in the DSR
discrepancy. From this, we infer that model negative bias in cloud IWP
explains a significant proportion of the overestimation of insolation
reaching the surface. This is particularly prevalent during the wet season,
with day-to-day variations in AOD accounting for dry season overestimation of
DSR at the surface.
Surface upwelling shortwave radiation
We consider the two factors which we would expect to produce an error in the
model USR: discrepancy in DSR and the incorrect characterization of surface
albedo. To estimate the surface albedo in the model and the observations, we
take the ratio of USR to DSR or the proportion of DSR which is reflected
upwards (inferred surface albedo, α). Figure b shows that
the model generally has a positive bias in α, ranging from 0.22 to 0.29,
contrasting with observations, where α varies between 0.14 and 0.29. The
seasonal contrast is due to the semi-arid nature of the region: dry
conditions during the dry seasons lead to a high albedo, but during the wet
season green vegetation and higher soil moisture reduce the surface albedo.
This is consistent with monthly values of the normalized difference
vegetation index (NDVI; Fig. b) from NASA MODIS, which peaks
during August/September, approximately corresponding to when there is a
minimum in surface albedo. Care must be taken when comparing these values
because we compare a model grid-average value with point measurements of DSR
and USR, which is only valid if the point measurements are representative of
the larger geographical area. This point is discussed further in
Sect. .
Plot (a) shows observed USR (blue), 43r1 USR (red) and
“adjusted” USR (calculated from 43r1 DSR and observed surface albedo,
green). Plot (b) shows inferred surface albedo as calculated by the
ratio USR/DSR from the observations (blue) and 43r1 (red), as well as NDVI
(see text). Plot (c) shows USR discrepancy (red) and linear model
(blue) of USR discrepancy with contributions in plot (d) from
discrepancy in surface albedo (pink) and DSR discrepancy (orange). Dashed
lines indicate the beginning and end of the wet season.
To quantify the impact of the model bias in α on USR, we calculate the
model USR that is consistent with using observed α values.
Figure a shows that this adjusted USR is much closer in
magnitude and variability to the observed USR than the original model USR
(r2=0.70), suggesting a major source of error for the model USR
originates from the poor characterization of the surface albedo over this
study site.
To determine whether the model bias in DSR or α has more of an impact
on model USR, we build a linear model of the USR observation–model
discrepancy. We use the discrepancy in α and in DSR as the predictor
variables (Table ) as both are highly correlated with the
discrepancy in USR (r2=0.48 and 0.82, respectively) but have a relatively
low correlation with each other (r2=0.13). Table and
Fig. c show that the linear model performs extremely well
(r2=0.97) over the whole year. Figure d shows that the larger
contribution comes from the discrepancy in α, although the discrepancy
in DSR is responsible for the large variations during the wet season. This
suggests that increased surface reflectivity and overestimated DSR in the
model combine to produce an overestimation in USR.
Surface downwelling longwave radiation
Following , we use observed Ta2, TCWV and AOD
to build a linear model that accounts for 88 % of the observed variability
in DLR (Table , Fig. a). A similar linear model but
without AOD explains 99 % of the 43r1 variability in DLR
(Fig. c). Considering the full year, the linear model for 43r1
gives greater weight to the contribution from TCWV with respect to
Ta2 than the observational linear model (compare
Fig. b to d), a feature which is dominated by wet season
behaviour.
Downwelling longwave surface fluxes: r2 values from
correlations between observed and 43r1 DLR, and their discrepancy, to other
variables for the whole year (days 7–341), first dry season (days 7–125), wet
season (days 126–301) and second dry season (days 302–341); statistically
significant (to p≤0.001) values are in bold. Italics indicate which
variables were used in the linear model.
The same as Fig. but for downwelling longwave
radiation, with observation–43r1 linear model contributions from
TCWV (blue), 2 m air temperature (orange) and AOD (green) in plots (b) and (d), and discrepancy model contributions from 2 m air temperature (orange) and AOD (pink) in plot (f).
The discrepancy in TCWV is found to have little correlation with
the observation–model discrepancy in DLR and is therefore not used further. In
contrast, the discrepancy in Ta2 has a stronger correlation with
DLR discrepancy in the wet season (r2=0.46), while the discrepancy in
AOD has a stronger correlation with DLR discrepancy in the first dry season
(r2=0.45). We therefore build a linear model of the discrepancy in DLR
with discrepancies from Ta2 and AOD (Fig. e). These
two variables collectively account for 75 % of the DLR discrepancy
variability over the whole year, with higher correlations in the dry season
than the wet season (r2=0.67 and 0.52, respectively).
Figure f shows that the discrepancy in DLR is largely driven by
the discrepancies in Ta2, with peaks in AOD contributing to
isolated events.
Surface upwelling longwave radiation
ULR is determined by variations in skin temperature, Ts and
emissivity, ϵ, through the Stefan–Boltzmann law:
ULR=ϵσTs4,
where σ denotes the Stefan–Boltzmann constant
(5.670 ×10-8 W m-2 K-4). ULR model error is therefore
likely to arise due to errors in either emissivity or in Ts. We
find that ULR and Ta2 (our proxy for Ts) are highly
correlated in both observations and 43r1 (Table ), as expected.
The observed minus model ULR discrepancy is also highly correlated to
Ta2, suggesting that the errors in ULR are linked to those in
Ts. Possible sources of error in Ts include errors in
surface heating, ground heat storage and the partitioning between ULR and the
turbulent (latent and sensible) heat fluxes.
Upwelling longwave surface fluxes: r2 values from correlations
between observed and 43r1 ULR, and their discrepancy, to other variables for
the whole year (days 7–341), first dry season (days 7–125), wet season (days
126–301) and second dry season (days 302–341); statistically significant (to
p≤0.001) values are in bold. Italics indicate which variables were
used in the linear model.
To explore the source of this temperature difference, we perform linear
regressions of ULR first with absorbed shortwave radiation at the surface
(net downward shortwave radiation flux or DSR minus USR) and then, with the
addition DLR, of total downward radiation flux (net downward shortwave
radiation flux plus DLR). The observed surface ULR is significantly correlated
with observed net downward shortwave radiation flux through the year (r2=0.33). We find that this correlation is improved when we linearly regress
observed ULR with the total downward radiation flux (r2=0.59), with the
largest correlation during the dry season (r2=0.96). We find a similar
result using 43r1. The observation–model discrepancy in ULR is highly
correlated to the discrepancy in total downward radiation flux. Although it
is difficult to fully disentangle the cause and effect relationship between
the upwelling and downwelling longwave radiation fluxes, the suggestion
through this analysis is that the dry season discrepancy in Ts
arises through an underestimation in model DLR which is partially offset by an
overestimation in net downward shortwave fluxes.
TOA radiative flux discrepancies
As with the surface budget, we examine the net radiation flux at the TOA
(incoming solar - (OLR + RSR)) in both the model and observations
(Fig. b). We find that despite large discrepancies in the
RSR and OLR (Figs. and ) there
is good agreement between the model and observed TOA budget, especially in
the dry season. This is likely due to the RSR underestimation counteracting the
OLR overestimation. The exception to this is the second part of the wet season,
where the model does not capture the large variations seen in the
observations. We now interpret OLR and RSR to establish which processes
control observed and model variations, and their discrepancies.
TOA reflected shortwave radiation
We find that the shortwave component of the TOA budget has similar controls
to DSR (Table ) with significant correlations between observed
RSR and cloud products (IWP and LWP, r2=0.56 and 0.31, respectively).
Control from IWP is strongest in the wet season, while the highest
correlation with LWP and AOD is during the first dry season. Using LWP, IWP
and AOD, we build a linear model which explains 73 % of the observed RSR
variability over the course of the year, with the highest correlation during
the wet season (Fig. a). IWP and LWP have the largest
contribution during the wet season, with AOD contributing more in the dry
seasons (Fig. b). Repeating the procedure with 43r1 shows very
similar dependencies of 43r1 RSR on LWP and IWP, suggesting that the response
to the cloud forcing is similar. Using the 43r1 IWP and LWP in a linear
model, we can recreate 74 % of the variability in 43r1 RSR
(Fig. c).
TOA RSR fluxes: r2 values from correlations between observed
and 43r1 RSR, and their discrepancy, to other variables for the whole year
(days 7–341), first dry season (days 7–125), wet season (days 126–301) and
second dry season (days 302–341); statistically significant (to p≤0.001)
values are in bold. Italics indicate which variables were used in the linear
model.
The same as Fig. but for reflected shortwave
radiation at the TOA, with observation–43r1 linear model
contributions from IWP (orange), LWP (blue) and (observations only) AOD (green) in plots (b) and (d), and
discrepancy model contributions from IWP (orange), LWP (blue) and
AOD (green) in plot (f).
Discrepancies in RSR and the predictor variables show that, as with DSR, the
largest correlation with RSR discrepancy is that in IWP, followed by that in
LWP (Table ). This suggests that the underlying discrepancies in
RSR have the same cause as those in DSR, namely the underestimation of IWP
and LWP, especially in the wet season. Indeed, we see a very strong
correlation between DSR and RSR both in the observations and 43r1
individually, as well as a high correlation between the observation–model
discrepancy in DSR and RSR. By combining the discrepancies in IWP, LWP and
AOD, we build a linear model which explains 55 % of the variability in the
discrepancy of RSR over the course of the year (Table ).
Comparison of the discrepancy models for both RSR (Fig. e and f)
and DSR (Fig. e and f) shows that the RSR discrepancy model is
less dependent on AOD but also includes a dependency on LWP.
TOA OLR fluxes: r2 values from correlations between observed
and 43r1 OLR, and their discrepancy, to other variables for the whole year
(days 7–341), first dry season (days 7–125), wet season (days 126–301) and
second dry season (days 302–341); statistically significant (to p≤0.001)
values are in bold. Italics indicate which variables were used in the linear
model.
We finish our examination of the radiation fluxes with the OLR. There is a
significant correlation between observed OLR and TCWV, IWP and LWP, with the
strongest correlation between IWP/LWP and OLR during the dry season, and TCWV
having a similar correlation in both the first dry and wet seasons. Upper
tropospheric humidity (UTH) has a statistically significant, albeit lower,
correlation during the wet season (r2=0.13). ULR, which we might expect
to influence OLR under clear-sky conditions, does not have a significant
correlation during the dry season, though it does during the wet season. By
combining IWP, LWP and TCWV, we build a linear model which explains 60 % of
the observed variability throughout the year (Table ,
Fig. a). Much of the seasonal cycle in this linear model is
driven by TCWV, while the day-to-day variability during the wet season comes
from variations in IWP and LWP (Fig. b). Again, these
contributions are shown as negatives in the contributions' plot, as an
increase in these variables leads to a decrease in OLR.
The same as Fig. but for outgoing longwave radiation
at the TOA, with observation–43r1 linear model contributions from
TCWV (orange), IWP (blue) and UTH (green) in plots (b) and (d), and discrepancy model contributions from
IWP (blue) and UTH (green) and LWP (pink) in plot (f). Negative contributions in plots (b),
(d) and (f) indicate that an increase in that variable corresponds to a decrease in OLR.
We see a similar pattern with 43r1, though LWP has a lower correlation with
OLR during the year (r2=0.20) and no significant correlation in any
individual seasons. Instead, IWP has a stronger correlation (r2=0.69),
and UTH has a higher correlation than the observations (r2=0.54 rising
to 0.87 during the wet season). The linear model from TCWV, IWP and UTH
(Fig. c) captures most of the variability in 43r1 OLR (r2=0.85), though TCWV contributes less to the linear model than in the
observations (Fig. d).
As noted above, for the majority of the days examined, 43r1 OLR falls within
the uncertainty bounds of the observations, with the exception of the wet
season. We therefore restrict our discrepancy model to just this period.
There is little correlation between the discrepancy in wet season IWP, ULR or
UTH and the discrepancy in OLR, and no significant correlation with TCWV or
LWP (Table ). We therefore build a linear model of the
discrepancy in wet season OLR using the discrepancy in IWP, ULR and UTH,
producing a model with correlation coefficient of r2=0.36
(Fig. ). From the contributions to this model
(Fig. ), we see that the largest contribution to this model is
from the discrepancy in UTH, with minor contributions from IWP and LWP,
suggesting that model humidity profiles are the largest cause of OLR
discrepancies.
Discussion and concluding remarks
The purpose of this study has been to characterize differences in TOA and
surface radiation fluxes between ECMWF's IFS 43r1 and observations from
GERB/SEVIRI; to link these discrepancies to differences in physical
processes; and to examine links between surface and TOA discrepancies. We do
this using some simple statistics and by extending the methods of
multivariate linear models used by .
We are able to link observation–model discrepancies in physical processes
to those in radiation fluxes. The most striking of these impacts arises from
a lack of ice clouds, manifested as an underestimation in the ice water path,
which causes large wet season discrepancies in shortwave radiation. This lack
of ice clouds leads to too much shortwave radiation reaching the surface and
not enough being reflected at the TOA. This effect is exacerbated by the use
of an aerosol loading climatology which typically underestimates the real
amount of aerosol present in the atmosphere over Niamey and is the major
source of the model overestimation of surface DSR in the dry season. This
agrees with an assessment of an earlier cycle
of the IFS, 32r3, used for a reanalysis by ,
though here we directly link the lack of cloud and aerosol loading to an
overestimation of DSR. , in their study of controls of
surface and TOA radiation budgets in a similar site in Algeria (Bordj Badji
Mokhtar at 21.4∘ N, 0.9∘ E), also find that net shortwave
radiation at the surface is controlled by a balance of clouds, AOD and TCWV,
consistent with our results here. The positive bias in model DSR leads to an
overestimation of net shortwave absorption by the surface and overestimation
of USR. However, we find that for RSR at the TOA, the largest contribution to the
observation–model discrepancy remains the underestimated cloud ice water
path.
Turning to the longwave regime, we find that the model bias in ice water path
also contributes to a positive bias in wet season OLR, though discrepancies
in upper tropospheric humidity and ULR also
play a role. Our analysis suggests that the discrepancy in ULR is itself due
to an underestimation of skin temperature. The origin of this discrepancy in
skin temperature is difficult to identify: we find that the model generally
shows a positive surface energy budget (where downwelling is the positive
direction), while the observations suggest the surface energy budget is
generally negative. This would logically result in an enhanced skin
temperature in the model and an overestimation of ULR. The higher correlation
of ULR with a combination of net DSR and DLR at the surface may indicate complex feedbacks. A cooler
near-surface temperature profile in the model could lead to lower DLR, which
would in turn lead to lower longwave absorption at the surface and therefore
a lower skin temperature. Clearly, separating the cause and effect of the
underestimated skin temperature is difficult. However, this temperature
discrepancy, regardless of origin, can be directly linked to the discrepancy
in DLR, particularly in the wet season. Marked peaks in model–observation DLR
discrepancies result from significant aerosol events which are not captured
in the model aerosol climatology.
There are limitations to the approach we have taken here. A significant
caveat relates to the comparison of point measurements with area averages.
However, assuming a non-static atmosphere, the use of daily averages rather
than instantaneous measurements from the surface (point site), satellite
(∼ 10 km resolution) and model (∼ 29 km resolution) should, to
some degree, account for the mismatch in spatial scales given the higher
native temporal resolution of the surface (1 s) and satellite observations
(15 min). The qualitative agreement in the temporal variability in NDVI from
MODIS (∼ 110 km) and the surface albedo derived from the ARM site
gives confidence in the general representativeness of the site in terms of
surface properties. We also use the upper bounds of instrumental uncertainty
to avoid over-interpreting model–observation discrepancies which lie within
the bounds of measurement error.
The method we have used here does rely heavily on the
availability of high-frequency measurements of radiative and meteorological
variables from surface sites, which are not widely available. However, we
find that larger-scale satellite products, such as ice and liquid water path,
have been integral to our analysis of TOA fluxes in particular, suggesting
this method could potentially be extended to larger spatial scales using
suitably validated satellite products.
In summary, we have shown in this study that the use of multivariate linear
models can give us deeper insight into how physical processes in 43r1 impact
the evolution of radiative fluxes at the surface and the TOA in Niamey, as
well as identify where shortcomings exist in the current version of the
model.
We acknowledge a number of organizations for provision of data.
Data were obtained from the Atmospheric Radiation Measurement (ARM) climate research
facility, a US Department of Energy Office of Science User Facility sponsored by the
Office of Biological and Environmental Research. Data used are qcrad1longM1.s2
(10.5439/1227214); nimmetM1.s2 (10.5439/1025220); nimsondewnpnM1.b1
(10.5439/1021460); nim30qcecorM1.b1 (10.5439/1097546) and AOD-FLYNN (10.5439/1169504).
Data were also obtained from CMSAF (Schulz et al., 2009), which are copyright (2017), EUMETSAT
(10.5676/EUM_SAF_CM/CLAAS/V002). GERB HR data were made available by the Royal
Meteorological Institute of Belgium (RMIB). NDVI data were obtained from NASA Earth
Observations (10.5067/MODIS/MOD13C2.006). ECMWF IFS 43r1 output were made available by Robin Hogan, ECMWF.
ERA-I–43r1 comparison
Figure showing ERA-Interim and IFS cycle 43r1 differences, as discussed in
Sect. .
Comparison between observations (red), ERA-Interim (blue) and 43r1
(green) for (a) TOA reflected shortwave; (b) TOA outgoing
longwave; (c) surface upwelling shortwave; (d) upwelling
longwave; (e) downwelling shortwave; and (f) downwelling
longwave radiation fluxes. Dashed lines indicate the beginning and end of the wet
season.
Linear model equations
The equations for the linear models used in Sect. ,
where primes indicate observation–model discrepancies.
The authors declare that they have no conflict of
interest.
Acknowledgements
Paul I. Palmer acknowledges his Royal
Society Wolfson Research Merit Award and funding from
the NERC National Centre for Earth Observaton. Anna Mackie was supported by the UK
Natural Environment Research Council (grant NE/L002558/1) through the University of
Edinburgh's E3 Doctoral Training Partnership. Additionally, the authors would like to
thank Robin Hogan at ECMWF for the 43r1 output and help with interpreting it, and Ronald L. Miller
for his helpful advice on the use of multivariate linear models.
Edited by: Stelios Kazadzis
Reviewed by: two anonymous referees
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