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GLOBAL ATMOSPHERIC DOWNWARD LONGWAVE RADIATION AT THE SURFACE FROM GROUND-BASED OBSERVATIONS, SATELLITE RETRIEVALS, AND REANALYSES Kaicun Wang1 and Robert E. Dickinson2 Received 7 November 2012; revised 27 March 2013; accepted 27 March 2013; published 28 May 2013.

[1] Atmospheric downward longwave radiation at the surface (Ld) varies with increasing CO2 and other greenhouse gases. This study quantifies the uncertainties of current estimates of global Ld at monthly to decadal timescales and its global climatology and trends during the past decades by a synthesis of the existing observations, reanalyses, and satellite products. We find that current Ld observations have a standard deviation error of ~3.5 W m
2 on a monthly scale. Observations of Ld by different pyrgeometers may differ substantially for lack of a standard reference. The calibration of a pyrgeometer significantly affects its quantification of annual variability. Compared with observations collected at 169 global land sites from 1992 to 2010, the Ld derived from state-of-the-art satellite cloud observations and reanalysis

temperature and humidity profiles at a grid scale of ~1 has a bias of 9 W m
2 and a standard deviation of 7 W m
2, with a nearly zero overall bias. The standard deviations are reduced to 4 W m
2 over tropical oceans when compared to Ld observations collected by 24 buoy sites from 2002 to 2011. The
4 W m
2 bias of satellite Ld retrievals over tropical oceans is likely because of the overestimation of Ld observations resulting from solar heating of the pyrgeometer. Our best estimate of global means Ld from 2003 to 2010 are 342  3 W m
2 (global), 307  3 W m
2 (land), and 356  3 W m
2 (ocean). Estimates of Ld trends are seriously compromised by the changes in satellite sensors giving changes of water vapor profiles.

Citation: Wang, K., and R. E. Dickinson (2013), Global atmospheric downward longwave radiation at the surface from ground-based observations, satellite retrievals, and reanalyses, Rev. Geophys., 51, 150–185, doi:10.1002/rog.20009.

1.

INTRODUCTION

[2] Surface temperatures are controlled locally by a surface energy balance, i.e., the balance between net radiation at the surface and turbulent fluxes of heat and moisture [Wang and Dickinson, 2012]. These terms have large geographical, seasonal, and (over land) diurnal variations. Net radiation consists of surface absorbed solar radiation minus the difference between upward and downward longwave radiation [Wang and Liang, 2009b]: 1

State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China. 2 Department of Geological Sciences, The University of Texas at Austin, Austin, Texas, USA. Corresponding author: K. Wang, State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. ([email protected])

©2013. American Geophysical Union. All Rights Reserved. 8755-1209/13/10.1002/rog.20009

Rn ¼ Sd
Su þ Ld
Lu ¼ lE þ H þ G

(1)

where Rn is the surface net radiation, Sd is the surface incident solar radiation, Su is the surface reflected solar radiation, Ld is atmospheric download longwave radiation, Lu is the surface emitted longwave radiation, lE is the latent heat flux, H is the sensible heat flux, and G is the heat storage in the ground. Of these terms, Ld is perhaps the most fundamental for understanding the impact of increasing CO2 and other greenhouse gases on climate [Iacono et al., 2008; Stephens and Greenwald, 1991; Stephens et al., 1994] but is also considered to be the most poorly quantified from observations [Trenberth and Fasullo, 2012; Trenberth et al., 2009]. [3] Rather than estimate Ld as that needed to balance measurements of the other terms in equation (1) [e.g., Trenberth, et al., 2009], a more direct approach is to obtain it from observations of other atmospheric quantities that serve as

Reviews of Geophysics, 51 / 2013 150 Paper number 2012RG000423

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION

input for its calculation from radiative transfer. Especially important for such calculation are the vertical distributions of atmospheric water vapor, temperatures, and clouds [Ellingson, 1995; Schmetz, 1989]. In warm or tropical humid climates, these atmospheric properties near the surface dominate the calculation of Ld. This approach has used two distinct frameworks for provision of the needed atmospheric properties: “reanalyses,” which introduces uncertainties because they only use modeled clouds [Trenberth and Fasullo, 2010], or satellite observations [Stephens et al., 2012] that also may be limited by inaccurate estimates of cloud bases. [4] The most direct measurement approach is with the use of “pyrgeometers.” Such measurements have been made through some internationally coordinated monitoring activities, such as the World Climate Research Programme (WCRP) Baseline Surface Radiation Network (BSRN) [Ohmura et al., 1998], the Global Energy and Water Cycle Experiment (GEWEX) Coordinated Energy and water cycle Observations Project (CEOP) [Koike, 2004; Lawford et al., 2006], and the FLUXNET [Baldocchi et al., 2001]. These networks have provided long-term continuous measurements of Ld for nearly 20 years at globally distributed sites, in particular at the BSRN sites. Different reanalysis and satellite products of Ld are available for an even longer time period globally. In light of climate change, it is an appropriate time now to investigate the capability of these estimates of Ld to quantify its monthly to decadal variability. [5] This paper synthesizes existing surface observations of Ld from 1992 to 2011 at 169 global land sites and 24 buoy sites in the tropical oceans, and estimates of global Ld from satellite and reanalysis products to answer the following questions: (1) What are the uncertainties of current Ld observations at monthly to decadal timescales? (2) What are the uncertainties of Ld from existing reanalysis and satellite products at monthly to decadal timescales? (3) What has been the climatology and trend of global Ld during the past decades? Attempts have been made previously to address this last question. However, we find it to be essential to address the first two questions before examining the third one. The answers to the first two questions, not previously considered, provide ways to clarify the discussion of the last one, i.e., what are the global means of Ld and its trends? 2.

REVIEW OF EXISTING ESTIMATES OF GLOBAL Ld

2.1. Pyrgeometers and Their Calibration [6] The instrument used to directly measure Ld is called a pyrgeometer. It has a body, a thermopile, and a dome filter that blocks out solar shortwave radiation (Figure 1). It was first described by Drummond et al. [1970] and evolved into the Eppley Precision Infrared Radiometer (PIR). Other types of widely used pyrgeometers include the CG4 pyrgeometer from Kipp & Zonen [Gröbner and Los, 2007; Miskolczi and Guzzi, 1993] and the chopped pyrgeometer [Lorenz et al., 1996]. Dome materials of early pyrgeometer had an ageing problem, which was substantially reduced when dome material was switched to silicon in 1976 [WMO, 2008]. A thermistor glued

Figure 1. A photo of a shaded and ventilated Eppley Precision Infrared Radiometer (PIR) pyrgeometer (front) to measure atmospheric downward longwave radiation at the surface (Ld) at the BSRN Boulder site (Latitude: 40.13, Longitude:
105.24). The photo was downloaded from http://www. esrl.noaa.gov/gmd/grad/surfrad/tmtpics/index.html. to the dome interior of PIR was also added in 1976, and since that time, the instrument has essentially remained unchanged [Payne and Anderson, 1999]. Pyrgeometers are usually operated with a shade ball (Figure 1). This prevents excessive heating of the dome by the sun and shields the sensor from receiving the infrared fraction of the solar beam, which is not regarded as part of Ld [Enz et al., 1975]. [7] Under ideal conditions, the voltage V from a thermopile is linearly related to the net gain of radiant power, and the thermopile sensor surface absorbs and emits as a blackbody at measured temperature Ts. An energy balance equation for this perfect thermopile can be written [Philipona et al., 1998]: Ld ¼

V þ esTs 4 C

(2)

where C is the responsivity of thermopile in mV W
2 and s is the Stefan-Boltzmann constant. However, Ts in equation (2) cannot directly be measured but only derived from the body temperature TB measured at the cold junction of the thermopile [Albrecht et al., 1974; Philipona et al., 1995]. [8] The transmission of silicon dome varies between 0.2 and 0.5 in the region of 4–50mm (Figure 2), depending on the manufacturer and type of the pyrgeometer [Miskolczi and Guzzi, 1993]. The high dome absorptance introduces temperature differences between the dome and the body of a pyrgeometer, hence an additional thermal irradiance on the sensor surface [Ji and Tsay, 2000]. Consequently, a thermistor has been mounted at the lower rim to measure temperature of the silicon dome (TD). Therefore, an improved energy balance equation therefore has been developed [Albrecht and Cox, 1977; Albrecht et al., 1974]: Ld ¼


V þ esTB 4
ks TD 4
TB 4 C

(3)

where k is a correction factor. However, different investigators have used different emissivities e of thermopile, which

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WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION

Figure 2. Transmittance of dome of Kipp & Zonnen pyrgeometer CG4. This dome only transmits longwave radiation in the spectral window from 4.5 mm to 42 mm (This figure was downloaded from http://www.kippzonen.com/). may introduce an error of 5–10 W m
2 to measure Ld [Fairall et al., 1998; Ji and Tsay, 2000]. Furthermore, equation (3) neglects the reflection of thermopile irradiance by the inside of the dome [Fairall et al., 1998; Philipona et al., 1995]. [9] This energy balance equation for a pyrgeometer has been revised by different investigators, resulting in different calibration methods for pyrgeometers. For example, Fairall et al. [1998] modified equation (3) into
V (4) Ld ¼ þ sTB 4
Bs TD 4
TB 4 A where A and B are two factors that need to be determined by calibration. The key to this calibration method lies in finding a point at which TD and TB are equal. At this point, the dome term drops out from equation (4), and the calibration constant A can be estimated directly [Payne and Anderson, 1999]. With different variations of TD and TB, B can be obtained by a least square fit of Ld
sTB 4
V/A with s(TD 4
TB 4). This method has been selected by different investigators [Burns et al., 2000] and the National Renewable Energy Laboratory (NREL) [Reda et al., 2002]. [10] Philipona et al. [1995] revised equation (3) into Ld ¼



V 1 þ k1 sTB 3 þ k2 sTB 4
k3 s TD 4
TB 4 C

(5)

[11] By varying TD and TB as well as the temperature of the blackbody cavity separately, C and ki can be determined individually [Philipona et al., 1995]. This calibration method is also widely used [Gröbner, 2008; Meloni et al., 2012; Philipona et al., 2001]. [12] Equations (4) and (5) are two basic energy equations used to calibrate a pyrgeometer, but different investigators may have different methods to search for optimal values for the calibration parameters in the equations [Philipona et al., 1998]. Before the establishment of Interim World Infrared Standard Group (WISG) by the PhysikalischMeteorologische Observatorium Davos, World Radiation Center (PMOD/WRC) in 2007, pyrgeometers were calibrated through artificial blackbody measurements conducted in a laboratory or manufacturer’s facility [Reda et al., 2012], with temperatures varying typically from 250 K to 300 K, as representative temperatures of Ld [Albrecht and Cox, 1977; Philipona et al., 1995; Reda et al., 2002]. The international pyrgeometer calibration

round-robin experiment compared pyrgeometers calibrated at 11 different laboratory blackbody sources and found the pyrgeometer to agree within 1–2% of the median value [Philipona et al., 1998]. [13] However, laboratory blackbody calibration provides no information about uncertainty of the absolute value of Ld measurements [Philipona et al., 2001]. Field measurements of Ld using pyrgeometers that were all calibrated with the same blackbody cavity showed discrepancies of up to 8 W m
2 [Philipona et al., 2001]. This poor performance led to the development of reference pyrgeometers to measure absolute values of Ld and the establishment of WISG references at PMOD/WRC. Many efforts have been made to develop reference pyrgeometers [Philipona et al., 2001; Reda et al., 2006; Reda et al., 2012]. At PMOD/WRC, a pyrgeometer is first calibrated with a laboratory blackbody, and then it is deployed in the field and compared to a reference group of pyrgeometers to adjust the laboratory calibration factors [Gröbner and Los, 2007; Meloni et al., 2012]. Field calibration factors may improve the precision of nighttime pyrgeometer measurements by a factor of 3 over the best blackbody calibration [Marty et al., 2003; Philipona et al., 2001]. However, the 95% absolute uncertainty on the WISG reference group is still on the order of 5 W m
2 [Forgan, 2009; Meloni et al., 2012]. [14] Most pyrgeometers with field calibration at the National Renewable Energy Laboratory (NREL; with equation (4)) and PMOD (with equation (5)) traceable to WISG reference have a measurement uncertainty of  2 W m
2 (one standard deviation) for cloudy conditions [Reda et al., 2012]. However, incident solar radiation at the pyrgeometer may produce a nonhomogeneous dome temperature under cloud-free conditions and so introduce substantial error in the Ld measurements. Unshaded PIR overestimates the Ld measured by a shaded PIR by up to 10 W m
2 compared to a shaded CGR4 [Meloni et al., 2012]. Therefore, shading pyrgeometers is necessary to achieve a high Ld measurement accuracy, as shown in Figure 1. Ventilation of the pyrgeometer is also recommend by the World Meteorological Organization (WMO) [WMO, 2008]. 2.2. Ground-Based Observations [15] The spectral response function of a pyrgeometer normally covers a wavelength from 3.5 mm to 50 mm, while its reading is calibrated to the total range of terrestrial longwave emission (~4 mm to 200 mm). A dome protects this instrument from impacts of weather events, such as dew, snow [Matsui et al., 2012], and rainfall (Figure 1). Solar heating of this dome may introduce errors in Ld measurements [Enz et al., 1975; Meloni et al., 2012; Udo, 2000] as also other phenomena (such as frost riming and deposition of aerosols). The error from solar heating depends on the intensity of surface incident solar radiation and ventilation conditions [Culf and Gash, 1993; Ji and Tsay, 2000; Pérez and Alados-Arboledas, 1999] and may be more than 10% during a clear and calm day [Meloni et al., 2012; Pérez and Alados-Arboledas, 1999; Udo, 2000]. To reduce this error, modern pyrgeometers are

152

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 1. Basic Empirical Methods to Estimate Atmospheric Emissivity «a (See Equation (6)), Where ea Is Water Vapor Pressure in hpa and Ta is Air Temperature at 2 m Height (Unit: K) Reference

Model

Angstrom [1924] ea ¼ 0:83
0:18  10ð
0:067ea Þ Brunt [1932] ea = 0.605 + 0.048  e10.5 a Brutsaert [1975] ea ¼ 1:24  ðea =Ta Þ =7 Idso and Jackson [1969] ea = 1
0.261  exp(
0.00077  (273
Ta)2) Idso [1981] ea = 0.7 + 5.95  10
5  ea  exp(1500/Ta) Prata [1996] ea = 1
(1 + w)  exp(
(1.2 + 3  w)0.5) w = 46.5  ea/Ta

shaded and ventilated (Figure 1). The inherent accuracy of Ld by an Eppley Precision Infrared Radiometer (PIR) can be improved to ~1.5% when the thermopile voltage and both dome and case temperatures are measured [Fairall et al., 1998; Payne and Anderson, 1999]. [16] A pyrgeometer to measure Ld in the field must be calibrated regularly, i.e., every 6 months or annually. It has to be regularly maintained and checked for dust deposition. The estimated accuracy of measurements of Ld improved from 30 W m
2 in 1990 to 10 W m
2 in 1995 due to improvement of the calibration process [Ohmura et al., 1998] (section 2.1). However, there is still a lack of a recognized world reference for calibration of pyrgeometers [Blonquist et al., 2009; Ohmura et al., 1998; Reda et al., 2002] even though substantial progress has been made in reference radiometer construction [Grobner, 2012; Reda et al., 2012]. References of WISG include the sky scanning radiometer for absolute measurements of atmospheric longwave radiation [Philipona, 2001], two PIR and two CG4 type instruments [Gröbner and Los, 2007]. The 95% absolute uncertainty of the references at WISG of PMOD/ WRC is approximately 5 W m
2 [Meloni et al., 2012]. Consequently, inconsistencies can still occur between Ld measurements by different calibration methods and systems (section 2.1). [17] The measurements of Ld are also affected by the environment. For example, they are especially difficult in Arctic winter due to low water vapor content and extreme meteorological conditions [Marty et al., 2003; Matsui et al., 2012; Ruckstuhl and Philipona, 2005]. According to a laboratory study, when the instrument body temperature is lowered to
60 C, the pyrgeometer’s sensitivity increases by 13% from the factory-default specification [Su et al., 2008]. As a consequence, the Ld measurements at a station on the western Tibetan Plateau (4420 m above sea level) have been shown to be underestimated by tens of W m
2 in winter [Wang et al., 2007]. [18] There are several ways to evaluate the accuracy of Ld measurements in the field. One suggestion is that a modern radiative transfer model combined with radiosonde profiles of temperature and moisture could be used as a standard to improve the absolute accuracy of pyrgeometer data from field programs [Charlock and Alberta, 1996; Dupont et al., 2008; Dutton, 1993; Fairall et al., 1998; Marty et al., 2003]. Current atmospheric radiative transfer models can

provide accurate estimates of Ld given accurate input data [Fu et al., 1997; Lubin et al., 1995]. However, the high quality atmospheric radiosonde data required to use this standard are not available at most sites where Ld is measured. The specially designed pyrgeometer intercomparison experiment is a more used method for evaluating the uncertainty of Ld measurements [Blonquist et al., 2009; Burns et al., 2003; Kohsiek et al., 2007; Marty et al., 2003; Michel et al., 2008; Philipona et al., 2001; Philipona et al., 1998; van den Broeke et al., 2004]. Because of their high costs, these intercomparisons have only collected data for a short period of time. Prior to this study, no attempts have been published to establish how well observations can quantify the annual variability of Ld. 2.3. Empirical Methods [19] Under clear-sky conditions, Ld primarily depends on temperature and humidity profiles of the lower atmosphere [Raisanen, 1996; Rossow and Zhang, 1995; Zhang et al., 1995], typically 80% of it originating from the lowest 500 m and more than 50% from the lowest 100 m of the atmosphere [Schmetz, 1989]. Therefore, Ld has been calculated from surface observations by assuming that temperature and humidity have standard atmospheric profiles [Brunt, 1932; Brutsaert, 1975; Culf and Gash, 1993; Dilley and O’Brien, 1998; Idso, 1981; Prata, 1996; Wang and Liang, 2009a]. [20] Ld under clear sky conditions can be estimated following the Stefan-Boltzman law: Ldc ¼ ea sTa4

(6)

where ea is atmospheric emissivity, s = 5.67  10 W m K
4 is the Stefan-Boltzman constant, and Ta in K is the atmospheric temperature at 2 m high. Most empirical methods estimate the atmospheric emissivity using water vapor pressure or Ta, or both (Table 1). According to the models in the Table 1, atmospheric emissivity (ea) increases with Ta and humidity. [21] Figure 3 compares six widely used empirical models to calculate ea (Table 1). Under extreme conditions, i.e., very hot or very cold, the differences between models are very large, enough to result in ~20% difference in the calculated Ldc. Except for very cold conditions, the estimates by the models of Brunt [1932], Brutsaert [1975], and Prata [1996] are in good agreement, as confirmed by existing studies [Bilbao and De Miguel, 2007; Choi et al., 2008; Kjaersgaard et al., 2007; Sugita and Brutsaert, 1993]. In particular, Brutsaert [1975] has been regarded as the best model in Oklahoma, USA [Sridhar and Elliott, 2002]; Ponta Grossa, Brazil [Duarte et al., 2006]; Denmark [Kjaersgaard et al., 2007]; and Andean Altiplano between Bolivia and Peru [Lhomme et al., 2007]. Other studies have reported the Brutsaert [1975] model to be the best [Rizou and Nnadi, 2007]. [22] Under hot and humid conditions, atmospheric emissivity calculated by the model of Idso [1981] is larger than unity, which is physically impossible. Under clear sky conditions, water vapor is the most important longwave

153


8


2

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION Difference between individual model and multi−model average

0.2

2008; Staiger and Matzarakis, 2010; Stephens et al., 2012]. They have a reasonable accuracy of ~20 W m
2 [Kjaersgaard et al., 2007; Wang and Liang, 2009a] or better [Dupont et al., 2008; Niemelä et al., 2001]. However, they strongly depend on their calibration data and do not fully consider the impact of cloud characteristics, such as cloud base, so may have larger biases outside the parameter range of their local calibration. It is also helpful to point out that none of the empirical models includes greenhouse gases other than water vapor.

0.15 0.1 0.05 0 −0.05 −0.1 −0.15 −0.2 0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

Multi−model average of atmospheric emissivity

Figure 3. The difference of atmospheric emissivity between individual model and multimodel average under conditions that air temperature varies from
20 C to 35 C, and relative humdity varies from 30% to 90%: (a) Angstrom [1924] (black dots), (b) Brunt [1932] (red dots), (c) Brutsaert [1975] (green dots), (d) Idso and Jackson [1969] (blue dots), (e) Idso [1981] (Magenta dots), and (f) Prata [1996] (yellow dots). Detailed information about these models is given in Table 1. Atmospheric emissivity (ea) increases with air temperature and humidity. The differences between models are largest under very cold and hot conditions. Except for very cold conditions, Brunt [1932], Brutsaert [1975], and Prata [1996] are in good agreement. Atmospheric emissivity from Idso [1981] is larger than one under hot and humid conditions, which is physically impossible. radiation absorber and emitter. It is therefore important to include humidity or water vapor pressure in the parameterization of atmospheric emissivity. Among the six models, the model of Idso and Jackson [1969] is the only one whose atmospheric emissivity solely depends on Ta, possibly the reason it deviates from others (Figure 3). [23] Cloud droplets effectively absorb and emit longwave radiation as a blackbody [Schneider et al., 1989] and therefore have a strong greenhouse effect [Mather and McFarlane, 2009; Mather et al., 2007]. In empirical models, cloud fraction (f) is used to quantify the cloud effects on Ld in two basic expressions [Crawford and Duchon, 1999; Duarte et al., 2006]:
Ld ¼ Ldc  1 þ a  f b Ld ¼ Ldc  ð1
f m Þ þ n  f m  s  Ta 4

(7) (8)

[24] Crowford and Duchon [1999] proposed a version of equation (8), where m = 1 and n = 1, regarded as the best by many authors [Cho et al., 2008; Duarte et al., 2006; Kjaersgaard et al., 2007; Lhomme et al., 2007]. However, other cloud characteristics substantially impact Ld [Bréon et al., 1991; Jakob et al., 2005; Stephens et al., 2012], in particular cloud base temperatures [Ellingson, 1995; Trenberth et al., 2009]. [25] These empirical methods using surface meteorological observations have been well recognized and are still being widely used [Abramowitz et al., 2012; Cho et al., 2008; Gubler et al., 2012; Marthews et al., 2012; Prata,

2.4. Satellite Retrievals [26] Satellite observations of clouds and atmosphere have also been used to estimate Ld [Collins and Inamdar, 1995; Darnell et al., 1983; Diak et al., 2000; Diak et al., 2004; Dilley and O’Brien, 1998; Ellingson, 1995; Gupta et al., 1992; Sellers et al., 1990]. However, satellites have difficulty in providing temperature and humidity profiles of the lower atmosphere with the accuracy required for estimation of Ld [Ellingson, 1995; Wang and Liang, 2009c]. Therefore, satellite cloud products and temperature and humidity estimates from atmospheric analysis or reanalysis systems have been combined to calculate Ld [Gupta et al., 1999; Gupta et al., 2010; Nussbaumer and Pinker, 2012; Pavlakis et al., 2004; Schmetz et al., 1986; Zhang et al., 2004]. Some satellite algorithms have empirically related satellite-derived total water vapor amount to Ld [Naud et al., 2012; Zhou et al., 2007]. [27] Furthermore, most passive satellite sensors can only provide observations of cloud top, but Ld is more closely related to parameters of cloud base [Ellingson, 1995; Trenberth et al., 2009]. Most low level cloud products from visible and thermal satellite observations only represent those low clouds that are not obstructed by higher clouds [Weare, 2000]. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite [Hunt et al., 2009; Winker et al., 2010] and the CloudSat Cloud Profiling Radar (CPR) [Stephens et al., 2002] are able to provide some observations or estimates of cloud base height, which have been used to improve the estimation of Ld [Kato et al., 2011]. However, CALIOP and CPR have very narrow swaths and so have some difficulty in providing global coverage of cloud base characteristics and they do not work well in detecting bases of optically thick low clouds [Brunke et al., 2010; Chan and Comiso, 2011; L’Ecuyer et al., 2008; Protat et al., 2010]. One of the principle hindrances of the CloudSat CPR is its low sensitivity and that of CALIOP is the attenuation due to liquid cloud drops [Kim et al., 2011]. These polar orbiting satellite sensors also have difficulty in depicting the diurnal cycle of clouds [Salby, 1989; Salby and Callaghan, 1997]. 2.5. Reanalysis [28] Global reanalysis products provide estimates of temperature and humidity profiles of the lower atmosphere, in part through their assimilation of atmospheric radiosonde and related satellite sounding observations that are accurate

154

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION

50

0

−50

−150

−100

−50

0

50

100

150

−150

−100

−50

0

50

100

150

50

0

−50

Figure 4. A map of sites where atmospheric downward longwave radiation at the surface (Ld) has been observed: AmeriFlux sites (red dots), AsiaFlux sites (green dots), CEOP sites (blue dots), BSRN sites (magenta dots, bottom), and Buoy sites over tropical oceans (blue cross, bottom). There are 193 sites in total. enough for a useful calculation of global Ld with a radiation transfer model. Such radiative transfer models have been substantially improved in recent decades [Fu, 1996; Iacono et al., 2000; Morcrette and Fouquart, 1985; Sun, 2011; ViúdezMora et al., 2009] in part through international atmosphere radiation code intercomparisons [Ellingson et al., 1991; Oreopoulos and Mlawer, 2010; Oreopoulos et al., 2012]. However, the modeled cloud fields used in reanalysis are sufficiently inaccurate to introduce tens of W m
2 error in Ld estimates [Bréon et al., 1991; Trenberth and Fasullo, 2010]. [29] In summary, Ld can be estimated from ground-based observations, empirical methods, reanalysis systems, and satellite retrievals. The accuracy of these estimations has been widely investigated through pyrgeometer calibration and intercomparison, reanalysis and satellite Ld product validation studies. However, all such existing studies have used short-term data and have focused on the uncertainties at time scales from minutes to months. How well these estimates provide monthly to decadal variability of global Ld has not been previously quantified. 3.

DATA

3.1. Ground-Based Observations [30] Although Ld is not routinely observed at weather stations [Gilgen et al., 1998; Wang and Liang, 2009b], as an important component of the surface energy budget, it is widely observed at different flux network stations. We have utilized long-term observations of Ld at 169 globally distributed sites from the BSRN, CEOP, AmeriFlux, and AsiaFlux networks. Measurements of Ld made by 24 buoy sites over tropical oceans are also used. Detailed information

about these sites can be found in Figure 4 and Table 2, and website links to download these data can be found in Table 3. [31] Some of these Ld data are available from 1991, and most are available from 2001 (Figure 5). Among the sites, 33 sites have Ld observations of more than 10 years and about 120 sites have Ld observations of no less than 5 years (Figure 5). The total length of all the data is about 1340 site years, covering all the climate regions from tropical to polar (Figure 4). The elevation of the sites varies from sea level to more than 5000 m above sea level. 3.1.1. BSRN Data [32] The WCRP BSRN started operation in 1992 and consists of more than 50 stations, covering the Earth’s surface from 80 N to 90 S. This study includes 47 of these sites whose Ld data are available to us (Figure 4 and Table 2). The BSRN aims at providing accurate shortwave and longwave radiation measurements for evaluating satellite and climate model simulations of these fluxes and for detecting long-term variations in these radiation fluxes at the Earth’s surface. The network and its instrumentation are designed (1) to cover major climate zones, (2) to provide the accuracy required to meet the objectives, and (3) to ensure homogenized standards for a long time period in the future [Ohmura et al., 1998]. Their Ld achieved an accuracy of 10 W m
2 in 1995 through improvement of its calibration process [Philipona et al., 1998]. [33] Table 2 shows that the instruments (pyrgeometer) for Ld measurements at BSRN stations include Eppley Precision Infrared Radiometers (PIR) and Kipp & Zonen CG 4 pyrgeometers. The Eppley PIR consists of a thermopile (a passive thermal sensing element ) and two thermistors. Its receiver is coated with “Parson’s black lacquer” (nonwavelength selective absorption). During daytime, it isolates longwave radiation from solar shortwave radiation by using a silicon dome. The inner surface of this hemisphere has a vacuum-deposited interference filter with a transmission range of approximately 3.5 to 50 mm. The uncertainty estimated by its manufacturer is 6.0% or 15 W m
2 at the 95% confidence level (two standard deviations). The real uncertainty of shaded and ventilated Eppley pyrgeometers can be much lower than this specification with field calibration [Philipona et al., 1998]. [34] The detector of the Kipp & Zonen CG 4 pyrgeometer is a thermopile. The CG 4 detector is shielded by a silicon meniscus dome. CG 4 has a 180 field of view with good cosine response, while pyrgeometers with a flat silicon window have a typical field of view of 150 . A drying cartridge in the radiometer housing is filled with silica gel that prevents dew accumulation on the inner sides of the domes, which can cool down considerably on clear windless nights. It covers a window from 4.5 mm to 42 mm (Figure 2). A CGR 4 is calibrated with a reference CGR 4 pyrgeometer. This reference pyrgeometer is calibrated outdoors regularly at the World Radiation Centre (WRC) at Davos, Switzerland. The maximum uncertainty of the Kipp & Zonen CG 4 is expected to be below 3% for daily totals at the 95% confidence level (http://www.kippzonen.com/?product/ 17152/CGR+4.aspx).

155

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 2. A Brief Summary of the Sites Where Atmospheric Downward Longwave Radiation at Surface (Ld) Has Been Observed Over Landa Site Name

Latitude

Longitude

Alice Springs Barrow Bermuda Billings Bondville Boulder Boulder Cabauw Camborne Carpentras Chesapeake Light Cener Cocos Island De Aar Darwin Concordia Station, Dome C Desert Rock S. Great Plains Florianopolis Fort Peck Fukuoka Goodwin Creek Georg von Neumayer Ilorin Ishigakijima Kwajalein Lauder Lerwick Lindenberg Momote Minamitorishima Nauru Island Ny-Ålesund Palaiseau, SIRTA Observatory Payerne Rock Springs Regina Sapporo Sede Boqer Solar Village South Pole Sioux Falls Syowa Tamanrasset Tateno Toravere Xianghe Cabauw LINDENBERG FALKENBERG LINDENBERG FOREST CAMP ChaoPhrayaRiver Lampang Korean Haenam Haenam Korean Peninsula DK NorthEastThai Nakhonrachasima SiberiaTaiga Larch Forest SiberiaTundra Tiksi Tibet Amdo-Tower Tibet BJ-Tower Tibet D105-AWS Tibet Gaize Tibet MS3478-AWS Tongyu Cropland Tongyu Grassland W-Pacific Ocean Peleliu SGP E2 Hillsboro SGP E4 Plevna SGP E22 Cordell SGP C1 Lamont SGP E2 Larned SGP E3 Le Roy


23.80 71.32 32.27 36.61 40.06 40.13 40.05 51.97 50.22 44.08 36.91 42.81
12.19
30.67
12.43
75.1 36.67 36.61
27.53 48.31 33.58 34.25
70.65 8.53 24.34 8.72
45.05 60.13 52.21
2.06 24.29
0.52 78.93 48.71 46.82 40.72 50.21 43.06 30.91 24.91
89.98 43.73
69.01 22.78 36.05 58.25 39.75 51.97 52.17 52.18 18.4 34.55 37.75 14.47 62.26 71.62 32.24 31.37 33.06 32.3 31.93 44.42 44.42 7.04 38.31 37.95 35.35 36.61 38.20 38.20

133.89
156.61
64.77
97.52
88.37
105.24
105.01 4.93
5.32 5.06
75.71
1.6 96.84 23.99 130.89 123.38
116.02
97.49
48.52
105.10 130.38
80.87
8.25 4.57 124.16 167.73 169.69
1.18 14.12 147.46 153.98 166.92 11.95 2.21 6.94
77.93
104.71 141.33 34.78 46.41
24.80
96.62 39.59 5.51 140.13 26.46 116.96 4.93 14.12 13.95 99.47 126.57 127.15 102.38 129.62 128.75 91.62 91.90 91.94 84.05 91.72 122.87 122.87 134.27
97.30
98.33
98.98
97.49
99.32
95.60

Elevation (m) 547 8 8 317 213 1689 1577 0 88 100 37 471 0 1287 30 3233 1007 318 11 634 3 98 42 350 6 10 350 84 125 6 7 11 156 491 376 578 17 500 650 2800 473 18 1385 25 70 32
0.7 73 49.1 241 13.7 330 311 220 38 4695 4509 5038 4416 4619.5 184 184 2 450 513 465 324 632 338

156

Period

Instrument

PI

1995–2008 1993–2010 1992–2010 1993–2009 1995–2009 1995–2009 1992–2010 2005–2011 2001–2007 1996–2011 2000–2011 2009–2011 2004–2004 2000–2004 2002–2007 2006–2010 1998–2009 1994–2009 1994–1999 1995–2009 2010–2011 1995–2009 1992–2011 1992–1998 2010–2011 1992–2010 1999–2007 2001–2007 1994–2006 1996–2009 2010–2011 1998–2007 1992–2011 2003–2007 1992–2009 1998–2009 1995–2007 2010–2011 2003–2011 1998–2002 1993–2010 2003–2009 1994–2010 2000–2011 1996–2011 2003–2011 2005–2008 2002–2009 2002–2008 2003–2008 2002–2004 2002–2003 2002–2003 2002–2004 2002–2004 2002–2004 2002–2004 2002–2004 2002–2004 2002–2004 2002–2004 2002–2004 2002–2004 2002–2003 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009

Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Kipp & Zonen CG4 Eppley, PIR Eppley, PIR Eppley, PIR Kipp & Zonen CG4 Eppley, PIR Eppley, PIR Eppley, PIR Kipp & Zonen CG4 Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Kipp & Zonen CG4 Eppley, PIR Eppley, PIR

Bruce Forgan Ellsworth Dutton Ellsworth Dutton Charles Long John A. Augustine John A. Augustine Ellsworth Dutton Wouter Knap Patrick Fishwick Jean-Philippe Morel Fred M. Denn Xabier Olano Bruce Forgan Danie Esterhuyse Charles Long Vito Vitale John A. Augustine Charles Long Sergio Colle John A. Augustine Hiroshi Tatsumi John A. Augustine Gert König-Langlo

Kipp & Zonen CG4 Eppley, PIR Kipp & Zonen, CH1 Eppley, PIR Eppley, PIR Eppley, PIR Kipp & Zonen CG4 Eppley, PIR Eppley, PIR Kipp & Zonen CG4 Eppley, PIR Eppley, PIR Eppley, PIR Kipp & Zonen CG4 Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Kipp & Zonen CG4 Eppley, PIR Kipp & Zonen CG4 Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Eppley, PIR EKO EKO MS-802F Eppley, PIR Eppley, PIR Eppley, PIR EKO MS-202 Eppley, PIR Kipp & Zonen CG4 Kipp & Zonen CG4 EKO MS-402 Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR

Hiroshi Tatsumi Ellsworth Dutton Bruce Forgan Patrick Fishwick Klaus Behrens Charles Long Hiroshi Tatsumi Charles Long Marion Maturilli Martial Haeffelin Laurent Vuilleumier John A. Augustine David Halliwell Hiroshi Tatsumi Vera Lyubansky Naif Al-Abbadi Ellsworth Dutton John A. Augustine Koji Kawashima Mohamed Mimouni Nozomu Ohkawara Ain Kallis Xiangao Xia Hans-Joerg Isemer Hans-Joerg Isemer Hans-Joerg Isemer Masatoshi Aoki Joon Kim Joon Kim Masatoshi Aoki Hironori Yabuki Tetsuo Ohata Yaoming Ma Yaoming Ma Yaoming Ma Shigenori Haginoya Yaoming Ma Wenjie Dong Wenjie Dong Ryuichi Shirooka Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 2. (Continued)

Site Name

Latitude

Longitude

Elevation (m)

Period

Instrument

PI

SGP E5 Halstead SGP E6 Towanda SGP E7 Elk Falls SGP E8 Coldwater SGP E9 Ashton SGP E10 Tyro SGP E11 Byron SGP E12 Pawhuska SGP E13 Lamont SGP E15 Ringwood SGP E16 Vici SGP E18 Morris SGP E19 El Reno SGP E20 Meeker SGP E21 Okmulgee SGP E24 Cyril SGP E27 Earlsboro Manaus Km34 Pantanal Pantanal Old Aspen Old Black Spruc Old Jack Pine Italy Forni NSA Barrow NSA Atqasuk TWP Manus TWP Nauru TWP Darwin ARM SGP Main Woodward Switch grass Audubon Research Ranch Black Hills Bondville Bondville Companion Site Brookings Canaan Valley Chestnut Ridge Cottonwood Duke Forest Hardwoods Duke Forest Loblolly Pine Duke Forest Open Field Flagstaff Managed Forest Flagstaff Unmanaged Forest Flagstaff Wildfire Fort Peck Freeman Ranch Mesquite Juniper GLEES Goodwin Creek Howland Forest East Tower Harvest Site Howland Forest Main Howland Forest WestTower Ivotuk Kansas Field Station Kendall Grassland Lucky Hills Shrubland Marys River Fir Site Mead Irrigated Mead Irrigated Rotation Mead Rainfed Metolius Intermediate Pine Missouri Ozark Morgan Monroe State Forest Niwot Ridge North Carolina Clearcut North Carolina Loblolly Pine Santa Rita Creosote Santa Rita Mesquite Savanna Sevilleta Desert Grassland Sevilleta Desert Shrubland Silas Little Experimental Forest Sioux Falls Portable

38.11 37.84 37.38 37.33 37.13 37.07 36.88 36.84 36.61 36.43 36.06 35.69 35.55 35.56 35.62 34.88 35.27
2.61
19.56 53.63 53.99 53.916 46.40 71.32 70.47
2.06
0.52
12.43 36.61 36.43 31.59 44.16 40.01 40.01 44.35 39.06 35.93 43.95 35.97 35.98 35.97 35.14 35.09 35.46 48.31 29.95 41.36 34.25 45.21


97.51
97.02
96.18
99.31
97.27
95.79
98.29
96.43
97.49
98.28
99.13
95.86
98.02
96.99
96.07
98.21
96.74
60.21
57.01
106.20
105.12
104.69 10.59
156.61
157.41 147.43 166.92 130.90
97.49
99.42
110.51
103.65
88.29
88.29
96.84
79.42
84.33
101.85
79.10
79.09
79.09
111.73
111.76
111.78
105.10
98.00
106.24
89.77
68.73

440 409 283 664 386 248 360 331 318 418 602 217 421 309 240 409 300 130 160 600 628 579 2669 8 20 4 7.1 29.9 314 611 1469 1718 219 219 510 994 286 744 168 163 168 2160 2180 2270 634 271.9 3190 87 61

2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2003–2009 2003–2005 2002–2003 2002–2004 2002–2004 2002–2004 2005–2009 2002–2009 2002–2009 2002–2009 2002–2009 2002–2009 2009–2010 2009–2010 2002–2009 2004–2010 2004–2008 2004–2008 2004–2009 2005–2010 2006–2009 2004–2008 2004–2008 2004–2008 2000–2008 2008–2008 2005–2010 2005–2010 2005–2010 2005–2010 2002–2006 2007–2009 2007–2009

Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR

Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Jin Huang Antonio Ocimar Manzi Antonio Ocimar Manzi Bob Crawford Bob Crawford Bob Crawford Guglielmina Adele Diolaiuti Michael Ritsche Michael Ritsche Michael Ritsche Michael Ritsche Michael Ritsche Dave Billesbach Dave Billesbach Tilden Meyers Tilden Meyers Tilden Meyers Steven Hollinger Tilden Meyers Tilden Meyers Tilden Meyers Tilden Meyers Gabriel Katul Gabriel Katul Gabriel Katul Tom Kolb Tom Kolb Tom Kolb Tilden Meyers Marcy Litvak William Massman Tilden Meyers David Hollinger

45.20 45.21 68.49 39.06 31.74 31.74 44.65 41.17 41.16 41.18 44.45 38.74 39.32 40.03 35.81 35.80 31.91 31.82 34.36 34.33 39.91 43.24


68.74
68.75
155.75
95.19
109.94
110.05
123.55
96.48
96.47
96.44
121.56
92.20
86.41
105.55
76.71
76.67
110.84
110.87
106.70
106.74
74.60
96.90

60 91 568 333 1531 1370 263 361 362 363 1253 219.4 275 3050 5 12 991 1116 1622 1593 30 386

2007–2009 2003–2006 2009–2010 1999–2009 2004–2009 2007–2009 2005–2010 2005–2009 2005–2010 1999–2010 2001–2010 2001–2010 2001–2010 2007–2009 2008–2009 2004–2010 2007–2008 2007–2008 2007–2009 2005–2011 2011–2011 2003–2010

157

Kipp & Zonen CM11 Kipp & Zonen CM11 Kipp & Zonen CM11 Kipp & Zonen CNR-1 Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Eppley, PIR Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 lite Kipp & Zonen CNR-1 lite Kipp & Zonen CNR-1 lite Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Eppley, PIR Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1

Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 lite

David Hollinger David Hollinger Walter Oechel Nathaniel Brunsell Russell Scott Russell Scott Beverly Law Shashi Verma Shashi Verma Shashi Verma Beverly Law Lianhong Gu Danilo Dragoni Russ Monson Asko Noormets Asko Noormets Shirley A. Papuga Russell Scott Marcy Litvak Marcy Litvak Kenneth Clark Tilden Meyers

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 2. (Continued)

Site Name Tablelands Juniper Savanna UMBS UMBS Disturbance Vaira Ranch Valles Caldera Ponderosa Pine Walker Branch Willow Creek Wind River Crane Site Bukit Soeharto Chi-Lan Mountain Research Site Fujiyoshida forest meteorology research site Kherlenbayan Ulaan Laoshan Moshiri Birch Forest Site Mae Klong Moshiri Mixed Forest Site Mase paddy flux site Palangkaraya drained forest Pasoh Forest Reserve Qinghai Flux Research Site Sakaerat Southern Khentei Taiga Seto Mixed Forest Site Takayama evergreen coniferous forest site Takayama deciduous broadleaf forest site Tomakomai Flux Research Site Siberia Yakutsk Larch Forest Site Yakutsk Pine Forest Site

Latitude

Longitude

Elevation (m)

Period

Instrument

PI

34.43 45.56 45.56 38.41 35.86 35.96 45.91 45.82
0.86 24.58 35.45


105.86
84.71
84.70
120.95
106.60
84.29
90.08
121.95 117.05 121.40 138.76

1926 234 239 129 2542 343 515 371 20 1600 1030

2007–2008 2001–2007 1998–2006 2009–2010 2008–2008 2004–2010 1998–2009 1996–2008 2001–2002 2007–2009 2000–2000

Kipp & Zonen CNR-1

Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Eppley, PIR

Marcy Litvak Gil Bohrer Gil Bohrer Dennis Baldocchi Marcy Litvak Tilden Meyers Ankur Desai Ken Bible Minoru Gamo Yue-Joe HSIA Yoshikazu Ohtani

47.21 45.28 44.38 14.58 44.32 36.05
2.35 2.97 37.60 14.29 48.25 35.25 36.14

108.74 137.42 142.32 98.84 142.26 140.03 114.04 102.30 101.33 101.92 108.65 137.07 137.37

1235 340 585 231 340 13 30 100 3250 543 1630 205 800

2003–2009 2002–2002 2003–2005 2003–2004 2003–2005 2001–2001 2002–2005 2003–2009 2002–2004 2001–2003 2003–2006 2002–2006 2007–2007

Kipp & Zonen CNR-1 EKO MR40 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Eppley, PIR Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 Kipp & Zonen CNR-1 CG3 (Kipp & Zonen)

Jun Asanuma Susumu Yamamoto Takeshi Ohta Minoru Gamo Takeshi Ohta Akira Miyata Takashi Hirano Yoshiko Kosugi Yanhong Tang Minoru Gamo Jun Asanuma Ayumi Kotani Ichiro TAMAGAWA

36.15

137.42

1420

1999–2007

EKO MR21

Hiroaki Kondo

42.74 62.26 62.24

141.52 129.24 129.65

140 220 220

2001–2003 2003–2004 2004–2004

EKO MR40 EKO MS-201F EKO MS-201F

Yasumi Fujinuma Takeshi Ohta Takeshi Ohta

Kipp & Zonen CNR-1

Information on pyrgeometers and principle investigators of the sites are also provided to acknowledge those providing data for this study. The first 47 sites are the BSRN sites, followed by 51 CEOP sites (beginning with site named Cabauw), 51 AmeriFlux sites (beginning with site named ARM SGP Main), and 20 AsiaFlux sites (beginning with site named Bukit Soeharto).

a

[35] The instruments at BSRN are well calibrated and maintained [Ohmura et al., 1998], and only 6.5% of the longwave radiation measurements have missing data [Roesch et al., 2011]. The impact of missing data on Ld is small as Ld has a small diurnal cycle. The largest error of monthly Ld caused by missing data averagesis is 2 W m
2 and is much less than measurement error (section 4). Thus, the BSRN Ld observations have sufficient accuracy and completeness for reliable estimates of their monthly mean values [Roesch et al., 2011]. 3.1.2. CEOP Data [36] The Coordinated Energy and water cycle Observations Project (CEOP) aims to produce consistent research quality data sets with error descriptions of the Earth’s energy budget and water cycle and their variability and trends on interannual to decadal time scales. For this study, we used Ld measurements at 51 CEOP sites from 2002 to 2009 (Table 2 and Figure 4); about half of the CEOP sites are located on the Great Southern Plains area of the U.S. Atmospheric Radiation Measurement (ARM) project. Information about these instruments can also be found in Table 2 (see also sections 3.1.1 and 3.1.3 for the information about the pyrgeometers used by CEOP network). 3.1.3. AmeriFlux and AsiaFlux Data [37] AmeriFlux and AsiaFlux are regional networks of the global network FLUXNET [Baldocchi et al., 2001]. The FLUXNET provides continuous observations of ecosystem

level exchanges of CO2, water, energy and momentum spanning diurnal, synoptic, seasonal, and interannual time scales. The AmeriFlux network was established in 1996 TABLE 3. Data Availability of Atmospheric Downward Longwave Radiation at Surface (Ld) From Ground-Based Observation, Satellite Retrieval, and Reanalysis and Their Websites for Data Downloading Data Type Ground-based observation

Network

http://www.bsrn.awi.de/ http://www.eol.ucar.edu/projects/ ceop/dm/ AmeriFlux http://public.ornl.gov/ameriflux/ AsiaFlux http://www.asiaflux.net/ http://www.pmel.noaa.gov/ TAO, PIRATA, RAMA tao/disdel/disdel.html Reanalysis data ERA-Interim http://www.ecmwf.int/research/era/ do/get/era-interim ERA-40 http://www.ecmwf.int/research/era/ do/get/era-40 CSFR http://rda.ucar.edu/pub/cfsr.html MERRA http://disc.sci.gsfc.nasa.gov/daac-bin/ DataHoldings.pl NCEP http://www.esrl.noaa.gov/psd/data/ reanalysis/reanalysis.shtml JRA-25 http://ds.data.jma.go.jp/gmd/jra/ download/download-e.html Satellite data CERES SYN http://eosweb.larc.nasa.gov/project/ ceres/level3_syn-avg-zavg_table.html GEWEX SRB http://eosweb.larc.nasa.gov/project/srb/ table_srb.html

158

BSRN CEOP

Website Link

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION

(a)

80

Number of Sites

Number of Sites

80 60 40 20 0

1995

2000

2005

60 40 20 0

2010

(b)

2000

1995

Accumulated Number of sites

50

Number of Sites

(c) 40 30 20 10 0

5

10

2005

2010

Data End Year

Data Start Year

15

20

Data Duration (Year)

(d) 150

100

50

0 20

15

10

5

Data Duration (Year)

Figure 5. Histograms of observational data available for evaluation of reanalysis and satellite atmospheric downward longwave radiation at the surface (Ld): (a) data start year, (b) data end year, (c) data duration (unit: year). (d) The accumulated number of sites (number of sites where data duration is larger than a certain year) as a function of data duration. Although some data tracked back to the early 1990s, most data started after 2001 so the data duration of most sites is more than 5 years. Among the 193 sites, 33 have Ld observations of more than 10 years and about 120 sites have Ld observations of no less than 5 years. and has generally covered the climatic and vegetation characteristics of the coterminous United States [Yang et al., 2008]. We selected Ld collected at 51 AmeriFlux sites and 20 AsiaFlux sites (Table 2 and Figure 4). [38] To reduce cost, AmeriFlux and AsiaFlux have used Kipp & Zonen net radiometers (CNR1 or CNR1-lite) to measure net radiation and its four components, including Ld. The CNR1 consists of a pair of pyrgeometers that face upward and a complementary pair that face downward (http://www.campbellsci.com/cnr1). The spectral response range of its pyrgeometers (CNR1) is from 5.0 mm to 50.0 mm. The spectral response range of its CNR1-lite pyrgeometers is 4.5 mm to 42 mm. The impact of a different spectral response range is expected to be much less than the measurement uncertainty. Uncertainty in the daily total of CNR1 (or CNR1-lite) is ~10% at 95% confidence level (or two standard deviations), which is higher than those of Eppley PIR and Kipp & Zonen CG 4 used in the BSRN. 3.1.4. Tropical Ocean Buoy Data [39] Most of the above-mentioned observations of Ld are available over land. We also utilized Ld observations made by buoy networks over tropical oceans. These data were collected by three networks: the TAO/Triangle TransOcean Buoy Networks (TAO/TRITON) buoy sites in the Pacific Ocean [Cronin et al., 2006; McPhaden et al., 1998], the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) in the Atlantic Ocean [Bourlès et al., 2008; Servain et al., 1998], and the Research Moored Array for African-Asian-Australian Monsoon Analysis and

Prediction (RAMA) in the Indian Ocean [McPhaden et al., 2009a; Ueki et al., 2010]. The Tropical Atmosphere Ocean (TAO) Project Office of NOAA Pacific Marine Environmental Laboratory (PMEL) has quality controlled these data and released them to the public [Bernard et al., 2008; Carpenter et al., 2011]. [40] A modified Eppley PIR has been used to measure Ld at these tropical ocean buoy sites [McPhaden et al., 2009b] that was modified to adapt to the corrosive and destructive conditions of ocean environment as well as to improve its calibration (Eppley PIR-TAO) [Payne and Anderson, 1999]. In particular, the range of these Eppley PIR-TAO is much smaller, i.e., 200 W m
2, which improves the sensitivity of pyrgeometer [McPhaden et al., 2009b]. The accuracy of Ld is expected to be 1% (3–4W m
2). However, the direct solar beam has not been shaded (see photos of buoys in McPhaden et al. [2009a] and Bourlès et al. [2008]), which can result in an overestimation of monthly Ld of ~5 W m
2 [Meloni et al., 2012; Pérez and Alados-Arboledas, 1999; Udo, 2000]. [41] Data of Ld are available over tropical oceans since 2002 (Figure 4). The data collection rate for the buoy measurements is approximately 80%, depending on the extent of fishing vandalism [Bourlès et al., 2008]. Realtime Ld estimates were averaged into daily and monthly averages and made publicly available through the TAO project office (Table 3). This study uses these monthly Ld. 3.2. Reanalysis Data [42] This study uses Ld from six reanalysis products, i.e., three first generation reanalyses—JRA-25 [Onogi et al.,

159

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 4. Details of the Reanalysis and Satellite Monthly Atmospheric Downward Longwave Radiation at the Surface (Ld) Products Used in This Studya Product

Data Period

Horizontal Resolution

Reference

JRA-25 ERA-40 NCEP ERA-Interim CSFR MERRA GEWEX SRB

1979–2009 1957–2002 1948–2010 1979–2011 1979–2010 1979–2011 1984–2007

T106 T159 T62 T255 T382 0.5 1  1

Onogi et al. [2007] Uppala et al. [2005] Kalnay et al. [1996] Dee et al. [2011] Saha et al. [2010] Rienecker et al. [2011] Gupta et al. [2010]

a

The data ending date depends on our processing date.

2007], the NCEP/NCAR Reanalysis [Kalnay et al., 1996], and ERA-40 [Uppala et al., 2005]—and three second generation reanalyses—NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) [Rienecker et al., 2011], NOAA’s Climate Forecast System Reanalysis (CFSR) [Saha et al., 2010], and the ERAInterim [Dee et al., 2011]. Detailed information about these reanalysis products can be found in Table 4 and in the references cited above. [43] A brief summary of these reanalyses can be also found in Trenberth et al. [2011]. The NCEP reanalysis did not use satellite atmospheric sounding data for air temperature and humidity and so had no information on water vapor over the oceans [Trenberth et al., 2011]. Therefore, its atmospheric water vapor is largely model generated value that is severely deficient in its spatial and temporal variability [Trenberth et al., 2005]. The ERA-40 and JRA-25 reanalyses addressed some of the shortcomings of that of NCEP, but many of the problems tied to observing system changes and model deficiencies remained. For example, spurious variability of the precipitable water in NCEP and ERA-40 over the oceans has been identified [Smith et al., 2001; Sterl, 2004; Sturaro, 2003; Trenberth et al., 2005]. The ERA-Interim reanalysis incorporates many important model improvements not in the ERA-40 system such as resolution and physics changes, the use of four-dimensional variational data assimilation, and various other changes in the analysis methodology [Trenberth et al., 2011]. CSFR has three main differences from the previous NCEP version: (1) much higher horizontal and vertical resolutions with sigma-pressure hybrid levels occur, (2) the guess forecast is generated from a coupled atmosphere-ocean-sea iceland system, and (3) radiance measurements from the historical satellites are assimilated [Trenberth et al., 2011]. [44] The MERRA has a special focus on the hydrological cycle and is intended to make advances in the land surface process and water cycle through its reanalysis [Rienecker et al., 2011]. The data assimilation system of MERRA is the Goddard Earth Observing System Model (GCOS), version 5. Temperature and humidity profiles from its earlier version have been used previously as input data to estimate Ld with satellite observations, including that of the Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) products (section 3.3).

3.3. Satellite Data [45] This study uses the latest versions of two satellite Ld products: the CERES Synoptic Radiative Fluxes and Clouds (SYN) product [Kato et al., 2011], version 3A, and the WCRP/GEWEX Surface Radiation Budget (SRB) product [Gupta et al., 2010], version 3.1. Both products use similar algorithms to estimate Ld [Gupta et al., 2010; Stackhouse et al., 2004]. These algorithms may overestimate Ld over arid regions [Kratz et al., 2010]. Both products also have similar temperature and humidity profile inputs: GEOS-4 (March 2000 to October 2007) or GEOS-5 (November 2007 to present) for CERES SYN [Kato et al., 2012b; Kato et al., 2011; Stephens et al., 2012] and the GEOS-4 [Bloom et al., 2005] for GEWEX SRB. The use of GEOS-5 versus GEOS-4 temperature and humidity yields a global mean difference in Ld of 1.2 W m
2 [Kato et al., 2011]. [46] The GEWEX SRB cloud fields are from the International Satellite Cloud Climatology Project (ISCCP) DX data product (nominal resolution of 30 km at nadir) [Rossow and Schiffer, 1999]. ISCCP cloud products combine the visible and infrared images from an international network of weather satellites, including polar orbiter satellites and geostationary satellites [Rossow and Schiffer, 1991; Rossow and Garder, 1993; Zygmuntowska et al., 2012]. The cloud pixels were separated into categories of high, middle, and low; and cloud fractions and optical depths were determined assuming random overlap of clouds [Rossow and Zhang, 2010]. [47] CERES SYN uses cloud properties from the Cloudsat radar (CPR) and CALIPSO Lidar profile data as well as Terra and Aqua MODIS cloud observations using the CERES cloud algorithm [Minnis et al., 2008]. These cloud products from NASA’s polar orbiting satellite cluster provide better cloud characteristics for optically thin clouds and cloud base height [Kato et al., 2011]. However, the polar orbiters lack a good capability for detecting the diurnal variation of clouds [Salby, 1989; Salby and Callaghan, 1997], and therefore, three-hourly geostationary satellitederived cloud properties are used to improve this aspect [Minnis et al., 2008]. The CERES instrument provides radiometric measurements at the top of atmosphere from three broadband channels: a shortwave channel (0.3–5 mm), a total channel (0.3–200 mm), and an infrared window channel (8–12 mm) [Wielicki et al., 1996]. These observations provide accurate constraints for calculated radiation fluxes for the top-of-atmosphere radiation of CERES SYN. We used the constrained Ld products as described by Kato et al. [2012b]. However, as Ld is largely unconnected to outgoing longwave radiation at the top of atmosphere, the impacts of the constraints by CERES observations are relatively small [Kato et al., 2012b]. [48] GEWEX SRB monthly Ld products are averaged from the original three-hourly data. In the latest version of CERES SYN (edition 3A), meteorological and cloud fields are interpolated into hourly data and hourly Ld are calculated, from which daily and monthly Ld are calculated and released. This better description of diurnal variation reduces monthly Ld by ~3 W m
2 compared to that averaged

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WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 5. A Summary of the Comparison of the Ld Observations at the BSRN and AmeriFlux Collocated Sites, Including Correlation Coefficients (R) and Standard Deviation (Std.)a Site Information Comparison Sites Goodwin Creek Fort Peck Boundville Boulder a

A: BSRN B: AmeriFlux A: BSRN B: AmeriFlux A: BSRN B: AmeriFlux C: AmeriFlux com A: BSRN 1 B: BSRN 2

Latitude (deg)

Longitude (deg)

Elevation (m)

Averaged Ld

34.25 34.25 48.31 48.31 40.05 40.01 40.01 40.125 40.048


89.87
89.87
105.10
105.10
88.37
88.29
88.29
105.237
105.007

98 87 634 634 213 219 219 1689 1577

352.46 340.03 280.06 266.78 325.44 336.84 337.76 287.51 290.65

R Between Monthly Lds.

Std. Between Monthly Lds

R Between ANNUAL Lds

1.00 (A, B)

3.61 (A, B)

0.99 (A, B)

7.50 (A, B)

0.43 (A, B)

0.88 (A, B) 0.88 (A, C) 1.00 (B, C) 0.99 (A, B)

24.75 (A, B) 24.65 (A, C) 1.27 (B, C) 5.86 (A, B)

0.62 (A, B) 0.65 (A, B)

In the last three columns, A, B, and C represent the sites, and (A, B) means that the parameters are calculated for the sites A and B.

from the three-hourly estimates of Ld (i.e., edition 2B as those of Kato et al. [2011]; personal communication with Seiji Kato, 2012). 4.

UNCERTAINTY OF Ld OBSERVATIONS

[49] AmeriFlux has set up several measurement sites collocated with those of the SURFRAD (Surface Radiation) network sites [Augustine et al., 2000; Augustine et al., 2005], the latter of which are a regional network of the BSRN. These instruments of AmeriFlux and SURFRAD are deployed at nearly the same locations (within 100 m of each other at the Fort Peck and Goodwin Creek sites and within 6 km at the Bondville site; see Table 2) [Wang et al., 2012]. AmeriFlux has several companion measurement projects (see section 4.1 for details), and each project has several measurement sites. These collocated measurements are similar to intercomparison experiments but provide long-term continuous measurements of Ld, which allows us to evaluate uncertainties of current Ld observations made at time scales from monthly to yearly. 4.1. Uncertainty of Ld Observations at Monthly Time Scales [50] Ld observations at half-hour or hourly time scales were downloaded from the websites of BSRN, CEOP, AmeriFlux, and AsiaFlux. We averaged them into daily values when more than 80% of the data were available during the day. These daily values were averaged into monthly values when daily Ld were available for no less than 24 days during the month (average scheme selected by the United States Climate Reference Networks (USCRN)). To calculate annual variability of Ld, we first calculated its monthly anomalies by removing its seasonal cycle and then averaged these monthly anomalies to obtain an annual anomaly. This annual anomaly is only regarded as reliable if monthly anomalies of Ld were available for no less than 10 months during the year. This section compares monthly Ld, while section 4.2 compares annual variability. [51] The BSRN and AmeriFlux sites at the Goodwin Creek and Fort Peck are collocated (see Table 5 for detailed information about these sites). The pyrgeometers (Eppley PIR for the BSRN site and Kipp & Zonen CNR-1 for the

AmeriFlux site) have been deployed within 100 m of each other at the Goodwin Creek and Fort Peck sites [Wang et al., 2012]. The comparison of monthly Ld at these sites is shown in Figures 6 and 7 and also summarized in Table 5. [52] At the Goodwin Creek site, monthly Ld measured by these two networks agree very well after bias removal, with a correlation coefficient of 1.00 (i.e., larger than 0.99) and a standard deviation error of 3.6 W m
2 (1% in relative value). However, the Ld measured by the AmeriFlux net radiometer (CNR-1, AmeriFlux site) is ~12.5 W m
2 less than that measured by the Eppley PIR pyrgeometer at the BSRN site. Figure 7 shows that this underestimation is systematic and does not change with the values of monthly Ld. It is attributed to their different calibration methods. [53] Similarly, at the Fort Peck site, monthly Ld measured by the net radiometer (Kipp & Zonen CNR-1) at the AmeriFlux site is ~13.3 W m
2 less than that measured by the Eppley PIR pyrgeometer at the BSRN site. However, its standard deviation error of 7.5 W m
2 (2.7% in relative value) is almost twice that at the Goodwin Creek site (Table 5). Figure 7 shows that the differences between instruments become larger when monthly Ld is larger. High monthly Ld generally corresponds to high surface incident solar radiation. While the Eppley PIR pyrgeometer at the BSRN site is shaded and ventilated, the net radiometer CNR-1 is neither shaded nor ventilated (http://www. campbellsci.com/cnr1). Thus, at the Fort Peck sites, error of the net radiometer CNR-1 from solar heating may explain the dependence of the difference of Ld on their monthly averages (Figure 7). [54] The Bondville (Table 5), BSRN, and AmeriFlux sites are separated by ~6 km, a distance that did not introduce much difference in monthly Ld before 2005 (Figure 6). However, after 2005, Ld measured at the two AmeriFlux sites are substantially higher than that measured at the BSRN sites. This difference could be due to calibration error of the net radiometer CNR-1 at the two AmeriFlux sites because of the following: (1) Ld measured at the BSRN sites is consistent before and after 2005, and Ld measured at the BSRN and AmeriFlux sites agree well before 2005. However, the measured Ld at the two AmeriFlux sites abruptly changed in 2005 and 2007 (Figure 6). (2) Ld measured at the two AmeriFlux sites agree very well after 2005, with a correlation

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Figure 6. Monthly atmospheric downward longwave radiation at the surface (Ld) collected by BSRN and AmeriFlux sites: (1) Goodwin Creek (a) BSRN site (red) and (b) AmeriFlux site (green); (2) Fort Peck (a) BSRN site (red) and (b) AmeriFlux site (green); (3) Bondville (a) BSRN site (red), (b) AmeriFlux site, (green) and (c) AmeriFlux comparison site (blue); and (4) Boulder (a) BSRN site 1 (red) and (b) BSRN site 2 (green). See Table 5 for detailed information about the sites and statistical results of the comparisons.

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Figure 7. Scatterplots of the monthly difference of atmospheric downward longwave radiation at the surface (Ld) collected by BSRN and AmeriFlux sites as a function of monthly averaged Ld : (1) Goodwin Creek AmeriFlux site minus BSRN site (red); (2) Fort Peck AmeriFlux site minus BSRN site (red); (3) Bondville (a) AmeriFlux site minus BSRN site (red), and (b) AmeriFlux comparison site minus BSRN site (green); and (4) Boulder BSRN site 2 minus BSRN site 1 (red). See Table 5 for detailed information about the sites and statistical results of the comparisons. 162

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 6. A Summary of the Comparison of Ld Observations at the AmeriFlux Companion Sites, Including Correlation Coefficients (R) and Standard Deviation (Std.)a Site Information Comparison Sites Duke Forest FlagStaff Mead North Carolina (NC) a

Latitude (deg)

Longitude (deg)

Elevation (m)

Averaged Ld

35.97 35.98 35.97 35.14 35.09 35.45 41.16 41.17 41.18 35.81 35.80


79.10
79.09
79.09
111.73
111.76
111.77
96.48
96.47
96.44
76.71
76.67

168 163 168 2160 2180 2270 361 362 363 5 12

321.04 332.22 319.67 282.70 271.99 256.33 313.32 310.89 307.71 343.41 347.43

A: Hardwoods B: Loblolly Pine C: Open Field A: Managed Forest B: Unmanaged Forest C: Wildfire A: Irrigated B: Irrigated Rotation C: Rainfed A: Clear cut B: Loblolly Pine

R Between Monthly Lds

Std. Between Monthly Lds

0.99 (A, B) 0.99 (A, C) 1.00 (B, C) 0.99 (A, B) 0.99 (A, C) 1.00 (B, C) 1.00 (A, B) 1.00 (A, C) 1.00 (B, C) 1.00 (A, B)

7.59 (A, B) 7.10 (A, C) 2.49 (B, C) 5.26 (A, B) 5.76 (A, C) 2.85 (B, C) 2.91 (A, B) 2.77 (A, C) 2.27 (B, C) 3.46 (A, B)

R Between ANNUAL Lds 0.83 (A, C)

0.97 (A, B) 0.96 (A, C) 0.98(B, C)

In the last three columns, A, B, and C represent the sites, and (A, B) means that the parameters are calculated for the sites A and B.

coefficient of 1.00 and a standard deviation error of 1.3 W m
2. This apparent calibration error of the CNR-1 introduced a standard deviation error of ~25 W m
2 compared with that measured by the Eppley PIR pyrgeometer at the BSRN sites. [55] The two BSRN sites at Boulder have a distance of about 20 km, and the first one is about 100 m higher (Table 5). This difference in elevation partly explains why Ld measured at the first BSRN sites is ~3 W m
2 lower. These two BSRN sites have a standard deviation error of 5.9 W m
2 in comparing their monthly Ld.

[56] The AmeriFlux network has a number of companion measurement projects, each with several companion sites (Table 6). These companion sites were selected to study the impact of different land cover types on land-atmosphere energy, water, and carbon exchange. This difference should affect the near surface air temperature and humidity and so change Ld. Therefore, the differences between Ld measured at the companion sites cannot be fully attributed to measurement error. The results of the comparison of monthly Ld at these sites are shown in Figures 8 and 9 and are summarized

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Figure 8. Monthly atmospheric downward longwave radiation at the surface (Ld) collected by AmeriFlux companion projects: (1) Duke Forest (a) Hardwood site (red), (b) Loblolly Pine site (green), and (c) open field site (blue); (2) Flagstaff (a) managed forest site (red), (b) unmanaged forest site (green), and (c) wildfire site (blue); (3) Mead (a) irrigated site (red), (b) irrigated rotation site (green), and (c) Rainfed site (blue); and (4) North Carolina (NC) (a) Clearcut site (red) and (b) Loblolly Pine site (green). See Table 6 for detailed information about the sites and statistical results of the comparisons. The agreements of monthly Ld in this figure are much better than those Figure 6. The pyrgeometers used in AmeriFlux sites have been calibrated with same method (this figure), while pyrgeometers used in BSRN and AmeriFlux sites have been calibrated differently (Figure 6). 163

Differences of Monthly Ld (W m−2) Differences of Monthly Ld (W m−2)

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Figure 9. Scatterplots of the monthly difference of atmospheric downward longwave radiation at the surface (Ld) between sites as a function of monthly Ld collected by AmeriFlux companion projects: (1) Duke Forest: Loblolly Pine site minus Hardwood site (red), (b) open field site minus Hardwood site (green), and (c) open field site minus Loblolly Pine site (blue); (2) Flagstaff (a) unmanaged forest site minus managed forest site (red), (b) wildfire site minus managed forest site (green), and (c) wildfire site minus unmanaged forest site (blue); (3) Mead (a) irrigated rotation site minus irrigated site (red), (b) Rainfed site minus irrigated site (green), and (c) Rainfed site minus irrigated rotation site (blue); and (4) North Carolina (NC) (a) Loblolly Pine site minus Clearcut site (red). See Table 6 for detailed information about the sites and statistical results of the comparisons.

in Table 6. In spite of this impact of different land cover types, the difference of site-averaged Ld at the Mead and NC companion sites is less than 5 W
2 (Table 6), i.e., less than those shown in Table 5. [57] The overall agreement shown in Figures 8 and 9 is much better than those in Figures 6 and 7, in part because the Ld were all measured by Kipp & Zonen CNR-1 at the AmeriFlux companion sites and their sensors have been calibrated using similar methods [Schmidt et al., 2012]. The correlation coefficients of the comparisons of monthly Ld are better than 0.99. Their standard deviation errors vary from 2.3 W m
2 to 7.6 W m
2, with a median (mean) of 3.5 W m
2 (4.2 W m
2). [58] In summary, comparisons between BSRN and AmeriFlux, or between AmeriFlux companion sites, show a good agreement between their monthly Ld observations, with a correlation coefficient higher than 0.99 if the impact of calibration error is removed. The standard deviation error of comparison of monthly Ld varies from 1.3 to 7.6 W m
2, with a median (mean) of 3.5 W m
2 (4.33 W m
2). These errors are lower than those inferred by previous instrument intercomparison studies which focused on shorter time scales, e.g., hourly [Blonquist et al., 2009; Kohsiek et al., 2007; Michel et al., 2008]. A lower error is expected as a result of cancellation of random errors with averaging. However, averages of Ld measured by different types of

pyrgeometers may differ by more than 13 W m
2 in the absence of a unified calibration method for pyrgeometers. The calibration method of the BSRN pyrgeometers is described [Ohmura et al., 1998; Philipona et al., 1998], as also that of the Atmospheric Radiation Measurement (ARM) at the Southern Great Plains sites of the U.S. (part of CEOP) [Reda et al., 2002]. The net radiometers at the AmeriFlux and AsiaFlux sites are calibrated by their respective manufacturers according to their recommended service intervals, typically every 2 years [Schmidt et al., 2012]. 4.2. Uncertainty of Ld Observations at an Annual Scale [59] Figure 10 compares the annual anomalies of Ld measured at the collocated BSRN and AmeriFlux sites. We calculated their correlation coefficients when both annual anomalies were available for no less than 5 years (Table 5). Similarly, Figure 11 and Table 6 compare results at AmeriFlux companion sites. [60] Generally, the agreement between annual anomalies at AmeriFlux companion sites (Figure 11) is better than those at the BSRN sites (Figure 10). The correlation coefficients between annual anomalies of Ld vary from 0.65 to 0.98, with a mean of 0.82 where Ld was measured by the same type of pyrgeometers (Figure 11). However, the correlation coefficients for AmeriFlux and BSRN sites are reduced to 0.43 and 0.62 (Table 5), respectively, when Ld was measured by

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Figure 10. Annual anomaly of atmospheric downward longwave radiation at the surface (Ld) collected by BSRN and AmeriFlux sites: (1) Goodwin Creek (a) BSRN site (red) and (b) AmeriFlux site (green); (2) Fort Peck (a) BSRN site (red) and (b) AmeriFlux site (green); (3) Bondville (a) BSRN site (red), (b) AmeriFlux site, and (c) AmeriFlux comparison site (blue); and (4) Boulder (a) BSRN site 1 (red) and (b) BSRN site 2 (green). See Table 5 for detailed information about the sites and statistical results of the comparisons. 10 5 0 −5

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Figure 11. Annual anomaly of atmospheric downward longwave radiation at the surface (Ld) collected by AmeriFlux companion projects: (1) Duke Forest (a) Hardwood site (red), (b) Loblolly Pine site (green), and (c) open field site (blue); (2) Flagstaff (a) managed forest site (red), (b) unmanaged forest site (green), and (c) wildfire site (blue); (3) Mead (a) irrigated site (red), (b) irrigated rotation site (green), and (c) Rainfed site (blue); and (4) North Carolina (NC) (a) Clearcut site (red) and (b) Loblolly Pine site (green). See Table 6 for detailed information about the sites and statistical results of the comparisons. The agreements of the annual anomalies of Ld in this figure are much better than those in Figure 10. The pyrgeometers used in AmeriFlux sites have been calibrated with same method (this figure), while pyrgeometers used in BSRN and AmeriFlux sites have been calibrated differently (Figure 10), which shows the importance of a unified international reference and calibration method for pyrgeometers. 165

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Figure 12. Maps of the biases of the comparisons of monthly atmospheric downward longwave radiation at the surface (Ld) from reanalyses and satellite retrievals with observations (unit: W m
2): (a) GEWEX-SRB, (b) CSFR, (c) ERA-Interim, and (d) CERES. If there are several observations of Ld in a model grid, they are compared with grid values of reanalyses and satellite retrievals first for different period of the observations, and then the biases are averaged in the grid and shown here. CERES has a near zero over-all bias and the range of the bias for different sites is lowest among the four estimates of Ld. different networks with different types of pyrgeometers that were calibrated with different methods (Figure 10).Trends of observed Ld from 1996 to 2011 at the seven SURFRAD sites (U.S. part of BSRN) are not supported by the observed changes of surface temperature, specific humidity, and cloud fraction [Augustine and Dutton, 2013]. These results show that it is difficult to accurately observe annual or decadal variation of Ld even at BSRN sites and that a unified calibration method is needed for pyrgeometers. 5. UNCERTAINTY OF REANALYSIS AND SATELLITE Ld PRODUCTS [61] This section uses ground-based “point” measurements to evaluate reanalysis and satellite Ld products. The three second generation reanalysis Ld products (ERA-Interim, MERRA, and CSFR) and two satellite Ld products (CERES SYN and GEWEX SRB) are carefully evaluated. These Ld products have a typical grid size of 1  1 (Table 4). We focus on monthly to a decadal time scales, as there are a number of evaluations at shorter time scales for previous versions of the reanalyses and satellite Ld products, i.e., from hourly to daily [Gui et al., 2010; Yang et al., 2006a; Yang et al., 2006b]. [62] Using ground observations of Ld to evaluate reanalysis and satellite grid Ld products is reasonable because (1) the spatial heterogeneity of Ld is much less at larger time scales than it is at short time scales [Wang et al., 2012] and (2) the spatial variability of Ld is rather small [Allan et al., 2004; Settle et al., 2008]. Section 4 has already shown that at the selected sites, the spatially

heterogeneity of Ld is less than the uncertainty of Ld observations at time scales of a month or longer. [63] The reanalysis (or satellite) grid of Ld at a scale of ~1 is directly compared to the nearest ground observations, and the bias, standard deviation, and correlation coefficient are calculated. If several ground observation sites are located in one grid box, the biases are directly averaged, and the best standard deviations and correlation coefficients are selected for the grid. This situation occurs when the CEOP and AmeriFlux sites are used for evaluation. We do not average the observational data first because observations at companion sites generally cover different time periods such that a direct average may cause a discontinuity in the data. [64] We have used the Ld observations at 193 globally distributed sites, including entire BSRN dataset, which is regarded as providing the most accurate observations of Ld (Figure 5). More importantly, 33 sites have more than 10 years of Ld data (Figure 5). This dataset allows us to evaluate the capability of reanalysis and satellite products for depicting the interannual and decadal variability of Ld. 5.1. Uncertainty of Reanalysis and Satellite Monthly Ld [65] The BSRN has used the highest grade pyrgeometers to measure Ld (section 3), and its sites are more evenly distributed (Figure 4). Therefore, we first compare the observations of Ld at the 47 BSRN sites with reanalysis and satellite retrievals of monthly Ld at grid scale when both data are available. Figures 12–14 show maps of the statistical parameters of the comparisons that are summarized in Table 7. To reduce the impact of the extreme values, this study uses median rather than mean values.

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Figure 13. Maps of the standard deviations of the comparisons of monthly atmospheric downward longwave radiation at the surface (Ld) from reanalysis and satellite retrievals with observations (unit: W m
2): (a) GEWEX-SRB, (b) CSFR, (c) ERA-Interim, and (d) CERES. If there are several observations of Ld in a model grid, they are compared with grid values of reanalyses and satellite retrievals first for different period of the observations, and then the standard deviations are averaged in the grid and shown here. CERES has the best standard deviations. [66] Generally speaking, reanalysis and satellite Ld agree very well with ground observations at a monthly scale, with a median correlation coefficient of 0.96–0.98. There are some lower correlation coefficients over tropical oceans (Figure 14), likely because the seasonal cycle of Ld in this region is very small, only ~20 W m
2 (Figure 15). Likewise,

the standard deviations of the comparison of monthly Ld in the tropics are rather small compared with other regions (Figure 13). [67] In general, these estimates of monthly Ld have their strongest biases at tropical and polar regions from the difficulty of accurately estimating clouds. The CERES SYN Ld

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WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 7. A Summary of the Evaluations of Monthly Ld From Reanalysis and Satellite Retrievals Using All the Data From BSRNa Products

Standard deviation

Bias

GEWEX-SRB 2.53 (9.63) CERES SYN 0.14 (9.29) CSFR
0.19 (12.67) ERA-Interim
2.31 (9.31) MERRA
14.51 (9.47)

9.95 7.22 8.38 7.93 8.47

Correlation Global average of coefficient Ld (W m-2) 0.96 (0.72) 0.98 (0.77) 0.97 (0.80) 0.97 (0.80) 0.97 (0.78)

308.20 307.15 305.23 304.23 295.07

a The medians of statistical parameters shown in Figures 12–14 at grid level are shown here. The standard deviations of the bias are shown in the brackets in the second column. The median of correlation coefficients between monthly anomalies of Ld (seasonal cycle removed) are shown in the brackets in the fourth column. For comparison, the last column of the table shows the global averaged Ld over land from the reanalysis and satellite retrieval products from 2001 to 2011 when most observations data are available (Figure 5).

Monthly Ld (W m−2)

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performs best among the five products (Table 7), with the least bias and standard deviation and the highest correlation coefficients. The range of bias of CERES SYN Ld at grid scale is also the least among the five Ld products. Evidently, a

better cloud description helps to improve the accuracy of CERES SYN Ld. CSFR has a similar small overall bias. However, its biases at different sites have a wide range with the largest standard deviation of 12.7 W m
2 (Table 7). The GEWEX SRB has a relatively small positive bias of 2.5 W m
2, and the ERA-Interim has a small negative bias of
2.3 W m
2. [68] The GEWEX SRB has the largest standard deviation error of 10 W m
2. Its air temperature and humidity profile data are from the NASA GEOS-4 model. Since errors of air temperature and humidity can be a large source of uncertainty for calculated Ld [Kato et al., 2012a; Zhang et al., 1995; Zhang et al., 2006], the comparisons here of Ld are consistent with existing studies of the accuracy of air temperature and humidity from reanalyses [Decker et al., 2012; Jakobson et al., 2012; Kishore et al., 2011; Mooney et al., 2011; Wang and Zeng, 2012] that indicate that the accuracy of the MERRA air temperature and humidity data is worse than that from ERA-Interim. [69] MERRA Ld has a large overall negative bias of
14.5W m
2, while GEWEX SRB Ld has a moderately

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Figure 15. Monthly averages of atmospheric downward longwave radiation at the surface (Ld) from ground-based observations (black), GEWEX SRB (Red), CSFR (green), ERA-Interim (blue), and CERES (magenta) at eight BSRN sites: (a) Barrow (71.32 ,
156.61 , Tundra), (b) Bermuda (32.26 ,
64.77 , Ocean), (c) Kwajalein (8.72 , 167.73 ,Ocean), (d) Darwin (
12.43 , 130.89 , Grass), (e) Desert Rock (36.67 ,
116.02 , Desert), (f) Tamanrasset (22.78 , 5.51 , Desert), (g) Georg von Neumayer (
70.65 ,
8.25 , Iceshelf), and (h) South Pole (
89.98 ,
24.80 , Glacier). Tropical oceans have much less seasonal cycle of Ld than those at middle latitudes. 168

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION

Figure 16. Differences of multiyear mean atmospheric downward longwave radiation at the surface (Ld) derived from different products: (a) GEWEX SRB minus CERES, (b) CSFR minus CERES, (c) ERAInterim minus CERES, and (d) MERRA minus CERES (unit: W m
2). positive bias of 2.5 W m
2. The differences between GEOS5 (for MERRA) and GEOS-4 (for GEWEX SRB) temperature and humidity only yield a global mean Ld difference of 1.2 W m
2 [Kato et al., 2011]. MERRA’s negative bias of Ld seen here is consistent with previous studies in polar regions [Cullather and Bosilovich, 2012; Zib et al., 2012]. [70] Figure 16 compares the other products with the CERES Ld, judged to be in best agreement with surface observations. Figure 16d indicates that MERRA underestimates Ld in middle latitudes by 30–40 W m
2. Figure 17 compares the MERRA cloud cover fraction with that of CERES. It is lower by ~30% over middle latitudes and higher by more than 30% over tropical regions, in particular over the Intertropical Convergence Zone (ITCZ). Its negative bias over middle latitudes is mostly for low clouds and appears to explain much of the negative bias of MERRA Ld since low clouds generally have an effect of 70 W m
2 on Ld over middle latitude oceans [Ghate et al., 2009; Kalisch and Macke, 2012], especially the low level stratus clouds over cool ocean surfaces [Harshvardhan, 1990]. Clouds can also increase Ld by ~50 W m
2 in polar regions [Cho et al., 2008; Shupe and Intrieri, 2004] as well as ~ 50 W m
2 over middle latitude land [Dong et al., 2006; Wang et al., 2004]. [71] We evaluated how well Ld from reanalysis and satellite products captures the seasonal cycle of Ld in different climate regions. Figures 15 and 18 show the results at eight BSRN sites, including one site in the Arctic region, one site

in a tropical grassland, two sites in ocean, two sites in deserts, and two sites in Antarctica. [72] Figure 15 and Table 7 show that reanalysis and satellite Ld products depict the seasonal cycle very well for various climate regions. As reported in recent studies, GEWEX SRB somewhat overestimates Ld at most sites, especially at

Figure 17. Difference of total cloud fraction: MERRA modeled cloud minus CERES satellite observations. The multiyear averaged cloud fraction are calculated from 2003 to 2010 when both data are available (unit: %). MERRA has a substantial positve bias in cloud cover fraction over tropical and polar areas, with a comparable negative bias in middle latitude regions.

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WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION

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Figure 18. Monthly anomalies of atmospheric downward longwave radiation at the surface (Ld) from observations (black), GEWEX SRB (red), CSFR (green), ERA-Interim (blue), CERES (magenta) at eight BSRN sites: (a) Barrow (71.32 ,
156.61 , Tundra), (b) Bermuda (32.26 ,
64.77 , Ocean), (c) Kwajalein (8.72 , 167.73 ,Ocean), (d) Darwin (
12.43 , 130.89 , Grass), (e) Desert Rock (36.673 ,
116.02 , Desert), (f) Tamanrasset (22.78 , 5.51 , Desert), (g) Georg von Neumayer (
70.65 ,
8.25 , Iceshelf), and (h) South Pole (
89.98 ,
24.80 , Glacier). Large deviation from zero of observations at ocean sites indicate that the difficulty in accurately measuring Ld over water. Similarly, GEWEX SRB shows obviously larger deviation over desert sites. desert sites (Figure 15f) [Gupta et al., 2010; Kratz et al., 2010]. An improved algorithm has been proposed with an empirical adjustment of lapse rate [Gupta et al., 2010]. This improvement does help to reduce the overestimation of Ld at the Desert Rock site (Figure 15e), which has been used as a validation site by Gupta et al. [2010]. However, it does not help at the Tamanrasset site (Figure 15f), which was not used by Gupta et al. [2010]. These overestimations of Ld by GEWEX SRB and CERES SYN are substantial at most desert areas (Figures 16b and 16c). The performance of CSFR varies for different sites, with a substantial underestimation of Ld at the Desert Rock and Georg Von Neumayer sites (Figures 15e and 15g). [73] At the tropical ocean sites (Figure 15c), the seasonal variation is very small. The performance of pyrgeometers over open water seems to be degraded by the highly humid environment (Figures 18b and 18c), possibly the same reason why reanalysis (or satellite) Ld has a lower correlation coefficient with observations at the tropical ocean site (Figure 14). Figure 18 shows that the Ld from observations and reanalysis are in reasonable agreement, except over

ocean where the performance of pyrgeometers may be degraded. Figure 18f confirms that the GEWEX SRB overestimates Ld during hot days at desert sites. [74] The high correlation coefficients in Table 7 and Figure 14 may be primarily from their similar seasonal cycles of Ld. Figure 19 and Table 7 compare the monthly anomalies of Ld from observations with those from reanalysis (or satellite) after removal of the seasonal cycle. The correlation coefficients are still reasonable, with medians of the correlation coefficients varying from 0.72 to 0.80. 5.2. Impact of Data Quality of Ld Observations on the Evaluations [75] To reduce cost, AmeriFlux and AsiaFlux use lowgrade pyrgeometers (in the net radiometer set) to measure Ld. Section 4 shows that their Ld measurements may be lower by more than ~13 W m
2 from those measured by higher grade pyrgeometers, such as the Eppley PIR and Kipp & Zonen CG4 used by BSRN sites, allowing us to evaluate how the quality of observations impacts our evaluation results. Some of the CEOP sites also use low-grade pyrgeometers.

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WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 8. Same as Table 7 Except That All the Data From CEOP, AmeriFlux and AsiaFlux are Used to Evaluate the Reanalysis and Satellite Monthly Ld Products Products

Standard Correlation Global Average of Deviation Coefficient Ld (W m
2)

Bias

GEWEX-SRB 2.67 (13.18) CERES SYN
1.95 (10.78) CSFR
0.31 (11.96) ERA-Interim
3.85 (11.93) MERRA
13.51 (12.72)

8.61 6.52 6.56 6.72 6.98

0.98 (0.70) 0.99 (0.79) 0.98 (0.85) 0.99 (0.84) 0.99 (0.86)

308.20 307.15 305.23 304.23 295.07

[76] Table 8 summarizes the results when using CEOP, AmeriFlux, and AsiaFlux data to evaluate the reanalysis (or satellite) Ld products. The results in Table 8 are similar to those in Table 7. CERES SYN and CSFR have the lowest bias, while MERRA has a substantial negative bias. GEWEX SRB has a similar small positive bias, and ERA-Interim has a similar negative bias. GEWEX SRB has the highest standard deviation error among the five products, also consistent with Table 7. [77] As expected, Table 8 shows that the range of the bias (quantified by the standard deviation of the bias) when using CEOP, AmeriFlux, and AsiaFlux data to evaluate reanalysis (or satellite) Ld products is approximately 2–3 W m
2 higher than that from the BSRN evaluation. In Table 7, the bias of the Ld products from the BSRN evaluation can be exactly related to their global averages over the land. However, this is not the case for the comparisons when CEOP, AmeriFlux,

and AsiaFlux data are used as reference data, in particular the bias of CSFR and ERA-Interim (Table 8). Evidently, the pyrgeometers in CEOP, AmeriFlux, and AsiaFlux sites are worse than those of BSRN in estimating absolute values of Ld. Such lower quality reference data can introduce an uncertainty of 3 W m
2 for a global evaluation of Ld products. [78] It is interesting that the overall results shown in Tables 7 and 8 are similar, with the largest differences no more than 2–3 W m
2 in spite of the huge difference of bias at certain sites (Figure 12). The biases at grid scale ~ 1 may differ by tens of W m
2 (not shown). However, these biases largely cancel out when determining a global average. [79] The standard deviation error of the five Ld products when CEOP, AmeriFlux, and AsiaFlux data are used for evaluation (Table 8) are substantially less than those shown in Table 7 for the BSRN data used for the evaluation. The correlation coefficients are also higher when CEOP, AmeriFlux, and AsiaFlux data are used, possibly because most of their sites are located in spatially homogeneous surfaces covered with dense vegetation [Yang et al., 2008], such as forests, where reanalysis (or satellite) determinations should correspond better to a local site. [80] In summary, the data quality of Ld observations substantially impacts the evaluation results at a grid scale. However, the errors of the observations can cancel out with averaging and may have a relatively small impact on the global overall evaluation.

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Figure 19. Histograms of the correlation coefficients of the comparisons of monthly anomalies of atmospheric downward longwave radiation at the surface (Ld) from reanalyses and satellite retrievals with ground-based observations. The correlation coefficients are only calculated if both observed and modeled annual anomalies are available for no less than 5 years. If there are several observations of Ld in a model grid, correlation coefficients are calculated first and then correlation coefficients are averaged in the grid and shown here. The correlation coefficients are comparable for different estimates while ERA-Interim and CSFR is a little better. 171

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 9. Same as Table 7 Except That All the Data Over 24 Tropical Ocean Stations Are Used to Evaluate the Reanalysis and Satellite Monthly Ld Productsa Products

Bias

GEWEX-SRB
3.13 (4.14) CERES SYN
4.17 (5.76) CSFR
2.79 (9.16) ERA-Interim
3.71 (5.45) MERRA
11.65 (7.93)

Standard Deviation 4.37 3.79 5.68 4.59 5.45

Correlation Global Average of Coefficient Ld (W m
2) 0.89 0.88 0.77 0.90 0.85

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a The pyrgeometers of the buoy sites did not shaded solar direct beam, which may introduce an overestimation of monthly Ld. This explain the negative biases of Ld from satellite retrievals and reanalysis.

5.3. Uncertainty of Reanalysis and Satellite Monthly Ld Over Tropical Oceans [81] Monthly Ld from reanalysis and satellite retrievals over global land are evaluated in 5.1, 5.2 with ground-based measurements collected at 169 sites. This section compares the monthly Ld from reanalyses and satellite retrievals using the measurements collected over tropical oceans and summarizes these results in Table 9 and Figure 20. [82] Compared with Ld collected by buoy sites over tropical oceans, CERES SYN Ld has the lowest standard deviation of 4 W m
2. This is much less that the 7 W m
2 over the land sites

found using BSRN measurements. The standard deviations for other reanalysis and satellite retrievals are also consistently less over ocean than those over the land sites (Table 7). Possibly, the meteorological variables and Ld are more spatially homogeneous making it easy to accurately estimate Ld from satellite remote sensing and reanalysis. [83] Similarly to that over land, MERRA has a substantial negative bias of
11.7 W m
2; GEWEX SRB, CERES SYN, ERA-interim, and CSFR Ld have a negative bias of 3–4 W m
2. The later biases are different from the near zero biases over land in comparing these products with BSRN Ld observations. At the BSRN sites, the pyrgeometers have been shaded with a solar tracking ball, which blocks out solar direct beam (Figure 1). Similar pyrgeometers (PIR) have been used at the buoy sites over the tropical oceans, but these pyrgeometers were not shaded (see photos of buoys in McPhaden et al. [2009a] and Bourlès et al. [2008]). The resulting excessive solar heating on the pyrgeometer may cause an overestimate of Ld by more than 10 W m
2 during daytime [Meloni et al., 2012; Pérez and Alados-Arboledas, 1999; Udo, 2000]. As monthly Ld is the average of observations during daytime and nighttime, we believe that the negative bias of 3–4 W m
2 of monthly Ld from satellite retrievals and reanalyses can be attributed to a overestimate of Ld by the pyrgeometer from its solar heating. The overestimation of Ld by an unshaded PIR has been found

Figure 20. Maps of the bias (up four panels) and standard deviations (STD, bottom four panels) of the comparisons of monthly atmospheric downward longwave radiation at the surface (Ld) from reanalyses and satellite retrievals with observations by buoy sites (unit: W m
2) over tropical oceans. Compared those over land, Ld from reanalyses and satellite retrievals have better standard deviation, with a median of 4–6 W m
2. However, Ld from reanalyses and satellite retrievals have a consistent negative bias, with a median of 3–4 W m
2. This is likely because the pyrgeometers of the buoy sites did not shaded solar direct beam and overestimated Ld. 172

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION 15

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Figure 21. Histograms of the correlation coefficients of the comparisons of annual anomalies of atmospheric downward longwave radiation at the surface (Ld) from reanalysis and satellite retrievals with observations. The correlation coefficients are only calculated if both observed and modeled annual anomalies are available for no less than 5 years. If there are several observations of Ld in a model grid, correlation coefficients are calculated first and then correlation coefficients are averaged in the grid and shown here. ERA-Interim has a best overall correlation coefficient between the annual anomalies of Ld at BSRN sites. to be 5.6 W m
2 in the north Atlantic Ocean in late spring [Pascal et al., 2000]. The negative bias of MERRA over tropical oceans is, however, weaker than that over global land, likely because MERRA substantially overestimates cloud fraction over tropical regions (Figure 17). [84] The correlation coefficients of the comparisons over the oceanic sites are slightly less than those over the land sites, likely because of the weaker seasonal cycle of Ld over the tropical oceans (Figure 15). The correlation coefficients are similar for different satellite and reanalysis estimates of Ld with ERA-interim being the highest. 5.4. Uncertainty of Reanalysis and Satellite Annual Ld [85] Figure 21 shows the distribution of correlation coefficients between annual anomalies of Ld from reanalyses (or satellite retrievals) and those from observations when both

are available for no less than 5 years, and Table 10 summarizes the results. The modeled annual anomalies have a median value of correlation coefficients varying from 0.56 to 0.71 (Table 10), similar or higher than those of observed annual anomalies derived from different measurement methods (Table 5). 5.5. Uncertainty of Trend of Reanalysis and Satellite Ld [86] Linear trends are calculated when both annual anomalies of Ld from reanalyses (or satellite retrievals) and observations are available for no less than 5 years. Figure 22 shows their scatterplots, and Table 10 lists their averages. The plots show considerable scatter, with correlation coefficients between linear trends varying from 0.22 to 0.58 (Table 10). This poor agreement in linear trends is a consequence of the difficulty in accurately quantifying

TABLE 10. A Summary of the Evaluations of Annual Ld From Reanalysis and Satellite Retrievals Using All the Data From BSRNa

Products

Median of Correlation Coefficients Between Annual Anomalies

Median of Correlation Coefficients Between Linear Trends

Averages of Linear Trend From Observations (W m
2 yr
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Averages of Linear Trends From Reanalysis or Satellite (W m
2 yr
1)

GEWEX-SRB CERES SYN CSFR ERA-Interim MERRA

0.56 0.60 0.68 0.71 0.70

0.42 0.36 0.22 0.58 0.29

0.56 0.00 0.46 0.44 0.46

0.47 0.40 0.36 0.24 0.25

a This table also summarizes the comparison of the long-term trends derived from observations and those derived from reanalysis and satellite products. Data shown here are only calculated from the annual anomalies of Ld both from observations and reanalysis (or satellite retrievals) available no Less than 5 years.

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WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION

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Figure 22. Scatterplots of linear trends of Ld derived from reanalysis and satellite retrievals as a function of the linear trends derived from observations at grid scale. The trends are calculated from annual anomalies only if the annual anomalies of Ld are available for no less than 5 years. The correlation coefficients between these linear trends vary from 0.22 to 0.58. Linear trends of Ld from ERA-Interim have a best agreement with those from ground-based measurements at BSRN sites.

6.

averages over ocean), with R2 = 0.99. Ideally, the best fit lines would have a slope of 1.00 and correlation of 1.00 if the biases evaluated at surface observations were to work exactly for global averages. It is important to note that for Bias from the Evaluations of Reanalysis or Satellite (W m−2)

trends of Ld at grid scale for both observations and reanalyses (or satellite). Section 4 shows that annual variability of Ld is similar in magnitude to the uncertainty of observations (Tables 5 and 6; Figures 10 and 11). [87] Trend estimates of Ld are highly dependent on the starting and ending dates of the data. We average the trends from both observations and reanalysis (or satellite) for each comparison and summarize the results in Table 10. On average, reanalyses (or satellites) may underestimate the trends of Ld by approximately 30% (Table 10). GLOBAL MEAN Ld AND ITS TREND

6.1. Global Mean Ld [88] Section 5 evaluates Ld estimates from three reanalyses and two satellite products. The evaluations show that CERES SYN Ld has the least overall bias compared to pyrgeometer measurements and that MERRA has a substantial negative overall bias. GEWEX SRB and ERA-Interim have moderate positive and negative overall biases, respectively. Strictly speaking, these biases are only estimated for the grids where observations are available (Figure 4). [89] We show, however, that these biases appear to be similar for their global averages (or climatology). We calculate global averages of Ld from the reanalyses and satellite products for 2001 to 2011 when most of the evaluation data were available (Figure 5) and correlate those averages with their biases (Figure 23). The fitting results in Figure 23 show a slope of 1.19 (1.28 for averages over land and 1.21 for

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Figure 23. A scatterplot of the bias of global evaluation of the reanalyses and satellite retrievals of atmospheric downward longwave radiation at the surface (Ld) (Table 6) as a function of their global average: global mean Ld (black star), mean Ld over global land (red cross), and mean Ld over global ocean (blue cross). The lines are the corresponding best fit lines. The slopes of the lines are 1.19 (black line), 1.28 (blue line), and 1.21 (red line), with R2 = 0.99. Ideally, the best fit lines would have a slope of 1.00 and correlation of 1.00 if the biases evaluated at surface observations were to exactly work for global averages.

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the global averages of Ld, since we only consider the period after 2001 (although there are some evaluation data available before 2001 (Figure 4)), some error may be introduced in the best fit lines (Figure 23). The best fit lines also may have considerable uncertainty because of the small number of samples and the dominance of single outlier points. [90] How good are the Ld of these reanalyses and satellite Ld products largely depends on their modeled clouds over the land and ocean. CERES SYN Ld products use state-ofthe-art satellite cloud observations, and our evaluation also shows that its better description of clouds helps improve its estimate of Ld (section 5). [91] The largest difference between the Ld estimates is over land. Compared to those of CERES SYN Ld, CSFR, ERA-Interim, and GEWEX SRB substantially overestimate Ld in the mountains, such as the Himalayas, Rockies, and Andes, likely because they overestimate clouds. The Ld from reanalysis products (CSFR, ERA-Interim, and MERRA) are substantially less than those from satellites over desert areas, partly because CERES SYN and GEWEX SRB Ld algorithms tend to substantially overestimate Ld over these regions [Gupta et al., 2010; Kratz et al., 2010]. Figures 15 and 16 also suggest that CSFR and MERRA underestimate Ld over dry areas. Considering the results shown in Figures 15 and 16, ERA-Interim may provide the best estimate of Ld over deserts. [92] Radar and Lidar cloud profiling provide better cloud descriptions [Kato et al., 2011] than previous passive satellite products of visible and infrared observations in polar regions [Rossow and Garder, 1993], i.e., Antarctica (Figure 16). Passive satellite sensors have difficulty in detecting clouds over ice and snow surfaces [Ackerman et al., 1998; Ackerman et al., 2008; Bromwich et al., 2012; Frey et al., 2008; Liu et al., 2004; Liu et al., 2010]. For surfaces covered by snow or ice in high latitudes, the contrast of surface characteristics between clouds and surface, such as reflectance and temperature, is very small because both snow (or ice) and clouds have high reflectance and low temperature. This difficulty has been reduced by CALIPSO Lidar and CloudSat Radar, and more clouds have been detected [Kato et al., 2011]. [93] The reanalyses including CSFR, ERA-Interim, and MERRA substantially overestimate cloud fractions within the Intertropical Convergence Zone (ITCZ) and polar front zones. These overestimates of clouds (e.g., Figure 17) result in an overestimation of Ld by ~10 W m
2 over these regions. These overestimations are strongest for CSFR. MERRA underestimates cloud fractions by ~30% in middle latitudes (Figure 17) [Trenberth and Fasullo, 2010; Trenberth et al., 2011]. [94] The differences between Ld products over oceans are substantially less than those over land (Figure 16) except for MERRA, which consistently underestimates Ld in middle latitudes. Our evaluation results show that the two other reanalysis Ld products perform rather well over the land. Evaluations over tropical oceans further show that Ld from satellite retrievals and reanalyses have a much smaller standard deviation error than that over land. The negative

bias of
4 W m
2 is likely from the lack of shading of Ld observations collected at the buoy sites. The reanalysis and satellite Ld products are good in the Arctic Ocean [Cox et al., 2012; Troy and Wood, 2009] and over tropical oceans (i.e., biases are less than 5 W m
2) [Fairall et al., 2008; Praveen Kumar et al., 2012]. In particular, Kato et al. [2012b] show that CERES SYN Ld has a similar or better accuracy over tropical oceans than over land by comparing with measurements collocated by 23 buoys. Existing studies have shown that their air temperature and humidity profile products are sufficiently accurate over both land and ocean [Decker et al., 2012; Jakobson et al., 2012; Jones et al., 2012; Kishore et al., 2011; Mooney et al., 2011; Wang and Zeng, 2012]. The reanalysis of CSFR also is substantially improved over oceans in air temperature, humidity, and longwave radiation compared to its precursors [Wang et al., 2011; Xue et al., 2011]. [95] In summary, differences between Ld products are largest over the land likely because of the difficulty in accurately quantifying clouds over a complex terrain. Figure 23 indicates that the global averages of Ld over ocean (or global) are proportional to those over land. Therefore, we conclude that biases inferred over land for the reanalysis and satellite Ld data are reasonably reliable for their global averages. [96] This study finds that the CERES SYN Ld products perform best and have the smallest bias because of their use of superior cloud characteristics. However, their algorithms substantially overestimate Ld over deserts. ERA-Interim may provide the best estimate of Ld over desert. Averaging CERES SYN Ld globally (after replacing Ld over deserts with those of ERA-Interim), we obtain averages of 306.5 W m
2 (land), 356.2 W m
2 (ocean), and 341.8 W m
2 (global). These values are similar to those obtained by directly averaging CERES SYN Ld products (Table 11). Their spatial distribution is shown in Figure 24. Previous studies have found a global land average Ld of 305 W m
2 (with data collected at 22 stations) and an estimate of 308, 292, and 296 W m
2 from different climate models [Garratt and Prata, 1996]. Similar results of global land averages have been reported by other investigators [Allan et al., 2007; Garratt et al., 1998; Wild, 2008; Wild and Cechet, 2002; Wild et al., 1995; Wild et al., 2001]. [97] It is very difficult to quantify the uncertainty of these estimates of global mean Ld. Previous studies have suggested 7 W m
2 [Kato et al., 2012a]. We have shown in this paper that this estimate corresponds to the global average of random errors of calculated Ld (i.e., standard deviation errors in Tables 6 and 7) but does not account for the systematic errors. For example, section 4.1 shows that the multiyear averages of Ld may differ by about 13.3 W m
2 at a site when observed by different pyrgeometers for lack of a standard calibration method (or pyrgeometer). However, such errors should be regarded as random rather than systematic for assessing global averages of Ld from observations.

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WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION TABLE 11. A Summary of Global Averages of Atmospheric Downward Longwave Radiation at the Surface (Ld) and Its Trenda

Products NCEP JRA-25 CSFR ERA-Interim ERA-40 GEWEX SRB MERRA CERES SRB Stephens et al. [2012] Prata [2008] Prata [2008] Wild [2008] Wang and Liang [2009a] Wild et al. [2001] PavLakis [2004] Kato et al. [2011] Gupta et al.[1999] Stephens et al. [2012] Stevens and Schwartz [2012] Wild [2012] Best estimates

Data Period 1979–2007 1979–2007 1979–2007 1979–2007 1979–2001 1984–2007 1979–2010 2003–2010 1988–2005 1964–1990 1992–2002 1986–2000 1973–2008 1985–1993 1984–1993 2008 1983–1991 2000–2004 2003–2010

Global Mean of Ld (W m
2)

Trend of Global Mean of Ld (W m
2 per Decade)

Globe

Land

Ocean

Globe

Land

Ocean

335 (337) 327 (327) 340 (343) 341 (342) 344 344 (344) 329(331) (342)

294 (295) 287 (287) 304 (305) 303(304) 306 308 (308) 293(295) (307)

352 (354) 343 (343) 355 (358) 357 (357) 359 359 (359) 343 (345) (356)

1.1*
0.2 1.8* 0.4* 1.2* 0.4 1.9* 1.0

0.6* 0.4 1.2* 0.7 0.4 0.2 1.4* 5.9*

1.3*
0.4 2.0* 0.3 1.5* 0.4 2.0*
0.9 1.8  0.3 (Clear sky)

1.7 (Clear sky) 2.6 (Clear sky) 2.1 2.2 344 342–344 345 348 344–350 342 342 342  3

307  3

356  3

a

The global mean of Ld in brackets are averages from 2001 to 2010. Note that JRA-25 and NCEP did not include changes of CO2 and this effect accounted for a trend of ~0.3 W m-2 per decade for global mean Ld. Trends with star pass the a = 0.05 student-t confidence test.

[98] The BSRN networks use the highest grade pyrgeometers to measure Ld, while AmeriFlux and AsiaFlux networks use lower grade pyrgeometers. Such quality of the observations does impact the bias of the evaluation at grid scale. However, these biases apparently cancel out for global averages. Tables 6 and 7 show that the biases based on the use of different datasets for global evaluation have differences less than 2–3 W m
2. This error can be regarded as the systematic error of global averages from observations. The limited evidence given by comparing Tables 6 and 7 indicates that this global average has an uncertainty of 3 W m
2 for land. Evaluations show that Ld from satellite retrievals and reanalysis over tropical oceans may be have a better accuracy because of the spatial homogeneity of surface meteorological variables and Ld. Considering that WISG reference pyrgeometers of PMOD/WRC have a 95% uncertainty (two standard deviation) of 5 W m
2, we estimate the uncertainty of global, land and ocean means of Ld as 3 W m
2. [99] Table 11 lists the global averages of Ld from existing studies. The different data periods may have some impact on the estimated values, which will be discussed in the next section (section 6.2). Among these estimates, the 348 W m
2 from Gupta et al. [1999] is the highest. The algorithm of Gupta et al. [1999] may substantially overestimate Ld over dry land [Gupta et al., 2010; Kratz et al., 2010]. Our best estimate of global average Ld of 342 W m
2 is consistent with two other recent estimates [Stevens and Schwartz, 2012; Wild, 2012]. [100] The value of 345 W m
2 from Kato et al. [2011] was based on an old diurnal cycle of Ld and has been reduced to 342 W m
2 from 2003 to 2010 after introduction of an improved diurnal cycle description of Ld (Table 11).

Stephens et al. [2012] estimated Ld to lie in the range of 344–350 W m
2 from 2000 to 2004 from a synthesis of satellite estimates, including that of the previous version of CERES [Kato et al., 2011]. However, all such past estimates were purely model calculations using the old diurnal cycle of Ld without a solid evaluation using direct observations. In their recent uncertainty analysis paper, Kato et al. [2012a] compared their estimates with observations at 62 sites but only used data in July from 2000 to 2005. Some biases may exist in their evaluation results, such as that from the overestimation of Ld by GEWEX SRB and CERES SYN over deserts during summer [Gupta et al., 2010; Kratz et al., 2010].

Figure 24. Multiyear mean atmospheric downward longwave radiation at the surface (Ld) averaged CERES SYN products from 2003 to 2010, with Ld of ERA-Interim over the deserts (unit: W m
2).

176

Global Averages of Ld (W m−2)

Global Averages of Ld (W m−2)

Global Averages of Ld (W m−2)

WANG AND DICKINSON: DOWNWARD LONGWAVE RADIATION 350

Globe 345 340 335 330 325

310

1950

1960

1970

1980

1990

2000

2010

1960

1970

1980

1990

2000

2010

1960

1970

1980

1990

2000

2010

Land

300

290

280

1950

365 360

Ocean

355 350 345 340 335

1950

Year

Figure 25. Time serious of global annual mean atmospheric downward longwave radiation at the surface (Ld) averaged over (a) global (upper), (b) land (middle), and (c) ocean (bottom): NCEP (red cross), JRA25 (green cross), ERA-40 (blue cross), CERES (Black cross), CSFR (red dot), ERA-Interim (blue dot), GEWEX SRB (green dot), and MERRA (black dot).

6.2. Trend of Global Mean Ld [101] Figure 25 shows the global annual mean Ld as obtained from six reanalyses and two satellite retrievals. Most of the data start in 1979. Therefore, we calculate their global averages and their trends after 1979, as summarized in Table 11. [102] The derived linear trends of global mean Ld vary from
0.2 W m
2 per decade to 1.9 W m
2 per decade. The trends vary from
0.1 to 1.5 W m
2 per decade for global mean Ld over land and from
0.2 to 2.1 W m
2 per decade for global mean Ld over oceans. Trends of global mean Ld over land are much less than those over ocean, consistent with observations that in the past decades, although land has undergone stronger warming, relative humidity over land has decreased, in particular after 2000 [Simmons et al., 2010]. It is important to note that the first generation reanalyses, including JRA-25 and NCEP, did not include changing CO2 concentrations [Trenberth et al., 2011]. Therefore, they did not include the ~0.3 W m
2 per decade trend of Ld resulting from the observed trend of atmospheric CO2 concentrations [Prata, 2008; Wang and Liang, 2009a].

[103] The reliability of the trends of Ld depends on how well the trends of air temperature, humidity (or precipitable water), and clouds are predicted by the reanalysis and satellite products. Reanalyses generally provides reliable interannual variability and long-term trends of air temperature [Jones et al., 2012; Matsui et al., 2012; Wang et al., 2011]. [104] The precipitable water estimated by reanalysis (also input for satellite Ld retrievals) depends highly on the satellite vertical sounding observations [Jackson and Stephens, 1995; Jackson et al., 2006], in particular over ocean where radiosonde data are unavailable [Robertson et al., 2011; Trenberth et al., 2005]. The weighting functions of the water vapor channels of the Advanced TIROS Operational Vertical Sounder (ATOVS) system launched in 1998 are substantially different from its predecessor, the TIROS Operational Vertical Sounder (TOVS) [Jackson and Soden, 2007; Jackson and Wick, 2010; Jackson et al., 2009]. These changes in satellite sensors caused a substantial increase in the estimation of precipitable water by MERRA [Bosilovich et al., 2011; Trenberth et al., 2011]

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and CSFR [Wang et al., 2011]. These spurious discontinuities in precipitable water introduce spurious trends of Ld by MERRA and CSFR [Robertson et al., 2011]. This change from TOVS to ATOVS also significantly impacted the JRA-25 precipitable water and Ld products [Onogi et al., 2005; Onogi et al., 2007]. TOVS data from different satellite platforms also produced spurious changes of precipitable water in the NCEP and ERA-40 reanalyses [Trenberth et al., 2005]. The methods used to correct discontinuities between TOVS and ATOVS in different reanalysis systems have produced substantially different trends of Ld (Figure 25 and Table 9). [105] The ISCCP clouds used by the GEWEX SRB are from satellite observations, which have large uncertainties. The ISCCP cloud data were originally produced for studying short-term variations. Its long-term cloud data contained spurious changes resulting from satellite changes between geostationary and polar orbit satellites, and progressive changes in orbit and instrumental parameters during the lifetime of individual satellites [Jacobowitz et al., 2003; Trenberth, 2002]. Considerable effort has been made [Rossow and Schiffer, 1999] to minimize spurious changes in long-term records of satellite cloud data due to calibration problems [Klein and Hartmann, 1993] and satellite drift [Foster and Heidinger, 2012]. However, long-term homogeneity is still elusive due to changing satellite view angles [Campbell, 2004; Dai et al., 2006; Evan et al., 2007; Marchand et al., 2010; Santer et al., 2003]. The geostationary satellites measure clouds over tropical regions with near nadir view angles, and polar orbiting satellites view these clouds from different view angles. Therefore, geostationary satellites have difficulty in detecting lower level clouds with their visible and infrared observations when multilayer clouds occur, and therefore, adding additional geostationary satellite observations may introduce discontinuities in the cloud data. Because Ld is more sensitive to occurrence of low level clouds than high clouds, such a discontinuity in low clouds can substantially affect the calculated trend of Ld, which has a scale of ~1 W m
2 per decade (Table 11). Similar issues also occur for ERA-Interim. Ld of ERA-Interim is not a reanalysis product. It is a forecast product and not necessarily based on consistent cloud products. JRA-25 also has notable problems with clouds [Trenberth et al., 2011]. [106] In summary, reanalysis and satellite products produce substantially different long-term trends of global mean Ld. Changes in satellite observations produce discontinuities in long-term trends of precipitable water and clouds, which seriously impact the reliability of estimates of Ld trends, especially over oceans where radiosonde data are unavailable [Trenberth et al., 2005; Xue et al., 2011]. However, the trends of Ld over land derived from reanalysis and satellite products are substantially less than empirical estimations based on observations of surface air temperature and humidity or atmospheric radiosonde data in Table 11. The latter do not have a global coverage and do not fully consider the impact of clouds. 7.

CONCLUSIONS AND DISCUSSION

[107] Accurate estimates of the downward longwave radiation at the surface (Ld) and its changes with time are

important for determining how greenhouse gases impact climate change and for understanding climate sensitivity. This paper quantifies (1) the uncertainties of current Ld surface observations at monthly to decadal timescales, (2) the uncertainties of Ld from reanalysis and satellite products at monthly to decadal timescales, and (3) climatology, and estimates of trends of global Ld during past decades. To do this, we have used Ld observations from 1992 to 2010 at 169 sites over global land from the BSRN (47 sites), CEOP (51 sites), AmeriFlux (51 sites), and AsiaFlux (20 sites). Ld measurements collected at 24 buoy sites over tropical oceans from 2002 to 2011 were also used. We have also utilized global Ld from six reanalyses (NCEP, ERA-40, JRA-25, ERA-Interim, MERRA, and CSFR) and two satellite products (CERES SYN and GEWEX SRB). [108] Based on the energy balance of a pyrgeometer, two basic calibration methods have been developed. Before 2007, when WISG of PMOD/WRC was established, pyrgeometers were calibrated by laboratory blackbodies. After that, the laboratory derived calibration parameters were adjusted by comparing with measurements of WISG. This advance substantially improved the accuracy of Ld measurements in the field. The pyrgeometers of WISG have a 95% uncertainty of 5 W m
2 (two standard deviations). Shading of the solar beam of a pyrgeometer is essential to maintain its accuracy. Otherwise, Ld measurements may be overestimated by more than 10 W m
2 during daytime. [109] We find that current Ld observations have a standard deviation error of ~3.5 W m
2 at a monthly scale. However, given the lack of a standard reference for pyrgeometers, accurately estimating absolute values of Ld is problematical. Multiyear-averaged Ld observed by different pyrgeometers may differ by more than 13 W m
2. The calibration of pyrgeometers also impacts the accuracy of the Ld observations in quantifying annual variability. At limited sites, we show that annual anomalies have a higher correlation coefficient (0.82) when Ld is measured by the same type of pyrgeometers than by different types of pyrgeometers. The performance of a pyrgeometer over open water can be impacted by its environment, such as extreme cold and high humidity. [110] We carefully evaluated CERES SYN, GEWEX SRB, CSFR, ERA-Interim, and MERRA Ld products. CERES SYN Ld products perform the best among the five when compared with pyrgeometers, with the least bias and standard deviation and the highest correlation coefficient. The range of bias of CERES SYN Ld products at grid scale is also the least. Good cloud descriptions used by the CERES SYN Ld products apparently help to improve their accuracy. The CSFR Ld has a similar overall bias, but its range of biases at grid scale is the worst among the five Ld products. GEWEX SRB has a moderately positive bias of 3 W m
2, and ERA-Interim has a moderately negative bias of
2 W m
2. [111] At a grid scale ~ 1 , the reanalysis and satellite Ld products have similar biases of 10–13 W m
2. At monthly scale, these reanalysis Ld products have a standard deviation error of 7–10 W m
2, with ERA-Interim best and GEWEX

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SRB worst. All five Ld products have a correlation coefficient of 0.96–0.98 for their monthly averaged Ld and 0.72–0.80 for their monthly anomaly of Ld. The correlation coefficients between observed and modeled annual anomalies of Ld vary from 0.63 to 0.71. However, linear trends of Ld derived from reanalysis and from satellite are nearly unrelated to observed trends. [112] The standard deviations, 4–6 W m
2, of satellite retrievals and reanalyses over tropical oceans are much less than those over land sites, likely because oceans are spatially homogenous. The 3–4 W m
2 negative bias of satellite retrievals (GEWEX SRB and CERES SYN) and reanalyses (ERA-Interim and CSFR) apparently primarily comes from the overestimation of Ld observations collected by buoy sites, a consequence of lack of solar shading and ventilation for the instruments [113] The data quality of Ld observations substantially impacts the evaluation results at grid scale, but the errors largely cancel out in global averaging and have but a relatively small impact on the overall global evaluation. [114] In climate models, Ld is calculated from radiative transfer codes with input of modeled profiles of air temperature and humidity and cloud characteristics. In the past, much of the differences between calculation and observations of Ld could be attributed to the algorithm used for radiative transfer [Wild and Roeckner, 2006; Wild et al., 1995; Wild et al., 2001]. However, atmosphere radiation transfer has algorithms that have been substantially improved in recent decades [Fu, 1996; Iacono et al., 2000; Morcrette and Fouquart, 1985; Sun, 2011; Viúdez-Mora et al., 2009] and facilitated international radiation code intercomparisons [Ellingson et al., 1991; Oreopoulos and Mlawer, 2010; Oreopoulos et al., 2012]. Currently, the greater source of error appears to be modeled clouds and water vapor. Modeled clouds are overestimated in tropical and high latitude regions and underestimated in middle latitude [Trenberth and Fasullo, 2010] such that reanalysis overestimates Ld at tropical and high latitude regions and underestimates Ld in middle latitude regions compared to the CERES SYN Ld retrievals that use satellite cloud observations as input data. CERES SYN and GEWEX SRB satellite products overestimate Ld over deserts because of their algorithms [Gupta et al., 2010; Kratz et al., 2010]. [115] Based on our evaluation, the global mean Ld from 2003 to 2010 is 342 W m
2 averaged globally, 307 W m
2 averaged over land, and 356 W m
2 averaged over ocean. The uncertainty of global mean is estimated to be 3 W m
2. [116] Reanalysis and satellite products produce substantially different long-term trends of global mean Ld. Changes of weighting function of satellite sensors have produced discontinuities in the long-term trend of precipitable water, which seriously degrade the reliability of these trends, especially over ocean where radiosonde data are not available [Trenberth et al., 2005; Xue et al., 2011]. However, the trends of Ld over land derived from reanalysis and satellite products are substantially less than empirical estimations based

on observations of surface air temperature and humidity or atmospheric radiosonde data, but the latter do not have global coverage or an accurate cloud description. [117] Accurate estimates of Ld and its changes with time are essential for determining the global energy and water cycle. Surface net radiation is balanced by turbulent fluxes, i.e., sensible heat flux and latent heat flux (lE, where l is the latent heat of vaporization and E is evapotranspiration; see equation (1)) [Wang and Dickinson, 2012]. On a global average, evapotranspiration is equal to total precipitation at the surface. Different estimates of Ld imply substantial differences in estimates of other terms in the global hydrological cycle [Stephens et al., 2012], including precipitation [Huffman et al., 2009; Trenberth, 2011] and evapotranspiration [Wang and Dickinson, 2012]. [118] Several areas in need of further research are as follows: [119] 1. The absolute values of Ld. Lack of a standard reference for pyrgeometers leads to inconsistency in the observations of Ld and consequently inadequate constraints on atmospheric radiation models and global climate models. Our evaluation shows that it is difficult to estimate Ld with an accuracy better than 2–3 W m
2 (one standard deviation). Ld calculated from a highly accurate atmospheric radiation model with input of accurate observations of atmospheric temperature and humidity profiles could be a good reference for the long-term performance of Ld observations. Thus, corresponding atmospheric profile observations are important for Ld observations. [120] 2. Clouds impact both values of Ld and their changes with time. We find that although MERRA and CERES SYN have similar atmospheric inputs from NASA’s GEOS-5, MERRA’s global average is less by ~18 W m
2, possibly from its underestimation of low clouds. Furthermore, the treatment of clouds substantially affects trends of the global mean Ld, such as that of ERA-Interim. Improvement of clouds in reanalysis should greatly improve their estimation of Ld and its change with time. [121] 3. Surface radiation calculations highly depend on the cloud overlap schemes assumed. This study found that CERES SYN Ld products perform best. The cloud fraction needed by its surface radiation calculation is derived from satellite cloud detection or cloud property products at pixel level with an assumption of a cloud overlap scheme. Studies have shown different schemes may produce large differences in cloud fraction [Chang and Li, 2005; Pincus et al., 2012; Rossow and Zhang, 2010]. [122] 4. Discontinuities in atmospheric precipitable water have seriously compromised the estimates of trends of the global mean Ld. Changes in satellite observations introduce this discontinuity. Better calibration of satellite sensors or better methods to use the satellite data of atmospheric precipitable water are needed for a better estimate of Ld.

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[123] ACKNOWLEDGMENTS. The first author is funded by the National Basic Research Program of China (2012CB955302), the National Natural Science Foundation of China (41175126), and the New Century Excellent Talents Program. The second author is funded by DOE grant DE-SC0002246 and NSF grant ATM-0720619. We thank Kevin Trenberth for his insightful suggestions. We also thank John T. Fasullo and Qian Ma for processing some data used in this study. We thank the data providers for the data used in this study (their names are listed in Tables 2 and 3). Buoy data were released by the Tropical Atmosphere Ocean project office (Table 3). The Editor on this paper was Alan Robock. He thanks Fred Prata and two anonymous reviewers for their review assistance on this manuscript. REFERENCES Abramowitz, G., L. Pouyanné, and H. Ajami (2012), On the information content of surface meteorology for downward atmospheric long-wave radiation synthesis, Geophys. Res. Lett., 39, L04808, doi:10.1029/2011gl050726. Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley (1998), Discriminating clear sky from clouds with MODIS, J. Geophys. Res., 103(D24), 32141–32157. Ackerman, S. A., R. E. Holz, R. Frey, E. W. Eloranta, B. C. Maddux, and M. McGill (2008), Cloud detection with MODIS. Part II: Validation, J. Atmos. Ocean. Technol., 25(7), 1073–1086. Albrecht, B., and S. K. Cox (1977), Procedures for improving pyrgeometer performance, J. Appl. Meteorol., 16(2), 188–197. Albrecht, B., M. Poellot, and S. K. Cox (1974), Pyrgeometer measurements from aircraft, Rev. Sci. Instrum., 45(1), 33–38. Allan, R. P., M. A. Ringer, J. A. Pamment, and A. Slingo (2004), Simulation of the Earth’s radiation budget by the European Centre for Medium-Range Weather Forecasts 40-year reanalysis (ERA40), J. Geophys. Res., 109, D18107. Allan, R. P., A. Slingo, S. F. Milton, and M. E. Brooks (2007), Evaluation of the Met Office global forecast model using Geostationary Earth Radiation Budget (GERB) data, Q. J. R. Meteorol. Soc., 133(629), 1993–2010. Angstrom, A. (1924), Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation, Q. J. R. Meteorol. Soc., 50(210), 121–126. Augustine, J. A., and E. G. Dutton (2013), Variability of the surface radiation budget over the United States from 1996 through 2011 from high-quality measurements, J. Geophy. Res., 118, 43–53, doi:10.1029/2012jd018551. Augustine, J. A., J. J. DeLuisi, and C. N. Long (2000), SURFRAD—A national surface radiation budget network for atmospheric research, Bull. Amer. Meteorol. Soc., 81(10), 2341–2357. Augustine, J. A., G. B. Hodges, C. R. Cornwall, J. J. Michalsky, and C. I. Medina (2005), An update on SURFRAD—The GCOS Surface Radiation budget network for the continental United States, J. Atmos. Ocean. Technol., 22(10). Baldocchi, D., et al. (2001), FLUXNET: A new tool to study the temporal and spatial variability of ecosystem scale carbon dioxide, water vapor, and energy flux densities, Bulletin of the American Meteorological Society, 82(11), 2415–2434. Bernard, L., K. Kern, Z. Jing, and C. Teng (2008), Refreshed data system for Tropical Atmosphere Ocean (TAO) buoy array, paper presented at OCEANS 2008—MTS/IEEE Kobe Techno-Ocean, 8–11 April 2008. Bilbao, J., and A. H. De Miguel (2007), Estimation of daylight downward longwave atmospheric irradiance under clear-sky and all-sky conditions, JAMC, 46(6), 878–889. Blonquist, J. M., B. D. Tanner, and B. Bugbee (2009), Evaluation of measurement accuracy and comparison of two new and three

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