Contribution of solar radiation to decadal temperature variability over land

Kaicun Wang; Robert E. Dickinson

doi:10.1073/pnas.1311433110

Edited by Mark H. Thiemens, University of California, San Diego, La Jolla, CA, and approved August 2, 2013 (received for review June 18, 2013)

Significance

Global air temperature has become the primary metric for judging global climate change. The variability of global temperature on a decadal timescale is still poorly understood. This paper shows that surface incident solar radiation (Rs) over land globally peaked in the 1930s, substantially decreased from the 1940s to the 1970s, and changed little after that. The cooling effect of this reduction of Rs accounts in part for the near-constant temperature from the 1930s into the 1970s. Since then, neither the rapid increase in temperature from the 1970s through the 1990s nor the slowdown of warming in the early twenty-first century appear to be significantly related to changes of Rs.

Abstract

Global air temperature has become the primary metric for judging global climate change. The variability of global temperature on a decadal timescale is still poorly understood. This paper examines further one suggested hypothesis, that variations in solar radiation reaching the surface (R_s) have caused much of the observed decadal temperature variability. Because R_s only heats air during the day, its variability is plausibly related to the variability of diurnal temperature range (daily maximum temperature minus its minimum). We show that the variability of diurnal temperature range is consistent with the variability of R_s at timescales from monthly to decadal. This paper uses long comprehensive datasets for diurnal temperature range to establish what has been the contribution of R_s to decadal temperature variability. It shows that R_s over land globally peaked in the 1930s, substantially decreased from the 1940s to the 1970s, and changed little after that. Reduction of R_s caused a reduction of more than 0.2 °C in mean temperature during May to October from the 1940s through the 1970s, and a reduction of nearly 0.2 °C in mean air temperature during November to April from the 1960s through the 1970s. This cooling accounts in part for the near-constant temperature from the 1930s into the 1970s. Since then, neither the rapid increase in temperature from the 1970s through the 1990s nor the slowdown of warming in the early twenty-first century appear to be significantly related to changes of R_s.

Global temperature has become the primary metric for judging global climate change, although many other factors are recognized to be of comparable importance. The overall increase of global temperature over the last century has been largely attributed to the increase of greenhouse gases (1). Less well understood are the reasons for the variability of this increase on a decadal timescale. In particular, warming from 1900 to 1940 was followed by three decades of flat or slightly decreasing temperature, then three decades of very rapid temperature increase, then so far in this century, very little additional increase. The two most plausible explanations for the decadal variability are natural climate variability and variable degrees of cooling from anthropogenic releases of sulfur gas producing sulfate aerosols (2). This effect has long been proposed as a mechanism to counter greenhouse warming (3), has become the basis for many geoengineering proposals (4), and has been used to attribute the lack of warming so far this century to the rapid growth of aerosols in Asia (5).

Besides the difference in sign of their temperature effects, sulfate aerosols are distinguished from greenhouse gases in that they only affect daytime radiation, i.e., surface incident solar radiation (R_s). Some kinds of natural variability can also act through affecting R_s, i.e., those involving cloud properties.

Changes of aerosol loading and cloud properties likely caused the rapid decrease of R_s, measured at the surface from the 1950s to the 1980s, referred to as “global dimming,” and its partial recovery after that (6). The plausible suggestion was made by Wild et al. (7) that the rapid warming in the late twentieth century was a consequence of the cessation of global dimming, possibly in part from the imposition of controls on sulfur emission in the industrialized nations (8, 9).

This paper examines further the hypothesis that variations in R_s have caused much of the observed decadal variability in the rate of warming. Direct measurements of R_s cannot be quantitatively related to such variability because they have been limited in their geographical coverage. The approach used here is to examine a global land dataset of diurnal temperature range (DTR). This concept is not new, indeed, Wild et al. (7) noted (compare with their figure 2) that the global pattern of DTR was similar to that of their global dimming and brightening. The present paper develops the longest and most comprehensive dataset for DTR possible, and, with some plausible assumptions, establishes what the contribution of R_s has been to decadal temperature variability. It indicates that a decrease of R_s from the 1940s through the 1970s reduced the global temperature trend over that period. However, global temperature does not appear to have been significantly affected by changing R_s after that. The method is limited in that it is only applicable over land. As the effects of aerosols are likely to be less over ocean, especially in the Southern Hemisphere, this approach may exaggerate the actual effect of aerosols on global temperature trends.

Results

Relationship Between R_s and DTR.

This section establishes that locally DTR is highly correlated with R_s, but that spatial and seasonal variability precludes direct use of this correlation to infer R_s where it is not already measured. In the absence of weather variability, near-surface air temperature T_a over land decreases with time at night from longwave radiative cooling and reaches T_min before sunrise. After sunrise, the surface is heated by R_s and this heat is transferred as sensible heat H to the overlying air, raising T_a to T_max in early afternoon. Therefore, changes of DTR = T_max − T_min, have been interpreted as directly related to changes of R_s (6, 7, 10 ⇓⇓–13). Here we explain how R_s and DTR connect physically and how their relationship varies with environment.

Fig. 1 shows the correlation of monthly anomalies of R_s collected by the Global Energy Balance Archive (GEBA) (14) with DTR from 1950 to 2005 at 524 globally distributed stations (see SI Text and Fig. S1 for DTR data sources and their quality control). The correlation coefficients between R_s and DTR are the highest in humid areas and lower in arid or semiarid areas because the fraction of absorbed R_s generating H also depends on variable soil moisture resulting from the frequency and intensity of precipitation (15, 16). Besides its dependence on surface wetness (17), the partitioning of surface-absorbed R_s between H and latent heat flux (λE) depends on land-cover conditions (18, 19) and atmospheric evaporative demand (20). In humid areas, both H and λE generally increase with R_s (21, 22), but under warm conditions the latter increases more (23). In arid or semiarid regions, λE is limited by soil water supply and H can account for a higher portion of surface absorbed R_s. Fig. 2 shows, as expected from the above discussion, that the sensitivity of DTR to R_s is higher in arid or semiarid areas than in humid areas.

Fig. 1.

Correlation coefficients between monthly anomalies of DTR and R_s. The R_s observations are from the GEBA, and the homogenized maximum and minimum air temperature at 2 m are from the Global Historical Climatology Network (GHCN). Both datasets cover the period from 1950 to 2005. Each point in the figure represents a weather station where both R_s and DTR are available for more than 120 mo. There are 524 stations in total.

Fig. 2.

The sensitivity of DTR to R_s (in °C per Wm⁻²) calculated from the monthly anomalies of R_s and DTR. The data used here are the same as in Fig. 1.

Surface aridity changes seasonally for most monsoon areas, i.e., where it is wet only in a rainy season, but its interannual variability is expected to be much less than such seasonal changes. To reduce the impact of seasonality, we used monthly and annual anomalies rather than absolute values of DTR and R_s. In the following discussion, we also divide a year into boreal warm seasons (May to October) and boreal cold seasons (November to April).

The correlations and sensitivity shown in Figs. 1 and 2 are the lowest in coastal areas. Evidently the impact of R_s on DTR in these areas is masked by the impact of energy advection with regular alteration between land breezes and ocean breezes. This masking can be substantially reduced by regional averaging of DTR and R_s (6, 24).

Figs. 3 and 4 compare Europe’s and China’s regional average annual anomalies of DTR with those of R_s. These quantities agree quite well, partly because of their better data density and data continuity (Fig. S3). The agreement between regional DTR and R_s over Europe has also been confirmed by both data analysis (10) and model simulation (24). In China, the decrease of R_s is in good agreement with the reduction of DTR before 1990 (11). However, R_s in China increased suddenly during the early 1990s but not DTR and sunshine duration (25). The introduction of new pyranometers from 1990 to 1993 introduced this inhomogeneity into the R_s observations (25, 26).

Fig. 3.

Scatterplots of annual anomalies of regional DTR as a function of annual anomaly of R_s during warm seasons (May to October) and cold seasons (November to April) from 1950 to 2005. The correlation coefficients are 0.61 and 0.83 over China and 0.86 and 0.73 over Europe during the warm and cold seasons, respectively.

Fig. 4.

Regionally averaged annual anomalies of R_s (in blue) and DTR (in green) during boreal warm seasons (May to October) and boreal cold seasons (November to April) from 1950 to 2005. Data used here are the same as in Fig. 3. Equivalent plots for the United States are given in Fig. S2.

Fig. 4 also shows that DTR has had a larger temporal variability than R_s, a consequence of the annual variability of precipitation leading to variations in the partitioning of surface absorbed R_s between λE and H. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) concluded that precipitation has had large annual variability during the last century, but that its long-term trend and thus its impact on the long-term trend of DTR has been negligible (1), as confirmed by Figs. 3 and 4 and the following sections. The impact of annual variability of precipitation is largely removed by using 5-y smoothing of the anomalies of DTR as in the following.

Variability of DTR, a Proxy of R_s, from 1900 to 2010.

This section establishes what is available as a global record for DTR variability. For estimation of DTR over land with optimum spatial and temporal coverage and the highest quality, we combined three data sources (27 ⇓–29) (see SI Text for detailed information) for the past 110 y. Monthly anomalies of DTR were derived by removing its seasonal cycle. Observations of DTR had the highest density in North America. To mitigate the impact of the different data densities, monthly anomalies of DTR were binned into 5° × 5° grids. Given the low correlation between DTR and R_s in coastal areas, we only selected the grids with more than 50% of their area over land, as shown in Fig. S4. The monthly anomalies at each grid were averaged into regional monthly anomalies, and then into annual values and 5-y average annual anomalies at each region, as plotted in Fig. 5.

Fig. 5.

Five-year average of annual anomaly (black) of regional DTR from 1900 to 2010 averaged from the monthly anomalies at 5° × 5° grids (Fig. S4), which is calculated from weather stations. For comparison, anomalies during boreal warm seasons (May to October, red) and boreal cold seasons (November to April) are shown.

Europe is the only region where measurements of both DTR and R_s extend back to the 1920s (6). DTR generally increased in Europe from the 1920s to the 1950s. After the late 1950s, it began to decrease until the 1980s, and since the 1990s increased. These variations of DTR are consistent with those of observed R_s (6, 24, 30). The better agreement of warm-season variability of DTR with that of R_s is consistent with the larger R_s during warm seasons.

Attempts have been made to correlate annual R_s and DTR both at regional and global scales (6, 7). However, existing studies have not recognized that DTR and R_s have different seasonal cycles; R_s is largest in summertime as a result of higher solar elevation. However, DTR is relatively low in moist summers because of the small fraction of R_s that is partitioned into H. Therefore, annual variability of DTR is primarily determined by its variability during seasons other than summer. DTR and R_s agree well both for warm and cold seasons, and variability of R_s over warm seasons is more representative of its annual variability. The reported annual variability of R_s, therefore, agrees better with DTR over warm seasons over Europe (and other regions) than that over an entire year or cold seasons. Variability of DTR over warm and cold seasons is substantially different at both the regional scale (Fig. 5) and the global scale (Fig. 6). For this reason, it is essential to consider these differences in reconstructing variability of R_s from DTR.

Fig. 6.

(A) The 5-y smoothed impact of R_s on mean air temperature (T_a) over global land during boreal warm seasons (May to October, red), boreal cold seasons (November to April, blue), and an entire year (black). The mean air temperatures over global land are also shown in B.

In Asia, DTR substantially decreased from the 1950s to the 1980s, was stable until 2000, and then decreased again, consistent with R_s derived from sunshine duration (25) and the dimming of directly measured R_s between 1960 and 1990 in China (11, 31). As already mentioned, after the 1990s, direct observations of R_s became inconsistent with those of DTR and sunshine (31), a result of the urban bias of R_s observations. When averaged over all stations (∼400 stations) rather than over the ∼50 urban stations in China with direct observations of R_s, R_s derived from sunshine duration was stable during the 1990s and decreased after 2000 (25).

DTR substantially decreased in North America from 1900 to 2010, consistent with the increase of cloudiness, in particular, of low clouds (32), and decrease of sunshine duration (33). Cloud cover alone accounted for up to 63% of the regional annual DTR variability in the United States from 1902 to 2002, with cloud-cover trends especially driving DTR in northern United States (34). Aerosol loading over North America was relatively light (35) and rather stable during the past few decades (8). Observations at six stations in the United States showed that R_s significantly increased from 1995 to 2007 (36, 37), primarily in the 1990s (38).

As there is a good agreement between R_s and DTR, changes of R_s are expected to be similar to those of DTR, especially during the warm seasons. Fig. 5 shows the variability of DTR over land during the past century, and hence provides qualitative estimates of R_s variability over this period. However, it is difficult to reconstruct R_s quantitatively using the variability of DTR because of the changes of their relationship with time (e.g., from wet to dry seasons; Fig. 3) and region (e.g., from humid to arid regions) (Figs. 1 and 2). Below, we describe another approach for using DTR to estimate the impact of R_s on T_a.

Estimation of the Impact of R_s on T_a from 1900 to 2010.

Elevated greenhouse gases (GHG) have increased atmospheric downward longwave radiation (L_d) (39, 40) and T_a (41) during the twentieth century. However, variability in radiative forcing from aerosols and clouds complicates the attribution of the observed climate change to the elevated GHG. The previous sections have established a more comprehensive climatology for DTR than that available previously and its long-term variability is highly consistent with that of R_s. This climatology allows us to address the question of how much of the observed temperature change has been a result of changes of R_s. For the following analysis, we assume: (i) T_min is not changed by R_s; and (ii) DTR is only changed by changes of R_s (elaborated on in Discussion and Conclusions).

The globally averaged anomaly of DTR is calculated directly from its grid values. Daily mean air temperature T_a is commonly estimated by T_a = 0.5 × (T_max + T_min). As DTR = T_max − T_min, we obtain T_a = T_min + 0.5 × DTR. For the given assumptions, the impact of R_s on daily mean T_a is 0.5 × DTR (Fig. 6). These assumptions can be inaccurate for various reasons, e.g., changes of daytime radiation can be stored and released to change nighttime temperature. Observations from global flux networks show that storage fraction is less than 10% of R_s at most surfaces (42, 43). Allowing for this effect would likely amplify our estimate of the impact of R_s on T_a over global land by a factor of ∼1.1.

We calculate the impact of R_s on mean T_a during the three time periods: (i) 1900–2010 (the whole time period when data are available); (ii) 1940–1984 (the global dimming period) and (iii) 1985–2010 (the global brightening period). The results are summarized in Table 1.

View this table:

Table 1.

The impact of R_s on daily mean air temperature (T_a) during three periods, 1900–2010, 1985–2010, and 1940–1984 (in °C per 100 y)

Table 1 and Fig. 6 indicate that a reduction in R_s has reduced T_a and that it decreased most rapidly during the dimming period of 1940–1984. The rate of temperature increase during the cold seasons has been reported to be much higher than that during the boreal warm seasons (May to October) (44). Fig. 6 shows that warm-season R_s substantially decreased from the 1940s to early 1950s and during the 1970s, resulting in a reduction of more than 0.2 °C in T_a. Similarly, cold-season R_s substantially decreased from the 1960s through the 1970s, resulting in a decrease of nearly 0.2 °C in T_a. A subsequent increase of R_s was only significant over Europe. In conclusion, the variations of R_s partly accounted for the near absence of warming from midcentury through the 1970s. The maximum cooling seen in the early 1980s and early 1990s were consistent with the effects expected from the El Chichón and Pinatubo volcanoes, respectively. Fig. 6 also shows that the results are substantially different for warm seasons, cold seasons, and the entire year when using DTR to quantify the impact of R_s on air temperature (7).

Discussion and Conclusions

This paper shows, using direct R_s observations (6, 45) and sunshine duration observations (25), that the interannual variability of DTR can be used as a proxy for the long-term variability of R_s. In principle, this relationship should also be applicable to model simulations. AR4 climate models (46) show a weak monotonic increase of DTR from 1950 (44), compare with their figure 5, suggesting that many of the models examined applied a slow constant ramp-up of aerosol forcing rather than concentrated increases before 1980 as indicated here. Changes of DTR are expected to be directly related to H from surface to overlying air but the magnitudes of these turbulent fluxes are not readily estimated (22). Many parameters affect the relationship between R_s and H, and consequently, the relationship between R_s and DTR.

Impacts of land-cover and land-use change (i.e., urbanization and irrigation) have been ignored here. In developing countries, such as China and India (47), there has been substantial urbanization and increased irrigation activity (48) since 1900 with opposing and possibly largely cancelling effects on DTR (16, 49, 50), so with impacts likely to be important locally, but likely to be small at a regional scale (1). Precipitation had a large annual variability but its long-term trend was negligible during the last century (1), and so likely also its impact on the long-term trend of DTR. At annual timescale or station-scale changes of precipitation and land-cover/land introduce substantial uncertainties. Therefore, use of DTR for estimates of variability of R_s and its impact on T_a should be confined to decadal timescale and regional space scale.

Because of the sparse distribution of measurement stations (1) and changes in measurement methods (38) and instruments (25, 51), direct observations cannot provide a reliable estimate of R_s over land during the past century, nor do current climate models generate long-term variability of R_s (52). This study qualitatively reconstructs R_s over land from 1990 to 2010 using the latest homogenized DTR observations at globally distributed weather stations. It infers that R_s over land globally peaked in the late 1930s, substantially decreased from the 1940s to the 1970s, and changed little after that. These estimates are consistent with observations of R_s and sunshine duration where these observations are available.

More importantly, the DTR observations allow us to estimate the impact of R_s on the observed changes of T_a. Only changes before 1984 appear related to the observed temperature trends and DTR variability after 1995 indicates a negligible global impact of R_s variability. The small impact of R_s on T_a may be partly a result of the low sensitivity of T_a to R_s, much lower than the sensitivity of T_a to longwave radiation caused by greenhouse gases (53).

The surface energy budget directly determines the Earth’s surface climate and its changes, but on more local scales strongly interacts with transport processes. In consequence, most existing studies have focused on the energy balance at the top of the atmosphere (5), which is indirectly related to surface T_a, depending on how clouds (54, 55), aerosols, and other feedbacks work. This paper provides a direct and simple method to estimate the variability of R_s over land, which is applied from 1900 to 2010 and estimates the impact of this variability on surface temperature change.

Changes of R_s are primarily determined by changes of clouds and aerosols. Aerosols are known to have accounted for variability of R_s in Europe and China (25, 56), while clouds have been used to explain changes of R_s in the United States (36, 37) during the last two decades. Natural variability from clouds is expected to be more regional and of shorter timescale than the trends from aerosols, but otherwise we are not able to separate their effects. This paper also does not address the mechanisms through which clouds and aerosols respond to climate change (57), i.e., through changes of cloud-cover fraction or cloud height (58).

Our analysis of impact of R_s on T_a does not account for warming effect of solar radiation absorbed by aerosols, i.e., from black carbon (59 ⇓–61). To zeroth order, aerosol absorption within the daytime boundary layer will return the solar energy removed from the surface, so will not change DTR but will contribute to warming T_a. Our analysis, in principle, cannot include the warming of absorbing aerosols in the aerosol layer although their scattering and absorption effects on surface R_s are included.

Acknowledgments

Chinese homogenized daily maximum and minimum temperature at 549 stations were provided by Prof. Zhongwei Yan. GEBA surface incident solar radiation data were kindly provided by Prof. Martin Wild. We thank Dr. Qian Ma for processing some data for this study. This study was supported by the National Basic Research Program of China (2012CB955302), the National Natural Science Foundation of China (41175126), and the US Department of Energy (BER) Grant DE-FG02-09ER64746.

Footnotes

↵¹To whom correspondence should be addressed. E-mail: kcwang{at}bnu.edu.cn.

Author contributions: K.W. designed research; K.W. performed research; K.W. and R.E.D. analyzed data; and K.W. and R.E.D. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1311433110/-/DCSupplemental.

Freely available online through the PNAS open access option.

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2. Dickinson RE
(2013) Global atmospheric downward longwave radiation at the surface from ground-based observations, satellite retrievals and reanalyses. Rev Geophys 51(2):150–185.
OpenUrl CrossRef
↵
1. Dickinson RE,
2. Cicerone RJ
(1986) Future global warming from atmospheric trace gases. Nature 319(6049):109–115.
OpenUrl CrossRef
↵
1. Jacobsen A
(1999) Estimation of the soil heat flux/net radiation ratio based on spectral vegetation indexes in high-latitude Arctic areas. Int J Remote Sens 20(2):445–461.
OpenUrl CrossRef
↵
1. Clothier BE,
2. et al.
(1986) Estimation of soil heat flux from net radiation during the growth of alfalfa. Agric For Meteorol 37(4):319–329.
OpenUrl CrossRef
↵
1. Wallace JM,
2. Fu Q,
3. Smoliak BV,
4. Lin P,
5. Johanson CM
(2012) Simulated versus observed patterns of warming over the extratropical Northern Hemisphere continents during the cold season. Proc Natl Acad Sci USA 109(36):14337–14342.
OpenUrl Abstract/FREE Full Text
↵
1. Wild M,
2. et al.
(2009) Global dimming and brightening: An update beyond 2000. J Geophys Res 114(D10):D00D13.
OpenUrl CrossRef
↵
1. Zhou LM,
2. Dickinson RE,
3. Dai AG,
4. Dirmeyer P
(2010) Detection and attribution of anthropogenic forcing to diurnal temperature range changes from 1950 to 1999: Comparing multi-model simulations with observations. Clim Dyn 35(7-8):1289–1307.
OpenUrl CrossRef
↵
1. Zhou LM,
2. et al.
(2004) Evidence for a significant urbanization effect on climate in China. Proc Natl Acad Sci USA 101(26):9540–9544.
OpenUrl Abstract/FREE Full Text
↵
1. Mukherji A,
2. et al.
(2009) Revitalizing Asia's Irrigation: To Sustainably Meet Tomorrow’s Food Needs (Food and Agricultural Organization of the United Nations, Rome) Rome,.
↵
1. Geerts B
(2002) On the effects of irrigation and urbanisation on the annual range of monthly-mean temperatures. Theor Appl Climatol 72(3-4):157–163.
OpenUrl CrossRef
↵
1. Sacks WJ,
2. Cook BI,
3. Buenning N,
4. Levis S,
5. Helkowski JH
(2009) Effects of global irrigation on the near-surface climate. Clim Dyn 33(2-3):159–175.
OpenUrl CrossRef
↵
1. Wang KC,
2. Augustine J,
3. Dickinson RE
(2012) Critical assessment of surface incident solar radiation observations collected by SURFRAD, USCRN and AmeriFlux networks from 1995 to 2011. J Geophys Res 117(D23):D23105.
OpenUrl CrossRef
↵
1. Wild M,
2. Schmucki E
(2011) Assessment of global dimming and brightening in IPCC-AR4/CMIP3 models and ERA40. Clim Dyn 37(7):1671–1688.
OpenUrl CrossRef
↵
1. Stanhill G
(2011) The role of water vapor and solar radiation in determining temperature changes and trends measured at Armagh, 1881-2000. J Geophys Res 116(D3):D03105.
OpenUrl CrossRef
↵
1. Spencer RW,
2. Braswell WD
(2010) On the diagnosis of radiative feedback in the presence of unknown radiative forcing. J Geophys Res 115(D16):D16109.
OpenUrl CrossRef
↵
1. Dessler AE
(2010) A determination of the cloud feedback from climate variations over the past decade. Science 330(6010):1523–1527.
OpenUrl Abstract/FREE Full Text
↵
1. Streets DG,
2. Wu Y,
3. Chin M
(2006) Two-decadal aerosol trends as a likely explanation of the global dimming/brightening transition. Geophys Res Lett 33(15):L15806.
OpenUrl CrossRef
↵
1. Kerr RA
(2010) Climate change. El Niño lends more confidence to strong global warming. Science 330(6010):1465.
OpenUrl Abstract/FREE Full Text
↵
1. Davies R,
2. Molloy M
(2012) Global cloud height fluctuations measured by MISR on Terra from 2000 to 2010. Geophys Res Lett 39(3):L03701.
OpenUrl
↵
1. Jacobson MZ
(2012) Investigating cloud absorption effects: Global absorption properties of black carbon, tar balls, and soil dust in clouds and aerosols. J Geophys Res 117(D6):D06205.
OpenUrl CrossRef
↵
1. Ramanathan V,
2. et al.
(2005) Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proc Natl Acad Sci USA 102(15):5326–5333.
OpenUrl Abstract/FREE Full Text
↵
1. Ramanathan V,
2. et al.
(2007) Warming trends in Asia amplified by brown cloud solar absorption. Nature 448(7153):575–578.
OpenUrl CrossRef PubMed

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Proceedings of the National Academy of Sciences: 110 (37)

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[149] Cicerone RJ

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[153] Clothier BE,

[154] et al.

[155] ↵
Wallace JM,
Fu Q,
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Lin P,
Johanson CM
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[156] Wallace JM,

[157] Fu Q,

[158] Smoliak BV,

[159] Lin P,

[160] Johanson CM

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[162] Wild M,

[163] et al.

[164] ↵
Zhou LM,
Dickinson RE,
Dai AG,
Dirmeyer P
(2010) Detection and attribution of anthropogenic forcing to diurnal temperature range changes from 1950 to 1999: Comparing multi-model simulations with observations. Clim Dyn 35(7-8):1289–1307.
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[165] Zhou LM,

[166] Dickinson RE,

[167] Dai AG,

[168] Dirmeyer P

[169] ↵
Zhou LM,
et al.
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[170] Zhou LM,

[171] et al.

[172] ↵
Mukherji A,
et al.
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[173] Mukherji A,

[174] et al.

[175] ↵
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(2002) On the effects of irrigation and urbanisation on the annual range of monthly-mean temperatures. Theor Appl Climatol 72(3-4):157–163.
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[176] Geerts B

[177] ↵
Sacks WJ,
Cook BI,
Buenning N,
Levis S,
Helkowski JH
(2009) Effects of global irrigation on the near-surface climate. Clim Dyn 33(2-3):159–175.
OpenUrl CrossRef

[178] Sacks WJ,

[179] Cook BI,

[180] Buenning N,

[181] Levis S,

[182] Helkowski JH

[183] ↵
Wang KC,
Augustine J,
Dickinson RE
(2012) Critical assessment of surface incident solar radiation observations collected by SURFRAD, USCRN and AmeriFlux networks from 1995 to 2011. J Geophys Res 117(D23):D23105.
OpenUrl CrossRef

[184] Wang KC,

[185] Augustine J,

[186] Dickinson RE

[187] ↵
Wild M,
Schmucki E
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OpenUrl CrossRef

[188] Wild M,

[189] Schmucki E

[190] ↵
Stanhill G
(2011) The role of water vapor and solar radiation in determining temperature changes and trends measured at Armagh, 1881-2000. J Geophys Res 116(D3):D03105.
OpenUrl CrossRef

[191] Stanhill G

[192] ↵
Spencer RW,
Braswell WD
(2010) On the diagnosis of radiative feedback in the presence of unknown radiative forcing. J Geophys Res 115(D16):D16109.
OpenUrl CrossRef

[193] Spencer RW,

[194] Braswell WD

[195] ↵
Dessler AE
(2010) A determination of the cloud feedback from climate variations over the past decade. Science 330(6010):1523–1527.
OpenUrl Abstract/FREE Full Text

[196] Dessler AE

[197] ↵
Streets DG,
Wu Y,
Chin M
(2006) Two-decadal aerosol trends as a likely explanation of the global dimming/brightening transition. Geophys Res Lett 33(15):L15806.
OpenUrl CrossRef

[198] Streets DG,

[199] Wu Y,

[200] Chin M

[201] ↵
Kerr RA
(2010) Climate change. El Niño lends more confidence to strong global warming. Science 330(6010):1465.
OpenUrl Abstract/FREE Full Text

[202] Kerr RA

[203] ↵
Davies R,
Molloy M
(2012) Global cloud height fluctuations measured by MISR on Terra from 2000 to 2010. Geophys Res Lett 39(3):L03701.
OpenUrl

[204] Davies R,

[205] Molloy M

[206] ↵
Jacobson MZ
(2012) Investigating cloud absorption effects: Global absorption properties of black carbon, tar balls, and soil dust in clouds and aerosols. J Geophys Res 117(D6):D06205.
OpenUrl CrossRef

[207] Jacobson MZ

[208] ↵
Ramanathan V,
et al.
(2005) Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proc Natl Acad Sci USA 102(15):5326–5333.
OpenUrl Abstract/FREE Full Text

[209] Ramanathan V,

[210] et al.

[211] ↵
Ramanathan V,
et al.
(2007) Warming trends in Asia amplified by brown cloud solar absorption. Nature 448(7153):575–578.
OpenUrl CrossRef PubMed

[212] Ramanathan V,

[213] et al.

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Contribution of solar radiation to decadal temperature variability over land

Significance

Abstract

Results

Relationship Between R_s and DTR.

Variability of DTR, a Proxy of R_s, from 1900 to 2010.

Estimation of the Impact of R_s on T_a from 1900 to 2010.

Discussion and Conclusions

Acknowledgments

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Significance

Abstract

Results

Relationship Between Rs and DTR.

Variability of DTR, a Proxy of Rs, from 1900 to 2010.

Estimation of the Impact of Rs on Ta from 1900 to 2010.

Discussion and Conclusions

Acknowledgments

Footnotes

References

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Relationship Between R_s and DTR.

Variability of DTR, a Proxy of R_s, from 1900 to 2010.

Estimation of the Impact of R_s on T_a from 1900 to 2010.