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Keywords = global dimming and brightening

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16 pages, 11260 KiB  
Article
A Numerical Modeling Study on the Earth’s Surface Brightening Effect of Cirrus Thinning
by Xiangjun Shi, Yuxin Liu and Jiaojiao Liu
Atmosphere 2024, 15(2), 189; https://doi.org/10.3390/atmos15020189 - 1 Feb 2024
Viewed by 1718
Abstract
Cirrus thinning, as one kind of geoengineering approach, not only cools our planet but also enhances the amount of sunlight reaching the Earth’s surface (brightening effect). This study delves into the brightening effect induced by cirrus thinning with a flexible seeding method. The [...] Read more.
Cirrus thinning, as one kind of geoengineering approach, not only cools our planet but also enhances the amount of sunlight reaching the Earth’s surface (brightening effect). This study delves into the brightening effect induced by cirrus thinning with a flexible seeding method. The thinning of cirrus clouds alone leads to a considerable globally averaged cooling effect (−2.46 W m−2), along with a notable globally averaged brightening effect (2.19 W m−2). Cirrus thinning also results in substantial reductions in the cloud radiative effects of the lower mixed-phase and liquid clouds. While these reductions counteract the cooling effect from cirrus clouds, they enhance the brightening effect from cirrus clouds. Consequently, the brightening effect caused by cirrus seeding (4.69 W m−2) is considerably stronger than its cooling effect (−1.21 W m−2). Furthermore, due to the more pronounced changes from the mixed-phase and liquid clouds at low and mid-latitudes, the cooling effect is primarily concentrated at high latitudes. In contrast, the brightening effect is stronger over most low- and mid-latitude regions. Overall, cirrus thinning could lead to a notable brightening effect, which can be leveraged to offset the dimming effect (the opposite of the brightening effect) of other geoengineering approaches. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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Figure 1

Figure 1
<p>Schematic diagram of cirrus thinning methods. Shown are the reference simulation without seeding (REF, black), seeding ice nuclei particles simulation (SEED, red), and only heterogeneous nucleation simulation (HET, green). The ice supersaturation (<span class="html-italic">S</span><sub>i</sub>, units: %) and number concentration of ice crystals (<span class="html-italic">N</span><sub>i</sub>, units: L<sup>−1</sup>) in the air parcel are represented by dashed and solid lines, respectively. All simulations start with common initial conditions (<span class="html-italic">N</span><sub>INP</sub> = 10 L<sup>−1</sup>, <span class="html-italic">N</span><sub>sul</sub> = 500,000 L<sup>−1</sup>, <span class="html-italic">P</span> = 330 hPa, <span class="html-italic">T</span> = 220 K, and <span class="html-italic">W</span> = 0.3 m s<sup>−1</sup>).</p>
Full article ">Figure 2
<p>Annual zonal mean of newly formed ice crystal number concentration in cirrus clouds (<span class="html-italic">N</span><sub>inuc</sub>, <b>first row</b>) and in-cloud ice crystal number concentration (<span class="html-italic">N</span><sub>i</sub>, <b>second row</b>), and spatial distributions of vertically integrated <span class="html-italic">N</span><sub>i</sub> (column <span class="html-italic">N</span><sub>i</sub>, <b>third row</b>). Simulation names and globally averaged values are displayed at the top. The zonal mean results are derived from model grids where the occurrence frequency of corresponding events is greater than 0.1%. The two black lines denote specific temperatures (0 and −37 °C).</p>
Full article ">Figure 3
<p>Annual zonal mean ice water content (IWC, <b>first row</b>) and spatial distribution of ice water path (IWP, <b>second row</b>). The <b>third and fourth rows</b> respectively denote the liquid water content (LWC) and the ice water path (IWP). The first column displays the REF simulation, while the second and third columns represent the discrepancies (“Δ”) in relation to the REF simulation from both HET and SEED simulations. Global mean values and corresponding standard deviations (in brackets) are shown in the upper right corner. Hatching represents the nonsignificant area at the 90% confidence level of <span class="html-italic">t</span>-test.</p>
Full article ">Figure 4
<p>Annual mean maps of cirrus cloud optical depth in longwave band (iCOD<sub>lw</sub>, <b>first row</b>) and shortwave band (iCOD<sub>sw</sub>, <b>third row</b>), and optical depth from mixed-phase and liquid clouds in long-wave band (mlCOD<sub>lw</sub>, <b>second row</b>) and short-wave band (mlCOD<sub>sw</sub>, <b>fourth row</b>). Global mean values and corresponding standard deviations (in brackets) are shown in the upper right corner. Hatching represents the nonsignificant area at the 90% confidence level of <span class="html-italic">t</span>-test.</p>
Full article ">Figure 5
<p>Annual mean maps of cirrus cloud radiative effect (iCRE<sub>TOA</sub>, <b>first row</b>), its longwave (iCRE<sub>TOAlw</sub>, <b>second row</b>) and shortwave (iCRE<sub>TOAsw</sub>, <b>third row</b>) components, and brightness radiative effect (iCRE<sub>bri</sub>, <b>fourth row</b>). Global mean values and corresponding standard deviations (in brackets) are shown in the upper right corner. Hatching represents the nonsignificant area at the 90% confidence level of <span class="html-italic">t</span>-test.</p>
Full article ">Figure 6
<p>Similar to <a href="#atmosphere-15-00189-f005" class="html-fig">Figure 5</a>, but for mixed-phase and liquid cloud radiative effects (mlCRE<sub>TOA</sub>, mlCRE<sub>TOAlw</sub>, mlCRE<sub>TOAsw</sub>, and mlCRE<sub>bri</sub>).</p>
Full article ">Figure 7
<p>Similar to <a href="#atmosphere-15-00189-f005" class="html-fig">Figure 5</a>, but for the entire clouds’ (ice, mixed-phase, and liquid) radiative effect (CRE<sub>TOA</sub>, CRE<sub>TOAlw</sub>, CRE<sub>TOAsw</sub>, and CRE<sub>bri</sub>).</p>
Full article ">
5 pages, 1284 KiB  
Proceeding Paper
The Global and Diffuse Solar Radiation Trends Using GEBA & BSRN Ground Based Measurements during 1984–2018
by Michael Stamatis, Pavlos Ioannou, Marios-Bruno Korras-Carraca and Nikolaos Hatzianastassiou
Environ. Sci. Proc. 2023, 26(1), 141; https://doi.org/10.3390/environsciproc2023026141 - 31 Aug 2023
Viewed by 715
Abstract
Surface solar radiation (SSR) is a crucial parameter for both the Earth’s climate and human activities, and it consists of two components: the direct beam from the sun and diffuse radiation, with the latter being scattered by atmospheric molecules, aerosols, or clouds. The [...] Read more.
Surface solar radiation (SSR) is a crucial parameter for both the Earth’s climate and human activities, and it consists of two components: the direct beam from the sun and diffuse radiation, with the latter being scattered by atmospheric molecules, aerosols, or clouds. The multidecadal variations of SSR, known as Global Dimming and Brightening (GDB), should also arise from a corresponding variability of either the direct or the diffuse radiation. Thus, the determination of the trends of both the direct and the diffuse radiation is important for showing the causes of GDB. In the present study, we estimate the trends of global and diffuse radiation on a global scale during the period 1984–2018, using worldwide reference ground-based measurements from the Global Energy Balance Archive (GEBA) and the Baseline Surface Radiation Network (BSRN). An increasing tendency of SSR is observed over most locations on our planet, while a decreasing trend occurs in India. On the other hand, the diffuse radiation has decreased over Europe and parts of Asia, whereas it has increased over the USA, India, and East Asia. Full article
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Figure 1

Figure 1
<p>Global distribution of changes in (<b>a</b>) SSR and (<b>b</b>) diffuse radiation (in W/m<sup>2</sup>) during the years 1984–2018 at 64 globally distributed GEBA stations. Reddish and yellow colors indicate increasing trends, while bluish colors indicate decreasing trends. The statistical significance of each trend is denoted by embedded “x” symbols.</p>
Full article ">Figure 2
<p>The global distribution of changes in (<b>a</b>) SSR and (<b>b</b>) diffuse radiation (in W/m<sup>2</sup>) during the years 1992–2018 at 23 globally distributed BSRN stations. Reddish and yellow colors indicate increasing trends, while bluish colors indicate decreasing trends. The statistical significance of each trend is denoted by embedded “x” symbols.</p>
Full article ">
5 pages, 1684 KiB  
Proceeding Paper
On the Contribution of Aerosols and Clouds to Global Dimming and Brightening Using a Radiative Transfer Model, ISCCP-H Cloud and MERRA-2 Aerosol Optical Properties
by Michael Stamatis, Nikolaos Hatzianastassiou, Marios-Bruno Korras-Carraca, Christos Matsoukas, Martin Wild and Ilias Vardavas
Environ. Sci. Proc. 2023, 26(1), 34; https://doi.org/10.3390/environsciproc2023026034 - 24 Aug 2023
Cited by 1 | Viewed by 878
Abstract
The interdecadal changes of the incident solar radiation at the Earth’s surface (SSR) are mainly driven by changes in clouds and aerosols. In order to investigate their contribution to the SSR changes (global dimming and brightening or GDB), the FORTH radiative transfer model [...] Read more.
The interdecadal changes of the incident solar radiation at the Earth’s surface (SSR) are mainly driven by changes in clouds and aerosols. In order to investigate their contribution to the SSR changes (global dimming and brightening or GDB), the FORTH radiative transfer model (RTM) is used to compute the SSR fluxes. The cloud input data were taken from satellite observations of ISCCP-H, while aerosols and meteorological data were taken from the MERRA-2 reanalysis dataset. The RTM operates on a monthly basis and in 0.5° × 0.625° latitude-longitude spatial resolution. The GDB was also computed keeping constant at their initial 1984 values, each input parameter that was examined, resulting in a GDB with the ‘frozen’ parameter. The contribution of each parameter to the GDB is defined as the subtraction of the frozen GDB from the base-run GDB, and the positive/negative values of the contribution indicate that the interdecadal variability of the examined parameter increased/decreased the SSR. The aerosol optical depth (AOD) produced a dimming in India, Amazonia, and S. China, whereas it induced a brightening in Europe and Mexico. On the other hand, the total cloud cover (TCC) changes caused a dimming over the Arctic, Australia, and the South Ocean against a brightening in Europe, Mexico, the Middle East, and South America. The global mean contribution of changing AOD is 0.37 W/m2, and for TCC, it is 4.7 W/m2, indicating that globally, the counteraction of cloud cover to the overall global dimming is larger. Opposite contributions to GDB from AOD and TCC may occur over specific regions, highlighting the complexity of the causes of the GDB phenomenon. Full article
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Figure 1

Figure 1
<p>Global distribution of the contribution of AOD interdecadal changes to the RTM GDB in W/m<sup>2</sup> during 1984–2018. Reddish and yellow colors indicate positive, while greenish and bluish colors have negative contributions.</p>
Full article ">Figure 2
<p>Global distribution of the contribution of TCC interdecadal changes to the RTM GDB in W/m<sup>2</sup> during 1984–2018. Reddish and yellow colors indicate positive, while greenish and bluish colors have negative contributions.</p>
Full article ">
37 pages, 24503 KiB  
Article
An Assessment of Global Dimming and Brightening during 1984–2018 Using the FORTH Radiative Transfer Model and ISCCP Satellite and MERRA-2 Reanalysis Data
by Michael Stamatis, Nikolaos Hatzianastassiou, Marios-Bruno Korras-Carraca, Christos Matsoukas, Martin Wild and Ilias Vardavas
Atmosphere 2023, 14(8), 1258; https://doi.org/10.3390/atmos14081258 - 8 Aug 2023
Cited by 6 | Viewed by 2502
Abstract
In this study, an assessment of the FORTH radiative transfer model (RTM) surface solar radiation (SSR) as well as its interdecadal changes (Δ(SSR)), namely global dimming and brightening (GDB), is performed during the 35-year period of 1984–2018. Furthermore, a thorough evaluation of SSR [...] Read more.
In this study, an assessment of the FORTH radiative transfer model (RTM) surface solar radiation (SSR) as well as its interdecadal changes (Δ(SSR)), namely global dimming and brightening (GDB), is performed during the 35-year period of 1984–2018. Furthermore, a thorough evaluation of SSR and (Δ(SSR)) is conducted against high-quality reference surface measurements from 1193 Global Energy Balance Archive (GEBA) and 66 Baseline Surface Radiation Network (BSRN) stations. For the first time, the FORTH-RTM Δ(SSR) was evaluated over an extended period of 35 years and with a spatial resolution of 0.5° × 0.625°. The RTM uses state-of-the-art input products such as MERRA-2 and ISCCP-H and computes 35-year-long monthly SSR and GDB, which are compared to a comprehensive dataset of reference measurements from GEBA and BSRN. Overall, the FORTH-RTM deseasonalized SSR anomalies correlate satisfactorily with either GEBA (R equal to 0.72) or BSRN (R equal to 0.80). The percentage of agreement between the sign of computed GEBA and FORTH-RTM Δ(SSR) is equal to 63.5% and the corresponding percentage for FORTH-RTM and BSRN is 54.5%. The obtained results indicate that a considerable and statistically significant increase in SSR (Brightening) took place over Europe, Mexico, Brazil, Argentina, Central and NW African areas, and some parts of the tropical oceans from the early 1980s to the late 2010s. On the other hand, during the same 35-year period, a strong and statistically significant decrease in SSR (Dimming) occurred over the western Tropical Pacific, India, Australia, Southern East China, Northern South America, and some parts of oceans. A statistically significant dimming at the 95% confidence level, equal to −0.063 Wm−2 year−1 (or −2.22 Wm−2) from 1984 to 2018 is found over the entire globe, which was more prevalent over oceanic than over continental regions (−0.07 Wm−2 year−1 and −0.03 Wm−2 year−1, statistically significant dimming at the 95% confidence level, respectively) in both hemispheres. Yet, this overall 35-year dimming arose from alternating decadal-scale changes, consisting of dimming during 1984–1989, brightening in the 1990s, turning into dimming over 2000–2009, and brightening during 2010–2018. Full article
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Figure 1

Figure 1
<p>Global Distribution of: (<b>a</b>) 1193 GEBA stations and (<b>b</b>) 66 BSRN stations, whose SSR data (availability in years indicated in the color bar) have been used in this study for the evaluation of the FORTH-RTM GDB.</p>
Full article ">Figure 2
<p>The methodology followed for the assessment/evaluation of the FORTH-RTM GDB and SSR.</p>
Full article ">Figure 3
<p>Global distribution of the long-term (1984–2018) average FORTH-RTM SSR. The average SSR of the GEBA and BSRN stations, which are used to validate the RTM, is also shown.</p>
Full article ">Figure 4
<p>Scatterplot comparison between FORTH-RTM and GEBA (<b>i-a</b>), and BSRN (<b>ii-a</b>) SSR fluxes, and between FORTH-RTM and GEBA (<b>i-b</b>) and BSRN (<b>ii-b</b>) deseasonalized SSR anomalies. The applied linear regression fit (red line), and the associated main statistical metrics, namely the equation of the applied linear fitting, the correlation coefficient (R), the root-mean-squared error (RMSE), the bias, and the total number of matched data pairs, are also shown.</p>
Full article ">Figure 5
<p>Seasonal variation of hemispherical averages of correlation coefficient R (<b>a</b>), bias in W/m<sup>2</sup> (<b>b</b>), percent relative bias in % (<b>c</b>), root mean squared error (RMSE in W/m<sup>2</sup>) (<b>d</b>), and percent relative RMSE in % (<b>e</b>) between the FORTH-RTM and GEBA (blue) and BSRN (red) stations. Results, computed using SSR fluxes (except for R, which is computed using deseasonalized SSR anomalies), are given for the North Hemisphere (<b>i-left column</b>) and South Hemisphere (<b>ii</b>-<b>right column</b>).</p>
Full article ">Figure 6
<p>Global distribution of correlation coefficient (<b>a</b>), bias (<b>b</b>) and relative percent bias (<b>c</b>) root mean squared error (<b>d</b>), relative root mean squared error (<b>e</b>), computed for the comparison between FORTH-RTM and each GEBA (<b>i</b>, <b>left column</b>) and BSRN (<b>ii</b>, <b>right column</b>) station SSR fluxes, except for R that is computed using deseasonalized SSR anomalies.</p>
Full article ">Figure 6 Cont.
<p>Global distribution of correlation coefficient (<b>a</b>), bias (<b>b</b>) and relative percent bias (<b>c</b>) root mean squared error (<b>d</b>), relative root mean squared error (<b>e</b>), computed for the comparison between FORTH-RTM and each GEBA (<b>i</b>, <b>left column</b>) and BSRN (<b>ii</b>, <b>right column</b>) station SSR fluxes, except for R that is computed using deseasonalized SSR anomalies.</p>
Full article ">Figure 7
<p>Scatterplot comparison between FORTH-RTM and GEBA (<b>a</b>), and FORTH-RTM and BSRN (<b>b</b>) Δ(SSR) or GDB (in Wm<sup>−2</sup>). The linear regression fit, and the associated statistical metrics, namely the equation of the applied linear fitting, the correlation coefficient (R), the root-mean-squared error (RMSE), the bias (FORTH-RTM minus stations), and the total number of matched data pairs, are also shown.</p>
Full article ">Figure 8
<p>Time series of 12-month moving averages of SSR anomalies for GEBA stations ((<b>a</b>), blue curve) and BSRN stations ((<b>b</b>), blue curve) along with those for the corresponding FORTH-RTM pixels (red curves). The number of available stations for each month is also shown in green color. The computed Δ(SSR) and the associated <span class="html-italic">p</span>-values for the two sets of time series (1984–2018 for GEBA and 1992–2018 for BSRN) are given at the top of each figure.</p>
Full article ">Figure 9
<p>Computed GDB (or Δ(SSR), in Wm<sup>−2</sup>) for GEBA (<b>i-a</b>) and BSRN (<b>ii-a</b>) stations and for the corresponding pixels of FORTH-RTM containing the GEBA (<b>i-b</b>) and BSRN (<b>ii-b</b>) stations. Negative values, shown in blue and green colors, indicate dimming, and positive values, in yellow, orange, and red colors, indicate brightening. The embedded white × symbols indicate trends that are statistically significant (at the 95% confidence level, assessed by applying the non-parametric Mann–Kendall test). The trends are estimated over the periods covered by each station (shown in <a href="#app1-atmosphere-14-01258" class="html-app">Figure S2</a>).</p>
Full article ">Figure 10
<p>Agreement (blue dots) and disagreement (red dots) between trends of FORTH-RTM and GEBA (<b>a</b>) and BSRN (<b>b</b>) station deseasonalized SSR anomalies. The embedded white “×” symbols indicate the statistically significant trends for both FORTH-RTM and stations. The numbers at the top of the plots provide the percentage of stations for which there is agreement/disagreement (first number) in the sign of GDB, while the corresponding numbers in parentheses refer to statistically significant trends.</p>
Full article ">Figure 11
<p>Agreement (blue dots) and disagreement (red dots) between trends of FORTH-RTM and station deseasonalized SSR anomalies (Δ(SSR)), for GEBA (<b>i-a</b>) and BSRN (<b>ii-a</b>) stations for the period 01/2001–01/2018. The stations showing agreement at the surface against GEBA stations (<b>i-a</b>) and BSRN stations (<b>ii-a</b>), also having agreement of Δ(OSR) with CERES at TOA, during the same period, are shown in (<b>i-b</b>,<b>ii-b</b>), respectively, with blue rectangles, while those having disagreement at TOA with red rectangles. The embedded white “×” symbols indicate the statistically significant trends for both FORTH-RTM and stations. At the top of each figure, the percentage number of stations (<b>a</b>) and CERES pixels (<b>b</b>) that agree in the sign of slopes with the FORTH-RTM pixels is shown.</p>
Full article ">Figure 12
<p>Global distribution of changes of FORTH-RTM GDB, namely changes of deseasonalized SSR anomalies, over the 35-year period 01/1984–12/2018. Areas shaded with reddish/bluish colors are those with positive/negative trends (brightening/dimming). Areas (pixels) with statistically significant trends are indicated by black dots. World land areas (Europe, India, and Japan) enclosed by red rectangles are characterized by homogeneous SSR trends (reference is made to them below).</p>
Full article ">Figure 13
<p>Time series of FORTH-RTM average global (black line) and hemispherical (NH and SH, green and red lines) monthly mean deseasonalized SSR anomalies for the period January 1984–December 2018. Linear regression fit line, along with associated Δ(SSR), the <span class="html-italic">p</span>-value inside the brackets, and the 4th-order polynomial fit applied to the global time series of FORTH-RTM SSR are also shown.</p>
Full article ">Figure 14
<p>Time series (01/1984–12/2018) of deseasonalized SSR anomalies for three selected world areas with homogeneous trends (see red rectangles in <a href="#atmosphere-14-01258-f012" class="html-fig">Figure 12</a>), namely, Europe (<b>a</b>), Japan (<b>b</b>), and India (<b>c</b>). For every area, results are given based on GEBA stations (red dotted lines), the corresponding FORTH-RTM pixels (including the stations, green dotted lines), and all the FORTH-RTM pixels of the areas (blue lines). Moreover, the linearly fitted lines to each time series are also displayed with similar colors, while the blue dashed line displaying the 4th-order polynomial fit applied to the time series of FORTH-RTM SSR for the entire world areas is also shown. The SSR changes along with their <span class="html-italic">p</span>-values inside the brackets are also shown.</p>
Full article ">Figure 14 Cont.
<p>Time series (01/1984–12/2018) of deseasonalized SSR anomalies for three selected world areas with homogeneous trends (see red rectangles in <a href="#atmosphere-14-01258-f012" class="html-fig">Figure 12</a>), namely, Europe (<b>a</b>), Japan (<b>b</b>), and India (<b>c</b>). For every area, results are given based on GEBA stations (red dotted lines), the corresponding FORTH-RTM pixels (including the stations, green dotted lines), and all the FORTH-RTM pixels of the areas (blue lines). Moreover, the linearly fitted lines to each time series are also displayed with similar colors, while the blue dashed line displaying the 4th-order polynomial fit applied to the time series of FORTH-RTM SSR for the entire world areas is also shown. The SSR changes along with their <span class="html-italic">p</span>-values inside the brackets are also shown.</p>
Full article ">
36 pages, 9516 KiB  
Article
Interdecadal Changes of the MERRA-2 Incoming Surface Solar Radiation (SSR) and Evaluation against GEBA & BSRN Stations
by Michael Stamatis, Nikolaos Hatzianastassiou, Marios Bruno Korras-Carraca, Christos Matsoukas, Martin Wild and Ilias Vardavas
Appl. Sci. 2022, 12(19), 10176; https://doi.org/10.3390/app121910176 - 10 Oct 2022
Cited by 8 | Viewed by 2697
Abstract
This study assesses and evaluates the 40-year (1980–2019) Modern-Era Retrospective Analysis for Research and Applications v.2 (MERRA-2) surface solar radiation (SSR) as well as its interdecadal changes (Δ(SSR)) against high quality reference surface measurements from 1397 Global Energy Balance Archive (GEBA) and 73 [...] Read more.
This study assesses and evaluates the 40-year (1980–2019) Modern-Era Retrospective Analysis for Research and Applications v.2 (MERRA-2) surface solar radiation (SSR) as well as its interdecadal changes (Δ(SSR)) against high quality reference surface measurements from 1397 Global Energy Balance Archive (GEBA) and 73 Baseline Surface Radiation Network (BSRN) stations. The study is innovative since MERRA-2 (Δ(SSR)) has never been evaluated in the past, while the MERRA-2 SSR fluxes themselves have not been evaluated in such large spatial scale, which is global here, and temporal basis, which counts 40-years. Other novelties of the study are the use of the highest quality BSRN stations, done for the first time in such an evaluation, as well as the use of a greater number of reference-GEBA stations than in other studies. Moreover, the assessment and evaluation in this study are largely based on SSR anomalies, while being done in depth, at spatial scales ranging from the local to global/hemispherical, and separately for land and ocean areas, and at temporal scales spanning intervals from decadal sub-periods to 40 years. Overall, the MERRA-2 deseasonalized SSR anomalies correlate well with either GEBA (R equal to 0.61) and BSRN (R equal to 0.62). The percentage of agreement between the sign of computed GEBA and MERRA-2 Δ(SSR) is equal to 63.4% and the corresponding percentage for MERRA-2 and BSRN is 50%. According to MERRA-2, strong and statistically significant positive Δ(SSR) (Brightening) is found over Europe, Central Africa, Mongolia, Mexico, Brazil, Argentina and some parts of the tropical oceans. In contrast, large and statistically significant negative Δ(SSR) (Dimming) occurs over the western Tropical Warm Pool, India, Southern East China, Amazonia, stratocumulus covered areas and some parts of oceans. MERRA-2 yields a dimming equal to −0.158 ± 0.005 W/m2/year over the globe from 1980 to 2019. This 40-year dimming, which occurred in both hemispheres, more over ocean than continental areas (−0.195 ± 0.006 and −0.064 ± 0.006 W/m2/year, respectively), underwent decadal scale variations. Full article
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Figure 1

Figure 1
<p>Global Distribution of: (<b>a</b>) Global Energy Balance Archive (GEBA) 1099 stations and (<b>b</b>) Baseline Surface Radiation Network (BSRN) 70 stations, whose surface solar radiation (SSR) data (availability in years indicated in the colorbar) have been used in this study.</p>
Full article ">Figure 2
<p>The steps of the applied methodology of assessment/evaluation of MERRA-2 GDB.</p>
Full article ">Figure 3
<p>Scatterplot comparison between MERRA-2 Reanalysis and GEBA (<b>i-a</b>), and BSRN (<b>ii-a</b>) SSR fluxes and between MERRA-2 and GEBA (<b>i-b</b>) and BSRN (<b>ii-b</b>) deseasonalized SSR anomalies. The linear regression fit, and the associated statistical metrics, namely the equation of the applied linear fitting, the correlation coefficient (R), the root-mean squared error (RMSE), the bias and the total number of matched data pairs, are also shown. The comparison is shown separately for each season (December–February, March–May, June–August and September–November) in different colors (the respective main statistical parameters are given in <a href="#applsci-12-10176-t001" class="html-table">Table 1</a>).</p>
Full article ">Figure 4
<p>Seasonal variation of hemispherical averages of correlation coefficient R (<b>i</b>,<b>ii-a</b>), bias in W/m<sup>2</sup> (<b>i</b>,<b>ii-b</b>), percent relative bias in % (<b>i</b>,<b>ii-c</b>), root mean squared error (RMSE in W/m<sup>2</sup>) (<b>i</b>,<b>ii-d</b>) and percent relative RMSE in % (<b>i</b>,<b>ii-e</b>) between MERRA-2 and GEBA (blue) and BSRN (red) stations, computed using SSR fluxes (except for R that is computed using deseasonalized SSR anomalies) for the North Hemisphere ((<b>i</b>)-left column) and South Hemisphere ((<b>ii</b>)-right column).</p>
Full article ">Figure 4 Cont.
<p>Seasonal variation of hemispherical averages of correlation coefficient R (<b>i</b>,<b>ii-a</b>), bias in W/m<sup>2</sup> (<b>i</b>,<b>ii-b</b>), percent relative bias in % (<b>i</b>,<b>ii-c</b>), root mean squared error (RMSE in W/m<sup>2</sup>) (<b>i</b>,<b>ii-d</b>) and percent relative RMSE in % (<b>i</b>,<b>ii-e</b>) between MERRA-2 and GEBA (blue) and BSRN (red) stations, computed using SSR fluxes (except for R that is computed using deseasonalized SSR anomalies) for the North Hemisphere ((<b>i</b>)-left column) and South Hemisphere ((<b>ii</b>)-right column).</p>
Full article ">Figure 5
<p>Global distribution of correlation coefficient (<b>i</b>,<b>ii-a</b>), bias (<b>i</b>,<b>ii-b</b>) and relative percent bias (<b>i</b>,<b>ii-c</b>) root mean squared error (<b>i</b>,<b>ii-d</b>), relative root mean squared error (<b>i</b>,<b>ii-e</b>), computed for the comparison between MERRA-2 and each GEBA ((<b>i</b>), left column) and BSRN ((<b>ii</b>), right column) station SSR fluxes, except for R that is computed using deseasonalized SSR anomalies.</p>
Full article ">Figure 5 Cont.
<p>Global distribution of correlation coefficient (<b>i</b>,<b>ii-a</b>), bias (<b>i</b>,<b>ii-b</b>) and relative percent bias (<b>i</b>,<b>ii-c</b>) root mean squared error (<b>i</b>,<b>ii-d</b>), relative root mean squared error (<b>i</b>,<b>ii-e</b>), computed for the comparison between MERRA-2 and each GEBA ((<b>i</b>), left column) and BSRN ((<b>ii</b>), right column) station SSR fluxes, except for R that is computed using deseasonalized SSR anomalies.</p>
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<p>Scatterplot comparison between MERRA-2 and GEBA (<b>a</b>), and MERRA-2 and BSRN (<b>b</b>) Δ(SSR) or GDB (in W/m<sup>2</sup>). The linear regression fit, and the associated statistical metrics, namely the slope, the slope error, the correlation coefficient (R), the root-mean squared error (RMSE), the bias (MERRA-2 minus stations) and the total number of matched data pairs, are also shown.</p>
Full article ">Figure 6 Cont.
<p>Scatterplot comparison between MERRA-2 and GEBA (<b>a</b>), and MERRA-2 and BSRN (<b>b</b>) Δ(SSR) or GDB (in W/m<sup>2</sup>). The linear regression fit, and the associated statistical metrics, namely the slope, the slope error, the correlation coefficient (R), the root-mean squared error (RMSE), the bias (MERRA-2 minus stations) and the total number of matched data pairs, are also shown.</p>
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<p>Time series of averaged SSR anomalies for GEBA stations ((<b>a</b>), blue curve) and BSRN stations ((<b>b</b>), blue curve) along with those for the corresponding MERRA-2 pixels (red curves). The number of available stations for each month is also shown in green color. 12-month moving averages for each time series are also shown in same colors. The computed Δ(SSR) and the associated standard deviation along with the correlation coefficient (R) between the two sets of time series (1980–2017 for GEBA and 1992–2020 for BSRN) are given at the top of figure.</p>
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<p>Computed GDB (or Δ(SSR), in W/m<sup>2</sup>) for GEBA (<b>i-a</b>) and BSRN (<b>ii-a</b>) stations and for the corresponding pixels of MERRA-2 containing the GEBA (<b>i-b</b>) and BSRN (<b>ii-b</b>) stations. Negative values, shown in blue and green colors, indicate dimming, and positive values, orange and red colors, indicate brightening. The embedded white x symbols indicate trends which are statistically significant (assessed at the 95% confidence level by applying the non-parametric Mann Kendall test). The trends are estimated over the periods covered by each station (shown in <a href="#app1-applsci-12-10176" class="html-app">Figure S2</a>).</p>
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<p>Agreement (blue dots) and disagreement (red dots) between trends of MERRA-2 and station deseasonalized SSR anomalies for GEBA (<b>a</b>) and BSRN (<b>b</b>) stations. The embedded white “x” symbols indicate the statistically significant trends for both MERRA-2 and stations. At the top left and top right parts above each panel figure, the numbers in parentheses provide the total number of stations for which there is agreement/disagreement (first number in parentheses) and the corresponding numbers for statistically significant trends (second number in parentheses). At the top center of each panel figure, the percent numbers of stations for which agreement, as well as those for which statistically significant agreement (in parenthesis), is found, are given.</p>
Full article ">Figure 9 Cont.
<p>Agreement (blue dots) and disagreement (red dots) between trends of MERRA-2 and station deseasonalized SSR anomalies for GEBA (<b>a</b>) and BSRN (<b>b</b>) stations. The embedded white “x” symbols indicate the statistically significant trends for both MERRA-2 and stations. At the top left and top right parts above each panel figure, the numbers in parentheses provide the total number of stations for which there is agreement/disagreement (first number in parentheses) and the corresponding numbers for statistically significant trends (second number in parentheses). At the top center of each panel figure, the percent numbers of stations for which agreement, as well as those for which statistically significant agreement (in parenthesis), is found, are given.</p>
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<p>Agreement (blue dots) and disagreement (red dots) between trends of MERRA-2 and station deseasonalized SSR anomalies (Δ(SSR)), for GEBA (<b>i-a</b>) and BSRN (<b>ii-a</b>) stations for the period 07/2000–06/2017. The stations showing agreement at the surface against GEBA stations (<b>i-a</b>) and BSRN stations (<b>ii-a</b>), also having agreement of Δ(OSR) with CERES at TOA, during the same period, are shown in (<b>i-b</b>) and (<b>ii-b</b>), respectively.</p>
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<p>Global distribution of GDB, namely changes of MERRA-2 deseasonalized SSR anomalies, over the 40-year period January 1980–December 2019. Areas shaded with reddish/bluish colors are those with positive/negative trends (brightening/dimming). Areas (pixels) with statistically significant trends are indicated by black dots. World areas (Europe, India and East Asia) enclosed by red rectangles are characterized by homogeneous SSR trends (reference is made to them below).</p>
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<p>Timeseries of MERRA-2 average global (black line) and hemispherical (NH and SH, red and green lines) monthly mean deseasonalized SSR anomalies for the period January 1980–December 2019. Linear regression fit line, along with the associated Δ(SSR) for the Globe and the two hemispheres (numbers in the embedded panel), as well as the 4th-order polynomial fit applied to the global time series of MERRA-2 SSR, are also shown.</p>
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<p>Timeseries (January 1980–December 2017) of deseasonalized anomalies of SSR for three selected world areas with homogeneous trends (see red rectangles in <a href="#applsci-12-10176-f011" class="html-fig">Figure 11</a>), namely, Europe (<b>a</b>), East Asia (<b>b</b>) and India (<b>c</b>). For every area, results are given based on GEBA stations (red dotted lines), corresponding MERRA-2 pixels (including the stations, green dot-dashed lines) and all MERRA-2 pixels of the areas (blue lines). Moreover, the linearly fitted lines to each timeseries are also displayed with similar colors, while blue line displaying the 4th-order polynomial fit applied to the time series of MERRA-2 SSR for the entire world areas are also shown.</p>
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15 pages, 1271 KiB  
Article
Shortwave Irradiance (1950 to 2020): Dimming, Brightening, and Urban Effects in Central Arizona?
by Anthony Brazel and Roger Tomalty
Climate 2021, 9(9), 137; https://doi.org/10.3390/cli9090137 - 28 Aug 2021
Cited by 1 | Viewed by 2476
Abstract
The objective of this study was to evaluate long-term change in shortwave irradiance in central Arizona (1950–2020) and to detect apparent dimming/brightening trends that may relate to many other global studies. Global Energy Budget Archives (GEBA) monthly data were accessed for the available [...] Read more.
The objective of this study was to evaluate long-term change in shortwave irradiance in central Arizona (1950–2020) and to detect apparent dimming/brightening trends that may relate to many other global studies. Global Energy Budget Archives (GEBA) monthly data were accessed for the available years 1950–1994 for Phoenix, Arizona and other selected sites in the Southwest desert. Monthly data of the database called gridMET were accessed, a 4-km gridded climate data based on NLDAS-2 and available for the years 1979–2020. Three Agricultural Meteorological Network (AZMET) automated weather stations in central Arizona have observed hourly shortwave irradiance over the period 1987–present. Two of the rural AZMET sites are located north and south of the Phoenix Metropolitan Area, and another site is in the center of the city of Phoenix. Using a combination of GEBA, gridMET, and AZMET data, annual time series demonstrate dimming up to late 1970s, early 1980s of −30 W/m2 (−13%), with brightening changes in the gridMET data post-1980 of +9 W/m2 (+4.6%). An urban site of the AZMET network showed significant reductions post-1987 up to 2020 of −9 W/m2 (3.8%) with no significant change at the two rural sites. Full article
(This article belongs to the Special Issue Climate Change and Solar Variability)
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Figure 1
<p>Study area of Phoenix, AZ in the desert SW USA. Sites used are shown with symbols and legend refers to the type of site, all mentioned in the paper. The orange pattern shows the urbanized area. Upper left corner of map = 34.38°N, 113.93°W; lower right = 32.76°N, 110.95°W.</p>
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<p>Annual mean shortwave irradiance K↓. Solid line-data from GEBA with interpolated missing years and correcting for known errors in instruments. Dashed line represents raw data from the GEBA Archives for the correction period.</p>
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<p>Three-year mean annual K↓ (W/m<sup>2</sup>). Clear days extracted from <a href="#climate-09-00137-f002" class="html-fig">Figure 2</a> in [<a href="#B18-climate-09-00137" class="html-bibr">18</a>] and GEBA data for all days.</p>
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<p>Annual trend GEBA, gridMET; Aguila, Encanto, Maricopa.</p>
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<p>(<b>a</b>) Transmissivity T (K↓/K<sub><span class="html-italic">etr</span></sub>) for urban site Encanto (black dots) vs. rural site Maricopa (red dots), (<b>b</b>) Urban vs. rural daily PM10 values. Urban (black dots) is mean of West Phoenix and Durango Complex; rural (red dots) is mean of Alamo Lake and Sacaton. Since data were for selected separate days over time, no curves were fit to the data.</p>
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23 pages, 7600 KiB  
Article
A New Long-Term Downward Surface Solar Radiation Dataset over China from 1958 to 2015
by Ning Hou, Xiaotong Zhang, Weiyu Zhang, Jiawen Xu, Chunjie Feng, Shuyue Yang, Kun Jia, Yunjun Yao, Jie Cheng and Bo Jiang
Sensors 2020, 20(21), 6167; https://doi.org/10.3390/s20216167 - 29 Oct 2020
Cited by 13 | Viewed by 2538
Abstract
Downward surface solar radiation (Rs) plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of Rs are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, [...] Read more.
Downward surface solar radiation (Rs) plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of Rs are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, so it is essential to build a long-term accurate Rs dataset. The extremely randomized trees (ERT) algorithm was used to generate Rs using routine meteorological observations (2000–2015) from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA). The estimated Rs values were validated against ground measurements at the national scale with an overall correlation coefficient value of 0.97, a mean bias of 0.04 Wm−2, a root-mean-square-error value of 23.12 Wm−2, and a mean relative error of 9.81%. It indicates that the estimated Rs from the ERT-based model is reasonably accurate. Moreover, the ERT-based model was used to generate a new daily Rs dataset at 756 CDC/CMA stations from 1958 to 2015. The long-term variation trends of Rs at 454 stations covering 46 consecutive years (1970–2015) were also analyzed. The Rs in China showed a significant decline trend (−1.1 Wm−2 per decade) during 1970–2015. A decreasing trend (−2.8 Wm−2 per decade) in Rs during 1970–1992 was observed, followed by a recovery trend (0.23 Wm−2 per decade) during 1992–2015. The recovery trends at individual stations were found at 233 out of 454 stations during 1970–2015, which were mainly located in southern and northern China. The new Rs dataset would substantially provide basic data for the related studies in agriculture, ecology, and meteorology. Full article
(This article belongs to the Section Remote Sensors)
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<p>(<b>a</b>) The spatial distribution of 454 Climate Data Centers of the Chinese Meteorological Administration (CDC/CMA) stations, the 96 radiation stations are denoted by five-pointed star symbols, (<b>b</b>) six climatic regions of China.</p>
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<p>Evaluation results of the extremely randomized trees (ERT)-based model for the (<b>a</b>) training dataset and (<b>b</b>) test dataset against ground-measured <span class="html-italic">Rs</span> during 2000–2015.</p>
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<p>Evaluation results of the ERT-based model in (<b>a</b>) northeast China (NE), (<b>b</b>) east China (EC), (<b>c</b>) north China (NC), (<b>d</b>) south China (SC), (<b>e</b>) southwest China (SW), and (<b>f</b>) Tibetan Plateau (TP) during 2000‒2015.</p>
Full article ">Figure 3 Cont.
<p>Evaluation results of the ERT-based model in (<b>a</b>) northeast China (NE), (<b>b</b>) east China (EC), (<b>c</b>) north China (NC), (<b>d</b>) south China (SC), (<b>e</b>) southwest China (SW), and (<b>f</b>) Tibetan Plateau (TP) during 2000‒2015.</p>
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<p>Spatial distribution of the ERT-based model performance on 96 CDC/CMA stations for (<b>a</b>) R, (<b>b</b>) RMSE, (<b>c</b>) mean bias error (MBE), and (<b>d</b>) mean relative error (MRE) during 2000–2015.</p>
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<p>Daily performance of the ERT-based model (<b>a</b>) R, (<b>b</b>) RMSE, (<b>c</b>) MBE, and (<b>d</b>) MRE during 2000–2015.</p>
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<p>Evaluation results of the ERT-based model in (<b>a</b>) spring (March to May), (<b>b</b>) summer (June to August), (<b>c</b>) autumn (September to November), and (<b>d</b>) winter (December to February) over China during 2000–2015.</p>
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<p>Evaluation results of the ERT-based model on the (<b>a</b>) monthly, (<b>b</b>) seasonal, and (<b>c</b>) annual timescales over China during 2000–2015.</p>
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<p>Spatial distribution of the annual mean <span class="html-italic">Rs</span> over China during 1970–2015.</p>
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<p>The monthly mean <span class="html-italic">Rs</span> over China during 1970–2015.</p>
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<p>Spatial distribution of the seasonal mean <span class="html-italic">Rs</span> over China in (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, and (<b>d</b>) winter during 1970–2015.</p>
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<p>The anomaly of the annual mean <span class="html-italic">Rs</span> in China during 1970–2015. The green dashed lines are linear trends of 1970–1992 and 1992–2015.</p>
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<p>The anomaly of the annual mean <span class="html-italic">Rs</span> in six climatic regions during 1970–2015. The green dashed lines are linear trends of 1970–1992 and 1992–2015.</p>
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<p>Decadal variation of the annual mean <span class="html-italic">Rs</span> on 454 stations during (<b>a</b>) 1970–2015, (<b>b</b>) 1970–1992, and (<b>c</b>) 1992–2015. Trends at the 95% significance level (<span class="html-italic">p</span> &lt; 0.05) are denoted by five-pointed black circle symbols.</p>
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<p>Comparison of the anomaly of the annual mean <span class="html-italic">Rs</span> and sunshine duration between 1970 and 2015. The green dashed lines are linear trends of 1970–1992 and 1992–2015.</p>
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<p>Comparison of the anomaly of the annual mean <span class="html-italic">Rs</span> and sunshine duration between 1970 and 2015 in six climatic regions.</p>
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16 pages, 3063 KiB  
Article
Global Dimming and Brightening Features during the First Decade of the 21st Century
by Nikolaos Hatzianastassiou, Eleftherios Ioannidis, Marios-Bruno Korras-Carraca, Maria Gavrouzou, Christos D. Papadimas, Christos Matsoukas, Nikolaos Benas, Angeliki Fotiadi, Martin Wild and Ilias Vardavas
Atmosphere 2020, 11(3), 308; https://doi.org/10.3390/atmos11030308 - 21 Mar 2020
Cited by 30 | Viewed by 5828
Abstract
Downward surface solar radiation (SSR) trends for the first decade of the 2000s were computed using a radiative transfer model and satellite and reanalysis input data and were validated against measurements from the reference global station networks Global Energy Balance Archive (GEBA) and [...] Read more.
Downward surface solar radiation (SSR) trends for the first decade of the 2000s were computed using a radiative transfer model and satellite and reanalysis input data and were validated against measurements from the reference global station networks Global Energy Balance Archive (GEBA) and Baseline Surface Radiation Network (BSRN). Under all-sky conditions, in spite of a somewhat patchy structure of global dimming and brightening (GDB), an overall dimming was found that is weaker in the Northern than in the Southern Hemisphere (−2.2 and −3.1 W m−2, respectively, over the 2001–2009 period). Dimming is observed over both land and ocean in the two hemispheres, but it is more remarkable over land areas of the Southern Hemisphere. The post-2000 dimming is found to have been primarily caused by clouds, and secondarily by aerosols, with total cloud cover contributing −1.4 W m−2 and aerosol optical thickness −0.7 W m−2 to the global average dimming of −2.65 W m−2. The evaluation of the model-computed GDB against BSRN and GEBA measurements indicates a good agreement, with the same trends for 65% and 64% of the examined stations, respectively. The obtained model results are in line with other studies for specific world regions and confirm the occurrence of an overall solar dimming over the globe during the first decade of 21st century. This post-2000 dimming has succeeded the global brightening observed in the 1990s and points to possible impacts on the ongoing global warming and climate change. Full article
(This article belongs to the Section Climatology)
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Figure 1
<p>Tendencies of model-computed downward surface solar radiation anomalies (changes in surface solar radiation (SSR), in W m<sup>−2</sup>) during the period 2001–2009. Dots indicate geographical cells for which the SSR tendencies are statistically significant.</p>
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<p>Time series of deseasonalized anomalies of monthly SSR fluxes averaged over land (green lines), ocean (blue lines), and land + ocean (black lines) regions of the Northern Hemisphere (<b>a</b>) and the Southern Hemisphere (<b>b</b>), over the period 2001–2009. The global dimming and brightening (GDB) magnitudes (SSR changes (ΔSSR), in W m<sup>−2</sup>) and the associated standard error values over the period 2001–2009 are also given for each hemisphere.</p>
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<p>Comparison between model-computed tendencies of SSR anomalies (ΔSSR) during 2001–2009 and similar anomalies of the Baseline Surface Radiation Network (BSRN) (<b>a</b>) and the Global Energy Balance Archive (GEBA) (<b>b</b>) station measurements. Blue and red circles indicate BSRN and GEBA stations for which the sign of tendencies of model-computed and station-measured SSR anomalies (ΔSSR) agree and disagree, respectively. The size of circles indicates the magnitude of computed correlation coefficients R between model-computed and ground-measured SSR anomalies.</p>
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<p>Tendencies of total cloud cover (<b>a</b>) and aerosol optical thickness (<b>b</b>) over the period 2001–2009. Total cloud cover (TCC) values are expressed in percentages (%) when multiplied by 100 and aerosol optical thickness (AOT) values are unitless. Dots indicate geographical cells for which the TCC and AOT tendencies are statistically significant.</p>
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<p>Contribution of changes in total cloud cover (<b>a</b>) and changes in aerosol optical thickness (<b>b</b>) to the model-computed changes of downward surface solar radiation (ΔSSR) over the period 2001–2009 (<a href="#atmosphere-11-00308-f001" class="html-fig">Figure 1</a>). The contribution is expressed in relative percent (%) terms with respect to (ΔSSR).</p>
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22 pages, 5654 KiB  
Article
Improvement of Air Pollution in China Inferred from Changes between Satellite-Based and Measured Surface Solar Radiation
by Yawen Wang, Jörg Trentmann, Uwe Pfeifroth, Wenping Yuan and Martin Wild
Remote Sens. 2019, 11(24), 2910; https://doi.org/10.3390/rs11242910 - 5 Dec 2019
Cited by 10 | Viewed by 3769
Abstract
The air pollution crisis in China has become a global concern due to its profound effects on the global environment and human health. To significantly improve the air quality, mandatory reductions were imposed on pollution emissions and energy consumption within the framework of [...] Read more.
The air pollution crisis in China has become a global concern due to its profound effects on the global environment and human health. To significantly improve the air quality, mandatory reductions were imposed on pollution emissions and energy consumption within the framework of the 11th and 12th Five Year Plans of China. This study takes the first step to quantify the implications of recent pollution control efforts for surface solar radiation (SSR), the primary energy source for our planet. The observed bias between satellite-retrieved and surface-observed SSR time series is proposed as a useful indicator for the radiative effects of aerosol changes. This is due to the fact that the effects of temporal variations of aerosols are neglected in satellite retrievals but well captured in surface observations of SSR. The implemented pollution control measures and actions have successfully brought back SSR by an average magnitude of 3.5 W m−2 decade−1 for the whole of China from 2008 onwards. Regionally, effective pollution regulations are indicated in the East Coast regions of South and North China, including the capital Beijing, with the SSR brightening induced by aerosol reduction of 7.5 W m−2 decade−1, 5.2 W m−2 decade−1, and 5.9 W m−2 decade−1, respectively. Seasonally, the SSR recovery in China mainly occurs in the warm seasons of spring and summer, with the magnitudes induced by the aerosol radiative effects of 5.9 W m−2 decade−1 and 4.7 W m−2 decade−1, respectively. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map showing the location of the 59 surface solar radiation stations across China, divided into four geographical regions: North China (NC, colored in red), South China (SC, green), Northwest China (NW, yellow), and Southwest China (SW, blue). Regional divisions are based on the three lines: Line A, also called Qinling-Huaihe Line, approximates the 0 °C January isotherm and the 800 mm isohyet in China. Line B represents the 400 mm isohyet and the boundary between monsoon and non-monsoon regions in China. Line C is the boundary between the first and second steps of China’s terrain with average elevation over 4000 m and around 1000~2000 m, respectively. The grey scale of the star symbols indicates the elevation (m) of the stations.</p>
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<p>Monthly anomalies of surface solar radiation (SSR, W m<sup>−2</sup>) for surface-based CMA (<b>a</b>) and collocated satellite-based CLARA-A2 (<b>b</b>) datasets, and their biases (<b>c</b>), averaged over the 59 stations across China for 1993–2015, plotted together with six-month moving average filters (thick black line). A stepwise linear regression has been applied for the periods of 1993–2007 and 2008–2015, respectively. Red and blue dashed lines denote upward and downward linear trends, respectively. Values given in the lower left corners of the panels represent the linear decadal trend slopes and the 95% confidence intervals (W m<sup>−2</sup> decade<sup>−1</sup>), marked with an asterisk * denoting significant trends (<span class="html-italic">p</span> &lt; 0.05). (<b>d</b>) shows the smoothed trends of the monthly SSR anomalies for CMA, CLARA-A2 and their biases as determined by the Mann–Kendall–Sneyers method.</p>
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<p>Trendraster-plots of mean SSR trends (W m<sup>−2</sup> decade<sup>−1</sup>): comparison between CMA and CLARA-A2 datasets for 1993–2015 over the 59 stations across China (<b>upper panels</b>) and Beijing (<b>lower panels</b>). The y and x axes show the starting and ending years, respectively, of the individual linear trend shown in each pixel. Please note that a different color-scale for the trend slopes is given to the CMA SSR of Bejing (<b>lower left panel</b>).</p>
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<p>Annual AOD time series derived from the reanalysis product of MERRA [<a href="#B40-remotesensing-11-02910" class="html-bibr">40</a>] and the combined satellite products of ASTR and MODIS [<a href="#B41-remotesensing-11-02910" class="html-bibr">41</a>] for 1993–2017 over China. Values are the decadal trend slopes and corresponding standard errors, red and blue colored values represent increasing and decreasing trends for the periods of 1993–2007 and 2008–2017, respectively, marked with an asterisk * denoting significant trends at the 95% confidence level.</p>
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<p>Seasonal (<b>a</b>) and regional (<b>b</b>) comparisons of the smoothed bias series between CLARA-A2 and CMA SSR determined by the Mann–Kendall–Sneyers method for 1993–2015. Subfigures (<b>c</b>) and (<b>d</b>) compare the seasonal and regional SSR trends (W m<sup>−2</sup> decade<sup>−1</sup>) of CMA (denoted by blue circle), CLARA-A2 (green squares) and their bias (red crosses) for the periods of 1993–2007 and 2008–2015, respectively. Seasons are defined as spring (MAM, March to May), summer (JJA, June to August), autumn (SON, September to November) and winter (DJF, December to February). The defined four regions are SW (Southwest China), SC (South China), NW (Northwest China), and NC (North China).</p>
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<p>Spatial patterns of the decadal trends (W m<sup>−2</sup> decade<sup>−1</sup>) in the monthly SSR anomalies of CLARA-A2 (<b>shown as the background layer</b>) compared against the CMA surface observations (<b>points</b>) over China for the periods of 1993–2007 <b>(upper panel</b>) and 2008–2015 (<b>lower panel</b>), respectively.</p>
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<p>Comparison of the monthly anomalies of surface solar radiation (SSR, W m<sup>−2</sup>, <b>a</b>–<b>c</b>) and cloud (represented by total cloud cover, TCC, and cloud fractional cover, CFC, %, <b>d</b>–<b>f</b>) between surface-based CMA and satellite-based CLARA-A2 datasets over Beijing for 1993–2015, plotted together with six-month moving average filters (thick black line). A stepwise linear regression has been applied for the periods of 1993–2007 and 2008–2015, respectively. Red and blue dashed lines denote upward and downward linear trends, respectively. Values stand for the linear decadal trend slopes and the 95% confidence intervals (W m<sup>−2</sup> decade<sup>−1</sup> for <b>a</b>-<b>c</b>, and % decade<sup>−1</sup> for <b>d</b>–<b>f</b>), marked with an asterisk * denoting significant trends (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Comparison of the aerosol (<b>a</b>) and cloud (<b>b</b>) climatologies used in CLARA-A2 (blue line) with the ones derived from surface observations of the AERONET and CMA networks (red line) for Beijing. The aerosol climatology used in CLARA-A2 is a slightly modified version of the monthly mean aerosol fields from the GADS/OPAC climatology [<a href="#B34-remotesensing-11-02910" class="html-bibr">34</a>], while the AERONET aerosol climatology is averaged over 2001–2015. Both of the CMA and CLARA-A2 cloud climatologies are calculated over 1993–2015.</p>
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<p>Monthly anomalies of aerosol-related information in terms of AOD (aerosol optical depth, <b>a</b>), API (air pollution index, <b>b</b>) and diffuse fraction (the ratio of diffuse to total radiation, %, <b>c</b>) in Beijing for 1993–2015, plotted together with six-month moving average filters (thick black line). A Stepwise linear regression has been applied for the periods of 1993–2007 and 2008–2015, respectively. Red and blue dashed lines denote upward and downward linear trends, respectively. Values correspond to the linear decadal trend slopes and the 95% confidence intervals in both absolute and relative (%) terms, marked with an asterisk * denoting significant trends (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Comparison of the seasonal trends in SSR (surface solar radiation (<b>a</b>) for CMA, CLARA-A2 and their bias, in W m<sup>−2</sup> decade<sup>−1</sup>), clouds (<b>b</b>) represented by total cloud cover (TCC) and cloud fractional cover (CFC), in % decade<sup>−1</sup>) and aerosol information (<b>c</b>) represented by aerosol optical depth (AOD), air pollution index (API), and the ratio of diffuse to total radiation (diffuse fraction), in % decade<sup>−1</sup>) in Beijing for the periods of 1993–2015, 1993–2007 and 2008–2015, respectively. Please note that AOD and API are only available for the periods of 2001–2015 and 2001–2012, respectively. * indicates significant trends at a 95% confidence level.</p>
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15 pages, 4941 KiB  
Article
Warming and Dimming: Interactive Impacts on Potential Summer Maize Yield in North China Plain
by Qi Hu, Xueqing Ma, Huayun He, Feifei Pan, Qijin He, Binxiang Huang and Xuebiao Pan
Sustainability 2019, 11(9), 2588; https://doi.org/10.3390/su11092588 - 5 May 2019
Cited by 8 | Viewed by 3006
Abstract
Global warming and dimming/brightening have significant implications for crop systems and exhibit regional variations. It is important to clarify the changes in regional thermal and solar radiation resources and estimate the impacts on potential crop production spatially and temporally. Based on daily observation [...] Read more.
Global warming and dimming/brightening have significant implications for crop systems and exhibit regional variations. It is important to clarify the changes in regional thermal and solar radiation resources and estimate the impacts on potential crop production spatially and temporally. Based on daily observation data during 1961–2015 in the North China Plain (NCP), the impacts of climate change associated with climate warming and global dimming/brightening on potential light–temperature productivity (PTP) of summer maize were assessed in this study. Results show that the NCP experienced a continuous warming and dimming trend in maize growing season during the past 55 years, and both ATT10 and solar radiation had an abrupt change in the mid-1990s. The period of 2000–2015 was warmer and dimmer than any other previous decade. Assuming the maize growing season remains unchanged, climate warming would increase PTP of summer maize by 4.6% over the period of 1961–2015, which mainly occurred in the start grain filling–maturity stage. On the other hand, as negative contribution value of solar radiation to the PTP was found in each stage, dimming would offset the increase of PTP due to warming climate, and lead to a 15.6% reduction in PTP in the past 55 years. This study reveals that the changes in thermal and solar radiation have reduced the PTP of summer maize in the NCP. However, the actual maize yield could benefit more from climate warming because solar radiation is not a limiting factor for the current low production level. Full article
(This article belongs to the Section Environmental Sustainability and Applications)
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<p>Boundary and meteorological stations in NCP. Digital Elevation Model (DEM) data were from Chinese Academy of Sciences and downloaded at <a href="http://www.resdc.cn/" target="_blank">http://www.resdc.cn/</a>.</p>
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<p>Changing characteristics of <span class="html-italic">ATT10</span> (°C·day) (<b>a</b>) and solar radiation (MJ·m<sup>−2</sup>) (<b>b</b>) in maize growing season at decadal scale. Three time periods are divided, i.e., Period 1 (P1, 1961–1979), Period 2 (P2, 1980–1999), and Period 3 (P3, 2000–2015).</p>
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<p>M-K test for <span class="html-italic">ATT10</span> (<b>a</b>) and solar radiation (<b>b</b>) in maize growing season over the period of 1961–2015. The values for each year were calculated for the average of 55 meteorological stations in NCP.</p>
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<p>Spatial distribution of climate trend rates for <span class="html-italic">ATT10</span> (°C·day·decade<sup>−1</sup>) (<b>a</b>) and solar radiation (MJ·m<sup>−2</sup>·decade<sup>−1</sup>) (<b>b</b>) in maize growing season in NCP over the period of 1961–2015.</p>
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<p>Changing characteristics of the <span class="html-italic">PTP</span> of summer maize (ton·ha<sup>−1</sup>) in NCP at decadal scale.</p>
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<p>Spatial distribution of climate trend rate for the <span class="html-italic">PTP</span> of summer maize (kg·ha<sup>−1</sup>·decade<sup>−1</sup>) in NCP over the period of 1961–2015.</p>
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<p>Annual anomaly percentage rate of the <span class="html-italic">PTP</span> of summer maize and the contribution rate of <span class="html-italic">ATT10</span> and solar radiation to the <span class="html-italic">PTP</span> in NCP over the period of 1961–2015.</p>
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<p>Changes for the <span class="html-italic">PTP</span> of summer maize, statistical yield, and yield gap in NCP at decadal scale. The statistical yield data were obtained from National Bureau of Statistics of China (<a href="http://www.stats.gov.cn/" target="_blank">http://www.stats.gov.cn/</a>).</p>
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17 pages, 5267 KiB  
Article
Impacts of 3D Aerosol, Cloud, and Water Vapor Variations on the Recent Brightening during the South Asian Monsoon Season
by Zengxin Pan, Feiyue Mao, Wei Wang, Bo Zhu, Xin Lu and Wei Gong
Remote Sens. 2018, 10(4), 651; https://doi.org/10.3390/rs10040651 - 23 Apr 2018
Cited by 14 | Viewed by 5267
Abstract
South Asia is experiencing a levelling-off trend in solar radiation and even a transition from dimming to brightening. Any change in incident solar radiation, which is the only significant energy source of the global ecosystem, profoundly affects our habitats. Here, we use multiple [...] Read more.
South Asia is experiencing a levelling-off trend in solar radiation and even a transition from dimming to brightening. Any change in incident solar radiation, which is the only significant energy source of the global ecosystem, profoundly affects our habitats. Here, we use multiple observations of the A-Train constellation to evaluate the impacts of three-dimensional (3D) aerosol, cloud, and water vapor variations on the changes in surface solar radiation during the monsoon season (June–September) in South Asia from 2006 to 2015. Results show that surface shortwave radiation (SSR) has possibly increased by 16.2 W m−2 during this period. However, an increase in aerosol loading is inconsistent with the SSR variations. Instead, clouds are generally reduced and thinned by approximately 8.8% and 280 m, respectively, with a decrease in both cloud water path (by 34.7 g m−2) and particle number concentration under cloudy conditions. Consequently, the shortwave cloud radiative effect decreases by approximately 45.5 W m−2 at the surface. Moreover, precipitable water in clear-sky conditions decreases by 2.8 mm (mainly below 2 km), and related solar brightening increases by 2.5 W m−2. Overall, the decreases in 3D water vapor and clouds distinctly result in increased absorption of SSR and subsequent surface brightening. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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Figure 1
<p>Temporal variations in spatial average AOD from (<b>a</b>) CALIPSO and MODIS during the monsoon season and (<b>b</b>) the vertical average aerosol extinction coefficient at 532 nm in South Asia from 2006 to 2015, respectively. The dashed lines indicate the linear fit line of the solid line with the same color; <span class="html-italic">p</span> is the significance level.</p>
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<p>Temporal variations in spatial average vertical cloud physical parameters from CloudSat during the monsoon season in South Asia from 2006 to 2015: (<b>a</b>) cloud vertical frequency distribution, and liquid and ice (<b>b</b>,<b>e</b>) water content, (<b>c</b>,<b>f</b>) effective radius, and (<b>d</b>,<b>g</b>) number concentration.</p>
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<p>Temporal variations in spatial average (<b>a</b>) cloud fraction and CWP, as well as (<b>b</b>) uppermost CTH, lowermost CBH and CGD; spatial distributions of the temporal changes in average (<b>c</b>) cloud fraction, (<b>d</b>) CH, (<b>e</b>) CWP, and (<b>f</b>) CGD from CloudSat during the monsoon season in South Asia from 2006 to 2015. The dashed lines indicate the linear fit line of the solid line with the same color; <span class="html-italic">p</span> is the significance level.</p>
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<p>Temporal variations in spatial average vertical (<b>a</b>) SW, (<b>b</b>) LW, and (<b>c</b>) net heat rating in all-sky conditions from CloudSat during the monsoon season in South Asia from 2006 to 2015.</p>
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<p>(<b>a</b>) Temporal variations in spatial average CRE at the TOA and surface, and (<b>b</b>) spatial distribution of the temporal changes in average SW CRE from CloudSat at the surface during the monsoon season in South Asia from 2006 to 2015. The dashed lines indicate the linear fit line of the solid line with the same color; <span class="html-italic">p</span> is the significance level.</p>
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<p>Temporal variations in spatial average vertical RH in (<b>a</b>) all-sky and (<b>c</b>) clear-sky conditions; spatial distribution of temporal changes in the average PW in (<b>b</b>) all-sky and (<b>d</b>) clear-sky conditions from the ECMWF-AUX during the monsoon season in South Asia from 2006 to 2015.</p>
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<p>Temporal variations in spatial average (<b>a</b>) PW (blue line) and SSR in clear-sky conditions from BUGSrad (red line) and CloudSat (orange line); spatial distribution of temporal changes in average (<b>b</b>) SSR from BUGSrad in clear-sky conditions during the monsoon season in South Asia from 2006 to 2015. The dashed lines indicate the linear fit line of the solid line with the same color; <span class="html-italic">p</span> is the significance level.</p>
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<p>Temporal variations in spatial average PW in (<b>a</b>) all-sky and (<b>b</b>) clear-sky conditions at different ranges of altitude during the monsoon season in South Asia from 2006 to 2015. The dashed lines indicate the linear fit line of the solid line with the same color; <span class="html-italic">p</span> is the significance level.</p>
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<p>Temporal variations in (<b>a</b>) spatial average AOD from MODIS and (<b>b</b>) SSR from CERES during the pre-monsoon, monsoon, and dry seasons in South Asia from 2006 to 2015, respectively. The dashed lines indicate the linear fit line of the solid line with the same color; <span class="html-italic">p</span> is the significance level.</p>
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<p>Schematic of the impacts of 3D aerosol, cloud, and water vapor variations on brightening during the monsoon season in South Asia from 2006 to 2015. Background gradient color represents the changing RH. The relative humidity is high when the color is dark.</p>
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